Python实现把多维数组展开成DataFrame
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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