Looking-up part of text in a column and if found true pass a Creating a new column by reflecting assigned text value instead of true or false

The easiest solution would be

import pandas as pd
df = your dataframe

assigning the default value first. You could set to None, or np.nan too

df['category'] = 'others'
df.loc[df.keyword.str.contains('therapist'),'category'] = 'therapist'

You can use a regex with all your keywords. Then, depending on whether you want to get the first match or all, use extract or extractall with aggregation, respectively.

I added the keyword "private" as example to see a difference in row 3:

import re
words = ['health', 'therapist', 'sales', 'private']
regex = '|'.join(map(re.escape, words))
# 'health|therapist|sales|private'

# option 1: get first match
df['category_first'] = (df['keyword']
 .str.extract(f'(?i)({regex})', expand=False)
 .fillna('other')
 )

# option 2: get all matches
df['category_all'] = (df['keyword']
 .str.extractall(f'(?i)({regex})')
 [0].groupby(level=0).agg(','.join)
 .reindex(df.index, fill_value='other')
 )

print(df)

Output:

                                              keyword   category category_first       category_all
0                     HR Consultancy UK-d-uk-159_bing      other          other              other
1           it support COMPANY LONDON-D-UK-G1161_bing      other          other              other
2             global sales training platform openings      sales          sales              sales
3                     tele private practice therapist  therapist        private  private,therapist
4                       asset grant management system      other          other              other
5   digital team project management solution openings      other          other              other
6                   global training platform openings      other          other              other
7                             tele practice therapist  therapist      therapist          therapist
8             global sales training platform openings      sales          sales              sales
9                                tele health practice     health         health             health
10                             asset grant management      other          other              other
11           digital team project management solution      other          other              other

1. Pandas has apply methods for this:

def f(s):
    if s.find('health')>=0:
        return 'health'
    if s.find('thera...')>=0:
        return 'thera'
    ...

df['category'] = df['text'].apply(f)

Read: https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.apply.html

2. pandas has map methods for this. First create a dictionary and then use map methods.

d ={keyword1:category1, ...}
df['category'] = df['keyword'].map(d)

This is for when you have a specific dictionary. Read: https://pandas.pydata.org/docs/reference/api/pandas.Series.map.html

You can create first a list of keywords to test or research on it . Then the empty ones fill it with "other".

import pandas as pd

#example df
df=pd.DataFrame(data=['health xxxxx','yyyy therapist','zzzzz'],columns=["keyword"])

keywords=['health','therapist']


df['category']=df['keyword'].str.findall('|'.join(keywords)).apply(set).str.join(',')
df = df.apply(lambda x: x.str.strip()).replace('', "other")