blocks|key|12800|text|import+pandas+as+pd

import+numpy+as+np

array=np.random.random((2,4))

df=pd.DataFrame(array,+columns=('Test1',+'toto',+'test2',+'riri'))

print+df

++++++Test1++++++toto+++++test2++++++riri
0++0.923249++0.572528++0.845464++0.144891
1++0.020438++0.332540++0.144455++0.741412

cols+=+[c+for+c+in+df.columns+if+c.lower()[:4]+!=+'test']

df=df[cols]

print+df
+++++++toto++++++riri
0++0.572528++0.144891
1++0.332540++0.741412|type|code-block|depth|inlineStyleRanges|entityRanges|data|syntax|javascript|12801|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>import pandas as pd

import numpy as np

array=np.random.random((2,4))

df=pd.DataFrame(array, columns=('Test1', 'toto', 'test2', 'riri'))

print df

 Test1 toto test2 riri
0 0.923249 0.572528 0.845464 0.144891
1 0.020438 0.332540 0.144455 0.741412

cols = [c for c in df.columns if c.lower()[:4] != 'test']

df=df[cols]

print df
 toto riri
0 0.572528 0.144891
1 0.332540 0.741412
</code></pre>

blocks|key|12817|text|使用DataFrame.select方法：|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|12818|In+[38]:+df+=+DataFrame({'Test1':+randn(10),+'Test2':+randn(10),+'awesome':+randn(10)})

In+[39]:+df.select(lambda+x:+not+re.search('Test\d%2B',+x),+axis=1)
Out[39]:
+++awesome
0++++1.215
1++++1.247
2++++0.142
3++++0.169
4++++0.137
5+++-0.971
6++++0.736
7++++0.214
8++++0.111
9+++-0.214|code-block|syntax|javascript|12819|entityMap^0|2|G|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|$]]

Use the <code>DataFrame.select</code> method:

<pre><code>In [38]: df = DataFrame({'Test1': randn(10), 'Test2': randn(10), 'awesome': randn(10)})

In [39]: df.select(lambda x: not re.search('Test\d+', x), axis=1)
Out[39]:
 awesome
0 1.215
1 1.247
2 0.142
3 0.169
4 0.137
5 -0.971
6 0.736
7 0.214
8 0.111
9 -0.214
</code></pre>

blocks|key|229330|text|你可以使用'+filter‘过滤掉你想要的列|type|unstyled|depth|inlineStyleRanges|entityRanges|data|229331|import+pandas+as+pd
import+numpy+as+np

data2+=+[{'test2':+1,+'result1':+2},+{'test':+5,+'result34':+10,+'c':+20}]

df+=+pd.DataFrame(data2)

df

++++c+++result1+++++result34++++test++++test2
0+++NaN+++++2.0+++++NaN+++++NaN+++++1.0
1+++20.0++++NaN+++++10.0++++5.0+++++NaN|code-block|syntax|javascript|229332|现在过滤|229333|df.filter(like='result',axis=1)|229334|得到..。|229335|+++result1++result34
0+++2.0+++++NaN
1+++NaN+++++10.0|229336|entityMap^0|0|0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|Q|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|R|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|S|8|@]|9|@]|A|$]]|$1|I|3|J|5|D|7|T|8|@]|9|@]|A|$E|F]]|$1|K|3|L|5|6|7|U|8|@]|9|@]|A|$]]|$1|M|3|N|5|D|7|V|8|@]|9|@]|A|$E|F]]|$1|O|3|-4|5|6|7|W|8|@]|9|@]|A|$]]]|P|$]]

You can filter out the columns you DO want using 'filter'

<pre><code>import pandas as pd
import numpy as np

data2 = [{'test2': 1, 'result1': 2}, {'test': 5, 'result34': 10, 'c': 20}]

df = pd.DataFrame(data2)

df

 c result1 result34 test test2
0 NaN 2.0 NaN NaN 1.0
1 20.0 NaN 10.0 5.0 NaN
</code></pre>

Now filter

<pre><code>df.filter(like='result',axis=1)
</code></pre>

Get..

<pre><code> result1 result34
0 2.0 NaN
1 NaN 10.0
</code></pre>

blocks|key|12870|text|以下是一种方法：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|12871|df+=+df[df.columns.drop(list(df.filter(regex='Test')))]|code-block|syntax|javascript|12872|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|$]]

Here is one way to do this:
<pre><code>df = df[df.columns.drop(list(df.filter(regex='Test')))]
</code></pre>

blocks|key|229402|text|只需一行代码即可完成此操作：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|229403|df+=+df.drop(df.filter(regex='Test').columns,+axis=1)|code-block|syntax|javascript|229404|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|$]]

This can be done neatly in one line with:

<pre><code>df = df.drop(df.filter(regex='Test').columns, axis=1)
</code></pre>

blocks|key|12994|text|这个方法做了所有的事情。许多其他答案都会创建副本，并且效率不高：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|12995|df.drop(df.columns[df.columns.str.contains('Test')],+axis=1,+inplace=True)|offset|length|style|CODE|12996|entityMap^0|0|0|22|0^^$0|@$1|2|3|4|5|6|7|J|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|K|8|@$D|L|E|M|F|G]]|9|@]|A|$]]|$1|H|3|-4|5|6|7|N|8|@]|9|@]|A|$]]]|I|$]]

This method does everything in place. Many of the other answers create copies and are not as efficient:

<code>df.drop(df.columns[df.columns.str.contains('Test')], axis=1, inplace=True)</code>

blocks|key|229499|text|使用正则表达式匹配不包含不需要的单词的所有列：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|229500|df+=+df.filter(regex='%5E((?!badword).)*$')|code-block|syntax|javascript|229501|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|$]]

Using a regex to match all columns not containing the unwanted word:
<pre class="lang-py prettyprint-override"><code>df = df.filter(regex='^((?!badword).)*$')
</code></pre>

blocks|key|229529|text|最短的方法是：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|229530|resdf+=+df.filter(like='Test',axis=1)|code-block|syntax|javascript|229531|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|$]]

the shortest way to do is is : 

<pre><code>resdf = df.filter(like='Test',axis=1)
</code></pre>

blocks|key|229552|text|删除包含正则表达式的列名列表时的解决方案。我更喜欢这种方法，因为我经常编辑下拉列表。对下拉列表使用负过滤器正则表达式。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|229553|drop_column_names+=+['A','B.%2B','C.*']
drop_columns_regex+=+'%5E(?!(?:'%2B'%7C'.join(drop_column_names)%2B')$)'
print('Dropping+columns:',',+'.join([c+for+c+in+df.columns+if+re.search(drop_columns_regex,c)]))
df+=+df.filter(regex=drop_columns_regex,axis=1)|code-block|syntax|javascript|229554|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|$]]

Solution when dropping a list of column names containing regex. I prefer this approach because I'm frequently editing the drop list. Uses a negative filter regex for the drop list.

<pre><code>drop_column_names = ['A','B.+','C.*']
drop_columns_regex = '^(?!(?:'+'|'.join(drop_column_names)+')$)'
print('Dropping columns:',', '.join([c for c in df.columns if re.search(drop_columns_regex,c)]))
df = df.filter(regex=drop_columns_regex,axis=1)
</code></pre>

blocks|key|229586|text|问题说明“我想删除名称中包含单词”Test“的所有列。”|type|unstyled|depth|inlineStyleRanges|entityRanges|data|229587|test_columns+=+[col+for+col+in+df+if+'Test'+in+col]
df.drop(columns=test_columns,+inplace=True)|code-block|syntax|javascript|229588|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|$]]

Question states 'I want to drop all the columns whose name contains the word &quot;Test&quot;.'
<pre class="lang-py prettyprint-override"><code>test_columns = [col for col in df if 'Test' in col]
df.drop(columns=test_columns, inplace=True)
</code></pre>

I have a pandas dataframe with the following column names:

Result1, Test1, Result2, Test2, Result3, Test3, etc...

I want to drop all the columns whose name contains the word "Test". The numbers of such columns is not static but depends on a previous function.

How can I do that?

Drop columns whose name contains a specific string from pandas DataFrame

我有一个pandas dataframe，它有以下列名：Result1、Test1、Result2、Test2、Result3、Test3等我想删除名称中包含单词"Test“的所有列。这些列的数量不是静态的，而是依赖于前一个函数。我该怎么做呢？

问从pandas DataFrame中删除名称包含特定字符串的列
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