Efficiently Creating A Pandas DataFrame From A Numpy 3d array

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.shapeout_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 -

>>> aarray([[[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  c0   0  2  01   0  1  72   0  3  83   1  5  04   1  0  75   1  8  06   2  2  57   2  8  28   2  1  29   3  5  310  3  1  611  3  3  2

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  1major minor      0     0      1  2      1      3  41     0      5  6      1      7  8

and c is :

   a  b  c0  0  1  21  0  3  42  1  5  63  1  7  8