df.str.get_dummies() 与 pd.get_dummies() (Python)

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英文:

df.str.get_dummies() vs pd.get_dummies() (Python)

问题

我有一个类似如下的系列：

  1. 0 mcdonalds, popeyes
  2. 1 wendys
  3. 2 popeyes
  4. 3 mcdonalds
  5. 4 mcdonalds

我使用以下代码：

  1. df.str.get_dummies(sep = ', ')

来获取以下数据框：

  1. popeyes wendys mcdonalds
  2. 1       0      1
  3. 0       1      0
  4. 1       0      0
  5. 0       0      1
  6. 0       0      1

我想要删除一列，以解决虚拟变量陷阱的问题。我应该如何做，就像pd.get_dummies()中的drop_first参数一样？

期望的输出可能类似于以下内容，但我不想硬编码删除一个随机列：

  1. popeyes wendys 
  2. 1       0      
  3. 0       1      
  4. 1       0      
  5. 0       0      
  6. 0       0
英文:

I have a series like so:

  1. 0 mcdonalds, popeyes
  2. 1 wendys
  3. 2 popeyes
  4. 3 mcdonalds
  5. 4 mcdonalds

I use the following code:

  1. df.str.get_dummies(sep = ', ')

to get the following data frame:

  1. popeyes wendys mcdonalds
  2. 1       0      1
  3. 0       1      0
  4. 1       0      0
  5. 0       0      1
  6. 0       0      1

I want to remove a column though to account for the dummy variable trap. how do i do this like in the drop_first argument in pd.get_dummies()?

expected output might look something like this, but i don't want to hardcode to drop a random column:

  1. popeyes wendys 
  2. 1       0      
  3. 0       1      
  4. 1       0      
  5. 0       0      
  6. 0       0

答案1

得分: 1

You can explode your Series before using pd.get_dummies:

  1. (pd.get_dummies(df.str.split(',').explode(), drop_first=True)
  2.    .groupby(level=0).max())
  4.    popeyes  wendys
  5. 0        1       0
  6. 1        0       1
  7. 2        1       0
  8. 3        0       0
  9. 4        0       0

Alternative:

  1. df.str.get_dummies(sep=',').drop(columns=df.iloc[0].split(',')[0])
  3.    popeyes  wendys
  4. 0        1       0
  5. 1        0       1
  6. 2        1       0
  7. 3        0       0
  8. 4        0       0
英文:

You can explode your Series before using pd.get_dummies:

  1. >>> (pd.get_dummies(df.str.split(', ').explode(), drop_first=True)
  2.        .groupby(level=0).max())
  4.    popeyes  wendys
  5. 0        1       0
  6. 1        0       1
  7. 2        1       0
  8. 3        0       0
  9. 4        0       0

Details:

  1. >>> df.str.split(', ').explode()
  2. 0    mcdonalds
  3. 0      popeyes
  4. 1       wendys
  5. 2      popeyes
  6. 3    mcdonalds
  7. 4    mcdonalds
  8. dtype: object
  10. >>> pd.get_dummies(df.str.split(', ').explode(), drop_first=True)
  11.    popeyes  wendys
  12. 0        0       0
  13. 0        1       0
  14. 1        0       1
  15. 2        1       0
  16. 3        0       0
  17. 4        0       0

Alternative:

  1. >>> df.str.get_dummies(sep=', ').drop(columns=df.iloc[0].split(', ')[0])
  3.    popeyes  wendys
  4. 0        1       0
  5. 1        0       1
  6. 2        1       0
  7. 3        0       0
  8. 4        0       0

答案2

得分: 0

可以使用切片来移除第一列：

  1. s.str.get_dummies(', ').iloc[:, 1:]

输出：

  1.    popeyes  wendys
  2. 0        1       0
  3. 1        0       1
  4. 2        1       0
  5. 3        0       0
  6. 4        0       0
英文:

You can slice to remove the first column:

  1. s.str.get_dummies(', ').iloc[:, 1:]

Output:

  1.    popeyes  wendys
  2. 0        1       0
  3. 1        0       1
  4. 2        1       0
  5. 3        0       0
  6. 4        0       0

huangapple
  • 本文由 发表于 2023年6月8日 02:47:50
  • 转载请务必保留本文链接：https://go.coder-hub.com/76426243.html
  • pandas
  • python
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