How to group by category and then count the frequency of words using Pandas

user_9161607 0
Viewed 635

my df looks like this:

category       text
--------       ----
soccer         soccer game is good
soccer         soccer game
basketball     game basketball
basketball     game
volleyball     sport volleyball sport

What I want to do is groupby category and then list the words by its frequency

category       text          frequency
--------       ----          ---------
soccer         soccer        2
               game          2 
               is            1
               good          1
basketball     game          2
               basketball    1  
volleyball     sport         2
               volleyball    1

what did I do?

  • I group all the text together

df.groupby(['category])['text'].sum()

Now all the text are on the same rows since I grouped it but I do not know how to do a Frequency Table using each word count.

Could someone please help me?

pandas nltk python
2 Answers

#Method 1:

You can use series.str.split with explode and the groupby.value_counts

(df.assign(text=df['text'].str.split()).explode("text")
 .groupby("category",sort=False)['text'].value_counts())

category    text      
soccer      game          2
            soccer        2
            good          1
            is            1
basketball  game          2
            basketball    1
volleyball  sport         2
            volleyball    1
Name: text, dtype: int64

#Method 2:

For older version of pandas using np.concatenate and index.repeat with df.join (There are other methods listed here)

s = df['text'].str.split()
(df[['category']].join(pd.Series(np.concatenate(s),
                      index=df.index.repeat(s.str.len()),name='text'))
.groupby("category",sort=False)['text'].value_counts())

#Method 3: using MultiLabelBinarizer from sklearn

from sklearn.preprocessing import MultiLabelBinarizer

s = df['text'].str.split()
mlb = MultiLabelBinarizer()
mlb.fit(s)
out = pd.DataFrame(mlb.transform(s),columns=mlb.classes_).groupby(df['category']).sum()
out.replace(0,np.nan).stack().astype(int)

category              
basketball  basketball    1
            game          2
soccer      game          2
            good          1
            is            1
            soccer        2
volleyball  sport         1
            volleyball    1
dtype: int32
user_98406370

value_counts is the right way. Usable inside a groupby too after split in words

user_31946180
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