把字串離散化

1.獲取字串的去重後列表

2.構造全為0的陣列(dataframe)， columns為字串的列表

3.給全為0的陣列賦值

第一步

import

pandas as pd
import
numpy as np
df = pd.dataframe()
#print(df)
print('
=' * 40)
print(df['c'
])"""
0        one,two,three
1              one,two
2             two,four
3    two,five,four,six
4      seven,eight,one
5    nine,ten,six,four
6    ten,six,two,seven
name: c, dtype: object
"""a = df['
c'].str.split(','
)print
(a)"""
0         [one, two, three]
1                [one, two]
2               [two, four]
3    [two, five, four, six]
4       [seven, eight, one]
5    [nine, ten, six, four]
6    [ten, six, two, seven]
name: c, dtype: object
"""print('
=' * 50)
a_lst = df['
c'].str.split(','
).tolist()
print
(a_lst)
#[['one', 'two', 'three'], ['one', 'two'], ['two', 'four'],
#['two', 'five', 'four', 'six'], ['seven', 'eight', 'one'],
#['nine', 'ten', 'six', 'four'], ['ten', 'six', 'two', 'seven']]
print('
*' * 60)
new_lst =
for i in
a_lst:
for j in
i:        
if j not
innew_lst:
print
(new_lst)
#['one', 'two', 'three', 'four', 'five',
#'six', 'seven', 'eight', 'nine', 'ten']

第二步

df_zeros = pd.dataframe(data=np.zeros((df.shape[0], len(new_lst))), columns=new_lst)

print
(df_zeros)
"""one  two  three  four  five  six  seven  eight  nine  ten
0  0.0  0.0    0.0   0.0   0.0  0.0    0.0    0.0   0.0  0.0
1  0.0  0.0    0.0   0.0   0.0  0.0    0.0    0.0   0.0  0.0
2  0.0  0.0    0.0   0.0   0.0  0.0    0.0    0.0   0.0  0.0
3  0.0  0.0    0.0   0.0   0.0  0.0    0.0    0.0   0.0  0.0
4  0.0  0.0    0.0   0.0   0.0  0.0    0.0    0.0   0.0  0.0
5  0.0  0.0    0.0   0.0   0.0  0.0    0.0    0.0   0.0  0.0
6  0.0  0.0    0.0   0.0   0.0  0.0    0.0    0.0   0.0  0.0
"""

方法二（資料量大的情況下使用）

for i in

new_lst:
df_zeros[i][df['c
'].str.contains(i)] = 1
print(df_zeros)

第三步

for i in

range(df_zeros.shape[0]):
df_zeros.loc[i, a_lst[i]] = 1
print
(df_zeros)
"""one  two  three  four  five  six  seven  eight  nine  ten
0  1.0  1.0    1.0   0.0   0.0  0.0    0.0    0.0   0.0  0.0
1  1.0  1.0    0.0   0.0   0.0  0.0    0.0    0.0   0.0  0.0
2  0.0  1.0    0.0   1.0   0.0  0.0    0.0    0.0   0.0  0.0
3  0.0  1.0    0.0   1.0   1.0  1.0    0.0    0.0   0.0  0.0
4  1.0  0.0    0.0   0.0   0.0  0.0    1.0    1.0   0.0  0.0
5  0.0  0.0    0.0   1.0   0.0  1.0    0.0    0.0   1.0  1.0
6  0.0  1.0    0.0   0.0   0.0  1.0    1.0    0.0   0.0  1.0
"""

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