- Find missing values:
df.isna().sum() - Remove them:
df.dropna(); fill them:df.fillna(value) - Duplicates:
df.duplicated(),df.drop_duplicates() - Fix types:
df["col"].astype(int),pd.to_datetime(df["date"])
Pandas Cleaning Data
Missing values, duplicates and wrong types.
✍️ 3 exercises + code challenge in this lessonKey Takeaways: Pandas Cleaning Data
Mastering Pandas Cleaning 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 Cleaning Data Code Example
Pandasimport pandas as pd
df = pd.DataFrame({
"name": ["Ali", "Sara", None, "Sara"],
"age": [25, None, 30, None],
})
print(df.isna().sum().tolist())
df["age"] = df["age"].fillna(df["age"].mean())
df = df.dropna(subset=["name"]).drop_duplicates()
print(df)Output
[1, 2] name age 0 Ali 25.0 1 Sara 27.5
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Pandas Cleaning Data Practice Exercises
Check what you learned in “Pandas Cleaning Data”.
/ 3 solved
Fill in the blank
Remove rows that contain missing values.
df = df.()
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Multiple choice
Which method removes duplicate rows?
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Code challenge
Fill the missing ages with 0 and print the ages as a list of ints.
Write the code in the live editor. Your answer is checked automatically.
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