Select columns if any of their rows contain a certain string

user_5525619 0
Viewed 4486

I am trying to obtain a list of columns in a DataFrame if any value in a column contains a string. For example in the below dataframe I would like a list of columns that have the % in the string. I am able to accomplish this using a for loop and the series.str.contains method but doens't seem optimal especially with a larger dataset. Is there a more efficient way to do this?

import pandas as pd

df = pd.DataFrame({'A': {0: '2019-06-01', 1: '2019-06-01', 2: '2019-06-01'},
                   'B': {0: '10', 1: '20', 2: '30'},
                   'C': {0: '10', 1: '20%', 2: '30%'},
                   'D': {0: '10%', 1: '20%', 2: '30'},
               })

DataFrame

            A   B    C    D
0  2019-06-01  10   10  10%
1  2019-06-01  20  20%  20%
2  2019-06-01  30  30%   30

Current Method

col_list = []
for col in df.columns:
    if (True in list(df[col].str.contains('%'))) is True:
        col_list.append(col)

Output

['C', 'D']
pandas dataframe python
6 Answers

stack with any

df.columns[df.stack().str.contains('%').any(level=1)]

Index(['C', 'D'], dtype='object')

comprehension

[c for c in df if df[c].str.contains('%').any()]

['C', 'D']

filter

[*filter(lambda c: df[c].str.contains('%').any(), df)]

['C', 'D']

Numpy's find

from numpy.core.defchararray import find

df.columns[(find(df.to_numpy().astype(str), '%') >= 0).any(0)]

Index(['C', 'D'], dtype='object')
user_23366540

First use DataFrame.select_dtypes for filter only object columns, obviously string columns.

Then use DataFrame.applymap for elementwise check values with DataFrame.any for return True if at least one per column, so possible filter columns:

c = df.columns[df.select_dtypes(object).applymap(lambda x: '%' in str(x)).any()].tolist()
print (c)
['C', 'D']

Or use Series.str.contains per columns, na parameter should be omit if all strings columns:

f = lambda x: x.str.contains('%', na=False)
c = df.columns[df.select_dtypes(object).apply(f).any()].tolist()
print (c)
['C', 'D']
user_29010020

Try this:

df.columns[df.apply(lambda x: x.str.contains("\%")).any()]
user_42384080

Compare with replace and create a mask to index the columns accordingly:

df.loc[:,(df != df.replace('%', '', regex=True)).any()]
     C    D
0   10  10%
1  20%  20%
2  30%   30

df.columns[(df != df.replace('%', '', regex=True)).any()]
# Index(['C', 'D'], dtype='object')

This avoids the need for a loop, apply, or applymap.

user_49090870

Let us do melt 

df.melt().loc[lambda x :x.value.str.contains('%'),'variable'].unique()
Out[556]: array(['C', 'D'], dtype=object)
user_79645270

Look at the below solution can work on object dtypes also.

[c for c in dfif df[c].eq("CSS").any()]
user_114530250
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