Merge & Join DataFrames

Combine two DataFrames on a common key — the pandas equivalent of SQL JOIN

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Explanation

pd.merge() combines two DataFrames based on a common column (like SQL JOIN).

python

orders = pd.DataFrame({'order_id':[1,2,3],'customer_id':[101,102,101],'amount':[50,80,30]})
customers = pd.DataFrame({'customer_id':[101,102,103],'name':['Alice','Bob','Carol']})

# INNER JOIN (default) — only matching rows
pd.merge(orders, customers, on='customer_id')

# LEFT JOIN — all orders, fill missing customer info with NaN
pd.merge(orders, customers, on='customer_id', how='left')

how= parameter:

'inner' — only rows with matches in BOTH (default)
'left' — all rows from left DataFrame
'right' — all rows from right DataFrame
'outer' — all rows from both, NaN where no match

Different column names:

python pd.merge(df1, df2, left_on='user_id', right_on='id')

join() vs merge(): df.join() is a shorthand that joins on the index — less common than merge().

Examples

Left join to keep all orders

how="left" keeps all rows from the left DataFrame

import pandas as pd

orders = pd.DataFrame({'id':[1,2,3],'customer_id':[101,102,999],'amount':[50,80,30]})
customers = pd.DataFrame({'customer_id':[101,102],'name':['Alice','Bob']})

result = pd.merge(orders, customers, on='customer_id', how='left')
print(result)
#    id  customer_id  amount   name
# 0   1          101      50  Alice
# 1   2          102      80    Bob
# 2   3          999      30    NaN  ← no match

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