How Can I Efficiently Replace NaN Values in Pandas DataFrames Without Loops?

Loop-Free NaN Replacement in Pandas DataFrames

Replacing NaN values in a Pandas DataFrame can be a common task. One approach is to iteratively replace NaNs with the first non-NaN value above it. However, this method is inefficient and can be prone to errors.

Fortunately, Pandas provides a more efficient and loop-free way to accomplish this using the fillna method. By specifying the method as 'ffill' (forward fill), Pandas will propagate the last valid observation forward to the next valid observation:

import pandas as pd

df = pd.DataFrame([[1, 2, 3], [4, None, None], [None, None, 9]])

df.fillna(method='ffill')

This results in the following DataFrame:

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

The fillna method also allows for backward filling (filling from the bottom) using the 'bfill' (backward fill) method:

df.fillna(method='bfill')

By default, the fillna method does not modify the original DataFrame inplace. To modify the original DataFrame, specify inplace=True:

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

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