How to transform a 3d arrays into a dataframe in python

Question

I have a 3d arrays as follows:

ThreeD_Arrays = np.random.randint(0, 1000, (5, 4, 3))

array([[[715, 226, 632],
        [305,  97, 534],
        [ 88, 592, 90         


        

Answer 1

You could convert your 3D array to a Pandas Panel, then flatten it to a 2D DataFrame (using .to_frame()):

import numpy as np
import pandas as pd
np.random.seed(2016)

arr = np.random.randint(0, 1000, (5, 4, 3))
pan = pd.Panel(arr)
df = pan.swapaxes(0, 2).to_frame()
df.index = df.index.droplevel('minor')
df.index.name = 'Date'
df.index = df.index+1
df.columns = list('ABC')

yields

        A    B    C
Date               
1     875  702  266
1     940  180  971
1     254  649  353
1     824  677  745
...
4     675  488  939
4     382  238  225
4     923  926  633
4     664  639  616
4     770  274  378

---

Alternatively, you could reshape the array to shape (20, 3), form the DataFrame as usual, and then fix the index:

import numpy as np
import pandas as pd
np.random.seed(2016)

arr = np.random.randint(0, 1000, (5, 4, 3))
df = pd.DataFrame(arr.reshape(-1, 3), columns=list('ABC'))
df.index = np.repeat(np.arange(arr.shape[0]), arr.shape[1]) + 1
df.index.name = 'Date'
print(df)

yields the same result.