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Merging, Joining & Concatenating DataFrames
SQL-style inner, left, right, and outer joins with merge() and concat()
Combining data from disparate sources is achieved using pd.merge() for relational key-based joins and pd.concat() for axis-aligned binding.
Join Types in pd.merge()
how="inner" (Default): Returns only records with matching keys in both tables.
how="left": Preserves all rows from the left table, filling unmatched right columns with NaN.
how="right": Preserves all rows from the right table.
how="outer": Full outer join, retaining all rows from both tables.
pd.concat([df1, df2], axis=0): Stacks DataFrames vertically (unions rows).
import pandas as pd
users = pd.DataFrame({
"user_id": [1, 2, 3],
"name": ["Alice", "Bob", "Charlie"]
})
orders = pd.DataFrame({
"order_id": [101, 102, 103],
"user_id": [1, 2, 1],
"amount": [99.50, 45.00, 150.25]
})
# Left join users onto their orders
merged = pd.merge(orders, users, on="user_id", how="left")
print("Merged Orders with User Details:
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Test Yourself With Exercise
Which join type in `pd.merge()` preserves all records from the left DataFrame regardless of whether a match exists in the right DataFrame?