如何选择除熊猫中的一列以外的所有列？

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?

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

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.

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

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'])]

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.

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]]]

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]

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