pd.merge(left, right, on="key") combines DataFrames on a shared column, like an SQL join.
how="inner"(default),"left","right","outer"pd.concat([df1, df2])stacks DataFrames on top of each other.
Pandas Merging Data
Join two DataFrames like SQL.
✍️ 3 exercises + code challenge in this lessonKey Takeaways: Pandas Merging Data
Mastering Pandas Merging Data helps you write clean and reliable Pandas code. Follow standard conventions, test edge cases, and keep logic simple. Try running the code example below and complete the exercises to build confidence.
Pandas Merging Data Code Example
Pandasimport pandas as pd
customers = pd.DataFrame({"id": [1, 2, 3], "name": ["Ali", "Sara", "Usman"]})
orders = pd.DataFrame({"customer_id": [1, 1, 3], "amount": [5000, 1200, 800]})
merged = pd.merge(orders, customers, left_on="customer_id", right_on="id")
print(merged[["name", "amount"]])
print(merged.groupby("name")["amount"].sum().to_dict())Output
name amount
0 Ali 5000
1 Ali 1200
2 Usman 800
{'Ali': 6200, 'Usman': 800}
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Pandas Merging Data Practice Exercises
Check what you learned in “Pandas Merging Data”.
/ 3 solved
Fill in the blank
Join two DataFrames on a shared column.
pd.(orders, customers, on="id")
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Multiple choice
Which "how" keeps all rows from the left DataFrame?
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Code challenge
Merge orders with customers on "id" and print the names of customers who ordered, as a list.
Write the code in the live editor. Your answer is checked automatically.
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