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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 | |