如何在熊猫中选择除一列之外的所有列？

Question

I have a dataframe that look like this:

import pandas as pd
import numpy as np
df = pd.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 b ?

Answer 1

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

Answer 2

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, eg df.drop(['a', 'b'], axis=1) will drop column a and b .

Answer 3

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

Answer 4

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'])]

Answer 5

You can drop columns in index:

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

or

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

Output:

          a         c         d
0  0.418762  0.869203  0.972314
1  0.991058  0.594784  0.534366
2  0.407472  0.396664  0.894202
3  0.726168  0.324932  0.906575

Answer 6

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.

Answer 7

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

Answer 8

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]

Answer 9

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]]]

Answer 10

我认为一个不错的解决方案是使用 pandas 和正则表达式的函数过滤器（匹配除“b”之外的所有内容）：

df.filter(regex="^(?!b$)")

Answer 11

与@Toms 回答类似，也可以在不使用 .loc 的情况下选择除“b”以外的所有列，如下所示：

df[df.columns[~df.columns.isin(['b'])]]