How to replace NaN values by Zeroes in a column of a Pandas Dataframe? How to replace NaN values by Zeroes in a column of a Pandas Dataframe? python python

How to replace NaN values by Zeroes in a column of a Pandas Dataframe?


I believe DataFrame.fillna() will do this for you.

Link to Docs for a dataframe and for a Series.

Example:

In [7]: dfOut[7]:           0         10       NaN       NaN1 -0.494375  0.5709942       NaN       NaN3  1.876360 -0.2297384       NaN       NaNIn [8]: df.fillna(0)Out[8]:           0         10  0.000000  0.0000001 -0.494375  0.5709942  0.000000  0.0000003  1.876360 -0.2297384  0.000000  0.000000

To fill the NaNs in only one column, select just that column. in this case I'm using inplace=True to actually change the contents of df.

In [12]: df[1].fillna(0, inplace=True)Out[12]: 0    0.0000001    0.5709942    0.0000003   -0.2297384    0.000000Name: 1In [13]: dfOut[13]:           0         10       NaN  0.0000001 -0.494375  0.5709942       NaN  0.0000003  1.876360 -0.2297384       NaN  0.000000

EDIT:

To avoid a SettingWithCopyWarning, use the built in column-specific functionality:

df.fillna({1:0}, inplace=True)


It is not guaranteed that the slicing returns a view or a copy. You can do

df['column'] = df['column'].fillna(value)


You could use replace to change NaN to 0:

import pandas as pdimport numpy as np# for columndf['column'] = df['column'].replace(np.nan, 0)# for whole dataframedf = df.replace(np.nan, 0)# inplacedf.replace(np.nan, 0, inplace=True)


matomo