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 NaN
s and replace them with the value in the same row of the Farheit
column?
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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
: