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import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np
import plotly.express as px
from matplotlib.gridspec import GridSpec
def average_sales_by_region(df):
"""
Generate a bar plot for average sales by region.
"""
df_bar = df[['region', 'sales']]
df_bar = df_bar.groupby('region').mean().sort_values(by='sales', ascending=False)
fig, ax = plt.subplots(figsize=[10, 6])
sns.barplot(x=df_bar.index, y='sales', data=df_bar, palette='viridis', ax=ax)
ax.set_title('Average Sales Across Different Regions')
ax.set_xlabel('Region')
ax.set_ylabel('Average Sales')
for index, value in enumerate(df_bar['sales']):
ax.text(index, value, f"{value:.2f}", ha='center', va='bottom')
return fig
def average_sales_and_profit_over_time(df):
"""
Generate a line plot for average sales and profit over time.
"""
df_line = df[['order_date', 'sales', 'profit']].sort_values('order_date')
df_line['order_date'] = pd.to_datetime(df_line['order_date'])
df_line = df_line.groupby(df_line['order_date'].dt.to_period("M")).mean()
df_line.index = df_line.index.to_timestamp()
fig, ax = plt.subplots(figsize=[10, 6])
ax.plot(df_line.index, 'sales', data=df_line, color='green', label='Avg Sales')
ax.plot(df_line.index, 'profit', data=df_line, color='red', label='Avg Profit')
ax.legend()
ax.set_title('Average Sales and Profit Over Time (Monthly)')
ax.set_xlabel('Time')
ax.set_ylabel('Value')
return fig
def segment_vs_region_distribution(df):
"""
Generate a count plot for segments across different regions.
"""
fig = plt.figure(figsize=(10, 6))
sns.countplot(x='segment', data=df, hue='region', palette='viridis')
plt.title('Segment vs. Region Distribution')
plt.xlabel('Segment')
plt.ylabel('Count')
plt.legend(title='Region')
return fig
def sales_vs_profit_across_segments(df):
"""
Generate a scatter plot comparing sales and profit across different customer segments.
"""
fig, ax = plt.subplots(figsize=(10, 6))
sns.scatterplot(x='sales', y='profit', hue='segment', data=df, palette='viridis', size='sales', sizes=(20, 200), ax=ax)
ax.set_title('Sales vs. Profit Across Different Customer Segments')
ax.set_xlabel('Sales')
ax.set_ylabel('Profit')
return fig
def category_composition_for_profit_and_sales(df):
"""
Generate pie charts for the composition of category for profit and sales.
"""
df_pie = df.groupby('category').agg({'sales': 'sum', 'profit': 'sum'}).reset_index()
fig, axs = plt.subplots(1, 2, figsize=(14, 7))
axs[0].pie(df_pie['sales'], labels=df_pie['category'], autopct='%1.1f%%', startangle=140, colors=['#ff9999','#66b3ff','#99ff99','#ffcc99'])
axs[0].set_title('Sales Composition by Category')
axs[1].pie(df_pie['profit'], labels=df_pie['category'], autopct='%1.1f%%', startangle=140, colors=['#ff9999','#66b3ff','#99ff99','#ffcc99'])
axs[1].set_title('Profit Composition by Category')
return fig
# Additional EDA functions can be added following the same pattern
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