Efficiently Creating A Pandas DataFrame From A Numpy 3d array

Question

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

Shouldn't we have `'b': [1, 3, 5, 7]` for that sample? Also, could you add another sample, like `a = np.random.randint(0,9,(4,3,2))`, just to see what to expect when the dimensions have different lengths? — Divakar, Mar 26 '16 at 12:26

Divakar · Accepted Answer · 2016-03-26T13:25:16.763

26

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

edited Mar 26 '16 at 13:25

answered Mar 26 '16 at 12:46

Divakar

218,885
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Thanks! This worked quite nicely. Although, I replaced m,n,r with x,y,z. – Abe Hoffman Jan 25 '18 at 19:09
The best solution that I've found to pass 3d array to pandas dataFrame!! – dbz Aug 04 '19 at 14:59
5

Since the Panel Object was just removed in pandas v0.25.0 this should probably become the canonical answer. – Drafter250 Aug 06 '19 at 14:03

score 5 · Answer 2 · edited Mar 26 '16 at 12:46

5

Using Panel:

a = np.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]]])
b=pd.Panel(rollaxis(a,2)).to_frame()
c=b.set_index(b.index.labels[0]).reset_index()
c.columns=list('abc')

then a is :

[[[1 2]
  [3 4]]

 [[5 6]
  [7 8]]]

b is :

             0  1
major minor      
0     0      1  2
      1      3  4
1     0      5  6
      1      7  8

and c is :

edited Mar 26 '16 at 12:46

Ami Tavory

74,578
11
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answered Mar 26 '16 at 12:41

B. M.

18,243
2
35
54

4

`Panel` has been deprecated, see answer below by @Divakar – mloning Aug 24 '19 at 12:30

score 1 · Answer 3 · answered Mar 22 '23 at 12:25

Here's a pure-Pandas solution without Panels.

To get a dataframe with MultiIndex, use pd.concat:

>>> df = pd.concat([pd.DataFrame(arr) for arr in a], keys=np.arange(len(a)))
>>> df
     0  1
0 0  0  6
  1  6  4
  2  3  4
1 0  5  1
  1  1  3
  2  6  4
2 0  8  0
  1  2  3
  2  3  1
3 0  2  2
  1  0  0
  2  6  3

To convert it to the non-MultiIndex form provided in the question:

>>> df.reset_index().drop('level_1',axis=1).set_axis(['a','b','c'], axis=1)

    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

Efficiently Creating A Pandas DataFrame From A Numpy 3d array

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