Missing Data

Code

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)

Explanation

isna() identifies missing values and sum() per column gives a quick null-count overview. fillna replaces NaN with constants, per-column mappings, or methods like ffill and interpolate. dropna removes rows or columns with too many missing values, configurable with thresh.

More Pandas Snippets