我需要将列列表中的每个元素转换为python panda中的一个新列

Question

我在Python中有一个数据帧，如下所示：

Name   Hobbies
0  Paul   ["Watch_NBA", "Play_PS4"]
1  Jeff   ["Play_hockey", "Read", "Play_PS4"]
2  Kyle   ["Sleep", "Watch_NBA"]

我需要在一个新列中转换列表中的每个元素，如果它出现在原始列表中，则分配值0或1。结果如下：

Name   Watch_NBA  Play_PS4 Play_hockey Read Sleep
0  Paul       1          1        0        0     0
1  Jeff       0          1        1        1     0
2  Kyle       1          0        0        0     1

有人知道我怎么能做到这一点。请记住，我会在专栏中使用很多Hobbies，所以它显示出一点自动化，而不是硬编码。谢谢

Answer 1

get_dummies()是好的，但sklearn'sMultiLabelBinarizer具有更好的性能：

from sklearn.preprocessing import MultiLabelBinarizer
mlb = MultiLabelBinarizer()
a = mlb.fit_transform(df["Hobbies"])
df_expanded = pd.DataFrame(a, columns=mlb.classes_, index=df.index)
# merge them using the following:
df_merged = df.merge(df_expanded, left_index=True, right_index=True)
print(df_merged)
index   Name    Hobbies                         Play_PS4    Play_hockey Read    Sleep   Watch_NBA
0       Paul    [Watch_NBA, Play_PS4]           1           0           0       0       1
1       Jeff    [Play_hockey, Read, Play_PS4]   1           1           1       0       0
2       Kyle    [Sleep, Watch_NBA]              0           0           0       1       1

Answer 2

您需要get_dummies()方法。此处提供文档。

例如：

names = df.Name
df = pd.get_dummies(df.Hobbies.apply(pd.Series).stack()).sum(level=0)
df.insert(0, 'Name', names)
#output:
Name  Play_PS4  Play_hockey  Read  Sleep  Watch_NBA
0  Paul         1            0     0      0          1
1  Jeff         1            1     1      0          0
2  Kyle         0            0     0      1          1

Answer 3

In [86]: df                                                                                                                                                                                                                                                                      
Out[86]: 
Name              Hobbies
0  Paul           [NBA, PS4]
1  Jeff  [Hockey, Read, PS4]
2  Kyle         [Sleep, NBA]
In [87]: df['dummy'] = 1                                                                                                                                                                                                                                                         
In [88]: df.explode("Hobbies").pivot(index='Name', columns='Hobbies', values='dummy').fillna(value=0)                                                                                                                                                                            
Out[88]: 
Hobbies  Hockey  NBA  PS4  Read  Sleep
Name                                  
Jeff        1.0  0.0  1.0   1.0    0.0
Kyle        0.0  1.0  0.0   0.0    1.0
Paul        0.0  1.0  1.0   0.0    0.0

Answer 4

你可以试试这个：

n = df['Name']
df = df['Hobbies'].apply(lambda x: pd.Series([1] * len(x), index=x)).fillna(0, downcast='infer')
df.insert(0, 'Name', n)
print(df)

输出：

Name  Watch_NBA  Play_PS4  Play_hockey  Read  Sleep
0  Paul          1         1            0     0      0
1  Jeff          0         1            1     1      0
2  Kyle          1         0            0     0      1