如何从具有不同长度的列表列表中创建 Pandas DataFrame? [英] How to create a Pandas DataFrame from a list of lists with different lengths?

问题描述

我有如下格式的数据

data = [["a", "b", "c"],
        ["b", "c"],
        ["d", "e", "f", "c"]]

并且我想要一个 DataFrame，其中所有唯一的字符串都作为列和出现的二进制值等

and I would like to have a DataFrame with all unique strings as columns and binary values of occurrence as such

    a  b  c  d  e  f
0   1  1  1  0  0  0
1   0  1  1  0  0  0
2   0  0  1  1  1  1

我有一个使用列表推导式的工作代码，但对于大数据来说速度很慢.

I have a working code using list comprehensions but it's pretty slow for large data.

# vocab_list contains all the unique keys, which is obtained when reading in data from file
df = pd.DataFrame([[1 if word in entry else 0 for word in vocab_list] for entry in data])

有没有办法优化这个任务?谢谢.

Is there any way to optimise this task? Thanks.

编辑(实际数据的小样本):

EDIT (a small sample of actual data):

[['a','关于'，'荒诞'，'再次'，'一个'，'同事','写'，'写','X'，'约克','你'，'你的']，['一种'，'坚持','年龄'，'加重','积极地','全部'，'几乎'，'独自的'，'已经'，'还'，'虽然']]

[['a', 'about', 'absurd', 'again', 'an', 'associates', 'writes', 'wrote', 'x', 'york', 'you', 'your'], ['a', 'abiding', 'age', 'aggravated', 'aggressively', 'all', 'almost', 'alone', 'already', 'also', 'although']]

推荐答案

为了获得更好的性能，请使用 MultiLabelBinarizer:

For better performance use MultiLabelBinarizer:

data = [["a", "b", "c"],
        ["b", "c"],
        ["d", "e", "f", "c"]]

from sklearn.preprocessing import MultiLabelBinarizer    
mlb = MultiLabelBinarizer()
df = pd.DataFrame(mlb.fit_transform(data),columns=mlb.classes_)
print (df)
   a  b  c  d  e  f
0  1  1  1  0  0  0
1  0  1  1  0  0  0
2  0  0  1  1  1  1

data = [['a', 'about', 'absurd', 'again', 'an', 'associates', 'writes', 'wrote', 'x', 'york', 'you', 'your'], ['a', 'abiding', 'age', 'aggravated', 'aggressively', 'all', 'almost', 'alone', 'already', 'also', 'although']]

from sklearn.preprocessing import MultiLabelBinarizer    
mlb = MultiLabelBinarizer()
df = pd.DataFrame(mlb.fit_transform(data),columns=mlb.classes_)
print (df)
   a  abiding  about  absurd  again  age  aggravated  aggressively  all  \
0  1        0      1       1      1    0           0             0    0   
1  1        1      0       0      0    1           1             1    1   

   almost  ...  also  although  an  associates  writes  wrote  x  york  you  \
0       0  ...     0         0   1           1       1      1  1     1    1   
1       1  ...     1         1   0           0       0      0  0     0    0   

   your  
0     1  
1     0  

[2 rows x 22 columns]

纯熊猫解决方案是可能的，但我想它应该更慢:

Pure pandas solution is possible, but I guess it should be slowier:

df = pd.get_dummies(pd.DataFrame(data), prefix='', prefix_sep='').max(level=0, axis=1)
print (df)
   a  b  d  c  e  f
0  1  1  0  1  0  0
1  0  1  0  1  0  0
2  0  0  1  1  1  1

df = pd.get_dummies(pd.DataFrame(data), prefix='', prefix_sep='').max(level=0, axis=1)
print (df)
   a  abiding  about  absurd  age  again  aggravated  aggressively  an  all  \
0  1        0      1       1    0      1           0             0   1    0   
1  1        1      0       0    1      0           1             1   0    1   

   ...  writes  alone  wrote  already  x  also  york  although  you  your  
0  ...       1      0      1        0  1     0     1         0    1     1  
1  ...       0      1      0        1  0     1     0         1    0     0  

[2 rows x 22 columns]

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