Split a list from Dataframe column into specific column name [duplicate]

Question

I have a question regarding splitting a list in a dataframe column into multiple columns. But every value that is splitted needs to be placed in a specific column.

Let's say I have this Dataframe:

date                   data
2020-01-01 00:00:00    [G07, G08, G10, G16]
2020-01-01 00:00:01    [G07, G08, G16]
2020-01-01 00:00:02    [G08, G10, G16, G20, G21]
2020-01-01 00:00:03    [G16, G20, G21, G26, G27, R02]
2020-01-01 00:00:04    [G07, G08, G26, G27]

And I'm looking for this kind of result:

date                   G07  G08  G10  G16  G20  G21  G26  G27  R02
2020-01-01 00:00:00    G07  G08  G10  G16  NaN  NaN  NaN  NaN  NaN
2020-01-01 00:00:01    G07  G08  NaN  G16  NaN  NaN  NaN  NaN  NaN
2020-01-01 00:00:02    NaN  G08  G10  G16  G20  G21  NaN  NaN  NaN
2020-01-01 00:00:03    NaN  NaN  NaN  G16  G20  G21  G26  G27  R02
2020-01-01 00:00:04    G07  G08  NaN  NaN  NaN  NaN  G26  G27  NaN

To finally get this kind of matrix:

date                   G07  G08  G10  G16  G20  G21  G26  G27  R02
2020-01-01 00:00:00    1    1    1    1    0    0    0    0    0
2020-01-01 00:00:01    1    1    0    1    0    0    0    0    0    
2020-01-01 00:00:02    0    1    1    1    1    1    0    0    0    
2020-01-01 00:00:03    0    0    0    1    1    1    1    1    1    
2020-01-01 00:00:04    1    1    0    0    0    0    1    1    0    

By doing this type of command :

In [1] pd.DataFrame(self.df['data'].to_list())

Out [1] date                   1    2    3    4    5    6    
        2020-01-01 00:00:00    G07  G08  G10  G16
        2020-01-01 00:00:01    G07  G08  G16
        2020-01-01 00:00:02    G08  G10  G16  G20  G21
        2020-01-01 00:00:03    G16  G20  G21  G26  G27  R02
        2020-01-01 00:00:04    G07  G08  G26  G27

I'm only allowed to split the list into other columns. But I cannot find a way to place each value into a specific column.

I've been thinking of making loops over each values of each dates but it is very slow and I have datasets that are more than 1,000,000 rows.

Answer 1

Check with MultiLabelBinarizer from sklearn

from sklearn.preprocessing import MultiLabelBinarizer

mlb = MultiLabelBinarizer()

s = pd.DataFrame(mlb.fit_transform(df['data']),columns=mlb.classes_, index=df.index)

df = df.join(s)