TypeError: cannot perform reduce with flexible type; does not print the pandas dataframe

Question

Create a program that has a function that takes two integer arguments and prints a Pandas dataframe.

• The two arguments will correspond to the number of rows and number of columns, respectively.
• The dataframe should be filled with random integers from 0 to 100.
• Set your random seed to 56.

randdf.py

import sys
import pandas 
import numpy

def randdf(rows=(sys.argv[1]), columns=(sys.argv[2])):
    numpy.random.seed(56)
    df = pandas.DataFrame(numpy.random.randint(0, 100, size = (rows, columns)))
    return df

print(randdf())

Test Case Examples:

python3 randdf.py 4 8
python3 randdf.py 3 5
python3 randdf.py 1 7

Expected Output:

Test Case 1

    0   1   2   3   4   5   6   7
0  85  15  64  34  14  87  22  57
1  90  55  24  12  66  43  11  80
2  33  89  10  31  28  37  34  15
3  28  89  19  69  41  66  74  65

Test Case 2

    0   1   2   3   4
0  85  15  64  34  14
1  87  22  57  90  55
2  24  12  66  43  11

Test Case 3

    0   1   2   3   4   5   6
0  85  15  64  34  14  87  22

Error Message

Traceback (most recent call last):
  File "randdf.py", line 10, in <module>
    print(randdf())
  File "randdf.py", line 7, in randdf
    df = pandas.DataFrame(numpy.random.randint(0, 100, size = (rows, columns)))
  File "mtrand.pyx", line 747, in numpy.random.mtrand.RandomState.randint
  File "_bounded_integers.pyx", line 1234, in numpy.random._bounded_integers._rand_int64
  File "<__array_function__ internals>", line 6, in prod
  File "/home/codegrade/.local/lib/python3.7/site-packages/numpy/core/fromnumeric.py", line 3031, in prod
    keepdims=keepdims, initial=initial, where=where)
  File "/home/codegrade/.local/lib/python3.7/site-packages/numpy/core/fromnumeric.py", line 87, in _wrapreduction
    return ufunc.reduce(obj, axis, dtype, out, **passkwargs)
TypeError: cannot perform reduce with flexible type

Answer 1

With newer version of numpy and pandas, you need to define dtype like this.

df = pandas.DataFrame(numpy.random.randint(0, 100, size=(10, 20), dtype=np.int32))