Reshape a 3D array to a 2D array in Python

Question

Suppose I have a data frame of length 5800000 which is a concatenation of 100 files where each file has 58000 rows. I have an array fv of shape (100, 10, 58000) which I want to add to the data frame by adding 10 columns. df has a length of 5800000 with two columns but only focuses on the first column index, i.e df.shape[0]

list_ = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j']  
fv = np.zeros(len(data), len(list_), int(df.shape[0]/len(files))

def add_fv_to_dataframe(_data, list_):
    for index in range(len(_data):
        for name_index, name_in_list in enumerate(list_):
            calculate something
            calcs_ = _data[index]
            fv[index, name_index, :] = calcs_
            # add the calculated values to the dataframe

            df['fv_{}'.format(name_in_list)] = pd.Series(fv.reshape(-1, (10,1)), index=df.index)

I would like to have my final data frame in the form;

df[0]	df[1]	fv_a	fv_b	fv_c	fv_d	fv_e	fv_f	fv_g	fv_h	fv_i	fv_j
1	1	1	1	1	1	1	1	1	1	1	1
:	:	:	:	:	:	:	:	:	:	:	:
5800000	5800000	5800000	5800000	5800000	5800000	5800000	5800000	5800000	5800000	5800000	5800000

Code Different · Accepted Answer · 2022-04-14 05:20:24Z

1

Use np.swapaxes:

for i, data in enumerate(np.swapaxes(fv, 0, 1)):
    df[f"fv_{i}"] = np.ravel(data)

answered Apr 14, 2022 at 5:20

Code Different

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