pandas 将嵌套字典转换为 mutiIndex 行和列

Posted 2023-03-11

【中文标题】pandas 将嵌套字典转换为 mutiIndex 行和列

【英文标题】：pandas convert a nested dictionary to mutiIndex rows and columns

【发布时间】：2020-06-11 21:20:27

【问题描述】：

我有一个嵌套字典，我想将它变成一个多索引行和列，如下所示。但是我的数据以某种方式丢失在表中。

    test= 12: 'Category 1': 'TestA': 'att_1': 1, 'att_2': 'whatever', 'TestB': 'att_1': 3, 'att_2': 'spring', 'Category 2': 'TestA': 'att_1': 23, 'att_2': 'another', 'TestB': 'att_1': 9, 'att_2': 'summer', 15: 'Category 1': 'TestA': 'att_1': 10, 'att_2': 'foo', 'TestB': 'att_1': 29, 'att_2': 'fall', 'Category 2': 'TestA': 'att_1': 30, 'att_2': 'bar', 'TestB': 'att_1': 36, 'att_2': 'winter'
columns=pd.MultiIndex.from_arrays([['TestA','TestA','TestB','TestB'],['att_1','att_2','att_1','att_2']])

我想要的格式：

              TestA       TestB      
              att_1 att_2 att_1 att_2
12 Category 1   NaN   NaN   NaN   NaN
   Category 2   NaN   NaN   NaN   NaN
15 Category 1   NaN   NaN   NaN   NaN
   Category 2   NaN   NaN   NaN   NaN

我做到了

    pd.DataFrame(test,index=pd.MultiIndex.from_arrays([[12,12,15,15],['Category 1','Category 2','Category 1','Category 2']]),columns=pd.MultiIndex.from_arrays([['TestA','TestA','TestB','TestB'],['att_1','att_2','att_1','att_2']]))

我的数据丢失如下：

             TestA       TestB      
              att_1 att_2 att_1 att_2
12 Category 1   NaN   NaN   NaN   NaN
   Category 2   NaN   NaN   NaN   NaN
15 Category 1   NaN   NaN   NaN   NaN
   Category 2   NaN   NaN   NaN   NaN

如果我只有 multiIndex 行，它会工作，但我想要 multiIndex 行和列。

     pd.DataFrame.from_dict((i,j): test[i][j] 
                           for i in test.keys() 
                           for j in test[i].keys(),
                       orient='index')

                                           TestA                             TestB
12 Category 1  'att_1': 1, 'att_2': 'whatever'   'att_1': 3, 'att_2': 'spring'
   Category 2  'att_1': 23, 'att_2': 'another'   'att_1': 9, 'att_2': 'summer'
15 Category 1      'att_1': 10, 'att_2': 'foo'    'att_1': 29, 'att_2': 'fall'
   Category 2      'att_1': 30, 'att_2': 'bar'  'att_1': 36, 'att_2': 'winter

【问题讨论】：

advanced indexing的pandas信息你查了吗？

【参考方案1】：

你可以得到所需的数据框为：

import pandas as pd
import numpy as np

test= 12: 'Category 1': 'TestA': 'att_1': 1, 'att_2': 'whatever', 'TestB': 'att_1': 3, 'att_2': 'spring', 'Category 2': 'TestA': 'att_1': 23, 'att_2': 'another', 'TestB': 'att_1': 9, 'att_2': 'summer', 15: 'Category 1': 'TestA': 'att_1': 10, 'att_2': 'foo', 'TestB': 'att_1': 29, 'att_2': 'fall', 'Category 2': 'TestA': 'att_1': 30, 'att_2': 'bar', 'TestB': 'att_1': 36, 'att_2': 'winter'

# Row indexes
row_index = [[12,12,15,15],['Category 1','Category 2','Category 1','Category 2']]

# Column indexes
col_index = [['TestA','TestA','TestB','TestB'],['att_1','att_2','att_1','att_2']]

# Values row wise
values =[1,'whatever',3,'spring',23,'another',9,'summer',10,'foo',29,'fall',30,'bar',36,'winter']

# Convert the list of values to numpy array
value = np.array(values)

# Reshape the value as (4,4) array as the matrix/dataframe is of shape (4,4)
value = value.reshape(4,4)

# Get your required data frame
pd.DataFrame(value, index=row_index, columns=col_index)