[英]Drop columns whose name contains a specific string from pandas DataFrame
I have a pandas dataframe with the following column names:我有一个带有以下列名称的熊猫数据框:
Result1, Test1, Result2, Test2, Result3, Test3, etc...结果 1、测试 1、结果 2、测试 2、结果 3、测试 3 等...
I want to drop all the columns whose name contains the word "Test".我想删除名称中包含“Test”一词的所有列。 The numbers of such columns is not static but depends on a previous function.
此类列的数量不是静态的,而是取决于先前的函数。
How can I do that?我该怎么做?
这是执行此操作的一种方法:
df = df[df.columns.drop(list(df.filter(regex='Test')))]
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
str.contains
str.contains
: str.contains
In recent versions of pandas, you can use string methods on the index and columns.在最新版本的 Pandas 中,您可以在索引和列上使用字符串方法。 Here,
str.startswith
seems like a good fit.在这里,
str.startswith
似乎很合适。
To remove all columns starting with a given substring:要删除以给定子字符串开头的所有列:
df.columns.str.startswith('Test')
# array([ True, False, False, False])
df.loc[:,~df.columns.str.startswith('Test')]
toto test2 riri
0 x x x
1 x x x
For case-insensitive matching, you can use regex-based matching with str.contains
with an SOL anchor:对于不区分大小写的匹配,您可以使用带有 SOL 锚点的
str.contains
基于正则表达式的匹配:
df.columns.str.contains('^test', case=False)
# array([ True, False, True, False])
df.loc[:,~df.columns.str.contains('^test', case=False)]
toto riri
0 x x
1 x x
if mixed-types is a possibility, specify na=False
as well.如果混合类型是可能的,也指定
na=False
。
这可以在一行中巧妙地完成:
df = df.drop(df.filter(regex='Test').columns, axis=1)
You can filter out the columns you DO want using 'filter'您可以使用“过滤器”过滤掉您想要的列
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
Now filter现在过滤
df.filter(like='result',axis=1)
Get..得到..
result1 result34
0 2.0 NaN
1 NaN 10.0
Use the DataFrame.select
method:使用
DataFrame.select
方法:
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
This method does everything in place.这个方法做的一切都到位了。 Many of the other answers create copies and are not as efficient:
许多其他答案创建副本并且效率不高:
df.drop(df.columns[df.columns.str.contains('Test')], axis=1, inplace=True)
Don't drop.不要掉。 Catch the opposite of what you want.
抓住你想要的反面。
df = df.filter(regex='^((?!badword).)*$').columns
Question states 'I want to drop all the columns whose name contains the word "Test".'问题说明“我想删除名称中包含“测试”一词的所有列。
test_columns = [col for col in df if 'Test' in col]
df.drop(columns=test_columns, inplace=True)
最短的方法是:
resdf = df.filter(like='Test',axis=1)
Solution when dropping a list of column names containing regex.删除包含正则表达式的列名列表时的解决方案。 I prefer this approach because I'm frequently editing the drop list.
我更喜欢这种方法,因为我经常编辑下拉列表。 Uses a negative filter regex for the drop list.
对下拉列表使用否定过滤器正则表达式。
drop_column_names = ['A','B.+','C.*']
drop_columns_regex = '^(?!(?:'+'|'.join(drop_column_names)+')$)'
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)
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