Retrieve DataFrame of all but one specified column

Question

Is there a way to select all but one column in a pandas DataFrame object? I've seen ways to delete a column, but I don't want to do that.

score 146 · Accepted Answer · answered Nov 27 '13 at 00:39

146

use drop method:

df.drop(column_name, axis=1)

answered Nov 27 '13 at 00:39

HYRY

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what does axis = 1 mean here? – uniquegino Mar 19 '17 at 20:20
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@uniquegino It instructs the function to search the column space when looking for the entity to drop. If `axis=0`, it will search the row space – Brian Mar 30 '17 at 23:48
@BrianVanover Thanks Brian! – uniquegino Apr 04 '17 at 18:33
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I've always used this to permanently drop a column. Never thought to use it for selection. `scott.mind == "blown"` – Him Jun 14 '18 at 14:30
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Do not forget to add `inplace=True` or to assign it to a variable. – Philippe Remy Nov 13 '18 at 09:10
@Scott Does it permanently remove a column ? Or if I print dp` again I would have it again ? – Revolucion for Monica Sep 11 '19 at 10:12

lev · Answer 2 · 2018-04-18T14:06:31.400

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df.loc[:, df.columns != col]

where col is the name of the column to leave out.

edited Apr 18 '18 at 14:06

answered Feb 05 '16 at 08:40

lev

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EdChum · Answer 3 · 2014-01-16T10:29:48.203

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you can just select the columns you want without deleting or dropping:

collist = ['col1', 'col2', 'col3']
df1 = df[collist]

Just pass a list of the columns you desire

You can also retrieve the list of columns and then select from that list

collist = df.columns.tolist()
# you can now select from this list any arbritrary range
df1 = df[collist[0:1]]
# or remove a column
collist.remove('col2')
# now select
df1 = df[collist]
# df1 will now only have 'col1' and 'col3'

edited Jan 16 '14 at 10:29

answered Nov 27 '13 at 02:06

EdChum

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Note: the question is for "all but one" columns not for selecting one column. – Yu Shen Jan 16 '14 at 10:12
@YuShen my answer shows how to select an arbritrary list of columns, it is not showing how to select a single column. – EdChum Jan 16 '14 at 10:21
@EdChum people seem to have been precipitate in downvoting this one. – Eric Walker Aug 21 '14 at 21:01
@EricWalker it's only rep nothing major plus I know this is an answer that is correct, others are free to think different but it doesn't make me think I should delete or edit this answer. In fact dropping a column would delete the column if you assigned the result of drop to itself e.g. `df = df.drop(col, axis=1)`. – EdChum Aug 21 '14 at 21:08

score 3 · Answer 4 · answered Nov 15 '17 at 17:48

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df[ df.columns[df.columns!='not_this_column'] ]

answered Nov 15 '17 at 17:48

pgalilea

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score 2 · Answer 5 · answered Feb 06 '15 at 13:34

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You could use numpy to build a mask:

import numpy as np
columns = df.columns
mask = np.ones(columns.shape, dtype=bool)
i = 4 #The specified column that you don't want to show
mask[i] = 0
df[columns[mask]]

answered Feb 06 '15 at 13:34

efajardo

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Ivan Calderon · Answer 6 · 2018-01-04T16:34:10.363

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Just as an option, you can select all columns but one (or many) using a list comprehension and df.loc method:

select = [x for x in df.columns if x != "column_you_don't_want"]
df.loc[:, select]

In case you want to leave out more than one column you can try this:

columns_dont_want = ["col1", "col2"]
select = [x for x in df.columns if x not in columns_dont_want]
df.loc[:, select]

edited Jan 04 '18 at 16:34

answered Dec 21 '17 at 15:10

Ivan Calderon

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Don't do that. Do this `df.loc[:, ~df.columns.isin(["col1", "col2"])]`. Even then, just use `drop` with the list `["col1", "col2"]`. – Little Bobby Tables Jan 04 '18 at 16:49

Retrieve DataFrame of all but one specified column

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