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?
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)
@Jonathan's answer is good, but an overkill, just use pop:
df['Temp_Rating'] = df['Temp_Rating'].fillna(df.pop('Farheit'))
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