I have a dataframe as below for example, i want to have only tests with certain regex to be part of my updated dataframe. I was wondering if there is a way to do it with fnmatch instead of regex?
data = {'part1':[0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1],
'part2':[0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1],
'part3':[0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1],
'part4':[0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 1],
'part5':[1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1],
'part6':[1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1],
'part7':[1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1],
'part8':[1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1],
'part9':[1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1 ],
'part10':[1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1],
'part11':[0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1],
'part12':[0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1]
}
df = pd.DataFrame(data, index =['test_gt1',
'test_gt2',
'test_gf3',
'test_gf4',
'test_gt5',
'test_gg6',
'test_gf7',
'test_gt8',
'test_gg9',
'test_gf10',
'test_gg11',
'test12'
])
i want to be able to create a new dataframe that only contains test_gg or test_gf or test_gt using fnmatch.filter? all examples i see are related to list, so how can i apply it to dataframe?
Import fnmatch.filter and filter on the index:
from fnmatch import filter
In [7]: df.loc[filter(df.index, '*g*')]
Out[7]:
part1 part2 part3 part4 part5 part6 part7 part8 part9 part10 part11 part12
test_gt1 0 0 0 0 1 1 1 1 1 1 0 0
test_gt2 1 1 1 0 0 1 1 0 0 1 1 1
test_gf3 0 0 0 0 1 1 1 1 1 1 0 0
test_gf4 0 1 1 1 0 1 1 1 0 1 0 1
test_gt5 0 1 0 1 0 1 0 1 0 1 0 1
test_gg6 0 0 0 0 1 1 1 1 1 1 0 0
test_gf7 1 1 1 0 0 1 1 0 0 1 0 1
test_gt8 0 1 1 1 0 1 1 1 0 1 0 0
test_gg9 1 0 1 0 1 0 1 0 1 0 1 0
test_gf10 0 1 0 1 0 1 0 1 0 1 0 1
test_gg11 0 0 0 0 0 0 0 0 0 0 0 0
You can also just use pandas' filter function with regex, and filter on the index:
In [8]: df.filter(regex=r".+g.+", axis='index')
Out[8]:
part1 part2 part3 part4 part5 part6 part7 part8 part9 part10 part11 part12
test_gt1 0 0 0 0 1 1 1 1 1 1 0 0
test_gt2 1 1 1 0 0 1 1 0 0 1 1 1
test_gf3 0 0 0 0 1 1 1 1 1 1 0 0
test_gf4 0 1 1 1 0 1 1 1 0 1 0 1
test_gt5 0 1 0 1 0 1 0 1 0 1 0 1
test_gg6 0 0 0 0 1 1 1 1 1 1 0 0
test_gf7 1 1 1 0 0 1 1 0 0 1 0 1
test_gt8 0 1 1 1 0 1 1 1 0 1 0 0
test_gg9 1 0 1 0 1 0 1 0 1 0 1 0
test_gf10 0 1 0 1 0 1 0 1 0 1 0 1
test_gg11 0 0 0 0 0 0 0 0 0 0 0 0
You can also just use like :
df.filter(like="g", axis='index')
Out[12]:
part1 part2 part3 part4 part5 part6 part7 part8 part9 part10 part11 part12
test_gt1 0 0 0 0 1 1 1 1 1 1 0 0
test_gt2 1 1 1 0 0 1 1 0 0 1 1 1
test_gf3 0 0 0 0 1 1 1 1 1 1 0 0
test_gf4 0 1 1 1 0 1 1 1 0 1 0 1
test_gt5 0 1 0 1 0 1 0 1 0 1 0 1
test_gg6 0 0 0 0 1 1 1 1 1 1 0 0
test_gf7 1 1 1 0 0 1 1 0 0 1 0 1
test_gt8 0 1 1 1 0 1 1 1 0 1 0 0
test_gg9 1 0 1 0 1 0 1 0 1 0 1 0
test_gf10 0 1 0 1 0 1 0 1 0 1 0 1
test_gg11 0 0 0 0 0 0 0 0 0 0 0 0