How does Pandas handle missing data?

In Pandas, missing data is usually represented by the value NaN (Not a Number). Pandas provides several methods to handle such data:

• isna() or isnull() — to detect missing values.
• dropna() — to remove rows or columns with missing values.
• fillna() — to fill missing values with a specified value or strategy (e.g., mean, median).

Example:

import pandas as pd
import numpy as np

df = pd.DataFrame({
    'A': [1, 2, np.nan, 4],
    'B': [5, np.nan, np.nan, 8]
})

# Find missing values
print(df.isna())

# Fill missing values with zeros
filled_df = df.fillna(0)

# Remove rows with missing values
clean_df = df.dropna()

Thus, Pandas offers flexible tools for detecting and handling missing data in tables.