从一个Numpy 3d数组高效创建Pandas DataFrame

问题描述

假设我们从

开始

import numpy as np
a = np.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]]])

如何有效地将其制作为等效于pandas的数据框

import pandas as pd
>>> pd.DataFrame({'a': [0, 0, 1, 1], 'b': [1, 3, 5, 7], 'c': [2, 4, 6, 8]})

   a  b  c
0  0  1  2
1  0  3  4
2  1  5  6
3  1  7  8

这个想法是让a列在原始数组的第一个维度上具有索引，而其余列则是二维数组在原始数组的后两个维度中的垂直串联. /p>

(这很容易使用循环；问题是在没有循环的情况下该如何做.)

---

更多示例

使用@Divakar的出色建议:

>>> np.random.randint(0,9,(4,3,2))
array([[[0, 6],
    [6, 4],
    [3, 4]],

   [[5, 1],
    [1, 3],
    [6, 4]],

   [[8, 0],
    [2, 3],
    [3, 1]],

   [[2, 2],
    [0, 0],
    [6, 3]]])

应该做成类似这样的东西:

>>> pd.DataFrame({
    'a': [0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3],
    'b': [0, 6, 3, 5, 1, 6, 8, 2, 3, 2, 0, 6],
    'c': [6, 4, 4, 1, 3, 4, 0, 3, 1, 2, 0, 3]})
    a  b  c
0   0  0  6
1   0  6  4
2   0  3  4
3   1  5  1
4   1  1  3
5   1  6  4
6   2  8  0
7   2  2  3
8   2  3  1
9   3  2  2
10  3  0  0
11  3  6  3

解决方案

这是在最终将其作为DataFrame投放之前，对NumPy进行大部分处理的一种方法，

m,n,r = a.shape
out_arr = np.column_stack((np.repeat(np.arange(m),n),a.reshape(m*n,-1)))
out_df = pd.DataFrame(out_arr)

如果您确切地知道列数为2，这样我们将以b和c作为最后两列，以a作为第一列，则可以添加如下列名所以-

out_df = pd.DataFrame(out_arr,columns=['a', 'b', 'c'])

样品运行-

>>> a
array([[[2, 0],
        [1, 7],
        [3, 8]],

       [[5, 0],
        [0, 7],
        [8, 0]],

       [[2, 5],
        [8, 2],
        [1, 2]],

       [[5, 3],
        [1, 6],
        [3, 2]]])
>>> out_df
    a  b  c
0   0  2  0
1   0  1  7
2   0  3  8
3   1  5  0
4   1  0  7
5   1  8  0
6   2  2  5
7   2  8  2
8   2  1  2
9   3  5  3
10  3  1  6
11  3  3  2

Suppose we start with

import numpy as np
a = np.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]]])

How can this be efficiently be made into a pandas DataFrame equivalent to

import pandas as pd
>>> pd.DataFrame({'a': [0, 0, 1, 1], 'b': [1, 3, 5, 7], 'c': [2, 4, 6, 8]})

   a  b  c
0  0  1  2
1  0  3  4
2  1  5  6
3  1  7  8

The idea is to have the a column have the index in the first dimension in the original array, and the rest of the columns be a vertical concatenation of the 2d arrays in the latter two dimensions in the original array.

(This is easy to do with loops; the question is how to do it without them.)

---

Longer Example

Using @Divakar's excellent suggestion:

>>> np.random.randint(0,9,(4,3,2))
array([[[0, 6],
    [6, 4],
    [3, 4]],

   [[5, 1],
    [1, 3],
    [6, 4]],

   [[8, 0],
    [2, 3],
    [3, 1]],

   [[2, 2],
    [0, 0],
    [6, 3]]])

Should be made to something like:

>>> pd.DataFrame({
    'a': [0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3],
    'b': [0, 6, 3, 5, 1, 6, 8, 2, 3, 2, 0, 6],
    'c': [6, 4, 4, 1, 3, 4, 0, 3, 1, 2, 0, 3]})
    a  b  c
0   0  0  6
1   0  6  4
2   0  3  4
3   1  5  1
4   1  1  3
5   1  6  4
6   2  8  0
7   2  2  3
8   2  3  1
9   3  2  2
10  3  0  0
11  3  6  3

解决方案

Here's one approach that does most of the processing on NumPy before finally putting it out as a DataFrame, like so -

m,n,r = a.shape
out_arr = np.column_stack((np.repeat(np.arange(m),n),a.reshape(m*n,-1)))
out_df = pd.DataFrame(out_arr)

If you precisely know that the number of columns would be 2, such that we would have b and c as the last two columns and a as the first one, you can add column names like so -

out_df = pd.DataFrame(out_arr,columns=['a', 'b', 'c'])

Sample run -

>>> a
array([[[2, 0],
        [1, 7],
        [3, 8]],

       [[5, 0],
        [0, 7],
        [8, 0]],

       [[2, 5],
        [8, 2],
        [1, 2]],

       [[5, 3],
        [1, 6],
        [3, 2]]])
>>> out_df
    a  b  c
0   0  2  0
1   0  1  7
2   0  3  8
3   1  5  0
4   1  0  7
5   1  8  0
6   2  2  5
7   2  8  2
8   2  1  2
9   3  5  3
10  3  1  6
11  3  3  2

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