将Pandas表转换为出现次数

huangapple
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英文:

Pandas transform table to occurrence

问题

把表格转化为如下所示：

  1.    Fruits and Region  Good  Bad  N/A
  2. 0   Apple America      1.0   0.0   0.0
  3. 1   Apple Europe       0.0   0.0   1.0
  4. 2   Apple Asia         0.0   1.0   0.0
  5. 3   Orange America     0.0   0.0   1.0
  6. 4   Orange Europe      0.0   1.0   0.0
  7. 5   Orange Asia        1.0   0.0   0.0

你可以尝试使用 Pandas 库中的 melt 函数来实现这种转换。

英文:

Hi I have a table similar to below

  1.   Fruits     America Europe Asia
  2. 0 Apple      Good   N/A   Bad
  3. 1 Orange     N/A    Bad  Good

and I would like to transform into something like this

  1.  Fruits and Region   Good Bad N/A
  2. 0 Apple America         1   0  0
  3. 1 Apple Europe.         0.  0. 1
  4. 2 Apple Asia.           0   1. 0
  5. 3 Orange America.       0.  0. 1
  6. 4 Orange Europe.        0.  1. 0
  7. 5 Orange Asia.          1.  0. 0

I have tried the stack function but it doesn't work as expected.

Thank you

答案1

得分: 1

以下是翻译好的部分：

你可以使用 melt 和 crosstab 函数：

  1. df2 = df.melt('Fruits', var_name='Region')
  3. out = (
  4.   pd.crosstab(df2['Fruits'].add(' '+df2['Region']).rename('Fruits and Region'),
  5.               df2['value'].fillna('N/A'), dropna=False)
  6.     .rename_axis(columns=None).reset_index()  
  7. )

输出：

  1.   Fruits and Region  Bad  Good  N/A
  2. 0     Apple America    0     1    0
  3. 1        Apple Asia    1     0    0
  4. 2      Apple Europe    0     0    1
  5. 3    Orange America    0     0    1
  6. 4       Orange Asia    0     1    0
  7. 5     Orange Europe    1     0    0
英文:

You can melt and crosstab:

  1. df2 = df.melt('Fruits', var_name='Region')
  3. out = (
  4.   pd.crosstab(df2['Fruits'].add(' '+df2['Region']).rename('Fruits and Region'),
  5.               df2['value'].fillna('N/A'), dropna=False)
  6.     .rename_axis(columns=None).reset_index()  
  7. )

Output:

  1.   Fruits and Region  Bad  Good  N/A
  2. 0     Apple America    0     1    0
  3. 1        Apple Asia    1     0    0
  4. 2      Apple Europe    0     0    1
  5. 3    Orange America    0     0    1
  6. 4       Orange Asia    0     1    0
  7. 5     Orange Europe    1     0    0

huangapple
  • 本文由 发表于 2023年3月7日 00:43:42
  • 转载请务必保留本文链接：https://go.coder-hub.com/75653530.html
  • pandas
  • python
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