Pandas E-commerce Data Analysis Practice

This section uses a complete e-commerce data analysis case to comprehensively apply various Pandas features for data analysis.

Case Overview

Analyze e-commerce platform order data, including sales trends, product performance, customer analysis, and other dimensions.

Data Preparation

Example

import pandas as pd
import numpy as np

# Simulate e-commerce order data
np.random.seed(42)
n_orders = 1000

orders = pd.DataFrame({
    "Order ID": range(1, n_orders + 1),
    "Customer ID": np.random.randint(100, 200, n_orders),
    "Product ID": np.random.randint(1, 20, n_orders),
    "Order Date": pd.date_range("2024-01-01", periods=n_orders, freq="30min"),
    "Quantity": np.random.randint(1, 5, n_orders),
    "Unit Price": np.random.uniform(10, 500, n_orders).round(2)
})

# Calculate order amount
orders["Order Amount"] = (orders["Quantity"] * orders["Unit Price"]).round(2)

print("Order data overview:")
print(orders.head(10))
print(f"\nData volume: {len(orders)} records")

Data Preprocessing

Example

# Extract date features
orders["Date"] = orders["Order Date"].dt.date
orders["Hour"] = orders["Order Date"].dt.hour
orders["Weekday"] = orders["Order Date"].dt.day_name()
orders["Month"] = orders["Order Date"].dt.month

print("After adding time features:")
print(orders.head())
print()

# Missing value check
print("Missing value check:")
print(orders.isnull().sum())

Sales Analysis

Overall Sales Situation

Example

# Overall sales metrics
print("=== Overall Sales Situation ===\n")
print(f"Total orders: {len(orders):,}")
print(f"Total sales: ¥{orders['Order Amount'].sum():,.2f}")
print(f"Average order amount: ¥{orders['Order Amount'].mean():,.2f}")
print(f"Median order amount: ¥{orders['Order Amount'].median():,.2f}")
print()

# Monthly statistics
monthly = orders.groupby("Month").agg({
    "Order ID": "count",
    "Order Amount": "sum",
    "Customer ID": "nunique"
}).rename(columns={
    "Order ID": "Order Count",
    "Order Amount": "Sales Amount",
    "Customer ID": "Customer Count"
})

print("Monthly sales trend:")
print(monthly)

Product Analysis

Example

# Product sales ranking
product_sales = orders.groupby("Product ID").agg({
    "Order ID": "count",
    "Quantity": "sum",
    "Order Amount": "sum"
}).rename(columns={
    "Order ID": "Order Count",
    "Quantity": "Sales Volume",
    "Order Amount": "Sales Amount"
}).sort_values("Sales Amount", ascending=False)

print("=== Top 10 Product Sales Ranking ===\n")
print(product_sales.head(10))
print()

# Best-selling product
print(f"Best-selling product: Product {product_sales.index)
print(f"Sales amount: ¥{product_sales.iloc)

Customer Analysis

Example

# Customer spending analysis
customer_sales = orders.groupby("Customer ID").agg({
    "Order ID": "count",
    "Order Amount": "sum"
}).rename(columns={
    "Order ID": "Order Count",
    "Order Amount": "Total Spending"
})

print("=== Customer Analysis ===\n")
print(f"Active customer count: {len(customer_sales)}")
print(f"Average orders per customer: {customer_sales['Order Count'].mean():.1f}")
print(f"Average spending per customer: ¥{customer_sales['Total Spending'].mean():,.2f}")
print()

# Customer segmentation
customer_sales["Segment"] = pd.cut(
    customer_sales["Total Spending"],
    bins=[0, 1000, 5000, 10000, float("inf")],
    labels=["Regular", "Silver", "Gold", "VIP"]
)

print("Customer segment statistics:")
print(customer_sales["Segment"].value_counts())

Time Analysis

Example

# Analysis by time period
hourly = orders.groupby("Hour")["Order Amount"].sum()

print("=== Time Period Sales Analysis ===\n")
print(f"Peak sales hour: {hourly.idxmax()} o'clock")
print(f"Sales amount during that period: ¥{hourly.max():,.2f}")
print()

# Analysis by weekday
weekday = orders.groupby("Weekday")["Order Amount"].sum().reindex([
    "Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday", "Sunday"
])

print("Weekday sales:")
for day, amount in weekday.items():
    print(f"{day}: ¥{amount:,.2f}")

Analysis Summary

Example

print("""
=== E-commerce Data Analysis Summary ===

1. Sales Overview
- Total orders: {0}
- Total sales: ¥{1:,.2f}
- Average order value: ¥{2:.2f}

2. Product Performance
- Best-selling product: Product {3}
- Product sales distribution is uneven, with top products contributing substantial revenue

3. Customer Insights
- Active customers: {4} people
- Recommend prioritizing retention of high-value customers

4. Time Patterns
- Sales peak around {5} o'clock
- Marketing strategies can be adjusted based on peak periods

5. Optimization Suggestions
- 1) Increase inventory and promotion for best-selling products
- 2) Provide personalized services to high-value customers
- 3) Increase customer service staffing during peak sales hours
""".format(
    len(orders),
    orders["Order Amount"].sum(),
    orders["Order Amount"].mean(),
    product_sales.index[0],
    len(customer_sales),
    hourly.idxmax()
))
Other Extensions