How to fill nan values with rolling mean in pandas
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())
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:
a
0 1.0
1 1.0
2 2.0
3 3.0
4 4.0
5 5.0
6 3.0
7 1.0
8 1.0
9 2.0
10 3.0
11 4.0
12 5.0
13 3.0