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

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 pdimport numpy as npts_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  115.02     1   NoT       63   13.03     2  YesT       43   71.0)