将 pandas DataFrame 旋转为正确的格式:`DataError: No numeric types to aggregate`

Pivot a pandas DataFrame to be the correct format: `DataError: No numeric types to aggregate`(将 pandas DataFrame 旋转为正确的格式:`DataError: No numeric types to aggregate`)

问题描述

这是我想要操作的 pandas DataFrame:

Here is a pandas DataFrame I would like to manipulate:

import pandas as pd

data = {"grouping": ["item1", "item1", "item1", "item2", "item2", "item2", "item2", ...],
        "labels": ["A", "B", "C", "A", "B", "C", "D", ...],
        "count": [5, 1, 8, 3, 731, 189, 9, ...]}

df = pd.DataFrame(data)

print(df)
>>>   grouping            labels       count
0        item1             A            5
1        item1             B            1
2        item1             C            8
3        item2             A            3
4        item2             B          731
5        item2             C          189
6        item2             D            9
7        ...               ...         ....

我想将此数据框展开"为以下格式:

I would like to "unfold" this dataframe into the following format:

grouping    A    B    C    D
item1       5    1    8    3
item2       3    731  189  9
....        ........

如何做到这一点?我认为这会起作用:

How would one do this? I would think that this would work:

pd.pivot_table(df,index=["grouping", "labels"]

但我收到以下错误:

DataError: No numeric types to aggregate

推荐答案

有四种惯用的 pandas 方法可以做到这一点.

There are four idiomatic pandas ways to do this.

• 分组列之间没有重复.不需要聚合
  • 枢轴
  • set_index
  • 数据透视表
  • 分组方式

  枢轴

  df.pivot('grouping', 'labels', 'count')
  

  set_index

  df.set_index(['grouping', 'labels'])['count'].unstack()
  

  pivot_table

  df.pivot_table('count', 'grouping', 'labels')
  

  groupby

  df.groupby(['grouping', 'labels'])['count'].sum().unstack()
  

  全部收益

  labels      A      B      C    D
  grouping                        
  item1     5.0    1.0    8.0  NaN
  item2     3.0  731.0  189.0  9.0
  

  时机

  使用 groupby、set_index 或 pivot_table 方法，您可以使用 fill_value=0

  With the groupby, set_index, or pivot_table approach, you can easily fill in missing values with fill_value=0

  df.pivot_table('count', 'grouping', 'labels', fill_value=0)
  
  df.groupby(['grouping', 'labels'])['count'].sum().unstack(fill_value=0)
  
  df.set_index(['grouping', 'labels'])['count'].sum().unstack(fill_value=0)
  

  全部收益

  labels    A    B    C  D
  grouping                
  item1     5    1    8  0
  item2     3  731  189  9
  

  <小时>

  关于groupby的其他想法

  因为我们不需要任何聚合.如果我们想使用 groupby，我们可以通过使用影响较小的聚合器来最小化隐式聚合的影响.

  Because we don't require any aggregation. If we wanted to use groupby, we can minimize the impact of the implicit aggregation by utilizing a less impactful aggregator.

  df.groupby(['grouping', 'labels'])['count'].max().unstack()
  

  或

  df.groupby(['grouping', 'labels'])['count'].first().unstack()
  

  定时groupby

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