Row wise frequency of each word in another column

Question

How to calculate:

frequency of each word, present in another column of same row/index, i.e.

case_description_new	item_new
This row contains word_13 word_43 word_11	word_11 word_12 word_13
This row contains word_31 word_34 word_22	word_21 word_22 word_23
This row contains word_33 word_33 word_51	word_31 word_32 word_33

Output:

case_description_new	item_new	items_frequency
This row contains word_13 word_43 word_11	word_11 word_12 word_13	word_11: 1, word_12: 0, word_13:1
This row contains word_31 word_34 word_22	word_21 word_22 word_23	word_21:0 word_22:1 word_23:0
This row contains word_33 word_33 word_51	word_31 word_32 word_33	word_31:0 word_32:0 word_33:2

Data

df_name = pd.DataFrame({
    'case_description_new': ['This row contains word_13 word_43 word_11', 'This row contains word_31 word_34 word_22', 'This row contains word_33 word_33 word_51'],
    'item_new': ['word_11 word_12 word_13', 'word_21 word_22 word_23', 'word_31 word_32 word_33']
    
})

score 1 · Accepted Answer · answered Aug 22 '22 at 16:39

Solution with comprehension and str.count

df['freq'] = [{z: x.count(z) for z in y.split()} for x, y in df.to_numpy()[:, :2]]

                        case_description_new                 item_new                                        freq
0  This row contains word_13 word_43 word_11  word_11 word_12 word_13  {'word_11': 1, 'word_12': 0, 'word_13': 1}
1  This row contains word_31 word_34 word_22  word_21 word_22 word_23  {'word_21': 0, 'word_22': 1, 'word_23': 0}
2  This row contains word_33 word_33 word_51  word_31 word_32 word_33  {'word_31': 0, 'word_32': 0, 'word_33': 2}

This solution works. Thank you. Making minor changes to your solution to make it easy to read & understand: df_name['freq'] = [{kw_count: case_description_new.count(kw_count) for kw_count in item_new.split()} for case_description_new, item_new in df_name.to_numpy()[:, :2]] — fast_crawler, Aug 22 '22 at 17:08

Row wise frequency of each word in another column

1 Answers1