How To Replace Missing Data In Dataframe
Lets say I have the following DataFrame: df = pd.DataFrame({'col1': [241, 123, 423], 'col2':[977, 78, np.NaN], 'col3':[76, 432, np.NaN], 'col4':[234, 321, 987]}, index=pd.date_rang
Solution 1:
We can use DataFrame.fillna
:
df=df.fillna(df2)
print(df)
col1 col2 col3 col4
Date
2019-01-01 241 977.0 76.0 234
2019-01-02 123 78.0 432.0 321
2019-01-03 423 111.0 222.0 987
if you had a series by columns like the one obtained with df2.iloc[0]
we can also do it:
my_serie=df2.iloc[0]print(my_serie)col2111col3222Name:2019-01-03 00:00:00,dtype:int64print(df.fillna(my_serie))col1col2col3col4Date2019-01-01 241977.076.02342019-01-02 12378.0432.03212019-01-03 423111.0222.0987
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