How do you handle missing values in Pandas?

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In Pandas, handling missing values is a common task in data cleaning. Missing values are typically represented as NaN (Not a Number). Pandas provides several ways to detect, remove, or fill these missing values to prepare the data for analysis.

✅ 1. Detect Missing Values

You can identify missing values using:

df.isnull()       # Returns a DataFrame of True/False

df.isnull().sum() # Count of missing values per column

🧹 2. Remove Missing Values

Remove rows with any NaN:

df.dropna()

Remove columns with any NaN:

df.dropna(axis=1)

✏️ 3. Fill Missing Values

Fill with a specific value:

df.fillna(0)  # Replace NaN with 0

Forward fill (use previous value):

df.fillna(method='ffill')

Backward fill (use next value):

df.fillna(method='bfill')

Fill with column mean/median/mode:

df['column'].fillna(df['column'].mean(), inplace=True)

🎯 4. Replace Missing Values Conditionally

df.loc[df['age'].isnull(), 'age'] = 25

Summary:

Use .isnull() to detect.

Use .dropna() to remove.

Use .fillna() to fill.

Choose strategy based on context and data type.

Proper handling of missing values ensures better model performance and more accurate insights in data analysis.

Read More:

What are Type I and Type II errors?

What is the difference between NumPy and Pandas?

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