Pandas Pivot Tables

Introduction

Pivot tables reshape data by aggregating values across different dimensions.

Basic Pivot Table

import pandas as pd

df = pd.DataFrame({
    "Category": ["A", "B", "A", "B", "A"],
    "Month": ["Jan", "Jan", "Feb", "Feb", "Jan"],
    "Sales": [100, 200, 150, 180, 120]
})

# Create pivot
pd.pivot_table(df, values="Sales", index="Category", columns="Month")

Aggregation

# Multiple aggregation functions
pd.pivot_table(df, values="Sales", index="Category", 
               aggfunc=["sum", "mean", "count"])

# Different functions per column
pd.pivot_table(df, 
               values=["Sales", "Quantity"],
               aggfunc={"Sales": "sum", "Quantity": "mean"})

Margins and Subtotals

pd.pivot_table(df, values="Sales", index="Category", 
               columns="Month", margins=True, margins_name="Total")

Practice Problems

1. Create pivot table from sales data
2. Use multiple aggregation functions
3. Add totals and subtotals
4. Handle missing values in pivot
5. Visualize pivot table results