Pandas Dataframe check if a value exists using regex

Question

I have a big dataframe and I want to check if any cell contains admin string.

   col1                   col2 ... coln
0   323           roster_admin ... rota_user
1   542  assignment_rule_admin ... application_admin
2   123           contact_user ... configuration_manager
3   235         admin_incident ... incident_user
... ...  ...                   ... ...

I tried to use df.isin(['*admin*']).any() but it seems like isin doesn't support regex. How can I search though all columns using regex?

I have avoided using loops because the dataframe contains over 10 million rows and many columns and the efficiency is important for me.

Answer 1

There are two solutions:

1. df.col.apply method is more straightforward but also a little bit slower:

    In [1]: import pandas as pd In [2]: import re In [3]: df = pd.DataFrame({'col1':[1,2,3,4,5], 'col2':['admin', 'aa', 'bb', 'c_admin_d', 'ee_admin']}) In [4]: df Out[4]: col1 col2 0 1 admin 1 2 aa 2 3 bb 3 4 c_admin_d 4 5 ee_admin In [5]: r = re.compile(r'.*(admin).*') In [6]: df.col2.apply(lambda x: bool(r.match(x))) Out[6]: 0 True 1 False 2 False 3 True 4 True Name: col2, dtype: bool In [7]: %timeit -n 100000 df.col2.apply(lambda x: bool(r.match(x))) 167 µs ± 1.02 µs per loop (mean ± std. dev. of 7 runs, 100000 loops each)

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2. np.vectorize method require import numpy , but it's more efficient (about 4 times faster in my timeit test).

    In [1]: import numpy as np In [2]: import pandas as pd In [3]: import re In [4]: df = pd.DataFrame({'col1':[1,2,3,4,5], 'col2':['admin', 'aa', 'bb', 'c_admin_d', 'ee_admin']}) In [5]: df Out[5]: col1 col2 0 1 admin 1 2 aa 2 3 bb 3 4 c_admin_d 4 5 ee_admin In [6]: r = re.compile(r'.*(admin).*') In [7]: regmatch = np.vectorize(lambda x: bool(r.match(x))) In [8]: regmatch(df.col2.values) Out[8]: array([ True, False, False, True, True]) In [9]: %timeit -n 100000 regmatch(df.col2.values) 43.4 µs ± 362 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)

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Since you have changed your question to check any cell, and also concern about time efficiency:

# if you want to check all columns no mater what `dtypes` they are
dfs = df.astype(str, copy=True, errors='raise')
regmatch(dfs.values) # This will return a 2-d array of booleans
regmatch(dfs.values).any() # For existence.

You can still use df.applymap method, but again, it will be slower.

dfs = df.astype(str, copy=True, errors='raise')
r = re.compile(r'.*(admin).*')
dfs.applymap(lambda x: bool(r.match(x))) # This will return a dataframe of booleans.
dfs.applymap(lambda x: bool(r.match(x))).any().any() # For existence.

Answer 2

Try this:

import pandas as pd

df=pd.DataFrame(
    {'col1': [323,542,123,235],
     'col2': ['roster_admin','assignment_rule_admin','contact_user','admin_incident'] ,
    })

df.apply(lambda row: row.astype(str).str.contains('admin').any(), axis=1)

Output:

0     True
1     True
2    False
3     True
dtype: bool