Pandas 基于布尔数组修改列值
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Pandas modify column values in place based on boolean array
提问by prooffreader
I know how to create a new column with applyor np.wherebased on the values of another column, but a way of selectively changing the values of an existing column is escaping me; I suspect df.ixis involved? Am I close?
我知道如何使用apply或np.where基于另一列的值创建新列，但是有选择地更改现有列的值的方法正在逃避我；我怀疑df.ix有牵连？我很亲近吗？
For example, here's a simple dataframe (mine has tens of thousands of rows). I would like to change the value in the 'flag' column (let's say to 'Blue') if the name ends with the letter 'e':
例如，这是一个简单的数据框（我的有数万行）。如果名称以字母“e”结尾，我想更改“flag”列中的值（假设为“Blue”）：
>>> import pandas as pd
>>> df = pd.DataFrame({'name':['Mick', 'John', 'Christine', 'Stevie', 'Lindsey'], \
'flag':['Purple', 'Red', nan, nan, nan]})[['name', 'flag']]
>>> print df
name flag
0 Mick Purple
1 John Red
2 Christine NaN
3 Stevie NaN
4 Lindsey NaN
[5 rows x 2 columns]
I can make a boolean series from my criteria:
我可以根据我的标准制作一个布尔系列：
>boolean_result = df.name.str.contains('e$')
>print boolean_result
0 False
1 False
2 True
3 True
4 False
Name: name, dtype: bool
I just need the crucial step to get the following result:
我只需要获得以下结果的关键步骤：
>>> print result_wanted
name flag
0 Mick Purple
1 John Red
2 Christine Blue
3 Stevie Blue
4 Lindsey NaN
回答by U2EF1
df['flag'][df.name.str.contains('e$')] = 'Blue'
