how find the most common tokens in 2 columns of a data frame where the first column has many items?-CodePudding

I have 2 columns. for each element in a column col2 I want to search in col1 to see which element of col2 has the most common token with the element of col1?

Example:

key     col1                                                         col2
ab    'summer hot, best friend, not possible, apple, let it go'      "let be hot"
cd     'do it better', 'I am sa'                                     "I need to go"
fg      'my best post, fun sunday'                                   "it's great"

output

key     col1                                                         col2            match
ab    'summer hot, best friend, not possible, apple, let it go'    "let be hot"    "let it go"
cd     'do it better, I am sa'                                  "I need it do sa"    "I am sa"
fg      'my best post, fun sunday'                                    "it's great"       None

the first row has a common token with both summer hot and let it go but it dosn't matter which one is selected.

I did the following but i am looking for a more efficient method:

name_norm=[]
for i in range(len(df)):
    name_max_tok=''
    max_tok=0
    for l in df['col1'].values[i].split(' '):
        count=0
        for k in df['col2'].values[i].split(' '):
            if k in l:
                count=count 1
            if max_tok<count:
                max_tok=count
                name_max_tok=l
    name_norm.append(name_max_tok)

CodePudding user response：

import pandas as pd
import numpy as np
df = pd.DataFrame([
    ['ab',    'summer hot, best friend, not possible, apple, let it go',      "let be hot"],
['cd',     'do it better, I am sa',                                     "I need to go"],
['fg',      'my best post, fun sunday' ,                                  "it's great"],
], columns=['key', 'col1', 'col2'])
df["col2_set"] = df["col2"].apply(lambda x: set(x.split(" ")))
def setter(x):
    data = x.col1.split(",")
    res = np.array([len(x.col2_set.intersection(y.split(" "))) for y in data])
    if res.sum() == 0:
        return None
    else:
        return data[res.argmax()]
df['match'] = df.apply(lambda x: setter(x), axis=1)
df.drop(columns=['col2_set'], inplace=True)
df