How to Efficiently Replace NaN Values in Pandas DataFrames using `ffill` and `bfill`?

Replacing NaNs with Preceding or Next Values in Pandas DataFrames

When dealing with Pandas DataFrames, missing data represented as NaNs (Not a Number) can pose a challenge for data analysis. One common task is to replace these NaNs with appropriate values derived from existing data. A straightforward approach is to iterate through the DataFrame and modify values explicitly. However, Pandas offers more efficient solutions that avoid the use of loops.

Forward Filling (ffill)

To replace NaNs with the first non-NaN value above them in the same column, use the fillna method with the ffill (forward fill) option. This method propagates the last valid observation forward to subsequent valid observations.

import pandas as pd
df = pd.DataFrame([[1, 2, 3], [4, None, None], [None, None, 9]])
df.fillna(method='ffill')

Output:

   0  1  2
0  1  2  3
1  4  2  3
2  4  2  9

Additional Considerations

• The first row in the DataFrame should not contain NaNs as it serves as the reference point for filling.
• The ffill method operates column-wise, meaning it fills NaNs in each column separately.
• To perform backward filling (replacing NaNs with the next non-NaN value below them), use the bfill (backward fill) option.
• To modify the DataFrame inplace without creating a new variable, set the inplace parameter to True:

df.fillna(method='ffill', inplace=True)

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