Python 如何通过pandas get_dummies() 方法为某些列创建虚拟对象？
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how to create dummies for certain columns by pandas get_dummies() method?
提问by Hyman
df = pd.DataFrame({'A': ['x', 'y', 'x'], 'B': ['z', 'u', 'z'],
'C': ['1', '2', '3'],
'D':['j', 'l', 'j']})
I just want Column A and D to get dummies not for Column B. If I used pd.get_dummies(df), all columns turned into dummies.
我只是想让 A 列和 D 列得到假人而不是 B 列。如果我使用pd.get_dummies(df)，所有列都变成了假人。
I want the final result containing all of columns , which means column C and column B exit,like 'A_x','A_y','B','C','D_j','D_l'.
我想要包含所有列的最终结果，这意味着列 C 和列 B 退出，如'A_x','A_y','B','C','D_j','D_l'.
回答by knagaev
It can be done without concatenation, using get_dummies() with required parameters
它可以在没有连接的情况下完成，使用带有所需参数的 get_dummies()
In [294]: pd.get_dummies(df, prefix=['A', 'D'], columns=['A', 'D'])
Out[294]:
B C A_x A_y D_j D_l
0 z 1 1.0 0.0 1.0 0.0
1 u 2 0.0 1.0 0.0 1.0
2 z 3 1.0 0.0 1.0 0.0
回答by Patric Fulop
Adding to the above perfect answers, in case you have a big dataset with lots of attributes, if you don't want to specify by hand all of the dummies you want, you can do set differences:
添加到上述完美答案中，如果您有一个包含大量属性的大型数据集，如果您不想手动指定您想要的所有虚拟对象，您可以设置差异：
len(df.columns) = 50
non_dummy_cols = ['A','B','C']
# Takes all 47 other columns
dummy_cols = list(set(df.columns) - set(non_dummy_cols))
df = pd.get_dummies(df, columns=dummy_cols)
回答by Stefan
Just select the two columns you want to .get_dummies()for - columnnames indicate source column and variable label represented as binary variable, and pd.concat()the original columns you want unchanged:
只需选择您想要的两列.get_dummies()-column名称表示源列和表示为二进制变量的变量标签，以及pd.concat()您希望保持不变的原始列：
pd.concat([pd.get_dummies(df[['A', 'D']]), df[['B', 'C']]], axis=1)
A_x A_y D_j D_l B C
0 1.0 0.0 1.0 0.0 z 1
1 0.0 1.0 0.0 1.0 u 2
2 1.0 0.0 1.0 0.0 z 3
