Fill NaN values in dataframe with previous values in column

Question

Hi I have a dataframe with some missing values ex:

The black numbers 40 and 50 are the values already inputted and the red ones are to autofill from the previous values. Row 2 is blank as there is no previous number to fill.

Any idea how I can do this efficiently? I was trying loops but maybe there is a better way

Possible duplicate of How to replace NaNs by preceding values in pandas DataFrame? — Mr. T
– Mr. T, Commented Jul 30, 2018 at 8:04

9769953 · Accepted Answer · 2018-07-30 08:02:37Z

9

It can be done easily with ffill method in pandas fillna.

To illustrate the working consider the following sample dataframe

df = pd.DataFrame()

df['Vals'] = [1, 2, 3, np.nan, np.nan, 6, 7, np.nan, 8]

    Vals
0   1.0
1   2.0
2   3.0
3   NaN
4   5.0
5   6.0
6   7.0
7   NaN
8   8.0

To fill the missing value do this

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

    Vals
0   1.0
1   2.0
2   3.0
3   3.0
4   3.0
5   6.0
6   7.0
7   7.0
8   8.0

edited Jul 30, 2018 at 8:02

9769953

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answered Jul 30, 2018 at 7:57

Sreeram TP

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Comments

smartass · Accepted Answer · 2018-07-30 08:18:43Z

0

There is a direct synonym function for this, pandas.DataFrame.ffill

df['Vals',inplace=True]

answered Jul 30, 2018 at 8:18

smartass

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