How to Fill Missing Values in pandas DataFrames Easily

Use the fillna() method in pandas to fill missing values in a DataFrame or Series. You can fill with a constant value, forward fill, backward fill, or use other methods to replace NaN values.

Syntax

The fillna() method replaces missing values (NaN) in pandas objects. You can specify a value to fill, or use methods like forward fill or backward fill.

• value: scalar or dict, value to replace NaN
• method: {'ffill', 'bfill'}, fill using previous or next valid value
• axis: 0 or 1, fill along rows or columns
• inplace: bool, whether to modify the original object

python

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

Example

This example shows how to fill missing values in a DataFrame using a constant value and 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 missing values with 0
filled_constant = df.fillna(0)

# Forward fill missing values
filled_ffill = df.fillna(method='ffill')

print('Original DataFrame:')
print(df)
print('\nFill with 0:')
print(filled_constant)
print('\nForward 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 with 0:
     A    B
0  1.0  0.0
1  0.0  2.0
2  3.0  0.0
3  0.0  4.0

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 filling missing values include:

• Using fillna() without inplace=True and expecting the original DataFrame to change.
• Filling with inappropriate values that distort data meaning.
• Using forward fill when the first rows are missing, which leaves them unchanged.

python

import pandas as pd
import numpy as np

df = pd.DataFrame({'A': [np.nan, 2, np.nan]})

# Wrong: fillna without inplace, original df unchanged
filled_wrong = df.fillna(0)

# Correct: either assign or use inplace
# df = df.fillna(0)
# or
# df.fillna(0, inplace=True)

print('Original DataFrame after fillna without inplace:')
print(df)
print('\nFilled DataFrame assigned to new variable:')
print(filled_wrong)

Output

Original DataFrame after fillna without inplace:
     A
0  NaN
1  2.0
2  NaN

Filled DataFrame assigned to new variable:
     A
0  0.0
1  2.0
2  0.0

Quick Reference

Summary of common fillna() options:

Parameter	Description
value	Fill missing values with this scalar or dict
method='ffill'	Fill missing values with previous valid value
method='bfill'	Fill missing values with next valid value
inplace=True	Modify the original DataFrame instead of returning a copy

Parameter	Description
value	Fill missing values with this scalar or dict
method='ffill'	Fill missing values with previous valid value
method='bfill'	Fill missing values with next valid value
inplace=True	Modify the original DataFrame instead of returning a copy

Key Takeaways

Use pandas' fillna() method to replace missing values easily.

Specify a value or method like 'ffill' to control how missing data is filled.

Remember to assign the result or use inplace=True to update your DataFrame.

Choose fill values carefully to avoid changing data meaning.

Forward fill does not fill missing values at the start of data.