How to highlight and count specific keywords in a pandas dataframe

Question

For each row in the Text column of my df, I want to do the following:

1. Highlight the keywords gross,suck,singing & ponzi

2. Count the number of keywords in each row and store them in a Count column

import pandas as pd

data = {'Text': ['The bread tastes good','Tuna is gross','Teddy is a beach bum','Angela suck at singing!','oneCoin was a ponzi scheme'],
        'ID': [1001,1002,1003,1004,1005]
        }

df = pd.DataFrame(data, columns = ['ID', 'Text'])

print(df)

The desired output should include the Count column and look like this :

My attempt (not the best! you can ignore this):

# keyword list
key_words = ['gross','suck','singing','ponzi']

# highlight the keywords
df['Text'].applymap(lambda x: "background-color: yellow" if x else "")

# count the keywords present in each row

df['Count'] = df['Text'].str.count(r"\b(?:{})\b".format("|".join(key_words)))

All attempts highly appreciated!

Answer 1

Use str, find all. That will give you a list. count elements in each list using str.len()

df['count']=df['Text'].str.findall('|'.join(key_words)).str.len()
df

Answer 2

Use Series.str.count:

>>> df['Text'].str.count(fr"\b(?:{'|'.join(key_words)})\b")
0    0
1    1
2    0
3    2
4    1
Name: Text, dtype: int64

\b is a word boundary, you can get whole word count with it.

You can't highlight separate words in Jupyter notebook. You can extract the words into a separate column:

df['Matches'] = df['Text'].str.findall(fr"\b(?:{'|'.join(key_words)})\b")