使用Pandas根据不同列中的值填充NaN条目，并以字典为指导 [英] Using Pandas to fill NaN entries based on values in a different column, using a dictionary as a guide

问题描述

我有一个很大的数据框，尝试使用字典作为指导，根据A列中的值填充B列的NaN条目.例如:

I have a large dataframe where I'm trying to populate the NaN entries of column B based on the values in column A, using a dictionary as a guide. For example:

df = 
   A    B 
0  Red  628  
1  Red  149  
2  Red  NaN  
3  Green  575  
4  Green  687
5  Green  NaN
6  Blue  159
7  Blue  NaN

而字典是(例如)

dict = {"Red": 123, "Green": 456, "Blue": 789}

我很好奇使用Pandas用字典中的相应数字替换每个NaN的最佳方法.我不确定在这种情况下如何使用.fillna()或.isnull()方法.我是Pandas的新手，所以我们感谢您的帮助！谢谢.

I am curious as to the best way to replace each NaN with the corresponding number from the dictionary using Pandas. I'm not sure how to use the .fillna() or .isnull() methods in this situation. I'm new to Pandas so any help is appreciated! Thanks.

推荐答案

使用boolean indexing rows. html#boolean-indexing"rel =" nofollow>(请参阅文档)，然后map您的dictionary在必要时将A转换为B值:

Select the relevant rows using boolean indexing (see docs), and map your dictionary to translate A to B values where necessary:

na_map = {"Red": 123, "Green": 456, "Blue": 789}
mask = df.B.isnull()

mask看起来如下:

0    False
1    False
2     True
3    False
4    False
5     True
6    False
7     True

最后:

df.loc[mask, 'B'] = df.loc[mask, 'A'].map(na_map)

       A    B
0    Red  628
1    Red  149
2    Red  123
3  Green  575
4  Green  687
5  Green  456
6   Blue  159
7   Blue  789

这篇关于使用Pandas根据不同列中的值填充NaN条目，并以字典为指导的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！