Pandas Missing Data

Detect, fill, and drop missing NaN values cleanly.

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Real-world datasets contain missing values (represented as NaN). Use isna() to identify nulls, dropna() to eliminate missing rows, and fillna() to substitute default values or column means.

Key Takeaways: Pandas Missing Data

Mastering Pandas Missing 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 Missing Data Code Example

Pandas

import pandas as pd
import numpy as np

df = pd.DataFrame({"age": [25, np.nan, 30, np.nan]})
# Impute with mean age
df["age_filled"] = df["age"].fillna(df["age"].mean())
print(df)

Output

age  age_filled
0  25.0       27.5
1   NaN       27.5
2  30.0       27.5
3   NaN       27.5

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Pandas Missing Data

💡 Key Takeaways: Pandas Missing Data

Pandas Missing Data Code Example

Key Takeaways: Pandas Missing Data