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如何使用Pandas重新排序多索引列?

分类: 技术问答 • 2022-06-03 16:10:25
问题描述：

表显示在此处

代码:

dff = pd.DataFrame({'Country':['France']*4+['China']*4,
                   'Progress':['Develop','Middle','Operate','Start']*2,
                   'NumTrans':np.random.randint(100,900,8),
                   'TransValue':np.random.randint(10000,9999999,8)})

dff = dff.set_index(['Country','Progress']).T

数据和代码如上所示.

Data and code are shown above.

我想知道有什么方法可以在使用Python进行start-develop-middle-operate时重新排序"Progress".

I want to know is there any way to re-order the "Progress" as start-develop-middle-operate using Python.

我尝试使用地图功能并为每个阶段设置了一个数字，但是无法从多索引中提取进度"

I tried using map function and set each stage with a number, but cannot extract "Progress" from multi-index

谢谢！

答

reindex

您可以指定要重新索引的级别

reindex

You can specify a level to reindex on

cats = ['Start', 'Develop', 'Middle', 'Operate']
dff.reindex(cats, axis=1, level=1)

Country      France                              China                         
Progress      Start  Develop   Middle  Operate   Start Develop  Middle  Operate
NumTrans        772      832      494      793     750     722     818      684
TransValue  7363187  2578816  9764430  4863178  159777  840700  978816  9674337

set_levels与CategoricalIndex

您可以定义第二级的顺序，然后进行排序.

set_levels with CategoricalIndex

You can define the order of the second level and then sort.

lvl1 = dff.columns.levels[1]
cats = ['Start', 'Develop', 'Middle', 'Operate']
cati = pd.CategoricalIndex(
  lvl1,
  categories=cats,
  ordered=True
)
dff.columns.set_levels(
  cati, level=1, inplace=True  
)

dff.sort_index(1)

Country      China                            France                           
Progress     Start Develop  Middle  Operate    Start  Develop   Middle  Operate
NumTrans       750     722     818      684      772      832      494      793
TransValue  159777  840700  978816  9674337  7363187  2578816  9764430  4863178

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