Python Pandas replace NaN in one column with value from corresponding row of second column

Question

Assuming your DataFrame is in df:

df.Temp_Rating.fillna(df.Farheit, inplace=True)
del df['Farheit']
df.columns = 'File heat Observations'.split()

First replace any NaN values with the corresponding value of df.Farheit. Delete the 'Farheit' column. Then rename the columns. Here's the resulting DataFrame:

The above mentioned solutions did not work for me. The method I used was:

df.loc[df['foo'].isnull(),'foo'] = df['bar']

An other way to solve this problem,

import pandas as pd
import numpy as np

ts_df = pd.DataFrame([[1,"YesQ",75,],[1,"NoR",115,],[1,"NoT",63,13],[2,"YesT",43,71]],columns=['File','heat','Farheit','Temp'])


def fx(x):
    if np.isnan(x['Temp']):
        return x['Farheit']
    else:
        return x['Temp']
print(1,ts_df)
ts_df['Temp']=ts_df.apply(lambda x : fx(x),axis=1)

print(2,ts_df)

returns:

(1,    File  heat  Farheit  Temp                                                                                    
0     1  YesQ       75   NaN                                                                                        
1     1   NoR      115   NaN                                                                                        
2     1   NoT       63  13.0                                                                                        
3     2  YesT       43  71.0)                                                                                       
(2,    File  heat  Farheit   Temp                                                                                   
0     1  YesQ       75   75.0                                                                                       
1     1   NoR      115  115.0
2     1   NoT       63   13.0
3     2  YesT       43   71.0)

The accepted answer uses fillna() which will fill in missing values where the two dataframes share indices. As explained nicely here, you can use combine_first to fill in missing values, rows and index values for situations where the indices of the two dataframes don't match.

df.Col1 = df.Col1.fillna(df.Col2) #fill in missing values if indices match

#or 
df.Col1 = df.Col1.combine_first(df.Col2) #fill in values, rows, and indices

@Jonathan's answer is good, but an overkill, just use pop:

df['Temp_Rating'] = df['Temp_Rating'].fillna(df.pop('Farheit'))