Column-wise string counts for multiple columns in a pandas DataFrame

Question

I have dataframe as below:

Name            Marks       Place               Points
John-->Hile     50          Germany-->Poland    1
Rog-->Oliver    60-->70     Australia-->US      2
Harry           80          UK                  3
Faye-->George   90          Poland              4

enter image description here

I want a result as below which finds counts of value having "-->"column wise and transpose it and result as below dataframe:

Column Count
Name   3
Marks  1
Place  1

This df is eg.This datframe is dynamic and can vary in each run like in 2nd Run we might have Name,Marks,Place or Name,Marks or anything else, So code should be dynamic which can run on any df.

Dataframe proper format is attached in the link :"Enter image description here" formatting is bit distorted. — ABS
– ABS, Commented Jul 7, 2020 at 6:55

cs95 · Accepted Answer · 2020-07-07 09:25:58Z

1

You can select object columns and column-wise perform a count and summation:

df.select_dtypes(object).apply(lambda x: x.str.contains('-->')).sum()

Name     3
Marks    1
Place    2
dtype: int64

Another weird, but interesting method with applymap:

(df.select_dtypes(object)
   .applymap(lambda x: '-->' in x if isinstance(x, str) else False)
   .sum())

Name     3
Marks    1
Place    2
dtype: int64

edited Jul 7, 2020 at 9:25

answered Jul 7, 2020 at 6:58

cs95

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3 Comments

ABS Over a year ago

I am receiving 0 if I am running 1st option

ABS Over a year ago

When running 2nd Option of applymap : ""argument of type 'numpy.int64' is not iterable occurred at index Number

cs95 Over a year ago

@Raunak you might've forgotten to assign it? As for the second problem, check my edit. Don't forget to assign it: df['result'] = df.select_dtypes(...)...

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