blocks|key|4514114|text|当列不是MultiIndex时，df.columns只是列名的数组，因此您可以这样做：|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|4514115|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|code-block|syntax|javascript|4514116|entityMap^0|G|A|0|0^^$0|@$1|2|3|4|5|6|7|M|8|@$9|N|A|O|B|C]]|D|@]|E|$]]|$1|F|3|G|5|H|7|P|8|@]|D|@]|E|$I|J]]|$1|K|3|-4|5|6|7|Q|8|@]|D|@]|E|$]]]|L|$]]

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

<pre><code>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
</code></pre>

blocks|key|6710|text|不要使用ix。我是deprecated。最具可读性和最常用的方法是df.drop()|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|6711|>>>+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|code-block|syntax|javascript|6712|请注意，在缺省情况下，.drop()不会就地运行；尽管名称不祥，但df不会受到此进程的影响。如果要从df中永久删除b，请执行df.drop('b',+inplace=True)。|6713|df.drop()还接受标签列表，例如，df.drop(['a',+'b'],+axis=1)将删除列a和b。|6714|entityMap|0|LINK|mutability|MUTABLE|url|https://pandas-docs.github.io/pandas-docs-travis/whatsnew.html#deprecate-ix^0|4|2|X|9|9|A|0|0|0|B|7|X|2|1E|2|1L|1|1Q|Q|0|0|9|K|R|1F|1|1H|1|0^^$0|@$1|2|3|4|5|6|7|W|8|@$9|X|A|Y|B|C]|$9|Z|A|10|B|C]]|D|@$9|11|A|12|1|13]]|E|$]]|$1|F|3|G|5|H|7|14|8|@]|D|@]|E|$I|J]]|$1|K|3|L|5|6|7|15|8|@$9|16|A|17|B|C]|$9|18|A|19|B|C]|$9|1A|A|1B|B|C]|$9|1C|A|1D|B|C]|$9|1E|A|1F|B|C]]|D|@]|E|$]]|$1|M|3|N|5|6|7|1G|8|@$9|1H|A|1I|B|C]|$9|1J|A|1K|B|C]|$9|1L|A|1M|B|C]|$9|1N|A|1O|B|C]]|D|@]|E|$]]|$1|O|3|-4|5|6|7|1P|8|@]|D|@]|E|$]]]|P|$Q|$5|R|S|T|E|$U|V]]]]

Don't use <code>ix</code>. It's <a href="https://pandas-docs.github.io/pandas-docs-travis/whatsnew.html#deprecate-ix" rel="noreferrer">deprecated</a>. The most readable and idiomatic way of doing this is <code>df.drop()</code>:

<pre><code>&gt;&gt;&gt; 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

&gt;&gt;&gt; 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
</code></pre>

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

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

blocks|key|4514177|text|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|type|code-block|depth|inlineStyleRanges|entityRanges|data|syntax|javascript|4514178|unstyled|entityMap^0|0^^$0|@$1|2|3|4|5|6|7|G|8|@]|9|@]|A|$B|C]]|$1|D|3|-4|5|E|7|H|8|@]|9|@]|A|$]]]|F|$]]

<pre><code>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
</code></pre>

blocks|key|4514270|text|您可以使用df.columns.isin()|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|4514271|df.loc[:,+~df.columns.isin(['b'])]|code-block|syntax|javascript|4514272|当您想要删除多个列时，就像这样简单：|4514273|df.loc[:,+~df.columns.isin(['col1',+'col2'])]|4514274|entityMap^0|5|H|0|0|0|0^^$0|@$1|2|3|4|5|6|7|Q|8|@$9|R|A|S|B|C]]|D|@]|E|$]]|$1|F|3|G|5|H|7|T|8|@]|D|@]|E|$I|J]]|$1|K|3|L|5|6|7|U|8|@]|D|@]|E|$]]|$1|M|3|N|5|H|7|V|8|@]|D|@]|E|$I|J]]|$1|O|3|-4|5|6|7|W|8|@]|D|@]|E|$]]]|P|$]]

You can use <code>df.columns.isin()</code>


<pre><code>df.loc[:, ~df.columns.isin(['b'])]
</code></pre>

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


<pre><code>df.loc[:, ~df.columns.isin(['col1', 'col2'])]
</code></pre>

blocks|key|6861|text|您可以在索引中drop列：|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|6862|df[df.columns.drop('b')]|code-block|syntax|javascript|6863|或|6864|df.loc[:,+df.columns.drop('b')]|6865|输出：|6866|++++++++++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|6867|entityMap|0|LINK|mutability|MUTABLE|url|http://pandas.pydata.org/docs/reference/api/pandas.Index.drop.html#pandas.Index.drop^0|7|4|7|4|0|0|0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|10|8|@$9|11|A|12|B|C]]|D|@$9|13|A|14|1|15]]|E|$]]|$1|F|3|G|5|H|7|16|8|@]|D|@]|E|$I|J]]|$1|K|3|L|5|6|7|17|8|@]|D|@]|E|$]]|$1|M|3|N|5|H|7|18|8|@]|D|@]|E|$I|J]]|$1|O|3|P|5|6|7|19|8|@]|D|@]|E|$]]|$1|Q|3|R|5|H|7|1A|8|@]|D|@]|E|$I|J]]|$1|S|3|-4|5|6|7|1B|8|@]|D|@]|E|$]]]|T|$U|$5|V|W|X|E|$Y|Z]]]]

You can <a href="http://pandas.pydata.org/docs/reference/api/pandas.Index.drop.html#pandas.Index.drop" rel="nofollow noreferrer"><code>drop</code></a> columns in index:
<pre><code>df[df.columns.drop('b')]
</code></pre>
or
<pre><code>df.loc[:, df.columns.drop('b')]
</code></pre>
Output:
<pre><code> 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
</code></pre>

blocks|key|6743|text|下面是另一种方法：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|6744|df[[i+for+i+in+list(df.columns)+if+i+!=+'<your+column>']]|code-block|syntax|javascript|6745|您只需传递要显示的所有列，但不需要的列除外。|6746|entityMap^0|0|0|0^^$0|@$1|2|3|4|5|6|7|K|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|L|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|M|8|@]|9|@]|A|$]]|$1|I|3|-4|5|6|7|N|8|@]|9|@]|A|$]]]|J|$]]

Here is another way:

<pre><code>df[[i for i in list(df.columns) if i != '&lt;your column&gt;']]
</code></pre>

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

blocks|key|4514232|text|对@Salvador的另一个细微修改允许排除列的列表：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|4514233|df[[i+for+i+in+list(df.columns)+if+i+not+in+[list_of_columns_to_exclude]]]|code-block|syntax|javascript|4514234|或|4514235|df.loc[:,[i+for+i+in+list(df.columns)+if+i+not+in+[list_of_columns_to_exclude]]]|4514236|entityMap^0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|M|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|N|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|O|8|@]|9|@]|A|$]]|$1|I|3|J|5|D|7|P|8|@]|9|@]|A|$E|F]]|$1|K|3|-4|5|6|7|Q|8|@]|9|@]|A|$]]]|L|$]]

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

<pre><code>df[[i for i in list(df.columns) if i not in [list_of_columns_to_exclude]]]
</code></pre>

or

<pre><code>df.loc[:,[i for i in list(df.columns) if i not in [list_of_columns_to_exclude]]]
</code></pre>

blocks|key|6769|text|我认为最好的方法是@Salvador提到的方法。并不是说其他人错了。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|6770|因为当您有一个数据集时，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个变量中，以便进行比较或计算。那么删除数据集的列可能没有帮助。当然，也有相应的用例。|6771|x_cols+=+[x+for+x+in+data.columns+if+x+!=+'name+of+column+to+be+excluded']|code-block|syntax|javascript|6772|然后，您可以将变量x_cols中的那些列集合放入另一个变量(如x_cols1+)中，以进行其他计算。|offset|length|style|CODE|6773|ex:+x_cols1+=+data[x_cols]|6774|entityMap^0|0|0|0|9|6|V|7|0|0^^$0|@$1|2|3|4|5|6|7|S|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|T|8|@]|9|@]|A|$]]|$1|D|3|E|5|F|7|U|8|@]|9|@]|A|$G|H]]|$1|I|3|J|5|6|7|V|8|@$K|W|L|X|M|N]|$K|Y|L|Z|M|N]]|9|@]|A|$]]|$1|O|3|P|5|F|7|10|8|@]|9|@]|A|$G|H]]|$1|Q|3|-4|5|6|7|11|8|@]|9|@]|A|$]]]|R|$]]

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. 

<pre><code>x_cols = [x for x in data.columns if x != 'name of column to be excluded']
</code></pre>

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

<pre><code>ex: x_cols1 = data[x_cols]
</code></pre>

blocks|key|6887|text|与@Toms类似，也可以不使用.loc选择除"b“之外的所有列，如下所示：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|6888|df[df.columns[~df.columns.isin(['b'])]]|code-block|syntax|javascript|6889|entityMap^0|0|0^^$0|@$1|2|3|4|5|6|7|I|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|J|8|@]|9|@]|A|$E|F]]|$1|G|3|-4|5|6|7|K|8|@]|9|@]|A|$]]]|H|$]]

Similar to @Toms answer, it is also possible to select all columns except &quot;b&quot; without using .loc, like so:
<pre><code>df[df.columns[~df.columns.isin(['b'])]]
</code></pre>

I have a dataframe look like this:

<pre><code>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
</code></pre>

How I can get all columns except <code>column b</code>?

How to select all columns, except one column in pandas?

我有一个数据帧，如下所示：import pandasimport numpy as npdf = DataFrame(np.random.rand(4,4), columns = list('abcd'))df      a         b         c         d0  0.418762  0.042369  0.869203  0.9723141  0.991058  0.51

问如何选择pandas中除一列之外的所有列？
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问如何选择pandas中除一列之外的所有列？EN

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问如何选择pandas中除一列之外的所有列？
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