df.groupby("column") splits rows into groups, then an aggregate function summarises each group:
df.groupby("dept")["salary"].mean()- Several at once:
.agg(["count", "mean", "max"]) df["city"].value_counts()counts each value.
Pandas Group By
Summarise data by category.
✍️ 3 exercises + code challenge in this lessonKey Takeaways: Pandas Group By
Mastering Pandas Group By 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 Group By Code Example
Pandasimport pandas as pd
df = pd.DataFrame({
"dept": ["IT", "HR", "IT", "Sales", "IT"],
"salary": [120000, 95000, 110000, 88000, 100000],
})
print(df.groupby("dept")["salary"].mean())
print(df["dept"].value_counts().to_dict())Output
dept
HR 95000.0
IT 110000.0
Sales 88000.0
Name: salary, dtype: float64
{'IT': 3, 'HR': 1, 'Sales': 1}
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Pandas Group By Practice Exercises
Check what you learned in “Pandas Group By”.
/ 3 solved
Fill in the blank
Group rows by department.
df.("dept")["salary"].mean()
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Multiple choice
Which method counts how often each value appears?
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Code challenge
Print the total salary of the IT department (use groupby and sum).
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