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如何选择除熊猫中的一列以外的所有列？

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问题：如何选择除熊猫中的一列以外的所有列？
回答 0
回答 1
回答 2
回答 3
回答 4
回答 5
回答 6
回答 7

问题：如何选择除熊猫中的一列以外的所有列？

我有一个数据框看起来像这样：

import pandas
import numpy as np
df = DataFrame(np.random.rand(4,4), columns = list('abcd'))
df
      a         b         c         d
0  0.418762  0.042369  0.869203  0.972314
1  0.991058  0.510228  0.594784  0.534366
2  0.407472  0.259811  0.396664  0.894202
3  0.726168  0.139531  0.324932  0.906575

我如何才能获得除以外的所有列column b？

点击查看英文原文

I have a dataframe look like this:

import pandas
import numpy as np
df = DataFrame(np.random.rand(4,4), columns = list('abcd'))
df
      a         b         c         d
0  0.418762  0.042369  0.869203  0.972314
1  0.991058  0.510228  0.594784  0.534366
2  0.407472  0.259811  0.396664  0.894202
3  0.726168  0.139531  0.324932  0.906575

How I can get all columns except column b?

回答 0

当列不是MultiIndex时，df.columns仅是列名称的数组，因此您可以执行以下操作：

df.loc[:, df.columns != 'b']

          a         c         d
0  0.561196  0.013768  0.772827
1  0.882641  0.615396  0.075381
2  0.368824  0.651378  0.397203
3  0.788730  0.568099  0.869127
点击查看英文原文

When the columns are not a MultiIndex, df.columns is just an array of column names so you can do:

df.loc[:, df.columns != 'b']

          a         c         d
0  0.561196  0.013768  0.772827
1  0.882641  0.615396  0.075381
2  0.368824  0.651378  0.397203
3  0.788730  0.568099  0.869127

回答 1

不要使用ix。它弃用。最可读和惯用的方法是df.drop()：

>>> df

          a         b         c         d
0  0.175127  0.191051  0.382122  0.869242
1  0.414376  0.300502  0.554819  0.497524
2  0.142878  0.406830  0.314240  0.093132
3  0.337368  0.851783  0.933441  0.949598

>>> df.drop('b', axis=1)

          a         c         d
0  0.175127  0.382122  0.869242
1  0.414376  0.554819  0.497524
2  0.142878  0.314240  0.093132
3  0.337368  0.933441  0.949598

请注意，默认情况下，.drop()它不会就地运行；尽管名称不祥，但df不受此过程的影响。如果你想永久删除b的df，做的df.drop('b', inplace=True)。

df.drop()还接受标签列表，例如df.drop(['a', 'b'], axis=1)将drop column a和b。

点击查看英文原文

Don’t use ix. It’s deprecated. The most readable and idiomatic way of doing this is df.drop():

>>> df

          a         b         c         d
0  0.175127  0.191051  0.382122  0.869242
1  0.414376  0.300502  0.554819  0.497524
2  0.142878  0.406830  0.314240  0.093132
3  0.337368  0.851783  0.933441  0.949598

>>> df.drop('b', axis=1)

          a         c         d
0  0.175127  0.382122  0.869242
1  0.414376  0.554819  0.497524
2  0.142878  0.314240  0.093132
3  0.337368  0.933441  0.949598

Note that by default, .drop() does not operate inplace; despite the ominous name, df is unharmed by this process. If you want to permanently remove b from df, do df.drop('b', inplace=True).

df.drop() also accepts a list of labels, e.g. df.drop(['a', 'b'], axis=1) will drop column a and b.

回答 2

df[df.columns.difference(['b'])]

Out: 
          a         c         d
0  0.427809  0.459807  0.333869
1  0.678031  0.668346  0.645951
2  0.996573  0.673730  0.314911
3  0.786942  0.719665  0.330833
点击查看英文原文 
df[df.columns.difference(['b'])]

Out: 
          a         c         d
0  0.427809  0.459807  0.333869
1  0.678031  0.668346  0.645951
2  0.996573  0.673730  0.314911
3  0.786942  0.719665  0.330833

回答 3

您可以使用 df.columns.isin() 

df.loc[:, ~df.columns.isin(['b'])]

当您要删除多列时，简单如下： 

df.loc[:, ~df.columns.isin(['col1', 'col2'])]
点击查看英文原文

You can use df.columns.isin() 

df.loc[:, ~df.columns.isin(['b'])]

When you want to drop multiple columns, as simple as: 

df.loc[:, ~df.columns.isin(['col1', 'col2'])]

回答 4

这是另一种方式：

df[[i for i in list(df.columns) if i != '<your column>']]

您只需要传递所有要显示的列即可，不需要的列除外。

点击查看英文原文

Here is another way:

df[[i for i in list(df.columns) if i != '<your column>']]

You just pass all columns to be shown except of the one you do not want.

回答 5

对@Salvador Dali的另一项轻微修改使列列表可以排除：

df[[i for i in list(df.columns) if i not in [list_of_columns_to_exclude]]]

要么

df.loc[:,[i for i in list(df.columns) if i not in [list_of_columns_to_exclude]]]
点击查看英文原文

Another slight modification to @Salvador Dali enables a list of columns to exclude:

df[[i for i in list(df.columns) if i not in [list_of_columns_to_exclude]]]

or

df.loc[:,[i for i in list(df.columns) if i not in [list_of_columns_to_exclude]]]

回答 6

我认为最好的方法是@Salvador Dali提到的方法。并不是说其他​​人是错的。

因为当您拥有一个数据集时，您只想选择一列并将其放入一个变量中，而将其余列放入另一变量中以进行比较或计算。然后删除数据集的列可能无济于事。当然，也有一些用例。

x_cols = [x for x in data.columns if x != 'name of column to be excluded']

然后，您可以x_cols像x_cols1其他计算一样将那些变量中的列集合放入另一个变量中。

ex: x_cols1 = data[x_cols]
点击查看英文原文

I think the best way to do is the way mentioned by @Salvador Dali. Not that the others are wrong.

Because when you have a data set where you just want to select one column and put it into one variable and the rest of the columns into another for comparison or computational purposes. Then dropping the column of the data set might not help. Of course there are use cases for that as well. 

x_cols = [x for x in data.columns if x != 'name of column to be excluded']

Then you can put those collection of columns in variable x_cols into another variable like x_cols1 for other computation.

ex: x_cols1 = data[x_cols]

回答 7

这是一行lambda：

df[map(lambda x :x not in ['b'], list(df.columns))]

之前：

import pandas
import numpy as np
df = pd.DataFrame(np.random.rand(4,4), columns = list('abcd'))
df

       a           b           c           d
0   0.774951    0.079351    0.118437    0.735799
1   0.615547    0.203062    0.437672    0.912781
2   0.804140    0.708514    0.156943    0.104416
3   0.226051    0.641862    0.739839    0.434230

之后：

df[map(lambda x :x not in ['b'], list(df.columns))]

        a          c          d
0   0.774951    0.118437    0.735799
1   0.615547    0.437672    0.912781
2   0.804140    0.156943    0.104416
3   0.226051    0.739839    0.434230
点击查看英文原文

Here is a one line lambda:

df[map(lambda x :x not in ['b'], list(df.columns))]

before:

import pandas
import numpy as np
df = pd.DataFrame(np.random.rand(4,4), columns = list('abcd'))
df

       a           b           c           d
0   0.774951    0.079351    0.118437    0.735799
1   0.615547    0.203062    0.437672    0.912781
2   0.804140    0.708514    0.156943    0.104416
3   0.226051    0.641862    0.739839    0.434230

after:

df[map(lambda x :x not in ['b'], list(df.columns))]

        a          c          d
0   0.774951    0.118437    0.735799
1   0.615547    0.437672    0.912781
2   0.804140    0.156943    0.104416
3   0.226051    0.739839    0.434230