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)
col2    111
col3    222
Name: 2019-01-03 00:00:00, dtype: int64

print(df.fillna(my_serie))
            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

Solution 2:

Alternative combine_first

df2.combine_first(df)
Out[8]: 
             col1   col2   col3   col4
Date                                  
2019-01-01  241.0  977.0   76.0  234.0
2019-01-02  123.0   78.0  432.0  321.0
2019-01-03  423.0  111.0  222.0  987.0

Or update

df.update(df2)
df
Out[10]: 
            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