Pandas explode column into rows

Question

I have a DataFrame where each row has two columns: date, and mentions. The end result would be a Dataframe of mentions per date, which should be easy via GroupBy if I can br

Answer 1

From sklearn

from sklearn.preprocessing import MultiLabelBinarizer
mlb = MultiLabelBinarizer()
pd.DataFrame(mlb.fit_transform(df['mentions'].str.split(',')),columns=mlb.classes_, index=df.date).sum(level=0)
Out[1745]: 
            alpha  beta  delta  gamma
date                                 
2018-01-01      2     1      0      1
2018-01-02      0     1      0      0
2018-01-03      0     0      1      0
2018-01-05      1     0      0      0
2018-01-07      1     0      0      0
2018-01-10      0     0      1      1
2018-01-11      0     0      0      1

Borrow Zero's resample('D')

pd.DataFrame(mlb.fit_transform(df['mentions'].str.split(',')),columns=mlb.classes_, index=df.date).sum(level=0).resample('D')

Answer 2

If your end result is dummy columns then use pd.Series.str.get_dummies

df.set_index('date').mentions.str.get_dummies(', ').sum(level=0)

            alpha  beta  delta  gamma
date                                 
2018-01-01      2     1      0      1
2018-01-02      0     1      0      0
2018-01-03      0     0      1      0
2018-01-05      1     0      0      0
2018-01-07      1     0      0      0
2018-01-10      0     0      1      1
2018-01-11      0     0      0      1

---

As mentioned by @Zero

df.set_index('date').mentions.str.get_dummies(', ').resample('D').sum()

            alpha  beta  delta  gamma
date                                 
2018-01-01      2     1      0      1
2018-01-02      0     1      0      0
2018-01-03      0     0      1      0
2018-01-04      0     0      0      0
2018-01-05      1     0      0      0
2018-01-06      0     0      0      0
2018-01-07      1     0      0      0
2018-01-08      0     0      0      0
2018-01-09      0     0      0      0
2018-01-10      0     0      1      1
2018-01-11      0     0      0      1