Converting an array of numpy arrays to DataFrame

Question

I have a numpy object that contains the following:

17506    [0, 0, 0, 0, 0, 0]
17507    [0, 0, 0, 0, 0, 0]
17508    [0, 0, 0, 0, 0, 0]
17509    [0, 0, 0, 0, 0, 0]
17510    [0, 0, 0, 0, 0, 0]
17511    [0, 0, 0, 0, 0, 0]
17512    [0, 0, 0, 0, 0, 0]
17513    [0, 0, 0, 0, 0, 0]
17514    [0, 0, 0, 0, 0, 0]
17515    [0, 0, 0, 0, 0, 0]
17516    [0, 0, 0, 0, 0, 0]
17517    [0, 0, 0, 0, 0, 0]
17518    [0, 0, 0, 0, 0, 0]
17519    [0, 0, 0, 0, 0, 0]

(An array that contains arrays of dtype('int32')) How can I efficiently convert this to data frame in pandas and concantenate it (vertically) to an existing dataframe?

if I simply do this: pd.DataFrame(arr) the result is that each row of the dataframe contains an array. But I want the nested arrays splitted into columns — kostas
– kostas, Commented Feb 18, 2016 at 14:32

Alexander · Accepted Answer · 2016-02-18 15:22:22Z

4

What seems to be the problem? You may need to further describe your data.

a = np.array([np.zeros(6) for _ in range(3)])

>>> pd.DataFrame(a)
   0  1  2  3  4  5
0  0  0  0  0  0  0
1  0  0  0  0  0  0
2  0  0  0  0  0  0

answered Feb 18, 2016 at 15:22

Alexander

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