How to Use fillna in pandas to Handle Missing Data

In pandas, use fillna() to replace missing values (NaN) in a DataFrame or Series with a specified value or method. This helps clean data by filling gaps with constants, forward-fill, or backward-fill. The method returns a new object or modifies in place if specified.

Syntax

The fillna() method has several parameters:

• value: The value to replace missing data with (number, string, dict, or Series).
• method: Use 'ffill' (forward fill) or 'bfill' (backward fill) to propagate non-missing values.
• axis: Choose rows (0) or columns (1) to fill along.
• inplace: If True, modifies the original object instead of returning a new one.
• limit: Maximum number of consecutive NaNs to fill.

python

DataFrame.fillna(value=None, method=None, axis=0, inplace=False, limit=None)

Example

This example shows how to fill missing values in a DataFrame with a constant and with forward fill method.

python

import pandas as pd
import numpy as np

data = {'A': [1, np.nan, 3, np.nan], 'B': [np.nan, 2, np.nan, 4]}
df = pd.DataFrame(data)

# Fill NaN with a constant value
filled_constant = df.fillna(0)

# Fill NaN using forward fill method
filled_ffill = df.fillna(method='ffill')

print('Original DataFrame:')
print(df)
print('\nFill NaN with 0:')
print(filled_constant)
print('\nFill NaN with forward fill:')
print(filled_ffill)

Output

Original DataFrame:
     A    B
0  1.0  NaN
1  NaN  2.0
2  3.0  NaN
3  NaN  4.0

Fill NaN with 0:
     A    B
0  1.0  0.0
1  0.0  2.0
2  3.0  0.0
3  0.0  4.0

Fill NaN with forward fill:
     A    B
0  1.0  NaN
1  1.0  2.0
2  3.0  2.0
3  3.0  4.0

Common Pitfalls

Common mistakes when using fillna() include:

• Using method and value together, which is not allowed.
• Forgetting to set inplace=True if you want to modify the original DataFrame.
• Not specifying limit when you want to restrict how many NaNs get filled.
• Trying to fill with incompatible types (e.g., filling numeric columns with strings).

python

import pandas as pd
import numpy as np

df = pd.DataFrame({'A': [1, np.nan, 3]})

# Wrong: Using value and method together raises error
try:
    df.fillna(value=0, method='ffill')
except ValueError as e:
    print(f'Error: {e}')

# Right: Use either value or method
filled = df.fillna(method='ffill')
print(filled)

Output

Error: Cannot specify both 'value' and 'method'
     A
0  1.0
1  1.0
2  3.0

Quick Reference

Here is a quick summary of fillna() options:

Parameter	Description	Example
value	Value to replace NaNs	df.fillna(0)
method	Fill method: 'ffill' or 'bfill'	df.fillna(method='ffill')
axis	Axis to fill along (0=rows, 1=columns)	df.fillna(0, axis=1)
inplace	Modify original DataFrame if True	df.fillna(0, inplace=True)
limit	Max number of NaNs to fill	df.fillna(0, limit=1)

Key Takeaways

Use fillna() to replace missing values in pandas DataFrames or Series easily.

Choose between filling with a constant value or using methods like forward fill.

Do not use both value and method parameters together in fillna().

Set inplace=True to modify the original data directly.

Use limit to control how many consecutive NaNs get filled.