Pandas Merging and Joining

Introduction

Pandas provides multiple ways to combine DataFrames based on their relationships.

Merge

# SQL-style join
left = pd.DataFrame({"key": ["A", "B", "C"], "value": [1, 2, 3]})
right = pd.DataFrame({"key": ["A", "B", "D"], "value2": [10, 20, 30]})

pd.merge(left, right, on="key")
pd.merge(left, right, on="key", how="left")
pd.merge(left, right, on="key", how="outer")
pd.merge(left, right, on="key", how="inner")

Concat

# Stack DataFrames
pd.concat([df1, df2])            # Vertical
pd.concat([df1, df2], axis=1)    # Horizontal

# Ignore index
pd.concat([df1, df2], ignore_index=True)

# Add only missing columns
pd.concat([df1, df2], join="inner")

Join

# Index-based join
left.set_index("key").join(right.set_index("key"))

Practice Problems

1. Merge customers and orders DataFrames
2. Concatenate monthly sales data
3. Perform left join to keep all customers
4. Combine DataFrames with different columns
5. Handle duplicate keys in merge