如何在pandas dataframe中的每行底部添加value_counts（）之类的东西？

发布于 2025-02-10 08:33:53 字数 1833 浏览 1 评论 0原文

我有一个带有自定义垃圾箱的列的数据框。我希望DataFrame在组的最后一行之后包含类似于垃圾箱的值计数的内容（另一列中的唯一值）。我有：

     |  Staff  |  Document A     |  Document B     |
     |  Bob    |  Expired        |  Expired        |
     |  Bob    |  Expiring soon  |  On Time        |
     |  Tom    |  On Time        |  Expired        |
     |  Tom    |  Expiring soon  |  On Time        |
     |  Tom    |  Expiring soon  |  Expired        |
     |  Tom    |  On Time        |  On Time        |

我想要：

     |  Staff            |  Document A     |  Document B     |
     |  Bob              |  Expired        |  Expired        |
     |  Bob              |  Expiring soon  |  On Time        |
     |  Expired          |      1          |      1          |
     |  Expiring soon    |      1          |      0          |
     |  On Time          |      0          |      1          |
     |  Tom              |  On Time        |  Expired        |
     |  Tom              |  Expiring soon  |  On Time        |
     |  Tom              |  Expiring soon  |  Expired        |
     |  Tom              |  On Time        |  On Time        |
     |  Expired          |      0          |      2          |
     |  Expiring soon    |      2          |      0          |
     |  On Time          |      2          |      2          |

如果那不实用。我还将数据框出口到由员工分组的同一Excel工作簿的单个床单中。如果更容易，我可以将工作簿导入到多个数据范围中，并在Python的数据集底部添加此摘要。因此，每张床单都想要：

     |  Staff            |  Document A     |  Document B     |
     |  Bob              |  Expired        |  Expired        |
     |  Bob              |  Expiring soon  |  On Time        |
     |  Expired          |      1          |      1          |
     |  Expiring soon    |      1          |      0          |
     |  On Time          |      0          |      1          |

原文

I have a Dataframe which has several columns with custom bins. I would like for the dataframe to include something similar to a value count of the bins after the last row of the group (unique value in another column). I have:

     |  Staff  |  Document A     |  Document B     |
     |  Bob    |  Expired        |  Expired        |
     |  Bob    |  Expiring soon  |  On Time        |
     |  Tom    |  On Time        |  Expired        |
     |  Tom    |  Expiring soon  |  On Time        |
     |  Tom    |  Expiring soon  |  Expired        |
     |  Tom    |  On Time        |  On Time        |

I would like:

     |  Staff            |  Document A     |  Document B     |
     |  Bob              |  Expired        |  Expired        |
     |  Bob              |  Expiring soon  |  On Time        |
     |  Expired          |      1          |      1          |
     |  Expiring soon    |      1          |      0          |
     |  On Time          |      0          |      1          |
     |  Tom              |  On Time        |  Expired        |
     |  Tom              |  Expiring soon  |  On Time        |
     |  Tom              |  Expiring soon  |  Expired        |
     |  Tom              |  On Time        |  On Time        |
     |  Expired          |      0          |      2          |
     |  Expiring soon    |      2          |      0          |
     |  On Time          |      2          |      2          |

If that is not practical. I have also exported my dataframe to individual sheets of the same Excel Workbook grouped by Staff. If easier, I could import the workbook into multiple dataframes and add this summary at the bottom of the dataset in Python. So then each sheet would like something like:

     |  Staff            |  Document A     |  Document B     |
     |  Bob              |  Expired        |  Expired        |
     |  Bob              |  Expiring soon  |  On Time        |
     |  Expired          |      1          |      1          |
     |  Expiring soon    |      1          |      0          |
     |  On Time          |      0          |      1          |

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当爱已成负担 2025-02-17 08:33:53

使用groupby与自定义组。最好将数据保存在新列中，以便以后与Excel不同。

 df.groupby(['Staff', pd.Categorical(df.Document)]).count()

输出

                     Document
Staff                        
Bob   Expired               1
      Expiring soon         1
      On Time               0
Tom   Expired               0
      Expiring soon         2
      On Time               2

Use groupby with custom groups. It is best to keep the data in the new column for better processing capabilities later unlike Excel.

 df.groupby(['Staff', pd.Categorical(df.Document)]).count()

Output

                     Document
Staff                        
Bob   Expired               1
      Expiring soon         1
      On Time               0
Tom   Expired               0
      Expiring soon         2
      On Time               2

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