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 c
You can filter out the columns you DO want using 'filter'
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
df.filter(like='result',axis=1)
Get..
result1 result34
0 2.0 NaN
1 NaN 10.0
This method does everything in place. Many of the other answers create copies and are not as efficient:
df.drop(df.columns[df.columns.str.contains('Test')], axis=1, inplace=True)
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
Use the DataFrame.select
method:
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 can be done neatly in one line with:
df = df.drop(df.filter(regex='Test').columns, axis=1)