how to map two rows of different dataframe based on a condition in pandas

Using set logic with Numpy broadcasting.

d1 = df1['Names'].fillna('').str.lower().str.split('[^a-z]+').apply(set).values
d2 = df2['values'].fillna('').str.lower().str.split('[^a-z]+').apply(set).values

i, j = np.where(d1 >= d2[:, None])

df2.assign(Names=pd.Series(df1['Names'].values[j], df2['values'].index[i]))

                   values                        Names
0                     sri         Sri is a good player
1                     NaN                          NaN
2                 sri, is         Sri is a good player
3  kumar,cricketer player  Kumar is a cricketer player

Try -

import pandas as pd

df1 = pd.read_csv('sample.csv')
df2 = pd.read_csv('sample_2.csv')

df2['values']= df2['values'].str.lower()
df1['names']= df1['names'].str.lower()

df2["values"] = df2['values'].str.replace('[^\w\s]',' ')
df2['values']= df2['values'].replace('\s+', ' ', regex=True)

df1["names"] = df1['names'].str.replace('[^\w\s]',' ')
df1['names']= df1['names'].replace('\s+', ' ', regex=True)

df2['list_values'] = df2['values'].apply(lambda x: str(x).split())
df1['list_names'] = df1['names'].apply(lambda x: str(x).split())

list_names = df1['list_names'].tolist()

def check_names(x, list_names):
    output = ''
    for list_name in list_names:
        if set(list_name) >= set(x):
            output = ' '.join(list_name)
            break
    return output

df2['Names'] = df2['list_values'].apply(lambda x: check_names(x, list_names))
print(df2)

Output

values                        Names
0                     sri         sri is a good player
1                     NaN                             
2                  sri is         sri is a good player
3  kumar cricketer player  kumar is a cricketer player

Exaplanation

It's a fuzzy matching problem. So here are the steps that I have applied -

1. Remove punctuations and split to get unique words on both df
2. Lowercase everything for standardized matching.
3. Convert by splitting the string into lists.
4. Finally doing the matching via the check_names() function to get the desired output