Python Pandas 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?
Answer
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:
