Python Pandas 用第二列对应行的值替换一列中的 NaN
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Python Pandas replace NaN in one column with value from corresponding row of second column
提问by edesz
I am working with this Pandas DataFrame in Python.
我正在 Python 中使用这个 Pandas DataFrame。
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_Ratingcolumn with the value from the Farheitcolumn.
我需要用Temp_Rating列中的值替换列中的所有 NaN Farheit。
This is what I need:
这就是我需要的：
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.
如果我进行布尔选择，我一次只能选择这些列中的一列。问题是如果我然后尝试加入他们，我无法在保留正确顺序的同时做到这一点。
How can I only find Temp_Ratingrows with the NaNs and replace them with the value in the same row of the Farheitcolumn?
如何只找到Temp_Rating带有NaNs 的行并用Farheit列的同一行中的值替换它们？
采纳答案by Jonathan Eunice
Assuming your DataFrame is in df:
假设您的 DataFrame 位于df：
df.Temp_Rating.fillna(df.Farheit, inplace=True)
del df['Farheit']
df.columns = 'File heat Observations'.split()
First replace any NaNvalues with the corresponding value of df.Farheit. Delete the 'Farheit'column. Then rename the columns. Here's the resulting DataFrame:
首先NaN用 的相应值替换任何值df.Farheit。删除该'Farheit'列。然后重命名列。结果DataFrame如下：
回答by zsad512
The above mentioned solutions did not work for me. The method I used was:
上述解决方案对我不起作用。我使用的方法是：
df.loc[df['foo'].isnull(),'foo'] = df['bar']
回答by felix_as
An other way to solve this problem,
解决这个问题的另一种方法，
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:
返回：
(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)
