Pandas

Missing Data

Detect, fill, and drop NaN values.

#missing-data#nan

Code

pandas

import pandas as pd
import numpy as np

df = pd.DataFrame({
    "a": [1, np.nan, 3, np.nan],
    "b": [10, 20, np.nan, 40],
})

# Detect
print(df.isna(), df.isna().sum())

# Fill
filled = df.fillna({"a": 0, "b": df["b"].mean()})
ffill = df.ffill()
interp = df.interpolate()

# Drop
dropped_rows = df.dropna()
dropped_cols = df.dropna(axis=1)
clean = df.dropna(thresh=1)

print(filled, dropped_rows)

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