我正在尝试对包含多个值的特定列中的值进行分组？

Question

I have this huge netflix dataset which I am trying to see which actors appeared in the most movies/tv shows specifically in America. First, I created a list of unique actors from the dataset. Then created a nested for loop to loop through each name in list3(containing unique actors which checked each row in df3(filtered dataset with 2000+rows) if the column cast contained the current actors name from list3. I believe using iterrows takes too long

myDict1 = {}


for name in list3:
    if name not in myDict1:
        myDict1[name] = 0
    for index, row in df3.iterrows():
        if name in row["cast"]:
            myDict1[name] += 1
            
myDict1

Title	cast
Movie1	Robert De Niro, Al Pacino, Tarantino
Movie2	Tom Hanks, Robert De Niro, Tom Cruise
Movie3	Tom Cruise, Zendaya, Seth Rogen

I want my output to be like this:

Name	Count
Robert De Niro	2
Tom Cruise	2

Answer 1

Use

out = df['cast'].str.split(', ').explode().value_counts()
out = pd.DataFrame({'Name': out.index, 'Count': out.values})


>>> out
             Name  Count
0      Tom Cruise      2
1  Robert De Niro      2
2         Zendaya      1
3      Seth Rogen      1
4       Tarantino      1
5       Al Pacino      1
6       Tom Hanks      1

Answer 2

l=['Robert De Niro','Tom Cruise']#list

df=df.assign(cast=df['cast'].str.split(',')).apply(pd.Series.explode)#convert cast into list and explode
df[df['cast'].str.contains("|".join(l))].groupby('cast').size().reset_index().rename(columns={'cast':'Name',0:'Count'})#groupby cast, find size and rename columns



              Name  Count
0  Robert De Niro      2
1      Tom Cruise      2

Answer 3

You could use collections.Counter to get the counts of the actors, after splitting the strings:

from collections import Counter

pd.DataFrame(Counter(df.cast.str.split(", ").sum()).items(), 
             columns = ['Name', 'Count'])
 
             Name  Count
0  Robert De Niro      2
1       Al Pacino      1
2       Tarantino      1
3       Tom Hanks      1
4      Tom Cruise      2
5         Zendaya      1
6      Seth Rogen      1

If you are keen about speed, and you have lots of data, you could dump the entire processing within plain python and rebuild the dataframe:

from itertools import chain
pd.DataFrame(Counter(chain.from_iterable(ent.split(", ") 
                                         for ent in df.cast)).items(), 
             columns = ['Name', 'Count'])