["python","pandas","dataframe","nan","fillna"]
Replace NaN in one column with value from corresponding row of second column

I am working with this Pandas DataFrame in Python. File heat Farheit Temp_Rating 1 YesQ 75 N/A 1 NoR 115 N/A 1 YesA 63 N/A 1 NoT 83 41 1 NoY 100 80 1 YesZ 56 12 2 YesQ 111 N/A 2 NoR 60 N/A 2 YesA 19 N/A 2 NoT 106 77 2 NoY 45 21 2 YesZ 40 54 3 YesQ 84 N/A 3 NoR 67 N/A 3 YesA 94 N/A 3 NoT 68 39 3 NoY 63 46 3 YesZ 34 81 I need to replace all NaNs in the Temp_Rating column with the value from the Farheit column. This is what I need: File heat Temp_Rating 1 YesQ 75 1 NoR 115 1 YesA 63 1 YesQ 41 1 NoR 80 1 YesA 12 2 YesQ 111 2 NoR 60 2 YesA 19 2 NoT 77 2 NoY 21 2 YesZ 54 3 YesQ 84 3 NoR 67 3 YesA 94 3 NoT 39 3 NoY 46 3 YesZ 81 If I do a Boolean selection, I can pick out only one of these columns at a time. The problem is if I then try to join them, I am not able to do this while preserving the correct order. How can I only find Temp_Rating rows with the NaNs and replace them with the value in the same row of the Farheit 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: File heat Observations 0 1 YesQ 75 1 1 NoR 115 2 1 YesA 63 3 1 NoT 41 4 1 NoY 80 5 1 YesZ 12 6 2 YesQ 111 7 2 NoR 60 8 2 YesA 19 9 2 NoT 77 10 2 NoY 21 11 2 YesZ 54 12 3 YesQ 84 13 3 NoR 67 14 3 YesA 94 15 3 NoT 39 16 3 NoY 46 17 3 YesZ 81