instruction
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Analyze loyalty score city
|
{
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": [
62.13,
111.4,
87.11,
154.16,
107.24,
146.92,
101.84,
175.28,
118.98,
196.76,
62.21,
133.22,
51.67,
129.18,
53.01,
85.66,
193.85,
114.23,
160.13,
162.11,
69.25,
96.35,
115.85,
149.45,
107.2,
165.05,
63.38,
184.09,
161.6,
174.27
],
"Category/Kategori": null,
"City/Kota": null,
"Conversion Rate/Rasio Konversi": [
0.022,
0.0869,
0.028800000000000003,
0.0658,
0.0323,
0.0151,
0.0472,
0.0443,
0.025500000000000002,
0.07,
0.0932,
0.0847,
0.0128,
0.0175,
0.0826,
0.0898,
0.0345,
0.051300000000000005,
0.020200000000000003,
0.0613,
0.0733,
0.0979,
0.0845,
0.046,
0.0143,
0.0877,
0.08650000000000001,
0.0257,
0.049100000000000005,
0.014100000000000001
],
"Country/Negara": null,
"Customer ID/ID Pelanggan": null,
"Date/Tanggal": [
"2025-03-23",
"2025-03-24",
"2025-03-25",
"2025-03-26",
"2025-03-27",
"2025-03-28",
"2025-03-29",
"2025-03-30",
"2025-03-31",
"2025-04-01",
"2025-04-02",
"2025-04-03",
"2025-04-04",
"2025-04-05",
"2025-04-06",
"2025-04-07",
"2025-04-08",
"2025-04-09",
"2025-04-10",
"2025-04-11",
"2025-04-12",
"2025-04-13",
"2025-04-14",
"2025-04-15",
"2025-04-16",
"2025-04-17",
"2025-04-18",
"2025-04-19",
"2025-04-20",
"2025-04-21"
],
"Discount/Diskon": null,
"Gender/Jenis Kelamin": null,
"Growth Rate/Pertumbuhan": null,
"Income/Pendapatan": null,
"Last Purchase Date/Tanggal Pembelian Terakhir": null,
"Latitude/Lintang": null,
"Longitude/Bujur": null,
"Loyalty Score/Skor Loyalitas": null,
"Market Share/Pangsa Pasar": null,
"Price/Harga": null,
"Product/Produk": null,
"Region/Wilayah": null,
"Revenue/Pendapatan": [
1929.56,
2117.12,
2109.38,
1986.4,
1992.98,
1972.83,
1987.38,
1867.88,
1737.17,
1630.69,
1612.29,
1619.72,
1596.98,
1547.03,
1559.29,
1737.04,
1906.76,
1920.3,
1791.25,
1914.52,
1786.25,
1734.57,
1653.68,
1486.43,
1521.8,
1345.34,
1437.81,
1495.01,
1476.22,
1532
],
"Sales/Penjualan": null,
"Stock/Stok": null,
"Visitors/Pengunjung": [
211,
108,
129,
213,
283,
288,
150,
103,
112,
74,
251,
54,
290,
121,
71,
80,
126,
302,
109,
244,
220,
155,
142,
251,
83,
97,
221,
169,
167,
198
]
}
|
import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Date/Tanggal': ['2025-03-23', '2025-03-24', '2025-03-25', '2025-03-26', '2025-03-27', '2025-03-28', '2025-03-29', '2025-03-30', '2025-03-31', '2025-04-01', '2025-04-02', '2025-04-03', '2025-04-04', '2025-04-05', '2025-04-06', '2025-04-07', '2025-04-08', '2025-04-09', '2025-04-10', '2025-04-11', '2025-04-12', '2025-04-13', '2025-04-14', '2025-04-15', '2025-04-16', '2025-04-17', '2025-04-18', '2025-04-19', '2025-04-20', '2025-04-21'], 'Revenue/Pendapatan': [1929.56, 2117.12, 2109.38, 1986.4, 1992.98, 1972.83, 1987.38, 1867.88, 1737.17, 1630.69, 1612.29, 1619.72, 1596.98, 1547.03, 1559.29, 1737.04, 1906.76, 1920.3, 1791.25, 1914.52, 1786.25, 1734.57, 1653.68, 1486.43, 1521.8, 1345.34, 1437.81, 1495.01, 1476.22, 1532.0], 'Visitors/Pengunjung': [211, 108, 129, 213, 283, 288, 150, 103, 112, 74, 251, 54, 290, 121, 71, 80, 126, 302, 109, 244, 220, 155, 142, 251, 83, 97, 221, 169, 167, 198], 'Conversion Rate/Rasio Konversi': [0.022, 0.0869, 0.0288, 0.0658, 0.0323, 0.0151, 0.0472, 0.0443, 0.0255, 0.07, 0.0932, 0.0847, 0.0128, 0.0175, 0.0826, 0.0898, 0.0345, 0.0513, 0.0202, 0.0613, 0.0733, 0.0979, 0.0845, 0.046, 0.0143, 0.0877, 0.0865, 0.0257, 0.0491, 0.0141], 'Avg. Order Value/Nilai Pesanan Rata2': [62.13, 111.4, 87.11, 154.16, 107.24, 146.92, 101.84, 175.28, 118.98, 196.76, 62.21, 133.22, 51.67, 129.18, 53.01, 85.66, 193.85, 114.23, 160.13, 162.11, 69.25, 96.35, 115.85, 149.45, 107.2, 165.05, 63.38, 184.09, 161.6, 174.27]})
# Basic data cleaning
df = df.dropna() # Remove missing values
df = df.drop_duplicates() # Remove duplicates
# Comprehensive data analysis
analysis_result = df.describe(include='all')
print(analysis_result)
# Additional insights
print('\nTop performing items:')
print(df.nlargest(3, df.columns[1]))
print('\nTrend analysis:')
if 'Date/Tanggal' in df.columns:
df['Month/Bulan'] = pd.to_datetime(df['Date/Tanggal']).dt.month
monthly_trend = df.groupby('Month/Bulan').mean(numeric_only=True)
print(monthly_trend)
|
Kelompokkan pendapatan wilayah
|
{
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": [
198.79,
196.59,
57.68,
123.24,
134.05,
137.3,
191.65,
179,
141.71,
55.59,
190.14,
142.75,
112.47,
85.63,
144.38,
175.87,
100.44,
126.06,
108.54,
63.68,
130.18,
67.88,
183.94,
191.18,
180.88,
170.04,
176.09,
51.36,
166.22,
156.79
],
"Category/Kategori": null,
"City/Kota": null,
"Conversion Rate/Rasio Konversi": [
0.0455,
0.0344,
0.0475,
0.057600000000000005,
0.0253,
0.0784,
0.025500000000000002,
0.0553,
0.0548,
0.0502,
0.0458,
0.024300000000000002,
0.0879,
0.0789,
0.0723,
0.037700000000000004,
0.0173,
0.058800000000000005,
0.0327,
0.033,
0.0262,
0.0155,
0.051300000000000005,
0.040400000000000005,
0.08660000000000001,
0.0949,
0.0145,
0.061500000000000006,
0.0993,
0.049800000000000004
],
"Country/Negara": null,
"Customer ID/ID Pelanggan": null,
"Date/Tanggal": [
"2025-03-23",
"2025-03-24",
"2025-03-25",
"2025-03-26",
"2025-03-27",
"2025-03-28",
"2025-03-29",
"2025-03-30",
"2025-03-31",
"2025-04-01",
"2025-04-02",
"2025-04-03",
"2025-04-04",
"2025-04-05",
"2025-04-06",
"2025-04-07",
"2025-04-08",
"2025-04-09",
"2025-04-10",
"2025-04-11",
"2025-04-12",
"2025-04-13",
"2025-04-14",
"2025-04-15",
"2025-04-16",
"2025-04-17",
"2025-04-18",
"2025-04-19",
"2025-04-20",
"2025-04-21"
],
"Discount/Diskon": null,
"Gender/Jenis Kelamin": null,
"Growth Rate/Pertumbuhan": null,
"Income/Pendapatan": null,
"Last Purchase Date/Tanggal Pembelian Terakhir": null,
"Latitude/Lintang": null,
"Longitude/Bujur": null,
"Loyalty Score/Skor Loyalitas": null,
"Market Share/Pangsa Pasar": null,
"Price/Harga": null,
"Product/Produk": null,
"Region/Wilayah": null,
"Revenue/Pendapatan": [
4861.23,
4947.73,
5082.19,
5032.59,
5131.35,
5023.4,
4956.69,
4921.76,
4809.65,
4737.92,
4684.5,
4636.31,
4754.67,
4764.03,
4837.01,
4864.16,
4899.43,
5073.01,
5064.28,
4960.93,
4908.85,
4863.06,
4707.71,
4729.92,
4602.12,
4604.99,
4561.8,
4605.6,
4698.84,
4778.39
],
"Sales/Penjualan": null,
"Stock/Stok": null,
"Visitors/Pengunjung": [
153,
259,
62,
250,
193,
259,
114,
122,
141,
149,
168,
64,
255,
282,
233,
77,
239,
253,
137,
70,
62,
105,
188,
46,
79,
289,
244,
214,
173,
180
]
}
|
import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Date/Tanggal': ['2025-03-23', '2025-03-24', '2025-03-25', '2025-03-26', '2025-03-27', '2025-03-28', '2025-03-29', '2025-03-30', '2025-03-31', '2025-04-01', '2025-04-02', '2025-04-03', '2025-04-04', '2025-04-05', '2025-04-06', '2025-04-07', '2025-04-08', '2025-04-09', '2025-04-10', '2025-04-11', '2025-04-12', '2025-04-13', '2025-04-14', '2025-04-15', '2025-04-16', '2025-04-17', '2025-04-18', '2025-04-19', '2025-04-20', '2025-04-21'], 'Revenue/Pendapatan': [4861.23, 4947.73, 5082.19, 5032.59, 5131.35, 5023.4, 4956.69, 4921.76, 4809.65, 4737.92, 4684.5, 4636.31, 4754.67, 4764.03, 4837.01, 4864.16, 4899.43, 5073.01, 5064.28, 4960.93, 4908.85, 4863.06, 4707.71, 4729.92, 4602.12, 4604.99, 4561.8, 4605.6, 4698.84, 4778.39], 'Visitors/Pengunjung': [153, 259, 62, 250, 193, 259, 114, 122, 141, 149, 168, 64, 255, 282, 233, 77, 239, 253, 137, 70, 62, 105, 188, 46, 79, 289, 244, 214, 173, 180], 'Conversion Rate/Rasio Konversi': [0.0455, 0.0344, 0.0475, 0.0576, 0.0253, 0.0784, 0.0255, 0.0553, 0.0548, 0.0502, 0.0458, 0.0243, 0.0879, 0.0789, 0.0723, 0.0377, 0.0173, 0.0588, 0.0327, 0.033, 0.0262, 0.0155, 0.0513, 0.0404, 0.0866, 0.0949, 0.0145, 0.0615, 0.0993, 0.0498], 'Avg. Order Value/Nilai Pesanan Rata2': [198.79, 196.59, 57.68, 123.24, 134.05, 137.3, 191.65, 179.0, 141.71, 55.59, 190.14, 142.75, 112.47, 85.63, 144.38, 175.87, 100.44, 126.06, 108.54, 63.68, 130.18, 67.88, 183.94, 191.18, 180.88, 170.04, 176.09, 51.36, 166.22, 156.79]})
# Basic data cleaning
df = df.dropna() # Remove missing values
df = df.drop_duplicates() # Remove duplicates
# Comprehensive data analysis
analysis_result = df.describe(include='all')
print(analysis_result)
# Additional insights
print('\nTop performing items:')
print(df.nlargest(3, df.columns[1]))
print('\nTrend analysis:')
if 'Date/Tanggal' in df.columns:
df['Month/Bulan'] = pd.to_datetime(df['Date/Tanggal']).dt.month
monthly_trend = df.groupby('Month/Bulan').mean(numeric_only=True)
print(monthly_trend)
|
Analisis diskon periode waktu
|
{
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": null,
"City/Kota": [
"Paris",
"Mexico City",
"Berlin",
"São Paulo",
"Tokyo"
],
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": [
"France",
"Mexico",
"Germany",
"Brazil",
"Japan"
],
"Customer ID/ID Pelanggan": null,
"Date/Tanggal": null,
"Discount/Diskon": null,
"Gender/Jenis Kelamin": null,
"Growth Rate/Pertumbuhan": [
0.19,
0.19,
0.04,
0.11,
0.15
],
"Income/Pendapatan": null,
"Last Purchase Date/Tanggal Pembelian Terakhir": null,
"Latitude/Lintang": [
-5.2544,
34.6242,
52.8123,
78.8455,
71.0469
],
"Longitude/Bujur": [
64.3982,
137.1681,
-85.9355,
122.9545,
147.7382
],
"Loyalty Score/Skor Loyalitas": null,
"Market Share/Pangsa Pasar": [
0.38,
0.15,
0.41000000000000003,
0.4,
0.32
],
"Price/Harga": null,
"Product/Produk": null,
"Region/Wilayah": [
"Europe",
"North America",
"Europe",
"South America",
"Asia"
],
"Revenue/Pendapatan": [
1165,
1522,
1802,
3046,
3632
],
"Sales/Penjualan": null,
"Stock/Stok": null,
"Visitors/Pengunjung": null
}
|
import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'City/Kota': ['Paris', 'Mexico City', 'Berlin', 'São Paulo', 'Tokyo'], 'Country/Negara': ['France', 'Mexico', 'Germany', 'Brazil', 'Japan'], 'Region/Wilayah': ['Europe', 'North America', 'Europe', 'South America', 'Asia'], 'Latitude/Lintang': [-5.2544, 34.6242, 52.8123, 78.8455, 71.0469], 'Longitude/Bujur': [64.3982, 137.1681, -85.9355, 122.9545, 147.7382], 'Revenue/Pendapatan': [1165, 1522, 1802, 3046, 3632], 'Market Share/Pangsa Pasar': [0.38, 0.15, 0.41, 0.4, 0.32], 'Growth Rate/Pertumbuhan': [0.19, 0.19, 0.04, 0.11, 0.15]})
# Basic data cleaning
df = df.dropna() # Remove missing values
df = df.drop_duplicates() # Remove duplicates
# Comprehensive data analysis
analysis_result = df.describe(include='all')
print(analysis_result)
# Additional insights
print('\nTop performing items:')
print(df.nlargest(3, df.columns[1]))
print('\nTrend analysis:')
if 'Date/Tanggal' in df.columns:
df['Month/Bulan'] = pd.to_datetime(df['Date/Tanggal']).dt.month
monthly_trend = df.groupby('Month/Bulan').mean(numeric_only=True)
print(monthly_trend)
|
Calculate loyalty score time period
|
{
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": [
"Automotive/Otomotif",
"Automotive/Otomotif",
"Automotive/Otomotif"
],
"City/Kota": null,
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": null,
"Customer ID/ID Pelanggan": null,
"Date/Tanggal": null,
"Discount/Diskon": [
0.07,
0.28,
0.2
],
"Gender/Jenis Kelamin": null,
"Growth Rate/Pertumbuhan": null,
"Income/Pendapatan": null,
"Last Purchase Date/Tanggal Pembelian Terakhir": null,
"Latitude/Lintang": null,
"Longitude/Bujur": null,
"Loyalty Score/Skor Loyalitas": null,
"Market Share/Pangsa Pasar": null,
"Price/Harga": [
29.16,
46.31,
144.04
],
"Product/Produk": [
"Oil_1",
"Tires_2",
"Accessories_3"
],
"Region/Wilayah": null,
"Revenue/Pendapatan": null,
"Sales/Penjualan": [
297,
929,
378
],
"Stock/Stok": [
181,
131,
25
],
"Visitors/Pengunjung": null
}
|
import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Product/Produk': ['Oil_1', 'Tires_2', 'Accessories_3'], 'Sales/Penjualan': [297, 929, 378], 'Price/Harga': [29.16, 46.31, 144.04], 'Category/Kategori': ['Automotive/Otomotif', 'Automotive/Otomotif', 'Automotive/Otomotif'], 'Discount/Diskon': [0.07, 0.28, 0.2], 'Stock/Stok': [181, 131, 25]})
# Basic data cleaning
df = df.dropna() # Remove missing values
df = df.drop_duplicates() # Remove duplicates
# profit margin calculation
result = df['Discount/Diskon'].(df['Revenue'] - df['Cost'])/df['Revenue']
print(result)
|
Compare growth rate gender
|
{
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": [
"Automotive/Otomotif",
"Automotive/Otomotif",
"Automotive/Otomotif"
],
"City/Kota": null,
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": null,
"Customer ID/ID Pelanggan": null,
"Date/Tanggal": null,
"Discount/Diskon": [
0.12,
0.29,
0.12
],
"Gender/Jenis Kelamin": null,
"Growth Rate/Pertumbuhan": null,
"Income/Pendapatan": null,
"Last Purchase Date/Tanggal Pembelian Terakhir": null,
"Latitude/Lintang": null,
"Longitude/Bujur": null,
"Loyalty Score/Skor Loyalitas": null,
"Market Share/Pangsa Pasar": null,
"Price/Harga": [
107.6,
161.95,
78.78
],
"Product/Produk": [
"Oil_1",
"Tools_2",
"Tires_3"
],
"Region/Wilayah": null,
"Revenue/Pendapatan": null,
"Sales/Penjualan": [
803,
723,
775
],
"Stock/Stok": [
28,
147,
127
],
"Visitors/Pengunjung": null
}
|
import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Product/Produk': ['Oil_1', 'Tools_2', 'Tires_3'], 'Sales/Penjualan': [803, 723, 775], 'Price/Harga': [107.6, 161.95, 78.78], 'Category/Kategori': ['Automotive/Otomotif', 'Automotive/Otomotif', 'Automotive/Otomotif'], 'Discount/Diskon': [0.12, 0.29, 0.12], 'Stock/Stok': [28, 147, 127]})
# Basic data cleaning
df = df.dropna() # Remove missing values
df = df.drop_duplicates() # Remove duplicates
# Comprehensive data analysis
analysis_result = df.describe(include='all')
print(analysis_result)
# Additional insights
print('\nTop performing items:')
print(df.nlargest(3, df.columns[1]))
print('\nTrend analysis:')
if 'Date/Tanggal' in df.columns:
df['Month/Bulan'] = pd.to_datetime(df['Date/Tanggal']).dt.month
monthly_trend = df.groupby('Month/Bulan').mean(numeric_only=True)
print(monthly_trend)
|
Analisis pangsa pasar negara
|
{
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": [
113.75,
144.75,
121.19,
74.37,
91.78,
60.88,
64.61,
96.77,
99.36,
143.42,
83.28,
168.7,
113.59,
98.14,
54.31,
199.02,
108.99,
89.33,
126.97,
138.8,
70.74,
191.31,
150.77,
85.52,
155.72,
172.99,
67.02,
198.59,
122.05,
163.97
],
"Category/Kategori": null,
"City/Kota": null,
"Conversion Rate/Rasio Konversi": [
0.0167,
0.0504,
0.0618,
0.0115,
0.0162,
0.029500000000000002,
0.0648,
0.0143,
0.0864,
0.0913,
0.0844,
0.0734,
0.0358,
0.0937,
0.0526,
0.0618,
0.016,
0.0227,
0.058,
0.08750000000000001,
0.07010000000000001,
0.02,
0.0415,
0.08850000000000001,
0.0472,
0.06810000000000001,
0.08,
0.07,
0.0823,
0.0114
],
"Country/Negara": null,
"Customer ID/ID Pelanggan": null,
"Date/Tanggal": [
"2025-03-23",
"2025-03-24",
"2025-03-25",
"2025-03-26",
"2025-03-27",
"2025-03-28",
"2025-03-29",
"2025-03-30",
"2025-03-31",
"2025-04-01",
"2025-04-02",
"2025-04-03",
"2025-04-04",
"2025-04-05",
"2025-04-06",
"2025-04-07",
"2025-04-08",
"2025-04-09",
"2025-04-10",
"2025-04-11",
"2025-04-12",
"2025-04-13",
"2025-04-14",
"2025-04-15",
"2025-04-16",
"2025-04-17",
"2025-04-18",
"2025-04-19",
"2025-04-20",
"2025-04-21"
],
"Discount/Diskon": null,
"Gender/Jenis Kelamin": null,
"Growth Rate/Pertumbuhan": null,
"Income/Pendapatan": null,
"Last Purchase Date/Tanggal Pembelian Terakhir": null,
"Latitude/Lintang": null,
"Longitude/Bujur": null,
"Loyalty Score/Skor Loyalitas": null,
"Market Share/Pangsa Pasar": null,
"Price/Harga": null,
"Product/Produk": null,
"Region/Wilayah": null,
"Revenue/Pendapatan": [
3010.64,
3045.43,
3060.29,
3128.93,
3143.19,
3084.98,
2989.56,
2915.09,
2812.27,
2721.09,
2585.29,
2678.03,
2725.86,
2804.14,
2747.48,
2949.53,
2996.84,
3050.78,
3038.66,
3075.41,
2915.4,
2991.14,
2928.31,
2690.9,
2736.94,
2610,
2587.68,
2620.29,
2688.18,
2785.11
],
"Sales/Penjualan": null,
"Stock/Stok": null,
"Visitors/Pengunjung": [
290,
294,
212,
305,
175,
55,
258,
76,
243,
262,
251,
187,
113,
90,
293,
232,
283,
240,
191,
75,
270,
291,
215,
68,
198,
265,
62,
154,
285,
211
]
}
|
import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Date/Tanggal': ['2025-03-23', '2025-03-24', '2025-03-25', '2025-03-26', '2025-03-27', '2025-03-28', '2025-03-29', '2025-03-30', '2025-03-31', '2025-04-01', '2025-04-02', '2025-04-03', '2025-04-04', '2025-04-05', '2025-04-06', '2025-04-07', '2025-04-08', '2025-04-09', '2025-04-10', '2025-04-11', '2025-04-12', '2025-04-13', '2025-04-14', '2025-04-15', '2025-04-16', '2025-04-17', '2025-04-18', '2025-04-19', '2025-04-20', '2025-04-21'], 'Revenue/Pendapatan': [3010.64, 3045.43, 3060.29, 3128.93, 3143.19, 3084.98, 2989.56, 2915.09, 2812.27, 2721.09, 2585.29, 2678.03, 2725.86, 2804.14, 2747.48, 2949.53, 2996.84, 3050.78, 3038.66, 3075.41, 2915.4, 2991.14, 2928.31, 2690.9, 2736.94, 2610.0, 2587.68, 2620.29, 2688.18, 2785.11], 'Visitors/Pengunjung': [290, 294, 212, 305, 175, 55, 258, 76, 243, 262, 251, 187, 113, 90, 293, 232, 283, 240, 191, 75, 270, 291, 215, 68, 198, 265, 62, 154, 285, 211], 'Conversion Rate/Rasio Konversi': [0.0167, 0.0504, 0.0618, 0.0115, 0.0162, 0.0295, 0.0648, 0.0143, 0.0864, 0.0913, 0.0844, 0.0734, 0.0358, 0.0937, 0.0526, 0.0618, 0.016, 0.0227, 0.058, 0.0875, 0.0701, 0.02, 0.0415, 0.0885, 0.0472, 0.0681, 0.08, 0.07, 0.0823, 0.0114], 'Avg. Order Value/Nilai Pesanan Rata2': [113.75, 144.75, 121.19, 74.37, 91.78, 60.88, 64.61, 96.77, 99.36, 143.42, 83.28, 168.7, 113.59, 98.14, 54.31, 199.02, 108.99, 89.33, 126.97, 138.8, 70.74, 191.31, 150.77, 85.52, 155.72, 172.99, 67.02, 198.59, 122.05, 163.97]})
# Basic data cleaning
df = df.dropna() # Remove missing values
df = df.drop_duplicates() # Remove duplicates
# Comprehensive data analysis
analysis_result = df.describe(include='all')
print(analysis_result)
# Additional insights
print('\nTop performing items:')
print(df.nlargest(3, df.columns[1]))
print('\nTrend analysis:')
if 'Date/Tanggal' in df.columns:
df['Month/Bulan'] = pd.to_datetime(df['Date/Tanggal']).dt.month
monthly_trend = df.groupby('Month/Bulan').mean(numeric_only=True)
print(monthly_trend)
|
Segment discount gender
|
{
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": null,
"City/Kota": [
"New York",
"Paris",
"Tokyo",
"London",
"Sydney"
],
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": [
"US",
"France",
"Japan",
"UK",
"Australia"
],
"Customer ID/ID Pelanggan": null,
"Date/Tanggal": null,
"Discount/Diskon": null,
"Gender/Jenis Kelamin": null,
"Growth Rate/Pertumbuhan": [
-0.02,
-0.02,
0.13,
-0.05,
0.07
],
"Income/Pendapatan": null,
"Last Purchase Date/Tanggal Pembelian Terakhir": null,
"Latitude/Lintang": [
6.7207,
69.9411,
23.6835,
-35.1688,
13.9049
],
"Longitude/Bujur": [
134.9165,
10.2526,
17.9766,
-55.5542,
49.4581
],
"Loyalty Score/Skor Loyalitas": null,
"Market Share/Pangsa Pasar": [
0.26,
0.1,
0.25,
0.32,
0.45
],
"Price/Harga": null,
"Product/Produk": null,
"Region/Wilayah": [
"North America",
"Europe",
"Asia",
"Europe",
"Oceania"
],
"Revenue/Pendapatan": [
4230,
1060,
4837,
1094,
4029
],
"Sales/Penjualan": null,
"Stock/Stok": null,
"Visitors/Pengunjung": null
}
|
import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'City/Kota': ['New York', 'Paris', 'Tokyo', 'London', 'Sydney'], 'Country/Negara': ['US', 'France', 'Japan', 'UK', 'Australia'], 'Region/Wilayah': ['North America', 'Europe', 'Asia', 'Europe', 'Oceania'], 'Latitude/Lintang': [6.7207, 69.9411, 23.6835, -35.1688, 13.9049], 'Longitude/Bujur': [134.9165, 10.2526, 17.9766, -55.5542, 49.4581], 'Revenue/Pendapatan': [4230, 1060, 4837, 1094, 4029], 'Market Share/Pangsa Pasar': [0.26, 0.1, 0.25, 0.32, 0.45], 'Growth Rate/Pertumbuhan': [-0.02, -0.02, 0.13, -0.05, 0.07]})
# Basic data cleaning
df = df.dropna() # Remove missing values
df = df.drop_duplicates() # Remove duplicates
# Customer segmentation using KMeans
from sklearn.cluster import KMeans
from sklearn.preprocessing import StandardScaler
# Select features and scale
X = df[['Age/Usia', 'Income/Pendapatan', 'Loyalty Score/Skor Loyalitas']]
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)
# Cluster into 3 segments
kmeans = KMeans(n_clusters=3, random_state=42)
df['Segment/Segmen'] = kmeans.fit_predict(X_scaled)
# Visualize segments
fig = px.scatter_3d(df, x='Age/Usia', y='Income/Pendapatan',
z='Loyalty Score/Skor Loyalitas',
color='Segment/Segmen',
title='Customer Segments')
fig.show()
|
Visualisasikan penjualan jenis kelamin
|
{
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": null,
"City/Kota": [
"Singapore",
"Berlin",
"Jakarta",
"Dubai",
"Tokyo"
],
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": [
"Singapore",
"Germany",
"Indonesia",
"UAE",
"Japan"
],
"Customer ID/ID Pelanggan": null,
"Date/Tanggal": null,
"Discount/Diskon": null,
"Gender/Jenis Kelamin": null,
"Growth Rate/Pertumbuhan": [
-0.07,
-0.03,
0.09,
0,
-0.05
],
"Income/Pendapatan": null,
"Last Purchase Date/Tanggal Pembelian Terakhir": null,
"Latitude/Lintang": [
-11.3435,
76.4062,
36.7018,
63.4598,
20.6415
],
"Longitude/Bujur": [
28.9065,
-158.1406,
-11.0484,
-79.435,
34.5191
],
"Loyalty Score/Skor Loyalitas": null,
"Market Share/Pangsa Pasar": [
0.49,
0.46,
0.24,
0.27,
0.17
],
"Price/Harga": null,
"Product/Produk": null,
"Region/Wilayah": [
"Asia",
"Europe",
"Asia",
"Middle East",
"Asia"
],
"Revenue/Pendapatan": [
3918,
2918,
4028,
3395,
1926
],
"Sales/Penjualan": null,
"Stock/Stok": null,
"Visitors/Pengunjung": null
}
|
import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'City/Kota': ['Singapore', 'Berlin', 'Jakarta', 'Dubai', 'Tokyo'], 'Country/Negara': ['Singapore', 'Germany', 'Indonesia', 'UAE', 'Japan'], 'Region/Wilayah': ['Asia', 'Europe', 'Asia', 'Middle East', 'Asia'], 'Latitude/Lintang': [-11.3435, 76.4062, 36.7018, 63.4598, 20.6415], 'Longitude/Bujur': [28.9065, -158.1406, -11.0484, -79.435, 34.5191], 'Revenue/Pendapatan': [3918, 2918, 4028, 3395, 1926], 'Market Share/Pangsa Pasar': [0.49, 0.46, 0.24, 0.27, 0.17], 'Growth Rate/Pertumbuhan': [-0.07, -0.03, 0.09, 0.0, -0.05]})
# Basic data cleaning
df = df.dropna() # Remove missing values
df = df.drop_duplicates() # Remove duplicates
# Geospatial visualization
fig = px.choropleth(df, locations='Country/Negara',
locationmode='country names',
color='Revenue/Pendapatan',
hover_name='City/Kota',
scope='world',
title='Revenue by Country')
fig.update_geos(showcountries=True)
fig.show()
|
Identifikasi skor loyalitas jenis kelamin
|
{
"Age/Usia": [
32,
46,
39,
67,
21,
34,
42,
53,
47,
34,
29,
29,
56,
44,
38,
53,
45,
22,
63,
26,
27,
29,
67,
63,
26,
27,
42,
44,
37,
57,
66,
37,
48,
68,
64,
48,
31,
70,
69,
38,
30,
39,
30,
37,
23,
35,
18,
61,
20,
67,
62,
53,
48,
32,
48,
41,
52,
28,
22,
35,
24,
62,
55,
23,
24,
51,
19,
40,
66,
70,
63,
53,
31,
40,
52,
29,
45,
42,
48,
57,
31,
64,
54,
48,
41,
62,
52,
52,
31,
51,
55,
64,
65,
51,
20,
56,
53,
25,
24
],
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": null,
"City/Kota": null,
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": null,
"Customer ID/ID Pelanggan": [
"CUST_0001",
"CUST_0002",
"CUST_0003",
"CUST_0004",
"CUST_0005",
"CUST_0006",
"CUST_0007",
"CUST_0008",
"CUST_0009",
"CUST_0010",
"CUST_0011",
"CUST_0012",
"CUST_0013",
"CUST_0014",
"CUST_0015",
"CUST_0016",
"CUST_0017",
"CUST_0018",
"CUST_0019",
"CUST_0020",
"CUST_0021",
"CUST_0022",
"CUST_0023",
"CUST_0024",
"CUST_0025",
"CUST_0026",
"CUST_0027",
"CUST_0028",
"CUST_0029",
"CUST_0030",
"CUST_0031",
"CUST_0032",
"CUST_0033",
"CUST_0034",
"CUST_0035",
"CUST_0036",
"CUST_0037",
"CUST_0038",
"CUST_0039",
"CUST_0040",
"CUST_0041",
"CUST_0042",
"CUST_0043",
"CUST_0044",
"CUST_0045",
"CUST_0046",
"CUST_0047",
"CUST_0048",
"CUST_0049",
"CUST_0050",
"CUST_0051",
"CUST_0052",
"CUST_0053",
"CUST_0054",
"CUST_0055",
"CUST_0056",
"CUST_0057",
"CUST_0058",
"CUST_0059",
"CUST_0060",
"CUST_0061",
"CUST_0062",
"CUST_0063",
"CUST_0064",
"CUST_0065",
"CUST_0066",
"CUST_0067",
"CUST_0068",
"CUST_0069",
"CUST_0070",
"CUST_0071",
"CUST_0072",
"CUST_0073",
"CUST_0074",
"CUST_0075",
"CUST_0076",
"CUST_0077",
"CUST_0078",
"CUST_0079",
"CUST_0080",
"CUST_0081",
"CUST_0082",
"CUST_0083",
"CUST_0084",
"CUST_0085",
"CUST_0086",
"CUST_0087",
"CUST_0088",
"CUST_0089",
"CUST_0090",
"CUST_0091",
"CUST_0092",
"CUST_0093",
"CUST_0094",
"CUST_0095",
"CUST_0096",
"CUST_0097",
"CUST_0098",
"CUST_0099"
],
"Date/Tanggal": null,
"Discount/Diskon": null,
"Gender/Jenis Kelamin": [
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Other/Lainnya",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Other/Lainnya",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan"
],
"Growth Rate/Pertumbuhan": null,
"Income/Pendapatan": [
4000,
2000,
2000,
7000,
7000,
2000,
3000,
8000,
8000,
4000,
1000,
5000,
7000,
1000,
8000,
10000,
6000,
5000,
9000,
9000,
2000,
9000,
9000,
4000,
10000,
5000,
2000,
3000,
10000,
3000,
3000,
2000,
7000,
9000,
5000,
3000,
6000,
5000,
8000,
5000,
6000,
7000,
5000,
6000,
9000,
8000,
5000,
5000,
6000,
10000,
5000,
7000,
1000,
9000,
7000,
7000,
1000,
8000,
6000,
4000,
4000,
1000,
5000,
1000,
2000,
10000,
2000,
3000,
2000,
8000,
10000,
5000,
8000,
7000,
4000,
3000,
10000,
4000,
6000,
6000,
4000,
1000,
2000,
6000,
10000,
2000,
5000,
6000,
4000,
2000,
4000,
4000,
3000,
7000,
3000,
3000,
2000,
5000,
6000
],
"Last Purchase Date/Tanggal Pembelian Terakhir": [
"2024-11-13",
"2025-03-27",
"2025-02-17",
"2025-01-28",
"2024-07-31",
"2024-09-27",
"2024-05-21",
"2025-01-23",
"2025-01-22",
"2024-05-17",
"2024-09-03",
"2025-04-18",
"2025-03-20",
"2024-11-25",
"2024-09-17",
"2024-06-27",
"2025-02-23",
"2024-10-04",
"2024-09-12",
"2025-03-12",
"2024-12-09",
"2024-05-19",
"2024-05-31",
"2024-11-16",
"2024-10-30",
"2025-01-11",
"2024-07-02",
"2024-07-26",
"2025-03-19",
"2025-02-21",
"2024-06-28",
"2024-05-25",
"2024-07-19",
"2025-03-10",
"2024-06-08",
"2025-02-15",
"2024-05-05",
"2025-02-04",
"2024-11-05",
"2024-06-21",
"2025-03-29",
"2025-02-26",
"2025-01-02",
"2024-12-26",
"2024-10-04",
"2025-03-14",
"2024-11-24",
"2025-03-14",
"2024-06-10",
"2025-01-10",
"2024-12-11",
"2024-09-13",
"2025-04-07",
"2024-07-20",
"2025-01-28",
"2024-05-14",
"2024-10-05",
"2025-03-29",
"2025-01-02",
"2024-10-09",
"2024-11-17",
"2024-08-26",
"2024-10-11",
"2025-04-05",
"2024-12-28",
"2024-10-03",
"2024-06-12",
"2024-10-15",
"2025-01-08",
"2024-09-04",
"2024-11-01",
"2024-10-08",
"2025-02-12",
"2025-04-16",
"2024-11-28",
"2024-12-02",
"2024-09-03",
"2024-12-11",
"2024-10-13",
"2025-02-14",
"2024-12-10",
"2024-10-04",
"2025-01-06",
"2025-03-05",
"2024-08-12",
"2024-05-10",
"2024-09-01",
"2024-12-13",
"2024-05-14",
"2024-11-07",
"2024-09-07",
"2024-08-01",
"2024-11-10",
"2025-03-04",
"2024-08-11",
"2025-01-16",
"2024-09-08",
"2025-01-11",
"2024-11-09"
],
"Latitude/Lintang": null,
"Longitude/Bujur": null,
"Loyalty Score/Skor Loyalitas": [
20,
89,
8,
39,
24,
37,
42,
19,
83,
3,
77,
85,
28,
4,
70,
31,
89,
63,
22,
6,
91,
21,
53,
89,
11,
40,
85,
6,
45,
27,
5,
50,
91,
15,
71,
67,
29,
65,
23,
32,
17,
80,
75,
33,
88,
39,
57,
30,
8,
9,
11,
96,
7,
18,
83,
95,
52,
81,
82,
5,
45,
99,
50,
65,
3,
72,
40,
96,
94,
5,
76,
51,
47,
43,
29,
96,
57,
65,
28,
7,
93,
89,
53,
34,
22,
87,
25,
62,
24,
50,
17,
40,
59,
8,
12,
54,
21,
91,
31
],
"Market Share/Pangsa Pasar": null,
"Price/Harga": null,
"Product/Produk": null,
"Region/Wilayah": null,
"Revenue/Pendapatan": null,
"Sales/Penjualan": null,
"Stock/Stok": null,
"Visitors/Pengunjung": null
}
|
import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Customer ID/ID Pelanggan': ['CUST_0001', 'CUST_0002', 'CUST_0003', 'CUST_0004', 'CUST_0005', 'CUST_0006', 'CUST_0007', 'CUST_0008', 'CUST_0009', 'CUST_0010', 'CUST_0011', 'CUST_0012', 'CUST_0013', 'CUST_0014', 'CUST_0015', 'CUST_0016', 'CUST_0017', 'CUST_0018', 'CUST_0019', 'CUST_0020', 'CUST_0021', 'CUST_0022', 'CUST_0023', 'CUST_0024', 'CUST_0025', 'CUST_0026', 'CUST_0027', 'CUST_0028', 'CUST_0029', 'CUST_0030', 'CUST_0031', 'CUST_0032', 'CUST_0033', 'CUST_0034', 'CUST_0035', 'CUST_0036', 'CUST_0037', 'CUST_0038', 'CUST_0039', 'CUST_0040', 'CUST_0041', 'CUST_0042', 'CUST_0043', 'CUST_0044', 'CUST_0045', 'CUST_0046', 'CUST_0047', 'CUST_0048', 'CUST_0049', 'CUST_0050', 'CUST_0051', 'CUST_0052', 'CUST_0053', 'CUST_0054', 'CUST_0055', 'CUST_0056', 'CUST_0057', 'CUST_0058', 'CUST_0059', 'CUST_0060', 'CUST_0061', 'CUST_0062', 'CUST_0063', 'CUST_0064', 'CUST_0065', 'CUST_0066', 'CUST_0067', 'CUST_0068', 'CUST_0069', 'CUST_0070', 'CUST_0071', 'CUST_0072', 'CUST_0073', 'CUST_0074', 'CUST_0075', 'CUST_0076', 'CUST_0077', 'CUST_0078', 'CUST_0079', 'CUST_0080', 'CUST_0081', 'CUST_0082', 'CUST_0083', 'CUST_0084', 'CUST_0085', 'CUST_0086', 'CUST_0087', 'CUST_0088', 'CUST_0089', 'CUST_0090', 'CUST_0091', 'CUST_0092', 'CUST_0093', 'CUST_0094', 'CUST_0095', 'CUST_0096', 'CUST_0097', 'CUST_0098', 'CUST_0099'], 'Age/Usia': [32, 46, 39, 67, 21, 34, 42, 53, 47, 34, 29, 29, 56, 44, 38, 53, 45, 22, 63, 26, 27, 29, 67, 63, 26, 27, 42, 44, 37, 57, 66, 37, 48, 68, 64, 48, 31, 70, 69, 38, 30, 39, 30, 37, 23, 35, 18, 61, 20, 67, 62, 53, 48, 32, 48, 41, 52, 28, 22, 35, 24, 62, 55, 23, 24, 51, 19, 40, 66, 70, 63, 53, 31, 40, 52, 29, 45, 42, 48, 57, 31, 64, 54, 48, 41, 62, 52, 52, 31, 51, 55, 64, 65, 51, 20, 56, 53, 25, 24], 'Gender/Jenis Kelamin': ['Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Other/Lainnya', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Other/Lainnya', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan'], 'Income/Pendapatan': [4000, 2000, 2000, 7000, 7000, 2000, 3000, 8000, 8000, 4000, 1000, 5000, 7000, 1000, 8000, 10000, 6000, 5000, 9000, 9000, 2000, 9000, 9000, 4000, 10000, 5000, 2000, 3000, 10000, 3000, 3000, 2000, 7000, 9000, 5000, 3000, 6000, 5000, 8000, 5000, 6000, 7000, 5000, 6000, 9000, 8000, 5000, 5000, 6000, 10000, 5000, 7000, 1000, 9000, 7000, 7000, 1000, 8000, 6000, 4000, 4000, 1000, 5000, 1000, 2000, 10000, 2000, 3000, 2000, 8000, 10000, 5000, 8000, 7000, 4000, 3000, 10000, 4000, 6000, 6000, 4000, 1000, 2000, 6000, 10000, 2000, 5000, 6000, 4000, 2000, 4000, 4000, 3000, 7000, 3000, 3000, 2000, 5000, 6000], 'Loyalty Score/Skor Loyalitas': [20, 89, 8, 39, 24, 37, 42, 19, 83, 3, 77, 85, 28, 4, 70, 31, 89, 63, 22, 6, 91, 21, 53, 89, 11, 40, 85, 6, 45, 27, 5, 50, 91, 15, 71, 67, 29, 65, 23, 32, 17, 80, 75, 33, 88, 39, 57, 30, 8, 9, 11, 96, 7, 18, 83, 95, 52, 81, 82, 5, 45, 99, 50, 65, 3, 72, 40, 96, 94, 5, 76, 51, 47, 43, 29, 96, 57, 65, 28, 7, 93, 89, 53, 34, 22, 87, 25, 62, 24, 50, 17, 40, 59, 8, 12, 54, 21, 91, 31], 'Last Purchase Date/Tanggal Pembelian Terakhir': ['2024-11-13', '2025-03-27', '2025-02-17', '2025-01-28', '2024-07-31', '2024-09-27', '2024-05-21', '2025-01-23', '2025-01-22', '2024-05-17', '2024-09-03', '2025-04-18', '2025-03-20', '2024-11-25', '2024-09-17', '2024-06-27', '2025-02-23', '2024-10-04', '2024-09-12', '2025-03-12', '2024-12-09', '2024-05-19', '2024-05-31', '2024-11-16', '2024-10-30', '2025-01-11', '2024-07-02', '2024-07-26', '2025-03-19', '2025-02-21', '2024-06-28', '2024-05-25', '2024-07-19', '2025-03-10', '2024-06-08', '2025-02-15', '2024-05-05', '2025-02-04', '2024-11-05', '2024-06-21', '2025-03-29', '2025-02-26', '2025-01-02', '2024-12-26', '2024-10-04', '2025-03-14', '2024-11-24', '2025-03-14', '2024-06-10', '2025-01-10', '2024-12-11', '2024-09-13', '2025-04-07', '2024-07-20', '2025-01-28', '2024-05-14', '2024-10-05', '2025-03-29', '2025-01-02', '2024-10-09', '2024-11-17', '2024-08-26', '2024-10-11', '2025-04-05', '2024-12-28', '2024-10-03', '2024-06-12', '2024-10-15', '2025-01-08', '2024-09-04', '2024-11-01', '2024-10-08', '2025-02-12', '2025-04-16', '2024-11-28', '2024-12-02', '2024-09-03', '2024-12-11', '2024-10-13', '2025-02-14', '2024-12-10', '2024-10-04', '2025-01-06', '2025-03-05', '2024-08-12', '2024-05-10', '2024-09-01', '2024-12-13', '2024-05-14', '2024-11-07', '2024-09-07', '2024-08-01', '2024-11-10', '2025-03-04', '2024-08-11', '2025-01-16', '2024-09-08', '2025-01-11', '2024-11-09']})
# Basic data cleaning
df = df.dropna() # Remove missing values
df = df.drop_duplicates() # Remove duplicates
# Comprehensive data analysis
analysis_result = df.describe(include='all')
print(analysis_result)
# Additional insights
print('\nTop performing items:')
print(df.nlargest(3, df.columns[1]))
print('\nTrend analysis:')
if 'Date/Tanggal' in df.columns:
df['Month/Bulan'] = pd.to_datetime(df['Date/Tanggal']).dt.month
monthly_trend = df.groupby('Month/Bulan').mean(numeric_only=True)
print(monthly_trend)
|
Segmentasi pertumbuhan grup usia
|
{
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": null,
"City/Kota": [
"Tokyo",
"Mumbai",
"Mexico City",
"London",
"New York"
],
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": [
"Japan",
"India",
"Mexico",
"UK",
"US"
],
"Customer ID/ID Pelanggan": null,
"Date/Tanggal": null,
"Discount/Diskon": null,
"Gender/Jenis Kelamin": null,
"Growth Rate/Pertumbuhan": [
-0.05,
0.07,
-0.07,
0.01,
0.09
],
"Income/Pendapatan": null,
"Last Purchase Date/Tanggal Pembelian Terakhir": null,
"Latitude/Lintang": [
41.5197,
21.7984,
66.6411,
-86.7752,
63.1073
],
"Longitude/Bujur": [
45.0023,
-10.0756,
36.7255,
174.7464,
-99.6719
],
"Loyalty Score/Skor Loyalitas": null,
"Market Share/Pangsa Pasar": [
0.49,
0.39,
0.23,
0.29,
0.19
],
"Price/Harga": null,
"Product/Produk": null,
"Region/Wilayah": [
"Asia",
"Asia",
"North America",
"Europe",
"North America"
],
"Revenue/Pendapatan": [
4688,
3787,
2954,
2237,
4598
],
"Sales/Penjualan": null,
"Stock/Stok": null,
"Visitors/Pengunjung": null
}
|
import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'City/Kota': ['Tokyo', 'Mumbai', 'Mexico City', 'London', 'New York'], 'Country/Negara': ['Japan', 'India', 'Mexico', 'UK', 'US'], 'Region/Wilayah': ['Asia', 'Asia', 'North America', 'Europe', 'North America'], 'Latitude/Lintang': [41.5197, 21.7984, 66.6411, -86.7752, 63.1073], 'Longitude/Bujur': [45.0023, -10.0756, 36.7255, 174.7464, -99.6719], 'Revenue/Pendapatan': [4688, 3787, 2954, 2237, 4598], 'Market Share/Pangsa Pasar': [0.49, 0.39, 0.23, 0.29, 0.19], 'Growth Rate/Pertumbuhan': [-0.05, 0.07, -0.07, 0.01, 0.09]})
# Basic data cleaning
df = df.dropna() # Remove missing values
df = df.drop_duplicates() # Remove duplicates
# Customer segmentation using KMeans
from sklearn.cluster import KMeans
from sklearn.preprocessing import StandardScaler
# Select features and scale
X = df[['Age/Usia', 'Income/Pendapatan', 'Loyalty Score/Skor Loyalitas']]
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)
# Cluster into 3 segments
kmeans = KMeans(n_clusters=3, random_state=42)
df['Segment/Segmen'] = kmeans.fit_predict(X_scaled)
# Visualize segments
fig = px.scatter_3d(df, x='Age/Usia', y='Income/Pendapatan',
z='Loyalty Score/Skor Loyalitas',
color='Segment/Segmen',
title='Customer Segments')
fig.show()
|
Optimize income gender
|
{
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": [
98.75,
122.42,
167.55,
170.74,
187.8,
181.51,
123.15,
136.72,
96.95,
162.23,
60.25,
87.53,
54.87,
129.71,
162.76,
146.62,
104.05,
105.14,
78.33,
114.94,
74.9,
67.73,
127.26,
61.33,
106.71,
168.17,
101.91,
104.36,
184.17,
101.27
],
"Category/Kategori": null,
"City/Kota": null,
"Conversion Rate/Rasio Konversi": [
0.0728,
0.0712,
0.0538,
0.07250000000000001,
0.0898,
0.0275,
0.020300000000000002,
0.0111,
0.016900000000000002,
0.0761,
0.0204,
0.034,
0.0177,
0.0945,
0.0256,
0.059300000000000005,
0.0853,
0.0625,
0.0471,
0.08560000000000001,
0.046200000000000005,
0.06770000000000001,
0.079,
0.0152,
0.0985,
0.0978,
0.0762,
0.016300000000000002,
0.0884,
0.0244
],
"Country/Negara": null,
"Customer ID/ID Pelanggan": null,
"Date/Tanggal": [
"2025-03-23",
"2025-03-24",
"2025-03-25",
"2025-03-26",
"2025-03-27",
"2025-03-28",
"2025-03-29",
"2025-03-30",
"2025-03-31",
"2025-04-01",
"2025-04-02",
"2025-04-03",
"2025-04-04",
"2025-04-05",
"2025-04-06",
"2025-04-07",
"2025-04-08",
"2025-04-09",
"2025-04-10",
"2025-04-11",
"2025-04-12",
"2025-04-13",
"2025-04-14",
"2025-04-15",
"2025-04-16",
"2025-04-17",
"2025-04-18",
"2025-04-19",
"2025-04-20",
"2025-04-21"
],
"Discount/Diskon": null,
"Gender/Jenis Kelamin": null,
"Growth Rate/Pertumbuhan": null,
"Income/Pendapatan": null,
"Last Purchase Date/Tanggal Pembelian Terakhir": null,
"Latitude/Lintang": null,
"Longitude/Bujur": null,
"Loyalty Score/Skor Loyalitas": null,
"Market Share/Pangsa Pasar": null,
"Price/Harga": null,
"Product/Produk": null,
"Region/Wilayah": null,
"Revenue/Pendapatan": [
1346.18,
1452.59,
1492.83,
1510.15,
1517.06,
1594.15,
1514.21,
1518.82,
1350.76,
1263.83,
1223.37,
1198.38,
1248.63,
1377.75,
1443.75,
1627.11,
1692.7,
1695.31,
1765.33,
1744.86,
1808.01,
1700.14,
1578.81,
1533,
1470.04,
1399.48,
1476.33,
1448.49,
1614.95,
1631.57
],
"Sales/Penjualan": null,
"Stock/Stok": null,
"Visitors/Pengunjung": [
164,
144,
74,
201,
157,
292,
110,
107,
251,
155,
182,
59,
260,
299,
174,
199,
103,
307,
127,
213,
205,
210,
230,
90,
254,
255,
61,
141,
168,
184
]
}
|
import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Date/Tanggal': ['2025-03-23', '2025-03-24', '2025-03-25', '2025-03-26', '2025-03-27', '2025-03-28', '2025-03-29', '2025-03-30', '2025-03-31', '2025-04-01', '2025-04-02', '2025-04-03', '2025-04-04', '2025-04-05', '2025-04-06', '2025-04-07', '2025-04-08', '2025-04-09', '2025-04-10', '2025-04-11', '2025-04-12', '2025-04-13', '2025-04-14', '2025-04-15', '2025-04-16', '2025-04-17', '2025-04-18', '2025-04-19', '2025-04-20', '2025-04-21'], 'Revenue/Pendapatan': [1346.18, 1452.59, 1492.83, 1510.15, 1517.06, 1594.15, 1514.21, 1518.82, 1350.76, 1263.83, 1223.37, 1198.38, 1248.63, 1377.75, 1443.75, 1627.11, 1692.7, 1695.31, 1765.33, 1744.86, 1808.01, 1700.14, 1578.81, 1533.0, 1470.04, 1399.48, 1476.33, 1448.49, 1614.95, 1631.57], 'Visitors/Pengunjung': [164, 144, 74, 201, 157, 292, 110, 107, 251, 155, 182, 59, 260, 299, 174, 199, 103, 307, 127, 213, 205, 210, 230, 90, 254, 255, 61, 141, 168, 184], 'Conversion Rate/Rasio Konversi': [0.0728, 0.0712, 0.0538, 0.0725, 0.0898, 0.0275, 0.0203, 0.0111, 0.0169, 0.0761, 0.0204, 0.034, 0.0177, 0.0945, 0.0256, 0.0593, 0.0853, 0.0625, 0.0471, 0.0856, 0.0462, 0.0677, 0.079, 0.0152, 0.0985, 0.0978, 0.0762, 0.0163, 0.0884, 0.0244], 'Avg. Order Value/Nilai Pesanan Rata2': [98.75, 122.42, 167.55, 170.74, 187.8, 181.51, 123.15, 136.72, 96.95, 162.23, 60.25, 87.53, 54.87, 129.71, 162.76, 146.62, 104.05, 105.14, 78.33, 114.94, 74.9, 67.73, 127.26, 61.33, 106.71, 168.17, 101.91, 104.36, 184.17, 101.27]})
# Basic data cleaning
df = df.dropna() # Remove missing values
df = df.drop_duplicates() # Remove duplicates
# Comprehensive data analysis
analysis_result = df.describe(include='all')
print(analysis_result)
# Additional insights
print('\nTop performing items:')
print(df.nlargest(3, df.columns[1]))
print('\nTrend analysis:')
if 'Date/Tanggal' in df.columns:
df['Month/Bulan'] = pd.to_datetime(df['Date/Tanggal']).dt.month
monthly_trend = df.groupby('Month/Bulan').mean(numeric_only=True)
print(monthly_trend)
|
Calculate conversion rate age group
|
{
"Age/Usia": [
19,
40,
63,
52,
68,
24,
53,
37,
48,
65,
39,
26,
46,
50,
61,
69,
20,
67,
46,
62,
28,
62,
45,
26,
59,
65,
21,
33,
57,
20,
25,
58,
47,
32,
27,
19,
62,
33,
43,
31,
44,
65,
26,
57,
38,
36,
31,
29,
62,
65,
37,
43,
29,
39,
36,
50,
44,
22,
22,
39,
48,
50,
54
],
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": null,
"City/Kota": null,
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": null,
"Customer ID/ID Pelanggan": [
"CUST_0001",
"CUST_0002",
"CUST_0003",
"CUST_0004",
"CUST_0005",
"CUST_0006",
"CUST_0007",
"CUST_0008",
"CUST_0009",
"CUST_0010",
"CUST_0011",
"CUST_0012",
"CUST_0013",
"CUST_0014",
"CUST_0015",
"CUST_0016",
"CUST_0017",
"CUST_0018",
"CUST_0019",
"CUST_0020",
"CUST_0021",
"CUST_0022",
"CUST_0023",
"CUST_0024",
"CUST_0025",
"CUST_0026",
"CUST_0027",
"CUST_0028",
"CUST_0029",
"CUST_0030",
"CUST_0031",
"CUST_0032",
"CUST_0033",
"CUST_0034",
"CUST_0035",
"CUST_0036",
"CUST_0037",
"CUST_0038",
"CUST_0039",
"CUST_0040",
"CUST_0041",
"CUST_0042",
"CUST_0043",
"CUST_0044",
"CUST_0045",
"CUST_0046",
"CUST_0047",
"CUST_0048",
"CUST_0049",
"CUST_0050",
"CUST_0051",
"CUST_0052",
"CUST_0053",
"CUST_0054",
"CUST_0055",
"CUST_0056",
"CUST_0057",
"CUST_0058",
"CUST_0059",
"CUST_0060",
"CUST_0061",
"CUST_0062",
"CUST_0063"
],
"Date/Tanggal": null,
"Discount/Diskon": null,
"Gender/Jenis Kelamin": [
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Other/Lainnya",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Other/Lainnya",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Other/Lainnya",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Other/Lainnya",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki"
],
"Growth Rate/Pertumbuhan": null,
"Income/Pendapatan": [
8000,
6000,
9000,
9000,
7000,
3000,
9000,
1000,
6000,
7000,
3000,
4000,
2000,
3000,
7000,
8000,
8000,
2000,
9000,
4000,
9000,
2000,
5000,
2000,
1000,
7000,
6000,
4000,
8000,
6000,
7000,
1000,
3000,
5000,
8000,
4000,
2000,
3000,
7000,
6000,
6000,
9000,
4000,
2000,
8000,
1000,
7000,
8000,
8000,
4000,
6000,
3000,
4000,
8000,
7000,
8000,
4000,
7000,
8000,
4000,
3000,
2000,
3000
],
"Last Purchase Date/Tanggal Pembelian Terakhir": [
"2024-12-05",
"2025-01-25",
"2025-03-24",
"2024-08-14",
"2024-06-10",
"2024-09-09",
"2024-10-10",
"2024-07-03",
"2024-05-10",
"2024-05-25",
"2025-01-17",
"2025-01-04",
"2024-10-28",
"2024-04-30",
"2024-07-03",
"2025-01-19",
"2024-12-07",
"2024-04-29",
"2025-03-19",
"2024-10-22",
"2024-12-15",
"2024-09-28",
"2024-07-28",
"2025-01-25",
"2024-08-29",
"2025-03-29",
"2024-05-02",
"2024-08-22",
"2024-10-01",
"2024-06-29",
"2025-02-12",
"2024-12-08",
"2025-01-03",
"2025-01-06",
"2025-03-14",
"2024-09-17",
"2024-10-14",
"2024-12-26",
"2024-07-05",
"2024-06-22",
"2024-05-16",
"2024-11-16",
"2024-09-25",
"2024-05-12",
"2024-10-01",
"2025-01-24",
"2024-07-23",
"2024-08-30",
"2024-07-31",
"2024-06-08",
"2025-01-04",
"2024-08-20",
"2024-06-08",
"2025-04-12",
"2024-09-30",
"2024-12-24",
"2024-12-16",
"2024-11-25",
"2024-09-21",
"2024-11-05",
"2025-03-27",
"2025-01-09",
"2024-12-04"
],
"Latitude/Lintang": null,
"Longitude/Bujur": null,
"Loyalty Score/Skor Loyalitas": [
25,
91,
93,
79,
27,
95,
69,
32,
20,
31,
29,
55,
69,
8,
40,
43,
14,
54,
77,
67,
40,
43,
4,
12,
77,
70,
31,
29,
54,
12,
29,
22,
90,
66,
60,
71,
78,
92,
97,
60,
13,
82,
81,
17,
66,
98,
63,
61,
69,
1,
40,
53,
13,
6,
97,
35,
98,
77,
52,
74,
70,
49,
15
],
"Market Share/Pangsa Pasar": null,
"Price/Harga": null,
"Product/Produk": null,
"Region/Wilayah": null,
"Revenue/Pendapatan": null,
"Sales/Penjualan": null,
"Stock/Stok": null,
"Visitors/Pengunjung": null
}
|
import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Customer ID/ID Pelanggan': ['CUST_0001', 'CUST_0002', 'CUST_0003', 'CUST_0004', 'CUST_0005', 'CUST_0006', 'CUST_0007', 'CUST_0008', 'CUST_0009', 'CUST_0010', 'CUST_0011', 'CUST_0012', 'CUST_0013', 'CUST_0014', 'CUST_0015', 'CUST_0016', 'CUST_0017', 'CUST_0018', 'CUST_0019', 'CUST_0020', 'CUST_0021', 'CUST_0022', 'CUST_0023', 'CUST_0024', 'CUST_0025', 'CUST_0026', 'CUST_0027', 'CUST_0028', 'CUST_0029', 'CUST_0030', 'CUST_0031', 'CUST_0032', 'CUST_0033', 'CUST_0034', 'CUST_0035', 'CUST_0036', 'CUST_0037', 'CUST_0038', 'CUST_0039', 'CUST_0040', 'CUST_0041', 'CUST_0042', 'CUST_0043', 'CUST_0044', 'CUST_0045', 'CUST_0046', 'CUST_0047', 'CUST_0048', 'CUST_0049', 'CUST_0050', 'CUST_0051', 'CUST_0052', 'CUST_0053', 'CUST_0054', 'CUST_0055', 'CUST_0056', 'CUST_0057', 'CUST_0058', 'CUST_0059', 'CUST_0060', 'CUST_0061', 'CUST_0062', 'CUST_0063'], 'Age/Usia': [19, 40, 63, 52, 68, 24, 53, 37, 48, 65, 39, 26, 46, 50, 61, 69, 20, 67, 46, 62, 28, 62, 45, 26, 59, 65, 21, 33, 57, 20, 25, 58, 47, 32, 27, 19, 62, 33, 43, 31, 44, 65, 26, 57, 38, 36, 31, 29, 62, 65, 37, 43, 29, 39, 36, 50, 44, 22, 22, 39, 48, 50, 54], 'Gender/Jenis Kelamin': ['Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Other/Lainnya', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Other/Lainnya', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Other/Lainnya', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Other/Lainnya', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki'], 'Income/Pendapatan': [8000, 6000, 9000, 9000, 7000, 3000, 9000, 1000, 6000, 7000, 3000, 4000, 2000, 3000, 7000, 8000, 8000, 2000, 9000, 4000, 9000, 2000, 5000, 2000, 1000, 7000, 6000, 4000, 8000, 6000, 7000, 1000, 3000, 5000, 8000, 4000, 2000, 3000, 7000, 6000, 6000, 9000, 4000, 2000, 8000, 1000, 7000, 8000, 8000, 4000, 6000, 3000, 4000, 8000, 7000, 8000, 4000, 7000, 8000, 4000, 3000, 2000, 3000], 'Loyalty Score/Skor Loyalitas': [25, 91, 93, 79, 27, 95, 69, 32, 20, 31, 29, 55, 69, 8, 40, 43, 14, 54, 77, 67, 40, 43, 4, 12, 77, 70, 31, 29, 54, 12, 29, 22, 90, 66, 60, 71, 78, 92, 97, 60, 13, 82, 81, 17, 66, 98, 63, 61, 69, 1, 40, 53, 13, 6, 97, 35, 98, 77, 52, 74, 70, 49, 15], 'Last Purchase Date/Tanggal Pembelian Terakhir': ['2024-12-05', '2025-01-25', '2025-03-24', '2024-08-14', '2024-06-10', '2024-09-09', '2024-10-10', '2024-07-03', '2024-05-10', '2024-05-25', '2025-01-17', '2025-01-04', '2024-10-28', '2024-04-30', '2024-07-03', '2025-01-19', '2024-12-07', '2024-04-29', '2025-03-19', '2024-10-22', '2024-12-15', '2024-09-28', '2024-07-28', '2025-01-25', '2024-08-29', '2025-03-29', '2024-05-02', '2024-08-22', '2024-10-01', '2024-06-29', '2025-02-12', '2024-12-08', '2025-01-03', '2025-01-06', '2025-03-14', '2024-09-17', '2024-10-14', '2024-12-26', '2024-07-05', '2024-06-22', '2024-05-16', '2024-11-16', '2024-09-25', '2024-05-12', '2024-10-01', '2025-01-24', '2024-07-23', '2024-08-30', '2024-07-31', '2024-06-08', '2025-01-04', '2024-08-20', '2024-06-08', '2025-04-12', '2024-09-30', '2024-12-24', '2024-12-16', '2024-11-25', '2024-09-21', '2024-11-05', '2025-03-27', '2025-01-09', '2024-12-04']})
# Basic data cleaning
df = df.dropna() # Remove missing values
df = df.drop_duplicates() # Remove duplicates
# growth rate calculation
result = df['Customer ID/ID Pelanggan'].pct_change()
print(result)
|
Bandingkan pendapatan kota
|
{
"Age/Usia": [
54,
53,
68,
42,
53,
56,
22,
42,
19,
40,
66,
68,
18,
18,
39,
64,
70,
53,
56,
23,
64,
45,
47,
32,
33,
61,
23,
22,
21,
50,
19,
19,
64,
35,
32,
66,
59,
68,
21,
55,
24,
35,
38,
29,
54,
43,
28,
67,
41,
27,
42,
62,
34,
49,
61,
26,
35,
54,
70
],
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": null,
"City/Kota": null,
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": null,
"Customer ID/ID Pelanggan": [
"CUST_0001",
"CUST_0002",
"CUST_0003",
"CUST_0004",
"CUST_0005",
"CUST_0006",
"CUST_0007",
"CUST_0008",
"CUST_0009",
"CUST_0010",
"CUST_0011",
"CUST_0012",
"CUST_0013",
"CUST_0014",
"CUST_0015",
"CUST_0016",
"CUST_0017",
"CUST_0018",
"CUST_0019",
"CUST_0020",
"CUST_0021",
"CUST_0022",
"CUST_0023",
"CUST_0024",
"CUST_0025",
"CUST_0026",
"CUST_0027",
"CUST_0028",
"CUST_0029",
"CUST_0030",
"CUST_0031",
"CUST_0032",
"CUST_0033",
"CUST_0034",
"CUST_0035",
"CUST_0036",
"CUST_0037",
"CUST_0038",
"CUST_0039",
"CUST_0040",
"CUST_0041",
"CUST_0042",
"CUST_0043",
"CUST_0044",
"CUST_0045",
"CUST_0046",
"CUST_0047",
"CUST_0048",
"CUST_0049",
"CUST_0050",
"CUST_0051",
"CUST_0052",
"CUST_0053",
"CUST_0054",
"CUST_0055",
"CUST_0056",
"CUST_0057",
"CUST_0058",
"CUST_0059"
],
"Date/Tanggal": null,
"Discount/Diskon": null,
"Gender/Jenis Kelamin": [
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Other/Lainnya",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Other/Lainnya",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Other/Lainnya",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan"
],
"Growth Rate/Pertumbuhan": null,
"Income/Pendapatan": [
9000,
2000,
7000,
9000,
6000,
9000,
9000,
3000,
9000,
1000,
4000,
3000,
6000,
9000,
8000,
4000,
8000,
1000,
7000,
5000,
5000,
7000,
9000,
5000,
9000,
3000,
3000,
8000,
6000,
3000,
6000,
6000,
6000,
7000,
5000,
1000,
3000,
3000,
2000,
8000,
5000,
3000,
6000,
3000,
9000,
2000,
4000,
9000,
9000,
3000,
5000,
7000,
4000,
4000,
1000,
6000,
6000,
4000,
2000
],
"Last Purchase Date/Tanggal Pembelian Terakhir": [
"2025-03-19",
"2025-01-29",
"2024-12-12",
"2024-10-20",
"2025-01-11",
"2025-01-27",
"2025-01-19",
"2024-11-24",
"2024-12-01",
"2024-12-30",
"2024-07-24",
"2024-07-30",
"2025-02-17",
"2024-11-10",
"2024-05-14",
"2024-12-31",
"2025-01-15",
"2024-05-01",
"2024-07-24",
"2024-05-26",
"2024-05-25",
"2024-06-24",
"2025-01-16",
"2024-08-25",
"2024-05-09",
"2024-10-07",
"2024-12-23",
"2025-03-13",
"2024-12-11",
"2025-01-23",
"2024-05-14",
"2024-10-28",
"2025-01-29",
"2024-12-30",
"2025-01-30",
"2024-09-04",
"2024-07-04",
"2025-03-18",
"2024-09-28",
"2025-01-27",
"2025-01-24",
"2024-07-02",
"2024-11-15",
"2025-02-09",
"2025-01-09",
"2024-10-18",
"2024-09-20",
"2025-02-21",
"2025-03-29",
"2025-01-14",
"2024-05-06",
"2024-12-27",
"2025-01-17",
"2024-09-14",
"2024-08-04",
"2024-12-13",
"2024-04-25",
"2025-04-09",
"2025-04-08"
],
"Latitude/Lintang": null,
"Longitude/Bujur": null,
"Loyalty Score/Skor Loyalitas": [
22,
92,
19,
30,
74,
63,
52,
22,
42,
3,
82,
90,
70,
42,
5,
15,
51,
12,
98,
39,
10,
89,
40,
59,
36,
33,
96,
27,
79,
99,
71,
68,
100,
25,
62,
50,
68,
37,
3,
24,
8,
78,
98,
64,
13,
53,
48,
4,
21,
25,
50,
61,
52,
77,
38,
61,
55,
28,
40
],
"Market Share/Pangsa Pasar": null,
"Price/Harga": null,
"Product/Produk": null,
"Region/Wilayah": null,
"Revenue/Pendapatan": null,
"Sales/Penjualan": null,
"Stock/Stok": null,
"Visitors/Pengunjung": null
}
|
import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Customer ID/ID Pelanggan': ['CUST_0001', 'CUST_0002', 'CUST_0003', 'CUST_0004', 'CUST_0005', 'CUST_0006', 'CUST_0007', 'CUST_0008', 'CUST_0009', 'CUST_0010', 'CUST_0011', 'CUST_0012', 'CUST_0013', 'CUST_0014', 'CUST_0015', 'CUST_0016', 'CUST_0017', 'CUST_0018', 'CUST_0019', 'CUST_0020', 'CUST_0021', 'CUST_0022', 'CUST_0023', 'CUST_0024', 'CUST_0025', 'CUST_0026', 'CUST_0027', 'CUST_0028', 'CUST_0029', 'CUST_0030', 'CUST_0031', 'CUST_0032', 'CUST_0033', 'CUST_0034', 'CUST_0035', 'CUST_0036', 'CUST_0037', 'CUST_0038', 'CUST_0039', 'CUST_0040', 'CUST_0041', 'CUST_0042', 'CUST_0043', 'CUST_0044', 'CUST_0045', 'CUST_0046', 'CUST_0047', 'CUST_0048', 'CUST_0049', 'CUST_0050', 'CUST_0051', 'CUST_0052', 'CUST_0053', 'CUST_0054', 'CUST_0055', 'CUST_0056', 'CUST_0057', 'CUST_0058', 'CUST_0059'], 'Age/Usia': [54, 53, 68, 42, 53, 56, 22, 42, 19, 40, 66, 68, 18, 18, 39, 64, 70, 53, 56, 23, 64, 45, 47, 32, 33, 61, 23, 22, 21, 50, 19, 19, 64, 35, 32, 66, 59, 68, 21, 55, 24, 35, 38, 29, 54, 43, 28, 67, 41, 27, 42, 62, 34, 49, 61, 26, 35, 54, 70], 'Gender/Jenis Kelamin': ['Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Other/Lainnya', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Other/Lainnya', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Other/Lainnya', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan'], 'Income/Pendapatan': [9000, 2000, 7000, 9000, 6000, 9000, 9000, 3000, 9000, 1000, 4000, 3000, 6000, 9000, 8000, 4000, 8000, 1000, 7000, 5000, 5000, 7000, 9000, 5000, 9000, 3000, 3000, 8000, 6000, 3000, 6000, 6000, 6000, 7000, 5000, 1000, 3000, 3000, 2000, 8000, 5000, 3000, 6000, 3000, 9000, 2000, 4000, 9000, 9000, 3000, 5000, 7000, 4000, 4000, 1000, 6000, 6000, 4000, 2000], 'Loyalty Score/Skor Loyalitas': [22, 92, 19, 30, 74, 63, 52, 22, 42, 3, 82, 90, 70, 42, 5, 15, 51, 12, 98, 39, 10, 89, 40, 59, 36, 33, 96, 27, 79, 99, 71, 68, 100, 25, 62, 50, 68, 37, 3, 24, 8, 78, 98, 64, 13, 53, 48, 4, 21, 25, 50, 61, 52, 77, 38, 61, 55, 28, 40], 'Last Purchase Date/Tanggal Pembelian Terakhir': ['2025-03-19', '2025-01-29', '2024-12-12', '2024-10-20', '2025-01-11', '2025-01-27', '2025-01-19', '2024-11-24', '2024-12-01', '2024-12-30', '2024-07-24', '2024-07-30', '2025-02-17', '2024-11-10', '2024-05-14', '2024-12-31', '2025-01-15', '2024-05-01', '2024-07-24', '2024-05-26', '2024-05-25', '2024-06-24', '2025-01-16', '2024-08-25', '2024-05-09', '2024-10-07', '2024-12-23', '2025-03-13', '2024-12-11', '2025-01-23', '2024-05-14', '2024-10-28', '2025-01-29', '2024-12-30', '2025-01-30', '2024-09-04', '2024-07-04', '2025-03-18', '2024-09-28', '2025-01-27', '2025-01-24', '2024-07-02', '2024-11-15', '2025-02-09', '2025-01-09', '2024-10-18', '2024-09-20', '2025-02-21', '2025-03-29', '2025-01-14', '2024-05-06', '2024-12-27', '2025-01-17', '2024-09-14', '2024-08-04', '2024-12-13', '2024-04-25', '2025-04-09', '2025-04-08']})
# Basic data cleaning
df = df.dropna() # Remove missing values
df = df.drop_duplicates() # Remove duplicates
# Comprehensive data analysis
analysis_result = df.describe(include='all')
print(analysis_result)
# Additional insights
print('\nTop performing items:')
print(df.nlargest(3, df.columns[1]))
print('\nTrend analysis:')
if 'Date/Tanggal' in df.columns:
df['Month/Bulan'] = pd.to_datetime(df['Date/Tanggal']).dt.month
monthly_trend = df.groupby('Month/Bulan').mean(numeric_only=True)
print(monthly_trend)
|
Segmentasi pangsa pasar grup usia
|
{
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": [
"Automotive/Otomotif",
"Automotive/Otomotif",
"Automotive/Otomotif"
],
"City/Kota": null,
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": null,
"Customer ID/ID Pelanggan": null,
"Date/Tanggal": null,
"Discount/Diskon": [
0.01,
0.16,
0.16
],
"Gender/Jenis Kelamin": null,
"Growth Rate/Pertumbuhan": null,
"Income/Pendapatan": null,
"Last Purchase Date/Tanggal Pembelian Terakhir": null,
"Latitude/Lintang": null,
"Longitude/Bujur": null,
"Loyalty Score/Skor Loyalitas": null,
"Market Share/Pangsa Pasar": null,
"Price/Harga": [
79.74,
69.58,
51.78
],
"Product/Produk": [
"Accessories_1",
"Oil_2",
"Tools_3"
],
"Region/Wilayah": null,
"Revenue/Pendapatan": null,
"Sales/Penjualan": [
271,
507,
383
],
"Stock/Stok": [
71,
23,
141
],
"Visitors/Pengunjung": null
}
|
import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Product/Produk': ['Accessories_1', 'Oil_2', 'Tools_3'], 'Sales/Penjualan': [271, 507, 383], 'Price/Harga': [79.74, 69.58, 51.78], 'Category/Kategori': ['Automotive/Otomotif', 'Automotive/Otomotif', 'Automotive/Otomotif'], 'Discount/Diskon': [0.01, 0.16, 0.16], 'Stock/Stok': [71, 23, 141]})
# Basic data cleaning
df = df.dropna() # Remove missing values
df = df.drop_duplicates() # Remove duplicates
# Customer segmentation using KMeans
from sklearn.cluster import KMeans
from sklearn.preprocessing import StandardScaler
# Select features and scale
X = df[['Age/Usia', 'Income/Pendapatan', 'Loyalty Score/Skor Loyalitas']]
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)
# Cluster into 3 segments
kmeans = KMeans(n_clusters=3, random_state=42)
df['Segment/Segmen'] = kmeans.fit_predict(X_scaled)
# Visualize segments
fig = px.scatter_3d(df, x='Age/Usia', y='Income/Pendapatan',
z='Loyalty Score/Skor Loyalitas',
color='Segment/Segmen',
title='Customer Segments')
fig.show()
|
Bandingkan pangsa pasar kota
|
{
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": [
172.48,
85.7,
188.02,
97.63,
185.68,
75.64,
130.46,
130.7,
126.33,
145,
96.98,
129.09,
54.07,
145.62,
191.73,
51.63,
119.15,
59.05,
125.72,
124.68,
128.19,
133.96,
55.92,
130,
98.77,
61.24,
63.03,
110.15,
81.04,
161.63
],
"Category/Kategori": null,
"City/Kota": null,
"Conversion Rate/Rasio Konversi": [
0.0969,
0.0173,
0.028200000000000003,
0.0356,
0.0908,
0.0478,
0.0584,
0.0772,
0.07930000000000001,
0.026000000000000002,
0.0782,
0.028800000000000003,
0.0864,
0.030100000000000002,
0.0727,
0.0818,
0.0782,
0.0614,
0.0937,
0.0567,
0.0714,
0.046,
0.0592,
0.0857,
0.0694,
0.0732,
0.07,
0.042100000000000005,
0.036500000000000005,
0.09280000000000001
],
"Country/Negara": null,
"Customer ID/ID Pelanggan": null,
"Date/Tanggal": [
"2025-03-23",
"2025-03-24",
"2025-03-25",
"2025-03-26",
"2025-03-27",
"2025-03-28",
"2025-03-29",
"2025-03-30",
"2025-03-31",
"2025-04-01",
"2025-04-02",
"2025-04-03",
"2025-04-04",
"2025-04-05",
"2025-04-06",
"2025-04-07",
"2025-04-08",
"2025-04-09",
"2025-04-10",
"2025-04-11",
"2025-04-12",
"2025-04-13",
"2025-04-14",
"2025-04-15",
"2025-04-16",
"2025-04-17",
"2025-04-18",
"2025-04-19",
"2025-04-20",
"2025-04-21"
],
"Discount/Diskon": null,
"Gender/Jenis Kelamin": null,
"Growth Rate/Pertumbuhan": null,
"Income/Pendapatan": null,
"Last Purchase Date/Tanggal Pembelian Terakhir": null,
"Latitude/Lintang": null,
"Longitude/Bujur": null,
"Loyalty Score/Skor Loyalitas": null,
"Market Share/Pangsa Pasar": null,
"Price/Harga": null,
"Product/Produk": null,
"Region/Wilayah": null,
"Revenue/Pendapatan": [
1375.32,
1477.47,
1475.19,
1528.11,
1517.37,
1474.4,
1425.08,
1363.82,
1258.41,
1096.3,
1188.2,
1169.2,
1080.2,
1110.14,
1194.24,
1205.47,
1378.74,
1340.1,
1404.61,
1393.52,
1311.56,
1231.01,
1139.49,
1000.63,
964.85,
968.06,
962.57,
954.1,
1024.57,
1180.15
],
"Sales/Penjualan": null,
"Stock/Stok": null,
"Visitors/Pengunjung": [
262,
297,
294,
200,
136,
110,
72,
296,
184,
114,
288,
271,
95,
242,
62,
263,
152,
261,
76,
257,
46,
226,
76,
243,
277,
229,
72,
81,
217,
242
]
}
|
import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Date/Tanggal': ['2025-03-23', '2025-03-24', '2025-03-25', '2025-03-26', '2025-03-27', '2025-03-28', '2025-03-29', '2025-03-30', '2025-03-31', '2025-04-01', '2025-04-02', '2025-04-03', '2025-04-04', '2025-04-05', '2025-04-06', '2025-04-07', '2025-04-08', '2025-04-09', '2025-04-10', '2025-04-11', '2025-04-12', '2025-04-13', '2025-04-14', '2025-04-15', '2025-04-16', '2025-04-17', '2025-04-18', '2025-04-19', '2025-04-20', '2025-04-21'], 'Revenue/Pendapatan': [1375.32, 1477.47, 1475.19, 1528.11, 1517.37, 1474.4, 1425.08, 1363.82, 1258.41, 1096.3, 1188.2, 1169.2, 1080.2, 1110.14, 1194.24, 1205.47, 1378.74, 1340.1, 1404.61, 1393.52, 1311.56, 1231.01, 1139.49, 1000.63, 964.85, 968.06, 962.57, 954.1, 1024.57, 1180.15], 'Visitors/Pengunjung': [262, 297, 294, 200, 136, 110, 72, 296, 184, 114, 288, 271, 95, 242, 62, 263, 152, 261, 76, 257, 46, 226, 76, 243, 277, 229, 72, 81, 217, 242], 'Conversion Rate/Rasio Konversi': [0.0969, 0.0173, 0.0282, 0.0356, 0.0908, 0.0478, 0.0584, 0.0772, 0.0793, 0.026, 0.0782, 0.0288, 0.0864, 0.0301, 0.0727, 0.0818, 0.0782, 0.0614, 0.0937, 0.0567, 0.0714, 0.046, 0.0592, 0.0857, 0.0694, 0.0732, 0.07, 0.0421, 0.0365, 0.0928], 'Avg. Order Value/Nilai Pesanan Rata2': [172.48, 85.7, 188.02, 97.63, 185.68, 75.64, 130.46, 130.7, 126.33, 145.0, 96.98, 129.09, 54.07, 145.62, 191.73, 51.63, 119.15, 59.05, 125.72, 124.68, 128.19, 133.96, 55.92, 130.0, 98.77, 61.24, 63.03, 110.15, 81.04, 161.63]})
# Basic data cleaning
df = df.dropna() # Remove missing values
df = df.drop_duplicates() # Remove duplicates
# Comprehensive data analysis
analysis_result = df.describe(include='all')
print(analysis_result)
# Additional insights
print('\nTop performing items:')
print(df.nlargest(3, df.columns[1]))
print('\nTrend analysis:')
if 'Date/Tanggal' in df.columns:
df['Month/Bulan'] = pd.to_datetime(df['Date/Tanggal']).dt.month
monthly_trend = df.groupby('Month/Bulan').mean(numeric_only=True)
print(monthly_trend)
|
Forecast market share for/untuk month
|
{
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": null,
"City/Kota": [
"London",
"Jakarta",
"Singapore",
"São Paulo",
"Mumbai"
],
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": [
"UK",
"Indonesia",
"Singapore",
"Brazil",
"India"
],
"Customer ID/ID Pelanggan": null,
"Date/Tanggal": null,
"Discount/Diskon": null,
"Gender/Jenis Kelamin": null,
"Growth Rate/Pertumbuhan": [
0.15,
0.19,
-0.03,
0.24,
0.08
],
"Income/Pendapatan": null,
"Last Purchase Date/Tanggal Pembelian Terakhir": null,
"Latitude/Lintang": [
11.884,
77.0865,
-35.2855,
3.0397,
-54.4591
],
"Longitude/Bujur": [
-151.3358,
-120.2734,
-62.8963,
20.5467,
-34.682900000000004
],
"Loyalty Score/Skor Loyalitas": null,
"Market Share/Pangsa Pasar": [
0.12,
0.15,
0.31,
0.29,
0.44
],
"Price/Harga": null,
"Product/Produk": null,
"Region/Wilayah": [
"Europe",
"Asia",
"Asia",
"South America",
"Asia"
],
"Revenue/Pendapatan": [
4302,
4025,
4238,
1680,
4559
],
"Sales/Penjualan": null,
"Stock/Stok": null,
"Visitors/Pengunjung": null
}
|
import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'City/Kota': ['London', 'Jakarta', 'Singapore', 'São Paulo', 'Mumbai'], 'Country/Negara': ['UK', 'Indonesia', 'Singapore', 'Brazil', 'India'], 'Region/Wilayah': ['Europe', 'Asia', 'Asia', 'South America', 'Asia'], 'Latitude/Lintang': [11.884, 77.0865, -35.2855, 3.0397, -54.4591], 'Longitude/Bujur': [-151.3358, -120.2734, -62.8963, 20.5467, -34.6829], 'Revenue/Pendapatan': [4302, 4025, 4238, 1680, 4559], 'Market Share/Pangsa Pasar': [0.12, 0.15, 0.31, 0.29, 0.44], 'Growth Rate/Pertumbuhan': [0.15, 0.19, -0.03, 0.24, 0.08]})
# Basic data cleaning
df = df.dropna() # Remove missing values
df = df.drop_duplicates() # Remove duplicates
# Time series forecasting
from statsmodels.tsa.arima.model import ARIMA
# Prepare data
ts_data = df.set_index('Date/Tanggal')['Revenue/Pendapatan']
model = ARIMA(ts_data, order=(1,1,1))
model_fit = model.fit()
forecast = model_fit.forecast(steps=7)
print('Forecast for next 7 days:', forecast)
|
Visualisasikan pengunjung grup usia
|
{
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": null,
"City/Kota": [
"London",
"Mumbai",
"Sydney",
"Berlin",
"New York"
],
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": [
"UK",
"India",
"Australia",
"Germany",
"US"
],
"Customer ID/ID Pelanggan": null,
"Date/Tanggal": null,
"Discount/Diskon": null,
"Gender/Jenis Kelamin": null,
"Growth Rate/Pertumbuhan": [
-0.05,
0.01,
-0.07,
0.18,
-0.04
],
"Income/Pendapatan": null,
"Last Purchase Date/Tanggal Pembelian Terakhir": null,
"Latitude/Lintang": [
72.3728,
-58.5947,
89.7596,
22.3944,
0.8445
],
"Longitude/Bujur": [
140.0561,
-161.8252,
93.5911,
97.8351,
-170.5593
],
"Loyalty Score/Skor Loyalitas": null,
"Market Share/Pangsa Pasar": [
0.46,
0.36,
0.48,
0.33,
0.28
],
"Price/Harga": null,
"Product/Produk": null,
"Region/Wilayah": [
"Europe",
"Asia",
"Oceania",
"Europe",
"North America"
],
"Revenue/Pendapatan": [
1130,
1401,
3581,
1703,
3700
],
"Sales/Penjualan": null,
"Stock/Stok": null,
"Visitors/Pengunjung": null
}
|
import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'City/Kota': ['London', 'Mumbai', 'Sydney', 'Berlin', 'New York'], 'Country/Negara': ['UK', 'India', 'Australia', 'Germany', 'US'], 'Region/Wilayah': ['Europe', 'Asia', 'Oceania', 'Europe', 'North America'], 'Latitude/Lintang': [72.3728, -58.5947, 89.7596, 22.3944, 0.8445], 'Longitude/Bujur': [140.0561, -161.8252, 93.5911, 97.8351, -170.5593], 'Revenue/Pendapatan': [1130, 1401, 3581, 1703, 3700], 'Market Share/Pangsa Pasar': [0.46, 0.36, 0.48, 0.33, 0.28], 'Growth Rate/Pertumbuhan': [-0.05, 0.01, -0.07, 0.18, -0.04]})
# Basic data cleaning
df = df.dropna() # Remove missing values
df = df.drop_duplicates() # Remove duplicates
# Geospatial visualization
fig = px.choropleth(df, locations='Country/Negara',
locationmode='country names',
color='Revenue/Pendapatan',
hover_name='City/Kota',
scope='world',
title='Revenue by Country')
fig.update_geos(showcountries=True)
fig.show()
|
Perkiraan pendapatan for/untuk minggu
|
{
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": null,
"City/Kota": [
"Paris",
"New York",
"Dubai",
"Sydney",
"London"
],
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": [
"France",
"US",
"UAE",
"Australia",
"UK"
],
"Customer ID/ID Pelanggan": null,
"Date/Tanggal": null,
"Discount/Diskon": null,
"Gender/Jenis Kelamin": null,
"Growth Rate/Pertumbuhan": [
0.24,
0.28,
0.05,
-0.04,
-0.08
],
"Income/Pendapatan": null,
"Last Purchase Date/Tanggal Pembelian Terakhir": null,
"Latitude/Lintang": [
17.3867,
36.0032,
28.9952,
22.4,
88.1219
],
"Longitude/Bujur": [
-169.4641,
0.12250000000000001,
99.2327,
-4.8377,
150.5256
],
"Loyalty Score/Skor Loyalitas": null,
"Market Share/Pangsa Pasar": [
0.21,
0.49,
0.30000000000000004,
0.38,
0.46
],
"Price/Harga": null,
"Product/Produk": null,
"Region/Wilayah": [
"Europe",
"North America",
"Middle East",
"Oceania",
"Europe"
],
"Revenue/Pendapatan": [
3692,
1823,
2049,
2757,
4673
],
"Sales/Penjualan": null,
"Stock/Stok": null,
"Visitors/Pengunjung": null
}
|
import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'City/Kota': ['Paris', 'New York', 'Dubai', 'Sydney', 'London'], 'Country/Negara': ['France', 'US', 'UAE', 'Australia', 'UK'], 'Region/Wilayah': ['Europe', 'North America', 'Middle East', 'Oceania', 'Europe'], 'Latitude/Lintang': [17.3867, 36.0032, 28.9952, 22.4, 88.1219], 'Longitude/Bujur': [-169.4641, 0.1225, 99.2327, -4.8377, 150.5256], 'Revenue/Pendapatan': [3692, 1823, 2049, 2757, 4673], 'Market Share/Pangsa Pasar': [0.21, 0.49, 0.3, 0.38, 0.46], 'Growth Rate/Pertumbuhan': [0.24, 0.28, 0.05, -0.04, -0.08]})
# Basic data cleaning
df = df.dropna() # Remove missing values
df = df.drop_duplicates() # Remove duplicates
# Time series forecasting
from statsmodels.tsa.arima.model import ARIMA
# Prepare data
ts_data = df.set_index('Date/Tanggal')['Revenue/Pendapatan']
model = ARIMA(ts_data, order=(1,1,1))
model_fit = model.fit()
forecast = model_fit.forecast(steps=7)
print('Forecast for next 7 days:', forecast)
|
Correlate loyalty score and/dan income
|
{
"Age/Usia": [
54,
19,
42,
56,
40,
70,
49,
23,
38,
68,
68,
38,
30,
44,
19,
69,
48,
39,
59,
59,
70,
54,
42,
51,
54,
25,
37,
41,
58,
18,
36,
55,
48,
33,
40,
53,
41,
59,
69,
34,
42,
39,
30,
25,
21,
50,
64,
45,
32,
61,
58,
40,
67
],
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": null,
"City/Kota": null,
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": null,
"Customer ID/ID Pelanggan": [
"CUST_0001",
"CUST_0002",
"CUST_0003",
"CUST_0004",
"CUST_0005",
"CUST_0006",
"CUST_0007",
"CUST_0008",
"CUST_0009",
"CUST_0010",
"CUST_0011",
"CUST_0012",
"CUST_0013",
"CUST_0014",
"CUST_0015",
"CUST_0016",
"CUST_0017",
"CUST_0018",
"CUST_0019",
"CUST_0020",
"CUST_0021",
"CUST_0022",
"CUST_0023",
"CUST_0024",
"CUST_0025",
"CUST_0026",
"CUST_0027",
"CUST_0028",
"CUST_0029",
"CUST_0030",
"CUST_0031",
"CUST_0032",
"CUST_0033",
"CUST_0034",
"CUST_0035",
"CUST_0036",
"CUST_0037",
"CUST_0038",
"CUST_0039",
"CUST_0040",
"CUST_0041",
"CUST_0042",
"CUST_0043",
"CUST_0044",
"CUST_0045",
"CUST_0046",
"CUST_0047",
"CUST_0048",
"CUST_0049",
"CUST_0050",
"CUST_0051",
"CUST_0052",
"CUST_0053"
],
"Date/Tanggal": null,
"Discount/Diskon": null,
"Gender/Jenis Kelamin": [
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Other/Lainnya",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Other/Lainnya",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Other/Lainnya",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki"
],
"Growth Rate/Pertumbuhan": null,
"Income/Pendapatan": [
2000,
6000,
2000,
4000,
3000,
10000,
1000,
9000,
9000,
1000,
7000,
3000,
2000,
2000,
4000,
7000,
3000,
7000,
10000,
6000,
5000,
8000,
8000,
10000,
7000,
8000,
6000,
7000,
8000,
2000,
6000,
4000,
4000,
3000,
5000,
6000,
2000,
9000,
4000,
3000,
4000,
1000,
3000,
6000,
5000,
7000,
4000,
7000,
5000,
7000,
9000,
9000,
6000
],
"Last Purchase Date/Tanggal Pembelian Terakhir": [
"2025-01-30",
"2024-05-05",
"2024-05-02",
"2024-10-19",
"2025-01-24",
"2024-12-17",
"2025-02-08",
"2024-10-12",
"2025-02-18",
"2024-10-28",
"2025-04-20",
"2025-02-14",
"2025-03-05",
"2025-03-19",
"2024-06-07",
"2025-02-13",
"2025-01-16",
"2025-04-09",
"2025-04-17",
"2024-10-08",
"2025-02-01",
"2024-07-01",
"2024-11-27",
"2024-11-21",
"2024-09-28",
"2025-02-05",
"2024-05-03",
"2024-11-13",
"2025-01-13",
"2025-03-15",
"2025-03-03",
"2024-12-02",
"2024-06-18",
"2024-07-06",
"2024-10-31",
"2024-10-22",
"2025-04-10",
"2025-01-31",
"2024-07-08",
"2025-03-19",
"2025-03-18",
"2024-08-27",
"2024-10-07",
"2024-10-05",
"2024-12-26",
"2024-11-06",
"2024-08-13",
"2024-05-02",
"2024-10-24",
"2024-07-24",
"2024-09-10",
"2024-07-24",
"2024-06-20"
],
"Latitude/Lintang": null,
"Longitude/Bujur": null,
"Loyalty Score/Skor Loyalitas": [
90,
99,
85,
64,
6,
40,
61,
19,
69,
78,
26,
42,
90,
17,
82,
84,
96,
40,
83,
41,
25,
21,
75,
18,
3,
50,
8,
29,
13,
85,
87,
6,
59,
30,
96,
92,
93,
83,
1,
92,
53,
10,
52,
68,
13,
66,
76,
98,
94,
91,
15,
69,
37
],
"Market Share/Pangsa Pasar": null,
"Price/Harga": null,
"Product/Produk": null,
"Region/Wilayah": null,
"Revenue/Pendapatan": null,
"Sales/Penjualan": null,
"Stock/Stok": null,
"Visitors/Pengunjung": null
}
|
import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Customer ID/ID Pelanggan': ['CUST_0001', 'CUST_0002', 'CUST_0003', 'CUST_0004', 'CUST_0005', 'CUST_0006', 'CUST_0007', 'CUST_0008', 'CUST_0009', 'CUST_0010', 'CUST_0011', 'CUST_0012', 'CUST_0013', 'CUST_0014', 'CUST_0015', 'CUST_0016', 'CUST_0017', 'CUST_0018', 'CUST_0019', 'CUST_0020', 'CUST_0021', 'CUST_0022', 'CUST_0023', 'CUST_0024', 'CUST_0025', 'CUST_0026', 'CUST_0027', 'CUST_0028', 'CUST_0029', 'CUST_0030', 'CUST_0031', 'CUST_0032', 'CUST_0033', 'CUST_0034', 'CUST_0035', 'CUST_0036', 'CUST_0037', 'CUST_0038', 'CUST_0039', 'CUST_0040', 'CUST_0041', 'CUST_0042', 'CUST_0043', 'CUST_0044', 'CUST_0045', 'CUST_0046', 'CUST_0047', 'CUST_0048', 'CUST_0049', 'CUST_0050', 'CUST_0051', 'CUST_0052', 'CUST_0053'], 'Age/Usia': [54, 19, 42, 56, 40, 70, 49, 23, 38, 68, 68, 38, 30, 44, 19, 69, 48, 39, 59, 59, 70, 54, 42, 51, 54, 25, 37, 41, 58, 18, 36, 55, 48, 33, 40, 53, 41, 59, 69, 34, 42, 39, 30, 25, 21, 50, 64, 45, 32, 61, 58, 40, 67], 'Gender/Jenis Kelamin': ['Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Other/Lainnya', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Other/Lainnya', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Other/Lainnya', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki'], 'Income/Pendapatan': [2000, 6000, 2000, 4000, 3000, 10000, 1000, 9000, 9000, 1000, 7000, 3000, 2000, 2000, 4000, 7000, 3000, 7000, 10000, 6000, 5000, 8000, 8000, 10000, 7000, 8000, 6000, 7000, 8000, 2000, 6000, 4000, 4000, 3000, 5000, 6000, 2000, 9000, 4000, 3000, 4000, 1000, 3000, 6000, 5000, 7000, 4000, 7000, 5000, 7000, 9000, 9000, 6000], 'Loyalty Score/Skor Loyalitas': [90, 99, 85, 64, 6, 40, 61, 19, 69, 78, 26, 42, 90, 17, 82, 84, 96, 40, 83, 41, 25, 21, 75, 18, 3, 50, 8, 29, 13, 85, 87, 6, 59, 30, 96, 92, 93, 83, 1, 92, 53, 10, 52, 68, 13, 66, 76, 98, 94, 91, 15, 69, 37], 'Last Purchase Date/Tanggal Pembelian Terakhir': ['2025-01-30', '2024-05-05', '2024-05-02', '2024-10-19', '2025-01-24', '2024-12-17', '2025-02-08', '2024-10-12', '2025-02-18', '2024-10-28', '2025-04-20', '2025-02-14', '2025-03-05', '2025-03-19', '2024-06-07', '2025-02-13', '2025-01-16', '2025-04-09', '2025-04-17', '2024-10-08', '2025-02-01', '2024-07-01', '2024-11-27', '2024-11-21', '2024-09-28', '2025-02-05', '2024-05-03', '2024-11-13', '2025-01-13', '2025-03-15', '2025-03-03', '2024-12-02', '2024-06-18', '2024-07-06', '2024-10-31', '2024-10-22', '2025-04-10', '2025-01-31', '2024-07-08', '2025-03-19', '2025-03-18', '2024-08-27', '2024-10-07', '2024-10-05', '2024-12-26', '2024-11-06', '2024-08-13', '2024-05-02', '2024-10-24', '2024-07-24', '2024-09-10', '2024-07-24', '2024-06-20']})
# Basic data cleaning
df = df.dropna() # Remove missing values
df = df.drop_duplicates() # Remove duplicates
# Correlation analysis
corr_matrix = df.corr(numeric_only=True)
fig = px.imshow(corr_matrix, text_auto=True,
title='Correlation Matrix')
fig.show()
|
Hitung pangsa pasar produk
|
{
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": null,
"City/Kota": [
"Mumbai",
"Paris",
"São Paulo",
"Tokyo",
"New York"
],
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": [
"India",
"France",
"Brazil",
"Japan",
"US"
],
"Customer ID/ID Pelanggan": null,
"Date/Tanggal": null,
"Discount/Diskon": null,
"Gender/Jenis Kelamin": null,
"Growth Rate/Pertumbuhan": [
0.12,
0.24,
0.02,
-0.05,
-0.1
],
"Income/Pendapatan": null,
"Last Purchase Date/Tanggal Pembelian Terakhir": null,
"Latitude/Lintang": [
59.244,
82.4541,
2.4613,
61.4826,
-24.3944
],
"Longitude/Bujur": [
52.2886,
176.4716,
-138.0176,
-98.3051,
71.9768
],
"Loyalty Score/Skor Loyalitas": null,
"Market Share/Pangsa Pasar": [
0.13,
0.19,
0.1,
0.46,
0.26
],
"Price/Harga": null,
"Product/Produk": null,
"Region/Wilayah": [
"Asia",
"Europe",
"South America",
"Asia",
"North America"
],
"Revenue/Pendapatan": [
4680,
3152,
3672,
4987,
4793
],
"Sales/Penjualan": null,
"Stock/Stok": null,
"Visitors/Pengunjung": null
}
|
import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'City/Kota': ['Mumbai', 'Paris', 'São Paulo', 'Tokyo', 'New York'], 'Country/Negara': ['India', 'France', 'Brazil', 'Japan', 'US'], 'Region/Wilayah': ['Asia', 'Europe', 'South America', 'Asia', 'North America'], 'Latitude/Lintang': [59.244, 82.4541, 2.4613, 61.4826, -24.3944], 'Longitude/Bujur': [52.2886, 176.4716, -138.0176, -98.3051, 71.9768], 'Revenue/Pendapatan': [4680, 3152, 3672, 4987, 4793], 'Market Share/Pangsa Pasar': [0.13, 0.19, 0.1, 0.46, 0.26], 'Growth Rate/Pertumbuhan': [0.12, 0.24, 0.02, -0.05, -0.1]})
# Basic data cleaning
df = df.dropna() # Remove missing values
df = df.drop_duplicates() # Remove duplicates
# moving average calculation
result = df['Longitude/Bujur'].rolling(7).mean()
print(result)
|
Analisis pendapatan periode waktu
|
{
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": [
"Electronics/Elektronik",
"Electronics/Elektronik",
"Electronics/Elektronik"
],
"City/Kota": null,
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": null,
"Customer ID/ID Pelanggan": null,
"Date/Tanggal": null,
"Discount/Diskon": [
0.2,
0.07,
0.16
],
"Gender/Jenis Kelamin": null,
"Growth Rate/Pertumbuhan": null,
"Income/Pendapatan": null,
"Last Purchase Date/Tanggal Pembelian Terakhir": null,
"Latitude/Lintang": null,
"Longitude/Bujur": null,
"Loyalty Score/Skor Loyalitas": null,
"Market Share/Pangsa Pasar": null,
"Price/Harga": [
20.56,
11.1,
138.72
],
"Product/Produk": [
"Smartphone_1",
"TV_2",
"Laptop_3"
],
"Region/Wilayah": null,
"Revenue/Pendapatan": null,
"Sales/Penjualan": [
497,
276,
658
],
"Stock/Stok": [
115,
115,
20
],
"Visitors/Pengunjung": null
}
|
import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Product/Produk': ['Smartphone_1', 'TV_2', 'Laptop_3'], 'Sales/Penjualan': [497, 276, 658], 'Price/Harga': [20.56, 11.1, 138.72], 'Category/Kategori': ['Electronics/Elektronik', 'Electronics/Elektronik', 'Electronics/Elektronik'], 'Discount/Diskon': [0.2, 0.07, 0.16], 'Stock/Stok': [115, 115, 20]})
# Basic data cleaning
df = df.dropna() # Remove missing values
df = df.drop_duplicates() # Remove duplicates
# Comprehensive data analysis
analysis_result = df.describe(include='all')
print(analysis_result)
# Additional insights
print('\nTop performing items:')
print(df.nlargest(3, df.columns[1]))
print('\nTrend analysis:')
if 'Date/Tanggal' in df.columns:
df['Month/Bulan'] = pd.to_datetime(df['Date/Tanggal']).dt.month
monthly_trend = df.groupby('Month/Bulan').mean(numeric_only=True)
print(monthly_trend)
|
Forecast revenue for/untuk year
|
{
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": null,
"City/Kota": [
"Paris",
"Mexico City",
"Mumbai",
"New York",
"Sydney"
],
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": [
"France",
"Mexico",
"India",
"US",
"Australia"
],
"Customer ID/ID Pelanggan": null,
"Date/Tanggal": null,
"Discount/Diskon": null,
"Gender/Jenis Kelamin": null,
"Growth Rate/Pertumbuhan": [
0.29,
0.05,
-0.04,
0.26,
-0.07
],
"Income/Pendapatan": null,
"Last Purchase Date/Tanggal Pembelian Terakhir": null,
"Latitude/Lintang": [
-54.8075,
18.1095,
73.0632,
-75.4714,
59.7417
],
"Longitude/Bujur": [
2.6261,
103.6082,
-145.1563,
83.3641,
-125.4302
],
"Loyalty Score/Skor Loyalitas": null,
"Market Share/Pangsa Pasar": [
0.41000000000000003,
0.29,
0.43,
0.34,
0.14
],
"Price/Harga": null,
"Product/Produk": null,
"Region/Wilayah": [
"Europe",
"North America",
"Asia",
"North America",
"Oceania"
],
"Revenue/Pendapatan": [
1564,
4896,
2098,
4459,
2281
],
"Sales/Penjualan": null,
"Stock/Stok": null,
"Visitors/Pengunjung": null
}
|
import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'City/Kota': ['Paris', 'Mexico City', 'Mumbai', 'New York', 'Sydney'], 'Country/Negara': ['France', 'Mexico', 'India', 'US', 'Australia'], 'Region/Wilayah': ['Europe', 'North America', 'Asia', 'North America', 'Oceania'], 'Latitude/Lintang': [-54.8075, 18.1095, 73.0632, -75.4714, 59.7417], 'Longitude/Bujur': [2.6261, 103.6082, -145.1563, 83.3641, -125.4302], 'Revenue/Pendapatan': [1564, 4896, 2098, 4459, 2281], 'Market Share/Pangsa Pasar': [0.41, 0.29, 0.43, 0.34, 0.14], 'Growth Rate/Pertumbuhan': [0.29, 0.05, -0.04, 0.26, -0.07]})
# Basic data cleaning
df = df.dropna() # Remove missing values
df = df.drop_duplicates() # Remove duplicates
# Time series forecasting
from statsmodels.tsa.arima.model import ARIMA
# Prepare data
ts_data = df.set_index('Date/Tanggal')['Revenue/Pendapatan']
model = ARIMA(ts_data, order=(1,1,1))
model_fit = model.fit()
forecast = model_fit.forecast(steps=7)
print('Forecast for next 7 days:', forecast)
|
Segment revenue time period
|
{
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": [
"Automotive/Otomotif",
"Automotive/Otomotif",
"Automotive/Otomotif"
],
"City/Kota": null,
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": null,
"Customer ID/ID Pelanggan": null,
"Date/Tanggal": null,
"Discount/Diskon": [
0.12,
0.09,
0.29
],
"Gender/Jenis Kelamin": null,
"Growth Rate/Pertumbuhan": null,
"Income/Pendapatan": null,
"Last Purchase Date/Tanggal Pembelian Terakhir": null,
"Latitude/Lintang": null,
"Longitude/Bujur": null,
"Loyalty Score/Skor Loyalitas": null,
"Market Share/Pangsa Pasar": null,
"Price/Harga": [
197.94,
177.55,
155.37
],
"Product/Produk": [
"Oil_1",
"Tires_2",
"Accessories_3"
],
"Region/Wilayah": null,
"Revenue/Pendapatan": null,
"Sales/Penjualan": [
177,
820,
234
],
"Stock/Stok": [
21,
34,
151
],
"Visitors/Pengunjung": null
}
|
import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Product/Produk': ['Oil_1', 'Tires_2', 'Accessories_3'], 'Sales/Penjualan': [177, 820, 234], 'Price/Harga': [197.94, 177.55, 155.37], 'Category/Kategori': ['Automotive/Otomotif', 'Automotive/Otomotif', 'Automotive/Otomotif'], 'Discount/Diskon': [0.12, 0.09, 0.29], 'Stock/Stok': [21, 34, 151]})
# Basic data cleaning
df = df.dropna() # Remove missing values
df = df.drop_duplicates() # Remove duplicates
# Customer segmentation using KMeans
from sklearn.cluster import KMeans
from sklearn.preprocessing import StandardScaler
# Select features and scale
X = df[['Age/Usia', 'Income/Pendapatan', 'Loyalty Score/Skor Loyalitas']]
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)
# Cluster into 3 segments
kmeans = KMeans(n_clusters=3, random_state=42)
df['Segment/Segmen'] = kmeans.fit_predict(X_scaled)
# Visualize segments
fig = px.scatter_3d(df, x='Age/Usia', y='Income/Pendapatan',
z='Loyalty Score/Skor Loyalitas',
color='Segment/Segmen',
title='Customer Segments')
fig.show()
|
Segmentasi nilai pesanan rata-rata produk
|
{
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": [
"Food/Makanan",
"Food/Makanan",
"Food/Makanan"
],
"City/Kota": null,
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": null,
"Customer ID/ID Pelanggan": null,
"Date/Tanggal": null,
"Discount/Diskon": [
0.14,
0.22,
0.2
],
"Gender/Jenis Kelamin": null,
"Growth Rate/Pertumbuhan": null,
"Income/Pendapatan": null,
"Last Purchase Date/Tanggal Pembelian Terakhir": null,
"Latitude/Lintang": null,
"Longitude/Bujur": null,
"Loyalty Score/Skor Loyalitas": null,
"Market Share/Pangsa Pasar": null,
"Price/Harga": [
90,
56,
73.15
],
"Product/Produk": [
"Dairy_1",
"Fruits_2",
"Snacks_3"
],
"Region/Wilayah": null,
"Revenue/Pendapatan": null,
"Sales/Penjualan": [
107,
103,
397
],
"Stock/Stok": [
87,
143,
53
],
"Visitors/Pengunjung": null
}
|
import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Product/Produk': ['Dairy_1', 'Fruits_2', 'Snacks_3'], 'Sales/Penjualan': [107, 103, 397], 'Price/Harga': [90.0, 56.0, 73.15], 'Category/Kategori': ['Food/Makanan', 'Food/Makanan', 'Food/Makanan'], 'Discount/Diskon': [0.14, 0.22, 0.2], 'Stock/Stok': [87, 143, 53]})
# Basic data cleaning
df = df.dropna() # Remove missing values
df = df.drop_duplicates() # Remove duplicates
# Customer segmentation using KMeans
from sklearn.cluster import KMeans
from sklearn.preprocessing import StandardScaler
# Select features and scale
X = df[['Age/Usia', 'Income/Pendapatan', 'Loyalty Score/Skor Loyalitas']]
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)
# Cluster into 3 segments
kmeans = KMeans(n_clusters=3, random_state=42)
df['Segment/Segmen'] = kmeans.fit_predict(X_scaled)
# Visualize segments
fig = px.scatter_3d(df, x='Age/Usia', y='Income/Pendapatan',
z='Loyalty Score/Skor Loyalitas',
color='Segment/Segmen',
title='Customer Segments')
fig.show()
|
Segmentasi pertumbuhan segmen pelanggan
|
{
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": [
58.28,
97.08,
188.67,
191.48,
137.45,
50.38,
147.9,
161.82,
97.03,
102.29,
108.59,
192.72,
75.99,
115.55,
185.04,
92.28,
152.81,
132.72,
144.53,
168.4,
63.73,
67.32,
53.66,
165.26,
61.51,
180.79,
56.46,
184.8,
112.93,
88.08
],
"Category/Kategori": null,
"City/Kota": null,
"Conversion Rate/Rasio Konversi": [
0.0644,
0.0361,
0.08510000000000001,
0.010700000000000001,
0.018500000000000003,
0.0746,
0.049800000000000004,
0.031100000000000003,
0.098,
0.0487,
0.0757,
0.0772,
0.0495,
0.09630000000000001,
0.0426,
0.09910000000000001,
0.09330000000000001,
0.0985,
0.08,
0.0131,
0.028800000000000003,
0.055,
0.032,
0.0918,
0.0227,
0.0126,
0.0815,
0.0649,
0.0194,
0.060700000000000004
],
"Country/Negara": null,
"Customer ID/ID Pelanggan": null,
"Date/Tanggal": [
"2025-03-23",
"2025-03-24",
"2025-03-25",
"2025-03-26",
"2025-03-27",
"2025-03-28",
"2025-03-29",
"2025-03-30",
"2025-03-31",
"2025-04-01",
"2025-04-02",
"2025-04-03",
"2025-04-04",
"2025-04-05",
"2025-04-06",
"2025-04-07",
"2025-04-08",
"2025-04-09",
"2025-04-10",
"2025-04-11",
"2025-04-12",
"2025-04-13",
"2025-04-14",
"2025-04-15",
"2025-04-16",
"2025-04-17",
"2025-04-18",
"2025-04-19",
"2025-04-20",
"2025-04-21"
],
"Discount/Diskon": null,
"Gender/Jenis Kelamin": null,
"Growth Rate/Pertumbuhan": null,
"Income/Pendapatan": null,
"Last Purchase Date/Tanggal Pembelian Terakhir": null,
"Latitude/Lintang": null,
"Longitude/Bujur": null,
"Loyalty Score/Skor Loyalitas": null,
"Market Share/Pangsa Pasar": null,
"Price/Harga": null,
"Product/Produk": null,
"Region/Wilayah": null,
"Revenue/Pendapatan": [
1274.72,
1416.61,
1413.81,
1412.13,
1459.62,
1370.37,
1318.63,
1279.75,
1115.48,
1115.38,
969.3,
995.12,
994.94,
1137.95,
1179.37,
1319.15,
1345.13,
1353.37,
1435.74,
1354.5,
1356.67,
1164.41,
1179.29,
1083.02,
1015.72,
1025.43,
953.98,
1020.91,
1112.51,
1193.95
],
"Sales/Penjualan": null,
"Stock/Stok": null,
"Visitors/Pengunjung": [
242,
86,
134,
290,
173,
299,
116,
84,
183,
200,
229,
131,
236,
122,
288,
147,
188,
210,
58,
143,
194,
140,
224,
182,
214,
168,
83,
83,
216,
194
]
}
|
import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Date/Tanggal': ['2025-03-23', '2025-03-24', '2025-03-25', '2025-03-26', '2025-03-27', '2025-03-28', '2025-03-29', '2025-03-30', '2025-03-31', '2025-04-01', '2025-04-02', '2025-04-03', '2025-04-04', '2025-04-05', '2025-04-06', '2025-04-07', '2025-04-08', '2025-04-09', '2025-04-10', '2025-04-11', '2025-04-12', '2025-04-13', '2025-04-14', '2025-04-15', '2025-04-16', '2025-04-17', '2025-04-18', '2025-04-19', '2025-04-20', '2025-04-21'], 'Revenue/Pendapatan': [1274.72, 1416.61, 1413.81, 1412.13, 1459.62, 1370.37, 1318.63, 1279.75, 1115.48, 1115.38, 969.3, 995.12, 994.94, 1137.95, 1179.37, 1319.15, 1345.13, 1353.37, 1435.74, 1354.5, 1356.67, 1164.41, 1179.29, 1083.02, 1015.72, 1025.43, 953.98, 1020.91, 1112.51, 1193.95], 'Visitors/Pengunjung': [242, 86, 134, 290, 173, 299, 116, 84, 183, 200, 229, 131, 236, 122, 288, 147, 188, 210, 58, 143, 194, 140, 224, 182, 214, 168, 83, 83, 216, 194], 'Conversion Rate/Rasio Konversi': [0.0644, 0.0361, 0.0851, 0.0107, 0.0185, 0.0746, 0.0498, 0.0311, 0.098, 0.0487, 0.0757, 0.0772, 0.0495, 0.0963, 0.0426, 0.0991, 0.0933, 0.0985, 0.08, 0.0131, 0.0288, 0.055, 0.032, 0.0918, 0.0227, 0.0126, 0.0815, 0.0649, 0.0194, 0.0607], 'Avg. Order Value/Nilai Pesanan Rata2': [58.28, 97.08, 188.67, 191.48, 137.45, 50.38, 147.9, 161.82, 97.03, 102.29, 108.59, 192.72, 75.99, 115.55, 185.04, 92.28, 152.81, 132.72, 144.53, 168.4, 63.73, 67.32, 53.66, 165.26, 61.51, 180.79, 56.46, 184.8, 112.93, 88.08]})
# Basic data cleaning
df = df.dropna() # Remove missing values
df = df.drop_duplicates() # Remove duplicates
# Customer segmentation using KMeans
from sklearn.cluster import KMeans
from sklearn.preprocessing import StandardScaler
# Select features and scale
X = df[['Age/Usia', 'Income/Pendapatan', 'Loyalty Score/Skor Loyalitas']]
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)
# Cluster into 3 segments
kmeans = KMeans(n_clusters=3, random_state=42)
df['Segment/Segmen'] = kmeans.fit_predict(X_scaled)
# Visualize segments
fig = px.scatter_3d(df, x='Age/Usia', y='Income/Pendapatan',
z='Loyalty Score/Skor Loyalitas',
color='Segment/Segmen',
title='Customer Segments')
fig.show()
|
Optimalkan nilai pesanan rata-rata negara
|
{
"Age/Usia": [
64,
68,
69,
66,
62,
51,
38,
20,
33,
70,
42,
21,
36,
25,
28,
43,
63,
51,
29,
19,
67,
43,
37,
49,
55,
41,
36,
33,
60,
60,
48,
25,
40,
36,
63,
56,
53,
68,
59,
70,
27,
69,
64,
24,
46,
29,
51,
42,
36,
29,
41,
19,
26,
36,
42,
21,
61,
70,
24,
40,
42,
44,
18,
39,
54,
21,
70,
60,
52,
33,
61,
62,
31,
47,
49,
64,
22,
22,
61,
51,
67,
27,
26,
48,
56,
23,
18,
55,
46,
45,
31,
30,
21,
19,
24,
40,
27,
65,
56,
24
],
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": null,
"City/Kota": null,
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": null,
"Customer ID/ID Pelanggan": [
"CUST_0001",
"CUST_0002",
"CUST_0003",
"CUST_0004",
"CUST_0005",
"CUST_0006",
"CUST_0007",
"CUST_0008",
"CUST_0009",
"CUST_0010",
"CUST_0011",
"CUST_0012",
"CUST_0013",
"CUST_0014",
"CUST_0015",
"CUST_0016",
"CUST_0017",
"CUST_0018",
"CUST_0019",
"CUST_0020",
"CUST_0021",
"CUST_0022",
"CUST_0023",
"CUST_0024",
"CUST_0025",
"CUST_0026",
"CUST_0027",
"CUST_0028",
"CUST_0029",
"CUST_0030",
"CUST_0031",
"CUST_0032",
"CUST_0033",
"CUST_0034",
"CUST_0035",
"CUST_0036",
"CUST_0037",
"CUST_0038",
"CUST_0039",
"CUST_0040",
"CUST_0041",
"CUST_0042",
"CUST_0043",
"CUST_0044",
"CUST_0045",
"CUST_0046",
"CUST_0047",
"CUST_0048",
"CUST_0049",
"CUST_0050",
"CUST_0051",
"CUST_0052",
"CUST_0053",
"CUST_0054",
"CUST_0055",
"CUST_0056",
"CUST_0057",
"CUST_0058",
"CUST_0059",
"CUST_0060",
"CUST_0061",
"CUST_0062",
"CUST_0063",
"CUST_0064",
"CUST_0065",
"CUST_0066",
"CUST_0067",
"CUST_0068",
"CUST_0069",
"CUST_0070",
"CUST_0071",
"CUST_0072",
"CUST_0073",
"CUST_0074",
"CUST_0075",
"CUST_0076",
"CUST_0077",
"CUST_0078",
"CUST_0079",
"CUST_0080",
"CUST_0081",
"CUST_0082",
"CUST_0083",
"CUST_0084",
"CUST_0085",
"CUST_0086",
"CUST_0087",
"CUST_0088",
"CUST_0089",
"CUST_0090",
"CUST_0091",
"CUST_0092",
"CUST_0093",
"CUST_0094",
"CUST_0095",
"CUST_0096",
"CUST_0097",
"CUST_0098",
"CUST_0099",
"CUST_0100"
],
"Date/Tanggal": null,
"Discount/Diskon": null,
"Gender/Jenis Kelamin": [
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Other/Lainnya",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Other/Lainnya",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Other/Lainnya",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Other/Lainnya",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan"
],
"Growth Rate/Pertumbuhan": null,
"Income/Pendapatan": [
3000,
7000,
8000,
5000,
10000,
1000,
6000,
4000,
4000,
6000,
6000,
2000,
7000,
4000,
9000,
1000,
4000,
5000,
1000,
4000,
9000,
8000,
5000,
7000,
9000,
10000,
7000,
5000,
9000,
3000,
9000,
9000,
3000,
10000,
3000,
4000,
4000,
1000,
2000,
8000,
6000,
5000,
4000,
5000,
9000,
1000,
2000,
4000,
7000,
10000,
7000,
3000,
8000,
10000,
6000,
8000,
4000,
7000,
1000,
1000,
7000,
6000,
4000,
4000,
3000,
3000,
6000,
6000,
2000,
5000,
6000,
10000,
5000,
6000,
6000,
5000,
2000,
5000,
6000,
2000,
10000,
3000,
4000,
3000,
4000,
3000,
8000,
7000,
8000,
4000,
5000,
2000,
2000,
8000,
2000,
8000,
6000,
2000,
9000,
6000
],
"Last Purchase Date/Tanggal Pembelian Terakhir": [
"2025-01-03",
"2024-09-22",
"2025-04-15",
"2024-05-05",
"2025-03-16",
"2024-07-14",
"2024-09-30",
"2024-04-24",
"2025-03-10",
"2025-04-20",
"2024-10-23",
"2024-11-15",
"2024-09-20",
"2024-10-10",
"2024-12-14",
"2024-05-23",
"2024-04-24",
"2025-02-27",
"2024-09-30",
"2024-08-12",
"2024-09-16",
"2024-05-16",
"2025-04-13",
"2024-11-07",
"2024-06-20",
"2024-07-06",
"2024-09-27",
"2024-10-12",
"2025-02-06",
"2024-05-14",
"2024-09-15",
"2024-10-18",
"2024-12-12",
"2024-05-12",
"2024-12-27",
"2024-09-11",
"2024-05-15",
"2024-06-04",
"2024-12-07",
"2024-08-11",
"2024-09-20",
"2024-10-17",
"2024-11-26",
"2025-01-14",
"2025-01-10",
"2024-07-26",
"2024-05-16",
"2025-03-31",
"2025-01-18",
"2024-10-31",
"2024-10-26",
"2024-05-26",
"2024-10-10",
"2024-07-26",
"2025-01-10",
"2024-12-20",
"2024-10-27",
"2025-01-02",
"2024-12-14",
"2025-01-26",
"2024-10-17",
"2024-11-15",
"2024-05-18",
"2024-08-20",
"2024-10-20",
"2024-10-12",
"2025-03-29",
"2025-04-11",
"2024-11-12",
"2024-05-13",
"2025-04-11",
"2024-08-14",
"2024-08-16",
"2025-02-13",
"2024-11-18",
"2024-05-27",
"2025-02-27",
"2024-05-28",
"2024-11-16",
"2024-06-20",
"2024-12-24",
"2024-06-02",
"2024-12-16",
"2024-05-21",
"2024-06-27",
"2024-08-13",
"2025-03-31",
"2024-10-03",
"2024-06-06",
"2025-03-25",
"2024-12-25",
"2024-08-18",
"2024-11-13",
"2024-09-17",
"2024-05-28",
"2024-12-21",
"2025-01-05",
"2024-06-04",
"2025-01-19",
"2024-07-10"
],
"Latitude/Lintang": null,
"Longitude/Bujur": null,
"Loyalty Score/Skor Loyalitas": [
89,
4,
65,
19,
31,
95,
35,
68,
47,
98,
60,
58,
93,
83,
34,
75,
84,
90,
30,
18,
46,
35,
19,
35,
63,
16,
26,
95,
4,
86,
21,
93,
100,
18,
53,
20,
82,
31,
46,
18,
19,
48,
42,
51,
83,
54,
55,
80,
39,
51,
4,
76,
41,
94,
55,
23,
33,
5,
41,
74,
82,
2,
44,
1,
61,
28,
91,
43,
61,
39,
33,
99,
43,
79,
53,
91,
70,
55,
38,
47,
62,
57,
1,
2,
91,
23,
47,
80,
67,
91,
3,
8,
34,
100,
91,
46,
67,
68,
38,
96
],
"Market Share/Pangsa Pasar": null,
"Price/Harga": null,
"Product/Produk": null,
"Region/Wilayah": null,
"Revenue/Pendapatan": null,
"Sales/Penjualan": null,
"Stock/Stok": null,
"Visitors/Pengunjung": null
}
|
import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Customer ID/ID Pelanggan': ['CUST_0001', 'CUST_0002', 'CUST_0003', 'CUST_0004', 'CUST_0005', 'CUST_0006', 'CUST_0007', 'CUST_0008', 'CUST_0009', 'CUST_0010', 'CUST_0011', 'CUST_0012', 'CUST_0013', 'CUST_0014', 'CUST_0015', 'CUST_0016', 'CUST_0017', 'CUST_0018', 'CUST_0019', 'CUST_0020', 'CUST_0021', 'CUST_0022', 'CUST_0023', 'CUST_0024', 'CUST_0025', 'CUST_0026', 'CUST_0027', 'CUST_0028', 'CUST_0029', 'CUST_0030', 'CUST_0031', 'CUST_0032', 'CUST_0033', 'CUST_0034', 'CUST_0035', 'CUST_0036', 'CUST_0037', 'CUST_0038', 'CUST_0039', 'CUST_0040', 'CUST_0041', 'CUST_0042', 'CUST_0043', 'CUST_0044', 'CUST_0045', 'CUST_0046', 'CUST_0047', 'CUST_0048', 'CUST_0049', 'CUST_0050', 'CUST_0051', 'CUST_0052', 'CUST_0053', 'CUST_0054', 'CUST_0055', 'CUST_0056', 'CUST_0057', 'CUST_0058', 'CUST_0059', 'CUST_0060', 'CUST_0061', 'CUST_0062', 'CUST_0063', 'CUST_0064', 'CUST_0065', 'CUST_0066', 'CUST_0067', 'CUST_0068', 'CUST_0069', 'CUST_0070', 'CUST_0071', 'CUST_0072', 'CUST_0073', 'CUST_0074', 'CUST_0075', 'CUST_0076', 'CUST_0077', 'CUST_0078', 'CUST_0079', 'CUST_0080', 'CUST_0081', 'CUST_0082', 'CUST_0083', 'CUST_0084', 'CUST_0085', 'CUST_0086', 'CUST_0087', 'CUST_0088', 'CUST_0089', 'CUST_0090', 'CUST_0091', 'CUST_0092', 'CUST_0093', 'CUST_0094', 'CUST_0095', 'CUST_0096', 'CUST_0097', 'CUST_0098', 'CUST_0099', 'CUST_0100'], 'Age/Usia': [64, 68, 69, 66, 62, 51, 38, 20, 33, 70, 42, 21, 36, 25, 28, 43, 63, 51, 29, 19, 67, 43, 37, 49, 55, 41, 36, 33, 60, 60, 48, 25, 40, 36, 63, 56, 53, 68, 59, 70, 27, 69, 64, 24, 46, 29, 51, 42, 36, 29, 41, 19, 26, 36, 42, 21, 61, 70, 24, 40, 42, 44, 18, 39, 54, 21, 70, 60, 52, 33, 61, 62, 31, 47, 49, 64, 22, 22, 61, 51, 67, 27, 26, 48, 56, 23, 18, 55, 46, 45, 31, 30, 21, 19, 24, 40, 27, 65, 56, 24], 'Gender/Jenis Kelamin': ['Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Other/Lainnya', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Other/Lainnya', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Other/Lainnya', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Other/Lainnya', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan'], 'Income/Pendapatan': [3000, 7000, 8000, 5000, 10000, 1000, 6000, 4000, 4000, 6000, 6000, 2000, 7000, 4000, 9000, 1000, 4000, 5000, 1000, 4000, 9000, 8000, 5000, 7000, 9000, 10000, 7000, 5000, 9000, 3000, 9000, 9000, 3000, 10000, 3000, 4000, 4000, 1000, 2000, 8000, 6000, 5000, 4000, 5000, 9000, 1000, 2000, 4000, 7000, 10000, 7000, 3000, 8000, 10000, 6000, 8000, 4000, 7000, 1000, 1000, 7000, 6000, 4000, 4000, 3000, 3000, 6000, 6000, 2000, 5000, 6000, 10000, 5000, 6000, 6000, 5000, 2000, 5000, 6000, 2000, 10000, 3000, 4000, 3000, 4000, 3000, 8000, 7000, 8000, 4000, 5000, 2000, 2000, 8000, 2000, 8000, 6000, 2000, 9000, 6000], 'Loyalty Score/Skor Loyalitas': [89, 4, 65, 19, 31, 95, 35, 68, 47, 98, 60, 58, 93, 83, 34, 75, 84, 90, 30, 18, 46, 35, 19, 35, 63, 16, 26, 95, 4, 86, 21, 93, 100, 18, 53, 20, 82, 31, 46, 18, 19, 48, 42, 51, 83, 54, 55, 80, 39, 51, 4, 76, 41, 94, 55, 23, 33, 5, 41, 74, 82, 2, 44, 1, 61, 28, 91, 43, 61, 39, 33, 99, 43, 79, 53, 91, 70, 55, 38, 47, 62, 57, 1, 2, 91, 23, 47, 80, 67, 91, 3, 8, 34, 100, 91, 46, 67, 68, 38, 96], 'Last Purchase Date/Tanggal Pembelian Terakhir': ['2025-01-03', '2024-09-22', '2025-04-15', '2024-05-05', '2025-03-16', '2024-07-14', '2024-09-30', '2024-04-24', '2025-03-10', '2025-04-20', '2024-10-23', '2024-11-15', '2024-09-20', '2024-10-10', '2024-12-14', '2024-05-23', '2024-04-24', '2025-02-27', '2024-09-30', '2024-08-12', '2024-09-16', '2024-05-16', '2025-04-13', '2024-11-07', '2024-06-20', '2024-07-06', '2024-09-27', '2024-10-12', '2025-02-06', '2024-05-14', '2024-09-15', '2024-10-18', '2024-12-12', '2024-05-12', '2024-12-27', '2024-09-11', '2024-05-15', '2024-06-04', '2024-12-07', '2024-08-11', '2024-09-20', '2024-10-17', '2024-11-26', '2025-01-14', '2025-01-10', '2024-07-26', '2024-05-16', '2025-03-31', '2025-01-18', '2024-10-31', '2024-10-26', '2024-05-26', '2024-10-10', '2024-07-26', '2025-01-10', '2024-12-20', '2024-10-27', '2025-01-02', '2024-12-14', '2025-01-26', '2024-10-17', '2024-11-15', '2024-05-18', '2024-08-20', '2024-10-20', '2024-10-12', '2025-03-29', '2025-04-11', '2024-11-12', '2024-05-13', '2025-04-11', '2024-08-14', '2024-08-16', '2025-02-13', '2024-11-18', '2024-05-27', '2025-02-27', '2024-05-28', '2024-11-16', '2024-06-20', '2024-12-24', '2024-06-02', '2024-12-16', '2024-05-21', '2024-06-27', '2024-08-13', '2025-03-31', '2024-10-03', '2024-06-06', '2025-03-25', '2024-12-25', '2024-08-18', '2024-11-13', '2024-09-17', '2024-05-28', '2024-12-21', '2025-01-05', '2024-06-04', '2025-01-19', '2024-07-10']})
# Basic data cleaning
df = df.dropna() # Remove missing values
df = df.drop_duplicates() # Remove duplicates
# Comprehensive data analysis
analysis_result = df.describe(include='all')
print(analysis_result)
# Additional insights
print('\nTop performing items:')
print(df.nlargest(3, df.columns[1]))
print('\nTrend analysis:')
if 'Date/Tanggal' in df.columns:
df['Month/Bulan'] = pd.to_datetime(df['Date/Tanggal']).dt.month
monthly_trend = df.groupby('Month/Bulan').mean(numeric_only=True)
print(monthly_trend)
|
Compare average order value city
|
{
"Age/Usia": [
48,
62,
64,
20,
56,
18,
27,
66,
27,
48,
38,
44,
41,
25,
28,
47,
40,
42,
30,
57,
67,
29,
53,
61,
42,
35,
24,
44,
53,
58,
19,
58,
53,
25,
23,
55,
58,
21,
59,
50,
29,
55,
65,
18,
28,
45,
51,
47,
62,
26,
28,
22,
18,
38,
36,
53,
39,
25,
51,
39,
70,
19,
31,
50,
66,
59,
28,
37,
59,
21,
29,
33,
54,
32,
18,
50,
55,
28,
64,
20,
53,
47,
41,
23,
19,
29,
68,
69,
33,
53,
48,
27,
58,
50,
65
],
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": null,
"City/Kota": null,
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": null,
"Customer ID/ID Pelanggan": [
"CUST_0001",
"CUST_0002",
"CUST_0003",
"CUST_0004",
"CUST_0005",
"CUST_0006",
"CUST_0007",
"CUST_0008",
"CUST_0009",
"CUST_0010",
"CUST_0011",
"CUST_0012",
"CUST_0013",
"CUST_0014",
"CUST_0015",
"CUST_0016",
"CUST_0017",
"CUST_0018",
"CUST_0019",
"CUST_0020",
"CUST_0021",
"CUST_0022",
"CUST_0023",
"CUST_0024",
"CUST_0025",
"CUST_0026",
"CUST_0027",
"CUST_0028",
"CUST_0029",
"CUST_0030",
"CUST_0031",
"CUST_0032",
"CUST_0033",
"CUST_0034",
"CUST_0035",
"CUST_0036",
"CUST_0037",
"CUST_0038",
"CUST_0039",
"CUST_0040",
"CUST_0041",
"CUST_0042",
"CUST_0043",
"CUST_0044",
"CUST_0045",
"CUST_0046",
"CUST_0047",
"CUST_0048",
"CUST_0049",
"CUST_0050",
"CUST_0051",
"CUST_0052",
"CUST_0053",
"CUST_0054",
"CUST_0055",
"CUST_0056",
"CUST_0057",
"CUST_0058",
"CUST_0059",
"CUST_0060",
"CUST_0061",
"CUST_0062",
"CUST_0063",
"CUST_0064",
"CUST_0065",
"CUST_0066",
"CUST_0067",
"CUST_0068",
"CUST_0069",
"CUST_0070",
"CUST_0071",
"CUST_0072",
"CUST_0073",
"CUST_0074",
"CUST_0075",
"CUST_0076",
"CUST_0077",
"CUST_0078",
"CUST_0079",
"CUST_0080",
"CUST_0081",
"CUST_0082",
"CUST_0083",
"CUST_0084",
"CUST_0085",
"CUST_0086",
"CUST_0087",
"CUST_0088",
"CUST_0089",
"CUST_0090",
"CUST_0091",
"CUST_0092",
"CUST_0093",
"CUST_0094",
"CUST_0095"
],
"Date/Tanggal": null,
"Discount/Diskon": null,
"Gender/Jenis Kelamin": [
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Other/Lainnya",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Other/Lainnya",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan"
],
"Growth Rate/Pertumbuhan": null,
"Income/Pendapatan": [
10000,
9000,
7000,
3000,
1000,
4000,
2000,
6000,
1000,
2000,
2000,
9000,
3000,
2000,
4000,
1000,
3000,
5000,
1000,
2000,
8000,
9000,
4000,
5000,
3000,
3000,
4000,
9000,
9000,
4000,
3000,
4000,
7000,
6000,
7000,
10000,
6000,
10000,
7000,
3000,
4000,
3000,
6000,
9000,
7000,
3000,
5000,
9000,
4000,
3000,
2000,
6000,
9000,
5000,
5000,
6000,
1000,
4000,
1000,
8000,
8000,
4000,
8000,
10000,
10000,
7000,
5000,
9000,
5000,
7000,
8000,
6000,
6000,
2000,
3000,
4000,
2000,
5000,
4000,
7000,
1000,
8000,
7000,
6000,
4000,
3000,
2000,
1000,
3000,
2000,
4000,
2000,
1000,
5000,
8000
],
"Last Purchase Date/Tanggal Pembelian Terakhir": [
"2025-01-05",
"2024-06-30",
"2024-10-06",
"2024-07-10",
"2025-01-09",
"2025-02-17",
"2025-03-24",
"2024-10-04",
"2024-05-28",
"2024-12-17",
"2025-01-13",
"2024-07-20",
"2025-03-17",
"2024-06-01",
"2025-03-16",
"2025-01-22",
"2024-09-28",
"2024-07-12",
"2025-03-16",
"2024-04-21",
"2024-10-12",
"2025-01-21",
"2024-12-29",
"2024-09-27",
"2025-04-10",
"2024-05-27",
"2024-07-12",
"2025-03-10",
"2024-12-02",
"2024-12-30",
"2024-11-17",
"2025-01-05",
"2025-02-27",
"2024-07-17",
"2024-12-25",
"2024-10-05",
"2024-06-30",
"2024-08-26",
"2025-02-14",
"2024-10-19",
"2025-03-07",
"2025-03-04",
"2024-11-04",
"2024-10-23",
"2024-05-19",
"2025-01-28",
"2025-04-13",
"2024-06-06",
"2024-06-23",
"2024-05-27",
"2025-02-07",
"2025-01-06",
"2024-10-06",
"2025-03-03",
"2024-09-26",
"2025-01-27",
"2024-11-30",
"2024-09-13",
"2025-03-17",
"2024-10-10",
"2025-01-31",
"2025-03-15",
"2024-10-07",
"2024-04-25",
"2024-12-05",
"2024-06-04",
"2024-12-29",
"2025-01-09",
"2024-05-27",
"2024-07-19",
"2024-11-25",
"2025-01-14",
"2024-09-12",
"2024-07-11",
"2024-10-13",
"2024-08-06",
"2024-05-17",
"2024-06-11",
"2024-10-01",
"2024-08-06",
"2024-10-09",
"2024-11-05",
"2025-03-24",
"2024-11-01",
"2025-03-01",
"2024-12-24",
"2024-06-15",
"2024-05-10",
"2024-07-08",
"2024-12-21",
"2024-08-23",
"2024-09-05",
"2024-11-05",
"2024-06-04",
"2024-05-17"
],
"Latitude/Lintang": null,
"Longitude/Bujur": null,
"Loyalty Score/Skor Loyalitas": [
96,
40,
41,
15,
54,
78,
15,
23,
63,
17,
51,
77,
90,
88,
87,
26,
22,
10,
88,
96,
19,
99,
85,
31,
83,
58,
12,
42,
92,
28,
73,
98,
63,
45,
36,
90,
98,
40,
5,
26,
99,
5,
6,
24,
81,
43,
54,
15,
19,
65,
7,
49,
48,
25,
2,
43,
15,
13,
23,
60,
3,
55,
44,
92,
1,
89,
48,
18,
15,
43,
40,
93,
93,
50,
29,
99,
51,
93,
1,
11,
31,
26,
76,
55,
93,
38,
40,
22,
83,
91,
99,
40,
36,
2,
26
],
"Market Share/Pangsa Pasar": null,
"Price/Harga": null,
"Product/Produk": null,
"Region/Wilayah": null,
"Revenue/Pendapatan": null,
"Sales/Penjualan": null,
"Stock/Stok": null,
"Visitors/Pengunjung": null
}
|
import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Customer ID/ID Pelanggan': ['CUST_0001', 'CUST_0002', 'CUST_0003', 'CUST_0004', 'CUST_0005', 'CUST_0006', 'CUST_0007', 'CUST_0008', 'CUST_0009', 'CUST_0010', 'CUST_0011', 'CUST_0012', 'CUST_0013', 'CUST_0014', 'CUST_0015', 'CUST_0016', 'CUST_0017', 'CUST_0018', 'CUST_0019', 'CUST_0020', 'CUST_0021', 'CUST_0022', 'CUST_0023', 'CUST_0024', 'CUST_0025', 'CUST_0026', 'CUST_0027', 'CUST_0028', 'CUST_0029', 'CUST_0030', 'CUST_0031', 'CUST_0032', 'CUST_0033', 'CUST_0034', 'CUST_0035', 'CUST_0036', 'CUST_0037', 'CUST_0038', 'CUST_0039', 'CUST_0040', 'CUST_0041', 'CUST_0042', 'CUST_0043', 'CUST_0044', 'CUST_0045', 'CUST_0046', 'CUST_0047', 'CUST_0048', 'CUST_0049', 'CUST_0050', 'CUST_0051', 'CUST_0052', 'CUST_0053', 'CUST_0054', 'CUST_0055', 'CUST_0056', 'CUST_0057', 'CUST_0058', 'CUST_0059', 'CUST_0060', 'CUST_0061', 'CUST_0062', 'CUST_0063', 'CUST_0064', 'CUST_0065', 'CUST_0066', 'CUST_0067', 'CUST_0068', 'CUST_0069', 'CUST_0070', 'CUST_0071', 'CUST_0072', 'CUST_0073', 'CUST_0074', 'CUST_0075', 'CUST_0076', 'CUST_0077', 'CUST_0078', 'CUST_0079', 'CUST_0080', 'CUST_0081', 'CUST_0082', 'CUST_0083', 'CUST_0084', 'CUST_0085', 'CUST_0086', 'CUST_0087', 'CUST_0088', 'CUST_0089', 'CUST_0090', 'CUST_0091', 'CUST_0092', 'CUST_0093', 'CUST_0094', 'CUST_0095'], 'Age/Usia': [48, 62, 64, 20, 56, 18, 27, 66, 27, 48, 38, 44, 41, 25, 28, 47, 40, 42, 30, 57, 67, 29, 53, 61, 42, 35, 24, 44, 53, 58, 19, 58, 53, 25, 23, 55, 58, 21, 59, 50, 29, 55, 65, 18, 28, 45, 51, 47, 62, 26, 28, 22, 18, 38, 36, 53, 39, 25, 51, 39, 70, 19, 31, 50, 66, 59, 28, 37, 59, 21, 29, 33, 54, 32, 18, 50, 55, 28, 64, 20, 53, 47, 41, 23, 19, 29, 68, 69, 33, 53, 48, 27, 58, 50, 65], 'Gender/Jenis Kelamin': ['Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Other/Lainnya', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Other/Lainnya', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan'], 'Income/Pendapatan': [10000, 9000, 7000, 3000, 1000, 4000, 2000, 6000, 1000, 2000, 2000, 9000, 3000, 2000, 4000, 1000, 3000, 5000, 1000, 2000, 8000, 9000, 4000, 5000, 3000, 3000, 4000, 9000, 9000, 4000, 3000, 4000, 7000, 6000, 7000, 10000, 6000, 10000, 7000, 3000, 4000, 3000, 6000, 9000, 7000, 3000, 5000, 9000, 4000, 3000, 2000, 6000, 9000, 5000, 5000, 6000, 1000, 4000, 1000, 8000, 8000, 4000, 8000, 10000, 10000, 7000, 5000, 9000, 5000, 7000, 8000, 6000, 6000, 2000, 3000, 4000, 2000, 5000, 4000, 7000, 1000, 8000, 7000, 6000, 4000, 3000, 2000, 1000, 3000, 2000, 4000, 2000, 1000, 5000, 8000], 'Loyalty Score/Skor Loyalitas': [96, 40, 41, 15, 54, 78, 15, 23, 63, 17, 51, 77, 90, 88, 87, 26, 22, 10, 88, 96, 19, 99, 85, 31, 83, 58, 12, 42, 92, 28, 73, 98, 63, 45, 36, 90, 98, 40, 5, 26, 99, 5, 6, 24, 81, 43, 54, 15, 19, 65, 7, 49, 48, 25, 2, 43, 15, 13, 23, 60, 3, 55, 44, 92, 1, 89, 48, 18, 15, 43, 40, 93, 93, 50, 29, 99, 51, 93, 1, 11, 31, 26, 76, 55, 93, 38, 40, 22, 83, 91, 99, 40, 36, 2, 26], 'Last Purchase Date/Tanggal Pembelian Terakhir': ['2025-01-05', '2024-06-30', '2024-10-06', '2024-07-10', '2025-01-09', '2025-02-17', '2025-03-24', '2024-10-04', '2024-05-28', '2024-12-17', '2025-01-13', '2024-07-20', '2025-03-17', '2024-06-01', '2025-03-16', '2025-01-22', '2024-09-28', '2024-07-12', '2025-03-16', '2024-04-21', '2024-10-12', '2025-01-21', '2024-12-29', '2024-09-27', '2025-04-10', '2024-05-27', '2024-07-12', '2025-03-10', '2024-12-02', '2024-12-30', '2024-11-17', '2025-01-05', '2025-02-27', '2024-07-17', '2024-12-25', '2024-10-05', '2024-06-30', '2024-08-26', '2025-02-14', '2024-10-19', '2025-03-07', '2025-03-04', '2024-11-04', '2024-10-23', '2024-05-19', '2025-01-28', '2025-04-13', '2024-06-06', '2024-06-23', '2024-05-27', '2025-02-07', '2025-01-06', '2024-10-06', '2025-03-03', '2024-09-26', '2025-01-27', '2024-11-30', '2024-09-13', '2025-03-17', '2024-10-10', '2025-01-31', '2025-03-15', '2024-10-07', '2024-04-25', '2024-12-05', '2024-06-04', '2024-12-29', '2025-01-09', '2024-05-27', '2024-07-19', '2024-11-25', '2025-01-14', '2024-09-12', '2024-07-11', '2024-10-13', '2024-08-06', '2024-05-17', '2024-06-11', '2024-10-01', '2024-08-06', '2024-10-09', '2024-11-05', '2025-03-24', '2024-11-01', '2025-03-01', '2024-12-24', '2024-06-15', '2024-05-10', '2024-07-08', '2024-12-21', '2024-08-23', '2024-09-05', '2024-11-05', '2024-06-04', '2024-05-17']})
# Basic data cleaning
df = df.dropna() # Remove missing values
df = df.drop_duplicates() # Remove duplicates
# Comprehensive data analysis
analysis_result = df.describe(include='all')
print(analysis_result)
# Additional insights
print('\nTop performing items:')
print(df.nlargest(3, df.columns[1]))
print('\nTrend analysis:')
if 'Date/Tanggal' in df.columns:
df['Month/Bulan'] = pd.to_datetime(df['Date/Tanggal']).dt.month
monthly_trend = df.groupby('Month/Bulan').mean(numeric_only=True)
print(monthly_trend)
|
Identifikasi skor loyalitas jenis kelamin
|
{
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": [
"Automotive/Otomotif",
"Automotive/Otomotif",
"Automotive/Otomotif"
],
"City/Kota": null,
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": null,
"Customer ID/ID Pelanggan": null,
"Date/Tanggal": null,
"Discount/Diskon": [
0.2,
0.25,
0.15
],
"Gender/Jenis Kelamin": null,
"Growth Rate/Pertumbuhan": null,
"Income/Pendapatan": null,
"Last Purchase Date/Tanggal Pembelian Terakhir": null,
"Latitude/Lintang": null,
"Longitude/Bujur": null,
"Loyalty Score/Skor Loyalitas": null,
"Market Share/Pangsa Pasar": null,
"Price/Harga": [
93.43,
126.36,
183.55
],
"Product/Produk": [
"Accessories_1",
"Tires_2",
"Oil_3"
],
"Region/Wilayah": null,
"Revenue/Pendapatan": null,
"Sales/Penjualan": [
868,
428,
790
],
"Stock/Stok": [
184,
130,
198
],
"Visitors/Pengunjung": null
}
|
import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Product/Produk': ['Accessories_1', 'Tires_2', 'Oil_3'], 'Sales/Penjualan': [868, 428, 790], 'Price/Harga': [93.43, 126.36, 183.55], 'Category/Kategori': ['Automotive/Otomotif', 'Automotive/Otomotif', 'Automotive/Otomotif'], 'Discount/Diskon': [0.2, 0.25, 0.15], 'Stock/Stok': [184, 130, 198]})
# Basic data cleaning
df = df.dropna() # Remove missing values
df = df.drop_duplicates() # Remove duplicates
# Comprehensive data analysis
analysis_result = df.describe(include='all')
print(analysis_result)
# Additional insights
print('\nTop performing items:')
print(df.nlargest(3, df.columns[1]))
print('\nTrend analysis:')
if 'Date/Tanggal' in df.columns:
df['Month/Bulan'] = pd.to_datetime(df['Date/Tanggal']).dt.month
monthly_trend = df.groupby('Month/Bulan').mean(numeric_only=True)
print(monthly_trend)
|
Segment growth rate region
|
{
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": null,
"City/Kota": [
"Berlin",
"Dubai",
"Sydney",
"Singapore",
"São Paulo"
],
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": [
"Germany",
"UAE",
"Australia",
"Singapore",
"Brazil"
],
"Customer ID/ID Pelanggan": null,
"Date/Tanggal": null,
"Discount/Diskon": null,
"Gender/Jenis Kelamin": null,
"Growth Rate/Pertumbuhan": [
0.17,
0.17,
-0.03,
-0.01,
-0.04
],
"Income/Pendapatan": null,
"Last Purchase Date/Tanggal Pembelian Terakhir": null,
"Latitude/Lintang": [
-40.1641,
47.8796,
39.1437,
-58.8714,
57.1894
],
"Longitude/Bujur": [
175.8081,
-136.9615,
51.1702,
166.526,
38.7624
],
"Loyalty Score/Skor Loyalitas": null,
"Market Share/Pangsa Pasar": [
0.46,
0.33,
0.15,
0.33,
0.2
],
"Price/Harga": null,
"Product/Produk": null,
"Region/Wilayah": [
"Europe",
"Middle East",
"Oceania",
"Asia",
"South America"
],
"Revenue/Pendapatan": [
4876,
3122,
3611,
2876,
1584
],
"Sales/Penjualan": null,
"Stock/Stok": null,
"Visitors/Pengunjung": null
}
|
import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'City/Kota': ['Berlin', 'Dubai', 'Sydney', 'Singapore', 'São Paulo'], 'Country/Negara': ['Germany', 'UAE', 'Australia', 'Singapore', 'Brazil'], 'Region/Wilayah': ['Europe', 'Middle East', 'Oceania', 'Asia', 'South America'], 'Latitude/Lintang': [-40.1641, 47.8796, 39.1437, -58.8714, 57.1894], 'Longitude/Bujur': [175.8081, -136.9615, 51.1702, 166.526, 38.7624], 'Revenue/Pendapatan': [4876, 3122, 3611, 2876, 1584], 'Market Share/Pangsa Pasar': [0.46, 0.33, 0.15, 0.33, 0.2], 'Growth Rate/Pertumbuhan': [0.17, 0.17, -0.03, -0.01, -0.04]})
# Basic data cleaning
df = df.dropna() # Remove missing values
df = df.drop_duplicates() # Remove duplicates
# Customer segmentation using KMeans
from sklearn.cluster import KMeans
from sklearn.preprocessing import StandardScaler
# Select features and scale
X = df[['Age/Usia', 'Income/Pendapatan', 'Loyalty Score/Skor Loyalitas']]
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)
# Cluster into 3 segments
kmeans = KMeans(n_clusters=3, random_state=42)
df['Segment/Segmen'] = kmeans.fit_predict(X_scaled)
# Visualize segments
fig = px.scatter_3d(df, x='Age/Usia', y='Income/Pendapatan',
z='Loyalty Score/Skor Loyalitas',
color='Segment/Segmen',
title='Customer Segments')
fig.show()
|
Perkiraan nilai pesanan rata-rata for/untuk 3 bulan
|
{
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": [
"Automotive/Otomotif",
"Automotive/Otomotif",
"Automotive/Otomotif"
],
"City/Kota": null,
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": null,
"Customer ID/ID Pelanggan": null,
"Date/Tanggal": null,
"Discount/Diskon": [
0.16,
0.23,
0.21
],
"Gender/Jenis Kelamin": null,
"Growth Rate/Pertumbuhan": null,
"Income/Pendapatan": null,
"Last Purchase Date/Tanggal Pembelian Terakhir": null,
"Latitude/Lintang": null,
"Longitude/Bujur": null,
"Loyalty Score/Skor Loyalitas": null,
"Market Share/Pangsa Pasar": null,
"Price/Harga": [
39.42,
72.33,
52.47
],
"Product/Produk": [
"Tools_1",
"Tires_2",
"Oil_3"
],
"Region/Wilayah": null,
"Revenue/Pendapatan": null,
"Sales/Penjualan": [
476,
233,
993
],
"Stock/Stok": [
181,
70,
109
],
"Visitors/Pengunjung": null
}
|
import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Product/Produk': ['Tools_1', 'Tires_2', 'Oil_3'], 'Sales/Penjualan': [476, 233, 993], 'Price/Harga': [39.42, 72.33, 52.47], 'Category/Kategori': ['Automotive/Otomotif', 'Automotive/Otomotif', 'Automotive/Otomotif'], 'Discount/Diskon': [0.16, 0.23, 0.21], 'Stock/Stok': [181, 70, 109]})
# Basic data cleaning
df = df.dropna() # Remove missing values
df = df.drop_duplicates() # Remove duplicates
# Time series forecasting
from statsmodels.tsa.arima.model import ARIMA
# Prepare data
ts_data = df.set_index('Date/Tanggal')['Revenue/Pendapatan']
model = ARIMA(ts_data, order=(1,1,1))
model_fit = model.fit()
forecast = model_fit.forecast(steps=7)
print('Forecast for next 7 days:', forecast)
|
Optimalkan rasio konversi kategori
|
{
"Age/Usia": [
67,
39,
58,
64,
69,
69,
70,
53,
61,
26,
57,
58,
41,
52,
26,
38,
21,
19,
54,
47,
23,
23,
60,
52,
45,
49,
66,
34,
59,
36,
25,
62,
57,
55,
21,
22,
62,
47,
45,
66,
33,
30,
32,
53,
50,
22,
51,
66,
68,
47,
23,
29,
64,
24,
37,
28,
21,
58
],
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": null,
"City/Kota": null,
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": null,
"Customer ID/ID Pelanggan": [
"CUST_0001",
"CUST_0002",
"CUST_0003",
"CUST_0004",
"CUST_0005",
"CUST_0006",
"CUST_0007",
"CUST_0008",
"CUST_0009",
"CUST_0010",
"CUST_0011",
"CUST_0012",
"CUST_0013",
"CUST_0014",
"CUST_0015",
"CUST_0016",
"CUST_0017",
"CUST_0018",
"CUST_0019",
"CUST_0020",
"CUST_0021",
"CUST_0022",
"CUST_0023",
"CUST_0024",
"CUST_0025",
"CUST_0026",
"CUST_0027",
"CUST_0028",
"CUST_0029",
"CUST_0030",
"CUST_0031",
"CUST_0032",
"CUST_0033",
"CUST_0034",
"CUST_0035",
"CUST_0036",
"CUST_0037",
"CUST_0038",
"CUST_0039",
"CUST_0040",
"CUST_0041",
"CUST_0042",
"CUST_0043",
"CUST_0044",
"CUST_0045",
"CUST_0046",
"CUST_0047",
"CUST_0048",
"CUST_0049",
"CUST_0050",
"CUST_0051",
"CUST_0052",
"CUST_0053",
"CUST_0054",
"CUST_0055",
"CUST_0056",
"CUST_0057",
"CUST_0058"
],
"Date/Tanggal": null,
"Discount/Diskon": null,
"Gender/Jenis Kelamin": [
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Other/Lainnya",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan"
],
"Growth Rate/Pertumbuhan": null,
"Income/Pendapatan": [
7000,
10000,
7000,
9000,
10000,
7000,
9000,
2000,
8000,
8000,
7000,
2000,
8000,
8000,
3000,
3000,
5000,
6000,
6000,
8000,
8000,
6000,
2000,
9000,
7000,
10000,
9000,
7000,
5000,
4000,
8000,
3000,
5000,
10000,
3000,
3000,
3000,
5000,
3000,
4000,
3000,
5000,
10000,
3000,
2000,
5000,
3000,
9000,
10000,
1000,
7000,
10000,
5000,
4000,
5000,
10000,
2000,
9000
],
"Last Purchase Date/Tanggal Pembelian Terakhir": [
"2024-11-01",
"2024-08-11",
"2024-07-31",
"2025-02-09",
"2024-10-21",
"2024-11-05",
"2024-07-28",
"2024-07-21",
"2025-02-04",
"2025-02-03",
"2024-12-23",
"2024-08-05",
"2025-04-15",
"2025-01-20",
"2025-03-15",
"2025-03-26",
"2024-12-06",
"2024-05-26",
"2024-08-08",
"2024-07-13",
"2024-12-08",
"2024-08-03",
"2024-08-27",
"2025-01-07",
"2024-11-18",
"2024-08-30",
"2025-02-16",
"2024-10-07",
"2025-03-04",
"2024-04-22",
"2025-02-19",
"2024-09-25",
"2025-03-26",
"2025-03-16",
"2024-06-01",
"2025-02-25",
"2024-06-08",
"2024-08-29",
"2024-12-10",
"2024-05-25",
"2024-11-02",
"2024-06-03",
"2025-03-10",
"2024-12-16",
"2024-12-01",
"2024-07-11",
"2025-02-25",
"2025-02-15",
"2024-11-20",
"2024-06-13",
"2025-03-31",
"2024-09-01",
"2024-10-25",
"2025-04-02",
"2024-12-22",
"2024-08-06",
"2024-09-23",
"2024-10-15"
],
"Latitude/Lintang": null,
"Longitude/Bujur": null,
"Loyalty Score/Skor Loyalitas": [
96,
66,
55,
68,
35,
96,
13,
85,
26,
48,
48,
43,
30,
56,
96,
24,
31,
18,
5,
71,
58,
29,
70,
15,
52,
89,
18,
96,
83,
84,
50,
63,
29,
4,
13,
88,
29,
89,
41,
23,
99,
53,
58,
59,
38,
14,
42,
46,
31,
14,
42,
58,
44,
57,
19,
33,
22,
88
],
"Market Share/Pangsa Pasar": null,
"Price/Harga": null,
"Product/Produk": null,
"Region/Wilayah": null,
"Revenue/Pendapatan": null,
"Sales/Penjualan": null,
"Stock/Stok": null,
"Visitors/Pengunjung": null
}
|
import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Customer ID/ID Pelanggan': ['CUST_0001', 'CUST_0002', 'CUST_0003', 'CUST_0004', 'CUST_0005', 'CUST_0006', 'CUST_0007', 'CUST_0008', 'CUST_0009', 'CUST_0010', 'CUST_0011', 'CUST_0012', 'CUST_0013', 'CUST_0014', 'CUST_0015', 'CUST_0016', 'CUST_0017', 'CUST_0018', 'CUST_0019', 'CUST_0020', 'CUST_0021', 'CUST_0022', 'CUST_0023', 'CUST_0024', 'CUST_0025', 'CUST_0026', 'CUST_0027', 'CUST_0028', 'CUST_0029', 'CUST_0030', 'CUST_0031', 'CUST_0032', 'CUST_0033', 'CUST_0034', 'CUST_0035', 'CUST_0036', 'CUST_0037', 'CUST_0038', 'CUST_0039', 'CUST_0040', 'CUST_0041', 'CUST_0042', 'CUST_0043', 'CUST_0044', 'CUST_0045', 'CUST_0046', 'CUST_0047', 'CUST_0048', 'CUST_0049', 'CUST_0050', 'CUST_0051', 'CUST_0052', 'CUST_0053', 'CUST_0054', 'CUST_0055', 'CUST_0056', 'CUST_0057', 'CUST_0058'], 'Age/Usia': [67, 39, 58, 64, 69, 69, 70, 53, 61, 26, 57, 58, 41, 52, 26, 38, 21, 19, 54, 47, 23, 23, 60, 52, 45, 49, 66, 34, 59, 36, 25, 62, 57, 55, 21, 22, 62, 47, 45, 66, 33, 30, 32, 53, 50, 22, 51, 66, 68, 47, 23, 29, 64, 24, 37, 28, 21, 58], 'Gender/Jenis Kelamin': ['Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Other/Lainnya', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan'], 'Income/Pendapatan': [7000, 10000, 7000, 9000, 10000, 7000, 9000, 2000, 8000, 8000, 7000, 2000, 8000, 8000, 3000, 3000, 5000, 6000, 6000, 8000, 8000, 6000, 2000, 9000, 7000, 10000, 9000, 7000, 5000, 4000, 8000, 3000, 5000, 10000, 3000, 3000, 3000, 5000, 3000, 4000, 3000, 5000, 10000, 3000, 2000, 5000, 3000, 9000, 10000, 1000, 7000, 10000, 5000, 4000, 5000, 10000, 2000, 9000], 'Loyalty Score/Skor Loyalitas': [96, 66, 55, 68, 35, 96, 13, 85, 26, 48, 48, 43, 30, 56, 96, 24, 31, 18, 5, 71, 58, 29, 70, 15, 52, 89, 18, 96, 83, 84, 50, 63, 29, 4, 13, 88, 29, 89, 41, 23, 99, 53, 58, 59, 38, 14, 42, 46, 31, 14, 42, 58, 44, 57, 19, 33, 22, 88], 'Last Purchase Date/Tanggal Pembelian Terakhir': ['2024-11-01', '2024-08-11', '2024-07-31', '2025-02-09', '2024-10-21', '2024-11-05', '2024-07-28', '2024-07-21', '2025-02-04', '2025-02-03', '2024-12-23', '2024-08-05', '2025-04-15', '2025-01-20', '2025-03-15', '2025-03-26', '2024-12-06', '2024-05-26', '2024-08-08', '2024-07-13', '2024-12-08', '2024-08-03', '2024-08-27', '2025-01-07', '2024-11-18', '2024-08-30', '2025-02-16', '2024-10-07', '2025-03-04', '2024-04-22', '2025-02-19', '2024-09-25', '2025-03-26', '2025-03-16', '2024-06-01', '2025-02-25', '2024-06-08', '2024-08-29', '2024-12-10', '2024-05-25', '2024-11-02', '2024-06-03', '2025-03-10', '2024-12-16', '2024-12-01', '2024-07-11', '2025-02-25', '2025-02-15', '2024-11-20', '2024-06-13', '2025-03-31', '2024-09-01', '2024-10-25', '2025-04-02', '2024-12-22', '2024-08-06', '2024-09-23', '2024-10-15']})
# Basic data cleaning
df = df.dropna() # Remove missing values
df = df.drop_duplicates() # Remove duplicates
# Comprehensive data analysis
analysis_result = df.describe(include='all')
print(analysis_result)
# Additional insights
print('\nTop performing items:')
print(df.nlargest(3, df.columns[1]))
print('\nTrend analysis:')
if 'Date/Tanggal' in df.columns:
df['Month/Bulan'] = pd.to_datetime(df['Date/Tanggal']).dt.month
monthly_trend = df.groupby('Month/Bulan').mean(numeric_only=True)
print(monthly_trend)
|
Korelasi stok and/dan pengunjung
|
{
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": [
53.35,
104.13,
82.59,
82.67,
82.7,
67.42,
170.61,
166.08,
93.25,
185.35,
197.22,
184.58,
165.21,
70.55,
81.46,
184.71,
155.57,
90.51,
52.58,
178.41,
151.51,
170.83,
132.97,
78.73,
98.18,
199.2,
176.3,
79.95,
116.52,
156.41
],
"Category/Kategori": null,
"City/Kota": null,
"Conversion Rate/Rasio Konversi": [
0.060200000000000004,
0.061900000000000004,
0.066,
0.0217,
0.0776,
0.0763,
0.0931,
0.0945,
0.0722,
0.0412,
0.039400000000000004,
0.0176,
0.0587,
0.0419,
0.014100000000000001,
0.0316,
0.0494,
0.0443,
0.0703,
0.051000000000000004,
0.0123,
0.0567,
0.045700000000000005,
0.074,
0.07350000000000001,
0.06430000000000001,
0.0424,
0.0413,
0.0189,
0.0437
],
"Country/Negara": null,
"Customer ID/ID Pelanggan": null,
"Date/Tanggal": [
"2025-03-23",
"2025-03-24",
"2025-03-25",
"2025-03-26",
"2025-03-27",
"2025-03-28",
"2025-03-29",
"2025-03-30",
"2025-03-31",
"2025-04-01",
"2025-04-02",
"2025-04-03",
"2025-04-04",
"2025-04-05",
"2025-04-06",
"2025-04-07",
"2025-04-08",
"2025-04-09",
"2025-04-10",
"2025-04-11",
"2025-04-12",
"2025-04-13",
"2025-04-14",
"2025-04-15",
"2025-04-16",
"2025-04-17",
"2025-04-18",
"2025-04-19",
"2025-04-20",
"2025-04-21"
],
"Discount/Diskon": null,
"Gender/Jenis Kelamin": null,
"Growth Rate/Pertumbuhan": null,
"Income/Pendapatan": null,
"Last Purchase Date/Tanggal Pembelian Terakhir": null,
"Latitude/Lintang": null,
"Longitude/Bujur": null,
"Loyalty Score/Skor Loyalitas": null,
"Market Share/Pangsa Pasar": null,
"Price/Harga": null,
"Product/Produk": null,
"Region/Wilayah": null,
"Revenue/Pendapatan": [
4514.44,
4610.49,
4569.13,
4686.26,
4626.8,
4655.8,
4491.58,
4522.54,
4275.25,
4205.12,
4135.88,
4033.26,
4131.85,
4112.06,
4254.95,
4308.77,
4380.46,
4408.72,
4437.6,
4386.69,
4342.46,
4343.31,
4194.86,
4039.69,
3948.55,
3848.28,
3864.8,
3855.2,
3914,
3971.34
],
"Sales/Penjualan": null,
"Stock/Stok": null,
"Visitors/Pengunjung": [
224,
249,
137,
227,
255,
244,
101,
97,
88,
228,
69,
50,
160,
176,
161,
137,
210,
100,
278,
92,
180,
87,
97,
201,
236,
253,
253,
208,
210,
173
]
}
|
import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Date/Tanggal': ['2025-03-23', '2025-03-24', '2025-03-25', '2025-03-26', '2025-03-27', '2025-03-28', '2025-03-29', '2025-03-30', '2025-03-31', '2025-04-01', '2025-04-02', '2025-04-03', '2025-04-04', '2025-04-05', '2025-04-06', '2025-04-07', '2025-04-08', '2025-04-09', '2025-04-10', '2025-04-11', '2025-04-12', '2025-04-13', '2025-04-14', '2025-04-15', '2025-04-16', '2025-04-17', '2025-04-18', '2025-04-19', '2025-04-20', '2025-04-21'], 'Revenue/Pendapatan': [4514.44, 4610.49, 4569.13, 4686.26, 4626.8, 4655.8, 4491.58, 4522.54, 4275.25, 4205.12, 4135.88, 4033.26, 4131.85, 4112.06, 4254.95, 4308.77, 4380.46, 4408.72, 4437.6, 4386.69, 4342.46, 4343.31, 4194.86, 4039.69, 3948.55, 3848.28, 3864.8, 3855.2, 3914.0, 3971.34], 'Visitors/Pengunjung': [224, 249, 137, 227, 255, 244, 101, 97, 88, 228, 69, 50, 160, 176, 161, 137, 210, 100, 278, 92, 180, 87, 97, 201, 236, 253, 253, 208, 210, 173], 'Conversion Rate/Rasio Konversi': [0.0602, 0.0619, 0.066, 0.0217, 0.0776, 0.0763, 0.0931, 0.0945, 0.0722, 0.0412, 0.0394, 0.0176, 0.0587, 0.0419, 0.0141, 0.0316, 0.0494, 0.0443, 0.0703, 0.051, 0.0123, 0.0567, 0.0457, 0.074, 0.0735, 0.0643, 0.0424, 0.0413, 0.0189, 0.0437], 'Avg. Order Value/Nilai Pesanan Rata2': [53.35, 104.13, 82.59, 82.67, 82.7, 67.42, 170.61, 166.08, 93.25, 185.35, 197.22, 184.58, 165.21, 70.55, 81.46, 184.71, 155.57, 90.51, 52.58, 178.41, 151.51, 170.83, 132.97, 78.73, 98.18, 199.2, 176.3, 79.95, 116.52, 156.41]})
# Basic data cleaning
df = df.dropna() # Remove missing values
df = df.drop_duplicates() # Remove duplicates
# Correlation analysis
corr_matrix = df.corr(numeric_only=True)
fig = px.imshow(corr_matrix, text_auto=True,
title='Correlation Matrix')
fig.show()
|
Optimalkan skor loyalitas jenis kelamin
|
{
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": [
134.42,
121.18,
108.88,
97.22,
102.79,
184.62,
109.87,
101.36,
172.34,
167.19,
155.34,
84.45,
183.35,
140.92,
92.77,
156.45,
107.36,
55.67,
59.39,
78.26,
138.42,
190.19,
144.13,
122.89,
79.76,
79.39,
133.72,
84.41,
81.43,
98.14
],
"Category/Kategori": null,
"City/Kota": null,
"Conversion Rate/Rasio Konversi": [
0.0709,
0.0892,
0.020800000000000003,
0.0403,
0.0206,
0.0852,
0.029900000000000003,
0.0531,
0.0603,
0.07100000000000001,
0.052000000000000005,
0.0782,
0.066,
0.057100000000000005,
0.018500000000000003,
0.0671,
0.06720000000000001,
0.0741,
0.099,
0.08660000000000001,
0.091,
0.07440000000000001,
0.0596,
0.027600000000000003,
0.030500000000000003,
0.0545,
0.0351,
0.0801,
0.075,
0.08610000000000001
],
"Country/Negara": null,
"Customer ID/ID Pelanggan": null,
"Date/Tanggal": [
"2025-03-23",
"2025-03-24",
"2025-03-25",
"2025-03-26",
"2025-03-27",
"2025-03-28",
"2025-03-29",
"2025-03-30",
"2025-03-31",
"2025-04-01",
"2025-04-02",
"2025-04-03",
"2025-04-04",
"2025-04-05",
"2025-04-06",
"2025-04-07",
"2025-04-08",
"2025-04-09",
"2025-04-10",
"2025-04-11",
"2025-04-12",
"2025-04-13",
"2025-04-14",
"2025-04-15",
"2025-04-16",
"2025-04-17",
"2025-04-18",
"2025-04-19",
"2025-04-20",
"2025-04-21"
],
"Discount/Diskon": null,
"Gender/Jenis Kelamin": null,
"Growth Rate/Pertumbuhan": null,
"Income/Pendapatan": null,
"Last Purchase Date/Tanggal Pembelian Terakhir": null,
"Latitude/Lintang": null,
"Longitude/Bujur": null,
"Loyalty Score/Skor Loyalitas": null,
"Market Share/Pangsa Pasar": null,
"Price/Harga": null,
"Product/Produk": null,
"Region/Wilayah": null,
"Revenue/Pendapatan": [
4885.67,
4923.74,
5045.85,
5030.3,
5131.55,
5005.91,
5051.45,
5039.22,
4913.45,
4851.07,
4773.86,
4869.94,
4811.08,
4854.35,
4966.83,
5119.77,
5118.75,
5295.45,
5197.78,
5242.79,
5266.27,
5248.47,
5196.57,
5155.52,
4903.49,
4999.3,
5091.51,
5046.53,
5152.16,
5160.01
],
"Sales/Penjualan": null,
"Stock/Stok": null,
"Visitors/Pengunjung": [
73,
80,
226,
180,
186,
208,
112,
280,
62,
192,
158,
133,
185,
248,
145,
282,
292,
76,
303,
247,
214,
256,
49,
56,
288,
278,
238,
291,
159,
275
]
}
|
import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Date/Tanggal': ['2025-03-23', '2025-03-24', '2025-03-25', '2025-03-26', '2025-03-27', '2025-03-28', '2025-03-29', '2025-03-30', '2025-03-31', '2025-04-01', '2025-04-02', '2025-04-03', '2025-04-04', '2025-04-05', '2025-04-06', '2025-04-07', '2025-04-08', '2025-04-09', '2025-04-10', '2025-04-11', '2025-04-12', '2025-04-13', '2025-04-14', '2025-04-15', '2025-04-16', '2025-04-17', '2025-04-18', '2025-04-19', '2025-04-20', '2025-04-21'], 'Revenue/Pendapatan': [4885.67, 4923.74, 5045.85, 5030.3, 5131.55, 5005.91, 5051.45, 5039.22, 4913.45, 4851.07, 4773.86, 4869.94, 4811.08, 4854.35, 4966.83, 5119.77, 5118.75, 5295.45, 5197.78, 5242.79, 5266.27, 5248.47, 5196.57, 5155.52, 4903.49, 4999.3, 5091.51, 5046.53, 5152.16, 5160.01], 'Visitors/Pengunjung': [73, 80, 226, 180, 186, 208, 112, 280, 62, 192, 158, 133, 185, 248, 145, 282, 292, 76, 303, 247, 214, 256, 49, 56, 288, 278, 238, 291, 159, 275], 'Conversion Rate/Rasio Konversi': [0.0709, 0.0892, 0.0208, 0.0403, 0.0206, 0.0852, 0.0299, 0.0531, 0.0603, 0.071, 0.052, 0.0782, 0.066, 0.0571, 0.0185, 0.0671, 0.0672, 0.0741, 0.099, 0.0866, 0.091, 0.0744, 0.0596, 0.0276, 0.0305, 0.0545, 0.0351, 0.0801, 0.075, 0.0861], 'Avg. Order Value/Nilai Pesanan Rata2': [134.42, 121.18, 108.88, 97.22, 102.79, 184.62, 109.87, 101.36, 172.34, 167.19, 155.34, 84.45, 183.35, 140.92, 92.77, 156.45, 107.36, 55.67, 59.39, 78.26, 138.42, 190.19, 144.13, 122.89, 79.76, 79.39, 133.72, 84.41, 81.43, 98.14]})
# Basic data cleaning
df = df.dropna() # Remove missing values
df = df.drop_duplicates() # Remove duplicates
# Comprehensive data analysis
analysis_result = df.describe(include='all')
print(analysis_result)
# Additional insights
print('\nTop performing items:')
print(df.nlargest(3, df.columns[1]))
print('\nTrend analysis:')
if 'Date/Tanggal' in df.columns:
df['Month/Bulan'] = pd.to_datetime(df['Date/Tanggal']).dt.month
monthly_trend = df.groupby('Month/Bulan').mean(numeric_only=True)
print(monthly_trend)
|
Analyze growth rate customer segment
|
{
"Age/Usia": [
66,
34,
56,
69,
24,
60,
34,
21,
25,
57,
44,
24,
45,
24,
19,
60,
38,
26,
21,
39,
33,
18,
59,
25,
54,
36,
68,
63,
28,
63,
56,
22,
65,
22,
43,
67,
29,
47,
32,
42,
54,
52,
36,
46,
49,
66,
25,
57,
65,
30,
44,
45,
32,
28,
41,
49,
48,
31,
34,
44,
19,
48,
37,
37,
42,
32,
45,
63,
48
],
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": null,
"City/Kota": null,
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": null,
"Customer ID/ID Pelanggan": [
"CUST_0001",
"CUST_0002",
"CUST_0003",
"CUST_0004",
"CUST_0005",
"CUST_0006",
"CUST_0007",
"CUST_0008",
"CUST_0009",
"CUST_0010",
"CUST_0011",
"CUST_0012",
"CUST_0013",
"CUST_0014",
"CUST_0015",
"CUST_0016",
"CUST_0017",
"CUST_0018",
"CUST_0019",
"CUST_0020",
"CUST_0021",
"CUST_0022",
"CUST_0023",
"CUST_0024",
"CUST_0025",
"CUST_0026",
"CUST_0027",
"CUST_0028",
"CUST_0029",
"CUST_0030",
"CUST_0031",
"CUST_0032",
"CUST_0033",
"CUST_0034",
"CUST_0035",
"CUST_0036",
"CUST_0037",
"CUST_0038",
"CUST_0039",
"CUST_0040",
"CUST_0041",
"CUST_0042",
"CUST_0043",
"CUST_0044",
"CUST_0045",
"CUST_0046",
"CUST_0047",
"CUST_0048",
"CUST_0049",
"CUST_0050",
"CUST_0051",
"CUST_0052",
"CUST_0053",
"CUST_0054",
"CUST_0055",
"CUST_0056",
"CUST_0057",
"CUST_0058",
"CUST_0059",
"CUST_0060",
"CUST_0061",
"CUST_0062",
"CUST_0063",
"CUST_0064",
"CUST_0065",
"CUST_0066",
"CUST_0067",
"CUST_0068",
"CUST_0069"
],
"Date/Tanggal": null,
"Discount/Diskon": null,
"Gender/Jenis Kelamin": [
"Male/Laki-laki",
"Male/Laki-laki",
"Other/Lainnya",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Other/Lainnya",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki"
],
"Growth Rate/Pertumbuhan": null,
"Income/Pendapatan": [
6000,
6000,
4000,
10000,
7000,
8000,
5000,
7000,
4000,
2000,
7000,
7000,
6000,
10000,
9000,
4000,
6000,
8000,
7000,
5000,
2000,
5000,
4000,
7000,
4000,
10000,
10000,
4000,
3000,
2000,
8000,
5000,
7000,
6000,
6000,
5000,
3000,
2000,
2000,
3000,
8000,
10000,
4000,
9000,
8000,
6000,
4000,
6000,
5000,
5000,
4000,
3000,
4000,
5000,
7000,
8000,
9000,
3000,
2000,
1000,
5000,
5000,
5000,
9000,
6000,
5000,
3000,
3000,
9000
],
"Last Purchase Date/Tanggal Pembelian Terakhir": [
"2024-10-21",
"2024-10-31",
"2024-07-11",
"2024-10-27",
"2024-08-07",
"2024-10-16",
"2024-12-11",
"2024-09-14",
"2024-06-12",
"2024-08-23",
"2024-08-24",
"2024-07-22",
"2024-09-29",
"2024-12-02",
"2024-08-20",
"2024-11-29",
"2024-12-09",
"2024-05-19",
"2024-11-19",
"2025-04-20",
"2025-02-02",
"2024-07-09",
"2024-05-26",
"2024-08-25",
"2024-10-31",
"2024-08-01",
"2025-01-19",
"2025-04-15",
"2024-06-05",
"2024-11-14",
"2025-01-10",
"2024-12-19",
"2025-03-15",
"2025-02-10",
"2025-04-06",
"2024-10-24",
"2024-09-26",
"2024-05-02",
"2024-10-11",
"2024-04-24",
"2024-05-03",
"2024-05-21",
"2025-04-03",
"2025-01-11",
"2025-01-04",
"2024-08-26",
"2024-08-26",
"2024-10-03",
"2025-01-10",
"2024-10-09",
"2024-07-20",
"2025-02-01",
"2024-05-31",
"2024-12-28",
"2024-11-05",
"2024-09-13",
"2024-09-12",
"2024-11-24",
"2025-02-10",
"2024-12-14",
"2024-08-07",
"2024-10-14",
"2024-10-03",
"2024-11-27",
"2024-11-15",
"2024-04-22",
"2024-10-25",
"2024-08-10",
"2024-12-22"
],
"Latitude/Lintang": null,
"Longitude/Bujur": null,
"Loyalty Score/Skor Loyalitas": [
23,
88,
22,
24,
66,
60,
91,
73,
55,
57,
16,
27,
78,
33,
76,
7,
38,
28,
89,
70,
15,
76,
65,
67,
77,
61,
53,
30,
96,
89,
66,
39,
98,
68,
83,
31,
89,
18,
71,
13,
22,
96,
71,
23,
56,
18,
66,
43,
52,
63,
24,
55,
71,
92,
96,
63,
66,
99,
44,
98,
71,
23,
87,
6,
5,
5,
50,
77,
23
],
"Market Share/Pangsa Pasar": null,
"Price/Harga": null,
"Product/Produk": null,
"Region/Wilayah": null,
"Revenue/Pendapatan": null,
"Sales/Penjualan": null,
"Stock/Stok": null,
"Visitors/Pengunjung": null
}
|
import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Customer ID/ID Pelanggan': ['CUST_0001', 'CUST_0002', 'CUST_0003', 'CUST_0004', 'CUST_0005', 'CUST_0006', 'CUST_0007', 'CUST_0008', 'CUST_0009', 'CUST_0010', 'CUST_0011', 'CUST_0012', 'CUST_0013', 'CUST_0014', 'CUST_0015', 'CUST_0016', 'CUST_0017', 'CUST_0018', 'CUST_0019', 'CUST_0020', 'CUST_0021', 'CUST_0022', 'CUST_0023', 'CUST_0024', 'CUST_0025', 'CUST_0026', 'CUST_0027', 'CUST_0028', 'CUST_0029', 'CUST_0030', 'CUST_0031', 'CUST_0032', 'CUST_0033', 'CUST_0034', 'CUST_0035', 'CUST_0036', 'CUST_0037', 'CUST_0038', 'CUST_0039', 'CUST_0040', 'CUST_0041', 'CUST_0042', 'CUST_0043', 'CUST_0044', 'CUST_0045', 'CUST_0046', 'CUST_0047', 'CUST_0048', 'CUST_0049', 'CUST_0050', 'CUST_0051', 'CUST_0052', 'CUST_0053', 'CUST_0054', 'CUST_0055', 'CUST_0056', 'CUST_0057', 'CUST_0058', 'CUST_0059', 'CUST_0060', 'CUST_0061', 'CUST_0062', 'CUST_0063', 'CUST_0064', 'CUST_0065', 'CUST_0066', 'CUST_0067', 'CUST_0068', 'CUST_0069'], 'Age/Usia': [66, 34, 56, 69, 24, 60, 34, 21, 25, 57, 44, 24, 45, 24, 19, 60, 38, 26, 21, 39, 33, 18, 59, 25, 54, 36, 68, 63, 28, 63, 56, 22, 65, 22, 43, 67, 29, 47, 32, 42, 54, 52, 36, 46, 49, 66, 25, 57, 65, 30, 44, 45, 32, 28, 41, 49, 48, 31, 34, 44, 19, 48, 37, 37, 42, 32, 45, 63, 48], 'Gender/Jenis Kelamin': ['Male/Laki-laki', 'Male/Laki-laki', 'Other/Lainnya', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Other/Lainnya', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki'], 'Income/Pendapatan': [6000, 6000, 4000, 10000, 7000, 8000, 5000, 7000, 4000, 2000, 7000, 7000, 6000, 10000, 9000, 4000, 6000, 8000, 7000, 5000, 2000, 5000, 4000, 7000, 4000, 10000, 10000, 4000, 3000, 2000, 8000, 5000, 7000, 6000, 6000, 5000, 3000, 2000, 2000, 3000, 8000, 10000, 4000, 9000, 8000, 6000, 4000, 6000, 5000, 5000, 4000, 3000, 4000, 5000, 7000, 8000, 9000, 3000, 2000, 1000, 5000, 5000, 5000, 9000, 6000, 5000, 3000, 3000, 9000], 'Loyalty Score/Skor Loyalitas': [23, 88, 22, 24, 66, 60, 91, 73, 55, 57, 16, 27, 78, 33, 76, 7, 38, 28, 89, 70, 15, 76, 65, 67, 77, 61, 53, 30, 96, 89, 66, 39, 98, 68, 83, 31, 89, 18, 71, 13, 22, 96, 71, 23, 56, 18, 66, 43, 52, 63, 24, 55, 71, 92, 96, 63, 66, 99, 44, 98, 71, 23, 87, 6, 5, 5, 50, 77, 23], 'Last Purchase Date/Tanggal Pembelian Terakhir': ['2024-10-21', '2024-10-31', '2024-07-11', '2024-10-27', '2024-08-07', '2024-10-16', '2024-12-11', '2024-09-14', '2024-06-12', '2024-08-23', '2024-08-24', '2024-07-22', '2024-09-29', '2024-12-02', '2024-08-20', '2024-11-29', '2024-12-09', '2024-05-19', '2024-11-19', '2025-04-20', '2025-02-02', '2024-07-09', '2024-05-26', '2024-08-25', '2024-10-31', '2024-08-01', '2025-01-19', '2025-04-15', '2024-06-05', '2024-11-14', '2025-01-10', '2024-12-19', '2025-03-15', '2025-02-10', '2025-04-06', '2024-10-24', '2024-09-26', '2024-05-02', '2024-10-11', '2024-04-24', '2024-05-03', '2024-05-21', '2025-04-03', '2025-01-11', '2025-01-04', '2024-08-26', '2024-08-26', '2024-10-03', '2025-01-10', '2024-10-09', '2024-07-20', '2025-02-01', '2024-05-31', '2024-12-28', '2024-11-05', '2024-09-13', '2024-09-12', '2024-11-24', '2025-02-10', '2024-12-14', '2024-08-07', '2024-10-14', '2024-10-03', '2024-11-27', '2024-11-15', '2024-04-22', '2024-10-25', '2024-08-10', '2024-12-22']})
# Basic data cleaning
df = df.dropna() # Remove missing values
df = df.drop_duplicates() # Remove duplicates
# Customer segmentation using KMeans
from sklearn.cluster import KMeans
from sklearn.preprocessing import StandardScaler
# Select features and scale
X = df[['Age/Usia', 'Income/Pendapatan', 'Loyalty Score/Skor Loyalitas']]
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)
# Cluster into 3 segments
kmeans = KMeans(n_clusters=3, random_state=42)
df['Segment/Segmen'] = kmeans.fit_predict(X_scaled)
# Visualize segments
fig = px.scatter_3d(df, x='Age/Usia', y='Income/Pendapatan',
z='Loyalty Score/Skor Loyalitas',
color='Segment/Segmen',
title='Customer Segments')
fig.show()
|
Forecast price for/untuk year
|
{
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": [
177.15,
179.1,
89.85,
128.85,
51.62,
66.95,
169.7,
175.1,
130.86,
189.79,
158.87,
131.49,
91.56,
86.42,
169.2,
190.49,
76.62,
169.76,
187.07,
104.54,
188.65,
162.4,
173.82,
83.12,
56.45,
151.11,
75.32,
54.1,
104.02,
185.2
],
"Category/Kategori": null,
"City/Kota": null,
"Conversion Rate/Rasio Konversi": [
0.0309,
0.0692,
0.085,
0.08700000000000001,
0.012400000000000001,
0.07440000000000001,
0.09870000000000001,
0.0171,
0.045700000000000005,
0.032600000000000004,
0.0497,
0.0881,
0.0993,
0.0951,
0.037700000000000004,
0.0844,
0.0494,
0.028300000000000002,
0.0971,
0.0325,
0.042800000000000005,
0.0625,
0.0844,
0.058,
0.044,
0.0599,
0.0142,
0.0426,
0.09430000000000001,
0.0488
],
"Country/Negara": null,
"Customer ID/ID Pelanggan": null,
"Date/Tanggal": [
"2025-03-23",
"2025-03-24",
"2025-03-25",
"2025-03-26",
"2025-03-27",
"2025-03-28",
"2025-03-29",
"2025-03-30",
"2025-03-31",
"2025-04-01",
"2025-04-02",
"2025-04-03",
"2025-04-04",
"2025-04-05",
"2025-04-06",
"2025-04-07",
"2025-04-08",
"2025-04-09",
"2025-04-10",
"2025-04-11",
"2025-04-12",
"2025-04-13",
"2025-04-14",
"2025-04-15",
"2025-04-16",
"2025-04-17",
"2025-04-18",
"2025-04-19",
"2025-04-20",
"2025-04-21"
],
"Discount/Diskon": null,
"Gender/Jenis Kelamin": null,
"Growth Rate/Pertumbuhan": null,
"Income/Pendapatan": null,
"Last Purchase Date/Tanggal Pembelian Terakhir": null,
"Latitude/Lintang": null,
"Longitude/Bujur": null,
"Loyalty Score/Skor Loyalitas": null,
"Market Share/Pangsa Pasar": null,
"Price/Harga": null,
"Product/Produk": null,
"Region/Wilayah": null,
"Revenue/Pendapatan": [
2911.36,
3103.26,
3179.19,
3202.28,
3175.56,
3093.76,
3129.49,
2993.54,
2938.08,
2806.02,
2762.95,
2706,
2713.89,
2864.16,
2856.41,
3005.64,
3003.82,
3067.74,
3101.77,
3039.46,
2889.51,
2871.83,
2837.44,
2657.35,
2622.05,
2626.15,
2584.07,
2683.03,
2719.88,
2885.58
],
"Sales/Penjualan": null,
"Stock/Stok": null,
"Visitors/Pengunjung": [
122,
268,
223,
190,
228,
100,
148,
190,
141,
121,
51,
157,
225,
118,
247,
139,
230,
183,
74,
225,
247,
231,
191,
223,
205,
155,
239,
277,
265,
271
]
}
|
import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Date/Tanggal': ['2025-03-23', '2025-03-24', '2025-03-25', '2025-03-26', '2025-03-27', '2025-03-28', '2025-03-29', '2025-03-30', '2025-03-31', '2025-04-01', '2025-04-02', '2025-04-03', '2025-04-04', '2025-04-05', '2025-04-06', '2025-04-07', '2025-04-08', '2025-04-09', '2025-04-10', '2025-04-11', '2025-04-12', '2025-04-13', '2025-04-14', '2025-04-15', '2025-04-16', '2025-04-17', '2025-04-18', '2025-04-19', '2025-04-20', '2025-04-21'], 'Revenue/Pendapatan': [2911.36, 3103.26, 3179.19, 3202.28, 3175.56, 3093.76, 3129.49, 2993.54, 2938.08, 2806.02, 2762.95, 2706.0, 2713.89, 2864.16, 2856.41, 3005.64, 3003.82, 3067.74, 3101.77, 3039.46, 2889.51, 2871.83, 2837.44, 2657.35, 2622.05, 2626.15, 2584.07, 2683.03, 2719.88, 2885.58], 'Visitors/Pengunjung': [122, 268, 223, 190, 228, 100, 148, 190, 141, 121, 51, 157, 225, 118, 247, 139, 230, 183, 74, 225, 247, 231, 191, 223, 205, 155, 239, 277, 265, 271], 'Conversion Rate/Rasio Konversi': [0.0309, 0.0692, 0.085, 0.087, 0.0124, 0.0744, 0.0987, 0.0171, 0.0457, 0.0326, 0.0497, 0.0881, 0.0993, 0.0951, 0.0377, 0.0844, 0.0494, 0.0283, 0.0971, 0.0325, 0.0428, 0.0625, 0.0844, 0.058, 0.044, 0.0599, 0.0142, 0.0426, 0.0943, 0.0488], 'Avg. Order Value/Nilai Pesanan Rata2': [177.15, 179.1, 89.85, 128.85, 51.62, 66.95, 169.7, 175.1, 130.86, 189.79, 158.87, 131.49, 91.56, 86.42, 169.2, 190.49, 76.62, 169.76, 187.07, 104.54, 188.65, 162.4, 173.82, 83.12, 56.45, 151.11, 75.32, 54.1, 104.02, 185.2]})
# Basic data cleaning
df = df.dropna() # Remove missing values
df = df.drop_duplicates() # Remove duplicates
# Time series forecasting
from statsmodels.tsa.arima.model import ARIMA
# Prepare data
ts_data = df.set_index('Date/Tanggal')['Revenue/Pendapatan']
model = ARIMA(ts_data, order=(1,1,1))
model_fit = model.fit()
forecast = model_fit.forecast(steps=7)
print('Forecast for next 7 days:', forecast)
|
Analyze average order value product
|
{
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": [
"Electronics/Elektronik",
"Electronics/Elektronik",
"Electronics/Elektronik"
],
"City/Kota": null,
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": null,
"Customer ID/ID Pelanggan": null,
"Date/Tanggal": null,
"Discount/Diskon": [
0.01,
0.05,
0.27
],
"Gender/Jenis Kelamin": null,
"Growth Rate/Pertumbuhan": null,
"Income/Pendapatan": null,
"Last Purchase Date/Tanggal Pembelian Terakhir": null,
"Latitude/Lintang": null,
"Longitude/Bujur": null,
"Loyalty Score/Skor Loyalitas": null,
"Market Share/Pangsa Pasar": null,
"Price/Harga": [
41.59,
152.91,
175.27
],
"Product/Produk": [
"Headphones_1",
"Laptop_2",
"TV_3"
],
"Region/Wilayah": null,
"Revenue/Pendapatan": null,
"Sales/Penjualan": [
246,
971,
721
],
"Stock/Stok": [
162,
58,
44
],
"Visitors/Pengunjung": null
}
|
import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Product/Produk': ['Headphones_1', 'Laptop_2', 'TV_3'], 'Sales/Penjualan': [246, 971, 721], 'Price/Harga': [41.59, 152.91, 175.27], 'Category/Kategori': ['Electronics/Elektronik', 'Electronics/Elektronik', 'Electronics/Elektronik'], 'Discount/Diskon': [0.01, 0.05, 0.27], 'Stock/Stok': [162, 58, 44]})
# Basic data cleaning
df = df.dropna() # Remove missing values
df = df.drop_duplicates() # Remove duplicates
# Comprehensive data analysis
analysis_result = df.describe(include='all')
print(analysis_result)
# Additional insights
print('\nTop performing items:')
print(df.nlargest(3, df.columns[1]))
print('\nTrend analysis:')
if 'Date/Tanggal' in df.columns:
df['Month/Bulan'] = pd.to_datetime(df['Date/Tanggal']).dt.month
monthly_trend = df.groupby('Month/Bulan').mean(numeric_only=True)
print(monthly_trend)
|
Kelompokkan pangsa pasar kategori
|
{
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": [
71.25,
111.18,
85.06,
129.36,
63.01,
187.63,
92.03,
189.45,
131.09,
161.71,
134.77,
128.22,
50.27,
74.57,
120.34,
178.23,
164.19,
171.93,
90.67,
142.15,
199.27,
95.58,
133.41,
132.34,
195.91,
77.9,
118.07,
75.04,
65.08,
151.97
],
"Category/Kategori": null,
"City/Kota": null,
"Conversion Rate/Rasio Konversi": [
0.0546,
0.09860000000000001,
0.0917,
0.0969,
0.09340000000000001,
0.07350000000000001,
0.0606,
0.0114,
0.089,
0.061700000000000005,
0.0442,
0.0891,
0.0859,
0.045700000000000005,
0.0823,
0.035500000000000004,
0.0762,
0.049,
0.0599,
0.0217,
0.0907,
0.0912,
0.0398,
0.07050000000000001,
0.0188,
0.079,
0.0171,
0.0776,
0.0268,
0.031400000000000004
],
"Country/Negara": null,
"Customer ID/ID Pelanggan": null,
"Date/Tanggal": [
"2025-03-23",
"2025-03-24",
"2025-03-25",
"2025-03-26",
"2025-03-27",
"2025-03-28",
"2025-03-29",
"2025-03-30",
"2025-03-31",
"2025-04-01",
"2025-04-02",
"2025-04-03",
"2025-04-04",
"2025-04-05",
"2025-04-06",
"2025-04-07",
"2025-04-08",
"2025-04-09",
"2025-04-10",
"2025-04-11",
"2025-04-12",
"2025-04-13",
"2025-04-14",
"2025-04-15",
"2025-04-16",
"2025-04-17",
"2025-04-18",
"2025-04-19",
"2025-04-20",
"2025-04-21"
],
"Discount/Diskon": null,
"Gender/Jenis Kelamin": null,
"Growth Rate/Pertumbuhan": null,
"Income/Pendapatan": null,
"Last Purchase Date/Tanggal Pembelian Terakhir": null,
"Latitude/Lintang": null,
"Longitude/Bujur": null,
"Loyalty Score/Skor Loyalitas": null,
"Market Share/Pangsa Pasar": null,
"Price/Harga": null,
"Product/Produk": null,
"Region/Wilayah": null,
"Revenue/Pendapatan": [
2424.36,
2543.69,
2630.15,
2622.91,
2724.41,
2570.61,
2558.62,
2512.34,
2415.35,
2336.93,
2255.42,
2313.75,
2325.81,
2343.68,
2470.08,
2505.31,
2585.32,
2606.36,
2610.37,
2580.67,
2596.07,
2590.04,
2376.33,
2330.5,
2245.88,
2267.9,
2148,
2273.95,
2389.22,
2443.11
],
"Sales/Penjualan": null,
"Stock/Stok": null,
"Visitors/Pengunjung": [
124,
192,
173,
114,
176,
166,
106,
137,
269,
262,
266,
57,
78,
289,
172,
271,
177,
160,
203,
99,
158,
83,
42,
202,
277,
216,
172,
252,
86,
246
]
}
|
import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Date/Tanggal': ['2025-03-23', '2025-03-24', '2025-03-25', '2025-03-26', '2025-03-27', '2025-03-28', '2025-03-29', '2025-03-30', '2025-03-31', '2025-04-01', '2025-04-02', '2025-04-03', '2025-04-04', '2025-04-05', '2025-04-06', '2025-04-07', '2025-04-08', '2025-04-09', '2025-04-10', '2025-04-11', '2025-04-12', '2025-04-13', '2025-04-14', '2025-04-15', '2025-04-16', '2025-04-17', '2025-04-18', '2025-04-19', '2025-04-20', '2025-04-21'], 'Revenue/Pendapatan': [2424.36, 2543.69, 2630.15, 2622.91, 2724.41, 2570.61, 2558.62, 2512.34, 2415.35, 2336.93, 2255.42, 2313.75, 2325.81, 2343.68, 2470.08, 2505.31, 2585.32, 2606.36, 2610.37, 2580.67, 2596.07, 2590.04, 2376.33, 2330.5, 2245.88, 2267.9, 2148.0, 2273.95, 2389.22, 2443.11], 'Visitors/Pengunjung': [124, 192, 173, 114, 176, 166, 106, 137, 269, 262, 266, 57, 78, 289, 172, 271, 177, 160, 203, 99, 158, 83, 42, 202, 277, 216, 172, 252, 86, 246], 'Conversion Rate/Rasio Konversi': [0.0546, 0.0986, 0.0917, 0.0969, 0.0934, 0.0735, 0.0606, 0.0114, 0.089, 0.0617, 0.0442, 0.0891, 0.0859, 0.0457, 0.0823, 0.0355, 0.0762, 0.049, 0.0599, 0.0217, 0.0907, 0.0912, 0.0398, 0.0705, 0.0188, 0.079, 0.0171, 0.0776, 0.0268, 0.0314], 'Avg. Order Value/Nilai Pesanan Rata2': [71.25, 111.18, 85.06, 129.36, 63.01, 187.63, 92.03, 189.45, 131.09, 161.71, 134.77, 128.22, 50.27, 74.57, 120.34, 178.23, 164.19, 171.93, 90.67, 142.15, 199.27, 95.58, 133.41, 132.34, 195.91, 77.9, 118.07, 75.04, 65.08, 151.97]})
# Basic data cleaning
df = df.dropna() # Remove missing values
df = df.drop_duplicates() # Remove duplicates
# Comprehensive data analysis
analysis_result = df.describe(include='all')
print(analysis_result)
# Additional insights
print('\nTop performing items:')
print(df.nlargest(3, df.columns[1]))
print('\nTrend analysis:')
if 'Date/Tanggal' in df.columns:
df['Month/Bulan'] = pd.to_datetime(df['Date/Tanggal']).dt.month
monthly_trend = df.groupby('Month/Bulan').mean(numeric_only=True)
print(monthly_trend)
|
Hitung pendapatan segmen pelanggan
|
{
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": [
"Electronics/Elektronik",
"Electronics/Elektronik",
"Electronics/Elektronik"
],
"City/Kota": null,
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": null,
"Customer ID/ID Pelanggan": null,
"Date/Tanggal": null,
"Discount/Diskon": [
0.01,
0.1,
0.1
],
"Gender/Jenis Kelamin": null,
"Growth Rate/Pertumbuhan": null,
"Income/Pendapatan": null,
"Last Purchase Date/Tanggal Pembelian Terakhir": null,
"Latitude/Lintang": null,
"Longitude/Bujur": null,
"Loyalty Score/Skor Loyalitas": null,
"Market Share/Pangsa Pasar": null,
"Price/Harga": [
154.13,
126.38,
84.91
],
"Product/Produk": [
"Headphones_1",
"Smartphone_2",
"Laptop_3"
],
"Region/Wilayah": null,
"Revenue/Pendapatan": null,
"Sales/Penjualan": [
907,
206,
648
],
"Stock/Stok": [
121,
72,
82
],
"Visitors/Pengunjung": null
}
|
import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Product/Produk': ['Headphones_1', 'Smartphone_2', 'Laptop_3'], 'Sales/Penjualan': [907, 206, 648], 'Price/Harga': [154.13, 126.38, 84.91], 'Category/Kategori': ['Electronics/Elektronik', 'Electronics/Elektronik', 'Electronics/Elektronik'], 'Discount/Diskon': [0.01, 0.1, 0.1], 'Stock/Stok': [121, 72, 82]})
# Basic data cleaning
df = df.dropna() # Remove missing values
df = df.drop_duplicates() # Remove duplicates
# z-score calculation
result = df['Category/Kategori'].(df['{col}'] - df['{col}'].mean())/df['{col}'].std()
print(result)
|
Bandingkan diskon kota
|
{
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": [
"Automotive/Otomotif",
"Automotive/Otomotif",
"Automotive/Otomotif"
],
"City/Kota": null,
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": null,
"Customer ID/ID Pelanggan": null,
"Date/Tanggal": null,
"Discount/Diskon": [
0.08,
0.21,
0.08
],
"Gender/Jenis Kelamin": null,
"Growth Rate/Pertumbuhan": null,
"Income/Pendapatan": null,
"Last Purchase Date/Tanggal Pembelian Terakhir": null,
"Latitude/Lintang": null,
"Longitude/Bujur": null,
"Loyalty Score/Skor Loyalitas": null,
"Market Share/Pangsa Pasar": null,
"Price/Harga": [
193.34,
83.85,
178.17
],
"Product/Produk": [
"Tires_1",
"Accessories_2",
"Tools_3"
],
"Region/Wilayah": null,
"Revenue/Pendapatan": null,
"Sales/Penjualan": [
699,
105,
121
],
"Stock/Stok": [
68,
183,
71
],
"Visitors/Pengunjung": null
}
|
import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Product/Produk': ['Tires_1', 'Accessories_2', 'Tools_3'], 'Sales/Penjualan': [699, 105, 121], 'Price/Harga': [193.34, 83.85, 178.17], 'Category/Kategori': ['Automotive/Otomotif', 'Automotive/Otomotif', 'Automotive/Otomotif'], 'Discount/Diskon': [0.08, 0.21, 0.08], 'Stock/Stok': [68, 183, 71]})
# Basic data cleaning
df = df.dropna() # Remove missing values
df = df.drop_duplicates() # Remove duplicates
# Comprehensive data analysis
analysis_result = df.describe(include='all')
print(analysis_result)
# Additional insights
print('\nTop performing items:')
print(df.nlargest(3, df.columns[1]))
print('\nTrend analysis:')
if 'Date/Tanggal' in df.columns:
df['Month/Bulan'] = pd.to_datetime(df['Date/Tanggal']).dt.month
monthly_trend = df.groupby('Month/Bulan').mean(numeric_only=True)
print(monthly_trend)
|
Compare discount time period
|
{
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": [
"Food/Makanan",
"Food/Makanan",
"Food/Makanan"
],
"City/Kota": null,
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": null,
"Customer ID/ID Pelanggan": null,
"Date/Tanggal": null,
"Discount/Diskon": [
0.05,
0.09,
0.14
],
"Gender/Jenis Kelamin": null,
"Growth Rate/Pertumbuhan": null,
"Income/Pendapatan": null,
"Last Purchase Date/Tanggal Pembelian Terakhir": null,
"Latitude/Lintang": null,
"Longitude/Bujur": null,
"Loyalty Score/Skor Loyalitas": null,
"Market Share/Pangsa Pasar": null,
"Price/Harga": [
181.55,
183.82,
137.98
],
"Product/Produk": [
"Fruits_1",
"Snacks_2",
"Dairy_3"
],
"Region/Wilayah": null,
"Revenue/Pendapatan": null,
"Sales/Penjualan": [
586,
879,
682
],
"Stock/Stok": [
75,
161,
174
],
"Visitors/Pengunjung": null
}
|
import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Product/Produk': ['Fruits_1', 'Snacks_2', 'Dairy_3'], 'Sales/Penjualan': [586, 879, 682], 'Price/Harga': [181.55, 183.82, 137.98], 'Category/Kategori': ['Food/Makanan', 'Food/Makanan', 'Food/Makanan'], 'Discount/Diskon': [0.05, 0.09, 0.14], 'Stock/Stok': [75, 161, 174]})
# Basic data cleaning
df = df.dropna() # Remove missing values
df = df.drop_duplicates() # Remove duplicates
# Comprehensive data analysis
analysis_result = df.describe(include='all')
print(analysis_result)
# Additional insights
print('\nTop performing items:')
print(df.nlargest(3, df.columns[1]))
print('\nTrend analysis:')
if 'Date/Tanggal' in df.columns:
df['Month/Bulan'] = pd.to_datetime(df['Date/Tanggal']).dt.month
monthly_trend = df.groupby('Month/Bulan').mean(numeric_only=True)
print(monthly_trend)
|
Optimalkan pengunjung negara
|
{
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": [
147.47,
109.1,
98.65,
106.21,
89.72,
190.1,
198.44,
110.11,
174.24,
157.71,
198.41,
83.41,
131.77,
128.38,
140.52,
62.34,
170.6,
61.26,
52.11,
58.17,
148.89,
155.73,
166.45,
191.49,
62.3,
99.5,
54.25,
88.95,
53.35,
60.28
],
"Category/Kategori": null,
"City/Kota": null,
"Conversion Rate/Rasio Konversi": [
0.049600000000000005,
0.06620000000000001,
0.0709,
0.0694,
0.020900000000000002,
0.0267,
0.0639,
0.0341,
0.038,
0.08750000000000001,
0.0341,
0.0398,
0.0441,
0.0158,
0.0393,
0.0412,
0.0412,
0.0286,
0.0655,
0.0495,
0.06860000000000001,
0.08990000000000001,
0.0413,
0.0763,
0.024800000000000003,
0.0188,
0.0261,
0.027800000000000002,
0.07680000000000001,
0.036500000000000005
],
"Country/Negara": null,
"Customer ID/ID Pelanggan": null,
"Date/Tanggal": [
"2025-03-23",
"2025-03-24",
"2025-03-25",
"2025-03-26",
"2025-03-27",
"2025-03-28",
"2025-03-29",
"2025-03-30",
"2025-03-31",
"2025-04-01",
"2025-04-02",
"2025-04-03",
"2025-04-04",
"2025-04-05",
"2025-04-06",
"2025-04-07",
"2025-04-08",
"2025-04-09",
"2025-04-10",
"2025-04-11",
"2025-04-12",
"2025-04-13",
"2025-04-14",
"2025-04-15",
"2025-04-16",
"2025-04-17",
"2025-04-18",
"2025-04-19",
"2025-04-20",
"2025-04-21"
],
"Discount/Diskon": null,
"Gender/Jenis Kelamin": null,
"Growth Rate/Pertumbuhan": null,
"Income/Pendapatan": null,
"Last Purchase Date/Tanggal Pembelian Terakhir": null,
"Latitude/Lintang": null,
"Longitude/Bujur": null,
"Loyalty Score/Skor Loyalitas": null,
"Market Share/Pangsa Pasar": null,
"Price/Harga": null,
"Product/Produk": null,
"Region/Wilayah": null,
"Revenue/Pendapatan": [
1646.04,
1788.65,
1821.35,
1882.7,
1820.9,
1781.82,
1762.55,
1702.37,
1647.74,
1476.48,
1420.16,
1345.21,
1391.81,
1499.9,
1608.16,
1546.25,
1626.12,
1790.72,
1753.63,
1736.55,
1665.52,
1553.72,
1413.28,
1253.74,
1299.75,
1152.54,
1168.46,
1207.52,
1286.17,
1392.67
],
"Sales/Penjualan": null,
"Stock/Stok": null,
"Visitors/Pengunjung": [
287,
229,
304,
132,
144,
161,
179,
68,
198,
234,
68,
157,
190,
95,
104,
151,
191,
76,
247,
126,
91,
163,
227,
241,
204,
216,
128,
274,
115,
295
]
}
|
import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Date/Tanggal': ['2025-03-23', '2025-03-24', '2025-03-25', '2025-03-26', '2025-03-27', '2025-03-28', '2025-03-29', '2025-03-30', '2025-03-31', '2025-04-01', '2025-04-02', '2025-04-03', '2025-04-04', '2025-04-05', '2025-04-06', '2025-04-07', '2025-04-08', '2025-04-09', '2025-04-10', '2025-04-11', '2025-04-12', '2025-04-13', '2025-04-14', '2025-04-15', '2025-04-16', '2025-04-17', '2025-04-18', '2025-04-19', '2025-04-20', '2025-04-21'], 'Revenue/Pendapatan': [1646.04, 1788.65, 1821.35, 1882.7, 1820.9, 1781.82, 1762.55, 1702.37, 1647.74, 1476.48, 1420.16, 1345.21, 1391.81, 1499.9, 1608.16, 1546.25, 1626.12, 1790.72, 1753.63, 1736.55, 1665.52, 1553.72, 1413.28, 1253.74, 1299.75, 1152.54, 1168.46, 1207.52, 1286.17, 1392.67], 'Visitors/Pengunjung': [287, 229, 304, 132, 144, 161, 179, 68, 198, 234, 68, 157, 190, 95, 104, 151, 191, 76, 247, 126, 91, 163, 227, 241, 204, 216, 128, 274, 115, 295], 'Conversion Rate/Rasio Konversi': [0.0496, 0.0662, 0.0709, 0.0694, 0.0209, 0.0267, 0.0639, 0.0341, 0.038, 0.0875, 0.0341, 0.0398, 0.0441, 0.0158, 0.0393, 0.0412, 0.0412, 0.0286, 0.0655, 0.0495, 0.0686, 0.0899, 0.0413, 0.0763, 0.0248, 0.0188, 0.0261, 0.0278, 0.0768, 0.0365], 'Avg. Order Value/Nilai Pesanan Rata2': [147.47, 109.1, 98.65, 106.21, 89.72, 190.1, 198.44, 110.11, 174.24, 157.71, 198.41, 83.41, 131.77, 128.38, 140.52, 62.34, 170.6, 61.26, 52.11, 58.17, 148.89, 155.73, 166.45, 191.49, 62.3, 99.5, 54.25, 88.95, 53.35, 60.28]})
# Basic data cleaning
df = df.dropna() # Remove missing values
df = df.drop_duplicates() # Remove duplicates
# Comprehensive data analysis
analysis_result = df.describe(include='all')
print(analysis_result)
# Additional insights
print('\nTop performing items:')
print(df.nlargest(3, df.columns[1]))
print('\nTrend analysis:')
if 'Date/Tanggal' in df.columns:
df['Month/Bulan'] = pd.to_datetime(df['Date/Tanggal']).dt.month
monthly_trend = df.groupby('Month/Bulan').mean(numeric_only=True)
print(monthly_trend)
|
Analyze income city
|
{
"Age/Usia": [
70,
60,
54,
28,
39,
24,
54,
30,
30,
18,
53,
46,
34,
32,
40,
61,
34,
26,
67,
24,
18,
48,
49,
68,
48,
26,
39,
28,
50,
61,
44,
18,
54,
32,
70,
35,
23,
57,
66,
21,
45,
51,
50,
64,
36,
38,
23,
56,
34,
59,
69,
40,
24,
51,
47,
56,
20,
62,
70,
29,
30,
24,
52,
26,
24,
19,
38,
55,
67,
42,
58,
40,
43,
36,
23,
69,
57,
32,
36,
53,
36,
46
],
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": null,
"City/Kota": null,
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": null,
"Customer ID/ID Pelanggan": [
"CUST_0001",
"CUST_0002",
"CUST_0003",
"CUST_0004",
"CUST_0005",
"CUST_0006",
"CUST_0007",
"CUST_0008",
"CUST_0009",
"CUST_0010",
"CUST_0011",
"CUST_0012",
"CUST_0013",
"CUST_0014",
"CUST_0015",
"CUST_0016",
"CUST_0017",
"CUST_0018",
"CUST_0019",
"CUST_0020",
"CUST_0021",
"CUST_0022",
"CUST_0023",
"CUST_0024",
"CUST_0025",
"CUST_0026",
"CUST_0027",
"CUST_0028",
"CUST_0029",
"CUST_0030",
"CUST_0031",
"CUST_0032",
"CUST_0033",
"CUST_0034",
"CUST_0035",
"CUST_0036",
"CUST_0037",
"CUST_0038",
"CUST_0039",
"CUST_0040",
"CUST_0041",
"CUST_0042",
"CUST_0043",
"CUST_0044",
"CUST_0045",
"CUST_0046",
"CUST_0047",
"CUST_0048",
"CUST_0049",
"CUST_0050",
"CUST_0051",
"CUST_0052",
"CUST_0053",
"CUST_0054",
"CUST_0055",
"CUST_0056",
"CUST_0057",
"CUST_0058",
"CUST_0059",
"CUST_0060",
"CUST_0061",
"CUST_0062",
"CUST_0063",
"CUST_0064",
"CUST_0065",
"CUST_0066",
"CUST_0067",
"CUST_0068",
"CUST_0069",
"CUST_0070",
"CUST_0071",
"CUST_0072",
"CUST_0073",
"CUST_0074",
"CUST_0075",
"CUST_0076",
"CUST_0077",
"CUST_0078",
"CUST_0079",
"CUST_0080",
"CUST_0081",
"CUST_0082"
],
"Date/Tanggal": null,
"Discount/Diskon": null,
"Gender/Jenis Kelamin": [
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Other/Lainnya",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Other/Lainnya",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan"
],
"Growth Rate/Pertumbuhan": null,
"Income/Pendapatan": [
9000,
6000,
7000,
4000,
7000,
5000,
4000,
9000,
4000,
1000,
5000,
6000,
9000,
4000,
10000,
2000,
4000,
3000,
9000,
8000,
1000,
1000,
5000,
7000,
3000,
2000,
6000,
3000,
8000,
2000,
5000,
8000,
9000,
6000,
2000,
2000,
4000,
5000,
8000,
6000,
7000,
4000,
6000,
8000,
8000,
5000,
5000,
9000,
10000,
2000,
6000,
7000,
9000,
7000,
9000,
1000,
5000,
5000,
1000,
5000,
1000,
4000,
3000,
10000,
10000,
2000,
9000,
9000,
8000,
3000,
9000,
10000,
5000,
6000,
9000,
9000,
3000,
5000,
10000,
2000,
4000,
10000
],
"Last Purchase Date/Tanggal Pembelian Terakhir": [
"2024-12-09",
"2025-02-26",
"2025-04-11",
"2024-11-22",
"2024-10-23",
"2025-01-08",
"2024-05-06",
"2024-10-23",
"2025-01-24",
"2024-09-01",
"2025-03-25",
"2024-11-03",
"2024-04-21",
"2024-09-18",
"2025-01-04",
"2024-07-11",
"2024-12-18",
"2024-11-29",
"2025-02-27",
"2024-07-05",
"2024-06-14",
"2024-05-14",
"2025-03-02",
"2025-02-05",
"2024-06-08",
"2024-09-07",
"2025-03-17",
"2025-03-17",
"2025-01-04",
"2024-10-18",
"2024-09-17",
"2024-10-19",
"2024-08-11",
"2024-11-03",
"2024-06-27",
"2025-02-17",
"2024-06-16",
"2024-05-03",
"2024-09-06",
"2024-06-22",
"2024-06-01",
"2024-09-18",
"2025-03-09",
"2024-09-02",
"2025-02-08",
"2025-03-22",
"2024-11-24",
"2024-09-18",
"2025-04-03",
"2025-01-18",
"2024-09-23",
"2024-04-27",
"2025-01-07",
"2024-07-30",
"2025-02-01",
"2024-05-03",
"2024-11-12",
"2024-08-15",
"2024-08-30",
"2024-05-08",
"2024-11-29",
"2024-11-06",
"2024-05-23",
"2024-05-17",
"2024-07-18",
"2024-08-13",
"2024-06-04",
"2024-11-08",
"2024-09-29",
"2025-02-03",
"2024-05-28",
"2024-12-06",
"2024-04-29",
"2024-07-04",
"2024-10-24",
"2024-11-14",
"2024-09-09",
"2024-09-14",
"2024-12-17",
"2025-03-01",
"2024-06-06",
"2024-06-25"
],
"Latitude/Lintang": null,
"Longitude/Bujur": null,
"Loyalty Score/Skor Loyalitas": [
9,
74,
11,
35,
37,
78,
62,
85,
88,
49,
2,
66,
58,
30,
87,
48,
2,
91,
56,
53,
62,
64,
31,
42,
63,
54,
14,
42,
96,
85,
3,
24,
26,
41,
19,
70,
45,
41,
89,
28,
44,
100,
48,
78,
69,
84,
54,
99,
24,
22,
45,
98,
10,
27,
45,
2,
34,
100,
28,
90,
10,
43,
56,
76,
87,
42,
50,
37,
78,
81,
40,
47,
8,
82,
85,
75,
53,
50,
94,
19,
46,
69
],
"Market Share/Pangsa Pasar": null,
"Price/Harga": null,
"Product/Produk": null,
"Region/Wilayah": null,
"Revenue/Pendapatan": null,
"Sales/Penjualan": null,
"Stock/Stok": null,
"Visitors/Pengunjung": null
}
|
import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Customer ID/ID Pelanggan': ['CUST_0001', 'CUST_0002', 'CUST_0003', 'CUST_0004', 'CUST_0005', 'CUST_0006', 'CUST_0007', 'CUST_0008', 'CUST_0009', 'CUST_0010', 'CUST_0011', 'CUST_0012', 'CUST_0013', 'CUST_0014', 'CUST_0015', 'CUST_0016', 'CUST_0017', 'CUST_0018', 'CUST_0019', 'CUST_0020', 'CUST_0021', 'CUST_0022', 'CUST_0023', 'CUST_0024', 'CUST_0025', 'CUST_0026', 'CUST_0027', 'CUST_0028', 'CUST_0029', 'CUST_0030', 'CUST_0031', 'CUST_0032', 'CUST_0033', 'CUST_0034', 'CUST_0035', 'CUST_0036', 'CUST_0037', 'CUST_0038', 'CUST_0039', 'CUST_0040', 'CUST_0041', 'CUST_0042', 'CUST_0043', 'CUST_0044', 'CUST_0045', 'CUST_0046', 'CUST_0047', 'CUST_0048', 'CUST_0049', 'CUST_0050', 'CUST_0051', 'CUST_0052', 'CUST_0053', 'CUST_0054', 'CUST_0055', 'CUST_0056', 'CUST_0057', 'CUST_0058', 'CUST_0059', 'CUST_0060', 'CUST_0061', 'CUST_0062', 'CUST_0063', 'CUST_0064', 'CUST_0065', 'CUST_0066', 'CUST_0067', 'CUST_0068', 'CUST_0069', 'CUST_0070', 'CUST_0071', 'CUST_0072', 'CUST_0073', 'CUST_0074', 'CUST_0075', 'CUST_0076', 'CUST_0077', 'CUST_0078', 'CUST_0079', 'CUST_0080', 'CUST_0081', 'CUST_0082'], 'Age/Usia': [70, 60, 54, 28, 39, 24, 54, 30, 30, 18, 53, 46, 34, 32, 40, 61, 34, 26, 67, 24, 18, 48, 49, 68, 48, 26, 39, 28, 50, 61, 44, 18, 54, 32, 70, 35, 23, 57, 66, 21, 45, 51, 50, 64, 36, 38, 23, 56, 34, 59, 69, 40, 24, 51, 47, 56, 20, 62, 70, 29, 30, 24, 52, 26, 24, 19, 38, 55, 67, 42, 58, 40, 43, 36, 23, 69, 57, 32, 36, 53, 36, 46], 'Gender/Jenis Kelamin': ['Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Other/Lainnya', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Other/Lainnya', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan'], 'Income/Pendapatan': [9000, 6000, 7000, 4000, 7000, 5000, 4000, 9000, 4000, 1000, 5000, 6000, 9000, 4000, 10000, 2000, 4000, 3000, 9000, 8000, 1000, 1000, 5000, 7000, 3000, 2000, 6000, 3000, 8000, 2000, 5000, 8000, 9000, 6000, 2000, 2000, 4000, 5000, 8000, 6000, 7000, 4000, 6000, 8000, 8000, 5000, 5000, 9000, 10000, 2000, 6000, 7000, 9000, 7000, 9000, 1000, 5000, 5000, 1000, 5000, 1000, 4000, 3000, 10000, 10000, 2000, 9000, 9000, 8000, 3000, 9000, 10000, 5000, 6000, 9000, 9000, 3000, 5000, 10000, 2000, 4000, 10000], 'Loyalty Score/Skor Loyalitas': [9, 74, 11, 35, 37, 78, 62, 85, 88, 49, 2, 66, 58, 30, 87, 48, 2, 91, 56, 53, 62, 64, 31, 42, 63, 54, 14, 42, 96, 85, 3, 24, 26, 41, 19, 70, 45, 41, 89, 28, 44, 100, 48, 78, 69, 84, 54, 99, 24, 22, 45, 98, 10, 27, 45, 2, 34, 100, 28, 90, 10, 43, 56, 76, 87, 42, 50, 37, 78, 81, 40, 47, 8, 82, 85, 75, 53, 50, 94, 19, 46, 69], 'Last Purchase Date/Tanggal Pembelian Terakhir': ['2024-12-09', '2025-02-26', '2025-04-11', '2024-11-22', '2024-10-23', '2025-01-08', '2024-05-06', '2024-10-23', '2025-01-24', '2024-09-01', '2025-03-25', '2024-11-03', '2024-04-21', '2024-09-18', '2025-01-04', '2024-07-11', '2024-12-18', '2024-11-29', '2025-02-27', '2024-07-05', '2024-06-14', '2024-05-14', '2025-03-02', '2025-02-05', '2024-06-08', '2024-09-07', '2025-03-17', '2025-03-17', '2025-01-04', '2024-10-18', '2024-09-17', '2024-10-19', '2024-08-11', '2024-11-03', '2024-06-27', '2025-02-17', '2024-06-16', '2024-05-03', '2024-09-06', '2024-06-22', '2024-06-01', '2024-09-18', '2025-03-09', '2024-09-02', '2025-02-08', '2025-03-22', '2024-11-24', '2024-09-18', '2025-04-03', '2025-01-18', '2024-09-23', '2024-04-27', '2025-01-07', '2024-07-30', '2025-02-01', '2024-05-03', '2024-11-12', '2024-08-15', '2024-08-30', '2024-05-08', '2024-11-29', '2024-11-06', '2024-05-23', '2024-05-17', '2024-07-18', '2024-08-13', '2024-06-04', '2024-11-08', '2024-09-29', '2025-02-03', '2024-05-28', '2024-12-06', '2024-04-29', '2024-07-04', '2024-10-24', '2024-11-14', '2024-09-09', '2024-09-14', '2024-12-17', '2025-03-01', '2024-06-06', '2024-06-25']})
# Basic data cleaning
df = df.dropna() # Remove missing values
df = df.drop_duplicates() # Remove duplicates
# Comprehensive data analysis
analysis_result = df.describe(include='all')
print(analysis_result)
# Additional insights
print('\nTop performing items:')
print(df.nlargest(3, df.columns[1]))
print('\nTrend analysis:')
if 'Date/Tanggal' in df.columns:
df['Month/Bulan'] = pd.to_datetime(df['Date/Tanggal']).dt.month
monthly_trend = df.groupby('Month/Bulan').mean(numeric_only=True)
print(monthly_trend)
|
Kelompokkan nilai pesanan rata-rata jenis kelamin
|
{
"Age/Usia": [
46,
39,
23,
18,
33,
41,
27,
66,
48,
69,
68,
68,
52,
37,
70,
46,
23,
58,
35,
21,
21,
25,
60,
25,
22,
23,
28,
69,
18,
28,
52,
67,
44,
28,
63,
32,
55,
67,
69,
66,
46,
30,
34,
43,
33,
23,
52,
36,
43,
67,
62,
65,
43,
25,
49,
26,
60,
26,
59,
39
],
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": null,
"City/Kota": null,
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": null,
"Customer ID/ID Pelanggan": [
"CUST_0001",
"CUST_0002",
"CUST_0003",
"CUST_0004",
"CUST_0005",
"CUST_0006",
"CUST_0007",
"CUST_0008",
"CUST_0009",
"CUST_0010",
"CUST_0011",
"CUST_0012",
"CUST_0013",
"CUST_0014",
"CUST_0015",
"CUST_0016",
"CUST_0017",
"CUST_0018",
"CUST_0019",
"CUST_0020",
"CUST_0021",
"CUST_0022",
"CUST_0023",
"CUST_0024",
"CUST_0025",
"CUST_0026",
"CUST_0027",
"CUST_0028",
"CUST_0029",
"CUST_0030",
"CUST_0031",
"CUST_0032",
"CUST_0033",
"CUST_0034",
"CUST_0035",
"CUST_0036",
"CUST_0037",
"CUST_0038",
"CUST_0039",
"CUST_0040",
"CUST_0041",
"CUST_0042",
"CUST_0043",
"CUST_0044",
"CUST_0045",
"CUST_0046",
"CUST_0047",
"CUST_0048",
"CUST_0049",
"CUST_0050",
"CUST_0051",
"CUST_0052",
"CUST_0053",
"CUST_0054",
"CUST_0055",
"CUST_0056",
"CUST_0057",
"CUST_0058",
"CUST_0059",
"CUST_0060"
],
"Date/Tanggal": null,
"Discount/Diskon": null,
"Gender/Jenis Kelamin": [
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Other/Lainnya",
"Male/Laki-laki",
"Other/Lainnya",
"Female/Perempuan",
"Other/Lainnya",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Other/Lainnya",
"Male/Laki-laki",
"Other/Lainnya",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Other/Lainnya",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan"
],
"Growth Rate/Pertumbuhan": null,
"Income/Pendapatan": [
2000,
6000,
5000,
7000,
3000,
8000,
9000,
8000,
3000,
3000,
3000,
6000,
2000,
4000,
2000,
6000,
10000,
8000,
9000,
4000,
2000,
4000,
5000,
4000,
5000,
4000,
9000,
9000,
4000,
7000,
3000,
3000,
1000,
4000,
6000,
10000,
4000,
7000,
9000,
6000,
10000,
6000,
9000,
10000,
2000,
6000,
1000,
9000,
10000,
3000,
9000,
1000,
10000,
2000,
2000,
6000,
7000,
9000,
6000,
3000
],
"Last Purchase Date/Tanggal Pembelian Terakhir": [
"2024-12-11",
"2024-06-21",
"2024-10-11",
"2025-02-26",
"2024-08-03",
"2025-04-06",
"2024-05-07",
"2024-12-31",
"2025-03-26",
"2024-08-17",
"2024-08-11",
"2024-05-14",
"2024-09-12",
"2024-06-04",
"2025-03-20",
"2025-03-16",
"2025-01-11",
"2025-01-22",
"2024-07-04",
"2024-08-05",
"2025-02-05",
"2024-06-26",
"2024-08-17",
"2025-03-10",
"2024-09-02",
"2024-08-27",
"2025-02-05",
"2025-02-27",
"2024-05-08",
"2025-03-30",
"2024-12-21",
"2025-01-12",
"2024-11-09",
"2024-04-24",
"2025-04-12",
"2024-10-09",
"2025-03-30",
"2024-07-05",
"2024-08-08",
"2024-07-08",
"2025-01-30",
"2025-01-25",
"2025-01-26",
"2024-11-04",
"2024-09-09",
"2025-04-13",
"2024-06-17",
"2024-07-21",
"2024-05-08",
"2024-11-16",
"2025-01-31",
"2025-04-02",
"2024-05-16",
"2025-01-11",
"2025-01-17",
"2024-05-17",
"2025-03-24",
"2024-11-16",
"2025-01-28",
"2024-08-31"
],
"Latitude/Lintang": null,
"Longitude/Bujur": null,
"Loyalty Score/Skor Loyalitas": [
93,
12,
79,
96,
14,
41,
14,
46,
15,
66,
71,
10,
43,
92,
91,
25,
30,
4,
60,
92,
29,
77,
79,
49,
35,
1,
9,
41,
56,
65,
61,
83,
18,
66,
19,
86,
33,
52,
51,
3,
64,
17,
68,
81,
33,
97,
53,
20,
72,
85,
20,
95,
27,
92,
25,
15,
95,
36,
50,
90
],
"Market Share/Pangsa Pasar": null,
"Price/Harga": null,
"Product/Produk": null,
"Region/Wilayah": null,
"Revenue/Pendapatan": null,
"Sales/Penjualan": null,
"Stock/Stok": null,
"Visitors/Pengunjung": null
}
|
import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Customer ID/ID Pelanggan': ['CUST_0001', 'CUST_0002', 'CUST_0003', 'CUST_0004', 'CUST_0005', 'CUST_0006', 'CUST_0007', 'CUST_0008', 'CUST_0009', 'CUST_0010', 'CUST_0011', 'CUST_0012', 'CUST_0013', 'CUST_0014', 'CUST_0015', 'CUST_0016', 'CUST_0017', 'CUST_0018', 'CUST_0019', 'CUST_0020', 'CUST_0021', 'CUST_0022', 'CUST_0023', 'CUST_0024', 'CUST_0025', 'CUST_0026', 'CUST_0027', 'CUST_0028', 'CUST_0029', 'CUST_0030', 'CUST_0031', 'CUST_0032', 'CUST_0033', 'CUST_0034', 'CUST_0035', 'CUST_0036', 'CUST_0037', 'CUST_0038', 'CUST_0039', 'CUST_0040', 'CUST_0041', 'CUST_0042', 'CUST_0043', 'CUST_0044', 'CUST_0045', 'CUST_0046', 'CUST_0047', 'CUST_0048', 'CUST_0049', 'CUST_0050', 'CUST_0051', 'CUST_0052', 'CUST_0053', 'CUST_0054', 'CUST_0055', 'CUST_0056', 'CUST_0057', 'CUST_0058', 'CUST_0059', 'CUST_0060'], 'Age/Usia': [46, 39, 23, 18, 33, 41, 27, 66, 48, 69, 68, 68, 52, 37, 70, 46, 23, 58, 35, 21, 21, 25, 60, 25, 22, 23, 28, 69, 18, 28, 52, 67, 44, 28, 63, 32, 55, 67, 69, 66, 46, 30, 34, 43, 33, 23, 52, 36, 43, 67, 62, 65, 43, 25, 49, 26, 60, 26, 59, 39], 'Gender/Jenis Kelamin': ['Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Other/Lainnya', 'Male/Laki-laki', 'Other/Lainnya', 'Female/Perempuan', 'Other/Lainnya', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Other/Lainnya', 'Male/Laki-laki', 'Other/Lainnya', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Other/Lainnya', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan'], 'Income/Pendapatan': [2000, 6000, 5000, 7000, 3000, 8000, 9000, 8000, 3000, 3000, 3000, 6000, 2000, 4000, 2000, 6000, 10000, 8000, 9000, 4000, 2000, 4000, 5000, 4000, 5000, 4000, 9000, 9000, 4000, 7000, 3000, 3000, 1000, 4000, 6000, 10000, 4000, 7000, 9000, 6000, 10000, 6000, 9000, 10000, 2000, 6000, 1000, 9000, 10000, 3000, 9000, 1000, 10000, 2000, 2000, 6000, 7000, 9000, 6000, 3000], 'Loyalty Score/Skor Loyalitas': [93, 12, 79, 96, 14, 41, 14, 46, 15, 66, 71, 10, 43, 92, 91, 25, 30, 4, 60, 92, 29, 77, 79, 49, 35, 1, 9, 41, 56, 65, 61, 83, 18, 66, 19, 86, 33, 52, 51, 3, 64, 17, 68, 81, 33, 97, 53, 20, 72, 85, 20, 95, 27, 92, 25, 15, 95, 36, 50, 90], 'Last Purchase Date/Tanggal Pembelian Terakhir': ['2024-12-11', '2024-06-21', '2024-10-11', '2025-02-26', '2024-08-03', '2025-04-06', '2024-05-07', '2024-12-31', '2025-03-26', '2024-08-17', '2024-08-11', '2024-05-14', '2024-09-12', '2024-06-04', '2025-03-20', '2025-03-16', '2025-01-11', '2025-01-22', '2024-07-04', '2024-08-05', '2025-02-05', '2024-06-26', '2024-08-17', '2025-03-10', '2024-09-02', '2024-08-27', '2025-02-05', '2025-02-27', '2024-05-08', '2025-03-30', '2024-12-21', '2025-01-12', '2024-11-09', '2024-04-24', '2025-04-12', '2024-10-09', '2025-03-30', '2024-07-05', '2024-08-08', '2024-07-08', '2025-01-30', '2025-01-25', '2025-01-26', '2024-11-04', '2024-09-09', '2025-04-13', '2024-06-17', '2024-07-21', '2024-05-08', '2024-11-16', '2025-01-31', '2025-04-02', '2024-05-16', '2025-01-11', '2025-01-17', '2024-05-17', '2025-03-24', '2024-11-16', '2025-01-28', '2024-08-31']})
# Basic data cleaning
df = df.dropna() # Remove missing values
df = df.drop_duplicates() # Remove duplicates
# Comprehensive data analysis
analysis_result = df.describe(include='all')
print(analysis_result)
# Additional insights
print('\nTop performing items:')
print(df.nlargest(3, df.columns[1]))
print('\nTrend analysis:')
if 'Date/Tanggal' in df.columns:
df['Month/Bulan'] = pd.to_datetime(df['Date/Tanggal']).dt.month
monthly_trend = df.groupby('Month/Bulan').mean(numeric_only=True)
print(monthly_trend)
|
Compare market share time period
|
{
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": [
"Food/Makanan",
"Food/Makanan",
"Food/Makanan"
],
"City/Kota": null,
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": null,
"Customer ID/ID Pelanggan": null,
"Date/Tanggal": null,
"Discount/Diskon": [
0.28,
0.04,
0.23
],
"Gender/Jenis Kelamin": null,
"Growth Rate/Pertumbuhan": null,
"Income/Pendapatan": null,
"Last Purchase Date/Tanggal Pembelian Terakhir": null,
"Latitude/Lintang": null,
"Longitude/Bujur": null,
"Loyalty Score/Skor Loyalitas": null,
"Market Share/Pangsa Pasar": null,
"Price/Harga": [
26.36,
50.69,
172.84
],
"Product/Produk": [
"Dairy_1",
"Beverages_2",
"Fruits_3"
],
"Region/Wilayah": null,
"Revenue/Pendapatan": null,
"Sales/Penjualan": [
941,
715,
129
],
"Stock/Stok": [
78,
96,
110
],
"Visitors/Pengunjung": null
}
|
import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Product/Produk': ['Dairy_1', 'Beverages_2', 'Fruits_3'], 'Sales/Penjualan': [941, 715, 129], 'Price/Harga': [26.36, 50.69, 172.84], 'Category/Kategori': ['Food/Makanan', 'Food/Makanan', 'Food/Makanan'], 'Discount/Diskon': [0.28, 0.04, 0.23], 'Stock/Stok': [78, 96, 110]})
# Basic data cleaning
df = df.dropna() # Remove missing values
df = df.drop_duplicates() # Remove duplicates
# Comprehensive data analysis
analysis_result = df.describe(include='all')
print(analysis_result)
# Additional insights
print('\nTop performing items:')
print(df.nlargest(3, df.columns[1]))
print('\nTrend analysis:')
if 'Date/Tanggal' in df.columns:
df['Month/Bulan'] = pd.to_datetime(df['Date/Tanggal']).dt.month
monthly_trend = df.groupby('Month/Bulan').mean(numeric_only=True)
print(monthly_trend)
|
Calculate average order value gender
|
{
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": [
"Food/Makanan",
"Food/Makanan",
"Food/Makanan"
],
"City/Kota": null,
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": null,
"Customer ID/ID Pelanggan": null,
"Date/Tanggal": null,
"Discount/Diskon": [
0.27,
0.28,
0.08
],
"Gender/Jenis Kelamin": null,
"Growth Rate/Pertumbuhan": null,
"Income/Pendapatan": null,
"Last Purchase Date/Tanggal Pembelian Terakhir": null,
"Latitude/Lintang": null,
"Longitude/Bujur": null,
"Loyalty Score/Skor Loyalitas": null,
"Market Share/Pangsa Pasar": null,
"Price/Harga": [
140.03,
118.54,
34.64
],
"Product/Produk": [
"Snacks_1",
"Beverages_2",
"Dairy_3"
],
"Region/Wilayah": null,
"Revenue/Pendapatan": null,
"Sales/Penjualan": [
287,
139,
621
],
"Stock/Stok": [
18,
151,
30
],
"Visitors/Pengunjung": null
}
|
import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Product/Produk': ['Snacks_1', 'Beverages_2', 'Dairy_3'], 'Sales/Penjualan': [287, 139, 621], 'Price/Harga': [140.03, 118.54, 34.64], 'Category/Kategori': ['Food/Makanan', 'Food/Makanan', 'Food/Makanan'], 'Discount/Diskon': [0.27, 0.28, 0.08], 'Stock/Stok': [18, 151, 30]})
# Basic data cleaning
df = df.dropna() # Remove missing values
df = df.drop_duplicates() # Remove duplicates
# cumulative sum calculation
result = df['Category/Kategori'].cumsum()
print(result)
|
Segment price time period
|
{
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": null,
"City/Kota": [
"Tokyo",
"Berlin",
"Sydney",
"London",
"São Paulo"
],
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": [
"Japan",
"Germany",
"Australia",
"UK",
"Brazil"
],
"Customer ID/ID Pelanggan": null,
"Date/Tanggal": null,
"Discount/Diskon": null,
"Gender/Jenis Kelamin": null,
"Growth Rate/Pertumbuhan": [
-0.08,
0.12,
0.02,
0.04,
0.01
],
"Income/Pendapatan": null,
"Last Purchase Date/Tanggal Pembelian Terakhir": null,
"Latitude/Lintang": [
-40.4542,
0.07970000000000001,
-41.4007,
-45.3629,
64.7025
],
"Longitude/Bujur": [
92.6457,
53.7816,
-137.037,
-5.8395,
20.3322
],
"Loyalty Score/Skor Loyalitas": null,
"Market Share/Pangsa Pasar": [
0.14,
0.2,
0.33,
0.26,
0.4
],
"Price/Harga": null,
"Product/Produk": null,
"Region/Wilayah": [
"Asia",
"Europe",
"Oceania",
"Europe",
"South America"
],
"Revenue/Pendapatan": [
3319,
1729,
3170,
1230,
2884
],
"Sales/Penjualan": null,
"Stock/Stok": null,
"Visitors/Pengunjung": null
}
|
import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'City/Kota': ['Tokyo', 'Berlin', 'Sydney', 'London', 'São Paulo'], 'Country/Negara': ['Japan', 'Germany', 'Australia', 'UK', 'Brazil'], 'Region/Wilayah': ['Asia', 'Europe', 'Oceania', 'Europe', 'South America'], 'Latitude/Lintang': [-40.4542, 0.0797, -41.4007, -45.3629, 64.7025], 'Longitude/Bujur': [92.6457, 53.7816, -137.037, -5.8395, 20.3322], 'Revenue/Pendapatan': [3319, 1729, 3170, 1230, 2884], 'Market Share/Pangsa Pasar': [0.14, 0.2, 0.33, 0.26, 0.4], 'Growth Rate/Pertumbuhan': [-0.08, 0.12, 0.02, 0.04, 0.01]})
# Basic data cleaning
df = df.dropna() # Remove missing values
df = df.drop_duplicates() # Remove duplicates
# Customer segmentation using KMeans
from sklearn.cluster import KMeans
from sklearn.preprocessing import StandardScaler
# Select features and scale
X = df[['Age/Usia', 'Income/Pendapatan', 'Loyalty Score/Skor Loyalitas']]
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)
# Cluster into 3 segments
kmeans = KMeans(n_clusters=3, random_state=42)
df['Segment/Segmen'] = kmeans.fit_predict(X_scaled)
# Visualize segments
fig = px.scatter_3d(df, x='Age/Usia', y='Income/Pendapatan',
z='Loyalty Score/Skor Loyalitas',
color='Segment/Segmen',
title='Customer Segments')
fig.show()
|
Visualize market share gender
|
{
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": null,
"City/Kota": [
"Singapore",
"Dubai",
"São Paulo",
"New York",
"Paris"
],
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": [
"Singapore",
"UAE",
"Brazil",
"US",
"France"
],
"Customer ID/ID Pelanggan": null,
"Date/Tanggal": null,
"Discount/Diskon": null,
"Gender/Jenis Kelamin": null,
"Growth Rate/Pertumbuhan": [
-0.06,
0.01,
0.17,
0.13,
0.18
],
"Income/Pendapatan": null,
"Last Purchase Date/Tanggal Pembelian Terakhir": null,
"Latitude/Lintang": [
-57.54,
34.0661,
86.5975,
13.2216,
-68.9136
],
"Longitude/Bujur": [
-41.8359,
-14.2114,
-69.3239,
36.8743,
-173.3371
],
"Loyalty Score/Skor Loyalitas": null,
"Market Share/Pangsa Pasar": [
0.49,
0.39,
0.33,
0.19,
0.28
],
"Price/Harga": null,
"Product/Produk": null,
"Region/Wilayah": [
"Asia",
"Middle East",
"South America",
"North America",
"Europe"
],
"Revenue/Pendapatan": [
2150,
2360,
4197,
2141,
1907
],
"Sales/Penjualan": null,
"Stock/Stok": null,
"Visitors/Pengunjung": null
}
|
import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'City/Kota': ['Singapore', 'Dubai', 'São Paulo', 'New York', 'Paris'], 'Country/Negara': ['Singapore', 'UAE', 'Brazil', 'US', 'France'], 'Region/Wilayah': ['Asia', 'Middle East', 'South America', 'North America', 'Europe'], 'Latitude/Lintang': [-57.54, 34.0661, 86.5975, 13.2216, -68.9136], 'Longitude/Bujur': [-41.8359, -14.2114, -69.3239, 36.8743, -173.3371], 'Revenue/Pendapatan': [2150, 2360, 4197, 2141, 1907], 'Market Share/Pangsa Pasar': [0.49, 0.39, 0.33, 0.19, 0.28], 'Growth Rate/Pertumbuhan': [-0.06, 0.01, 0.17, 0.13, 0.18]})
# Basic data cleaning
df = df.dropna() # Remove missing values
df = df.drop_duplicates() # Remove duplicates
# Geospatial visualization
fig = px.choropleth(df, locations='Country/Negara',
locationmode='country names',
color='Revenue/Pendapatan',
hover_name='City/Kota',
scope='world',
title='Revenue by Country')
fig.update_geos(showcountries=True)
fig.show()
|
Analisis pengunjung negara
|
{
"Age/Usia": [
23,
26,
18,
66,
41,
31,
59,
40,
27,
37,
46,
26,
58,
37,
18,
70,
63,
29,
19,
33,
68,
56,
49,
59,
41,
53,
65,
21,
66,
61,
34,
54,
68,
64,
62,
68,
55,
57,
48,
60,
65,
21,
70,
26,
25,
51,
20,
26,
59,
37,
42,
53,
18,
52,
31,
28,
22,
53,
27,
44,
55,
22,
59,
31,
43,
40,
55,
36,
26,
51,
60,
70,
31,
69,
68,
47,
68,
31,
36,
30,
28,
24,
26,
51,
41,
46,
23,
32,
31,
29,
50,
63,
65,
27,
40,
60,
58,
41,
59,
63
],
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": null,
"City/Kota": null,
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": null,
"Customer ID/ID Pelanggan": [
"CUST_0001",
"CUST_0002",
"CUST_0003",
"CUST_0004",
"CUST_0005",
"CUST_0006",
"CUST_0007",
"CUST_0008",
"CUST_0009",
"CUST_0010",
"CUST_0011",
"CUST_0012",
"CUST_0013",
"CUST_0014",
"CUST_0015",
"CUST_0016",
"CUST_0017",
"CUST_0018",
"CUST_0019",
"CUST_0020",
"CUST_0021",
"CUST_0022",
"CUST_0023",
"CUST_0024",
"CUST_0025",
"CUST_0026",
"CUST_0027",
"CUST_0028",
"CUST_0029",
"CUST_0030",
"CUST_0031",
"CUST_0032",
"CUST_0033",
"CUST_0034",
"CUST_0035",
"CUST_0036",
"CUST_0037",
"CUST_0038",
"CUST_0039",
"CUST_0040",
"CUST_0041",
"CUST_0042",
"CUST_0043",
"CUST_0044",
"CUST_0045",
"CUST_0046",
"CUST_0047",
"CUST_0048",
"CUST_0049",
"CUST_0050",
"CUST_0051",
"CUST_0052",
"CUST_0053",
"CUST_0054",
"CUST_0055",
"CUST_0056",
"CUST_0057",
"CUST_0058",
"CUST_0059",
"CUST_0060",
"CUST_0061",
"CUST_0062",
"CUST_0063",
"CUST_0064",
"CUST_0065",
"CUST_0066",
"CUST_0067",
"CUST_0068",
"CUST_0069",
"CUST_0070",
"CUST_0071",
"CUST_0072",
"CUST_0073",
"CUST_0074",
"CUST_0075",
"CUST_0076",
"CUST_0077",
"CUST_0078",
"CUST_0079",
"CUST_0080",
"CUST_0081",
"CUST_0082",
"CUST_0083",
"CUST_0084",
"CUST_0085",
"CUST_0086",
"CUST_0087",
"CUST_0088",
"CUST_0089",
"CUST_0090",
"CUST_0091",
"CUST_0092",
"CUST_0093",
"CUST_0094",
"CUST_0095",
"CUST_0096",
"CUST_0097",
"CUST_0098",
"CUST_0099",
"CUST_0100"
],
"Date/Tanggal": null,
"Discount/Diskon": null,
"Gender/Jenis Kelamin": [
"Male/Laki-laki",
"Male/Laki-laki",
"Other/Lainnya",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Other/Lainnya",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Other/Lainnya",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Other/Lainnya",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Other/Lainnya",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Other/Lainnya",
"Male/Laki-laki",
"Other/Lainnya",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Other/Lainnya",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki"
],
"Growth Rate/Pertumbuhan": null,
"Income/Pendapatan": [
2000,
4000,
3000,
4000,
8000,
3000,
7000,
2000,
5000,
6000,
4000,
4000,
7000,
9000,
6000,
3000,
4000,
3000,
8000,
2000,
10000,
9000,
8000,
7000,
2000,
1000,
9000,
8000,
3000,
10000,
9000,
10000,
9000,
2000,
2000,
6000,
6000,
9000,
3000,
9000,
3000,
4000,
10000,
6000,
2000,
3000,
2000,
6000,
8000,
6000,
3000,
4000,
5000,
4000,
3000,
7000,
1000,
5000,
8000,
8000,
5000,
5000,
5000,
4000,
9000,
6000,
1000,
7000,
3000,
9000,
2000,
1000,
5000,
6000,
2000,
3000,
2000,
3000,
2000,
7000,
9000,
5000,
5000,
7000,
7000,
1000,
3000,
4000,
3000,
9000,
3000,
7000,
8000,
3000,
4000,
6000,
2000,
5000,
6000,
1000
],
"Last Purchase Date/Tanggal Pembelian Terakhir": [
"2024-09-24",
"2025-03-21",
"2024-10-02",
"2024-05-18",
"2024-08-11",
"2024-06-21",
"2024-07-23",
"2025-02-06",
"2024-10-09",
"2024-05-11",
"2024-06-23",
"2024-07-03",
"2025-01-20",
"2025-02-27",
"2025-02-27",
"2024-06-24",
"2024-05-10",
"2024-12-27",
"2024-05-08",
"2024-09-25",
"2024-11-20",
"2024-11-10",
"2024-06-26",
"2024-06-24",
"2024-08-24",
"2025-03-15",
"2024-05-30",
"2025-02-15",
"2024-11-09",
"2024-05-13",
"2024-11-26",
"2024-09-07",
"2024-10-25",
"2024-12-28",
"2024-08-24",
"2024-07-13",
"2024-07-06",
"2024-06-12",
"2024-08-28",
"2024-08-25",
"2024-09-14",
"2024-10-28",
"2024-11-04",
"2024-08-08",
"2025-03-30",
"2024-11-28",
"2025-01-10",
"2024-10-01",
"2024-05-30",
"2024-12-21",
"2025-03-27",
"2024-10-11",
"2024-10-07",
"2025-01-05",
"2025-03-28",
"2024-11-30",
"2024-08-04",
"2024-05-31",
"2024-05-08",
"2025-04-04",
"2025-03-18",
"2024-10-28",
"2025-02-16",
"2024-06-11",
"2025-03-13",
"2024-05-04",
"2024-08-16",
"2025-02-22",
"2025-03-25",
"2024-05-20",
"2024-05-30",
"2024-04-30",
"2025-01-07",
"2024-10-30",
"2025-02-13",
"2025-02-20",
"2024-12-28",
"2024-10-09",
"2024-09-11",
"2024-06-08",
"2024-06-16",
"2025-01-21",
"2024-12-11",
"2024-07-26",
"2025-01-12",
"2024-12-13",
"2024-10-14",
"2024-07-01",
"2024-08-20",
"2024-10-01",
"2024-05-26",
"2024-06-10",
"2024-06-19",
"2024-10-02",
"2024-05-06",
"2025-02-28",
"2025-01-05",
"2025-03-16",
"2024-06-28",
"2024-10-12"
],
"Latitude/Lintang": null,
"Longitude/Bujur": null,
"Loyalty Score/Skor Loyalitas": [
91,
79,
51,
37,
65,
64,
63,
1,
11,
96,
52,
28,
39,
92,
83,
65,
6,
21,
15,
96,
48,
31,
47,
31,
4,
81,
16,
11,
69,
16,
1,
6,
7,
57,
61,
96,
39,
33,
61,
4,
89,
15,
97,
86,
47,
35,
56,
57,
43,
33,
22,
46,
20,
2,
38,
57,
30,
66,
79,
57,
24,
9,
54,
9,
23,
69,
43,
47,
64,
40,
14,
48,
72,
7,
20,
48,
62,
82,
8,
36,
28,
91,
76,
18,
88,
14,
66,
60,
89,
82,
29,
84,
95,
54,
92,
20,
1,
76,
95,
43
],
"Market Share/Pangsa Pasar": null,
"Price/Harga": null,
"Product/Produk": null,
"Region/Wilayah": null,
"Revenue/Pendapatan": null,
"Sales/Penjualan": null,
"Stock/Stok": null,
"Visitors/Pengunjung": null
}
|
import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Customer ID/ID Pelanggan': ['CUST_0001', 'CUST_0002', 'CUST_0003', 'CUST_0004', 'CUST_0005', 'CUST_0006', 'CUST_0007', 'CUST_0008', 'CUST_0009', 'CUST_0010', 'CUST_0011', 'CUST_0012', 'CUST_0013', 'CUST_0014', 'CUST_0015', 'CUST_0016', 'CUST_0017', 'CUST_0018', 'CUST_0019', 'CUST_0020', 'CUST_0021', 'CUST_0022', 'CUST_0023', 'CUST_0024', 'CUST_0025', 'CUST_0026', 'CUST_0027', 'CUST_0028', 'CUST_0029', 'CUST_0030', 'CUST_0031', 'CUST_0032', 'CUST_0033', 'CUST_0034', 'CUST_0035', 'CUST_0036', 'CUST_0037', 'CUST_0038', 'CUST_0039', 'CUST_0040', 'CUST_0041', 'CUST_0042', 'CUST_0043', 'CUST_0044', 'CUST_0045', 'CUST_0046', 'CUST_0047', 'CUST_0048', 'CUST_0049', 'CUST_0050', 'CUST_0051', 'CUST_0052', 'CUST_0053', 'CUST_0054', 'CUST_0055', 'CUST_0056', 'CUST_0057', 'CUST_0058', 'CUST_0059', 'CUST_0060', 'CUST_0061', 'CUST_0062', 'CUST_0063', 'CUST_0064', 'CUST_0065', 'CUST_0066', 'CUST_0067', 'CUST_0068', 'CUST_0069', 'CUST_0070', 'CUST_0071', 'CUST_0072', 'CUST_0073', 'CUST_0074', 'CUST_0075', 'CUST_0076', 'CUST_0077', 'CUST_0078', 'CUST_0079', 'CUST_0080', 'CUST_0081', 'CUST_0082', 'CUST_0083', 'CUST_0084', 'CUST_0085', 'CUST_0086', 'CUST_0087', 'CUST_0088', 'CUST_0089', 'CUST_0090', 'CUST_0091', 'CUST_0092', 'CUST_0093', 'CUST_0094', 'CUST_0095', 'CUST_0096', 'CUST_0097', 'CUST_0098', 'CUST_0099', 'CUST_0100'], 'Age/Usia': [23, 26, 18, 66, 41, 31, 59, 40, 27, 37, 46, 26, 58, 37, 18, 70, 63, 29, 19, 33, 68, 56, 49, 59, 41, 53, 65, 21, 66, 61, 34, 54, 68, 64, 62, 68, 55, 57, 48, 60, 65, 21, 70, 26, 25, 51, 20, 26, 59, 37, 42, 53, 18, 52, 31, 28, 22, 53, 27, 44, 55, 22, 59, 31, 43, 40, 55, 36, 26, 51, 60, 70, 31, 69, 68, 47, 68, 31, 36, 30, 28, 24, 26, 51, 41, 46, 23, 32, 31, 29, 50, 63, 65, 27, 40, 60, 58, 41, 59, 63], 'Gender/Jenis Kelamin': ['Male/Laki-laki', 'Male/Laki-laki', 'Other/Lainnya', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Other/Lainnya', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Other/Lainnya', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Other/Lainnya', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Other/Lainnya', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Other/Lainnya', 'Male/Laki-laki', 'Other/Lainnya', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Other/Lainnya', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki'], 'Income/Pendapatan': [2000, 4000, 3000, 4000, 8000, 3000, 7000, 2000, 5000, 6000, 4000, 4000, 7000, 9000, 6000, 3000, 4000, 3000, 8000, 2000, 10000, 9000, 8000, 7000, 2000, 1000, 9000, 8000, 3000, 10000, 9000, 10000, 9000, 2000, 2000, 6000, 6000, 9000, 3000, 9000, 3000, 4000, 10000, 6000, 2000, 3000, 2000, 6000, 8000, 6000, 3000, 4000, 5000, 4000, 3000, 7000, 1000, 5000, 8000, 8000, 5000, 5000, 5000, 4000, 9000, 6000, 1000, 7000, 3000, 9000, 2000, 1000, 5000, 6000, 2000, 3000, 2000, 3000, 2000, 7000, 9000, 5000, 5000, 7000, 7000, 1000, 3000, 4000, 3000, 9000, 3000, 7000, 8000, 3000, 4000, 6000, 2000, 5000, 6000, 1000], 'Loyalty Score/Skor Loyalitas': [91, 79, 51, 37, 65, 64, 63, 1, 11, 96, 52, 28, 39, 92, 83, 65, 6, 21, 15, 96, 48, 31, 47, 31, 4, 81, 16, 11, 69, 16, 1, 6, 7, 57, 61, 96, 39, 33, 61, 4, 89, 15, 97, 86, 47, 35, 56, 57, 43, 33, 22, 46, 20, 2, 38, 57, 30, 66, 79, 57, 24, 9, 54, 9, 23, 69, 43, 47, 64, 40, 14, 48, 72, 7, 20, 48, 62, 82, 8, 36, 28, 91, 76, 18, 88, 14, 66, 60, 89, 82, 29, 84, 95, 54, 92, 20, 1, 76, 95, 43], 'Last Purchase Date/Tanggal Pembelian Terakhir': ['2024-09-24', '2025-03-21', '2024-10-02', '2024-05-18', '2024-08-11', '2024-06-21', '2024-07-23', '2025-02-06', '2024-10-09', '2024-05-11', '2024-06-23', '2024-07-03', '2025-01-20', '2025-02-27', '2025-02-27', '2024-06-24', '2024-05-10', '2024-12-27', '2024-05-08', '2024-09-25', '2024-11-20', '2024-11-10', '2024-06-26', '2024-06-24', '2024-08-24', '2025-03-15', '2024-05-30', '2025-02-15', '2024-11-09', '2024-05-13', '2024-11-26', '2024-09-07', '2024-10-25', '2024-12-28', '2024-08-24', '2024-07-13', '2024-07-06', '2024-06-12', '2024-08-28', '2024-08-25', '2024-09-14', '2024-10-28', '2024-11-04', '2024-08-08', '2025-03-30', '2024-11-28', '2025-01-10', '2024-10-01', '2024-05-30', '2024-12-21', '2025-03-27', '2024-10-11', '2024-10-07', '2025-01-05', '2025-03-28', '2024-11-30', '2024-08-04', '2024-05-31', '2024-05-08', '2025-04-04', '2025-03-18', '2024-10-28', '2025-02-16', '2024-06-11', '2025-03-13', '2024-05-04', '2024-08-16', '2025-02-22', '2025-03-25', '2024-05-20', '2024-05-30', '2024-04-30', '2025-01-07', '2024-10-30', '2025-02-13', '2025-02-20', '2024-12-28', '2024-10-09', '2024-09-11', '2024-06-08', '2024-06-16', '2025-01-21', '2024-12-11', '2024-07-26', '2025-01-12', '2024-12-13', '2024-10-14', '2024-07-01', '2024-08-20', '2024-10-01', '2024-05-26', '2024-06-10', '2024-06-19', '2024-10-02', '2024-05-06', '2025-02-28', '2025-01-05', '2025-03-16', '2024-06-28', '2024-10-12']})
# Basic data cleaning
df = df.dropna() # Remove missing values
df = df.drop_duplicates() # Remove duplicates
# Comprehensive data analysis
analysis_result = df.describe(include='all')
print(analysis_result)
# Additional insights
print('\nTop performing items:')
print(df.nlargest(3, df.columns[1]))
print('\nTrend analysis:')
if 'Date/Tanggal' in df.columns:
df['Month/Bulan'] = pd.to_datetime(df['Date/Tanggal']).dt.month
monthly_trend = df.groupby('Month/Bulan').mean(numeric_only=True)
print(monthly_trend)
|
Calculate revenue gender
|
{
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": [
"Automotive/Otomotif",
"Automotive/Otomotif",
"Automotive/Otomotif"
],
"City/Kota": null,
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": null,
"Customer ID/ID Pelanggan": null,
"Date/Tanggal": null,
"Discount/Diskon": [
0.29,
0.05,
0.11
],
"Gender/Jenis Kelamin": null,
"Growth Rate/Pertumbuhan": null,
"Income/Pendapatan": null,
"Last Purchase Date/Tanggal Pembelian Terakhir": null,
"Latitude/Lintang": null,
"Longitude/Bujur": null,
"Loyalty Score/Skor Loyalitas": null,
"Market Share/Pangsa Pasar": null,
"Price/Harga": [
128.98,
155.41,
18.57
],
"Product/Produk": [
"Tools_1",
"Tires_2",
"Accessories_3"
],
"Region/Wilayah": null,
"Revenue/Pendapatan": null,
"Sales/Penjualan": [
487,
734,
625
],
"Stock/Stok": [
68,
190,
156
],
"Visitors/Pengunjung": null
}
|
import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Product/Produk': ['Tools_1', 'Tires_2', 'Accessories_3'], 'Sales/Penjualan': [487, 734, 625], 'Price/Harga': [128.98, 155.41, 18.57], 'Category/Kategori': ['Automotive/Otomotif', 'Automotive/Otomotif', 'Automotive/Otomotif'], 'Discount/Diskon': [0.29, 0.05, 0.11], 'Stock/Stok': [68, 190, 156]})
# Basic data cleaning
df = df.dropna() # Remove missing values
df = df.drop_duplicates() # Remove duplicates
# cumulative sum calculation
result = df['Discount/Diskon'].cumsum()
print(result)
|
Segment growth rate product
|
{
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": null,
"City/Kota": [
"Mumbai",
"Jakarta",
"New York",
"Paris",
"Tokyo"
],
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": [
"India",
"Indonesia",
"US",
"France",
"Japan"
],
"Customer ID/ID Pelanggan": null,
"Date/Tanggal": null,
"Discount/Diskon": null,
"Gender/Jenis Kelamin": null,
"Growth Rate/Pertumbuhan": [
0.13,
0.23,
0.07,
0.27,
0.18
],
"Income/Pendapatan": null,
"Last Purchase Date/Tanggal Pembelian Terakhir": null,
"Latitude/Lintang": [
79.9486,
81.1343,
31.4768,
-89.953,
36.8456
],
"Longitude/Bujur": [
147.6153,
12.6877,
-103.4012,
52.6226,
-27.6525
],
"Loyalty Score/Skor Loyalitas": null,
"Market Share/Pangsa Pasar": [
0.12,
0.35000000000000003,
0.39,
0.36,
0.41000000000000003
],
"Price/Harga": null,
"Product/Produk": null,
"Region/Wilayah": [
"Asia",
"Asia",
"North America",
"Europe",
"Asia"
],
"Revenue/Pendapatan": [
1683,
2342,
4361,
2948,
3607
],
"Sales/Penjualan": null,
"Stock/Stok": null,
"Visitors/Pengunjung": null
}
|
import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'City/Kota': ['Mumbai', 'Jakarta', 'New York', 'Paris', 'Tokyo'], 'Country/Negara': ['India', 'Indonesia', 'US', 'France', 'Japan'], 'Region/Wilayah': ['Asia', 'Asia', 'North America', 'Europe', 'Asia'], 'Latitude/Lintang': [79.9486, 81.1343, 31.4768, -89.953, 36.8456], 'Longitude/Bujur': [147.6153, 12.6877, -103.4012, 52.6226, -27.6525], 'Revenue/Pendapatan': [1683, 2342, 4361, 2948, 3607], 'Market Share/Pangsa Pasar': [0.12, 0.35, 0.39, 0.36, 0.41], 'Growth Rate/Pertumbuhan': [0.13, 0.23, 0.07, 0.27, 0.18]})
# Basic data cleaning
df = df.dropna() # Remove missing values
df = df.drop_duplicates() # Remove duplicates
# Customer segmentation using KMeans
from sklearn.cluster import KMeans
from sklearn.preprocessing import StandardScaler
# Select features and scale
X = df[['Age/Usia', 'Income/Pendapatan', 'Loyalty Score/Skor Loyalitas']]
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)
# Cluster into 3 segments
kmeans = KMeans(n_clusters=3, random_state=42)
df['Segment/Segmen'] = kmeans.fit_predict(X_scaled)
# Visualize segments
fig = px.scatter_3d(df, x='Age/Usia', y='Income/Pendapatan',
z='Loyalty Score/Skor Loyalitas',
color='Segment/Segmen',
title='Customer Segments')
fig.show()
|
Segment average order value customer segment
|
{
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": [
"Food/Makanan",
"Food/Makanan",
"Food/Makanan"
],
"City/Kota": null,
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": null,
"Customer ID/ID Pelanggan": null,
"Date/Tanggal": null,
"Discount/Diskon": [
0.09,
0.2,
0.25
],
"Gender/Jenis Kelamin": null,
"Growth Rate/Pertumbuhan": null,
"Income/Pendapatan": null,
"Last Purchase Date/Tanggal Pembelian Terakhir": null,
"Latitude/Lintang": null,
"Longitude/Bujur": null,
"Loyalty Score/Skor Loyalitas": null,
"Market Share/Pangsa Pasar": null,
"Price/Harga": [
132.38,
179.32,
131.21
],
"Product/Produk": [
"Dairy_1",
"Beverages_2",
"Snacks_3"
],
"Region/Wilayah": null,
"Revenue/Pendapatan": null,
"Sales/Penjualan": [
377,
949,
233
],
"Stock/Stok": [
60,
64,
115
],
"Visitors/Pengunjung": null
}
|
import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Product/Produk': ['Dairy_1', 'Beverages_2', 'Snacks_3'], 'Sales/Penjualan': [377, 949, 233], 'Price/Harga': [132.38, 179.32, 131.21], 'Category/Kategori': ['Food/Makanan', 'Food/Makanan', 'Food/Makanan'], 'Discount/Diskon': [0.09, 0.2, 0.25], 'Stock/Stok': [60, 64, 115]})
# Basic data cleaning
df = df.dropna() # Remove missing values
df = df.drop_duplicates() # Remove duplicates
# Customer segmentation using KMeans
from sklearn.cluster import KMeans
from sklearn.preprocessing import StandardScaler
# Select features and scale
X = df[['Age/Usia', 'Income/Pendapatan', 'Loyalty Score/Skor Loyalitas']]
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)
# Cluster into 3 segments
kmeans = KMeans(n_clusters=3, random_state=42)
df['Segment/Segmen'] = kmeans.fit_predict(X_scaled)
# Visualize segments
fig = px.scatter_3d(df, x='Age/Usia', y='Income/Pendapatan',
z='Loyalty Score/Skor Loyalitas',
color='Segment/Segmen',
title='Customer Segments')
fig.show()
|
Korelasi penjualan and/dan diskon
|
{
"Age/Usia": [
35,
52,
50,
28,
45,
39,
39,
57,
64,
63,
23,
25,
50,
26,
37,
40,
35,
28,
35,
57,
70,
69,
35,
48,
32,
45,
46,
19,
60,
45,
50,
67,
62,
43,
20,
66,
64,
21,
40,
33,
47,
28,
26,
38,
41,
36,
54,
55,
20,
44,
35,
25,
47,
43,
22
],
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": null,
"City/Kota": null,
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": null,
"Customer ID/ID Pelanggan": [
"CUST_0001",
"CUST_0002",
"CUST_0003",
"CUST_0004",
"CUST_0005",
"CUST_0006",
"CUST_0007",
"CUST_0008",
"CUST_0009",
"CUST_0010",
"CUST_0011",
"CUST_0012",
"CUST_0013",
"CUST_0014",
"CUST_0015",
"CUST_0016",
"CUST_0017",
"CUST_0018",
"CUST_0019",
"CUST_0020",
"CUST_0021",
"CUST_0022",
"CUST_0023",
"CUST_0024",
"CUST_0025",
"CUST_0026",
"CUST_0027",
"CUST_0028",
"CUST_0029",
"CUST_0030",
"CUST_0031",
"CUST_0032",
"CUST_0033",
"CUST_0034",
"CUST_0035",
"CUST_0036",
"CUST_0037",
"CUST_0038",
"CUST_0039",
"CUST_0040",
"CUST_0041",
"CUST_0042",
"CUST_0043",
"CUST_0044",
"CUST_0045",
"CUST_0046",
"CUST_0047",
"CUST_0048",
"CUST_0049",
"CUST_0050",
"CUST_0051",
"CUST_0052",
"CUST_0053",
"CUST_0054",
"CUST_0055"
],
"Date/Tanggal": null,
"Discount/Diskon": null,
"Gender/Jenis Kelamin": [
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Other/Lainnya",
"Male/Laki-laki",
"Male/Laki-laki",
"Other/Lainnya",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Other/Lainnya",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan"
],
"Growth Rate/Pertumbuhan": null,
"Income/Pendapatan": [
2000,
2000,
6000,
4000,
2000,
3000,
6000,
2000,
5000,
3000,
5000,
4000,
2000,
1000,
8000,
4000,
8000,
8000,
4000,
3000,
10000,
3000,
9000,
4000,
7000,
9000,
6000,
4000,
7000,
2000,
9000,
2000,
5000,
7000,
9000,
2000,
3000,
4000,
3000,
3000,
4000,
9000,
8000,
5000,
2000,
2000,
7000,
10000,
5000,
4000,
7000,
2000,
3000,
4000,
2000
],
"Last Purchase Date/Tanggal Pembelian Terakhir": [
"2024-06-12",
"2024-09-17",
"2024-06-14",
"2025-02-09",
"2025-03-03",
"2024-10-21",
"2024-06-13",
"2025-03-12",
"2025-01-10",
"2024-10-29",
"2024-06-02",
"2024-07-15",
"2024-11-15",
"2024-11-29",
"2024-05-26",
"2024-08-17",
"2024-06-25",
"2024-04-23",
"2024-09-13",
"2025-02-09",
"2024-05-14",
"2024-08-20",
"2024-09-18",
"2025-01-04",
"2024-11-26",
"2024-10-29",
"2025-01-07",
"2025-03-13",
"2024-09-02",
"2024-05-30",
"2024-08-21",
"2024-07-08",
"2025-04-02",
"2024-12-30",
"2024-11-01",
"2024-04-23",
"2025-01-18",
"2024-12-30",
"2024-09-04",
"2024-05-26",
"2024-08-21",
"2024-07-15",
"2024-06-14",
"2025-03-27",
"2024-10-08",
"2024-06-17",
"2024-12-04",
"2024-06-16",
"2024-09-19",
"2024-08-13",
"2025-01-14",
"2025-02-03",
"2024-10-07",
"2024-11-08",
"2024-05-12"
],
"Latitude/Lintang": null,
"Longitude/Bujur": null,
"Loyalty Score/Skor Loyalitas": [
96,
47,
24,
7,
3,
95,
37,
3,
96,
32,
83,
36,
35,
93,
63,
37,
41,
97,
95,
23,
19,
23,
9,
48,
58,
28,
89,
68,
36,
40,
88,
52,
79,
51,
29,
84,
67,
99,
27,
9,
23,
10,
41,
12,
64,
16,
76,
92,
45,
73,
93,
83,
15,
6,
73
],
"Market Share/Pangsa Pasar": null,
"Price/Harga": null,
"Product/Produk": null,
"Region/Wilayah": null,
"Revenue/Pendapatan": null,
"Sales/Penjualan": null,
"Stock/Stok": null,
"Visitors/Pengunjung": null
}
|
import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Customer ID/ID Pelanggan': ['CUST_0001', 'CUST_0002', 'CUST_0003', 'CUST_0004', 'CUST_0005', 'CUST_0006', 'CUST_0007', 'CUST_0008', 'CUST_0009', 'CUST_0010', 'CUST_0011', 'CUST_0012', 'CUST_0013', 'CUST_0014', 'CUST_0015', 'CUST_0016', 'CUST_0017', 'CUST_0018', 'CUST_0019', 'CUST_0020', 'CUST_0021', 'CUST_0022', 'CUST_0023', 'CUST_0024', 'CUST_0025', 'CUST_0026', 'CUST_0027', 'CUST_0028', 'CUST_0029', 'CUST_0030', 'CUST_0031', 'CUST_0032', 'CUST_0033', 'CUST_0034', 'CUST_0035', 'CUST_0036', 'CUST_0037', 'CUST_0038', 'CUST_0039', 'CUST_0040', 'CUST_0041', 'CUST_0042', 'CUST_0043', 'CUST_0044', 'CUST_0045', 'CUST_0046', 'CUST_0047', 'CUST_0048', 'CUST_0049', 'CUST_0050', 'CUST_0051', 'CUST_0052', 'CUST_0053', 'CUST_0054', 'CUST_0055'], 'Age/Usia': [35, 52, 50, 28, 45, 39, 39, 57, 64, 63, 23, 25, 50, 26, 37, 40, 35, 28, 35, 57, 70, 69, 35, 48, 32, 45, 46, 19, 60, 45, 50, 67, 62, 43, 20, 66, 64, 21, 40, 33, 47, 28, 26, 38, 41, 36, 54, 55, 20, 44, 35, 25, 47, 43, 22], 'Gender/Jenis Kelamin': ['Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Other/Lainnya', 'Male/Laki-laki', 'Male/Laki-laki', 'Other/Lainnya', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Other/Lainnya', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan'], 'Income/Pendapatan': [2000, 2000, 6000, 4000, 2000, 3000, 6000, 2000, 5000, 3000, 5000, 4000, 2000, 1000, 8000, 4000, 8000, 8000, 4000, 3000, 10000, 3000, 9000, 4000, 7000, 9000, 6000, 4000, 7000, 2000, 9000, 2000, 5000, 7000, 9000, 2000, 3000, 4000, 3000, 3000, 4000, 9000, 8000, 5000, 2000, 2000, 7000, 10000, 5000, 4000, 7000, 2000, 3000, 4000, 2000], 'Loyalty Score/Skor Loyalitas': [96, 47, 24, 7, 3, 95, 37, 3, 96, 32, 83, 36, 35, 93, 63, 37, 41, 97, 95, 23, 19, 23, 9, 48, 58, 28, 89, 68, 36, 40, 88, 52, 79, 51, 29, 84, 67, 99, 27, 9, 23, 10, 41, 12, 64, 16, 76, 92, 45, 73, 93, 83, 15, 6, 73], 'Last Purchase Date/Tanggal Pembelian Terakhir': ['2024-06-12', '2024-09-17', '2024-06-14', '2025-02-09', '2025-03-03', '2024-10-21', '2024-06-13', '2025-03-12', '2025-01-10', '2024-10-29', '2024-06-02', '2024-07-15', '2024-11-15', '2024-11-29', '2024-05-26', '2024-08-17', '2024-06-25', '2024-04-23', '2024-09-13', '2025-02-09', '2024-05-14', '2024-08-20', '2024-09-18', '2025-01-04', '2024-11-26', '2024-10-29', '2025-01-07', '2025-03-13', '2024-09-02', '2024-05-30', '2024-08-21', '2024-07-08', '2025-04-02', '2024-12-30', '2024-11-01', '2024-04-23', '2025-01-18', '2024-12-30', '2024-09-04', '2024-05-26', '2024-08-21', '2024-07-15', '2024-06-14', '2025-03-27', '2024-10-08', '2024-06-17', '2024-12-04', '2024-06-16', '2024-09-19', '2024-08-13', '2025-01-14', '2025-02-03', '2024-10-07', '2024-11-08', '2024-05-12']})
# Basic data cleaning
df = df.dropna() # Remove missing values
df = df.drop_duplicates() # Remove duplicates
# Correlation analysis
corr_matrix = df.corr(numeric_only=True)
fig = px.imshow(corr_matrix, text_auto=True,
title='Correlation Matrix')
fig.show()
|
Optimalkan diskon kota
|
{
"Age/Usia": [
34,
39,
68,
20,
44,
43,
65,
30,
46,
60,
42,
59,
22,
67,
21,
54,
53,
45,
55,
23,
40,
45,
27,
52,
55,
28,
25,
62,
22,
21,
54,
38,
31,
39,
30,
69,
25,
65,
39,
31,
63,
52,
22,
51,
24,
65,
58,
35,
35,
62,
21,
57,
61,
22,
60,
37,
49,
24,
32,
30,
34,
60,
21,
36,
44,
68
],
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": null,
"City/Kota": null,
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": null,
"Customer ID/ID Pelanggan": [
"CUST_0001",
"CUST_0002",
"CUST_0003",
"CUST_0004",
"CUST_0005",
"CUST_0006",
"CUST_0007",
"CUST_0008",
"CUST_0009",
"CUST_0010",
"CUST_0011",
"CUST_0012",
"CUST_0013",
"CUST_0014",
"CUST_0015",
"CUST_0016",
"CUST_0017",
"CUST_0018",
"CUST_0019",
"CUST_0020",
"CUST_0021",
"CUST_0022",
"CUST_0023",
"CUST_0024",
"CUST_0025",
"CUST_0026",
"CUST_0027",
"CUST_0028",
"CUST_0029",
"CUST_0030",
"CUST_0031",
"CUST_0032",
"CUST_0033",
"CUST_0034",
"CUST_0035",
"CUST_0036",
"CUST_0037",
"CUST_0038",
"CUST_0039",
"CUST_0040",
"CUST_0041",
"CUST_0042",
"CUST_0043",
"CUST_0044",
"CUST_0045",
"CUST_0046",
"CUST_0047",
"CUST_0048",
"CUST_0049",
"CUST_0050",
"CUST_0051",
"CUST_0052",
"CUST_0053",
"CUST_0054",
"CUST_0055",
"CUST_0056",
"CUST_0057",
"CUST_0058",
"CUST_0059",
"CUST_0060",
"CUST_0061",
"CUST_0062",
"CUST_0063",
"CUST_0064",
"CUST_0065",
"CUST_0066"
],
"Date/Tanggal": null,
"Discount/Diskon": null,
"Gender/Jenis Kelamin": [
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Other/Lainnya",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Other/Lainnya",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Other/Lainnya",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Other/Lainnya",
"Male/Laki-laki",
"Other/Lainnya",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Other/Lainnya",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan"
],
"Growth Rate/Pertumbuhan": null,
"Income/Pendapatan": [
8000,
5000,
3000,
10000,
5000,
1000,
8000,
2000,
8000,
1000,
8000,
5000,
7000,
7000,
9000,
4000,
4000,
10000,
6000,
3000,
6000,
6000,
9000,
3000,
3000,
3000,
2000,
8000,
10000,
2000,
9000,
8000,
6000,
7000,
5000,
4000,
10000,
8000,
6000,
1000,
3000,
5000,
7000,
6000,
4000,
3000,
10000,
7000,
6000,
3000,
2000,
6000,
7000,
9000,
1000,
3000,
9000,
2000,
4000,
3000,
2000,
2000,
4000,
9000,
5000,
1000
],
"Last Purchase Date/Tanggal Pembelian Terakhir": [
"2024-11-02",
"2024-05-01",
"2025-03-16",
"2025-01-04",
"2025-01-12",
"2025-01-03",
"2024-10-13",
"2024-08-30",
"2024-09-24",
"2024-11-14",
"2025-01-30",
"2025-01-03",
"2024-04-30",
"2025-03-23",
"2025-04-12",
"2025-04-17",
"2024-04-25",
"2024-07-09",
"2024-11-11",
"2024-05-11",
"2025-03-21",
"2024-10-11",
"2024-04-29",
"2024-11-04",
"2024-11-08",
"2025-01-22",
"2024-06-30",
"2024-09-04",
"2024-06-10",
"2024-08-11",
"2024-06-16",
"2024-05-20",
"2025-03-12",
"2024-11-20",
"2025-02-11",
"2025-04-07",
"2024-10-31",
"2024-09-18",
"2024-10-05",
"2024-06-12",
"2025-01-06",
"2025-01-15",
"2024-05-21",
"2024-09-20",
"2025-03-24",
"2024-07-03",
"2024-05-09",
"2024-07-16",
"2024-08-15",
"2025-01-06",
"2025-03-03",
"2025-04-08",
"2024-12-25",
"2025-02-05",
"2024-05-26",
"2024-09-09",
"2024-05-05",
"2024-05-18",
"2025-04-13",
"2024-07-15",
"2024-12-20",
"2024-10-19",
"2024-04-23",
"2024-08-18",
"2024-09-30",
"2024-09-13"
],
"Latitude/Lintang": null,
"Longitude/Bujur": null,
"Loyalty Score/Skor Loyalitas": [
6,
57,
2,
50,
25,
60,
13,
48,
53,
9,
83,
4,
9,
6,
30,
56,
58,
20,
89,
85,
95,
93,
27,
41,
86,
24,
92,
19,
36,
50,
91,
87,
46,
76,
40,
35,
33,
27,
83,
60,
27,
4,
30,
62,
74,
14,
30,
94,
55,
59,
99,
66,
88,
50,
25,
15,
97,
58,
45,
62,
76,
47,
57,
83,
6,
4
],
"Market Share/Pangsa Pasar": null,
"Price/Harga": null,
"Product/Produk": null,
"Region/Wilayah": null,
"Revenue/Pendapatan": null,
"Sales/Penjualan": null,
"Stock/Stok": null,
"Visitors/Pengunjung": null
}
|
import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Customer ID/ID Pelanggan': ['CUST_0001', 'CUST_0002', 'CUST_0003', 'CUST_0004', 'CUST_0005', 'CUST_0006', 'CUST_0007', 'CUST_0008', 'CUST_0009', 'CUST_0010', 'CUST_0011', 'CUST_0012', 'CUST_0013', 'CUST_0014', 'CUST_0015', 'CUST_0016', 'CUST_0017', 'CUST_0018', 'CUST_0019', 'CUST_0020', 'CUST_0021', 'CUST_0022', 'CUST_0023', 'CUST_0024', 'CUST_0025', 'CUST_0026', 'CUST_0027', 'CUST_0028', 'CUST_0029', 'CUST_0030', 'CUST_0031', 'CUST_0032', 'CUST_0033', 'CUST_0034', 'CUST_0035', 'CUST_0036', 'CUST_0037', 'CUST_0038', 'CUST_0039', 'CUST_0040', 'CUST_0041', 'CUST_0042', 'CUST_0043', 'CUST_0044', 'CUST_0045', 'CUST_0046', 'CUST_0047', 'CUST_0048', 'CUST_0049', 'CUST_0050', 'CUST_0051', 'CUST_0052', 'CUST_0053', 'CUST_0054', 'CUST_0055', 'CUST_0056', 'CUST_0057', 'CUST_0058', 'CUST_0059', 'CUST_0060', 'CUST_0061', 'CUST_0062', 'CUST_0063', 'CUST_0064', 'CUST_0065', 'CUST_0066'], 'Age/Usia': [34, 39, 68, 20, 44, 43, 65, 30, 46, 60, 42, 59, 22, 67, 21, 54, 53, 45, 55, 23, 40, 45, 27, 52, 55, 28, 25, 62, 22, 21, 54, 38, 31, 39, 30, 69, 25, 65, 39, 31, 63, 52, 22, 51, 24, 65, 58, 35, 35, 62, 21, 57, 61, 22, 60, 37, 49, 24, 32, 30, 34, 60, 21, 36, 44, 68], 'Gender/Jenis Kelamin': ['Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Other/Lainnya', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Other/Lainnya', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Other/Lainnya', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Other/Lainnya', 'Male/Laki-laki', 'Other/Lainnya', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Other/Lainnya', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan'], 'Income/Pendapatan': [8000, 5000, 3000, 10000, 5000, 1000, 8000, 2000, 8000, 1000, 8000, 5000, 7000, 7000, 9000, 4000, 4000, 10000, 6000, 3000, 6000, 6000, 9000, 3000, 3000, 3000, 2000, 8000, 10000, 2000, 9000, 8000, 6000, 7000, 5000, 4000, 10000, 8000, 6000, 1000, 3000, 5000, 7000, 6000, 4000, 3000, 10000, 7000, 6000, 3000, 2000, 6000, 7000, 9000, 1000, 3000, 9000, 2000, 4000, 3000, 2000, 2000, 4000, 9000, 5000, 1000], 'Loyalty Score/Skor Loyalitas': [6, 57, 2, 50, 25, 60, 13, 48, 53, 9, 83, 4, 9, 6, 30, 56, 58, 20, 89, 85, 95, 93, 27, 41, 86, 24, 92, 19, 36, 50, 91, 87, 46, 76, 40, 35, 33, 27, 83, 60, 27, 4, 30, 62, 74, 14, 30, 94, 55, 59, 99, 66, 88, 50, 25, 15, 97, 58, 45, 62, 76, 47, 57, 83, 6, 4], 'Last Purchase Date/Tanggal Pembelian Terakhir': ['2024-11-02', '2024-05-01', '2025-03-16', '2025-01-04', '2025-01-12', '2025-01-03', '2024-10-13', '2024-08-30', '2024-09-24', '2024-11-14', '2025-01-30', '2025-01-03', '2024-04-30', '2025-03-23', '2025-04-12', '2025-04-17', '2024-04-25', '2024-07-09', '2024-11-11', '2024-05-11', '2025-03-21', '2024-10-11', '2024-04-29', '2024-11-04', '2024-11-08', '2025-01-22', '2024-06-30', '2024-09-04', '2024-06-10', '2024-08-11', '2024-06-16', '2024-05-20', '2025-03-12', '2024-11-20', '2025-02-11', '2025-04-07', '2024-10-31', '2024-09-18', '2024-10-05', '2024-06-12', '2025-01-06', '2025-01-15', '2024-05-21', '2024-09-20', '2025-03-24', '2024-07-03', '2024-05-09', '2024-07-16', '2024-08-15', '2025-01-06', '2025-03-03', '2025-04-08', '2024-12-25', '2025-02-05', '2024-05-26', '2024-09-09', '2024-05-05', '2024-05-18', '2025-04-13', '2024-07-15', '2024-12-20', '2024-10-19', '2024-04-23', '2024-08-18', '2024-09-30', '2024-09-13']})
# Basic data cleaning
df = df.dropna() # Remove missing values
df = df.drop_duplicates() # Remove duplicates
# Comprehensive data analysis
analysis_result = df.describe(include='all')
print(analysis_result)
# Additional insights
print('\nTop performing items:')
print(df.nlargest(3, df.columns[1]))
print('\nTrend analysis:')
if 'Date/Tanggal' in df.columns:
df['Month/Bulan'] = pd.to_datetime(df['Date/Tanggal']).dt.month
monthly_trend = df.groupby('Month/Bulan').mean(numeric_only=True)
print(monthly_trend)
|
Optimize growth rate region
|
{
"Age/Usia": [
20,
58,
22,
36,
23,
34,
60,
66,
49,
70,
70,
58,
31,
37,
53,
25,
19,
24,
59,
41,
64,
32,
57,
21,
50,
42,
48,
63,
40,
69,
37,
29,
62,
59,
21,
26,
64,
26,
64,
49,
61,
68,
18,
20,
65,
29,
39,
39,
69,
50,
18,
27,
61,
33,
48,
58,
43,
64,
24,
39,
49,
27,
35,
48,
56,
45,
27,
59,
58,
63,
28,
22,
32,
67,
61,
67,
27,
34,
26,
34,
63,
28,
48,
43,
54,
36,
41,
22,
47,
57,
24,
39,
48,
39,
20,
56,
35,
43
],
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": null,
"City/Kota": null,
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": null,
"Customer ID/ID Pelanggan": [
"CUST_0001",
"CUST_0002",
"CUST_0003",
"CUST_0004",
"CUST_0005",
"CUST_0006",
"CUST_0007",
"CUST_0008",
"CUST_0009",
"CUST_0010",
"CUST_0011",
"CUST_0012",
"CUST_0013",
"CUST_0014",
"CUST_0015",
"CUST_0016",
"CUST_0017",
"CUST_0018",
"CUST_0019",
"CUST_0020",
"CUST_0021",
"CUST_0022",
"CUST_0023",
"CUST_0024",
"CUST_0025",
"CUST_0026",
"CUST_0027",
"CUST_0028",
"CUST_0029",
"CUST_0030",
"CUST_0031",
"CUST_0032",
"CUST_0033",
"CUST_0034",
"CUST_0035",
"CUST_0036",
"CUST_0037",
"CUST_0038",
"CUST_0039",
"CUST_0040",
"CUST_0041",
"CUST_0042",
"CUST_0043",
"CUST_0044",
"CUST_0045",
"CUST_0046",
"CUST_0047",
"CUST_0048",
"CUST_0049",
"CUST_0050",
"CUST_0051",
"CUST_0052",
"CUST_0053",
"CUST_0054",
"CUST_0055",
"CUST_0056",
"CUST_0057",
"CUST_0058",
"CUST_0059",
"CUST_0060",
"CUST_0061",
"CUST_0062",
"CUST_0063",
"CUST_0064",
"CUST_0065",
"CUST_0066",
"CUST_0067",
"CUST_0068",
"CUST_0069",
"CUST_0070",
"CUST_0071",
"CUST_0072",
"CUST_0073",
"CUST_0074",
"CUST_0075",
"CUST_0076",
"CUST_0077",
"CUST_0078",
"CUST_0079",
"CUST_0080",
"CUST_0081",
"CUST_0082",
"CUST_0083",
"CUST_0084",
"CUST_0085",
"CUST_0086",
"CUST_0087",
"CUST_0088",
"CUST_0089",
"CUST_0090",
"CUST_0091",
"CUST_0092",
"CUST_0093",
"CUST_0094",
"CUST_0095",
"CUST_0096",
"CUST_0097",
"CUST_0098"
],
"Date/Tanggal": null,
"Discount/Diskon": null,
"Gender/Jenis Kelamin": [
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Other/Lainnya",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Other/Lainnya",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Other/Lainnya",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Other/Lainnya",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki"
],
"Growth Rate/Pertumbuhan": null,
"Income/Pendapatan": [
1000,
7000,
3000,
8000,
10000,
7000,
3000,
7000,
10000,
4000,
7000,
9000,
7000,
7000,
6000,
9000,
2000,
5000,
10000,
5000,
4000,
6000,
8000,
8000,
7000,
3000,
3000,
8000,
4000,
7000,
1000,
3000,
2000,
5000,
4000,
8000,
5000,
4000,
4000,
2000,
4000,
9000,
8000,
2000,
5000,
4000,
7000,
10000,
2000,
6000,
4000,
9000,
9000,
7000,
2000,
6000,
1000,
9000,
2000,
3000,
3000,
2000,
5000,
4000,
3000,
3000,
7000,
5000,
8000,
3000,
9000,
2000,
5000,
10000,
7000,
10000,
9000,
7000,
5000,
9000,
7000,
3000,
10000,
7000,
3000,
5000,
5000,
10000,
7000,
8000,
1000,
4000,
5000,
6000,
7000,
4000,
9000,
8000
],
"Last Purchase Date/Tanggal Pembelian Terakhir": [
"2025-03-19",
"2024-08-22",
"2024-12-04",
"2025-02-28",
"2025-01-14",
"2024-08-24",
"2025-01-09",
"2024-12-01",
"2024-09-09",
"2024-12-25",
"2024-06-15",
"2024-08-05",
"2024-05-10",
"2025-03-08",
"2025-04-06",
"2024-05-21",
"2024-07-01",
"2024-09-15",
"2025-02-14",
"2024-11-01",
"2024-12-10",
"2024-05-24",
"2025-04-09",
"2024-07-29",
"2024-09-12",
"2025-01-24",
"2024-11-27",
"2024-12-24",
"2024-05-31",
"2024-12-31",
"2024-11-06",
"2024-11-10",
"2025-01-01",
"2024-12-28",
"2025-02-04",
"2024-10-27",
"2024-09-29",
"2024-05-11",
"2024-06-03",
"2025-04-02",
"2024-07-19",
"2025-01-22",
"2024-09-28",
"2025-04-15",
"2025-04-19",
"2025-04-09",
"2024-06-25",
"2025-01-11",
"2024-08-23",
"2024-11-07",
"2024-10-31",
"2024-12-20",
"2024-06-01",
"2024-11-02",
"2024-10-10",
"2024-11-11",
"2024-09-16",
"2024-11-20",
"2024-11-07",
"2024-06-10",
"2024-08-05",
"2024-06-03",
"2024-10-07",
"2024-11-01",
"2025-03-18",
"2024-07-11",
"2024-05-27",
"2024-10-07",
"2024-07-01",
"2024-07-06",
"2024-07-05",
"2024-06-14",
"2024-11-24",
"2024-05-11",
"2024-06-26",
"2024-07-07",
"2024-08-18",
"2024-08-22",
"2025-03-13",
"2024-10-25",
"2025-02-15",
"2024-12-14",
"2025-02-12",
"2024-05-31",
"2024-08-09",
"2024-12-05",
"2024-09-09",
"2024-11-12",
"2024-10-24",
"2024-11-23",
"2024-09-06",
"2024-04-21",
"2025-04-16",
"2024-06-07",
"2025-01-30",
"2024-05-13",
"2025-03-11",
"2025-04-08"
],
"Latitude/Lintang": null,
"Longitude/Bujur": null,
"Loyalty Score/Skor Loyalitas": [
84,
43,
49,
15,
12,
79,
9,
33,
66,
12,
48,
89,
87,
34,
72,
99,
72,
57,
61,
1,
66,
59,
77,
44,
48,
10,
34,
76,
99,
13,
94,
5,
46,
48,
49,
40,
13,
84,
8,
38,
54,
92,
22,
6,
44,
70,
18,
97,
92,
2,
19,
10,
18,
13,
66,
5,
14,
21,
72,
41,
65,
32,
4,
19,
47,
73,
97,
52,
93,
58,
94,
53,
1,
70,
65,
12,
91,
48,
74,
88,
27,
86,
43,
37,
47,
50,
94,
76,
25,
17,
68,
34,
44,
70,
54,
89,
91,
32
],
"Market Share/Pangsa Pasar": null,
"Price/Harga": null,
"Product/Produk": null,
"Region/Wilayah": null,
"Revenue/Pendapatan": null,
"Sales/Penjualan": null,
"Stock/Stok": null,
"Visitors/Pengunjung": null
}
|
import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Customer ID/ID Pelanggan': ['CUST_0001', 'CUST_0002', 'CUST_0003', 'CUST_0004', 'CUST_0005', 'CUST_0006', 'CUST_0007', 'CUST_0008', 'CUST_0009', 'CUST_0010', 'CUST_0011', 'CUST_0012', 'CUST_0013', 'CUST_0014', 'CUST_0015', 'CUST_0016', 'CUST_0017', 'CUST_0018', 'CUST_0019', 'CUST_0020', 'CUST_0021', 'CUST_0022', 'CUST_0023', 'CUST_0024', 'CUST_0025', 'CUST_0026', 'CUST_0027', 'CUST_0028', 'CUST_0029', 'CUST_0030', 'CUST_0031', 'CUST_0032', 'CUST_0033', 'CUST_0034', 'CUST_0035', 'CUST_0036', 'CUST_0037', 'CUST_0038', 'CUST_0039', 'CUST_0040', 'CUST_0041', 'CUST_0042', 'CUST_0043', 'CUST_0044', 'CUST_0045', 'CUST_0046', 'CUST_0047', 'CUST_0048', 'CUST_0049', 'CUST_0050', 'CUST_0051', 'CUST_0052', 'CUST_0053', 'CUST_0054', 'CUST_0055', 'CUST_0056', 'CUST_0057', 'CUST_0058', 'CUST_0059', 'CUST_0060', 'CUST_0061', 'CUST_0062', 'CUST_0063', 'CUST_0064', 'CUST_0065', 'CUST_0066', 'CUST_0067', 'CUST_0068', 'CUST_0069', 'CUST_0070', 'CUST_0071', 'CUST_0072', 'CUST_0073', 'CUST_0074', 'CUST_0075', 'CUST_0076', 'CUST_0077', 'CUST_0078', 'CUST_0079', 'CUST_0080', 'CUST_0081', 'CUST_0082', 'CUST_0083', 'CUST_0084', 'CUST_0085', 'CUST_0086', 'CUST_0087', 'CUST_0088', 'CUST_0089', 'CUST_0090', 'CUST_0091', 'CUST_0092', 'CUST_0093', 'CUST_0094', 'CUST_0095', 'CUST_0096', 'CUST_0097', 'CUST_0098'], 'Age/Usia': [20, 58, 22, 36, 23, 34, 60, 66, 49, 70, 70, 58, 31, 37, 53, 25, 19, 24, 59, 41, 64, 32, 57, 21, 50, 42, 48, 63, 40, 69, 37, 29, 62, 59, 21, 26, 64, 26, 64, 49, 61, 68, 18, 20, 65, 29, 39, 39, 69, 50, 18, 27, 61, 33, 48, 58, 43, 64, 24, 39, 49, 27, 35, 48, 56, 45, 27, 59, 58, 63, 28, 22, 32, 67, 61, 67, 27, 34, 26, 34, 63, 28, 48, 43, 54, 36, 41, 22, 47, 57, 24, 39, 48, 39, 20, 56, 35, 43], 'Gender/Jenis Kelamin': ['Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Other/Lainnya', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Other/Lainnya', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Other/Lainnya', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Other/Lainnya', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki'], 'Income/Pendapatan': [1000, 7000, 3000, 8000, 10000, 7000, 3000, 7000, 10000, 4000, 7000, 9000, 7000, 7000, 6000, 9000, 2000, 5000, 10000, 5000, 4000, 6000, 8000, 8000, 7000, 3000, 3000, 8000, 4000, 7000, 1000, 3000, 2000, 5000, 4000, 8000, 5000, 4000, 4000, 2000, 4000, 9000, 8000, 2000, 5000, 4000, 7000, 10000, 2000, 6000, 4000, 9000, 9000, 7000, 2000, 6000, 1000, 9000, 2000, 3000, 3000, 2000, 5000, 4000, 3000, 3000, 7000, 5000, 8000, 3000, 9000, 2000, 5000, 10000, 7000, 10000, 9000, 7000, 5000, 9000, 7000, 3000, 10000, 7000, 3000, 5000, 5000, 10000, 7000, 8000, 1000, 4000, 5000, 6000, 7000, 4000, 9000, 8000], 'Loyalty Score/Skor Loyalitas': [84, 43, 49, 15, 12, 79, 9, 33, 66, 12, 48, 89, 87, 34, 72, 99, 72, 57, 61, 1, 66, 59, 77, 44, 48, 10, 34, 76, 99, 13, 94, 5, 46, 48, 49, 40, 13, 84, 8, 38, 54, 92, 22, 6, 44, 70, 18, 97, 92, 2, 19, 10, 18, 13, 66, 5, 14, 21, 72, 41, 65, 32, 4, 19, 47, 73, 97, 52, 93, 58, 94, 53, 1, 70, 65, 12, 91, 48, 74, 88, 27, 86, 43, 37, 47, 50, 94, 76, 25, 17, 68, 34, 44, 70, 54, 89, 91, 32], 'Last Purchase Date/Tanggal Pembelian Terakhir': ['2025-03-19', '2024-08-22', '2024-12-04', '2025-02-28', '2025-01-14', '2024-08-24', '2025-01-09', '2024-12-01', '2024-09-09', '2024-12-25', '2024-06-15', '2024-08-05', '2024-05-10', '2025-03-08', '2025-04-06', '2024-05-21', '2024-07-01', '2024-09-15', '2025-02-14', '2024-11-01', '2024-12-10', '2024-05-24', '2025-04-09', '2024-07-29', '2024-09-12', '2025-01-24', '2024-11-27', '2024-12-24', '2024-05-31', '2024-12-31', '2024-11-06', '2024-11-10', '2025-01-01', '2024-12-28', '2025-02-04', '2024-10-27', '2024-09-29', '2024-05-11', '2024-06-03', '2025-04-02', '2024-07-19', '2025-01-22', '2024-09-28', '2025-04-15', '2025-04-19', '2025-04-09', '2024-06-25', '2025-01-11', '2024-08-23', '2024-11-07', '2024-10-31', '2024-12-20', '2024-06-01', '2024-11-02', '2024-10-10', '2024-11-11', '2024-09-16', '2024-11-20', '2024-11-07', '2024-06-10', '2024-08-05', '2024-06-03', '2024-10-07', '2024-11-01', '2025-03-18', '2024-07-11', '2024-05-27', '2024-10-07', '2024-07-01', '2024-07-06', '2024-07-05', '2024-06-14', '2024-11-24', '2024-05-11', '2024-06-26', '2024-07-07', '2024-08-18', '2024-08-22', '2025-03-13', '2024-10-25', '2025-02-15', '2024-12-14', '2025-02-12', '2024-05-31', '2024-08-09', '2024-12-05', '2024-09-09', '2024-11-12', '2024-10-24', '2024-11-23', '2024-09-06', '2024-04-21', '2025-04-16', '2024-06-07', '2025-01-30', '2024-05-13', '2025-03-11', '2025-04-08']})
# Basic data cleaning
df = df.dropna() # Remove missing values
df = df.drop_duplicates() # Remove duplicates
# Comprehensive data analysis
analysis_result = df.describe(include='all')
print(analysis_result)
# Additional insights
print('\nTop performing items:')
print(df.nlargest(3, df.columns[1]))
print('\nTrend analysis:')
if 'Date/Tanggal' in df.columns:
df['Month/Bulan'] = pd.to_datetime(df['Date/Tanggal']).dt.month
monthly_trend = df.groupby('Month/Bulan').mean(numeric_only=True)
print(monthly_trend)
|
Forecast visitors for/untuk quarter
|
{
"Age/Usia": [
55,
67,
49,
67,
70,
40,
46,
39,
57,
58,
52,
48,
47,
57,
29,
65,
66,
42,
67,
54,
23,
40,
54,
45,
52,
30,
19,
32,
26,
63,
55,
62,
40,
58,
44,
27,
25,
25,
30,
37,
24,
46,
51,
23,
57,
69,
51,
35,
29,
61,
64,
41,
44,
31,
28
],
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": null,
"City/Kota": null,
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": null,
"Customer ID/ID Pelanggan": [
"CUST_0001",
"CUST_0002",
"CUST_0003",
"CUST_0004",
"CUST_0005",
"CUST_0006",
"CUST_0007",
"CUST_0008",
"CUST_0009",
"CUST_0010",
"CUST_0011",
"CUST_0012",
"CUST_0013",
"CUST_0014",
"CUST_0015",
"CUST_0016",
"CUST_0017",
"CUST_0018",
"CUST_0019",
"CUST_0020",
"CUST_0021",
"CUST_0022",
"CUST_0023",
"CUST_0024",
"CUST_0025",
"CUST_0026",
"CUST_0027",
"CUST_0028",
"CUST_0029",
"CUST_0030",
"CUST_0031",
"CUST_0032",
"CUST_0033",
"CUST_0034",
"CUST_0035",
"CUST_0036",
"CUST_0037",
"CUST_0038",
"CUST_0039",
"CUST_0040",
"CUST_0041",
"CUST_0042",
"CUST_0043",
"CUST_0044",
"CUST_0045",
"CUST_0046",
"CUST_0047",
"CUST_0048",
"CUST_0049",
"CUST_0050",
"CUST_0051",
"CUST_0052",
"CUST_0053",
"CUST_0054",
"CUST_0055"
],
"Date/Tanggal": null,
"Discount/Diskon": null,
"Gender/Jenis Kelamin": [
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Other/Lainnya",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Other/Lainnya",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Other/Lainnya",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan"
],
"Growth Rate/Pertumbuhan": null,
"Income/Pendapatan": [
7000,
8000,
2000,
9000,
6000,
6000,
5000,
7000,
10000,
7000,
8000,
8000,
8000,
3000,
2000,
4000,
5000,
8000,
6000,
7000,
4000,
5000,
9000,
9000,
4000,
3000,
6000,
8000,
2000,
8000,
2000,
9000,
9000,
7000,
3000,
2000,
7000,
7000,
5000,
6000,
10000,
4000,
5000,
2000,
8000,
2000,
6000,
2000,
2000,
9000,
8000,
9000,
4000,
5000,
9000
],
"Last Purchase Date/Tanggal Pembelian Terakhir": [
"2025-01-14",
"2025-02-08",
"2025-01-23",
"2024-09-26",
"2024-08-26",
"2024-05-23",
"2024-10-23",
"2025-01-12",
"2024-05-10",
"2024-09-14",
"2025-04-16",
"2024-08-25",
"2024-05-24",
"2024-05-10",
"2024-10-08",
"2024-08-24",
"2024-10-21",
"2024-10-04",
"2024-09-16",
"2024-06-05",
"2024-09-17",
"2024-08-10",
"2024-12-13",
"2024-05-15",
"2024-07-02",
"2024-06-25",
"2024-07-11",
"2024-12-05",
"2025-03-02",
"2024-11-11",
"2024-12-21",
"2025-04-14",
"2024-11-13",
"2024-06-21",
"2025-01-23",
"2025-01-02",
"2024-10-17",
"2024-05-24",
"2024-09-13",
"2024-09-30",
"2025-01-08",
"2024-12-25",
"2025-01-01",
"2025-04-09",
"2024-09-06",
"2025-01-27",
"2025-03-01",
"2024-04-27",
"2024-06-11",
"2024-08-01",
"2025-03-18",
"2024-09-30",
"2024-12-17",
"2024-06-03",
"2024-07-10"
],
"Latitude/Lintang": null,
"Longitude/Bujur": null,
"Loyalty Score/Skor Loyalitas": [
5,
60,
67,
17,
4,
21,
14,
59,
21,
58,
6,
30,
94,
36,
95,
30,
91,
52,
80,
100,
72,
99,
80,
21,
15,
31,
96,
100,
9,
13,
70,
27,
55,
9,
77,
64,
37,
49,
26,
71,
53,
69,
19,
88,
58,
52,
53,
77,
11,
80,
21,
26,
61,
63,
44
],
"Market Share/Pangsa Pasar": null,
"Price/Harga": null,
"Product/Produk": null,
"Region/Wilayah": null,
"Revenue/Pendapatan": null,
"Sales/Penjualan": null,
"Stock/Stok": null,
"Visitors/Pengunjung": null
}
|
import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Customer ID/ID Pelanggan': ['CUST_0001', 'CUST_0002', 'CUST_0003', 'CUST_0004', 'CUST_0005', 'CUST_0006', 'CUST_0007', 'CUST_0008', 'CUST_0009', 'CUST_0010', 'CUST_0011', 'CUST_0012', 'CUST_0013', 'CUST_0014', 'CUST_0015', 'CUST_0016', 'CUST_0017', 'CUST_0018', 'CUST_0019', 'CUST_0020', 'CUST_0021', 'CUST_0022', 'CUST_0023', 'CUST_0024', 'CUST_0025', 'CUST_0026', 'CUST_0027', 'CUST_0028', 'CUST_0029', 'CUST_0030', 'CUST_0031', 'CUST_0032', 'CUST_0033', 'CUST_0034', 'CUST_0035', 'CUST_0036', 'CUST_0037', 'CUST_0038', 'CUST_0039', 'CUST_0040', 'CUST_0041', 'CUST_0042', 'CUST_0043', 'CUST_0044', 'CUST_0045', 'CUST_0046', 'CUST_0047', 'CUST_0048', 'CUST_0049', 'CUST_0050', 'CUST_0051', 'CUST_0052', 'CUST_0053', 'CUST_0054', 'CUST_0055'], 'Age/Usia': [55, 67, 49, 67, 70, 40, 46, 39, 57, 58, 52, 48, 47, 57, 29, 65, 66, 42, 67, 54, 23, 40, 54, 45, 52, 30, 19, 32, 26, 63, 55, 62, 40, 58, 44, 27, 25, 25, 30, 37, 24, 46, 51, 23, 57, 69, 51, 35, 29, 61, 64, 41, 44, 31, 28], 'Gender/Jenis Kelamin': ['Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Other/Lainnya', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Other/Lainnya', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Other/Lainnya', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan'], 'Income/Pendapatan': [7000, 8000, 2000, 9000, 6000, 6000, 5000, 7000, 10000, 7000, 8000, 8000, 8000, 3000, 2000, 4000, 5000, 8000, 6000, 7000, 4000, 5000, 9000, 9000, 4000, 3000, 6000, 8000, 2000, 8000, 2000, 9000, 9000, 7000, 3000, 2000, 7000, 7000, 5000, 6000, 10000, 4000, 5000, 2000, 8000, 2000, 6000, 2000, 2000, 9000, 8000, 9000, 4000, 5000, 9000], 'Loyalty Score/Skor Loyalitas': [5, 60, 67, 17, 4, 21, 14, 59, 21, 58, 6, 30, 94, 36, 95, 30, 91, 52, 80, 100, 72, 99, 80, 21, 15, 31, 96, 100, 9, 13, 70, 27, 55, 9, 77, 64, 37, 49, 26, 71, 53, 69, 19, 88, 58, 52, 53, 77, 11, 80, 21, 26, 61, 63, 44], 'Last Purchase Date/Tanggal Pembelian Terakhir': ['2025-01-14', '2025-02-08', '2025-01-23', '2024-09-26', '2024-08-26', '2024-05-23', '2024-10-23', '2025-01-12', '2024-05-10', '2024-09-14', '2025-04-16', '2024-08-25', '2024-05-24', '2024-05-10', '2024-10-08', '2024-08-24', '2024-10-21', '2024-10-04', '2024-09-16', '2024-06-05', '2024-09-17', '2024-08-10', '2024-12-13', '2024-05-15', '2024-07-02', '2024-06-25', '2024-07-11', '2024-12-05', '2025-03-02', '2024-11-11', '2024-12-21', '2025-04-14', '2024-11-13', '2024-06-21', '2025-01-23', '2025-01-02', '2024-10-17', '2024-05-24', '2024-09-13', '2024-09-30', '2025-01-08', '2024-12-25', '2025-01-01', '2025-04-09', '2024-09-06', '2025-01-27', '2025-03-01', '2024-04-27', '2024-06-11', '2024-08-01', '2025-03-18', '2024-09-30', '2024-12-17', '2024-06-03', '2024-07-10']})
# Basic data cleaning
df = df.dropna() # Remove missing values
df = df.drop_duplicates() # Remove duplicates
# Time series forecasting
from statsmodels.tsa.arima.model import ARIMA
# Prepare data
ts_data = df.set_index('Date/Tanggal')['Revenue/Pendapatan']
model = ARIMA(ts_data, order=(1,1,1))
model_fit = model.fit()
forecast = model_fit.forecast(steps=7)
print('Forecast for next 7 days:', forecast)
|
Korelasi skor loyalitas and/dan nilai pesanan rata-rata
|
{
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": [
"Automotive/Otomotif",
"Automotive/Otomotif",
"Automotive/Otomotif"
],
"City/Kota": null,
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": null,
"Customer ID/ID Pelanggan": null,
"Date/Tanggal": null,
"Discount/Diskon": [
0.15,
0.12,
0.07
],
"Gender/Jenis Kelamin": null,
"Growth Rate/Pertumbuhan": null,
"Income/Pendapatan": null,
"Last Purchase Date/Tanggal Pembelian Terakhir": null,
"Latitude/Lintang": null,
"Longitude/Bujur": null,
"Loyalty Score/Skor Loyalitas": null,
"Market Share/Pangsa Pasar": null,
"Price/Harga": [
156.75,
106.13,
72.02
],
"Product/Produk": [
"Oil_1",
"Accessories_2",
"Tires_3"
],
"Region/Wilayah": null,
"Revenue/Pendapatan": null,
"Sales/Penjualan": [
668,
1000,
174
],
"Stock/Stok": [
31,
124,
80
],
"Visitors/Pengunjung": null
}
|
import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Product/Produk': ['Oil_1', 'Accessories_2', 'Tires_3'], 'Sales/Penjualan': [668, 1000, 174], 'Price/Harga': [156.75, 106.13, 72.02], 'Category/Kategori': ['Automotive/Otomotif', 'Automotive/Otomotif', 'Automotive/Otomotif'], 'Discount/Diskon': [0.15, 0.12, 0.07], 'Stock/Stok': [31, 124, 80]})
# Basic data cleaning
df = df.dropna() # Remove missing values
df = df.drop_duplicates() # Remove duplicates
# Correlation analysis
corr_matrix = df.corr(numeric_only=True)
fig = px.imshow(corr_matrix, text_auto=True,
title='Correlation Matrix')
fig.show()
|
Analisis pangsa pasar segmen pelanggan
|
{
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": null,
"City/Kota": [
"Mexico City",
"Singapore",
"Dubai",
"Tokyo",
"London"
],
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": [
"Mexico",
"Singapore",
"UAE",
"Japan",
"UK"
],
"Customer ID/ID Pelanggan": null,
"Date/Tanggal": null,
"Discount/Diskon": null,
"Gender/Jenis Kelamin": null,
"Growth Rate/Pertumbuhan": [
-0.07,
0.26,
0.2,
0.14,
0.07
],
"Income/Pendapatan": null,
"Last Purchase Date/Tanggal Pembelian Terakhir": null,
"Latitude/Lintang": [
81.8079,
-30.6587,
55.9382,
-37.8074,
53.2723
],
"Longitude/Bujur": [
106.2646,
-19.9066,
-107.6793,
29.0186,
-97.2467
],
"Loyalty Score/Skor Loyalitas": null,
"Market Share/Pangsa Pasar": [
0.44,
0.42,
0.2,
0.23,
0.26
],
"Price/Harga": null,
"Product/Produk": null,
"Region/Wilayah": [
"North America",
"Asia",
"Middle East",
"Asia",
"Europe"
],
"Revenue/Pendapatan": [
3333,
1303,
1706,
1441,
4412
],
"Sales/Penjualan": null,
"Stock/Stok": null,
"Visitors/Pengunjung": null
}
|
import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'City/Kota': ['Mexico City', 'Singapore', 'Dubai', 'Tokyo', 'London'], 'Country/Negara': ['Mexico', 'Singapore', 'UAE', 'Japan', 'UK'], 'Region/Wilayah': ['North America', 'Asia', 'Middle East', 'Asia', 'Europe'], 'Latitude/Lintang': [81.8079, -30.6587, 55.9382, -37.8074, 53.2723], 'Longitude/Bujur': [106.2646, -19.9066, -107.6793, 29.0186, -97.2467], 'Revenue/Pendapatan': [3333, 1303, 1706, 1441, 4412], 'Market Share/Pangsa Pasar': [0.44, 0.42, 0.2, 0.23, 0.26], 'Growth Rate/Pertumbuhan': [-0.07, 0.26, 0.2, 0.14, 0.07]})
# Basic data cleaning
df = df.dropna() # Remove missing values
df = df.drop_duplicates() # Remove duplicates
# Comprehensive data analysis
analysis_result = df.describe(include='all')
print(analysis_result)
# Additional insights
print('\nTop performing items:')
print(df.nlargest(3, df.columns[1]))
print('\nTrend analysis:')
if 'Date/Tanggal' in df.columns:
df['Month/Bulan'] = pd.to_datetime(df['Date/Tanggal']).dt.month
monthly_trend = df.groupby('Month/Bulan').mean(numeric_only=True)
print(monthly_trend)
|
Identifikasi pendapatan negara
|
{
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": [
"Fashion",
"Fashion",
"Fashion"
],
"City/Kota": null,
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": null,
"Customer ID/ID Pelanggan": null,
"Date/Tanggal": null,
"Discount/Diskon": [
0.13,
0.04,
0.30000000000000004
],
"Gender/Jenis Kelamin": null,
"Growth Rate/Pertumbuhan": null,
"Income/Pendapatan": null,
"Last Purchase Date/Tanggal Pembelian Terakhir": null,
"Latitude/Lintang": null,
"Longitude/Bujur": null,
"Loyalty Score/Skor Loyalitas": null,
"Market Share/Pangsa Pasar": null,
"Price/Harga": [
194.49,
159.57,
38.84
],
"Product/Produk": [
"T-Shirt_1",
"Jeans_2",
"Shoes_3"
],
"Region/Wilayah": null,
"Revenue/Pendapatan": null,
"Sales/Penjualan": [
693,
827,
495
],
"Stock/Stok": [
139,
119,
24
],
"Visitors/Pengunjung": null
}
|
import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Product/Produk': ['T-Shirt_1', 'Jeans_2', 'Shoes_3'], 'Sales/Penjualan': [693, 827, 495], 'Price/Harga': [194.49, 159.57, 38.84], 'Category/Kategori': ['Fashion', 'Fashion', 'Fashion'], 'Discount/Diskon': [0.13, 0.04, 0.3], 'Stock/Stok': [139, 119, 24]})
# Basic data cleaning
df = df.dropna() # Remove missing values
df = df.drop_duplicates() # Remove duplicates
# Comprehensive data analysis
analysis_result = df.describe(include='all')
print(analysis_result)
# Additional insights
print('\nTop performing items:')
print(df.nlargest(3, df.columns[1]))
print('\nTrend analysis:')
if 'Date/Tanggal' in df.columns:
df['Month/Bulan'] = pd.to_datetime(df['Date/Tanggal']).dt.month
monthly_trend = df.groupby('Month/Bulan').mean(numeric_only=True)
print(monthly_trend)
|
Optimize conversion rate customer segment
|
{
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": null,
"City/Kota": [
"Sydney",
"Singapore",
"Dubai",
"New York",
"Mumbai"
],
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": [
"Australia",
"Singapore",
"UAE",
"US",
"India"
],
"Customer ID/ID Pelanggan": null,
"Date/Tanggal": null,
"Discount/Diskon": null,
"Gender/Jenis Kelamin": null,
"Growth Rate/Pertumbuhan": [
-0.03,
0.29,
0.25,
0.11,
0.26
],
"Income/Pendapatan": null,
"Last Purchase Date/Tanggal Pembelian Terakhir": null,
"Latitude/Lintang": [
-3.7821,
21.9056,
-72.378,
-13.7583,
55.902
],
"Longitude/Bujur": [
-48.0211,
-168.8784,
-126.7716,
171.8496,
87.3951
],
"Loyalty Score/Skor Loyalitas": null,
"Market Share/Pangsa Pasar": [
0.22,
0.24,
0.15,
0.12,
0.36
],
"Price/Harga": null,
"Product/Produk": null,
"Region/Wilayah": [
"Oceania",
"Asia",
"Middle East",
"North America",
"Asia"
],
"Revenue/Pendapatan": [
2918,
1044,
1830,
2451,
1321
],
"Sales/Penjualan": null,
"Stock/Stok": null,
"Visitors/Pengunjung": null
}
|
import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'City/Kota': ['Sydney', 'Singapore', 'Dubai', 'New York', 'Mumbai'], 'Country/Negara': ['Australia', 'Singapore', 'UAE', 'US', 'India'], 'Region/Wilayah': ['Oceania', 'Asia', 'Middle East', 'North America', 'Asia'], 'Latitude/Lintang': [-3.7821, 21.9056, -72.378, -13.7583, 55.902], 'Longitude/Bujur': [-48.0211, -168.8784, -126.7716, 171.8496, 87.3951], 'Revenue/Pendapatan': [2918, 1044, 1830, 2451, 1321], 'Market Share/Pangsa Pasar': [0.22, 0.24, 0.15, 0.12, 0.36], 'Growth Rate/Pertumbuhan': [-0.03, 0.29, 0.25, 0.11, 0.26]})
# Basic data cleaning
df = df.dropna() # Remove missing values
df = df.drop_duplicates() # Remove duplicates
# Customer segmentation using KMeans
from sklearn.cluster import KMeans
from sklearn.preprocessing import StandardScaler
# Select features and scale
X = df[['Age/Usia', 'Income/Pendapatan', 'Loyalty Score/Skor Loyalitas']]
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)
# Cluster into 3 segments
kmeans = KMeans(n_clusters=3, random_state=42)
df['Segment/Segmen'] = kmeans.fit_predict(X_scaled)
# Visualize segments
fig = px.scatter_3d(df, x='Age/Usia', y='Income/Pendapatan',
z='Loyalty Score/Skor Loyalitas',
color='Segment/Segmen',
title='Customer Segments')
fig.show()
|
Korelasi stok and/dan pangsa pasar
|
{
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": null,
"City/Kota": [
"Mumbai",
"New York",
"Jakarta",
"Tokyo",
"Dubai"
],
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": [
"India",
"US",
"Indonesia",
"Japan",
"UAE"
],
"Customer ID/ID Pelanggan": null,
"Date/Tanggal": null,
"Discount/Diskon": null,
"Gender/Jenis Kelamin": null,
"Growth Rate/Pertumbuhan": [
0.04,
0.1,
0.19,
0.03,
0.14
],
"Income/Pendapatan": null,
"Last Purchase Date/Tanggal Pembelian Terakhir": null,
"Latitude/Lintang": [
-37.7819,
-36.3422,
-28.3427,
-86.7913,
66.665
],
"Longitude/Bujur": [
-51.767,
109.1282,
-87.7083,
112.206,
26.0203
],
"Loyalty Score/Skor Loyalitas": null,
"Market Share/Pangsa Pasar": [
0.33,
0.23,
0.26,
0.27,
0.44
],
"Price/Harga": null,
"Product/Produk": null,
"Region/Wilayah": [
"Asia",
"North America",
"Asia",
"Asia",
"Middle East"
],
"Revenue/Pendapatan": [
4838,
3649,
3778,
1163,
2585
],
"Sales/Penjualan": null,
"Stock/Stok": null,
"Visitors/Pengunjung": null
}
|
import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'City/Kota': ['Mumbai', 'New York', 'Jakarta', 'Tokyo', 'Dubai'], 'Country/Negara': ['India', 'US', 'Indonesia', 'Japan', 'UAE'], 'Region/Wilayah': ['Asia', 'North America', 'Asia', 'Asia', 'Middle East'], 'Latitude/Lintang': [-37.7819, -36.3422, -28.3427, -86.7913, 66.665], 'Longitude/Bujur': [-51.767, 109.1282, -87.7083, 112.206, 26.0203], 'Revenue/Pendapatan': [4838, 3649, 3778, 1163, 2585], 'Market Share/Pangsa Pasar': [0.33, 0.23, 0.26, 0.27, 0.44], 'Growth Rate/Pertumbuhan': [0.04, 0.1, 0.19, 0.03, 0.14]})
# Basic data cleaning
df = df.dropna() # Remove missing values
df = df.drop_duplicates() # Remove duplicates
# Correlation analysis
corr_matrix = df.corr(numeric_only=True)
fig = px.imshow(corr_matrix, text_auto=True,
title='Correlation Matrix')
fig.show()
|
Analyze visitors time period
|
{
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": [
"Electronics/Elektronik",
"Electronics/Elektronik",
"Electronics/Elektronik"
],
"City/Kota": null,
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": null,
"Customer ID/ID Pelanggan": null,
"Date/Tanggal": null,
"Discount/Diskon": [
0.09,
0.12,
0
],
"Gender/Jenis Kelamin": null,
"Growth Rate/Pertumbuhan": null,
"Income/Pendapatan": null,
"Last Purchase Date/Tanggal Pembelian Terakhir": null,
"Latitude/Lintang": null,
"Longitude/Bujur": null,
"Loyalty Score/Skor Loyalitas": null,
"Market Share/Pangsa Pasar": null,
"Price/Harga": [
20.08,
150.18,
126.77
],
"Product/Produk": [
"TV_1",
"Headphones_2",
"Smartphone_3"
],
"Region/Wilayah": null,
"Revenue/Pendapatan": null,
"Sales/Penjualan": [
778,
283,
759
],
"Stock/Stok": [
98,
13,
87
],
"Visitors/Pengunjung": null
}
|
import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Product/Produk': ['TV_1', 'Headphones_2', 'Smartphone_3'], 'Sales/Penjualan': [778, 283, 759], 'Price/Harga': [20.08, 150.18, 126.77], 'Category/Kategori': ['Electronics/Elektronik', 'Electronics/Elektronik', 'Electronics/Elektronik'], 'Discount/Diskon': [0.09, 0.12, 0.0], 'Stock/Stok': [98, 13, 87]})
# Basic data cleaning
df = df.dropna() # Remove missing values
df = df.drop_duplicates() # Remove duplicates
# Comprehensive data analysis
analysis_result = df.describe(include='all')
print(analysis_result)
# Additional insights
print('\nTop performing items:')
print(df.nlargest(3, df.columns[1]))
print('\nTrend analysis:')
if 'Date/Tanggal' in df.columns:
df['Month/Bulan'] = pd.to_datetime(df['Date/Tanggal']).dt.month
monthly_trend = df.groupby('Month/Bulan').mean(numeric_only=True)
print(monthly_trend)
|
Analisis nilai pesanan rata-rata periode waktu
|
{
"Age/Usia": [
26,
68,
28,
21,
54,
53,
20,
32,
59,
24,
53,
43,
55,
66,
35,
39,
43,
33,
36,
68,
30,
20,
70,
41,
64,
38,
53,
28,
53,
21,
63,
59,
52,
59,
61,
58,
46,
51,
20,
57,
68,
24,
51,
33,
59,
64,
38,
20,
30,
57,
33,
52,
26,
68,
64,
64,
51,
32,
55,
26,
28,
42,
42,
42,
24,
36,
38,
65,
31,
30,
45,
61,
38,
51,
67,
64,
21,
20,
47,
60,
29,
40,
26,
48,
32,
47,
66
],
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": null,
"City/Kota": null,
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": null,
"Customer ID/ID Pelanggan": [
"CUST_0001",
"CUST_0002",
"CUST_0003",
"CUST_0004",
"CUST_0005",
"CUST_0006",
"CUST_0007",
"CUST_0008",
"CUST_0009",
"CUST_0010",
"CUST_0011",
"CUST_0012",
"CUST_0013",
"CUST_0014",
"CUST_0015",
"CUST_0016",
"CUST_0017",
"CUST_0018",
"CUST_0019",
"CUST_0020",
"CUST_0021",
"CUST_0022",
"CUST_0023",
"CUST_0024",
"CUST_0025",
"CUST_0026",
"CUST_0027",
"CUST_0028",
"CUST_0029",
"CUST_0030",
"CUST_0031",
"CUST_0032",
"CUST_0033",
"CUST_0034",
"CUST_0035",
"CUST_0036",
"CUST_0037",
"CUST_0038",
"CUST_0039",
"CUST_0040",
"CUST_0041",
"CUST_0042",
"CUST_0043",
"CUST_0044",
"CUST_0045",
"CUST_0046",
"CUST_0047",
"CUST_0048",
"CUST_0049",
"CUST_0050",
"CUST_0051",
"CUST_0052",
"CUST_0053",
"CUST_0054",
"CUST_0055",
"CUST_0056",
"CUST_0057",
"CUST_0058",
"CUST_0059",
"CUST_0060",
"CUST_0061",
"CUST_0062",
"CUST_0063",
"CUST_0064",
"CUST_0065",
"CUST_0066",
"CUST_0067",
"CUST_0068",
"CUST_0069",
"CUST_0070",
"CUST_0071",
"CUST_0072",
"CUST_0073",
"CUST_0074",
"CUST_0075",
"CUST_0076",
"CUST_0077",
"CUST_0078",
"CUST_0079",
"CUST_0080",
"CUST_0081",
"CUST_0082",
"CUST_0083",
"CUST_0084",
"CUST_0085",
"CUST_0086",
"CUST_0087"
],
"Date/Tanggal": null,
"Discount/Diskon": null,
"Gender/Jenis Kelamin": [
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Other/Lainnya",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Other/Lainnya",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan"
],
"Growth Rate/Pertumbuhan": null,
"Income/Pendapatan": [
1000,
4000,
9000,
7000,
5000,
9000,
6000,
6000,
8000,
9000,
7000,
4000,
9000,
2000,
4000,
4000,
3000,
3000,
1000,
3000,
6000,
4000,
6000,
5000,
6000,
9000,
6000,
4000,
8000,
3000,
7000,
3000,
8000,
7000,
6000,
3000,
5000,
6000,
8000,
6000,
5000,
5000,
8000,
7000,
3000,
5000,
9000,
6000,
3000,
10000,
7000,
3000,
7000,
10000,
8000,
9000,
10000,
4000,
10000,
2000,
6000,
3000,
6000,
8000,
7000,
8000,
7000,
8000,
7000,
10000,
7000,
7000,
5000,
8000,
5000,
8000,
7000,
2000,
7000,
6000,
7000,
9000,
9000,
3000,
8000,
7000,
7000
],
"Last Purchase Date/Tanggal Pembelian Terakhir": [
"2024-07-02",
"2024-05-28",
"2025-04-07",
"2025-02-18",
"2024-07-25",
"2024-09-05",
"2025-02-13",
"2024-11-22",
"2025-04-15",
"2024-07-27",
"2024-05-28",
"2024-09-19",
"2024-08-26",
"2024-09-17",
"2024-08-06",
"2024-07-08",
"2024-08-28",
"2025-02-02",
"2024-10-24",
"2024-11-26",
"2025-01-26",
"2024-08-04",
"2024-10-27",
"2024-10-06",
"2025-03-20",
"2024-09-11",
"2024-09-20",
"2024-05-03",
"2025-02-10",
"2024-07-18",
"2024-09-09",
"2024-11-10",
"2024-09-17",
"2024-07-06",
"2025-02-25",
"2024-12-30",
"2024-09-18",
"2024-04-24",
"2024-07-03",
"2025-01-21",
"2024-11-13",
"2024-06-29",
"2024-10-19",
"2024-09-17",
"2025-01-12",
"2024-10-11",
"2024-10-19",
"2024-08-25",
"2024-08-21",
"2025-01-13",
"2025-02-03",
"2024-12-26",
"2025-02-02",
"2024-06-24",
"2024-05-09",
"2024-11-06",
"2024-10-10",
"2024-10-27",
"2024-07-30",
"2024-10-20",
"2024-09-11",
"2024-12-09",
"2025-03-29",
"2024-06-11",
"2025-02-24",
"2024-09-15",
"2024-09-21",
"2025-03-06",
"2024-11-02",
"2024-10-31",
"2024-12-26",
"2025-04-20",
"2024-11-11",
"2024-09-20",
"2024-06-29",
"2025-01-16",
"2024-06-17",
"2024-07-03",
"2025-02-26",
"2024-12-05",
"2024-07-15",
"2025-03-14",
"2024-05-16",
"2024-05-28",
"2025-02-05",
"2024-05-03",
"2024-10-31"
],
"Latitude/Lintang": null,
"Longitude/Bujur": null,
"Loyalty Score/Skor Loyalitas": [
37,
94,
88,
73,
30,
54,
64,
50,
21,
5,
98,
58,
50,
85,
24,
45,
20,
40,
16,
88,
56,
59,
33,
83,
27,
62,
57,
2,
4,
47,
7,
39,
90,
9,
93,
23,
21,
94,
33,
74,
33,
72,
72,
14,
69,
33,
63,
8,
97,
47,
94,
86,
60,
29,
89,
77,
52,
48,
54,
77,
66,
34,
40,
29,
42,
62,
51,
73,
61,
93,
34,
48,
7,
74,
57,
15,
55,
92,
46,
79,
35,
18,
45,
54,
49,
41,
57
],
"Market Share/Pangsa Pasar": null,
"Price/Harga": null,
"Product/Produk": null,
"Region/Wilayah": null,
"Revenue/Pendapatan": null,
"Sales/Penjualan": null,
"Stock/Stok": null,
"Visitors/Pengunjung": null
}
|
import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Customer ID/ID Pelanggan': ['CUST_0001', 'CUST_0002', 'CUST_0003', 'CUST_0004', 'CUST_0005', 'CUST_0006', 'CUST_0007', 'CUST_0008', 'CUST_0009', 'CUST_0010', 'CUST_0011', 'CUST_0012', 'CUST_0013', 'CUST_0014', 'CUST_0015', 'CUST_0016', 'CUST_0017', 'CUST_0018', 'CUST_0019', 'CUST_0020', 'CUST_0021', 'CUST_0022', 'CUST_0023', 'CUST_0024', 'CUST_0025', 'CUST_0026', 'CUST_0027', 'CUST_0028', 'CUST_0029', 'CUST_0030', 'CUST_0031', 'CUST_0032', 'CUST_0033', 'CUST_0034', 'CUST_0035', 'CUST_0036', 'CUST_0037', 'CUST_0038', 'CUST_0039', 'CUST_0040', 'CUST_0041', 'CUST_0042', 'CUST_0043', 'CUST_0044', 'CUST_0045', 'CUST_0046', 'CUST_0047', 'CUST_0048', 'CUST_0049', 'CUST_0050', 'CUST_0051', 'CUST_0052', 'CUST_0053', 'CUST_0054', 'CUST_0055', 'CUST_0056', 'CUST_0057', 'CUST_0058', 'CUST_0059', 'CUST_0060', 'CUST_0061', 'CUST_0062', 'CUST_0063', 'CUST_0064', 'CUST_0065', 'CUST_0066', 'CUST_0067', 'CUST_0068', 'CUST_0069', 'CUST_0070', 'CUST_0071', 'CUST_0072', 'CUST_0073', 'CUST_0074', 'CUST_0075', 'CUST_0076', 'CUST_0077', 'CUST_0078', 'CUST_0079', 'CUST_0080', 'CUST_0081', 'CUST_0082', 'CUST_0083', 'CUST_0084', 'CUST_0085', 'CUST_0086', 'CUST_0087'], 'Age/Usia': [26, 68, 28, 21, 54, 53, 20, 32, 59, 24, 53, 43, 55, 66, 35, 39, 43, 33, 36, 68, 30, 20, 70, 41, 64, 38, 53, 28, 53, 21, 63, 59, 52, 59, 61, 58, 46, 51, 20, 57, 68, 24, 51, 33, 59, 64, 38, 20, 30, 57, 33, 52, 26, 68, 64, 64, 51, 32, 55, 26, 28, 42, 42, 42, 24, 36, 38, 65, 31, 30, 45, 61, 38, 51, 67, 64, 21, 20, 47, 60, 29, 40, 26, 48, 32, 47, 66], 'Gender/Jenis Kelamin': ['Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Other/Lainnya', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Other/Lainnya', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan'], 'Income/Pendapatan': [1000, 4000, 9000, 7000, 5000, 9000, 6000, 6000, 8000, 9000, 7000, 4000, 9000, 2000, 4000, 4000, 3000, 3000, 1000, 3000, 6000, 4000, 6000, 5000, 6000, 9000, 6000, 4000, 8000, 3000, 7000, 3000, 8000, 7000, 6000, 3000, 5000, 6000, 8000, 6000, 5000, 5000, 8000, 7000, 3000, 5000, 9000, 6000, 3000, 10000, 7000, 3000, 7000, 10000, 8000, 9000, 10000, 4000, 10000, 2000, 6000, 3000, 6000, 8000, 7000, 8000, 7000, 8000, 7000, 10000, 7000, 7000, 5000, 8000, 5000, 8000, 7000, 2000, 7000, 6000, 7000, 9000, 9000, 3000, 8000, 7000, 7000], 'Loyalty Score/Skor Loyalitas': [37, 94, 88, 73, 30, 54, 64, 50, 21, 5, 98, 58, 50, 85, 24, 45, 20, 40, 16, 88, 56, 59, 33, 83, 27, 62, 57, 2, 4, 47, 7, 39, 90, 9, 93, 23, 21, 94, 33, 74, 33, 72, 72, 14, 69, 33, 63, 8, 97, 47, 94, 86, 60, 29, 89, 77, 52, 48, 54, 77, 66, 34, 40, 29, 42, 62, 51, 73, 61, 93, 34, 48, 7, 74, 57, 15, 55, 92, 46, 79, 35, 18, 45, 54, 49, 41, 57], 'Last Purchase Date/Tanggal Pembelian Terakhir': ['2024-07-02', '2024-05-28', '2025-04-07', '2025-02-18', '2024-07-25', '2024-09-05', '2025-02-13', '2024-11-22', '2025-04-15', '2024-07-27', '2024-05-28', '2024-09-19', '2024-08-26', '2024-09-17', '2024-08-06', '2024-07-08', '2024-08-28', '2025-02-02', '2024-10-24', '2024-11-26', '2025-01-26', '2024-08-04', '2024-10-27', '2024-10-06', '2025-03-20', '2024-09-11', '2024-09-20', '2024-05-03', '2025-02-10', '2024-07-18', '2024-09-09', '2024-11-10', '2024-09-17', '2024-07-06', '2025-02-25', '2024-12-30', '2024-09-18', '2024-04-24', '2024-07-03', '2025-01-21', '2024-11-13', '2024-06-29', '2024-10-19', '2024-09-17', '2025-01-12', '2024-10-11', '2024-10-19', '2024-08-25', '2024-08-21', '2025-01-13', '2025-02-03', '2024-12-26', '2025-02-02', '2024-06-24', '2024-05-09', '2024-11-06', '2024-10-10', '2024-10-27', '2024-07-30', '2024-10-20', '2024-09-11', '2024-12-09', '2025-03-29', '2024-06-11', '2025-02-24', '2024-09-15', '2024-09-21', '2025-03-06', '2024-11-02', '2024-10-31', '2024-12-26', '2025-04-20', '2024-11-11', '2024-09-20', '2024-06-29', '2025-01-16', '2024-06-17', '2024-07-03', '2025-02-26', '2024-12-05', '2024-07-15', '2025-03-14', '2024-05-16', '2024-05-28', '2025-02-05', '2024-05-03', '2024-10-31']})
# Basic data cleaning
df = df.dropna() # Remove missing values
df = df.drop_duplicates() # Remove duplicates
# Comprehensive data analysis
analysis_result = df.describe(include='all')
print(analysis_result)
# Additional insights
print('\nTop performing items:')
print(df.nlargest(3, df.columns[1]))
print('\nTrend analysis:')
if 'Date/Tanggal' in df.columns:
df['Month/Bulan'] = pd.to_datetime(df['Date/Tanggal']).dt.month
monthly_trend = df.groupby('Month/Bulan').mean(numeric_only=True)
print(monthly_trend)
|
Visualisasikan pendapatan segmen pelanggan
|
{
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": [
112.78,
146.34,
114.85,
144.22,
161.49,
150.67,
101.69,
179.94,
77.52,
66.49,
150.08,
195.67,
91.44,
199.01,
100.35,
152.81,
57.23,
114.01,
160.31,
119.5,
80.17,
128.94,
101.74,
83.61,
98.9,
68.02,
105.6,
58.39,
167.79,
115.51
],
"Category/Kategori": null,
"City/Kota": null,
"Conversion Rate/Rasio Konversi": [
0.068,
0.07930000000000001,
0.038700000000000005,
0.049100000000000005,
0.0629,
0.0756,
0.0149,
0.041100000000000005,
0.0196,
0.021,
0.09480000000000001,
0.0175,
0.0165,
0.0279,
0.0874,
0.0241,
0.0921,
0.0198,
0.0863,
0.044,
0.0135,
0.09580000000000001,
0.019200000000000002,
0.0167,
0.0453,
0.061900000000000004,
0.0568,
0.026600000000000002,
0.063,
0.0347
],
"Country/Negara": null,
"Customer ID/ID Pelanggan": null,
"Date/Tanggal": [
"2025-03-23",
"2025-03-24",
"2025-03-25",
"2025-03-26",
"2025-03-27",
"2025-03-28",
"2025-03-29",
"2025-03-30",
"2025-03-31",
"2025-04-01",
"2025-04-02",
"2025-04-03",
"2025-04-04",
"2025-04-05",
"2025-04-06",
"2025-04-07",
"2025-04-08",
"2025-04-09",
"2025-04-10",
"2025-04-11",
"2025-04-12",
"2025-04-13",
"2025-04-14",
"2025-04-15",
"2025-04-16",
"2025-04-17",
"2025-04-18",
"2025-04-19",
"2025-04-20",
"2025-04-21"
],
"Discount/Diskon": null,
"Gender/Jenis Kelamin": null,
"Growth Rate/Pertumbuhan": null,
"Income/Pendapatan": null,
"Last Purchase Date/Tanggal Pembelian Terakhir": null,
"Latitude/Lintang": null,
"Longitude/Bujur": null,
"Loyalty Score/Skor Loyalitas": null,
"Market Share/Pangsa Pasar": null,
"Price/Harga": null,
"Product/Produk": null,
"Region/Wilayah": null,
"Revenue/Pendapatan": [
989.44,
1186.59,
1179.51,
1194.43,
1327.94,
1247.32,
1177.38,
1120.3,
1160.71,
959.04,
993.47,
900.88,
1026.22,
1068.18,
1217.52,
1193.29,
1336.55,
1428.41,
1481.92,
1432.88,
1407.49,
1387.8,
1261.78,
1160.59,
1144.16,
1144.28,
1195.16,
1300.82,
1251.21,
1493.75
],
"Sales/Penjualan": null,
"Stock/Stok": null,
"Visitors/Pengunjung": [
206,
181,
293,
65,
164,
245,
223,
66,
63,
154,
106,
142,
247,
60,
297,
77,
108,
212,
90,
220,
232,
159,
249,
218,
187,
271,
183,
204,
176,
286
]
}
|
import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Date/Tanggal': ['2025-03-23', '2025-03-24', '2025-03-25', '2025-03-26', '2025-03-27', '2025-03-28', '2025-03-29', '2025-03-30', '2025-03-31', '2025-04-01', '2025-04-02', '2025-04-03', '2025-04-04', '2025-04-05', '2025-04-06', '2025-04-07', '2025-04-08', '2025-04-09', '2025-04-10', '2025-04-11', '2025-04-12', '2025-04-13', '2025-04-14', '2025-04-15', '2025-04-16', '2025-04-17', '2025-04-18', '2025-04-19', '2025-04-20', '2025-04-21'], 'Revenue/Pendapatan': [989.44, 1186.59, 1179.51, 1194.43, 1327.94, 1247.32, 1177.38, 1120.3, 1160.71, 959.04, 993.47, 900.88, 1026.22, 1068.18, 1217.52, 1193.29, 1336.55, 1428.41, 1481.92, 1432.88, 1407.49, 1387.8, 1261.78, 1160.59, 1144.16, 1144.28, 1195.16, 1300.82, 1251.21, 1493.75], 'Visitors/Pengunjung': [206, 181, 293, 65, 164, 245, 223, 66, 63, 154, 106, 142, 247, 60, 297, 77, 108, 212, 90, 220, 232, 159, 249, 218, 187, 271, 183, 204, 176, 286], 'Conversion Rate/Rasio Konversi': [0.068, 0.0793, 0.0387, 0.0491, 0.0629, 0.0756, 0.0149, 0.0411, 0.0196, 0.021, 0.0948, 0.0175, 0.0165, 0.0279, 0.0874, 0.0241, 0.0921, 0.0198, 0.0863, 0.044, 0.0135, 0.0958, 0.0192, 0.0167, 0.0453, 0.0619, 0.0568, 0.0266, 0.063, 0.0347], 'Avg. Order Value/Nilai Pesanan Rata2': [112.78, 146.34, 114.85, 144.22, 161.49, 150.67, 101.69, 179.94, 77.52, 66.49, 150.08, 195.67, 91.44, 199.01, 100.35, 152.81, 57.23, 114.01, 160.31, 119.5, 80.17, 128.94, 101.74, 83.61, 98.9, 68.02, 105.6, 58.39, 167.79, 115.51]})
# Basic data cleaning
df = df.dropna() # Remove missing values
df = df.drop_duplicates() # Remove duplicates
# Time series visualization
fig = px.line(df, x='Date/Tanggal', y='Revenue/Pendapatan',
title='Revenue Over Time',
markers=True)
fig.update_xaxes(rangeslider_visible=True)
fig.show()
|
Korelasi penjualan and/dan pendapatan
|
{
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": [
"Electronics/Elektronik",
"Electronics/Elektronik",
"Electronics/Elektronik"
],
"City/Kota": null,
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": null,
"Customer ID/ID Pelanggan": null,
"Date/Tanggal": null,
"Discount/Diskon": [
0.28,
0.14,
0.2
],
"Gender/Jenis Kelamin": null,
"Growth Rate/Pertumbuhan": null,
"Income/Pendapatan": null,
"Last Purchase Date/Tanggal Pembelian Terakhir": null,
"Latitude/Lintang": null,
"Longitude/Bujur": null,
"Loyalty Score/Skor Loyalitas": null,
"Market Share/Pangsa Pasar": null,
"Price/Harga": [
103.51,
165.5,
121.25
],
"Product/Produk": [
"Smartphone_1",
"Laptop_2",
"TV_3"
],
"Region/Wilayah": null,
"Revenue/Pendapatan": null,
"Sales/Penjualan": [
928,
390,
612
],
"Stock/Stok": [
21,
60,
124
],
"Visitors/Pengunjung": null
}
|
import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Product/Produk': ['Smartphone_1', 'Laptop_2', 'TV_3'], 'Sales/Penjualan': [928, 390, 612], 'Price/Harga': [103.51, 165.5, 121.25], 'Category/Kategori': ['Electronics/Elektronik', 'Electronics/Elektronik', 'Electronics/Elektronik'], 'Discount/Diskon': [0.28, 0.14, 0.2], 'Stock/Stok': [21, 60, 124]})
# Basic data cleaning
df = df.dropna() # Remove missing values
df = df.drop_duplicates() # Remove duplicates
# Correlation analysis
corr_matrix = df.corr(numeric_only=True)
fig = px.imshow(corr_matrix, text_auto=True,
title='Correlation Matrix')
fig.show()
|
Visualisasikan pengunjung kategori
|
{
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": [
"Food/Makanan",
"Food/Makanan",
"Food/Makanan"
],
"City/Kota": null,
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": null,
"Customer ID/ID Pelanggan": null,
"Date/Tanggal": null,
"Discount/Diskon": [
0.1,
0.01,
0.02
],
"Gender/Jenis Kelamin": null,
"Growth Rate/Pertumbuhan": null,
"Income/Pendapatan": null,
"Last Purchase Date/Tanggal Pembelian Terakhir": null,
"Latitude/Lintang": null,
"Longitude/Bujur": null,
"Loyalty Score/Skor Loyalitas": null,
"Market Share/Pangsa Pasar": null,
"Price/Harga": [
179.79,
38.51,
49.73
],
"Product/Produk": [
"Fruits_1",
"Dairy_2",
"Beverages_3"
],
"Region/Wilayah": null,
"Revenue/Pendapatan": null,
"Sales/Penjualan": [
990,
959,
563
],
"Stock/Stok": [
166,
184,
133
],
"Visitors/Pengunjung": null
}
|
import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Product/Produk': ['Fruits_1', 'Dairy_2', 'Beverages_3'], 'Sales/Penjualan': [990, 959, 563], 'Price/Harga': [179.79, 38.51, 49.73], 'Category/Kategori': ['Food/Makanan', 'Food/Makanan', 'Food/Makanan'], 'Discount/Diskon': [0.1, 0.01, 0.02], 'Stock/Stok': [166, 184, 133]})
# Basic data cleaning
df = df.dropna() # Remove missing values
df = df.drop_duplicates() # Remove duplicates
# Scatter chart visualization
fig = px.scatter(df, x=df.columns[1], y=df.columns[2],
color=df.columns[3] if len(df.columns) > 3 else None,
title='Visualisasikan pengunjung kategori')
fig.show()
|
Korelasi pangsa pasar and/dan diskon
|
{
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": [
145.37,
81.41,
97.64,
96.1,
157.2,
168.41,
121.35,
149.41,
53.62,
146.14,
111.57,
134.7,
73.83,
68.37,
191.61,
166.85,
70.77,
189.06,
179.58,
71.58,
110.1,
195.89,
104.63,
163.23,
145.02,
196.54,
101.64,
69.59,
82.53,
181.08
],
"Category/Kategori": null,
"City/Kota": null,
"Conversion Rate/Rasio Konversi": [
0.0495,
0.0205,
0.08850000000000001,
0.0495,
0.014400000000000001,
0.0974,
0.08750000000000001,
0.0775,
0.0847,
0.0115,
0.0669,
0.0868,
0.061200000000000004,
0.0978,
0.0407,
0.022600000000000002,
0.0172,
0.0726,
0.0582,
0.0264,
0.07200000000000001,
0.075,
0.0441,
0.0347,
0.0383,
0.0494,
0.0888,
0.0922,
0.0361,
0.043000000000000003
],
"Country/Negara": null,
"Customer ID/ID Pelanggan": null,
"Date/Tanggal": [
"2025-03-23",
"2025-03-24",
"2025-03-25",
"2025-03-26",
"2025-03-27",
"2025-03-28",
"2025-03-29",
"2025-03-30",
"2025-03-31",
"2025-04-01",
"2025-04-02",
"2025-04-03",
"2025-04-04",
"2025-04-05",
"2025-04-06",
"2025-04-07",
"2025-04-08",
"2025-04-09",
"2025-04-10",
"2025-04-11",
"2025-04-12",
"2025-04-13",
"2025-04-14",
"2025-04-15",
"2025-04-16",
"2025-04-17",
"2025-04-18",
"2025-04-19",
"2025-04-20",
"2025-04-21"
],
"Discount/Diskon": null,
"Gender/Jenis Kelamin": null,
"Growth Rate/Pertumbuhan": null,
"Income/Pendapatan": null,
"Last Purchase Date/Tanggal Pembelian Terakhir": null,
"Latitude/Lintang": null,
"Longitude/Bujur": null,
"Loyalty Score/Skor Loyalitas": null,
"Market Share/Pangsa Pasar": null,
"Price/Harga": null,
"Product/Produk": null,
"Region/Wilayah": null,
"Revenue/Pendapatan": [
2304.28,
2493.72,
2453.26,
2526.34,
2583.4,
2466.69,
2459.28,
2371.86,
2330.1,
2295.46,
2298.81,
2254.89,
2262.37,
2396.75,
2416.79,
2621.84,
2689.07,
2692.74,
2840.85,
2777.33,
2748.62,
2714.11,
2597.21,
2502.07,
2453.04,
2450.5,
2524.94,
2527.63,
2622.4,
2711.12
],
"Sales/Penjualan": null,
"Stock/Stok": null,
"Visitors/Pengunjung": [
106,
245,
235,
147,
193,
212,
194,
273,
272,
234,
157,
94,
286,
253,
263,
233,
272,
170,
59,
96,
159,
122,
230,
131,
281,
80,
88,
174,
198,
264
]
}
|
import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Date/Tanggal': ['2025-03-23', '2025-03-24', '2025-03-25', '2025-03-26', '2025-03-27', '2025-03-28', '2025-03-29', '2025-03-30', '2025-03-31', '2025-04-01', '2025-04-02', '2025-04-03', '2025-04-04', '2025-04-05', '2025-04-06', '2025-04-07', '2025-04-08', '2025-04-09', '2025-04-10', '2025-04-11', '2025-04-12', '2025-04-13', '2025-04-14', '2025-04-15', '2025-04-16', '2025-04-17', '2025-04-18', '2025-04-19', '2025-04-20', '2025-04-21'], 'Revenue/Pendapatan': [2304.28, 2493.72, 2453.26, 2526.34, 2583.4, 2466.69, 2459.28, 2371.86, 2330.1, 2295.46, 2298.81, 2254.89, 2262.37, 2396.75, 2416.79, 2621.84, 2689.07, 2692.74, 2840.85, 2777.33, 2748.62, 2714.11, 2597.21, 2502.07, 2453.04, 2450.5, 2524.94, 2527.63, 2622.4, 2711.12], 'Visitors/Pengunjung': [106, 245, 235, 147, 193, 212, 194, 273, 272, 234, 157, 94, 286, 253, 263, 233, 272, 170, 59, 96, 159, 122, 230, 131, 281, 80, 88, 174, 198, 264], 'Conversion Rate/Rasio Konversi': [0.0495, 0.0205, 0.0885, 0.0495, 0.0144, 0.0974, 0.0875, 0.0775, 0.0847, 0.0115, 0.0669, 0.0868, 0.0612, 0.0978, 0.0407, 0.0226, 0.0172, 0.0726, 0.0582, 0.0264, 0.072, 0.075, 0.0441, 0.0347, 0.0383, 0.0494, 0.0888, 0.0922, 0.0361, 0.043], 'Avg. Order Value/Nilai Pesanan Rata2': [145.37, 81.41, 97.64, 96.1, 157.2, 168.41, 121.35, 149.41, 53.62, 146.14, 111.57, 134.7, 73.83, 68.37, 191.61, 166.85, 70.77, 189.06, 179.58, 71.58, 110.1, 195.89, 104.63, 163.23, 145.02, 196.54, 101.64, 69.59, 82.53, 181.08]})
# Basic data cleaning
df = df.dropna() # Remove missing values
df = df.drop_duplicates() # Remove duplicates
# Correlation analysis
corr_matrix = df.corr(numeric_only=True)
fig = px.imshow(corr_matrix, text_auto=True,
title='Correlation Matrix')
fig.show()
|
Calculate income gender
|
{
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": [
"Electronics/Elektronik",
"Electronics/Elektronik",
"Electronics/Elektronik"
],
"City/Kota": null,
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": null,
"Customer ID/ID Pelanggan": null,
"Date/Tanggal": null,
"Discount/Diskon": [
0.18,
0.13,
0.27
],
"Gender/Jenis Kelamin": null,
"Growth Rate/Pertumbuhan": null,
"Income/Pendapatan": null,
"Last Purchase Date/Tanggal Pembelian Terakhir": null,
"Latitude/Lintang": null,
"Longitude/Bujur": null,
"Loyalty Score/Skor Loyalitas": null,
"Market Share/Pangsa Pasar": null,
"Price/Harga": [
15.14,
173.53,
31.62
],
"Product/Produk": [
"Smartphone_1",
"Laptop_2",
"TV_3"
],
"Region/Wilayah": null,
"Revenue/Pendapatan": null,
"Sales/Penjualan": [
684,
578,
655
],
"Stock/Stok": [
64,
187,
94
],
"Visitors/Pengunjung": null
}
|
import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Product/Produk': ['Smartphone_1', 'Laptop_2', 'TV_3'], 'Sales/Penjualan': [684, 578, 655], 'Price/Harga': [15.14, 173.53, 31.62], 'Category/Kategori': ['Electronics/Elektronik', 'Electronics/Elektronik', 'Electronics/Elektronik'], 'Discount/Diskon': [0.18, 0.13, 0.27], 'Stock/Stok': [64, 187, 94]})
# Basic data cleaning
df = df.dropna() # Remove missing values
df = df.drop_duplicates() # Remove duplicates
# moving average calculation
result = df['Stock/Stok'].rolling(7).mean()
print(result)
|
Cluster loyalty score product
|
{
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": [
90.44,
129.78,
133.4,
69.49,
74.56,
152.69,
91.23,
192.64,
120.65,
169.61,
115.34,
105.25,
76.82,
51.93,
159.19,
60.39,
128.37,
132.5,
64.09,
94.58,
94.43,
54.32,
149.65,
165.13,
54.81,
68.78,
107,
142.14,
132.84,
114.37
],
"Category/Kategori": null,
"City/Kota": null,
"Conversion Rate/Rasio Konversi": [
0.0522,
0.0198,
0.043500000000000004,
0.0281,
0.0589,
0.012700000000000001,
0.012700000000000001,
0.016,
0.0359,
0.038200000000000005,
0.0506,
0.0391,
0.0529,
0.0902,
0.059000000000000004,
0.0558,
0.0187,
0.06720000000000001,
0.0636,
0.046400000000000004,
0.0984,
0.0392,
0.0256,
0.0219,
0.081,
0.016900000000000002,
0.0177,
0.06760000000000001,
0.0728,
0.0533
],
"Country/Negara": null,
"Customer ID/ID Pelanggan": null,
"Date/Tanggal": [
"2025-03-23",
"2025-03-24",
"2025-03-25",
"2025-03-26",
"2025-03-27",
"2025-03-28",
"2025-03-29",
"2025-03-30",
"2025-03-31",
"2025-04-01",
"2025-04-02",
"2025-04-03",
"2025-04-04",
"2025-04-05",
"2025-04-06",
"2025-04-07",
"2025-04-08",
"2025-04-09",
"2025-04-10",
"2025-04-11",
"2025-04-12",
"2025-04-13",
"2025-04-14",
"2025-04-15",
"2025-04-16",
"2025-04-17",
"2025-04-18",
"2025-04-19",
"2025-04-20",
"2025-04-21"
],
"Discount/Diskon": null,
"Gender/Jenis Kelamin": null,
"Growth Rate/Pertumbuhan": null,
"Income/Pendapatan": null,
"Last Purchase Date/Tanggal Pembelian Terakhir": null,
"Latitude/Lintang": null,
"Longitude/Bujur": null,
"Loyalty Score/Skor Loyalitas": null,
"Market Share/Pangsa Pasar": null,
"Price/Harga": null,
"Product/Produk": null,
"Region/Wilayah": null,
"Revenue/Pendapatan": [
1948.25,
2038.86,
2129.02,
2134.4,
2215.89,
2038.39,
2039.82,
2006.97,
1820.48,
1725.53,
1624.36,
1658.23,
1630.05,
1764.08,
1823.3,
1864.84,
1994,
2051.87,
1973.45,
1975.7,
2000.86,
1803.07,
1731.14,
1722.58,
1641.9,
1556.75,
1521.35,
1612.3,
1646.22,
1717.52
],
"Sales/Penjualan": null,
"Stock/Stok": null,
"Visitors/Pengunjung": [
160,
303,
62,
291,
128,
66,
270,
148,
50,
274,
241,
55,
132,
142,
92,
70,
72,
293,
189,
292,
67,
273,
144,
171,
242,
121,
282,
235,
232,
198
]
}
|
import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Date/Tanggal': ['2025-03-23', '2025-03-24', '2025-03-25', '2025-03-26', '2025-03-27', '2025-03-28', '2025-03-29', '2025-03-30', '2025-03-31', '2025-04-01', '2025-04-02', '2025-04-03', '2025-04-04', '2025-04-05', '2025-04-06', '2025-04-07', '2025-04-08', '2025-04-09', '2025-04-10', '2025-04-11', '2025-04-12', '2025-04-13', '2025-04-14', '2025-04-15', '2025-04-16', '2025-04-17', '2025-04-18', '2025-04-19', '2025-04-20', '2025-04-21'], 'Revenue/Pendapatan': [1948.25, 2038.86, 2129.02, 2134.4, 2215.89, 2038.39, 2039.82, 2006.97, 1820.48, 1725.53, 1624.36, 1658.23, 1630.05, 1764.08, 1823.3, 1864.84, 1994.0, 2051.87, 1973.45, 1975.7, 2000.86, 1803.07, 1731.14, 1722.58, 1641.9, 1556.75, 1521.35, 1612.3, 1646.22, 1717.52], 'Visitors/Pengunjung': [160, 303, 62, 291, 128, 66, 270, 148, 50, 274, 241, 55, 132, 142, 92, 70, 72, 293, 189, 292, 67, 273, 144, 171, 242, 121, 282, 235, 232, 198], 'Conversion Rate/Rasio Konversi': [0.0522, 0.0198, 0.0435, 0.0281, 0.0589, 0.0127, 0.0127, 0.016, 0.0359, 0.0382, 0.0506, 0.0391, 0.0529, 0.0902, 0.059, 0.0558, 0.0187, 0.0672, 0.0636, 0.0464, 0.0984, 0.0392, 0.0256, 0.0219, 0.081, 0.0169, 0.0177, 0.0676, 0.0728, 0.0533], 'Avg. Order Value/Nilai Pesanan Rata2': [90.44, 129.78, 133.4, 69.49, 74.56, 152.69, 91.23, 192.64, 120.65, 169.61, 115.34, 105.25, 76.82, 51.93, 159.19, 60.39, 128.37, 132.5, 64.09, 94.58, 94.43, 54.32, 149.65, 165.13, 54.81, 68.78, 107.0, 142.14, 132.84, 114.37]})
# Basic data cleaning
df = df.dropna() # Remove missing values
df = df.drop_duplicates() # Remove duplicates
# Comprehensive data analysis
analysis_result = df.describe(include='all')
print(analysis_result)
# Additional insights
print('\nTop performing items:')
print(df.nlargest(3, df.columns[1]))
print('\nTrend analysis:')
if 'Date/Tanggal' in df.columns:
df['Month/Bulan'] = pd.to_datetime(df['Date/Tanggal']).dt.month
monthly_trend = df.groupby('Month/Bulan').mean(numeric_only=True)
print(monthly_trend)
|
Correlate average order value and/dan price
|
{
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": [
"Food/Makanan",
"Food/Makanan",
"Food/Makanan"
],
"City/Kota": null,
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": null,
"Customer ID/ID Pelanggan": null,
"Date/Tanggal": null,
"Discount/Diskon": [
0.22,
0.21,
0.22
],
"Gender/Jenis Kelamin": null,
"Growth Rate/Pertumbuhan": null,
"Income/Pendapatan": null,
"Last Purchase Date/Tanggal Pembelian Terakhir": null,
"Latitude/Lintang": null,
"Longitude/Bujur": null,
"Loyalty Score/Skor Loyalitas": null,
"Market Share/Pangsa Pasar": null,
"Price/Harga": [
92.95,
46.68,
162.54
],
"Product/Produk": [
"Fruits_1",
"Snacks_2",
"Dairy_3"
],
"Region/Wilayah": null,
"Revenue/Pendapatan": null,
"Sales/Penjualan": [
503,
130,
660
],
"Stock/Stok": [
131,
74,
193
],
"Visitors/Pengunjung": null
}
|
import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Product/Produk': ['Fruits_1', 'Snacks_2', 'Dairy_3'], 'Sales/Penjualan': [503, 130, 660], 'Price/Harga': [92.95, 46.68, 162.54], 'Category/Kategori': ['Food/Makanan', 'Food/Makanan', 'Food/Makanan'], 'Discount/Diskon': [0.22, 0.21, 0.22], 'Stock/Stok': [131, 74, 193]})
# Basic data cleaning
df = df.dropna() # Remove missing values
df = df.drop_duplicates() # Remove duplicates
# Correlation analysis
corr_matrix = df.corr(numeric_only=True)
fig = px.imshow(corr_matrix, text_auto=True,
title='Correlation Matrix')
fig.show()
|
Identify loyalty score gender
|
{
"Age/Usia": [
19,
66,
22,
33,
61,
58,
26,
27,
67,
35,
39,
37,
46,
39,
61,
63,
66,
24,
25,
43,
69,
24,
29,
30,
65,
70,
45,
21,
51,
70,
27,
50,
57,
36,
68,
24,
41,
19,
56,
43,
23,
36,
64,
56,
32,
50,
70,
29,
46,
40,
29,
36,
21,
51,
56,
43,
26,
18,
38,
70,
39,
34
],
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": null,
"City/Kota": null,
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": null,
"Customer ID/ID Pelanggan": [
"CUST_0001",
"CUST_0002",
"CUST_0003",
"CUST_0004",
"CUST_0005",
"CUST_0006",
"CUST_0007",
"CUST_0008",
"CUST_0009",
"CUST_0010",
"CUST_0011",
"CUST_0012",
"CUST_0013",
"CUST_0014",
"CUST_0015",
"CUST_0016",
"CUST_0017",
"CUST_0018",
"CUST_0019",
"CUST_0020",
"CUST_0021",
"CUST_0022",
"CUST_0023",
"CUST_0024",
"CUST_0025",
"CUST_0026",
"CUST_0027",
"CUST_0028",
"CUST_0029",
"CUST_0030",
"CUST_0031",
"CUST_0032",
"CUST_0033",
"CUST_0034",
"CUST_0035",
"CUST_0036",
"CUST_0037",
"CUST_0038",
"CUST_0039",
"CUST_0040",
"CUST_0041",
"CUST_0042",
"CUST_0043",
"CUST_0044",
"CUST_0045",
"CUST_0046",
"CUST_0047",
"CUST_0048",
"CUST_0049",
"CUST_0050",
"CUST_0051",
"CUST_0052",
"CUST_0053",
"CUST_0054",
"CUST_0055",
"CUST_0056",
"CUST_0057",
"CUST_0058",
"CUST_0059",
"CUST_0060",
"CUST_0061",
"CUST_0062"
],
"Date/Tanggal": null,
"Discount/Diskon": null,
"Gender/Jenis Kelamin": [
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Other/Lainnya",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Other/Lainnya",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan"
],
"Growth Rate/Pertumbuhan": null,
"Income/Pendapatan": [
9000,
7000,
5000,
1000,
1000,
5000,
8000,
8000,
6000,
9000,
4000,
1000,
6000,
4000,
5000,
4000,
2000,
7000,
1000,
9000,
7000,
9000,
7000,
8000,
5000,
7000,
1000,
3000,
10000,
9000,
6000,
9000,
7000,
6000,
5000,
6000,
4000,
2000,
5000,
3000,
1000,
7000,
6000,
10000,
1000,
5000,
10000,
6000,
4000,
6000,
7000,
9000,
6000,
2000,
4000,
7000,
2000,
4000,
8000,
6000,
9000,
3000
],
"Last Purchase Date/Tanggal Pembelian Terakhir": [
"2024-09-23",
"2024-07-05",
"2024-08-28",
"2024-05-21",
"2024-05-26",
"2025-04-10",
"2024-11-17",
"2024-07-15",
"2024-08-27",
"2024-06-01",
"2025-03-12",
"2025-01-11",
"2024-06-24",
"2025-01-13",
"2024-05-26",
"2025-02-28",
"2024-12-25",
"2024-05-06",
"2024-08-13",
"2025-04-04",
"2024-09-13",
"2024-08-19",
"2024-11-05",
"2024-08-06",
"2024-08-23",
"2024-12-08",
"2024-08-04",
"2025-04-19",
"2024-09-07",
"2025-03-24",
"2024-11-14",
"2024-06-07",
"2025-01-01",
"2024-11-07",
"2025-04-09",
"2024-07-11",
"2024-05-10",
"2024-11-22",
"2024-12-16",
"2025-04-06",
"2024-08-28",
"2024-09-13",
"2025-01-30",
"2024-07-11",
"2024-12-13",
"2024-09-12",
"2024-10-05",
"2024-07-20",
"2025-01-08",
"2024-10-12",
"2024-10-16",
"2025-01-07",
"2024-09-10",
"2024-09-29",
"2024-06-03",
"2025-03-08",
"2024-11-18",
"2025-03-05",
"2024-05-05",
"2024-08-14",
"2024-05-13",
"2024-10-16"
],
"Latitude/Lintang": null,
"Longitude/Bujur": null,
"Loyalty Score/Skor Loyalitas": [
40,
42,
14,
72,
59,
11,
84,
78,
88,
97,
64,
90,
69,
85,
35,
76,
61,
69,
3,
24,
61,
87,
12,
79,
6,
28,
64,
5,
25,
4,
92,
26,
28,
61,
58,
30,
43,
56,
99,
100,
17,
21,
32,
40,
85,
87,
30,
3,
60,
90,
35,
78,
46,
44,
53,
6,
43,
86,
12,
73,
52,
14
],
"Market Share/Pangsa Pasar": null,
"Price/Harga": null,
"Product/Produk": null,
"Region/Wilayah": null,
"Revenue/Pendapatan": null,
"Sales/Penjualan": null,
"Stock/Stok": null,
"Visitors/Pengunjung": null
}
|
import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Customer ID/ID Pelanggan': ['CUST_0001', 'CUST_0002', 'CUST_0003', 'CUST_0004', 'CUST_0005', 'CUST_0006', 'CUST_0007', 'CUST_0008', 'CUST_0009', 'CUST_0010', 'CUST_0011', 'CUST_0012', 'CUST_0013', 'CUST_0014', 'CUST_0015', 'CUST_0016', 'CUST_0017', 'CUST_0018', 'CUST_0019', 'CUST_0020', 'CUST_0021', 'CUST_0022', 'CUST_0023', 'CUST_0024', 'CUST_0025', 'CUST_0026', 'CUST_0027', 'CUST_0028', 'CUST_0029', 'CUST_0030', 'CUST_0031', 'CUST_0032', 'CUST_0033', 'CUST_0034', 'CUST_0035', 'CUST_0036', 'CUST_0037', 'CUST_0038', 'CUST_0039', 'CUST_0040', 'CUST_0041', 'CUST_0042', 'CUST_0043', 'CUST_0044', 'CUST_0045', 'CUST_0046', 'CUST_0047', 'CUST_0048', 'CUST_0049', 'CUST_0050', 'CUST_0051', 'CUST_0052', 'CUST_0053', 'CUST_0054', 'CUST_0055', 'CUST_0056', 'CUST_0057', 'CUST_0058', 'CUST_0059', 'CUST_0060', 'CUST_0061', 'CUST_0062'], 'Age/Usia': [19, 66, 22, 33, 61, 58, 26, 27, 67, 35, 39, 37, 46, 39, 61, 63, 66, 24, 25, 43, 69, 24, 29, 30, 65, 70, 45, 21, 51, 70, 27, 50, 57, 36, 68, 24, 41, 19, 56, 43, 23, 36, 64, 56, 32, 50, 70, 29, 46, 40, 29, 36, 21, 51, 56, 43, 26, 18, 38, 70, 39, 34], 'Gender/Jenis Kelamin': ['Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Other/Lainnya', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Other/Lainnya', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan'], 'Income/Pendapatan': [9000, 7000, 5000, 1000, 1000, 5000, 8000, 8000, 6000, 9000, 4000, 1000, 6000, 4000, 5000, 4000, 2000, 7000, 1000, 9000, 7000, 9000, 7000, 8000, 5000, 7000, 1000, 3000, 10000, 9000, 6000, 9000, 7000, 6000, 5000, 6000, 4000, 2000, 5000, 3000, 1000, 7000, 6000, 10000, 1000, 5000, 10000, 6000, 4000, 6000, 7000, 9000, 6000, 2000, 4000, 7000, 2000, 4000, 8000, 6000, 9000, 3000], 'Loyalty Score/Skor Loyalitas': [40, 42, 14, 72, 59, 11, 84, 78, 88, 97, 64, 90, 69, 85, 35, 76, 61, 69, 3, 24, 61, 87, 12, 79, 6, 28, 64, 5, 25, 4, 92, 26, 28, 61, 58, 30, 43, 56, 99, 100, 17, 21, 32, 40, 85, 87, 30, 3, 60, 90, 35, 78, 46, 44, 53, 6, 43, 86, 12, 73, 52, 14], 'Last Purchase Date/Tanggal Pembelian Terakhir': ['2024-09-23', '2024-07-05', '2024-08-28', '2024-05-21', '2024-05-26', '2025-04-10', '2024-11-17', '2024-07-15', '2024-08-27', '2024-06-01', '2025-03-12', '2025-01-11', '2024-06-24', '2025-01-13', '2024-05-26', '2025-02-28', '2024-12-25', '2024-05-06', '2024-08-13', '2025-04-04', '2024-09-13', '2024-08-19', '2024-11-05', '2024-08-06', '2024-08-23', '2024-12-08', '2024-08-04', '2025-04-19', '2024-09-07', '2025-03-24', '2024-11-14', '2024-06-07', '2025-01-01', '2024-11-07', '2025-04-09', '2024-07-11', '2024-05-10', '2024-11-22', '2024-12-16', '2025-04-06', '2024-08-28', '2024-09-13', '2025-01-30', '2024-07-11', '2024-12-13', '2024-09-12', '2024-10-05', '2024-07-20', '2025-01-08', '2024-10-12', '2024-10-16', '2025-01-07', '2024-09-10', '2024-09-29', '2024-06-03', '2025-03-08', '2024-11-18', '2025-03-05', '2024-05-05', '2024-08-14', '2024-05-13', '2024-10-16']})
# Basic data cleaning
df = df.dropna() # Remove missing values
df = df.drop_duplicates() # Remove duplicates
# Comprehensive data analysis
analysis_result = df.describe(include='all')
print(analysis_result)
# Additional insights
print('\nTop performing items:')
print(df.nlargest(3, df.columns[1]))
print('\nTrend analysis:')
if 'Date/Tanggal' in df.columns:
df['Month/Bulan'] = pd.to_datetime(df['Date/Tanggal']).dt.month
monthly_trend = df.groupby('Month/Bulan').mean(numeric_only=True)
print(monthly_trend)
|
Correlate sales and/dan discount
|
{
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": [
141.77,
115.13,
78.61,
73.1,
107.99,
150.88,
190.91,
160.75,
55.41,
86.53,
69.24,
131.66,
163.5,
94.94,
107.2,
64.27,
53.4,
68.59,
113.06,
91.56,
179.34,
66.58,
186.36,
103.43,
95.23,
181.17,
142.07,
184.7,
67.76,
103.29
],
"Category/Kategori": null,
"City/Kota": null,
"Conversion Rate/Rasio Konversi": [
0.0752,
0.09680000000000001,
0.023,
0.0766,
0.0146,
0.0429,
0.019200000000000002,
0.0632,
0.0848,
0.043000000000000003,
0.0526,
0.0543,
0.033100000000000004,
0.0877,
0.0325,
0.09680000000000001,
0.0287,
0.0252,
0.0316,
0.0546,
0.0155,
0.010100000000000001,
0.0357,
0.0391,
0.0978,
0.0391,
0.027,
0.030100000000000002,
0.0888,
0.057600000000000005
],
"Country/Negara": null,
"Customer ID/ID Pelanggan": null,
"Date/Tanggal": [
"2025-03-23",
"2025-03-24",
"2025-03-25",
"2025-03-26",
"2025-03-27",
"2025-03-28",
"2025-03-29",
"2025-03-30",
"2025-03-31",
"2025-04-01",
"2025-04-02",
"2025-04-03",
"2025-04-04",
"2025-04-05",
"2025-04-06",
"2025-04-07",
"2025-04-08",
"2025-04-09",
"2025-04-10",
"2025-04-11",
"2025-04-12",
"2025-04-13",
"2025-04-14",
"2025-04-15",
"2025-04-16",
"2025-04-17",
"2025-04-18",
"2025-04-19",
"2025-04-20",
"2025-04-21"
],
"Discount/Diskon": null,
"Gender/Jenis Kelamin": null,
"Growth Rate/Pertumbuhan": null,
"Income/Pendapatan": null,
"Last Purchase Date/Tanggal Pembelian Terakhir": null,
"Latitude/Lintang": null,
"Longitude/Bujur": null,
"Loyalty Score/Skor Loyalitas": null,
"Market Share/Pangsa Pasar": null,
"Price/Harga": null,
"Product/Produk": null,
"Region/Wilayah": null,
"Revenue/Pendapatan": [
3453.91,
3519.63,
3606.95,
3673.96,
3542.69,
3491.05,
3495.79,
3386.47,
3235.69,
3136.07,
3084.5,
3111.97,
3099.52,
3184.16,
3189.64,
3245.05,
3365.82,
3324.45,
3455.62,
3448.92,
3299.51,
3306.85,
3135.38,
3091.41,
2955.01,
2918.55,
2914.92,
2977.47,
2993.09,
3026.78
],
"Sales/Penjualan": null,
"Stock/Stok": null,
"Visitors/Pengunjung": [
253,
165,
267,
156,
95,
145,
198,
194,
270,
214,
187,
112,
249,
282,
145,
165,
110,
129,
187,
116,
130,
98,
119,
274,
175,
228,
106,
266,
208,
249
]
}
|
import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Date/Tanggal': ['2025-03-23', '2025-03-24', '2025-03-25', '2025-03-26', '2025-03-27', '2025-03-28', '2025-03-29', '2025-03-30', '2025-03-31', '2025-04-01', '2025-04-02', '2025-04-03', '2025-04-04', '2025-04-05', '2025-04-06', '2025-04-07', '2025-04-08', '2025-04-09', '2025-04-10', '2025-04-11', '2025-04-12', '2025-04-13', '2025-04-14', '2025-04-15', '2025-04-16', '2025-04-17', '2025-04-18', '2025-04-19', '2025-04-20', '2025-04-21'], 'Revenue/Pendapatan': [3453.91, 3519.63, 3606.95, 3673.96, 3542.69, 3491.05, 3495.79, 3386.47, 3235.69, 3136.07, 3084.5, 3111.97, 3099.52, 3184.16, 3189.64, 3245.05, 3365.82, 3324.45, 3455.62, 3448.92, 3299.51, 3306.85, 3135.38, 3091.41, 2955.01, 2918.55, 2914.92, 2977.47, 2993.09, 3026.78], 'Visitors/Pengunjung': [253, 165, 267, 156, 95, 145, 198, 194, 270, 214, 187, 112, 249, 282, 145, 165, 110, 129, 187, 116, 130, 98, 119, 274, 175, 228, 106, 266, 208, 249], 'Conversion Rate/Rasio Konversi': [0.0752, 0.0968, 0.023, 0.0766, 0.0146, 0.0429, 0.0192, 0.0632, 0.0848, 0.043, 0.0526, 0.0543, 0.0331, 0.0877, 0.0325, 0.0968, 0.0287, 0.0252, 0.0316, 0.0546, 0.0155, 0.0101, 0.0357, 0.0391, 0.0978, 0.0391, 0.027, 0.0301, 0.0888, 0.0576], 'Avg. Order Value/Nilai Pesanan Rata2': [141.77, 115.13, 78.61, 73.1, 107.99, 150.88, 190.91, 160.75, 55.41, 86.53, 69.24, 131.66, 163.5, 94.94, 107.2, 64.27, 53.4, 68.59, 113.06, 91.56, 179.34, 66.58, 186.36, 103.43, 95.23, 181.17, 142.07, 184.7, 67.76, 103.29]})
# Basic data cleaning
df = df.dropna() # Remove missing values
df = df.drop_duplicates() # Remove duplicates
# Correlation analysis
corr_matrix = df.corr(numeric_only=True)
fig = px.imshow(corr_matrix, text_auto=True,
title='Correlation Matrix')
fig.show()
|
Analisis rasio konversi segmen pelanggan
|
{
"Age/Usia": [
51,
26,
18,
20,
53,
54,
35,
52,
27,
55,
31,
25,
41,
34,
46,
41,
54,
56,
66,
46,
36,
54,
56,
49,
50,
31,
57,
45,
43,
44,
32,
47,
27,
20,
65,
23,
54,
54,
21,
56,
30,
29,
18,
27,
60,
38,
61,
55,
49,
51,
62,
70,
62,
46,
39,
48,
41,
47,
32,
38,
64,
48,
57,
44,
66,
50,
62,
49,
48,
32,
57,
26,
67,
50,
58,
30,
65,
66,
38,
32,
37,
48,
45,
51,
26,
63,
37,
58,
36,
21,
30,
66,
55,
38
],
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": null,
"City/Kota": null,
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": null,
"Customer ID/ID Pelanggan": [
"CUST_0001",
"CUST_0002",
"CUST_0003",
"CUST_0004",
"CUST_0005",
"CUST_0006",
"CUST_0007",
"CUST_0008",
"CUST_0009",
"CUST_0010",
"CUST_0011",
"CUST_0012",
"CUST_0013",
"CUST_0014",
"CUST_0015",
"CUST_0016",
"CUST_0017",
"CUST_0018",
"CUST_0019",
"CUST_0020",
"CUST_0021",
"CUST_0022",
"CUST_0023",
"CUST_0024",
"CUST_0025",
"CUST_0026",
"CUST_0027",
"CUST_0028",
"CUST_0029",
"CUST_0030",
"CUST_0031",
"CUST_0032",
"CUST_0033",
"CUST_0034",
"CUST_0035",
"CUST_0036",
"CUST_0037",
"CUST_0038",
"CUST_0039",
"CUST_0040",
"CUST_0041",
"CUST_0042",
"CUST_0043",
"CUST_0044",
"CUST_0045",
"CUST_0046",
"CUST_0047",
"CUST_0048",
"CUST_0049",
"CUST_0050",
"CUST_0051",
"CUST_0052",
"CUST_0053",
"CUST_0054",
"CUST_0055",
"CUST_0056",
"CUST_0057",
"CUST_0058",
"CUST_0059",
"CUST_0060",
"CUST_0061",
"CUST_0062",
"CUST_0063",
"CUST_0064",
"CUST_0065",
"CUST_0066",
"CUST_0067",
"CUST_0068",
"CUST_0069",
"CUST_0070",
"CUST_0071",
"CUST_0072",
"CUST_0073",
"CUST_0074",
"CUST_0075",
"CUST_0076",
"CUST_0077",
"CUST_0078",
"CUST_0079",
"CUST_0080",
"CUST_0081",
"CUST_0082",
"CUST_0083",
"CUST_0084",
"CUST_0085",
"CUST_0086",
"CUST_0087",
"CUST_0088",
"CUST_0089",
"CUST_0090",
"CUST_0091",
"CUST_0092",
"CUST_0093",
"CUST_0094"
],
"Date/Tanggal": null,
"Discount/Diskon": null,
"Gender/Jenis Kelamin": [
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Other/Lainnya",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Other/Lainnya",
"Other/Lainnya",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Other/Lainnya",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Other/Lainnya",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Other/Lainnya",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan"
],
"Growth Rate/Pertumbuhan": null,
"Income/Pendapatan": [
6000,
8000,
3000,
2000,
5000,
2000,
9000,
7000,
8000,
5000,
2000,
3000,
9000,
9000,
2000,
9000,
6000,
3000,
9000,
8000,
7000,
6000,
9000,
2000,
1000,
2000,
3000,
2000,
1000,
6000,
9000,
2000,
7000,
9000,
8000,
7000,
8000,
8000,
4000,
2000,
7000,
2000,
8000,
7000,
5000,
2000,
8000,
3000,
5000,
5000,
7000,
6000,
7000,
9000,
7000,
5000,
9000,
7000,
9000,
9000,
1000,
1000,
7000,
4000,
1000,
5000,
7000,
4000,
1000,
8000,
5000,
8000,
1000,
8000,
3000,
6000,
1000,
2000,
6000,
8000,
5000,
9000,
3000,
7000,
4000,
8000,
3000,
7000,
9000,
7000,
8000,
7000,
4000,
4000
],
"Last Purchase Date/Tanggal Pembelian Terakhir": [
"2024-09-15",
"2024-10-07",
"2024-08-01",
"2024-07-15",
"2024-09-11",
"2024-09-10",
"2024-05-15",
"2024-09-25",
"2024-12-20",
"2024-08-28",
"2024-05-21",
"2025-01-10",
"2025-04-10",
"2025-02-25",
"2025-02-13",
"2025-02-25",
"2024-10-26",
"2025-01-08",
"2025-04-13",
"2024-07-15",
"2024-05-16",
"2024-04-22",
"2025-01-07",
"2024-09-18",
"2024-09-16",
"2024-06-04",
"2024-09-20",
"2024-08-21",
"2025-04-03",
"2024-06-24",
"2024-07-02",
"2024-07-11",
"2024-08-20",
"2024-12-26",
"2025-04-09",
"2024-11-10",
"2025-01-17",
"2025-03-19",
"2024-09-23",
"2024-09-20",
"2024-06-07",
"2024-09-02",
"2024-09-04",
"2024-10-25",
"2024-07-29",
"2024-12-26",
"2024-07-23",
"2024-08-11",
"2025-03-31",
"2024-11-02",
"2024-10-06",
"2025-03-05",
"2024-10-25",
"2025-01-08",
"2024-05-24",
"2024-10-15",
"2024-08-07",
"2024-10-13",
"2025-02-17",
"2024-08-05",
"2024-10-03",
"2024-12-19",
"2024-08-31",
"2024-05-18",
"2025-01-25",
"2024-07-29",
"2024-08-25",
"2024-10-19",
"2024-07-15",
"2024-07-16",
"2024-04-26",
"2024-08-31",
"2024-05-04",
"2024-06-13",
"2024-12-04",
"2025-04-01",
"2024-12-29",
"2024-10-02",
"2024-09-04",
"2025-01-27",
"2025-04-09",
"2024-11-02",
"2024-05-21",
"2024-06-11",
"2024-09-21",
"2024-09-27",
"2025-02-09",
"2025-03-05",
"2025-02-15",
"2024-08-06",
"2025-01-09",
"2025-03-12",
"2024-09-07",
"2025-03-18"
],
"Latitude/Lintang": null,
"Longitude/Bujur": null,
"Loyalty Score/Skor Loyalitas": [
48,
100,
80,
25,
13,
59,
8,
92,
70,
100,
70,
82,
55,
94,
19,
29,
92,
53,
2,
63,
35,
37,
70,
86,
26,
92,
5,
96,
90,
37,
62,
64,
62,
71,
94,
93,
61,
77,
63,
49,
15,
95,
74,
19,
63,
56,
40,
45,
52,
7,
64,
99,
19,
45,
66,
3,
67,
22,
2,
64,
69,
100,
28,
91,
96,
73,
29,
81,
44,
47,
48,
89,
27,
28,
90,
30,
94,
84,
45,
88,
39,
99,
90,
37,
99,
97,
52,
75,
83,
43,
3,
52,
27,
22
],
"Market Share/Pangsa Pasar": null,
"Price/Harga": null,
"Product/Produk": null,
"Region/Wilayah": null,
"Revenue/Pendapatan": null,
"Sales/Penjualan": null,
"Stock/Stok": null,
"Visitors/Pengunjung": null
}
|
import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Customer ID/ID Pelanggan': ['CUST_0001', 'CUST_0002', 'CUST_0003', 'CUST_0004', 'CUST_0005', 'CUST_0006', 'CUST_0007', 'CUST_0008', 'CUST_0009', 'CUST_0010', 'CUST_0011', 'CUST_0012', 'CUST_0013', 'CUST_0014', 'CUST_0015', 'CUST_0016', 'CUST_0017', 'CUST_0018', 'CUST_0019', 'CUST_0020', 'CUST_0021', 'CUST_0022', 'CUST_0023', 'CUST_0024', 'CUST_0025', 'CUST_0026', 'CUST_0027', 'CUST_0028', 'CUST_0029', 'CUST_0030', 'CUST_0031', 'CUST_0032', 'CUST_0033', 'CUST_0034', 'CUST_0035', 'CUST_0036', 'CUST_0037', 'CUST_0038', 'CUST_0039', 'CUST_0040', 'CUST_0041', 'CUST_0042', 'CUST_0043', 'CUST_0044', 'CUST_0045', 'CUST_0046', 'CUST_0047', 'CUST_0048', 'CUST_0049', 'CUST_0050', 'CUST_0051', 'CUST_0052', 'CUST_0053', 'CUST_0054', 'CUST_0055', 'CUST_0056', 'CUST_0057', 'CUST_0058', 'CUST_0059', 'CUST_0060', 'CUST_0061', 'CUST_0062', 'CUST_0063', 'CUST_0064', 'CUST_0065', 'CUST_0066', 'CUST_0067', 'CUST_0068', 'CUST_0069', 'CUST_0070', 'CUST_0071', 'CUST_0072', 'CUST_0073', 'CUST_0074', 'CUST_0075', 'CUST_0076', 'CUST_0077', 'CUST_0078', 'CUST_0079', 'CUST_0080', 'CUST_0081', 'CUST_0082', 'CUST_0083', 'CUST_0084', 'CUST_0085', 'CUST_0086', 'CUST_0087', 'CUST_0088', 'CUST_0089', 'CUST_0090', 'CUST_0091', 'CUST_0092', 'CUST_0093', 'CUST_0094'], 'Age/Usia': [51, 26, 18, 20, 53, 54, 35, 52, 27, 55, 31, 25, 41, 34, 46, 41, 54, 56, 66, 46, 36, 54, 56, 49, 50, 31, 57, 45, 43, 44, 32, 47, 27, 20, 65, 23, 54, 54, 21, 56, 30, 29, 18, 27, 60, 38, 61, 55, 49, 51, 62, 70, 62, 46, 39, 48, 41, 47, 32, 38, 64, 48, 57, 44, 66, 50, 62, 49, 48, 32, 57, 26, 67, 50, 58, 30, 65, 66, 38, 32, 37, 48, 45, 51, 26, 63, 37, 58, 36, 21, 30, 66, 55, 38], 'Gender/Jenis Kelamin': ['Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Other/Lainnya', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Other/Lainnya', 'Other/Lainnya', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Other/Lainnya', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Other/Lainnya', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Other/Lainnya', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan'], 'Income/Pendapatan': [6000, 8000, 3000, 2000, 5000, 2000, 9000, 7000, 8000, 5000, 2000, 3000, 9000, 9000, 2000, 9000, 6000, 3000, 9000, 8000, 7000, 6000, 9000, 2000, 1000, 2000, 3000, 2000, 1000, 6000, 9000, 2000, 7000, 9000, 8000, 7000, 8000, 8000, 4000, 2000, 7000, 2000, 8000, 7000, 5000, 2000, 8000, 3000, 5000, 5000, 7000, 6000, 7000, 9000, 7000, 5000, 9000, 7000, 9000, 9000, 1000, 1000, 7000, 4000, 1000, 5000, 7000, 4000, 1000, 8000, 5000, 8000, 1000, 8000, 3000, 6000, 1000, 2000, 6000, 8000, 5000, 9000, 3000, 7000, 4000, 8000, 3000, 7000, 9000, 7000, 8000, 7000, 4000, 4000], 'Loyalty Score/Skor Loyalitas': [48, 100, 80, 25, 13, 59, 8, 92, 70, 100, 70, 82, 55, 94, 19, 29, 92, 53, 2, 63, 35, 37, 70, 86, 26, 92, 5, 96, 90, 37, 62, 64, 62, 71, 94, 93, 61, 77, 63, 49, 15, 95, 74, 19, 63, 56, 40, 45, 52, 7, 64, 99, 19, 45, 66, 3, 67, 22, 2, 64, 69, 100, 28, 91, 96, 73, 29, 81, 44, 47, 48, 89, 27, 28, 90, 30, 94, 84, 45, 88, 39, 99, 90, 37, 99, 97, 52, 75, 83, 43, 3, 52, 27, 22], 'Last Purchase Date/Tanggal Pembelian Terakhir': ['2024-09-15', '2024-10-07', '2024-08-01', '2024-07-15', '2024-09-11', '2024-09-10', '2024-05-15', '2024-09-25', '2024-12-20', '2024-08-28', '2024-05-21', '2025-01-10', '2025-04-10', '2025-02-25', '2025-02-13', '2025-02-25', '2024-10-26', '2025-01-08', '2025-04-13', '2024-07-15', '2024-05-16', '2024-04-22', '2025-01-07', '2024-09-18', '2024-09-16', '2024-06-04', '2024-09-20', '2024-08-21', '2025-04-03', '2024-06-24', '2024-07-02', '2024-07-11', '2024-08-20', '2024-12-26', '2025-04-09', '2024-11-10', '2025-01-17', '2025-03-19', '2024-09-23', '2024-09-20', '2024-06-07', '2024-09-02', '2024-09-04', '2024-10-25', '2024-07-29', '2024-12-26', '2024-07-23', '2024-08-11', '2025-03-31', '2024-11-02', '2024-10-06', '2025-03-05', '2024-10-25', '2025-01-08', '2024-05-24', '2024-10-15', '2024-08-07', '2024-10-13', '2025-02-17', '2024-08-05', '2024-10-03', '2024-12-19', '2024-08-31', '2024-05-18', '2025-01-25', '2024-07-29', '2024-08-25', '2024-10-19', '2024-07-15', '2024-07-16', '2024-04-26', '2024-08-31', '2024-05-04', '2024-06-13', '2024-12-04', '2025-04-01', '2024-12-29', '2024-10-02', '2024-09-04', '2025-01-27', '2025-04-09', '2024-11-02', '2024-05-21', '2024-06-11', '2024-09-21', '2024-09-27', '2025-02-09', '2025-03-05', '2025-02-15', '2024-08-06', '2025-01-09', '2025-03-12', '2024-09-07', '2025-03-18']})
# Basic data cleaning
df = df.dropna() # Remove missing values
df = df.drop_duplicates() # Remove duplicates
# Comprehensive data analysis
analysis_result = df.describe(include='all')
print(analysis_result)
# Additional insights
print('\nTop performing items:')
print(df.nlargest(3, df.columns[1]))
print('\nTrend analysis:')
if 'Date/Tanggal' in df.columns:
df['Month/Bulan'] = pd.to_datetime(df['Date/Tanggal']).dt.month
monthly_trend = df.groupby('Month/Bulan').mean(numeric_only=True)
print(monthly_trend)
|
Bandingkan pendapatan kategori
|
{
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": [
"Food/Makanan",
"Food/Makanan",
"Food/Makanan"
],
"City/Kota": null,
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": null,
"Customer ID/ID Pelanggan": null,
"Date/Tanggal": null,
"Discount/Diskon": [
0.13,
0.11,
0.08
],
"Gender/Jenis Kelamin": null,
"Growth Rate/Pertumbuhan": null,
"Income/Pendapatan": null,
"Last Purchase Date/Tanggal Pembelian Terakhir": null,
"Latitude/Lintang": null,
"Longitude/Bujur": null,
"Loyalty Score/Skor Loyalitas": null,
"Market Share/Pangsa Pasar": null,
"Price/Harga": [
193.64,
30.01,
25.58
],
"Product/Produk": [
"Snacks_1",
"Beverages_2",
"Dairy_3"
],
"Region/Wilayah": null,
"Revenue/Pendapatan": null,
"Sales/Penjualan": [
291,
161,
981
],
"Stock/Stok": [
190,
125,
25
],
"Visitors/Pengunjung": null
}
|
import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Product/Produk': ['Snacks_1', 'Beverages_2', 'Dairy_3'], 'Sales/Penjualan': [291, 161, 981], 'Price/Harga': [193.64, 30.01, 25.58], 'Category/Kategori': ['Food/Makanan', 'Food/Makanan', 'Food/Makanan'], 'Discount/Diskon': [0.13, 0.11, 0.08], 'Stock/Stok': [190, 125, 25]})
# Basic data cleaning
df = df.dropna() # Remove missing values
df = df.drop_duplicates() # Remove duplicates
# Comprehensive data analysis
analysis_result = df.describe(include='all')
print(analysis_result)
# Additional insights
print('\nTop performing items:')
print(df.nlargest(3, df.columns[1]))
print('\nTrend analysis:')
if 'Date/Tanggal' in df.columns:
df['Month/Bulan'] = pd.to_datetime(df['Date/Tanggal']).dt.month
monthly_trend = df.groupby('Month/Bulan').mean(numeric_only=True)
print(monthly_trend)
|
Analyze income gender
|
{
"Age/Usia": [
62,
56,
50,
63,
22,
36,
63,
45,
55,
66,
45,
59,
34,
58,
63,
61,
20,
33,
55,
57,
43,
52,
41,
56,
20,
53,
50,
31,
42,
60,
48,
57,
70,
51,
49,
47,
35,
56,
30,
57,
52,
59,
67,
18,
44,
69,
61,
53,
57,
23,
30
],
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": null,
"City/Kota": null,
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": null,
"Customer ID/ID Pelanggan": [
"CUST_0001",
"CUST_0002",
"CUST_0003",
"CUST_0004",
"CUST_0005",
"CUST_0006",
"CUST_0007",
"CUST_0008",
"CUST_0009",
"CUST_0010",
"CUST_0011",
"CUST_0012",
"CUST_0013",
"CUST_0014",
"CUST_0015",
"CUST_0016",
"CUST_0017",
"CUST_0018",
"CUST_0019",
"CUST_0020",
"CUST_0021",
"CUST_0022",
"CUST_0023",
"CUST_0024",
"CUST_0025",
"CUST_0026",
"CUST_0027",
"CUST_0028",
"CUST_0029",
"CUST_0030",
"CUST_0031",
"CUST_0032",
"CUST_0033",
"CUST_0034",
"CUST_0035",
"CUST_0036",
"CUST_0037",
"CUST_0038",
"CUST_0039",
"CUST_0040",
"CUST_0041",
"CUST_0042",
"CUST_0043",
"CUST_0044",
"CUST_0045",
"CUST_0046",
"CUST_0047",
"CUST_0048",
"CUST_0049",
"CUST_0050",
"CUST_0051"
],
"Date/Tanggal": null,
"Discount/Diskon": null,
"Gender/Jenis Kelamin": [
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Other/Lainnya",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Other/Lainnya",
"Female/Perempuan",
"Other/Lainnya",
"Male/Laki-laki",
"Male/Laki-laki",
"Other/Lainnya",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki"
],
"Growth Rate/Pertumbuhan": null,
"Income/Pendapatan": [
4000,
5000,
5000,
3000,
8000,
3000,
6000,
4000,
8000,
3000,
7000,
7000,
8000,
6000,
5000,
6000,
3000,
9000,
5000,
5000,
7000,
1000,
8000,
10000,
4000,
8000,
5000,
3000,
6000,
7000,
6000,
9000,
7000,
8000,
1000,
7000,
2000,
9000,
10000,
9000,
1000,
5000,
5000,
1000,
9000,
8000,
1000,
4000,
6000,
2000,
6000
],
"Last Purchase Date/Tanggal Pembelian Terakhir": [
"2024-12-23",
"2025-03-21",
"2024-11-22",
"2025-02-12",
"2024-09-12",
"2025-01-08",
"2024-05-12",
"2025-01-04",
"2024-06-11",
"2024-05-03",
"2024-09-17",
"2024-09-05",
"2025-04-19",
"2024-10-18",
"2024-10-26",
"2025-02-18",
"2024-12-15",
"2024-09-16",
"2024-10-07",
"2024-12-19",
"2024-05-07",
"2024-07-17",
"2025-01-11",
"2024-10-17",
"2024-11-23",
"2024-11-15",
"2024-07-27",
"2025-01-08",
"2024-07-21",
"2024-04-29",
"2024-07-22",
"2025-02-20",
"2024-11-26",
"2024-08-18",
"2024-05-11",
"2024-09-11",
"2025-04-07",
"2024-08-21",
"2025-01-25",
"2024-09-08",
"2024-04-30",
"2025-03-12",
"2024-09-08",
"2024-05-03",
"2024-11-13",
"2024-07-05",
"2024-11-18",
"2024-09-22",
"2024-09-29",
"2025-04-14",
"2025-02-03"
],
"Latitude/Lintang": null,
"Longitude/Bujur": null,
"Loyalty Score/Skor Loyalitas": [
68,
89,
95,
57,
17,
25,
28,
1,
88,
88,
15,
42,
66,
87,
10,
62,
6,
65,
44,
33,
5,
74,
14,
71,
52,
43,
53,
9,
53,
22,
39,
95,
46,
57,
47,
74,
32,
29,
5,
74,
86,
13,
30,
32,
35,
24,
35,
89,
72,
76,
32
],
"Market Share/Pangsa Pasar": null,
"Price/Harga": null,
"Product/Produk": null,
"Region/Wilayah": null,
"Revenue/Pendapatan": null,
"Sales/Penjualan": null,
"Stock/Stok": null,
"Visitors/Pengunjung": null
}
|
import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Customer ID/ID Pelanggan': ['CUST_0001', 'CUST_0002', 'CUST_0003', 'CUST_0004', 'CUST_0005', 'CUST_0006', 'CUST_0007', 'CUST_0008', 'CUST_0009', 'CUST_0010', 'CUST_0011', 'CUST_0012', 'CUST_0013', 'CUST_0014', 'CUST_0015', 'CUST_0016', 'CUST_0017', 'CUST_0018', 'CUST_0019', 'CUST_0020', 'CUST_0021', 'CUST_0022', 'CUST_0023', 'CUST_0024', 'CUST_0025', 'CUST_0026', 'CUST_0027', 'CUST_0028', 'CUST_0029', 'CUST_0030', 'CUST_0031', 'CUST_0032', 'CUST_0033', 'CUST_0034', 'CUST_0035', 'CUST_0036', 'CUST_0037', 'CUST_0038', 'CUST_0039', 'CUST_0040', 'CUST_0041', 'CUST_0042', 'CUST_0043', 'CUST_0044', 'CUST_0045', 'CUST_0046', 'CUST_0047', 'CUST_0048', 'CUST_0049', 'CUST_0050', 'CUST_0051'], 'Age/Usia': [62, 56, 50, 63, 22, 36, 63, 45, 55, 66, 45, 59, 34, 58, 63, 61, 20, 33, 55, 57, 43, 52, 41, 56, 20, 53, 50, 31, 42, 60, 48, 57, 70, 51, 49, 47, 35, 56, 30, 57, 52, 59, 67, 18, 44, 69, 61, 53, 57, 23, 30], 'Gender/Jenis Kelamin': ['Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Other/Lainnya', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Other/Lainnya', 'Female/Perempuan', 'Other/Lainnya', 'Male/Laki-laki', 'Male/Laki-laki', 'Other/Lainnya', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki'], 'Income/Pendapatan': [4000, 5000, 5000, 3000, 8000, 3000, 6000, 4000, 8000, 3000, 7000, 7000, 8000, 6000, 5000, 6000, 3000, 9000, 5000, 5000, 7000, 1000, 8000, 10000, 4000, 8000, 5000, 3000, 6000, 7000, 6000, 9000, 7000, 8000, 1000, 7000, 2000, 9000, 10000, 9000, 1000, 5000, 5000, 1000, 9000, 8000, 1000, 4000, 6000, 2000, 6000], 'Loyalty Score/Skor Loyalitas': [68, 89, 95, 57, 17, 25, 28, 1, 88, 88, 15, 42, 66, 87, 10, 62, 6, 65, 44, 33, 5, 74, 14, 71, 52, 43, 53, 9, 53, 22, 39, 95, 46, 57, 47, 74, 32, 29, 5, 74, 86, 13, 30, 32, 35, 24, 35, 89, 72, 76, 32], 'Last Purchase Date/Tanggal Pembelian Terakhir': ['2024-12-23', '2025-03-21', '2024-11-22', '2025-02-12', '2024-09-12', '2025-01-08', '2024-05-12', '2025-01-04', '2024-06-11', '2024-05-03', '2024-09-17', '2024-09-05', '2025-04-19', '2024-10-18', '2024-10-26', '2025-02-18', '2024-12-15', '2024-09-16', '2024-10-07', '2024-12-19', '2024-05-07', '2024-07-17', '2025-01-11', '2024-10-17', '2024-11-23', '2024-11-15', '2024-07-27', '2025-01-08', '2024-07-21', '2024-04-29', '2024-07-22', '2025-02-20', '2024-11-26', '2024-08-18', '2024-05-11', '2024-09-11', '2025-04-07', '2024-08-21', '2025-01-25', '2024-09-08', '2024-04-30', '2025-03-12', '2024-09-08', '2024-05-03', '2024-11-13', '2024-07-05', '2024-11-18', '2024-09-22', '2024-09-29', '2025-04-14', '2025-02-03']})
# Basic data cleaning
df = df.dropna() # Remove missing values
df = df.drop_duplicates() # Remove duplicates
# Comprehensive data analysis
analysis_result = df.describe(include='all')
print(analysis_result)
# Additional insights
print('\nTop performing items:')
print(df.nlargest(3, df.columns[1]))
print('\nTrend analysis:')
if 'Date/Tanggal' in df.columns:
df['Month/Bulan'] = pd.to_datetime(df['Date/Tanggal']).dt.month
monthly_trend = df.groupby('Month/Bulan').mean(numeric_only=True)
print(monthly_trend)
|
Correlate discount and/dan average order value
|
{
"Age/Usia": [
40,
67,
37,
60,
37,
31,
66,
33,
32,
46,
49,
29,
36,
25,
70,
43,
40,
23,
65,
63,
39,
66,
35,
63,
26,
43,
27,
21,
69,
44,
66,
29,
54,
44,
31,
37,
42,
60,
66,
31,
37,
54,
45,
26,
60,
67,
20,
65,
47,
55,
23,
49,
23,
35,
19,
58,
20,
43,
31,
36,
19,
23,
43,
34,
41,
58,
33,
62,
29,
24,
70,
32,
70,
34,
52,
60,
66,
57,
29
],
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": null,
"City/Kota": null,
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": null,
"Customer ID/ID Pelanggan": [
"CUST_0001",
"CUST_0002",
"CUST_0003",
"CUST_0004",
"CUST_0005",
"CUST_0006",
"CUST_0007",
"CUST_0008",
"CUST_0009",
"CUST_0010",
"CUST_0011",
"CUST_0012",
"CUST_0013",
"CUST_0014",
"CUST_0015",
"CUST_0016",
"CUST_0017",
"CUST_0018",
"CUST_0019",
"CUST_0020",
"CUST_0021",
"CUST_0022",
"CUST_0023",
"CUST_0024",
"CUST_0025",
"CUST_0026",
"CUST_0027",
"CUST_0028",
"CUST_0029",
"CUST_0030",
"CUST_0031",
"CUST_0032",
"CUST_0033",
"CUST_0034",
"CUST_0035",
"CUST_0036",
"CUST_0037",
"CUST_0038",
"CUST_0039",
"CUST_0040",
"CUST_0041",
"CUST_0042",
"CUST_0043",
"CUST_0044",
"CUST_0045",
"CUST_0046",
"CUST_0047",
"CUST_0048",
"CUST_0049",
"CUST_0050",
"CUST_0051",
"CUST_0052",
"CUST_0053",
"CUST_0054",
"CUST_0055",
"CUST_0056",
"CUST_0057",
"CUST_0058",
"CUST_0059",
"CUST_0060",
"CUST_0061",
"CUST_0062",
"CUST_0063",
"CUST_0064",
"CUST_0065",
"CUST_0066",
"CUST_0067",
"CUST_0068",
"CUST_0069",
"CUST_0070",
"CUST_0071",
"CUST_0072",
"CUST_0073",
"CUST_0074",
"CUST_0075",
"CUST_0076",
"CUST_0077",
"CUST_0078",
"CUST_0079"
],
"Date/Tanggal": null,
"Discount/Diskon": null,
"Gender/Jenis Kelamin": [
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Other/Lainnya",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki"
],
"Growth Rate/Pertumbuhan": null,
"Income/Pendapatan": [
7000,
8000,
9000,
9000,
5000,
7000,
4000,
4000,
6000,
6000,
8000,
4000,
8000,
4000,
7000,
6000,
7000,
4000,
2000,
4000,
9000,
5000,
10000,
9000,
7000,
7000,
1000,
7000,
4000,
6000,
5000,
2000,
10000,
1000,
3000,
8000,
8000,
10000,
1000,
10000,
7000,
9000,
2000,
4000,
1000,
10000,
2000,
2000,
9000,
10000,
5000,
10000,
7000,
2000,
3000,
6000,
2000,
10000,
2000,
5000,
5000,
4000,
1000,
3000,
7000,
6000,
8000,
9000,
7000,
3000,
8000,
6000,
9000,
7000,
3000,
5000,
5000,
8000,
3000
],
"Last Purchase Date/Tanggal Pembelian Terakhir": [
"2025-03-01",
"2024-08-26",
"2025-03-10",
"2024-12-20",
"2025-03-25",
"2025-02-10",
"2024-05-05",
"2024-08-12",
"2024-08-04",
"2024-10-07",
"2024-08-27",
"2024-05-13",
"2024-11-08",
"2025-02-04",
"2024-07-12",
"2024-10-18",
"2025-01-11",
"2024-09-19",
"2025-02-12",
"2024-10-25",
"2024-08-21",
"2024-07-10",
"2024-12-13",
"2024-09-24",
"2024-12-30",
"2024-05-02",
"2025-01-15",
"2024-08-24",
"2024-10-21",
"2024-08-08",
"2024-05-12",
"2024-10-22",
"2025-04-01",
"2024-11-17",
"2025-03-25",
"2024-10-01",
"2024-08-10",
"2024-08-17",
"2025-02-13",
"2024-12-16",
"2025-01-06",
"2024-10-03",
"2024-06-27",
"2025-03-29",
"2024-08-16",
"2024-10-30",
"2025-01-12",
"2025-03-07",
"2024-10-29",
"2024-08-11",
"2024-08-03",
"2024-07-15",
"2024-07-30",
"2024-08-04",
"2024-05-13",
"2025-01-10",
"2024-12-25",
"2025-02-21",
"2024-05-29",
"2024-08-30",
"2025-02-08",
"2025-02-22",
"2024-09-29",
"2025-04-01",
"2024-08-01",
"2025-04-04",
"2025-03-28",
"2024-06-19",
"2024-11-06",
"2024-05-24",
"2024-09-06",
"2024-12-14",
"2024-05-23",
"2024-08-25",
"2024-04-27",
"2024-07-22",
"2025-04-07",
"2024-10-02",
"2025-01-24"
],
"Latitude/Lintang": null,
"Longitude/Bujur": null,
"Loyalty Score/Skor Loyalitas": [
11,
11,
7,
97,
74,
88,
3,
72,
98,
76,
84,
2,
35,
58,
10,
90,
64,
85,
32,
7,
100,
98,
64,
87,
28,
71,
59,
81,
36,
99,
46,
65,
69,
14,
5,
67,
5,
6,
14,
43,
86,
4,
100,
64,
49,
40,
12,
39,
18,
89,
7,
84,
15,
88,
28,
96,
55,
52,
3,
75,
41,
47,
57,
2,
1,
10,
58,
77,
55,
67,
58,
71,
53,
66,
90,
75,
3,
77,
14
],
"Market Share/Pangsa Pasar": null,
"Price/Harga": null,
"Product/Produk": null,
"Region/Wilayah": null,
"Revenue/Pendapatan": null,
"Sales/Penjualan": null,
"Stock/Stok": null,
"Visitors/Pengunjung": null
}
|
import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Customer ID/ID Pelanggan': ['CUST_0001', 'CUST_0002', 'CUST_0003', 'CUST_0004', 'CUST_0005', 'CUST_0006', 'CUST_0007', 'CUST_0008', 'CUST_0009', 'CUST_0010', 'CUST_0011', 'CUST_0012', 'CUST_0013', 'CUST_0014', 'CUST_0015', 'CUST_0016', 'CUST_0017', 'CUST_0018', 'CUST_0019', 'CUST_0020', 'CUST_0021', 'CUST_0022', 'CUST_0023', 'CUST_0024', 'CUST_0025', 'CUST_0026', 'CUST_0027', 'CUST_0028', 'CUST_0029', 'CUST_0030', 'CUST_0031', 'CUST_0032', 'CUST_0033', 'CUST_0034', 'CUST_0035', 'CUST_0036', 'CUST_0037', 'CUST_0038', 'CUST_0039', 'CUST_0040', 'CUST_0041', 'CUST_0042', 'CUST_0043', 'CUST_0044', 'CUST_0045', 'CUST_0046', 'CUST_0047', 'CUST_0048', 'CUST_0049', 'CUST_0050', 'CUST_0051', 'CUST_0052', 'CUST_0053', 'CUST_0054', 'CUST_0055', 'CUST_0056', 'CUST_0057', 'CUST_0058', 'CUST_0059', 'CUST_0060', 'CUST_0061', 'CUST_0062', 'CUST_0063', 'CUST_0064', 'CUST_0065', 'CUST_0066', 'CUST_0067', 'CUST_0068', 'CUST_0069', 'CUST_0070', 'CUST_0071', 'CUST_0072', 'CUST_0073', 'CUST_0074', 'CUST_0075', 'CUST_0076', 'CUST_0077', 'CUST_0078', 'CUST_0079'], 'Age/Usia': [40, 67, 37, 60, 37, 31, 66, 33, 32, 46, 49, 29, 36, 25, 70, 43, 40, 23, 65, 63, 39, 66, 35, 63, 26, 43, 27, 21, 69, 44, 66, 29, 54, 44, 31, 37, 42, 60, 66, 31, 37, 54, 45, 26, 60, 67, 20, 65, 47, 55, 23, 49, 23, 35, 19, 58, 20, 43, 31, 36, 19, 23, 43, 34, 41, 58, 33, 62, 29, 24, 70, 32, 70, 34, 52, 60, 66, 57, 29], 'Gender/Jenis Kelamin': ['Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Other/Lainnya', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki'], 'Income/Pendapatan': [7000, 8000, 9000, 9000, 5000, 7000, 4000, 4000, 6000, 6000, 8000, 4000, 8000, 4000, 7000, 6000, 7000, 4000, 2000, 4000, 9000, 5000, 10000, 9000, 7000, 7000, 1000, 7000, 4000, 6000, 5000, 2000, 10000, 1000, 3000, 8000, 8000, 10000, 1000, 10000, 7000, 9000, 2000, 4000, 1000, 10000, 2000, 2000, 9000, 10000, 5000, 10000, 7000, 2000, 3000, 6000, 2000, 10000, 2000, 5000, 5000, 4000, 1000, 3000, 7000, 6000, 8000, 9000, 7000, 3000, 8000, 6000, 9000, 7000, 3000, 5000, 5000, 8000, 3000], 'Loyalty Score/Skor Loyalitas': [11, 11, 7, 97, 74, 88, 3, 72, 98, 76, 84, 2, 35, 58, 10, 90, 64, 85, 32, 7, 100, 98, 64, 87, 28, 71, 59, 81, 36, 99, 46, 65, 69, 14, 5, 67, 5, 6, 14, 43, 86, 4, 100, 64, 49, 40, 12, 39, 18, 89, 7, 84, 15, 88, 28, 96, 55, 52, 3, 75, 41, 47, 57, 2, 1, 10, 58, 77, 55, 67, 58, 71, 53, 66, 90, 75, 3, 77, 14], 'Last Purchase Date/Tanggal Pembelian Terakhir': ['2025-03-01', '2024-08-26', '2025-03-10', '2024-12-20', '2025-03-25', '2025-02-10', '2024-05-05', '2024-08-12', '2024-08-04', '2024-10-07', '2024-08-27', '2024-05-13', '2024-11-08', '2025-02-04', '2024-07-12', '2024-10-18', '2025-01-11', '2024-09-19', '2025-02-12', '2024-10-25', '2024-08-21', '2024-07-10', '2024-12-13', '2024-09-24', '2024-12-30', '2024-05-02', '2025-01-15', '2024-08-24', '2024-10-21', '2024-08-08', '2024-05-12', '2024-10-22', '2025-04-01', '2024-11-17', '2025-03-25', '2024-10-01', '2024-08-10', '2024-08-17', '2025-02-13', '2024-12-16', '2025-01-06', '2024-10-03', '2024-06-27', '2025-03-29', '2024-08-16', '2024-10-30', '2025-01-12', '2025-03-07', '2024-10-29', '2024-08-11', '2024-08-03', '2024-07-15', '2024-07-30', '2024-08-04', '2024-05-13', '2025-01-10', '2024-12-25', '2025-02-21', '2024-05-29', '2024-08-30', '2025-02-08', '2025-02-22', '2024-09-29', '2025-04-01', '2024-08-01', '2025-04-04', '2025-03-28', '2024-06-19', '2024-11-06', '2024-05-24', '2024-09-06', '2024-12-14', '2024-05-23', '2024-08-25', '2024-04-27', '2024-07-22', '2025-04-07', '2024-10-02', '2025-01-24']})
# Basic data cleaning
df = df.dropna() # Remove missing values
df = df.drop_duplicates() # Remove duplicates
# Correlation analysis
corr_matrix = df.corr(numeric_only=True)
fig = px.imshow(corr_matrix, text_auto=True,
title='Correlation Matrix')
fig.show()
|
Korelasi pengunjung and/dan pendapatan
|
{
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": null,
"City/Kota": [
"Sydney",
"Singapore",
"New York",
"Mumbai",
"Dubai"
],
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": [
"Australia",
"Singapore",
"US",
"India",
"UAE"
],
"Customer ID/ID Pelanggan": null,
"Date/Tanggal": null,
"Discount/Diskon": null,
"Gender/Jenis Kelamin": null,
"Growth Rate/Pertumbuhan": [
0.02,
-0.09,
0.02,
0.27,
-0.07
],
"Income/Pendapatan": null,
"Last Purchase Date/Tanggal Pembelian Terakhir": null,
"Latitude/Lintang": [
-88.4235,
85.2578,
-26.927,
-5.1132,
24.821
],
"Longitude/Bujur": [
-93.6849,
35.0648,
-166.4055,
-47.5911,
-170.4995
],
"Loyalty Score/Skor Loyalitas": null,
"Market Share/Pangsa Pasar": [
0.34,
0.26,
0.22,
0.43,
0.19
],
"Price/Harga": null,
"Product/Produk": null,
"Region/Wilayah": [
"Oceania",
"Asia",
"North America",
"Asia",
"Middle East"
],
"Revenue/Pendapatan": [
4831,
3062,
4960,
4305,
3474
],
"Sales/Penjualan": null,
"Stock/Stok": null,
"Visitors/Pengunjung": null
}
|
import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'City/Kota': ['Sydney', 'Singapore', 'New York', 'Mumbai', 'Dubai'], 'Country/Negara': ['Australia', 'Singapore', 'US', 'India', 'UAE'], 'Region/Wilayah': ['Oceania', 'Asia', 'North America', 'Asia', 'Middle East'], 'Latitude/Lintang': [-88.4235, 85.2578, -26.927, -5.1132, 24.821], 'Longitude/Bujur': [-93.6849, 35.0648, -166.4055, -47.5911, -170.4995], 'Revenue/Pendapatan': [4831, 3062, 4960, 4305, 3474], 'Market Share/Pangsa Pasar': [0.34, 0.26, 0.22, 0.43, 0.19], 'Growth Rate/Pertumbuhan': [0.02, -0.09, 0.02, 0.27, -0.07]})
# Basic data cleaning
df = df.dropna() # Remove missing values
df = df.drop_duplicates() # Remove duplicates
# Correlation analysis
corr_matrix = df.corr(numeric_only=True)
fig = px.imshow(corr_matrix, text_auto=True,
title='Correlation Matrix')
fig.show()
|
Identify income gender
|
{
"Age/Usia": [
24,
38,
67,
52,
26,
67,
60,
55,
67,
40,
49,
25,
68,
66,
39,
52,
20,
32,
58,
49,
20,
41,
45,
23,
49,
22,
47,
42,
43,
58,
43,
34,
41,
33,
41,
51,
24,
21,
21,
47,
54,
37,
54,
41,
47,
19,
54,
22,
63,
25,
25,
57,
63,
66,
19,
60,
41,
47,
38,
33,
47,
35,
27,
68,
32,
35,
67,
44,
49,
61,
44,
35,
46,
36,
26,
51,
39,
29,
35,
25,
36,
60,
47,
69,
57,
58,
51,
31,
26,
60
],
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": null,
"City/Kota": null,
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": null,
"Customer ID/ID Pelanggan": [
"CUST_0001",
"CUST_0002",
"CUST_0003",
"CUST_0004",
"CUST_0005",
"CUST_0006",
"CUST_0007",
"CUST_0008",
"CUST_0009",
"CUST_0010",
"CUST_0011",
"CUST_0012",
"CUST_0013",
"CUST_0014",
"CUST_0015",
"CUST_0016",
"CUST_0017",
"CUST_0018",
"CUST_0019",
"CUST_0020",
"CUST_0021",
"CUST_0022",
"CUST_0023",
"CUST_0024",
"CUST_0025",
"CUST_0026",
"CUST_0027",
"CUST_0028",
"CUST_0029",
"CUST_0030",
"CUST_0031",
"CUST_0032",
"CUST_0033",
"CUST_0034",
"CUST_0035",
"CUST_0036",
"CUST_0037",
"CUST_0038",
"CUST_0039",
"CUST_0040",
"CUST_0041",
"CUST_0042",
"CUST_0043",
"CUST_0044",
"CUST_0045",
"CUST_0046",
"CUST_0047",
"CUST_0048",
"CUST_0049",
"CUST_0050",
"CUST_0051",
"CUST_0052",
"CUST_0053",
"CUST_0054",
"CUST_0055",
"CUST_0056",
"CUST_0057",
"CUST_0058",
"CUST_0059",
"CUST_0060",
"CUST_0061",
"CUST_0062",
"CUST_0063",
"CUST_0064",
"CUST_0065",
"CUST_0066",
"CUST_0067",
"CUST_0068",
"CUST_0069",
"CUST_0070",
"CUST_0071",
"CUST_0072",
"CUST_0073",
"CUST_0074",
"CUST_0075",
"CUST_0076",
"CUST_0077",
"CUST_0078",
"CUST_0079",
"CUST_0080",
"CUST_0081",
"CUST_0082",
"CUST_0083",
"CUST_0084",
"CUST_0085",
"CUST_0086",
"CUST_0087",
"CUST_0088",
"CUST_0089",
"CUST_0090"
],
"Date/Tanggal": null,
"Discount/Diskon": null,
"Gender/Jenis Kelamin": [
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Other/Lainnya",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Other/Lainnya",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Other/Lainnya",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Other/Lainnya",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki"
],
"Growth Rate/Pertumbuhan": null,
"Income/Pendapatan": [
5000,
3000,
4000,
4000,
8000,
5000,
4000,
4000,
2000,
6000,
7000,
7000,
2000,
2000,
7000,
4000,
1000,
2000,
4000,
7000,
5000,
7000,
7000,
6000,
10000,
9000,
6000,
7000,
3000,
4000,
6000,
1000,
6000,
5000,
6000,
5000,
2000,
2000,
7000,
2000,
8000,
7000,
6000,
1000,
3000,
6000,
3000,
6000,
4000,
9000,
7000,
6000,
5000,
2000,
8000,
6000,
6000,
1000,
1000,
6000,
10000,
5000,
10000,
5000,
7000,
2000,
2000,
8000,
10000,
6000,
9000,
2000,
2000,
5000,
6000,
4000,
2000,
7000,
2000,
5000,
2000,
4000,
10000,
8000,
2000,
8000,
7000,
8000,
9000,
3000
],
"Last Purchase Date/Tanggal Pembelian Terakhir": [
"2024-07-29",
"2025-03-26",
"2025-03-04",
"2024-11-17",
"2025-02-21",
"2024-10-14",
"2025-01-06",
"2025-04-10",
"2024-05-27",
"2024-07-20",
"2025-01-07",
"2024-08-22",
"2025-03-03",
"2025-02-09",
"2024-12-07",
"2024-06-27",
"2024-06-01",
"2024-12-19",
"2024-06-06",
"2024-06-02",
"2024-11-17",
"2024-08-30",
"2024-11-06",
"2024-05-24",
"2025-03-12",
"2024-12-22",
"2025-03-18",
"2024-04-30",
"2024-12-08",
"2024-04-22",
"2025-02-06",
"2024-05-11",
"2024-12-27",
"2025-01-20",
"2024-06-03",
"2024-07-20",
"2024-09-16",
"2025-04-10",
"2025-01-10",
"2024-06-30",
"2024-10-23",
"2024-05-07",
"2024-05-23",
"2024-07-12",
"2025-02-25",
"2025-04-02",
"2025-01-17",
"2025-04-12",
"2024-05-10",
"2025-03-14",
"2025-04-13",
"2024-09-15",
"2024-06-28",
"2024-08-29",
"2025-01-16",
"2024-09-14",
"2024-05-12",
"2025-04-17",
"2024-05-03",
"2025-03-26",
"2024-11-04",
"2024-12-22",
"2025-02-19",
"2024-09-28",
"2024-08-18",
"2024-12-16",
"2024-12-15",
"2025-03-17",
"2024-12-15",
"2024-07-23",
"2024-08-21",
"2025-03-13",
"2024-08-13",
"2024-06-16",
"2024-11-28",
"2024-07-15",
"2025-03-15",
"2024-05-27",
"2024-09-09",
"2024-05-30",
"2024-09-03",
"2024-04-27",
"2025-03-22",
"2024-10-03",
"2024-06-26",
"2025-02-10",
"2024-08-11",
"2025-02-21",
"2024-12-27",
"2024-08-30"
],
"Latitude/Lintang": null,
"Longitude/Bujur": null,
"Loyalty Score/Skor Loyalitas": [
97,
32,
34,
62,
24,
69,
90,
4,
11,
33,
33,
75,
3,
29,
74,
54,
49,
100,
2,
47,
42,
10,
37,
28,
45,
95,
71,
22,
26,
48,
3,
49,
76,
61,
14,
88,
94,
49,
79,
53,
99,
38,
54,
11,
58,
33,
48,
20,
77,
43,
40,
27,
15,
85,
23,
33,
16,
86,
61,
15,
59,
61,
24,
6,
64,
41,
41,
68,
4,
57,
18,
92,
66,
4,
19,
38,
96,
47,
33,
80,
95,
32,
50,
27,
87,
99,
21,
57,
8,
1
],
"Market Share/Pangsa Pasar": null,
"Price/Harga": null,
"Product/Produk": null,
"Region/Wilayah": null,
"Revenue/Pendapatan": null,
"Sales/Penjualan": null,
"Stock/Stok": null,
"Visitors/Pengunjung": null
}
|
import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Customer ID/ID Pelanggan': ['CUST_0001', 'CUST_0002', 'CUST_0003', 'CUST_0004', 'CUST_0005', 'CUST_0006', 'CUST_0007', 'CUST_0008', 'CUST_0009', 'CUST_0010', 'CUST_0011', 'CUST_0012', 'CUST_0013', 'CUST_0014', 'CUST_0015', 'CUST_0016', 'CUST_0017', 'CUST_0018', 'CUST_0019', 'CUST_0020', 'CUST_0021', 'CUST_0022', 'CUST_0023', 'CUST_0024', 'CUST_0025', 'CUST_0026', 'CUST_0027', 'CUST_0028', 'CUST_0029', 'CUST_0030', 'CUST_0031', 'CUST_0032', 'CUST_0033', 'CUST_0034', 'CUST_0035', 'CUST_0036', 'CUST_0037', 'CUST_0038', 'CUST_0039', 'CUST_0040', 'CUST_0041', 'CUST_0042', 'CUST_0043', 'CUST_0044', 'CUST_0045', 'CUST_0046', 'CUST_0047', 'CUST_0048', 'CUST_0049', 'CUST_0050', 'CUST_0051', 'CUST_0052', 'CUST_0053', 'CUST_0054', 'CUST_0055', 'CUST_0056', 'CUST_0057', 'CUST_0058', 'CUST_0059', 'CUST_0060', 'CUST_0061', 'CUST_0062', 'CUST_0063', 'CUST_0064', 'CUST_0065', 'CUST_0066', 'CUST_0067', 'CUST_0068', 'CUST_0069', 'CUST_0070', 'CUST_0071', 'CUST_0072', 'CUST_0073', 'CUST_0074', 'CUST_0075', 'CUST_0076', 'CUST_0077', 'CUST_0078', 'CUST_0079', 'CUST_0080', 'CUST_0081', 'CUST_0082', 'CUST_0083', 'CUST_0084', 'CUST_0085', 'CUST_0086', 'CUST_0087', 'CUST_0088', 'CUST_0089', 'CUST_0090'], 'Age/Usia': [24, 38, 67, 52, 26, 67, 60, 55, 67, 40, 49, 25, 68, 66, 39, 52, 20, 32, 58, 49, 20, 41, 45, 23, 49, 22, 47, 42, 43, 58, 43, 34, 41, 33, 41, 51, 24, 21, 21, 47, 54, 37, 54, 41, 47, 19, 54, 22, 63, 25, 25, 57, 63, 66, 19, 60, 41, 47, 38, 33, 47, 35, 27, 68, 32, 35, 67, 44, 49, 61, 44, 35, 46, 36, 26, 51, 39, 29, 35, 25, 36, 60, 47, 69, 57, 58, 51, 31, 26, 60], 'Gender/Jenis Kelamin': ['Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Other/Lainnya', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Other/Lainnya', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Other/Lainnya', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Other/Lainnya', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki'], 'Income/Pendapatan': [5000, 3000, 4000, 4000, 8000, 5000, 4000, 4000, 2000, 6000, 7000, 7000, 2000, 2000, 7000, 4000, 1000, 2000, 4000, 7000, 5000, 7000, 7000, 6000, 10000, 9000, 6000, 7000, 3000, 4000, 6000, 1000, 6000, 5000, 6000, 5000, 2000, 2000, 7000, 2000, 8000, 7000, 6000, 1000, 3000, 6000, 3000, 6000, 4000, 9000, 7000, 6000, 5000, 2000, 8000, 6000, 6000, 1000, 1000, 6000, 10000, 5000, 10000, 5000, 7000, 2000, 2000, 8000, 10000, 6000, 9000, 2000, 2000, 5000, 6000, 4000, 2000, 7000, 2000, 5000, 2000, 4000, 10000, 8000, 2000, 8000, 7000, 8000, 9000, 3000], 'Loyalty Score/Skor Loyalitas': [97, 32, 34, 62, 24, 69, 90, 4, 11, 33, 33, 75, 3, 29, 74, 54, 49, 100, 2, 47, 42, 10, 37, 28, 45, 95, 71, 22, 26, 48, 3, 49, 76, 61, 14, 88, 94, 49, 79, 53, 99, 38, 54, 11, 58, 33, 48, 20, 77, 43, 40, 27, 15, 85, 23, 33, 16, 86, 61, 15, 59, 61, 24, 6, 64, 41, 41, 68, 4, 57, 18, 92, 66, 4, 19, 38, 96, 47, 33, 80, 95, 32, 50, 27, 87, 99, 21, 57, 8, 1], 'Last Purchase Date/Tanggal Pembelian Terakhir': ['2024-07-29', '2025-03-26', '2025-03-04', '2024-11-17', '2025-02-21', '2024-10-14', '2025-01-06', '2025-04-10', '2024-05-27', '2024-07-20', '2025-01-07', '2024-08-22', '2025-03-03', '2025-02-09', '2024-12-07', '2024-06-27', '2024-06-01', '2024-12-19', '2024-06-06', '2024-06-02', '2024-11-17', '2024-08-30', '2024-11-06', '2024-05-24', '2025-03-12', '2024-12-22', '2025-03-18', '2024-04-30', '2024-12-08', '2024-04-22', '2025-02-06', '2024-05-11', '2024-12-27', '2025-01-20', '2024-06-03', '2024-07-20', '2024-09-16', '2025-04-10', '2025-01-10', '2024-06-30', '2024-10-23', '2024-05-07', '2024-05-23', '2024-07-12', '2025-02-25', '2025-04-02', '2025-01-17', '2025-04-12', '2024-05-10', '2025-03-14', '2025-04-13', '2024-09-15', '2024-06-28', '2024-08-29', '2025-01-16', '2024-09-14', '2024-05-12', '2025-04-17', '2024-05-03', '2025-03-26', '2024-11-04', '2024-12-22', '2025-02-19', '2024-09-28', '2024-08-18', '2024-12-16', '2024-12-15', '2025-03-17', '2024-12-15', '2024-07-23', '2024-08-21', '2025-03-13', '2024-08-13', '2024-06-16', '2024-11-28', '2024-07-15', '2025-03-15', '2024-05-27', '2024-09-09', '2024-05-30', '2024-09-03', '2024-04-27', '2025-03-22', '2024-10-03', '2024-06-26', '2025-02-10', '2024-08-11', '2025-02-21', '2024-12-27', '2024-08-30']})
# Basic data cleaning
df = df.dropna() # Remove missing values
df = df.drop_duplicates() # Remove duplicates
# Comprehensive data analysis
analysis_result = df.describe(include='all')
print(analysis_result)
# Additional insights
print('\nTop performing items:')
print(df.nlargest(3, df.columns[1]))
print('\nTrend analysis:')
if 'Date/Tanggal' in df.columns:
df['Month/Bulan'] = pd.to_datetime(df['Date/Tanggal']).dt.month
monthly_trend = df.groupby('Month/Bulan').mean(numeric_only=True)
print(monthly_trend)
|
Identify loyalty score region
|
{
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": [
"Fashion",
"Fashion",
"Fashion"
],
"City/Kota": null,
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": null,
"Customer ID/ID Pelanggan": null,
"Date/Tanggal": null,
"Discount/Diskon": [
0.29,
0.01,
0
],
"Gender/Jenis Kelamin": null,
"Growth Rate/Pertumbuhan": null,
"Income/Pendapatan": null,
"Last Purchase Date/Tanggal Pembelian Terakhir": null,
"Latitude/Lintang": null,
"Longitude/Bujur": null,
"Loyalty Score/Skor Loyalitas": null,
"Market Share/Pangsa Pasar": null,
"Price/Harga": [
94.79,
195.26,
39.68
],
"Product/Produk": [
"Shoes_1",
"T-Shirt_2",
"Dress_3"
],
"Region/Wilayah": null,
"Revenue/Pendapatan": null,
"Sales/Penjualan": [
931,
478,
389
],
"Stock/Stok": [
17,
139,
19
],
"Visitors/Pengunjung": null
}
|
import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Product/Produk': ['Shoes_1', 'T-Shirt_2', 'Dress_3'], 'Sales/Penjualan': [931, 478, 389], 'Price/Harga': [94.79, 195.26, 39.68], 'Category/Kategori': ['Fashion', 'Fashion', 'Fashion'], 'Discount/Diskon': [0.29, 0.01, 0.0], 'Stock/Stok': [17, 139, 19]})
# Basic data cleaning
df = df.dropna() # Remove missing values
df = df.drop_duplicates() # Remove duplicates
# Comprehensive data analysis
analysis_result = df.describe(include='all')
print(analysis_result)
# Additional insights
print('\nTop performing items:')
print(df.nlargest(3, df.columns[1]))
print('\nTrend analysis:')
if 'Date/Tanggal' in df.columns:
df['Month/Bulan'] = pd.to_datetime(df['Date/Tanggal']).dt.month
monthly_trend = df.groupby('Month/Bulan').mean(numeric_only=True)
print(monthly_trend)
|
Compare price country
|
{
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": [
"Automotive/Otomotif",
"Automotive/Otomotif",
"Automotive/Otomotif"
],
"City/Kota": null,
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": null,
"Customer ID/ID Pelanggan": null,
"Date/Tanggal": null,
"Discount/Diskon": [
0.25,
0.15,
0.08
],
"Gender/Jenis Kelamin": null,
"Growth Rate/Pertumbuhan": null,
"Income/Pendapatan": null,
"Last Purchase Date/Tanggal Pembelian Terakhir": null,
"Latitude/Lintang": null,
"Longitude/Bujur": null,
"Loyalty Score/Skor Loyalitas": null,
"Market Share/Pangsa Pasar": null,
"Price/Harga": [
40.78,
92.48,
66.94
],
"Product/Produk": [
"Oil_1",
"Tools_2",
"Tires_3"
],
"Region/Wilayah": null,
"Revenue/Pendapatan": null,
"Sales/Penjualan": [
486,
302,
181
],
"Stock/Stok": [
103,
77,
56
],
"Visitors/Pengunjung": null
}
|
import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Product/Produk': ['Oil_1', 'Tools_2', 'Tires_3'], 'Sales/Penjualan': [486, 302, 181], 'Price/Harga': [40.78, 92.48, 66.94], 'Category/Kategori': ['Automotive/Otomotif', 'Automotive/Otomotif', 'Automotive/Otomotif'], 'Discount/Diskon': [0.25, 0.15, 0.08], 'Stock/Stok': [103, 77, 56]})
# Basic data cleaning
df = df.dropna() # Remove missing values
df = df.drop_duplicates() # Remove duplicates
# Comprehensive data analysis
analysis_result = df.describe(include='all')
print(analysis_result)
# Additional insights
print('\nTop performing items:')
print(df.nlargest(3, df.columns[1]))
print('\nTrend analysis:')
if 'Date/Tanggal' in df.columns:
df['Month/Bulan'] = pd.to_datetime(df['Date/Tanggal']).dt.month
monthly_trend = df.groupby('Month/Bulan').mean(numeric_only=True)
print(monthly_trend)
|
Korelasi penjualan and/dan pendapatan
|
{
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": null,
"City/Kota": [
"Jakarta",
"Paris",
"Dubai",
"Sydney",
"New York"
],
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": [
"Indonesia",
"France",
"UAE",
"Australia",
"US"
],
"Customer ID/ID Pelanggan": null,
"Date/Tanggal": null,
"Discount/Diskon": null,
"Gender/Jenis Kelamin": null,
"Growth Rate/Pertumbuhan": [
-0.02,
0.06,
0.24,
0.22,
0.06
],
"Income/Pendapatan": null,
"Last Purchase Date/Tanggal Pembelian Terakhir": null,
"Latitude/Lintang": [
64.9689,
88.3034,
-73.343,
-20.3746,
-72.4041
],
"Longitude/Bujur": [
170.481,
8.1583,
-41.3543,
-167.0418,
141.9367
],
"Loyalty Score/Skor Loyalitas": null,
"Market Share/Pangsa Pasar": [
0.2,
0.46,
0.11,
0.35000000000000003,
0.21
],
"Price/Harga": null,
"Product/Produk": null,
"Region/Wilayah": [
"Asia",
"Europe",
"Middle East",
"Oceania",
"North America"
],
"Revenue/Pendapatan": [
3004,
3032,
3550,
4381,
4084
],
"Sales/Penjualan": null,
"Stock/Stok": null,
"Visitors/Pengunjung": null
}
|
import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'City/Kota': ['Jakarta', 'Paris', 'Dubai', 'Sydney', 'New York'], 'Country/Negara': ['Indonesia', 'France', 'UAE', 'Australia', 'US'], 'Region/Wilayah': ['Asia', 'Europe', 'Middle East', 'Oceania', 'North America'], 'Latitude/Lintang': [64.9689, 88.3034, -73.343, -20.3746, -72.4041], 'Longitude/Bujur': [170.481, 8.1583, -41.3543, -167.0418, 141.9367], 'Revenue/Pendapatan': [3004, 3032, 3550, 4381, 4084], 'Market Share/Pangsa Pasar': [0.2, 0.46, 0.11, 0.35, 0.21], 'Growth Rate/Pertumbuhan': [-0.02, 0.06, 0.24, 0.22, 0.06]})
# Basic data cleaning
df = df.dropna() # Remove missing values
df = df.drop_duplicates() # Remove duplicates
# Correlation analysis
corr_matrix = df.corr(numeric_only=True)
fig = px.imshow(corr_matrix, text_auto=True,
title='Correlation Matrix')
fig.show()
|
Optimize market share customer segment
|
{
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": null,
"City/Kota": [
"Tokyo",
"São Paulo",
"Singapore",
"New York",
"Sydney"
],
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": [
"Japan",
"Brazil",
"Singapore",
"US",
"Australia"
],
"Customer ID/ID Pelanggan": null,
"Date/Tanggal": null,
"Discount/Diskon": null,
"Gender/Jenis Kelamin": null,
"Growth Rate/Pertumbuhan": [
0.1,
0.25,
0.21,
0.30000000000000004,
0.16
],
"Income/Pendapatan": null,
"Last Purchase Date/Tanggal Pembelian Terakhir": null,
"Latitude/Lintang": [
-35.5345,
-8.5461,
-14.5142,
-11.0631,
-54.8508
],
"Longitude/Bujur": [
27.2884,
147.2505,
-28.9529,
89.8145,
-152.5137
],
"Loyalty Score/Skor Loyalitas": null,
"Market Share/Pangsa Pasar": [
0.37,
0.41000000000000003,
0.43,
0.43,
0.34
],
"Price/Harga": null,
"Product/Produk": null,
"Region/Wilayah": [
"Asia",
"South America",
"Asia",
"North America",
"Oceania"
],
"Revenue/Pendapatan": [
1140,
1372,
3001,
4278,
4647
],
"Sales/Penjualan": null,
"Stock/Stok": null,
"Visitors/Pengunjung": null
}
|
import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'City/Kota': ['Tokyo', 'São Paulo', 'Singapore', 'New York', 'Sydney'], 'Country/Negara': ['Japan', 'Brazil', 'Singapore', 'US', 'Australia'], 'Region/Wilayah': ['Asia', 'South America', 'Asia', 'North America', 'Oceania'], 'Latitude/Lintang': [-35.5345, -8.5461, -14.5142, -11.0631, -54.8508], 'Longitude/Bujur': [27.2884, 147.2505, -28.9529, 89.8145, -152.5137], 'Revenue/Pendapatan': [1140, 1372, 3001, 4278, 4647], 'Market Share/Pangsa Pasar': [0.37, 0.41, 0.43, 0.43, 0.34], 'Growth Rate/Pertumbuhan': [0.1, 0.25, 0.21, 0.3, 0.16]})
# Basic data cleaning
df = df.dropna() # Remove missing values
df = df.drop_duplicates() # Remove duplicates
# Customer segmentation using KMeans
from sklearn.cluster import KMeans
from sklearn.preprocessing import StandardScaler
# Select features and scale
X = df[['Age/Usia', 'Income/Pendapatan', 'Loyalty Score/Skor Loyalitas']]
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)
# Cluster into 3 segments
kmeans = KMeans(n_clusters=3, random_state=42)
df['Segment/Segmen'] = kmeans.fit_predict(X_scaled)
# Visualize segments
fig = px.scatter_3d(df, x='Age/Usia', y='Income/Pendapatan',
z='Loyalty Score/Skor Loyalitas',
color='Segment/Segmen',
title='Customer Segments')
fig.show()
|
Optimalkan pendapatan kota
|
{
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": [
132.44,
70.91,
109.81,
74.49,
126.92,
171.3,
173.45,
198.28,
124.9,
160.92,
55.64,
132.64,
170,
193.39,
132.39,
115.33,
140.15,
162.13,
169.97,
166.54,
99.65,
127.21,
115.01,
130.54,
163.79,
64.24,
190.02,
143.98,
89.06,
139.37
],
"Category/Kategori": null,
"City/Kota": null,
"Conversion Rate/Rasio Konversi": [
0.0261,
0.047900000000000005,
0.027800000000000002,
0.0618,
0.0409,
0.0383,
0.0882,
0.051000000000000004,
0.0106,
0.0567,
0.011600000000000001,
0.0313,
0.054,
0.044700000000000004,
0.0128,
0.0454,
0.08650000000000001,
0.043300000000000005,
0.0626,
0.06,
0.0723,
0.0341,
0.09630000000000001,
0.057800000000000004,
0.0181,
0.0568,
0.0729,
0.0658,
0.0679,
0.09720000000000001
],
"Country/Negara": null,
"Customer ID/ID Pelanggan": null,
"Date/Tanggal": [
"2025-03-23",
"2025-03-24",
"2025-03-25",
"2025-03-26",
"2025-03-27",
"2025-03-28",
"2025-03-29",
"2025-03-30",
"2025-03-31",
"2025-04-01",
"2025-04-02",
"2025-04-03",
"2025-04-04",
"2025-04-05",
"2025-04-06",
"2025-04-07",
"2025-04-08",
"2025-04-09",
"2025-04-10",
"2025-04-11",
"2025-04-12",
"2025-04-13",
"2025-04-14",
"2025-04-15",
"2025-04-16",
"2025-04-17",
"2025-04-18",
"2025-04-19",
"2025-04-20",
"2025-04-21"
],
"Discount/Diskon": null,
"Gender/Jenis Kelamin": null,
"Growth Rate/Pertumbuhan": null,
"Income/Pendapatan": null,
"Last Purchase Date/Tanggal Pembelian Terakhir": null,
"Latitude/Lintang": null,
"Longitude/Bujur": null,
"Loyalty Score/Skor Loyalitas": null,
"Market Share/Pangsa Pasar": null,
"Price/Harga": null,
"Product/Produk": null,
"Region/Wilayah": null,
"Revenue/Pendapatan": [
4016.54,
4063.13,
4145.97,
4205.44,
4165.05,
4141.48,
4075.21,
3977.46,
3888.97,
3864.29,
3742.99,
3792.21,
3721.44,
3905.74,
3951.98,
3974.54,
4004.43,
4060.04,
4094.78,
4054.88,
4100.36,
3927.9,
3972.4,
3809.92,
3762.6,
3606.27,
3620.15,
3694.61,
3786.58,
3887.17
],
"Sales/Penjualan": null,
"Stock/Stok": null,
"Visitors/Pengunjung": [
181,
266,
220,
219,
103,
282,
287,
143,
61,
248,
106,
114,
268,
230,
151,
215,
204,
189,
302,
153,
125,
266,
204,
211,
221,
178,
164,
267,
157,
75
]
}
|
import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Date/Tanggal': ['2025-03-23', '2025-03-24', '2025-03-25', '2025-03-26', '2025-03-27', '2025-03-28', '2025-03-29', '2025-03-30', '2025-03-31', '2025-04-01', '2025-04-02', '2025-04-03', '2025-04-04', '2025-04-05', '2025-04-06', '2025-04-07', '2025-04-08', '2025-04-09', '2025-04-10', '2025-04-11', '2025-04-12', '2025-04-13', '2025-04-14', '2025-04-15', '2025-04-16', '2025-04-17', '2025-04-18', '2025-04-19', '2025-04-20', '2025-04-21'], 'Revenue/Pendapatan': [4016.54, 4063.13, 4145.97, 4205.44, 4165.05, 4141.48, 4075.21, 3977.46, 3888.97, 3864.29, 3742.99, 3792.21, 3721.44, 3905.74, 3951.98, 3974.54, 4004.43, 4060.04, 4094.78, 4054.88, 4100.36, 3927.9, 3972.4, 3809.92, 3762.6, 3606.27, 3620.15, 3694.61, 3786.58, 3887.17], 'Visitors/Pengunjung': [181, 266, 220, 219, 103, 282, 287, 143, 61, 248, 106, 114, 268, 230, 151, 215, 204, 189, 302, 153, 125, 266, 204, 211, 221, 178, 164, 267, 157, 75], 'Conversion Rate/Rasio Konversi': [0.0261, 0.0479, 0.0278, 0.0618, 0.0409, 0.0383, 0.0882, 0.051, 0.0106, 0.0567, 0.0116, 0.0313, 0.054, 0.0447, 0.0128, 0.0454, 0.0865, 0.0433, 0.0626, 0.06, 0.0723, 0.0341, 0.0963, 0.0578, 0.0181, 0.0568, 0.0729, 0.0658, 0.0679, 0.0972], 'Avg. Order Value/Nilai Pesanan Rata2': [132.44, 70.91, 109.81, 74.49, 126.92, 171.3, 173.45, 198.28, 124.9, 160.92, 55.64, 132.64, 170.0, 193.39, 132.39, 115.33, 140.15, 162.13, 169.97, 166.54, 99.65, 127.21, 115.01, 130.54, 163.79, 64.24, 190.02, 143.98, 89.06, 139.37]})
# Basic data cleaning
df = df.dropna() # Remove missing values
df = df.drop_duplicates() # Remove duplicates
# Comprehensive data analysis
analysis_result = df.describe(include='all')
print(analysis_result)
# Additional insights
print('\nTop performing items:')
print(df.nlargest(3, df.columns[1]))
print('\nTrend analysis:')
if 'Date/Tanggal' in df.columns:
df['Month/Bulan'] = pd.to_datetime(df['Date/Tanggal']).dt.month
monthly_trend = df.groupby('Month/Bulan').mean(numeric_only=True)
print(monthly_trend)
|
Optimalkan stok kategori
|
{
"Age/Usia": [
51,
36,
20,
22,
24,
28,
41,
51,
26,
24,
47,
27,
45,
53,
58,
61,
52,
19,
55,
22,
59,
64,
43,
51,
52,
34,
20,
29,
25,
19,
45,
57,
49,
28,
44,
65,
48,
52,
69,
42,
65,
49,
60,
52,
63,
26,
63,
64,
51,
42,
42,
18,
27,
36
],
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": null,
"City/Kota": null,
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": null,
"Customer ID/ID Pelanggan": [
"CUST_0001",
"CUST_0002",
"CUST_0003",
"CUST_0004",
"CUST_0005",
"CUST_0006",
"CUST_0007",
"CUST_0008",
"CUST_0009",
"CUST_0010",
"CUST_0011",
"CUST_0012",
"CUST_0013",
"CUST_0014",
"CUST_0015",
"CUST_0016",
"CUST_0017",
"CUST_0018",
"CUST_0019",
"CUST_0020",
"CUST_0021",
"CUST_0022",
"CUST_0023",
"CUST_0024",
"CUST_0025",
"CUST_0026",
"CUST_0027",
"CUST_0028",
"CUST_0029",
"CUST_0030",
"CUST_0031",
"CUST_0032",
"CUST_0033",
"CUST_0034",
"CUST_0035",
"CUST_0036",
"CUST_0037",
"CUST_0038",
"CUST_0039",
"CUST_0040",
"CUST_0041",
"CUST_0042",
"CUST_0043",
"CUST_0044",
"CUST_0045",
"CUST_0046",
"CUST_0047",
"CUST_0048",
"CUST_0049",
"CUST_0050",
"CUST_0051",
"CUST_0052",
"CUST_0053",
"CUST_0054"
],
"Date/Tanggal": null,
"Discount/Diskon": null,
"Gender/Jenis Kelamin": [
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Other/Lainnya",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki"
],
"Growth Rate/Pertumbuhan": null,
"Income/Pendapatan": [
5000,
1000,
8000,
1000,
6000,
2000,
8000,
4000,
3000,
2000,
7000,
9000,
8000,
8000,
9000,
2000,
9000,
2000,
10000,
10000,
9000,
4000,
8000,
2000,
6000,
6000,
2000,
1000,
2000,
6000,
8000,
10000,
9000,
2000,
5000,
6000,
9000,
5000,
4000,
2000,
2000,
6000,
6000,
1000,
1000,
8000,
1000,
10000,
8000,
2000,
4000,
2000,
8000,
5000
],
"Last Purchase Date/Tanggal Pembelian Terakhir": [
"2024-09-24",
"2025-04-16",
"2024-06-05",
"2024-07-27",
"2025-01-14",
"2025-03-20",
"2024-08-28",
"2024-06-19",
"2024-11-02",
"2025-03-24",
"2025-01-09",
"2024-07-29",
"2024-09-22",
"2024-12-11",
"2024-04-28",
"2024-08-23",
"2024-12-12",
"2024-07-09",
"2025-03-20",
"2025-01-31",
"2024-10-14",
"2024-08-29",
"2024-07-25",
"2024-10-09",
"2025-01-18",
"2025-03-13",
"2025-02-11",
"2024-05-29",
"2024-11-18",
"2025-04-13",
"2024-07-14",
"2024-12-16",
"2024-12-15",
"2024-06-10",
"2025-02-18",
"2025-01-05",
"2024-08-08",
"2025-03-02",
"2024-08-17",
"2024-08-18",
"2025-01-12",
"2024-07-04",
"2025-02-10",
"2024-12-30",
"2024-11-07",
"2024-08-25",
"2025-03-31",
"2024-12-30",
"2025-02-24",
"2025-02-12",
"2024-09-05",
"2024-08-22",
"2024-09-04",
"2025-01-02"
],
"Latitude/Lintang": null,
"Longitude/Bujur": null,
"Loyalty Score/Skor Loyalitas": [
82,
78,
51,
32,
10,
47,
99,
46,
96,
72,
27,
81,
27,
30,
40,
13,
46,
25,
10,
11,
57,
22,
58,
98,
26,
79,
28,
41,
67,
39,
93,
8,
32,
80,
34,
5,
39,
79,
41,
45,
15,
96,
74,
88,
15,
30,
44,
84,
75,
100,
27,
31,
68,
29
],
"Market Share/Pangsa Pasar": null,
"Price/Harga": null,
"Product/Produk": null,
"Region/Wilayah": null,
"Revenue/Pendapatan": null,
"Sales/Penjualan": null,
"Stock/Stok": null,
"Visitors/Pengunjung": null
}
|
import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Customer ID/ID Pelanggan': ['CUST_0001', 'CUST_0002', 'CUST_0003', 'CUST_0004', 'CUST_0005', 'CUST_0006', 'CUST_0007', 'CUST_0008', 'CUST_0009', 'CUST_0010', 'CUST_0011', 'CUST_0012', 'CUST_0013', 'CUST_0014', 'CUST_0015', 'CUST_0016', 'CUST_0017', 'CUST_0018', 'CUST_0019', 'CUST_0020', 'CUST_0021', 'CUST_0022', 'CUST_0023', 'CUST_0024', 'CUST_0025', 'CUST_0026', 'CUST_0027', 'CUST_0028', 'CUST_0029', 'CUST_0030', 'CUST_0031', 'CUST_0032', 'CUST_0033', 'CUST_0034', 'CUST_0035', 'CUST_0036', 'CUST_0037', 'CUST_0038', 'CUST_0039', 'CUST_0040', 'CUST_0041', 'CUST_0042', 'CUST_0043', 'CUST_0044', 'CUST_0045', 'CUST_0046', 'CUST_0047', 'CUST_0048', 'CUST_0049', 'CUST_0050', 'CUST_0051', 'CUST_0052', 'CUST_0053', 'CUST_0054'], 'Age/Usia': [51, 36, 20, 22, 24, 28, 41, 51, 26, 24, 47, 27, 45, 53, 58, 61, 52, 19, 55, 22, 59, 64, 43, 51, 52, 34, 20, 29, 25, 19, 45, 57, 49, 28, 44, 65, 48, 52, 69, 42, 65, 49, 60, 52, 63, 26, 63, 64, 51, 42, 42, 18, 27, 36], 'Gender/Jenis Kelamin': ['Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Other/Lainnya', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki'], 'Income/Pendapatan': [5000, 1000, 8000, 1000, 6000, 2000, 8000, 4000, 3000, 2000, 7000, 9000, 8000, 8000, 9000, 2000, 9000, 2000, 10000, 10000, 9000, 4000, 8000, 2000, 6000, 6000, 2000, 1000, 2000, 6000, 8000, 10000, 9000, 2000, 5000, 6000, 9000, 5000, 4000, 2000, 2000, 6000, 6000, 1000, 1000, 8000, 1000, 10000, 8000, 2000, 4000, 2000, 8000, 5000], 'Loyalty Score/Skor Loyalitas': [82, 78, 51, 32, 10, 47, 99, 46, 96, 72, 27, 81, 27, 30, 40, 13, 46, 25, 10, 11, 57, 22, 58, 98, 26, 79, 28, 41, 67, 39, 93, 8, 32, 80, 34, 5, 39, 79, 41, 45, 15, 96, 74, 88, 15, 30, 44, 84, 75, 100, 27, 31, 68, 29], 'Last Purchase Date/Tanggal Pembelian Terakhir': ['2024-09-24', '2025-04-16', '2024-06-05', '2024-07-27', '2025-01-14', '2025-03-20', '2024-08-28', '2024-06-19', '2024-11-02', '2025-03-24', '2025-01-09', '2024-07-29', '2024-09-22', '2024-12-11', '2024-04-28', '2024-08-23', '2024-12-12', '2024-07-09', '2025-03-20', '2025-01-31', '2024-10-14', '2024-08-29', '2024-07-25', '2024-10-09', '2025-01-18', '2025-03-13', '2025-02-11', '2024-05-29', '2024-11-18', '2025-04-13', '2024-07-14', '2024-12-16', '2024-12-15', '2024-06-10', '2025-02-18', '2025-01-05', '2024-08-08', '2025-03-02', '2024-08-17', '2024-08-18', '2025-01-12', '2024-07-04', '2025-02-10', '2024-12-30', '2024-11-07', '2024-08-25', '2025-03-31', '2024-12-30', '2025-02-24', '2025-02-12', '2024-09-05', '2024-08-22', '2024-09-04', '2025-01-02']})
# Basic data cleaning
df = df.dropna() # Remove missing values
df = df.drop_duplicates() # Remove duplicates
# Comprehensive data analysis
analysis_result = df.describe(include='all')
print(analysis_result)
# Additional insights
print('\nTop performing items:')
print(df.nlargest(3, df.columns[1]))
print('\nTrend analysis:')
if 'Date/Tanggal' in df.columns:
df['Month/Bulan'] = pd.to_datetime(df['Date/Tanggal']).dt.month
monthly_trend = df.groupby('Month/Bulan').mean(numeric_only=True)
print(monthly_trend)
|
Forecast price for/untuk week
|
{
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": [
92.23,
177.68,
136.39,
66.14,
104.57,
65.07,
156.06,
137.85,
134.79,
178.3,
60.41,
144.51,
60.44,
71.36,
82.55,
144.06,
91.44,
104.54,
135.04,
121.7,
94.92,
182.61,
109.86,
114.54,
192.17,
134.92,
159.2,
70.35,
96.29,
157.66
],
"Category/Kategori": null,
"City/Kota": null,
"Conversion Rate/Rasio Konversi": [
0.0574,
0.0388,
0.0148,
0.0432,
0.056100000000000004,
0.0912,
0.031100000000000003,
0.0949,
0.0804,
0.0862,
0.0536,
0.027200000000000002,
0.0129,
0.09620000000000001,
0.0437,
0.0506,
0.061700000000000005,
0.0424,
0.0375,
0.09960000000000001,
0.0378,
0.0954,
0.0858,
0.041,
0.06420000000000001,
0.09040000000000001,
0.0409,
0.09390000000000001,
0.07830000000000001,
0.0829
],
"Country/Negara": null,
"Customer ID/ID Pelanggan": null,
"Date/Tanggal": [
"2025-03-23",
"2025-03-24",
"2025-03-25",
"2025-03-26",
"2025-03-27",
"2025-03-28",
"2025-03-29",
"2025-03-30",
"2025-03-31",
"2025-04-01",
"2025-04-02",
"2025-04-03",
"2025-04-04",
"2025-04-05",
"2025-04-06",
"2025-04-07",
"2025-04-08",
"2025-04-09",
"2025-04-10",
"2025-04-11",
"2025-04-12",
"2025-04-13",
"2025-04-14",
"2025-04-15",
"2025-04-16",
"2025-04-17",
"2025-04-18",
"2025-04-19",
"2025-04-20",
"2025-04-21"
],
"Discount/Diskon": null,
"Gender/Jenis Kelamin": null,
"Growth Rate/Pertumbuhan": null,
"Income/Pendapatan": null,
"Last Purchase Date/Tanggal Pembelian Terakhir": null,
"Latitude/Lintang": null,
"Longitude/Bujur": null,
"Loyalty Score/Skor Loyalitas": null,
"Market Share/Pangsa Pasar": null,
"Price/Harga": null,
"Product/Produk": null,
"Region/Wilayah": null,
"Revenue/Pendapatan": [
4078.61,
4256.05,
4256.89,
4302.49,
4388.02,
4310.42,
4289.44,
4149.84,
4164.29,
4052.32,
4087.27,
4018.09,
4034.44,
4053.15,
4120.91,
4372.48,
4383.22,
4522.49,
4520.14,
4481.34,
4350.28,
4376.4,
4330.07,
4365.68,
4111.23,
4112.28,
4200.71,
4195.32,
4305.65,
4468.31
],
"Sales/Penjualan": null,
"Stock/Stok": null,
"Visitors/Pengunjung": [
159,
273,
224,
103,
88,
131,
206,
149,
48,
265,
65,
185,
130,
78,
84,
304,
217,
112,
272,
131,
153,
91,
42,
212,
237,
72,
95,
192,
273,
203
]
}
|
import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Date/Tanggal': ['2025-03-23', '2025-03-24', '2025-03-25', '2025-03-26', '2025-03-27', '2025-03-28', '2025-03-29', '2025-03-30', '2025-03-31', '2025-04-01', '2025-04-02', '2025-04-03', '2025-04-04', '2025-04-05', '2025-04-06', '2025-04-07', '2025-04-08', '2025-04-09', '2025-04-10', '2025-04-11', '2025-04-12', '2025-04-13', '2025-04-14', '2025-04-15', '2025-04-16', '2025-04-17', '2025-04-18', '2025-04-19', '2025-04-20', '2025-04-21'], 'Revenue/Pendapatan': [4078.61, 4256.05, 4256.89, 4302.49, 4388.02, 4310.42, 4289.44, 4149.84, 4164.29, 4052.32, 4087.27, 4018.09, 4034.44, 4053.15, 4120.91, 4372.48, 4383.22, 4522.49, 4520.14, 4481.34, 4350.28, 4376.4, 4330.07, 4365.68, 4111.23, 4112.28, 4200.71, 4195.32, 4305.65, 4468.31], 'Visitors/Pengunjung': [159, 273, 224, 103, 88, 131, 206, 149, 48, 265, 65, 185, 130, 78, 84, 304, 217, 112, 272, 131, 153, 91, 42, 212, 237, 72, 95, 192, 273, 203], 'Conversion Rate/Rasio Konversi': [0.0574, 0.0388, 0.0148, 0.0432, 0.0561, 0.0912, 0.0311, 0.0949, 0.0804, 0.0862, 0.0536, 0.0272, 0.0129, 0.0962, 0.0437, 0.0506, 0.0617, 0.0424, 0.0375, 0.0996, 0.0378, 0.0954, 0.0858, 0.041, 0.0642, 0.0904, 0.0409, 0.0939, 0.0783, 0.0829], 'Avg. Order Value/Nilai Pesanan Rata2': [92.23, 177.68, 136.39, 66.14, 104.57, 65.07, 156.06, 137.85, 134.79, 178.3, 60.41, 144.51, 60.44, 71.36, 82.55, 144.06, 91.44, 104.54, 135.04, 121.7, 94.92, 182.61, 109.86, 114.54, 192.17, 134.92, 159.2, 70.35, 96.29, 157.66]})
# Basic data cleaning
df = df.dropna() # Remove missing values
df = df.drop_duplicates() # Remove duplicates
# Time series forecasting
from statsmodels.tsa.arima.model import ARIMA
# Prepare data
ts_data = df.set_index('Date/Tanggal')['Revenue/Pendapatan']
model = ARIMA(ts_data, order=(1,1,1))
model_fit = model.fit()
forecast = model_fit.forecast(steps=7)
print('Forecast for next 7 days:', forecast)
|
Optimalkan harga wilayah
|
{
"Age/Usia": [
29,
36,
21,
52,
46,
50,
64,
18,
24,
60,
52,
48,
23,
26,
66,
22,
58,
40,
63,
35,
56,
59,
23,
65,
31,
49,
50,
20,
47,
24,
40,
22,
46,
29,
24,
41,
26,
58,
65,
65,
40,
58,
29,
21,
32,
46,
31,
20,
20,
40,
70,
70,
63
],
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": null,
"City/Kota": null,
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": null,
"Customer ID/ID Pelanggan": [
"CUST_0001",
"CUST_0002",
"CUST_0003",
"CUST_0004",
"CUST_0005",
"CUST_0006",
"CUST_0007",
"CUST_0008",
"CUST_0009",
"CUST_0010",
"CUST_0011",
"CUST_0012",
"CUST_0013",
"CUST_0014",
"CUST_0015",
"CUST_0016",
"CUST_0017",
"CUST_0018",
"CUST_0019",
"CUST_0020",
"CUST_0021",
"CUST_0022",
"CUST_0023",
"CUST_0024",
"CUST_0025",
"CUST_0026",
"CUST_0027",
"CUST_0028",
"CUST_0029",
"CUST_0030",
"CUST_0031",
"CUST_0032",
"CUST_0033",
"CUST_0034",
"CUST_0035",
"CUST_0036",
"CUST_0037",
"CUST_0038",
"CUST_0039",
"CUST_0040",
"CUST_0041",
"CUST_0042",
"CUST_0043",
"CUST_0044",
"CUST_0045",
"CUST_0046",
"CUST_0047",
"CUST_0048",
"CUST_0049",
"CUST_0050",
"CUST_0051",
"CUST_0052",
"CUST_0053"
],
"Date/Tanggal": null,
"Discount/Diskon": null,
"Gender/Jenis Kelamin": [
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Other/Lainnya",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki"
],
"Growth Rate/Pertumbuhan": null,
"Income/Pendapatan": [
2000,
5000,
3000,
10000,
3000,
4000,
4000,
2000,
4000,
7000,
5000,
9000,
5000,
1000,
3000,
9000,
8000,
8000,
7000,
5000,
3000,
2000,
4000,
4000,
7000,
3000,
2000,
5000,
5000,
8000,
7000,
4000,
8000,
5000,
2000,
3000,
8000,
9000,
3000,
5000,
5000,
2000,
5000,
2000,
4000,
5000,
5000,
8000,
6000,
4000,
8000,
5000,
7000
],
"Last Purchase Date/Tanggal Pembelian Terakhir": [
"2025-01-20",
"2024-11-24",
"2024-08-10",
"2024-12-31",
"2024-11-16",
"2024-11-05",
"2024-10-16",
"2024-09-21",
"2024-12-18",
"2024-08-28",
"2025-01-31",
"2024-10-25",
"2025-04-03",
"2024-12-02",
"2024-12-20",
"2024-08-27",
"2024-06-29",
"2024-06-04",
"2024-08-26",
"2024-07-02",
"2024-08-08",
"2025-03-26",
"2024-10-10",
"2024-11-30",
"2024-05-10",
"2024-09-28",
"2025-03-09",
"2025-03-10",
"2025-04-03",
"2025-01-13",
"2024-07-04",
"2024-11-20",
"2024-05-10",
"2024-11-10",
"2024-07-23",
"2024-05-16",
"2024-12-16",
"2024-08-25",
"2024-05-14",
"2024-08-28",
"2024-12-24",
"2025-03-08",
"2024-11-14",
"2024-05-31",
"2024-10-06",
"2024-11-08",
"2024-08-26",
"2024-06-24",
"2024-11-06",
"2025-02-13",
"2024-09-23",
"2024-05-13",
"2024-06-24"
],
"Latitude/Lintang": null,
"Longitude/Bujur": null,
"Loyalty Score/Skor Loyalitas": [
53,
40,
85,
78,
44,
18,
52,
41,
78,
60,
35,
60,
81,
38,
38,
19,
31,
92,
81,
64,
100,
78,
28,
82,
74,
86,
64,
96,
57,
72,
16,
16,
24,
100,
63,
21,
33,
62,
68,
60,
33,
59,
3,
14,
25,
60,
93,
17,
65,
52,
70,
93,
79
],
"Market Share/Pangsa Pasar": null,
"Price/Harga": null,
"Product/Produk": null,
"Region/Wilayah": null,
"Revenue/Pendapatan": null,
"Sales/Penjualan": null,
"Stock/Stok": null,
"Visitors/Pengunjung": null
}
|
import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Customer ID/ID Pelanggan': ['CUST_0001', 'CUST_0002', 'CUST_0003', 'CUST_0004', 'CUST_0005', 'CUST_0006', 'CUST_0007', 'CUST_0008', 'CUST_0009', 'CUST_0010', 'CUST_0011', 'CUST_0012', 'CUST_0013', 'CUST_0014', 'CUST_0015', 'CUST_0016', 'CUST_0017', 'CUST_0018', 'CUST_0019', 'CUST_0020', 'CUST_0021', 'CUST_0022', 'CUST_0023', 'CUST_0024', 'CUST_0025', 'CUST_0026', 'CUST_0027', 'CUST_0028', 'CUST_0029', 'CUST_0030', 'CUST_0031', 'CUST_0032', 'CUST_0033', 'CUST_0034', 'CUST_0035', 'CUST_0036', 'CUST_0037', 'CUST_0038', 'CUST_0039', 'CUST_0040', 'CUST_0041', 'CUST_0042', 'CUST_0043', 'CUST_0044', 'CUST_0045', 'CUST_0046', 'CUST_0047', 'CUST_0048', 'CUST_0049', 'CUST_0050', 'CUST_0051', 'CUST_0052', 'CUST_0053'], 'Age/Usia': [29, 36, 21, 52, 46, 50, 64, 18, 24, 60, 52, 48, 23, 26, 66, 22, 58, 40, 63, 35, 56, 59, 23, 65, 31, 49, 50, 20, 47, 24, 40, 22, 46, 29, 24, 41, 26, 58, 65, 65, 40, 58, 29, 21, 32, 46, 31, 20, 20, 40, 70, 70, 63], 'Gender/Jenis Kelamin': ['Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Other/Lainnya', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki'], 'Income/Pendapatan': [2000, 5000, 3000, 10000, 3000, 4000, 4000, 2000, 4000, 7000, 5000, 9000, 5000, 1000, 3000, 9000, 8000, 8000, 7000, 5000, 3000, 2000, 4000, 4000, 7000, 3000, 2000, 5000, 5000, 8000, 7000, 4000, 8000, 5000, 2000, 3000, 8000, 9000, 3000, 5000, 5000, 2000, 5000, 2000, 4000, 5000, 5000, 8000, 6000, 4000, 8000, 5000, 7000], 'Loyalty Score/Skor Loyalitas': [53, 40, 85, 78, 44, 18, 52, 41, 78, 60, 35, 60, 81, 38, 38, 19, 31, 92, 81, 64, 100, 78, 28, 82, 74, 86, 64, 96, 57, 72, 16, 16, 24, 100, 63, 21, 33, 62, 68, 60, 33, 59, 3, 14, 25, 60, 93, 17, 65, 52, 70, 93, 79], 'Last Purchase Date/Tanggal Pembelian Terakhir': ['2025-01-20', '2024-11-24', '2024-08-10', '2024-12-31', '2024-11-16', '2024-11-05', '2024-10-16', '2024-09-21', '2024-12-18', '2024-08-28', '2025-01-31', '2024-10-25', '2025-04-03', '2024-12-02', '2024-12-20', '2024-08-27', '2024-06-29', '2024-06-04', '2024-08-26', '2024-07-02', '2024-08-08', '2025-03-26', '2024-10-10', '2024-11-30', '2024-05-10', '2024-09-28', '2025-03-09', '2025-03-10', '2025-04-03', '2025-01-13', '2024-07-04', '2024-11-20', '2024-05-10', '2024-11-10', '2024-07-23', '2024-05-16', '2024-12-16', '2024-08-25', '2024-05-14', '2024-08-28', '2024-12-24', '2025-03-08', '2024-11-14', '2024-05-31', '2024-10-06', '2024-11-08', '2024-08-26', '2024-06-24', '2024-11-06', '2025-02-13', '2024-09-23', '2024-05-13', '2024-06-24']})
# Basic data cleaning
df = df.dropna() # Remove missing values
df = df.drop_duplicates() # Remove duplicates
# Comprehensive data analysis
analysis_result = df.describe(include='all')
print(analysis_result)
# Additional insights
print('\nTop performing items:')
print(df.nlargest(3, df.columns[1]))
print('\nTrend analysis:')
if 'Date/Tanggal' in df.columns:
df['Month/Bulan'] = pd.to_datetime(df['Date/Tanggal']).dt.month
monthly_trend = df.groupby('Month/Bulan').mean(numeric_only=True)
print(monthly_trend)
|
Forecast discount for/untuk month
|
{
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": null,
"City/Kota": [
"São Paulo",
"Mumbai",
"Dubai",
"Sydney",
"Berlin"
],
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": [
"Brazil",
"India",
"UAE",
"Australia",
"Germany"
],
"Customer ID/ID Pelanggan": null,
"Date/Tanggal": null,
"Discount/Diskon": null,
"Gender/Jenis Kelamin": null,
"Growth Rate/Pertumbuhan": [
0.12,
-0.04,
0.06,
0.08,
0.05
],
"Income/Pendapatan": null,
"Last Purchase Date/Tanggal Pembelian Terakhir": null,
"Latitude/Lintang": [
-27.0867,
-45.0015,
-62.9004,
-31.8637,
-55.4581
],
"Longitude/Bujur": [
-117.6781,
-78.733,
72.2282,
138.1928,
161.9819
],
"Loyalty Score/Skor Loyalitas": null,
"Market Share/Pangsa Pasar": [
0.19,
0.45,
0.15,
0.44,
0.39
],
"Price/Harga": null,
"Product/Produk": null,
"Region/Wilayah": [
"South America",
"Asia",
"Middle East",
"Oceania",
"Europe"
],
"Revenue/Pendapatan": [
3375,
3519,
3556,
3301,
2922
],
"Sales/Penjualan": null,
"Stock/Stok": null,
"Visitors/Pengunjung": null
}
|
import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'City/Kota': ['São Paulo', 'Mumbai', 'Dubai', 'Sydney', 'Berlin'], 'Country/Negara': ['Brazil', 'India', 'UAE', 'Australia', 'Germany'], 'Region/Wilayah': ['South America', 'Asia', 'Middle East', 'Oceania', 'Europe'], 'Latitude/Lintang': [-27.0867, -45.0015, -62.9004, -31.8637, -55.4581], 'Longitude/Bujur': [-117.6781, -78.733, 72.2282, 138.1928, 161.9819], 'Revenue/Pendapatan': [3375, 3519, 3556, 3301, 2922], 'Market Share/Pangsa Pasar': [0.19, 0.45, 0.15, 0.44, 0.39], 'Growth Rate/Pertumbuhan': [0.12, -0.04, 0.06, 0.08, 0.05]})
# Basic data cleaning
df = df.dropna() # Remove missing values
df = df.drop_duplicates() # Remove duplicates
# Time series forecasting
from statsmodels.tsa.arima.model import ARIMA
# Prepare data
ts_data = df.set_index('Date/Tanggal')['Revenue/Pendapatan']
model = ARIMA(ts_data, order=(1,1,1))
model_fit = model.fit()
forecast = model_fit.forecast(steps=7)
print('Forecast for next 7 days:', forecast)
|
Perkiraan pendapatan for/untuk tahun
|
{
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": [
"Electronics/Elektronik",
"Electronics/Elektronik",
"Electronics/Elektronik"
],
"City/Kota": null,
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": null,
"Customer ID/ID Pelanggan": null,
"Date/Tanggal": null,
"Discount/Diskon": [
0.14,
0.21,
0.28
],
"Gender/Jenis Kelamin": null,
"Growth Rate/Pertumbuhan": null,
"Income/Pendapatan": null,
"Last Purchase Date/Tanggal Pembelian Terakhir": null,
"Latitude/Lintang": null,
"Longitude/Bujur": null,
"Loyalty Score/Skor Loyalitas": null,
"Market Share/Pangsa Pasar": null,
"Price/Harga": [
101,
166.71,
92.88
],
"Product/Produk": [
"Headphones_1",
"Laptop_2",
"TV_3"
],
"Region/Wilayah": null,
"Revenue/Pendapatan": null,
"Sales/Penjualan": [
243,
438,
359
],
"Stock/Stok": [
70,
124,
135
],
"Visitors/Pengunjung": null
}
|
import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Product/Produk': ['Headphones_1', 'Laptop_2', 'TV_3'], 'Sales/Penjualan': [243, 438, 359], 'Price/Harga': [101.0, 166.71, 92.88], 'Category/Kategori': ['Electronics/Elektronik', 'Electronics/Elektronik', 'Electronics/Elektronik'], 'Discount/Diskon': [0.14, 0.21, 0.28], 'Stock/Stok': [70, 124, 135]})
# Basic data cleaning
df = df.dropna() # Remove missing values
df = df.drop_duplicates() # Remove duplicates
# Time series forecasting
from statsmodels.tsa.arima.model import ARIMA
# Prepare data
ts_data = df.set_index('Date/Tanggal')['Revenue/Pendapatan']
model = ARIMA(ts_data, order=(1,1,1))
model_fit = model.fit()
forecast = model_fit.forecast(steps=7)
print('Forecast for next 7 days:', forecast)
|
Segment discount category
|
{
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": [
"Fashion",
"Fashion",
"Fashion"
],
"City/Kota": null,
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": null,
"Customer ID/ID Pelanggan": null,
"Date/Tanggal": null,
"Discount/Diskon": [
0.1,
0.1,
0.05
],
"Gender/Jenis Kelamin": null,
"Growth Rate/Pertumbuhan": null,
"Income/Pendapatan": null,
"Last Purchase Date/Tanggal Pembelian Terakhir": null,
"Latitude/Lintang": null,
"Longitude/Bujur": null,
"Loyalty Score/Skor Loyalitas": null,
"Market Share/Pangsa Pasar": null,
"Price/Harga": [
71.49,
24.93,
134.44
],
"Product/Produk": [
"T-Shirt_1",
"Jeans_2",
"Shoes_3"
],
"Region/Wilayah": null,
"Revenue/Pendapatan": null,
"Sales/Penjualan": [
829,
436,
853
],
"Stock/Stok": [
89,
129,
138
],
"Visitors/Pengunjung": null
}
|
import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Product/Produk': ['T-Shirt_1', 'Jeans_2', 'Shoes_3'], 'Sales/Penjualan': [829, 436, 853], 'Price/Harga': [71.49, 24.93, 134.44], 'Category/Kategori': ['Fashion', 'Fashion', 'Fashion'], 'Discount/Diskon': [0.1, 0.1, 0.05], 'Stock/Stok': [89, 129, 138]})
# Basic data cleaning
df = df.dropna() # Remove missing values
df = df.drop_duplicates() # Remove duplicates
# Customer segmentation using KMeans
from sklearn.cluster import KMeans
from sklearn.preprocessing import StandardScaler
# Select features and scale
X = df[['Age/Usia', 'Income/Pendapatan', 'Loyalty Score/Skor Loyalitas']]
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)
# Cluster into 3 segments
kmeans = KMeans(n_clusters=3, random_state=42)
df['Segment/Segmen'] = kmeans.fit_predict(X_scaled)
# Visualize segments
fig = px.scatter_3d(df, x='Age/Usia', y='Income/Pendapatan',
z='Loyalty Score/Skor Loyalitas',
color='Segment/Segmen',
title='Customer Segments')
fig.show()
|
Bandingkan pendapatan jenis kelamin
|
{
"Age/Usia": [
49,
51,
61,
57,
62,
69,
41,
45,
67,
32,
24,
51,
33,
66,
54,
18,
42,
70,
49,
39,
20,
25,
59,
19,
44,
35,
45,
63,
68,
59,
40,
57,
31,
44,
18,
45,
67,
38,
61,
59,
38,
68,
18,
62,
28,
24,
53,
51,
61,
42,
38,
22,
66,
24
],
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": null,
"City/Kota": null,
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": null,
"Customer ID/ID Pelanggan": [
"CUST_0001",
"CUST_0002",
"CUST_0003",
"CUST_0004",
"CUST_0005",
"CUST_0006",
"CUST_0007",
"CUST_0008",
"CUST_0009",
"CUST_0010",
"CUST_0011",
"CUST_0012",
"CUST_0013",
"CUST_0014",
"CUST_0015",
"CUST_0016",
"CUST_0017",
"CUST_0018",
"CUST_0019",
"CUST_0020",
"CUST_0021",
"CUST_0022",
"CUST_0023",
"CUST_0024",
"CUST_0025",
"CUST_0026",
"CUST_0027",
"CUST_0028",
"CUST_0029",
"CUST_0030",
"CUST_0031",
"CUST_0032",
"CUST_0033",
"CUST_0034",
"CUST_0035",
"CUST_0036",
"CUST_0037",
"CUST_0038",
"CUST_0039",
"CUST_0040",
"CUST_0041",
"CUST_0042",
"CUST_0043",
"CUST_0044",
"CUST_0045",
"CUST_0046",
"CUST_0047",
"CUST_0048",
"CUST_0049",
"CUST_0050",
"CUST_0051",
"CUST_0052",
"CUST_0053",
"CUST_0054"
],
"Date/Tanggal": null,
"Discount/Diskon": null,
"Gender/Jenis Kelamin": [
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan"
],
"Growth Rate/Pertumbuhan": null,
"Income/Pendapatan": [
4000,
9000,
2000,
9000,
3000,
5000,
7000,
1000,
2000,
10000,
5000,
5000,
4000,
3000,
3000,
3000,
6000,
7000,
6000,
4000,
9000,
5000,
8000,
8000,
5000,
1000,
5000,
8000,
7000,
2000,
6000,
7000,
5000,
9000,
9000,
10000,
1000,
7000,
7000,
9000,
1000,
9000,
9000,
1000,
7000,
6000,
5000,
10000,
10000,
4000,
6000,
10000,
1000,
1000
],
"Last Purchase Date/Tanggal Pembelian Terakhir": [
"2024-07-05",
"2024-09-27",
"2024-11-30",
"2024-07-28",
"2024-10-22",
"2024-11-28",
"2025-01-30",
"2024-10-05",
"2025-02-10",
"2024-12-10",
"2025-01-28",
"2025-01-22",
"2024-10-25",
"2024-04-24",
"2024-09-09",
"2024-09-30",
"2024-04-30",
"2024-08-11",
"2024-08-24",
"2024-09-12",
"2025-02-26",
"2025-04-06",
"2024-05-12",
"2024-05-19",
"2024-06-06",
"2025-02-19",
"2024-05-04",
"2024-06-10",
"2024-05-13",
"2025-02-02",
"2025-01-27",
"2024-12-12",
"2025-01-21",
"2025-01-15",
"2024-08-29",
"2024-08-16",
"2024-09-07",
"2024-07-25",
"2024-05-22",
"2024-08-05",
"2024-10-25",
"2024-11-05",
"2025-04-08",
"2024-11-04",
"2024-11-23",
"2024-12-11",
"2024-07-01",
"2024-12-01",
"2024-09-20",
"2024-12-23",
"2025-01-02",
"2025-03-31",
"2024-09-15",
"2024-10-22"
],
"Latitude/Lintang": null,
"Longitude/Bujur": null,
"Loyalty Score/Skor Loyalitas": [
68,
70,
75,
45,
92,
50,
25,
52,
47,
14,
44,
28,
62,
74,
37,
77,
74,
56,
91,
50,
64,
53,
4,
66,
41,
30,
39,
29,
82,
3,
81,
30,
77,
7,
67,
52,
5,
9,
92,
45,
34,
2,
51,
79,
31,
53,
91,
10,
69,
65,
7,
94,
79,
12
],
"Market Share/Pangsa Pasar": null,
"Price/Harga": null,
"Product/Produk": null,
"Region/Wilayah": null,
"Revenue/Pendapatan": null,
"Sales/Penjualan": null,
"Stock/Stok": null,
"Visitors/Pengunjung": null
}
|
import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Customer ID/ID Pelanggan': ['CUST_0001', 'CUST_0002', 'CUST_0003', 'CUST_0004', 'CUST_0005', 'CUST_0006', 'CUST_0007', 'CUST_0008', 'CUST_0009', 'CUST_0010', 'CUST_0011', 'CUST_0012', 'CUST_0013', 'CUST_0014', 'CUST_0015', 'CUST_0016', 'CUST_0017', 'CUST_0018', 'CUST_0019', 'CUST_0020', 'CUST_0021', 'CUST_0022', 'CUST_0023', 'CUST_0024', 'CUST_0025', 'CUST_0026', 'CUST_0027', 'CUST_0028', 'CUST_0029', 'CUST_0030', 'CUST_0031', 'CUST_0032', 'CUST_0033', 'CUST_0034', 'CUST_0035', 'CUST_0036', 'CUST_0037', 'CUST_0038', 'CUST_0039', 'CUST_0040', 'CUST_0041', 'CUST_0042', 'CUST_0043', 'CUST_0044', 'CUST_0045', 'CUST_0046', 'CUST_0047', 'CUST_0048', 'CUST_0049', 'CUST_0050', 'CUST_0051', 'CUST_0052', 'CUST_0053', 'CUST_0054'], 'Age/Usia': [49, 51, 61, 57, 62, 69, 41, 45, 67, 32, 24, 51, 33, 66, 54, 18, 42, 70, 49, 39, 20, 25, 59, 19, 44, 35, 45, 63, 68, 59, 40, 57, 31, 44, 18, 45, 67, 38, 61, 59, 38, 68, 18, 62, 28, 24, 53, 51, 61, 42, 38, 22, 66, 24], 'Gender/Jenis Kelamin': ['Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan'], 'Income/Pendapatan': [4000, 9000, 2000, 9000, 3000, 5000, 7000, 1000, 2000, 10000, 5000, 5000, 4000, 3000, 3000, 3000, 6000, 7000, 6000, 4000, 9000, 5000, 8000, 8000, 5000, 1000, 5000, 8000, 7000, 2000, 6000, 7000, 5000, 9000, 9000, 10000, 1000, 7000, 7000, 9000, 1000, 9000, 9000, 1000, 7000, 6000, 5000, 10000, 10000, 4000, 6000, 10000, 1000, 1000], 'Loyalty Score/Skor Loyalitas': [68, 70, 75, 45, 92, 50, 25, 52, 47, 14, 44, 28, 62, 74, 37, 77, 74, 56, 91, 50, 64, 53, 4, 66, 41, 30, 39, 29, 82, 3, 81, 30, 77, 7, 67, 52, 5, 9, 92, 45, 34, 2, 51, 79, 31, 53, 91, 10, 69, 65, 7, 94, 79, 12], 'Last Purchase Date/Tanggal Pembelian Terakhir': ['2024-07-05', '2024-09-27', '2024-11-30', '2024-07-28', '2024-10-22', '2024-11-28', '2025-01-30', '2024-10-05', '2025-02-10', '2024-12-10', '2025-01-28', '2025-01-22', '2024-10-25', '2024-04-24', '2024-09-09', '2024-09-30', '2024-04-30', '2024-08-11', '2024-08-24', '2024-09-12', '2025-02-26', '2025-04-06', '2024-05-12', '2024-05-19', '2024-06-06', '2025-02-19', '2024-05-04', '2024-06-10', '2024-05-13', '2025-02-02', '2025-01-27', '2024-12-12', '2025-01-21', '2025-01-15', '2024-08-29', '2024-08-16', '2024-09-07', '2024-07-25', '2024-05-22', '2024-08-05', '2024-10-25', '2024-11-05', '2025-04-08', '2024-11-04', '2024-11-23', '2024-12-11', '2024-07-01', '2024-12-01', '2024-09-20', '2024-12-23', '2025-01-02', '2025-03-31', '2024-09-15', '2024-10-22']})
# Basic data cleaning
df = df.dropna() # Remove missing values
df = df.drop_duplicates() # Remove duplicates
# Comprehensive data analysis
analysis_result = df.describe(include='all')
print(analysis_result)
# Additional insights
print('\nTop performing items:')
print(df.nlargest(3, df.columns[1]))
print('\nTrend analysis:')
if 'Date/Tanggal' in df.columns:
df['Month/Bulan'] = pd.to_datetime(df['Date/Tanggal']).dt.month
monthly_trend = df.groupby('Month/Bulan').mean(numeric_only=True)
print(monthly_trend)
|
Hitung nilai pesanan rata-rata produk
|
{
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": [
100.05,
164.48,
107.65,
92.07,
55.82,
106.24,
190.07,
100.59,
154.71,
55.14,
60.74,
174.77,
192.34,
112.99,
181.82,
151.33,
114.45,
99.02,
131.52,
142.67,
100.67,
153.33,
99.18,
113.93,
94.17,
151.17,
87.64,
105.59,
159.66,
163.53
],
"Category/Kategori": null,
"City/Kota": null,
"Conversion Rate/Rasio Konversi": [
0.040600000000000004,
0.0442,
0.0511,
0.0235,
0.0915,
0.0621,
0.0236,
0.0645,
0.0843,
0.0216,
0.0763,
0.019,
0.09280000000000001,
0.0239,
0.07350000000000001,
0.037,
0.0613,
0.06,
0.039400000000000004,
0.07780000000000001,
0.0184,
0.0472,
0.0745,
0.0632,
0.07,
0.0698,
0.0776,
0.0151,
0.033,
0.0223
],
"Country/Negara": null,
"Customer ID/ID Pelanggan": null,
"Date/Tanggal": [
"2025-03-23",
"2025-03-24",
"2025-03-25",
"2025-03-26",
"2025-03-27",
"2025-03-28",
"2025-03-29",
"2025-03-30",
"2025-03-31",
"2025-04-01",
"2025-04-02",
"2025-04-03",
"2025-04-04",
"2025-04-05",
"2025-04-06",
"2025-04-07",
"2025-04-08",
"2025-04-09",
"2025-04-10",
"2025-04-11",
"2025-04-12",
"2025-04-13",
"2025-04-14",
"2025-04-15",
"2025-04-16",
"2025-04-17",
"2025-04-18",
"2025-04-19",
"2025-04-20",
"2025-04-21"
],
"Discount/Diskon": null,
"Gender/Jenis Kelamin": null,
"Growth Rate/Pertumbuhan": null,
"Income/Pendapatan": null,
"Last Purchase Date/Tanggal Pembelian Terakhir": null,
"Latitude/Lintang": null,
"Longitude/Bujur": null,
"Loyalty Score/Skor Loyalitas": null,
"Market Share/Pangsa Pasar": null,
"Price/Harga": null,
"Product/Produk": null,
"Region/Wilayah": null,
"Revenue/Pendapatan": [
3029.28,
3169.31,
3123.16,
3173.11,
3309.43,
3344.6,
3145.88,
3144,
2982.08,
2976.37,
2879.88,
2885.55,
2942.06,
3102.08,
3135.3,
3249.93,
3341.09,
3518.11,
3393.99,
3375.07,
3401.58,
3205.46,
3238.14,
3186.8,
3175.34,
3189.35,
3238.73,
3194.02,
3252.27,
3316.67
],
"Sales/Penjualan": null,
"Stock/Stok": null,
"Visitors/Pengunjung": [
232,
168,
94,
207,
145,
57,
206,
135,
135,
204,
213,
241,
146,
133,
94,
298,
306,
240,
61,
164,
260,
69,
258,
71,
177,
100,
76,
142,
308,
91
]
}
|
import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Date/Tanggal': ['2025-03-23', '2025-03-24', '2025-03-25', '2025-03-26', '2025-03-27', '2025-03-28', '2025-03-29', '2025-03-30', '2025-03-31', '2025-04-01', '2025-04-02', '2025-04-03', '2025-04-04', '2025-04-05', '2025-04-06', '2025-04-07', '2025-04-08', '2025-04-09', '2025-04-10', '2025-04-11', '2025-04-12', '2025-04-13', '2025-04-14', '2025-04-15', '2025-04-16', '2025-04-17', '2025-04-18', '2025-04-19', '2025-04-20', '2025-04-21'], 'Revenue/Pendapatan': [3029.28, 3169.31, 3123.16, 3173.11, 3309.43, 3344.6, 3145.88, 3144.0, 2982.08, 2976.37, 2879.88, 2885.55, 2942.06, 3102.08, 3135.3, 3249.93, 3341.09, 3518.11, 3393.99, 3375.07, 3401.58, 3205.46, 3238.14, 3186.8, 3175.34, 3189.35, 3238.73, 3194.02, 3252.27, 3316.67], 'Visitors/Pengunjung': [232, 168, 94, 207, 145, 57, 206, 135, 135, 204, 213, 241, 146, 133, 94, 298, 306, 240, 61, 164, 260, 69, 258, 71, 177, 100, 76, 142, 308, 91], 'Conversion Rate/Rasio Konversi': [0.0406, 0.0442, 0.0511, 0.0235, 0.0915, 0.0621, 0.0236, 0.0645, 0.0843, 0.0216, 0.0763, 0.019, 0.0928, 0.0239, 0.0735, 0.037, 0.0613, 0.06, 0.0394, 0.0778, 0.0184, 0.0472, 0.0745, 0.0632, 0.07, 0.0698, 0.0776, 0.0151, 0.033, 0.0223], 'Avg. Order Value/Nilai Pesanan Rata2': [100.05, 164.48, 107.65, 92.07, 55.82, 106.24, 190.07, 100.59, 154.71, 55.14, 60.74, 174.77, 192.34, 112.99, 181.82, 151.33, 114.45, 99.02, 131.52, 142.67, 100.67, 153.33, 99.18, 113.93, 94.17, 151.17, 87.64, 105.59, 159.66, 163.53]})
# Basic data cleaning
df = df.dropna() # Remove missing values
df = df.drop_duplicates() # Remove duplicates
# cumulative sum calculation
result = df['Date/Tanggal'].cumsum()
print(result)
|
Identify average order value product
|
{
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": [
"Automotive/Otomotif",
"Automotive/Otomotif",
"Automotive/Otomotif"
],
"City/Kota": null,
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": null,
"Customer ID/ID Pelanggan": null,
"Date/Tanggal": null,
"Discount/Diskon": [
0.18,
0.19,
0.28
],
"Gender/Jenis Kelamin": null,
"Growth Rate/Pertumbuhan": null,
"Income/Pendapatan": null,
"Last Purchase Date/Tanggal Pembelian Terakhir": null,
"Latitude/Lintang": null,
"Longitude/Bujur": null,
"Loyalty Score/Skor Loyalitas": null,
"Market Share/Pangsa Pasar": null,
"Price/Harga": [
181.95,
32.51,
115.04
],
"Product/Produk": [
"Tools_1",
"Oil_2",
"Tires_3"
],
"Region/Wilayah": null,
"Revenue/Pendapatan": null,
"Sales/Penjualan": [
500,
633,
458
],
"Stock/Stok": [
93,
103,
85
],
"Visitors/Pengunjung": null
}
|
import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Product/Produk': ['Tools_1', 'Oil_2', 'Tires_3'], 'Sales/Penjualan': [500, 633, 458], 'Price/Harga': [181.95, 32.51, 115.04], 'Category/Kategori': ['Automotive/Otomotif', 'Automotive/Otomotif', 'Automotive/Otomotif'], 'Discount/Diskon': [0.18, 0.19, 0.28], 'Stock/Stok': [93, 103, 85]})
# Basic data cleaning
df = df.dropna() # Remove missing values
df = df.drop_duplicates() # Remove duplicates
# Comprehensive data analysis
analysis_result = df.describe(include='all')
print(analysis_result)
# Additional insights
print('\nTop performing items:')
print(df.nlargest(3, df.columns[1]))
print('\nTrend analysis:')
if 'Date/Tanggal' in df.columns:
df['Month/Bulan'] = pd.to_datetime(df['Date/Tanggal']).dt.month
monthly_trend = df.groupby('Month/Bulan').mean(numeric_only=True)
print(monthly_trend)
|
Calculate stock gender
|
{
"Age/Usia": [
20,
55,
56,
47,
63,
37,
49,
28,
59,
64,
60,
23,
55,
36,
22,
20,
63,
52,
55,
54,
51,
18,
35,
44,
57,
31,
58,
59,
28,
19,
39,
56,
66,
20,
55,
69,
36,
38,
55,
33,
30,
23,
52,
22,
63,
36,
48,
66,
53,
48,
41,
39,
48,
21,
62,
69,
51
],
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": null,
"City/Kota": null,
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": null,
"Customer ID/ID Pelanggan": [
"CUST_0001",
"CUST_0002",
"CUST_0003",
"CUST_0004",
"CUST_0005",
"CUST_0006",
"CUST_0007",
"CUST_0008",
"CUST_0009",
"CUST_0010",
"CUST_0011",
"CUST_0012",
"CUST_0013",
"CUST_0014",
"CUST_0015",
"CUST_0016",
"CUST_0017",
"CUST_0018",
"CUST_0019",
"CUST_0020",
"CUST_0021",
"CUST_0022",
"CUST_0023",
"CUST_0024",
"CUST_0025",
"CUST_0026",
"CUST_0027",
"CUST_0028",
"CUST_0029",
"CUST_0030",
"CUST_0031",
"CUST_0032",
"CUST_0033",
"CUST_0034",
"CUST_0035",
"CUST_0036",
"CUST_0037",
"CUST_0038",
"CUST_0039",
"CUST_0040",
"CUST_0041",
"CUST_0042",
"CUST_0043",
"CUST_0044",
"CUST_0045",
"CUST_0046",
"CUST_0047",
"CUST_0048",
"CUST_0049",
"CUST_0050",
"CUST_0051",
"CUST_0052",
"CUST_0053",
"CUST_0054",
"CUST_0055",
"CUST_0056",
"CUST_0057"
],
"Date/Tanggal": null,
"Discount/Diskon": null,
"Gender/Jenis Kelamin": [
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Other/Lainnya",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan"
],
"Growth Rate/Pertumbuhan": null,
"Income/Pendapatan": [
4000,
7000,
3000,
6000,
7000,
1000,
5000,
2000,
9000,
1000,
7000,
10000,
5000,
5000,
6000,
5000,
6000,
1000,
3000,
3000,
8000,
3000,
5000,
6000,
2000,
1000,
10000,
6000,
2000,
1000,
2000,
6000,
6000,
6000,
2000,
7000,
6000,
7000,
3000,
8000,
6000,
3000,
2000,
2000,
2000,
3000,
4000,
1000,
9000,
5000,
1000,
7000,
10000,
2000,
10000,
3000,
4000
],
"Last Purchase Date/Tanggal Pembelian Terakhir": [
"2024-10-29",
"2025-02-24",
"2024-09-26",
"2024-07-31",
"2025-01-14",
"2025-03-25",
"2024-06-12",
"2025-01-26",
"2025-01-16",
"2024-08-21",
"2024-06-10",
"2024-05-18",
"2024-08-06",
"2025-04-05",
"2025-02-09",
"2024-08-17",
"2024-08-04",
"2024-11-12",
"2025-01-02",
"2025-02-12",
"2025-02-02",
"2024-05-19",
"2024-07-18",
"2024-11-06",
"2024-09-23",
"2024-07-02",
"2024-06-09",
"2024-10-10",
"2024-06-29",
"2024-07-30",
"2025-04-05",
"2024-10-10",
"2024-05-10",
"2024-06-30",
"2024-06-10",
"2024-10-05",
"2024-11-06",
"2024-12-29",
"2024-08-12",
"2024-05-02",
"2024-11-03",
"2024-09-28",
"2024-09-18",
"2024-10-03",
"2024-05-05",
"2025-01-03",
"2024-11-26",
"2024-09-29",
"2024-05-03",
"2024-12-04",
"2025-03-25",
"2024-06-01",
"2024-10-04",
"2025-02-27",
"2025-04-10",
"2024-05-15",
"2024-06-16"
],
"Latitude/Lintang": null,
"Longitude/Bujur": null,
"Loyalty Score/Skor Loyalitas": [
63,
19,
18,
77,
40,
10,
94,
100,
2,
5,
39,
69,
98,
28,
46,
1,
71,
65,
65,
22,
15,
9,
5,
4,
78,
78,
66,
85,
28,
51,
41,
2,
93,
19,
48,
56,
82,
76,
66,
44,
32,
31,
100,
62,
28,
25,
34,
23,
20,
81,
15,
91,
49,
46,
62,
65,
90
],
"Market Share/Pangsa Pasar": null,
"Price/Harga": null,
"Product/Produk": null,
"Region/Wilayah": null,
"Revenue/Pendapatan": null,
"Sales/Penjualan": null,
"Stock/Stok": null,
"Visitors/Pengunjung": null
}
|
import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Customer ID/ID Pelanggan': ['CUST_0001', 'CUST_0002', 'CUST_0003', 'CUST_0004', 'CUST_0005', 'CUST_0006', 'CUST_0007', 'CUST_0008', 'CUST_0009', 'CUST_0010', 'CUST_0011', 'CUST_0012', 'CUST_0013', 'CUST_0014', 'CUST_0015', 'CUST_0016', 'CUST_0017', 'CUST_0018', 'CUST_0019', 'CUST_0020', 'CUST_0021', 'CUST_0022', 'CUST_0023', 'CUST_0024', 'CUST_0025', 'CUST_0026', 'CUST_0027', 'CUST_0028', 'CUST_0029', 'CUST_0030', 'CUST_0031', 'CUST_0032', 'CUST_0033', 'CUST_0034', 'CUST_0035', 'CUST_0036', 'CUST_0037', 'CUST_0038', 'CUST_0039', 'CUST_0040', 'CUST_0041', 'CUST_0042', 'CUST_0043', 'CUST_0044', 'CUST_0045', 'CUST_0046', 'CUST_0047', 'CUST_0048', 'CUST_0049', 'CUST_0050', 'CUST_0051', 'CUST_0052', 'CUST_0053', 'CUST_0054', 'CUST_0055', 'CUST_0056', 'CUST_0057'], 'Age/Usia': [20, 55, 56, 47, 63, 37, 49, 28, 59, 64, 60, 23, 55, 36, 22, 20, 63, 52, 55, 54, 51, 18, 35, 44, 57, 31, 58, 59, 28, 19, 39, 56, 66, 20, 55, 69, 36, 38, 55, 33, 30, 23, 52, 22, 63, 36, 48, 66, 53, 48, 41, 39, 48, 21, 62, 69, 51], 'Gender/Jenis Kelamin': ['Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Other/Lainnya', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan'], 'Income/Pendapatan': [4000, 7000, 3000, 6000, 7000, 1000, 5000, 2000, 9000, 1000, 7000, 10000, 5000, 5000, 6000, 5000, 6000, 1000, 3000, 3000, 8000, 3000, 5000, 6000, 2000, 1000, 10000, 6000, 2000, 1000, 2000, 6000, 6000, 6000, 2000, 7000, 6000, 7000, 3000, 8000, 6000, 3000, 2000, 2000, 2000, 3000, 4000, 1000, 9000, 5000, 1000, 7000, 10000, 2000, 10000, 3000, 4000], 'Loyalty Score/Skor Loyalitas': [63, 19, 18, 77, 40, 10, 94, 100, 2, 5, 39, 69, 98, 28, 46, 1, 71, 65, 65, 22, 15, 9, 5, 4, 78, 78, 66, 85, 28, 51, 41, 2, 93, 19, 48, 56, 82, 76, 66, 44, 32, 31, 100, 62, 28, 25, 34, 23, 20, 81, 15, 91, 49, 46, 62, 65, 90], 'Last Purchase Date/Tanggal Pembelian Terakhir': ['2024-10-29', '2025-02-24', '2024-09-26', '2024-07-31', '2025-01-14', '2025-03-25', '2024-06-12', '2025-01-26', '2025-01-16', '2024-08-21', '2024-06-10', '2024-05-18', '2024-08-06', '2025-04-05', '2025-02-09', '2024-08-17', '2024-08-04', '2024-11-12', '2025-01-02', '2025-02-12', '2025-02-02', '2024-05-19', '2024-07-18', '2024-11-06', '2024-09-23', '2024-07-02', '2024-06-09', '2024-10-10', '2024-06-29', '2024-07-30', '2025-04-05', '2024-10-10', '2024-05-10', '2024-06-30', '2024-06-10', '2024-10-05', '2024-11-06', '2024-12-29', '2024-08-12', '2024-05-02', '2024-11-03', '2024-09-28', '2024-09-18', '2024-10-03', '2024-05-05', '2025-01-03', '2024-11-26', '2024-09-29', '2024-05-03', '2024-12-04', '2025-03-25', '2024-06-01', '2024-10-04', '2025-02-27', '2025-04-10', '2024-05-15', '2024-06-16']})
# Basic data cleaning
df = df.dropna() # Remove missing values
df = df.drop_duplicates() # Remove duplicates
# cumulative sum calculation
result = df['Last Purchase Date/Tanggal Pembelian Terakhir'].cumsum()
print(result)
|
Compare visitors country
|
{
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": [
"Food/Makanan",
"Food/Makanan",
"Food/Makanan"
],
"City/Kota": null,
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": null,
"Customer ID/ID Pelanggan": null,
"Date/Tanggal": null,
"Discount/Diskon": [
0.02,
0.22,
0.03
],
"Gender/Jenis Kelamin": null,
"Growth Rate/Pertumbuhan": null,
"Income/Pendapatan": null,
"Last Purchase Date/Tanggal Pembelian Terakhir": null,
"Latitude/Lintang": null,
"Longitude/Bujur": null,
"Loyalty Score/Skor Loyalitas": null,
"Market Share/Pangsa Pasar": null,
"Price/Harga": [
193.32,
32.54,
176.28
],
"Product/Produk": [
"Fruits_1",
"Beverages_2",
"Dairy_3"
],
"Region/Wilayah": null,
"Revenue/Pendapatan": null,
"Sales/Penjualan": [
678,
225,
485
],
"Stock/Stok": [
107,
171,
141
],
"Visitors/Pengunjung": null
}
|
import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Product/Produk': ['Fruits_1', 'Beverages_2', 'Dairy_3'], 'Sales/Penjualan': [678, 225, 485], 'Price/Harga': [193.32, 32.54, 176.28], 'Category/Kategori': ['Food/Makanan', 'Food/Makanan', 'Food/Makanan'], 'Discount/Diskon': [0.02, 0.22, 0.03], 'Stock/Stok': [107, 171, 141]})
# Basic data cleaning
df = df.dropna() # Remove missing values
df = df.drop_duplicates() # Remove duplicates
# Comprehensive data analysis
analysis_result = df.describe(include='all')
print(analysis_result)
# Additional insights
print('\nTop performing items:')
print(df.nlargest(3, df.columns[1]))
print('\nTrend analysis:')
if 'Date/Tanggal' in df.columns:
df['Month/Bulan'] = pd.to_datetime(df['Date/Tanggal']).dt.month
monthly_trend = df.groupby('Month/Bulan').mean(numeric_only=True)
print(monthly_trend)
|
Segmentasi stok grup usia
|
{
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": [
"Food/Makanan",
"Food/Makanan",
"Food/Makanan"
],
"City/Kota": null,
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": null,
"Customer ID/ID Pelanggan": null,
"Date/Tanggal": null,
"Discount/Diskon": [
0.08,
0.1,
0.11
],
"Gender/Jenis Kelamin": null,
"Growth Rate/Pertumbuhan": null,
"Income/Pendapatan": null,
"Last Purchase Date/Tanggal Pembelian Terakhir": null,
"Latitude/Lintang": null,
"Longitude/Bujur": null,
"Loyalty Score/Skor Loyalitas": null,
"Market Share/Pangsa Pasar": null,
"Price/Harga": [
169.15,
156.44,
195.68
],
"Product/Produk": [
"Fruits_1",
"Dairy_2",
"Beverages_3"
],
"Region/Wilayah": null,
"Revenue/Pendapatan": null,
"Sales/Penjualan": [
476,
253,
682
],
"Stock/Stok": [
152,
21,
61
],
"Visitors/Pengunjung": null
}
|
import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Product/Produk': ['Fruits_1', 'Dairy_2', 'Beverages_3'], 'Sales/Penjualan': [476, 253, 682], 'Price/Harga': [169.15, 156.44, 195.68], 'Category/Kategori': ['Food/Makanan', 'Food/Makanan', 'Food/Makanan'], 'Discount/Diskon': [0.08, 0.1, 0.11], 'Stock/Stok': [152, 21, 61]})
# Basic data cleaning
df = df.dropna() # Remove missing values
df = df.drop_duplicates() # Remove duplicates
# Customer segmentation using KMeans
from sklearn.cluster import KMeans
from sklearn.preprocessing import StandardScaler
# Select features and scale
X = df[['Age/Usia', 'Income/Pendapatan', 'Loyalty Score/Skor Loyalitas']]
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)
# Cluster into 3 segments
kmeans = KMeans(n_clusters=3, random_state=42)
df['Segment/Segmen'] = kmeans.fit_predict(X_scaled)
# Visualize segments
fig = px.scatter_3d(df, x='Age/Usia', y='Income/Pendapatan',
z='Loyalty Score/Skor Loyalitas',
color='Segment/Segmen',
title='Customer Segments')
fig.show()
|
Optimize income customer segment
|
{
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": [
154.76,
50.67,
145.3,
83.4,
131.2,
99.75,
169.72,
122.08,
169.63,
132.9,
165.96,
111.98,
83.42,
185.62,
138.66,
158.74,
145.9,
152.57,
94.65,
67.98,
162.19,
141.95,
163.64,
180.44,
136.73,
134.44,
68.54,
105.17,
92.34,
77.83
],
"Category/Kategori": null,
"City/Kota": null,
"Conversion Rate/Rasio Konversi": [
0.0328,
0.055200000000000006,
0.0151,
0.0511,
0.0444,
0.0795,
0.049800000000000004,
0.0611,
0.0743,
0.0106,
0.048,
0.0392,
0.0134,
0.0385,
0.06760000000000001,
0.0279,
0.0896,
0.0636,
0.012400000000000001,
0.0522,
0.0392,
0.0688,
0.0751,
0.025900000000000003,
0.09240000000000001,
0.0949,
0.0639,
0.0233,
0.0599,
0.09620000000000001
],
"Country/Negara": null,
"Customer ID/ID Pelanggan": null,
"Date/Tanggal": [
"2025-03-23",
"2025-03-24",
"2025-03-25",
"2025-03-26",
"2025-03-27",
"2025-03-28",
"2025-03-29",
"2025-03-30",
"2025-03-31",
"2025-04-01",
"2025-04-02",
"2025-04-03",
"2025-04-04",
"2025-04-05",
"2025-04-06",
"2025-04-07",
"2025-04-08",
"2025-04-09",
"2025-04-10",
"2025-04-11",
"2025-04-12",
"2025-04-13",
"2025-04-14",
"2025-04-15",
"2025-04-16",
"2025-04-17",
"2025-04-18",
"2025-04-19",
"2025-04-20",
"2025-04-21"
],
"Discount/Diskon": null,
"Gender/Jenis Kelamin": null,
"Growth Rate/Pertumbuhan": null,
"Income/Pendapatan": null,
"Last Purchase Date/Tanggal Pembelian Terakhir": null,
"Latitude/Lintang": null,
"Longitude/Bujur": null,
"Loyalty Score/Skor Loyalitas": null,
"Market Share/Pangsa Pasar": null,
"Price/Harga": null,
"Product/Produk": null,
"Region/Wilayah": null,
"Revenue/Pendapatan": [
2691.11,
2831.59,
2825.23,
3049.85,
2935.52,
2997.61,
2902.61,
2863.59,
2709,
2705.98,
2599.71,
2742.79,
2767.35,
2712.49,
2965.07,
2929.2,
3198.26,
3179.75,
3241.61,
3207.1,
3207.83,
3021.73,
3059.83,
2904.97,
2910.77,
2949.97,
2911.07,
2977.72,
3105.66,
3175.03
],
"Sales/Penjualan": null,
"Stock/Stok": null,
"Visitors/Pengunjung": [
118,
94,
288,
144,
233,
196,
174,
176,
282,
42,
204,
58,
228,
122,
126,
199,
282,
305,
136,
52,
72,
164,
74,
216,
273,
187,
247,
196,
62,
282
]
}
|
import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Date/Tanggal': ['2025-03-23', '2025-03-24', '2025-03-25', '2025-03-26', '2025-03-27', '2025-03-28', '2025-03-29', '2025-03-30', '2025-03-31', '2025-04-01', '2025-04-02', '2025-04-03', '2025-04-04', '2025-04-05', '2025-04-06', '2025-04-07', '2025-04-08', '2025-04-09', '2025-04-10', '2025-04-11', '2025-04-12', '2025-04-13', '2025-04-14', '2025-04-15', '2025-04-16', '2025-04-17', '2025-04-18', '2025-04-19', '2025-04-20', '2025-04-21'], 'Revenue/Pendapatan': [2691.11, 2831.59, 2825.23, 3049.85, 2935.52, 2997.61, 2902.61, 2863.59, 2709.0, 2705.98, 2599.71, 2742.79, 2767.35, 2712.49, 2965.07, 2929.2, 3198.26, 3179.75, 3241.61, 3207.1, 3207.83, 3021.73, 3059.83, 2904.97, 2910.77, 2949.97, 2911.07, 2977.72, 3105.66, 3175.03], 'Visitors/Pengunjung': [118, 94, 288, 144, 233, 196, 174, 176, 282, 42, 204, 58, 228, 122, 126, 199, 282, 305, 136, 52, 72, 164, 74, 216, 273, 187, 247, 196, 62, 282], 'Conversion Rate/Rasio Konversi': [0.0328, 0.0552, 0.0151, 0.0511, 0.0444, 0.0795, 0.0498, 0.0611, 0.0743, 0.0106, 0.048, 0.0392, 0.0134, 0.0385, 0.0676, 0.0279, 0.0896, 0.0636, 0.0124, 0.0522, 0.0392, 0.0688, 0.0751, 0.0259, 0.0924, 0.0949, 0.0639, 0.0233, 0.0599, 0.0962], 'Avg. Order Value/Nilai Pesanan Rata2': [154.76, 50.67, 145.3, 83.4, 131.2, 99.75, 169.72, 122.08, 169.63, 132.9, 165.96, 111.98, 83.42, 185.62, 138.66, 158.74, 145.9, 152.57, 94.65, 67.98, 162.19, 141.95, 163.64, 180.44, 136.73, 134.44, 68.54, 105.17, 92.34, 77.83]})
# Basic data cleaning
df = df.dropna() # Remove missing values
df = df.drop_duplicates() # Remove duplicates
# Customer segmentation using KMeans
from sklearn.cluster import KMeans
from sklearn.preprocessing import StandardScaler
# Select features and scale
X = df[['Age/Usia', 'Income/Pendapatan', 'Loyalty Score/Skor Loyalitas']]
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)
# Cluster into 3 segments
kmeans = KMeans(n_clusters=3, random_state=42)
df['Segment/Segmen'] = kmeans.fit_predict(X_scaled)
# Visualize segments
fig = px.scatter_3d(df, x='Age/Usia', y='Income/Pendapatan',
z='Loyalty Score/Skor Loyalitas',
color='Segment/Segmen',
title='Customer Segments')
fig.show()
|
Kelompokkan skor loyalitas kota
|
{
"Age/Usia": [
23,
51,
70,
43,
33,
48,
58,
22,
40,
38,
23,
52,
52,
67,
63,
18,
62,
42,
18,
18,
57,
21,
45,
41,
43,
52,
46,
53,
69,
59,
58,
45,
56,
53,
59,
63,
23,
18,
23,
61,
46,
49,
61,
44,
64,
66,
31,
63,
20,
30,
47,
50,
28,
54,
53,
50,
48,
33,
55,
46,
26,
38,
44,
32,
39,
43,
50,
35,
37,
51
],
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": null,
"City/Kota": null,
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": null,
"Customer ID/ID Pelanggan": [
"CUST_0001",
"CUST_0002",
"CUST_0003",
"CUST_0004",
"CUST_0005",
"CUST_0006",
"CUST_0007",
"CUST_0008",
"CUST_0009",
"CUST_0010",
"CUST_0011",
"CUST_0012",
"CUST_0013",
"CUST_0014",
"CUST_0015",
"CUST_0016",
"CUST_0017",
"CUST_0018",
"CUST_0019",
"CUST_0020",
"CUST_0021",
"CUST_0022",
"CUST_0023",
"CUST_0024",
"CUST_0025",
"CUST_0026",
"CUST_0027",
"CUST_0028",
"CUST_0029",
"CUST_0030",
"CUST_0031",
"CUST_0032",
"CUST_0033",
"CUST_0034",
"CUST_0035",
"CUST_0036",
"CUST_0037",
"CUST_0038",
"CUST_0039",
"CUST_0040",
"CUST_0041",
"CUST_0042",
"CUST_0043",
"CUST_0044",
"CUST_0045",
"CUST_0046",
"CUST_0047",
"CUST_0048",
"CUST_0049",
"CUST_0050",
"CUST_0051",
"CUST_0052",
"CUST_0053",
"CUST_0054",
"CUST_0055",
"CUST_0056",
"CUST_0057",
"CUST_0058",
"CUST_0059",
"CUST_0060",
"CUST_0061",
"CUST_0062",
"CUST_0063",
"CUST_0064",
"CUST_0065",
"CUST_0066",
"CUST_0067",
"CUST_0068",
"CUST_0069",
"CUST_0070"
],
"Date/Tanggal": null,
"Discount/Diskon": null,
"Gender/Jenis Kelamin": [
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Other/Lainnya",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Other/Lainnya",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Other/Lainnya",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan"
],
"Growth Rate/Pertumbuhan": null,
"Income/Pendapatan": [
2000,
6000,
9000,
2000,
2000,
6000,
2000,
6000,
7000,
2000,
5000,
3000,
8000,
9000,
6000,
3000,
7000,
6000,
6000,
5000,
2000,
3000,
9000,
2000,
6000,
8000,
9000,
3000,
8000,
1000,
2000,
7000,
8000,
5000,
10000,
5000,
8000,
4000,
3000,
7000,
10000,
6000,
1000,
1000,
4000,
8000,
7000,
3000,
5000,
2000,
6000,
8000,
3000,
1000,
7000,
2000,
2000,
5000,
9000,
10000,
3000,
10000,
5000,
8000,
8000,
3000,
2000,
1000,
9000,
10000
],
"Last Purchase Date/Tanggal Pembelian Terakhir": [
"2025-01-24",
"2024-06-11",
"2024-06-15",
"2024-08-29",
"2025-02-03",
"2024-09-06",
"2024-07-08",
"2024-06-04",
"2024-10-12",
"2024-12-30",
"2024-12-15",
"2024-05-07",
"2024-10-18",
"2024-08-27",
"2025-03-08",
"2025-02-09",
"2025-04-08",
"2025-03-23",
"2024-04-27",
"2024-11-09",
"2025-02-07",
"2024-06-07",
"2024-09-06",
"2024-04-30",
"2024-11-05",
"2024-10-12",
"2024-05-25",
"2025-01-10",
"2024-08-25",
"2025-03-09",
"2024-09-01",
"2024-08-07",
"2025-01-17",
"2024-05-24",
"2025-03-02",
"2024-12-03",
"2024-09-12",
"2024-04-24",
"2025-04-13",
"2024-12-09",
"2024-06-07",
"2024-12-23",
"2024-10-20",
"2025-04-05",
"2025-02-24",
"2024-10-26",
"2025-04-17",
"2024-08-16",
"2024-10-29",
"2024-12-03",
"2024-05-30",
"2025-01-03",
"2024-09-22",
"2024-09-09",
"2024-10-29",
"2024-12-15",
"2024-12-31",
"2024-09-29",
"2024-07-26",
"2024-08-23",
"2024-06-27",
"2024-10-16",
"2024-06-07",
"2025-02-15",
"2024-10-13",
"2024-05-31",
"2024-10-14",
"2024-10-31",
"2024-09-22",
"2025-01-26"
],
"Latitude/Lintang": null,
"Longitude/Bujur": null,
"Loyalty Score/Skor Loyalitas": [
10,
25,
54,
68,
43,
55,
85,
21,
63,
21,
99,
43,
29,
81,
89,
70,
23,
90,
48,
81,
6,
49,
62,
61,
98,
81,
20,
53,
91,
96,
50,
69,
78,
30,
97,
80,
18,
92,
58,
100,
48,
17,
28,
36,
93,
80,
67,
13,
45,
60,
87,
95,
37,
68,
58,
89,
27,
26,
21,
16,
56,
52,
81,
7,
25,
26,
70,
78,
89,
70
],
"Market Share/Pangsa Pasar": null,
"Price/Harga": null,
"Product/Produk": null,
"Region/Wilayah": null,
"Revenue/Pendapatan": null,
"Sales/Penjualan": null,
"Stock/Stok": null,
"Visitors/Pengunjung": null
}
|
import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Customer ID/ID Pelanggan': ['CUST_0001', 'CUST_0002', 'CUST_0003', 'CUST_0004', 'CUST_0005', 'CUST_0006', 'CUST_0007', 'CUST_0008', 'CUST_0009', 'CUST_0010', 'CUST_0011', 'CUST_0012', 'CUST_0013', 'CUST_0014', 'CUST_0015', 'CUST_0016', 'CUST_0017', 'CUST_0018', 'CUST_0019', 'CUST_0020', 'CUST_0021', 'CUST_0022', 'CUST_0023', 'CUST_0024', 'CUST_0025', 'CUST_0026', 'CUST_0027', 'CUST_0028', 'CUST_0029', 'CUST_0030', 'CUST_0031', 'CUST_0032', 'CUST_0033', 'CUST_0034', 'CUST_0035', 'CUST_0036', 'CUST_0037', 'CUST_0038', 'CUST_0039', 'CUST_0040', 'CUST_0041', 'CUST_0042', 'CUST_0043', 'CUST_0044', 'CUST_0045', 'CUST_0046', 'CUST_0047', 'CUST_0048', 'CUST_0049', 'CUST_0050', 'CUST_0051', 'CUST_0052', 'CUST_0053', 'CUST_0054', 'CUST_0055', 'CUST_0056', 'CUST_0057', 'CUST_0058', 'CUST_0059', 'CUST_0060', 'CUST_0061', 'CUST_0062', 'CUST_0063', 'CUST_0064', 'CUST_0065', 'CUST_0066', 'CUST_0067', 'CUST_0068', 'CUST_0069', 'CUST_0070'], 'Age/Usia': [23, 51, 70, 43, 33, 48, 58, 22, 40, 38, 23, 52, 52, 67, 63, 18, 62, 42, 18, 18, 57, 21, 45, 41, 43, 52, 46, 53, 69, 59, 58, 45, 56, 53, 59, 63, 23, 18, 23, 61, 46, 49, 61, 44, 64, 66, 31, 63, 20, 30, 47, 50, 28, 54, 53, 50, 48, 33, 55, 46, 26, 38, 44, 32, 39, 43, 50, 35, 37, 51], 'Gender/Jenis Kelamin': ['Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Other/Lainnya', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Other/Lainnya', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Other/Lainnya', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan'], 'Income/Pendapatan': [2000, 6000, 9000, 2000, 2000, 6000, 2000, 6000, 7000, 2000, 5000, 3000, 8000, 9000, 6000, 3000, 7000, 6000, 6000, 5000, 2000, 3000, 9000, 2000, 6000, 8000, 9000, 3000, 8000, 1000, 2000, 7000, 8000, 5000, 10000, 5000, 8000, 4000, 3000, 7000, 10000, 6000, 1000, 1000, 4000, 8000, 7000, 3000, 5000, 2000, 6000, 8000, 3000, 1000, 7000, 2000, 2000, 5000, 9000, 10000, 3000, 10000, 5000, 8000, 8000, 3000, 2000, 1000, 9000, 10000], 'Loyalty Score/Skor Loyalitas': [10, 25, 54, 68, 43, 55, 85, 21, 63, 21, 99, 43, 29, 81, 89, 70, 23, 90, 48, 81, 6, 49, 62, 61, 98, 81, 20, 53, 91, 96, 50, 69, 78, 30, 97, 80, 18, 92, 58, 100, 48, 17, 28, 36, 93, 80, 67, 13, 45, 60, 87, 95, 37, 68, 58, 89, 27, 26, 21, 16, 56, 52, 81, 7, 25, 26, 70, 78, 89, 70], 'Last Purchase Date/Tanggal Pembelian Terakhir': ['2025-01-24', '2024-06-11', '2024-06-15', '2024-08-29', '2025-02-03', '2024-09-06', '2024-07-08', '2024-06-04', '2024-10-12', '2024-12-30', '2024-12-15', '2024-05-07', '2024-10-18', '2024-08-27', '2025-03-08', '2025-02-09', '2025-04-08', '2025-03-23', '2024-04-27', '2024-11-09', '2025-02-07', '2024-06-07', '2024-09-06', '2024-04-30', '2024-11-05', '2024-10-12', '2024-05-25', '2025-01-10', '2024-08-25', '2025-03-09', '2024-09-01', '2024-08-07', '2025-01-17', '2024-05-24', '2025-03-02', '2024-12-03', '2024-09-12', '2024-04-24', '2025-04-13', '2024-12-09', '2024-06-07', '2024-12-23', '2024-10-20', '2025-04-05', '2025-02-24', '2024-10-26', '2025-04-17', '2024-08-16', '2024-10-29', '2024-12-03', '2024-05-30', '2025-01-03', '2024-09-22', '2024-09-09', '2024-10-29', '2024-12-15', '2024-12-31', '2024-09-29', '2024-07-26', '2024-08-23', '2024-06-27', '2024-10-16', '2024-06-07', '2025-02-15', '2024-10-13', '2024-05-31', '2024-10-14', '2024-10-31', '2024-09-22', '2025-01-26']})
# Basic data cleaning
df = df.dropna() # Remove missing values
df = df.drop_duplicates() # Remove duplicates
# Comprehensive data analysis
analysis_result = df.describe(include='all')
print(analysis_result)
# Additional insights
print('\nTop performing items:')
print(df.nlargest(3, df.columns[1]))
print('\nTrend analysis:')
if 'Date/Tanggal' in df.columns:
df['Month/Bulan'] = pd.to_datetime(df['Date/Tanggal']).dt.month
monthly_trend = df.groupby('Month/Bulan').mean(numeric_only=True)
print(monthly_trend)
|
Bandingkan pendapatan grup usia
|
{
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": null,
"City/Kota": [
"New York",
"Paris",
"Mexico City",
"Sydney",
"São Paulo"
],
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": [
"US",
"France",
"Mexico",
"Australia",
"Brazil"
],
"Customer ID/ID Pelanggan": null,
"Date/Tanggal": null,
"Discount/Diskon": null,
"Gender/Jenis Kelamin": null,
"Growth Rate/Pertumbuhan": [
0.17,
0.08,
0.03,
-0.07,
0.02
],
"Income/Pendapatan": null,
"Last Purchase Date/Tanggal Pembelian Terakhir": null,
"Latitude/Lintang": [
27.9175,
85.1983,
77.4074,
-23.6537,
0.47800000000000004
],
"Longitude/Bujur": [
80.8811,
126.1749,
-162.9106,
-9.6177,
-112.4169
],
"Loyalty Score/Skor Loyalitas": null,
"Market Share/Pangsa Pasar": [
0.14,
0.42,
0.37,
0.22,
0.34
],
"Price/Harga": null,
"Product/Produk": null,
"Region/Wilayah": [
"North America",
"Europe",
"North America",
"Oceania",
"South America"
],
"Revenue/Pendapatan": [
1737,
2029,
1575,
4623,
2820
],
"Sales/Penjualan": null,
"Stock/Stok": null,
"Visitors/Pengunjung": null
}
|
import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'City/Kota': ['New York', 'Paris', 'Mexico City', 'Sydney', 'São Paulo'], 'Country/Negara': ['US', 'France', 'Mexico', 'Australia', 'Brazil'], 'Region/Wilayah': ['North America', 'Europe', 'North America', 'Oceania', 'South America'], 'Latitude/Lintang': [27.9175, 85.1983, 77.4074, -23.6537, 0.478], 'Longitude/Bujur': [80.8811, 126.1749, -162.9106, -9.6177, -112.4169], 'Revenue/Pendapatan': [1737, 2029, 1575, 4623, 2820], 'Market Share/Pangsa Pasar': [0.14, 0.42, 0.37, 0.22, 0.34], 'Growth Rate/Pertumbuhan': [0.17, 0.08, 0.03, -0.07, 0.02]})
# Basic data cleaning
df = df.dropna() # Remove missing values
df = df.drop_duplicates() # Remove duplicates
# Comprehensive data analysis
analysis_result = df.describe(include='all')
print(analysis_result)
# Additional insights
print('\nTop performing items:')
print(df.nlargest(3, df.columns[1]))
print('\nTrend analysis:')
if 'Date/Tanggal' in df.columns:
df['Month/Bulan'] = pd.to_datetime(df['Date/Tanggal']).dt.month
monthly_trend = df.groupby('Month/Bulan').mean(numeric_only=True)
print(monthly_trend)
|
Analyze conversion rate city
|
{
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": [
146.18,
69.99,
136.88,
199.1,
73.31,
58.49,
123.39,
149.21,
91.01,
114.37,
153.42,
173.5,
65.95,
72.23,
199.07,
138.56,
50.34,
130.83,
71.59,
197.22,
116.69,
197.49,
175.43,
132.43,
185.26,
82.76,
133.41,
52.55,
112.02,
109.55
],
"Category/Kategori": null,
"City/Kota": null,
"Conversion Rate/Rasio Konversi": [
0.0489,
0.09730000000000001,
0.017400000000000002,
0.028200000000000003,
0.09620000000000001,
0.057,
0.0702,
0.0371,
0.0954,
0.053200000000000004,
0.0443,
0.0281,
0.0412,
0.0205,
0.0897,
0.0182,
0.038900000000000004,
0.0285,
0.0463,
0.0771,
0.073,
0.0526,
0.0734,
0.0684,
0.013800000000000002,
0.0286,
0.0385,
0.026000000000000002,
0.0117,
0.0926
],
"Country/Negara": null,
"Customer ID/ID Pelanggan": null,
"Date/Tanggal": [
"2025-03-23",
"2025-03-24",
"2025-03-25",
"2025-03-26",
"2025-03-27",
"2025-03-28",
"2025-03-29",
"2025-03-30",
"2025-03-31",
"2025-04-01",
"2025-04-02",
"2025-04-03",
"2025-04-04",
"2025-04-05",
"2025-04-06",
"2025-04-07",
"2025-04-08",
"2025-04-09",
"2025-04-10",
"2025-04-11",
"2025-04-12",
"2025-04-13",
"2025-04-14",
"2025-04-15",
"2025-04-16",
"2025-04-17",
"2025-04-18",
"2025-04-19",
"2025-04-20",
"2025-04-21"
],
"Discount/Diskon": null,
"Gender/Jenis Kelamin": null,
"Growth Rate/Pertumbuhan": null,
"Income/Pendapatan": null,
"Last Purchase Date/Tanggal Pembelian Terakhir": null,
"Latitude/Lintang": null,
"Longitude/Bujur": null,
"Loyalty Score/Skor Loyalitas": null,
"Market Share/Pangsa Pasar": null,
"Price/Harga": null,
"Product/Produk": null,
"Region/Wilayah": null,
"Revenue/Pendapatan": [
1275.01,
1435.29,
1450.9,
1586.68,
1481.51,
1413.02,
1418.99,
1368.91,
1299.53,
1219.75,
1221.47,
1073.28,
1157.49,
1377.36,
1346.26,
1450.28,
1510.65,
1679.5,
1619.57,
1559.6,
1541.91,
1446.31,
1455.94,
1223.34,
1274.11,
1211.36,
1228.34,
1272.62,
1415,
1388.51
],
"Sales/Penjualan": null,
"Stock/Stok": null,
"Visitors/Pengunjung": [
210,
273,
212,
298,
307,
160,
131,
219,
190,
217,
257,
153,
83,
228,
129,
148,
154,
66,
232,
54,
110,
202,
121,
196,
290,
203,
279,
172,
287,
232
]
}
|
import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Date/Tanggal': ['2025-03-23', '2025-03-24', '2025-03-25', '2025-03-26', '2025-03-27', '2025-03-28', '2025-03-29', '2025-03-30', '2025-03-31', '2025-04-01', '2025-04-02', '2025-04-03', '2025-04-04', '2025-04-05', '2025-04-06', '2025-04-07', '2025-04-08', '2025-04-09', '2025-04-10', '2025-04-11', '2025-04-12', '2025-04-13', '2025-04-14', '2025-04-15', '2025-04-16', '2025-04-17', '2025-04-18', '2025-04-19', '2025-04-20', '2025-04-21'], 'Revenue/Pendapatan': [1275.01, 1435.29, 1450.9, 1586.68, 1481.51, 1413.02, 1418.99, 1368.91, 1299.53, 1219.75, 1221.47, 1073.28, 1157.49, 1377.36, 1346.26, 1450.28, 1510.65, 1679.5, 1619.57, 1559.6, 1541.91, 1446.31, 1455.94, 1223.34, 1274.11, 1211.36, 1228.34, 1272.62, 1415.0, 1388.51], 'Visitors/Pengunjung': [210, 273, 212, 298, 307, 160, 131, 219, 190, 217, 257, 153, 83, 228, 129, 148, 154, 66, 232, 54, 110, 202, 121, 196, 290, 203, 279, 172, 287, 232], 'Conversion Rate/Rasio Konversi': [0.0489, 0.0973, 0.0174, 0.0282, 0.0962, 0.057, 0.0702, 0.0371, 0.0954, 0.0532, 0.0443, 0.0281, 0.0412, 0.0205, 0.0897, 0.0182, 0.0389, 0.0285, 0.0463, 0.0771, 0.073, 0.0526, 0.0734, 0.0684, 0.0138, 0.0286, 0.0385, 0.026, 0.0117, 0.0926], 'Avg. Order Value/Nilai Pesanan Rata2': [146.18, 69.99, 136.88, 199.1, 73.31, 58.49, 123.39, 149.21, 91.01, 114.37, 153.42, 173.5, 65.95, 72.23, 199.07, 138.56, 50.34, 130.83, 71.59, 197.22, 116.69, 197.49, 175.43, 132.43, 185.26, 82.76, 133.41, 52.55, 112.02, 109.55]})
# Basic data cleaning
df = df.dropna() # Remove missing values
df = df.drop_duplicates() # Remove duplicates
# Comprehensive data analysis
analysis_result = df.describe(include='all')
print(analysis_result)
# Additional insights
print('\nTop performing items:')
print(df.nlargest(3, df.columns[1]))
print('\nTrend analysis:')
if 'Date/Tanggal' in df.columns:
df['Month/Bulan'] = pd.to_datetime(df['Date/Tanggal']).dt.month
monthly_trend = df.groupby('Month/Bulan').mean(numeric_only=True)
print(monthly_trend)
|
Visualize revenue gender
|
{
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": null,
"City/Kota": [
"Mumbai",
"Jakarta",
"Tokyo",
"Dubai",
"New York"
],
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": [
"India",
"Indonesia",
"Japan",
"UAE",
"US"
],
"Customer ID/ID Pelanggan": null,
"Date/Tanggal": null,
"Discount/Diskon": null,
"Gender/Jenis Kelamin": null,
"Growth Rate/Pertumbuhan": [
0.03,
-0.1,
0,
0.21,
0.03
],
"Income/Pendapatan": null,
"Last Purchase Date/Tanggal Pembelian Terakhir": null,
"Latitude/Lintang": [
-46.6167,
46.9083,
-8.9655,
-28.8306,
-23.1585
],
"Longitude/Bujur": [
21.3763,
-61.1691,
-96.5059,
55.064,
-144.7706
],
"Loyalty Score/Skor Loyalitas": null,
"Market Share/Pangsa Pasar": [
0.48,
0.38,
0.13,
0.15,
0.27
],
"Price/Harga": null,
"Product/Produk": null,
"Region/Wilayah": [
"Asia",
"Asia",
"Asia",
"Middle East",
"North America"
],
"Revenue/Pendapatan": [
1902,
1844,
1685,
2445,
4617
],
"Sales/Penjualan": null,
"Stock/Stok": null,
"Visitors/Pengunjung": null
}
|
import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'City/Kota': ['Mumbai', 'Jakarta', 'Tokyo', 'Dubai', 'New York'], 'Country/Negara': ['India', 'Indonesia', 'Japan', 'UAE', 'US'], 'Region/Wilayah': ['Asia', 'Asia', 'Asia', 'Middle East', 'North America'], 'Latitude/Lintang': [-46.6167, 46.9083, -8.9655, -28.8306, -23.1585], 'Longitude/Bujur': [21.3763, -61.1691, -96.5059, 55.064, -144.7706], 'Revenue/Pendapatan': [1902, 1844, 1685, 2445, 4617], 'Market Share/Pangsa Pasar': [0.48, 0.38, 0.13, 0.15, 0.27], 'Growth Rate/Pertumbuhan': [0.03, -0.1, -0.0, 0.21, 0.03]})
# Basic data cleaning
df = df.dropna() # Remove missing values
df = df.drop_duplicates() # Remove duplicates
# Geospatial visualization
fig = px.choropleth(df, locations='Country/Negara',
locationmode='country names',
color='Revenue/Pendapatan',
hover_name='City/Kota',
scope='world',
title='Revenue by Country')
fig.update_geos(showcountries=True)
fig.show()
|
Optimize price age group
|
{
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": null,
"City/Kota": [
"São Paulo",
"New York",
"Sydney",
"Tokyo",
"Paris"
],
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": [
"Brazil",
"US",
"Australia",
"Japan",
"France"
],
"Customer ID/ID Pelanggan": null,
"Date/Tanggal": null,
"Discount/Diskon": null,
"Gender/Jenis Kelamin": null,
"Growth Rate/Pertumbuhan": [
0.17,
0.12,
0.18,
0,
0.09
],
"Income/Pendapatan": null,
"Last Purchase Date/Tanggal Pembelian Terakhir": null,
"Latitude/Lintang": [
71.5829,
25.0132,
58.4267,
3.5686999999999998,
69.8904
],
"Longitude/Bujur": [
145.6496,
6.2671,
-20.7972,
99.0281,
-140.1458
],
"Loyalty Score/Skor Loyalitas": null,
"Market Share/Pangsa Pasar": [
0.29,
0.28,
0.17,
0.32,
0.36
],
"Price/Harga": null,
"Product/Produk": null,
"Region/Wilayah": [
"South America",
"North America",
"Oceania",
"Asia",
"Europe"
],
"Revenue/Pendapatan": [
2709,
4792,
2104,
2882,
3761
],
"Sales/Penjualan": null,
"Stock/Stok": null,
"Visitors/Pengunjung": null
}
|
import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'City/Kota': ['São Paulo', 'New York', 'Sydney', 'Tokyo', 'Paris'], 'Country/Negara': ['Brazil', 'US', 'Australia', 'Japan', 'France'], 'Region/Wilayah': ['South America', 'North America', 'Oceania', 'Asia', 'Europe'], 'Latitude/Lintang': [71.5829, 25.0132, 58.4267, 3.5687, 69.8904], 'Longitude/Bujur': [145.6496, 6.2671, -20.7972, 99.0281, -140.1458], 'Revenue/Pendapatan': [2709, 4792, 2104, 2882, 3761], 'Market Share/Pangsa Pasar': [0.29, 0.28, 0.17, 0.32, 0.36], 'Growth Rate/Pertumbuhan': [0.17, 0.12, 0.18, 0.0, 0.09]})
# Basic data cleaning
df = df.dropna() # Remove missing values
df = df.drop_duplicates() # Remove duplicates
# Comprehensive data analysis
analysis_result = df.describe(include='all')
print(analysis_result)
# Additional insights
print('\nTop performing items:')
print(df.nlargest(3, df.columns[1]))
print('\nTrend analysis:')
if 'Date/Tanggal' in df.columns:
df['Month/Bulan'] = pd.to_datetime(df['Date/Tanggal']).dt.month
monthly_trend = df.groupby('Month/Bulan').mean(numeric_only=True)
print(monthly_trend)
|
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