I would Convert 3d numpy array to a pandas dataframe that has 1 column, made out of 2d numpy arrays.
concrete example:
np_array = np.zeros((10,3,5))
print(np_array.shape) # (10, 3, 5)
so from this numpy array I would like to create a dataframe with 1 column, that has 10 rows, each row has an item of the shape (3,5).
trying to convert it as-is to pd.DataFrame(np_array) throws out ValueError: Must pass 2-d input error.
Thanks!
Perhaps a list comprehension?
import numpy as np
import pandas as pd
np_array = np.zeros((10,3,5))
pd.DataFrame(data = [[x] for x in np_array])
you can do this ,initialize the data frame with a dict to decalre what exactly is a columns, and turn the top level of the np.array to list (it turns it to list of 2d arrays):
>>>pd.DataFrame(dict(rows = list(np_array)))
rows
0 [[0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0....
1 [[0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0....
2 [[0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0....
3 [[0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0....
4 [[0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0....
5 [[0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0....
6 [[0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0....
7 [[0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0....
8 [[0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0....
9 [[0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0....
each row has an item of the shape (3,5)? Can you provide the structure of the desireddataframe?