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?](https://stackoverflow.com/questions/27905295/how-to-replace-nans-by-preceding-values-in-pandas-dataframe) — Mr. T, Jul 30 '18 at 08:04

score 9 · Accepted Answer · edited Jul 30 '18 at 08:02

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

score 0 · Answer 2 · answered Jul 30 '18 at 08:18

0

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

df['Vals',inplace=True]

answered Jul 30 '18 at 08:18

smartass

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Fill NaN values in dataframe with previous values in column

2 Answers2