this is the data:

data
       Positions
0        ST, LW
1    CF, RW, ST
2       LW, CAM
3            GK
4       CAM, CM
5            CB
6            ST
7       CM, CDM
8        LW, CF
9            GK

here is a another list

lst = ['CAM',
 'CB',
 'LB',
 'LWB',
 'LW',
 'CF',
 'ST',
 'RWB',
 'GK',
 'RM',
 'LM',
 'CM',
 'CDM',
 'RW',
 'RB']

this problem is just like one hot encoding.

for every rows,when a row has a element in the list, then fill 1, if not have, fill 0.

result`s shape:(10,15). list has 15 elements, number of data rows is 10.

blow is a demo to describe this result.

CAM CB LB LW  CF
 0  0  0   1  0
 0  0  0   0  1

CodePudding user response：

Assuming you have a string on the column, you can get_dummies, then reindex:

out = (data['Positions'].str.get_dummies(sep=', ')
        .reindex(lst, axis=1, fill_value=0)
       )

If you have lists:

out = (pd
   .get_dummies(data['Positions'].explode() 
   .groupby(level=0).max()
   .reindex(lst, axis=1, fill_value=0)
 )

Output:

   CAM  CB  LB  LWB  LW  CF  ST  RWB  GK  RM  LM  CM  CDM  RW  RB
0    0   0   0    0   1   0   1    0   0   0   0   0    0   0   0
1    0   0   0    0   0   1   1    0   0   0   0   0    0   1   0
2    1   0   0    0   1   0   0    0   0   0   0   0    0   0   0
3    0   0   0    0   0   0   0    0   1   0   0   0    0   0   0
4    1   0   0    0   0   0   0    0   0   0   0   1    0   0   0
5    0   1   0    0   0   0   0    0   0   0   0   0    0   0   0
6    0   0   0    0   0   0   1    0   0   0   0   0    0   0   0
7    0   0   0    0   0   0   0    0   0   0   0   1    1   0   0
8    0   0   0    0   1   1   0    0   0   0   0   0    0   0   0
9    0   0   0    0   0   0   0    0   1   0   0   0    0   0   0

CodePudding user response：

import pandas as pd

positions = data = [
    ['ST', 'LW'],
    ['CF', 'RW', 'ST'],
    ['LW', 'CAM'],
    ['GK'],
    ['CAM', 'CM'],
    ['CB'],
    ['ST'],
    ['CM', 'CDM'],
    ['LW', 'CF'],
    ['GK']
]
data = pd.DataFrame({'Positions': positions})
list_ = ['CAM', 'CB', 'LB', 'LWB', 'LW', 'CF', 'ST', 'RWB', 'GK', 'RM', 'LM', 'CM', 'CDM', 'RW', 'RB']
result = pd.DataFrame(0, index=range(data.shape[0]), columns=list_)
for i, row in enumerate(data.Positions):
    for e in row:
        result.loc[i, e] = 1
print(result)

prints