How to fill nan values with rolling mean in pandas

Question

I have a dataframe which contains nan values at few places. I am trying to perform data cleaning in which I fill the nan values with mean of it’s previous five instances. To do so, I have come up with the following.

input_data_frame[var_list].fillna(input_data_frame[var_list].rolling(5).mean(), inplace=True)

But, this is not working. It isn’t filling the nan values. There is no change in the dataframe’s null count before and after the above operation. Assuming I have a dataframe with just integer column, How can I fill NaN values with mean of the previous five instances? Thanks in advance.

Asked By: VaM999

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Source

Answer 1

This should work:

input_data_frame[var_list]= input_data_frame[var_list].fillna(pd.rolling_mean(input_data_frame[var_list], 6, min_periods=1))

Note that the window is 6 because it includes the value of NaN itself (which is not counted in the average). Also the other NaN values are not used for the averages, so if less that 5 values are found in the window, the average is calculated on the actual values.

Example:

df = {'a': [1, 1,2,3,4,5, np.nan, 1, 1, 2, 3, 4, 5, np.nan] }
df = pd.DataFrame(data=df)
print df

      a
0   1.0
1   1.0
2   2.0
3   3.0
4   4.0
5   5.0
6   NaN
7   1.0
8   1.0
9   2.0
10  3.0
11  4.0
12  5.0
13  NaN

Output:

Answered By: Joe

Answer 2

rolling_mean function has been modified in pandas. If you fill the entire dataset, you can use;

filled_dataset = dataset.fillna(dataset.rolling(6,min_periods=1).mean())

Answered By: Caner Erden

Answer 3

you can simply use interpolate()

df = {'a': [1,5, np.nan, np.nan, np.nan, 2, 5, np.nan] }
df = pd.DataFrame(data=df)
print(df)


df['a'].interpolate()

Answered By: Franz Eigner

How to fill nan values with rolling mean in pandas

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