计算另一列中一列的子串的出现次数

Question

I have two dataframes I am working with, one which contains a list of players and another that contains play by play data for the players from the other dataframe. Portions of the rows of interest within these two dataframes are shown below.

0          Matt Carpenter
1           Jason Heyward
2           Peter Bourjos
3           Matt Holliday
4          Jhonny Peralta
5              Matt Adams
...
Name: Name, dtype: object


0     Matt Carpenter grounded out to second (Grounder).
1               Jason Heyward doubled to right (Liner).
2     Matt Holliday singled to right (Liner). Jason Heyward scored.
...
Name: Play, dtype: object

What I am trying to do is create a column in the first dataframe that counts the number of occurrences of the string (df['Name'] + ' scored') in the column in the other dataframe. For example, it would search for instances of "Matt Carpenter scored", "Jason Heyward scored", etc. I know you can use str.contains to do this type of thing, but it only seems to work if you put in the explicit string. For example,

batter_game_logs_df['R vs SP'] = len(play_by_play_SP_df[play_by_play_SP_df['Play'].str.contains('Jason Heyward scored')].index)

works fine but if I try

batter_game_logs_df['R vs SP'] = len(play_by_play_SP_df[play_by_play_SP_df['Play'].str.contains(batter_game_logs_df['Name'].astype(str) + ' scored')].index)

it returns the error 'Series' objects are mutable, thus they cannot be hashed. I have looked at various similar questions but cannot find the solution to this problem for the life of me. Any assistance on this would be greatly appreciated, thank you!

Answer 1

I think need findall by regex with join all values of Name , then create indicator columns by MultiLabelBinarizer and add all missing columns by reindex :

s = df1['Name'] + ' scored'
pat = r'\b{}\b'.format('|'.join(s))

from sklearn.preprocessing import MultiLabelBinarizer

mlb = MultiLabelBinarizer()
df = pd.DataFrame(mlb.fit_transform(df2['Play'].str.findall(pat)),
                  columns=mlb.classes_, 
                  index=df2.index).reindex(columns=s, fill_value=0)
print (df)
Name  Matt Carpenter scored  Jason Heyward scored  Peter Bourjos scored  \
0                         0                     0                     0   
1                         0                     0                     0   
2                         0                     1                     0   

Name  Matt Holliday scored  Jhonny Peralta scored  Matt Adams scored  
0                        0                      0                  0  
1                        0                      0                  0  
2                        0                      0                  0  

Last if necessary join to df1 :

df = df2.join(df)
print (df)
                                                Play  Matt Carpenter scored  \
0  Matt Carpenter grounded out to second (Grounder).                      0   
1            Jason Heyward doubled to right (Liner).                      0   
2  Matt Holliday singled to right (Liner). Jason ...                      0   

   Jason Heyward scored  Peter Bourjos scored  Matt Holliday scored  \
0                     0                     0                     0   
1                     0                     0                     0   
2                     1                     0                     0   

   Jhonny Peralta scored  Matt Adams scored  
0                      0                  0  
1                      0                  0  
2                      0                  0