从 Dataframe 中所有列的列名中删除最后两个字符 - Pandas
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Remove last two characters from column names of all the columns in Dataframe - Pandas
提问by Observer
I am joining the two dataframes (a,b) with identical columns / column names using the user ID key and while joining, I had to give suffix characters, in order for it to get created. The following is the command I used,
我正在使用用户 ID 键加入具有相同列/列名称的两个数据框 (a,b)，在加入时，我必须提供后缀字符，以便创建它。以下是我使用的命令，
a.join(b,how='inner', on='userId',lsuffix="_1")
If I dont use this suffix, I am getting error. But I dont want the column names to change because, that is causing a problem while running other analysis. So I want to remove this "_1" character from all the column names of the resulting dataframe. Can anybody suggest me an efficient way to remove last two characters of names of all the columns in the Pandas dataframe?
如果我不使用此后缀，则会出错。但我不希望更改列名，因为这会在运行其他分析时导致问题。所以我想从结果数据框的所有列名中删除这个“_1”字符。任何人都可以建议我一种有效的方法来删除 Pandas 数据框中所有列的名称的最后两个字符吗？
Thanks
谢谢
回答by Thtu
This snippet should get the job done :
这个片段应该可以完成工作：
df.columns = pd.Index(map(lambda x : str(x)[:-2], df.columns))
Edit : This is a better way to do it
编辑：这是一个更好的方法
df.rename(columns = lambda x : str(x)[:-2])
In both cases, all we're doing is iterating through the columns and apply some function. In this case, the function converts something into a string and takes everything up until the last two characters.
在这两种情况下，我们所做的只是遍历列并应用一些函数。在这种情况下，该函数将某些内容转换为字符串，并将所有内容转换为最后两个字符。
I'm sure there are a few other ways you could do this.
我敢肯定还有其他一些方法可以做到这一点。
回答by aydow
You could use str.rstriplike so
你可以str.rstrip像这样使用
In [214]: import functools as ft
In [215]: f = ft.partial(np.random.choice, *[5, 3])
In [225]: df = pd.DataFrame({'a': f(), 'b': f(), 'c': f(), 'a_1': f(), 'b_1': f(), 'c_1': f()})
In [226]: df
Out[226]:
a b c a_1 b_1 c_1
0 4 2 0 2 3 2
1 0 0 3 2 1 1
2 4 0 4 4 4 3
In [227]: df.columns = df.columns.str.rstrip('_1')
In [228]: df
Out[228]:
a b c a b c
0 4 2 0 2 3 2
1 0 0 3 2 1 1
2 4 0 4 4 4 3
However if you need something more flexible (albeit probably a bit slower), you can use str.extractwhich, with the power of regexes, will allow you to select which part of the column name you would like to keep
但是，如果您需要更灵活的东西（尽管可能会慢一点），您可以使用str.extract它，借助正则表达式的强大功能，您可以选择要保留的列名的哪一部分
In [216]: df = pd.DataFrame({f'{c}_{i}': f() for i in range(3) for c in 'abc'})
In [217]: df
Out[217]:
a_0 b_0 c_0 a_1 b_1 c_1 a_2 b_2 c_2
0 0 1 0 2 2 4 0 0 3
1 0 0 3 1 4 2 4 3 2
2 2 0 1 0 0 2 2 2 1
In [223]: df.columns = df.columns.str.extract(r'(.*)_\d+')[0]
In [224]: df
Out[224]:
0 a b c a b c a b c
0 1 1 0 0 0 2 1 1 2
1 1 0 1 0 1 2 0 4 1
2 1 3 1 3 4 2 0 1 1
Idea to use df.columns.strcame from thisanswer
使用的想法df.columns.str来自这个答案
