Merging and Joining DataFrames

Merging & Joining

Combine data from multiple sources with relational operations.

merge (SQL‑like joins)

orders = pd.read_csv('orders.csv')
customers = pd.read_csv('customers.csv')

# Inner join on customer_id
merged = pd.merge(orders, customers, on='customer_id', how='inner')
print(merged.head())

Types of joins

• inner: keep only matches
• left: keep all left rows
• right: keep all right rows
• outer: keep all rows, fill missing with NaN

left_join = pd.merge(df_left, df_right, on='key', how='left')
right_join = pd.merge(df_left, df_right, on='key', how='right')
full_outer = pd.merge(df_left, df_right, on='key', how='outer')

Concatenating vertically or horizontally

# Append rows (vertical)
combined = pd.concat([df1, df2], ignore_index=True)

# Append columns (horizontal)
combined_h = pd.concat([df1, df2], axis=1)

Handling duplicate column names after merge

merged = pd.merge(df1, df2, on='id', suffixes=('_left', '_right'))

pd.merge(df_a, df_b, on='key', how='inner', validate='many_to_one')
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