How do you merge and join DataFrames in Pandas, and what do the different join types mean?

Real-world data lives in multiple tables. Pandas merge() implements SQL-style joins, and concat() stacks DataFrames. Choosing the right join type prevents silently losing or duplicating rows.

import pandas as pd

customers = pd.DataFrame({
    'cust_id': [1, 2, 3, 4],
    'name':    ['Alice', 'Bob', 'Carol', 'Dave'],
})
orders = pd.DataFrame({
    'order_id': [101, 102, 103],
    'cust_id':  [1, 2, 9],     # cust_id 9 has no match; Dave has no order
    'amount':   [200, 150, 80],
})

# Inner join — only rows that match in both
pd.merge(customers, orders, on='cust_id', how='inner')   # 2 rows

# Left join — all customers, NaN where no order
pd.merge(customers, orders, on='cust_id', how='left')    # 4 rows

# Different key names in each table
pd.merge(customers, orders,
         left_on='cust_id', right_on='cust_id')  # same here, but shows syntax

# Merge on index
pd.merge(customers.set_index('cust_id'), orders,
         left_index=True, right_on='cust_id')

# concat — stack vertically (rows) or horizontally (columns)
pd.concat([df1, df2], axis=0, ignore_index=True)   # stack rows
pd.concat([df1, df2], axis=1)                       # add columns side by side

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