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?
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
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