How to replace NaNs by preceding values in pandas DataFrame?

Suppose I have a DataFrame with some NaNs:

    >>> import pandas as pd
    >>> df = pd.DataFrame([[1, 2, 3], [4, None, None], [None, None, 9]])
    >>> df
        0   1   2
    0   1   2   3
    1   4 NaN NaN
    2 NaN NaN   9

What I need to do is replace every NaN with the first non-NaN value in the same column above it. It is assumed that the first row will never contain a NaN. So for the previous example the result would be

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

I can just loop through the whole DataFrame column-by-column, element-by-element and set the values directly, but is there an easy (optimally a loop-free) way of achieving this?

You could use the fillna method on the DataFrame and specify the method as ffill (forward fill):

    >>> df = pd.DataFrame([[1, 2, 3], [4, None, None], [None, None, 9]])
    >>> df.fillna(method='ffill')
       0  1  2
    0  1  2  3
    1  4  2  3
    2  4  2  9

This method...

propagate[s] last valid observation forward to next valid

To go the opposite way, there's also a bfill method.

This method doesn't modify the DataFrame inplace - you'll need to rebind the returned DataFrame to a variable or else specify inplace=True:

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

From: stackoverflow.com/q/27905295

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