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    Python实现把多维数组展开成DataFrame

    栏目:Linux/apache问题 时间:2019-12-01 22:35

    如下所示:

    import numpy as np
    import pandas as pd
    
    ################# 准备数据 #################
    a1 = np.arange(1,101)
    a3 = a1.reshape((2,5,10))
    a3
    '''
    array([[[ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
      [ 11, 12, 13, 14, 15, 16, 17, 18, 19, 20],
      [ 21, 22, 23, 24, 25, 26, 27, 28, 29, 30],
      [ 31, 32, 33, 34, 35, 36, 37, 38, 39, 40],
      [ 41, 42, 43, 44, 45, 46, 47, 48, 49, 50]],  
      [[ 51, 52, 53, 54, 55, 56, 57, 58, 59, 60],
      [ 61, 62, 63, 64, 65, 66, 67, 68, 69, 70],
      [ 71, 72, 73, 74, 75, 76, 77, 78, 79, 80],
      [ 81, 82, 83, 84, 85, 86, 87, 88, 89, 90],
      [ 91, 92, 93, 94, 95, 96, 97, 98, 99, 100]]])
    '''
    
    ################# 准备标签 #################
    # 第 1 维的标签
    index1 = pd.Series(np.arange(1,11))
    index1 = index1.astype(str)
    index1 = 'A'+index1
    index1
    '''
    0  A1
    1  A2
    2  A3
    3  A4
    4  A5
    5  A6
    6  A7
    7  A8
    8  A9
    9 A10
    '''
    
    # 第 2 维的标签
    index2 = pd.Series(np.arange(1,6))
    index2 = index2.astype(str)
    index2 = 'B'+index2
    index2
    '''
    0 B1
    1 B2
    2 B3
    3 B4
    4 B5
    '''
    
    # 第 3 维的标签
    index3 = pd.Series(np.arange(1,3))
    index3 = index3.astype(str)
    index3 = 'C'+index3
    index3
    '''
    0 C1
    1 C2
    '''
    
    ################# 展开数据 #################
    # 把三维数组展开
    value = a3.flatten()
    value = pd.Series(value)
    value.name = 'value'
    value
    '''
    0  1
    1  2
    2  3
      ... 
    97  98
    98  99
    99 100
    Name: value, Length: 100, dtype: int64
    '''
    
    ################# 展开标签 #################
    import itertools
    
    # index的笛卡尔乘积。注意:高维在前,低维在后
    prod = itertools.product(index3, index2, index1 )
    # 转换为DataFrame
    prod = pd.DataFrame([x for x in prod])
    prod.columns = ['C', 'B', 'A']
    prod.T
    '''
     0 1 2 3 4 5 6 7 8 9 ... 90 91 92 93 94 95 96 \
    C C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 ... C2 C2 C2 C2 C2 C2 C2 
    B B1 B1 B1 B1 B1 B1 B1 B1 B1 B1 ... B5 B5 B5 B5 B5 B5 B5 
    A A1 A2 A3 A4 A5 A6 A7 A8 A9 A10 ... A1 A2 A3 A4 A5 A6 A7 
     97 98 99 
    C C2 C2 C2 
    B B5 B5 B5 
    A A8 A9 A10 
    [3 rows x 100 columns]
    '''
    
    ################# 最终数据 #################
    # 合并成一个DataFrame
    pd.concat([prod, value], axis=1)
    '''
      C B A value
    0 C1 B1 A1  1
    1 C1 B1 A2  2
    2 C1 B1 A3  3
    3 C1 B1 A4  4
    4 C1 B1 A5  5
    5 C1 B1 A6  6
    6 C1 B1 A7  7
    7 C1 B1 A8  8
    8 C1 B1 A9  9
    9 C1 B1 A10  10
    10 C1 B2 A1  11
    11 C1 B2 A2  12
    12 C1 B2 A3  13
    13 C1 B2 A4  14
    14 C1 B2 A5  15
    15 C1 B2 A6  16
    16 C1 B2 A7  17
    17 C1 B2 A8  18
    18 C1 B2 A9  19
    19 C1 B2 A10  20
    20 C1 B3 A1  21
    21 C1 B3 A2  22
    22 C1 B3 A3  23
    23 C1 B3 A4  24
    24 C1 B3 A5  25
    25 C1 B3 A6  26
    26 C1 B3 A7  27
    27 C1 B3 A8  28
    28 C1 B3 A9  29
    29 C1 B3 A10  30
    .. .. .. ... ...
    70 C2 B3 A1  71
    71 C2 B3 A2  72
    72 C2 B3 A3  73
    73 C2 B3 A4  74
    74 C2 B3 A5  75
    75 C2 B3 A6  76
    76 C2 B3 A7  77
    77 C2 B3 A8  78
    78 C2 B3 A9  79
    79 C2 B3 A10  80
    80 C2 B4 A1  81
    81 C2 B4 A2  82
    82 C2 B4 A3  83
    83 C2 B4 A4  84
    84 C2 B4 A5  85
    85 C2 B4 A6  86
    86 C2 B4 A7  87
    87 C2 B4 A8  88
    88 C2 B4 A9  89
    89 C2 B4 A10  90
    90 C2 B5 A1  91
    91 C2 B5 A2  92
    92 C2 B5 A3  93
    93 C2 B5 A4  94
    94 C2 B5 A5  95
    95 C2 B5 A6  96
    96 C2 B5 A7  97
    97 C2 B5 A8  98
    98 C2 B5 A9  99
    99 C2 B5 A10 100
    [100 rows x 4 columns]
    '''
    
    

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