How to slice array by its index in 2D array in python using numpy

Question

I have written the following code:

import numpy as np
n_rows = int(input("Enter number of rows:"))
n_columns = int(input("Enter number of columns:"))
print("Enter 2D array values---")
matrix = []
for i in range(n_rows):
    a=[]
    for j in range(n_columns):
        a.append(int(input()))
    matrix.append(a)
arr=np.array(matrix)
arr

if i input the following values this will give the following output:

array([[1, 2, 3],
       [4, 5, 6]])

but i want first row of matrix to enter as string values like:

["John","Alex","Smith"]

and 2nd row of matrix as integer values like:

[50,60,70]

and then i want to get the following output:

Name: John , Marks: 50
Name: Alex , Marks: 60
Name: Smith, Marks: 70

halfer · Accepted Answer · 2023-03-18 12:18:51Z

2

Numpy requires that all values in a matrix are of the same type. This is due to how it searches for items in an array (for more information look for strides).

Therefore, if you want text data in your array, you must change the type of an entire array to a type which supports strings.

An alternative would be to have an array for names and a separate ones for values. Also, you could use pandas.DataFrame as it a direct solution to your problem.

edited Mar 18, 2023 at 12:18

halfer

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answered Dec 23, 2021 at 8:32

w_sz

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Comments

hpaulj · Accepted Answer · 2021-12-23 17:29:59Z

A list of lists:

In [274]: alist = [["John","Alex","Smith"],[50,60,70]]
In [275]: alist
Out[275]: [['John', 'Alex', 'Smith'], [50, 60, 70]]

Simply calling np.array makes an array that contains the strings, the minimal common dtype:

In [276]: np.array(alist)
Out[276]: 
array([['John', 'Alex', 'Smith'],
       ['50', '60', '70']], dtype='<U21')

We can also specify object, but such an array is virtually the same as the original list:

In [277]: np.array(alist, dtype=object)
Out[277]: 
array([['John', 'Alex', 'Smith'],
       [50, 60, 70]], dtype=object)

A "transpose" of that list:

In [278]: altlist = list(zip(*alist))
In [279]: altlist
Out[279]: [('John', 50), ('Alex', 60), ('Smith', 70)]

that can be used to make a structured array with a compound dtype:

In [280]: np.array(altlist, dtype='U10,int')
Out[280]: 
array([('John', 50), ('Alex', 60), ('Smith', 70)],
      dtype=[('f0', '<U10'), ('f1', '<i8')])

or a dataframe:

In [281]: pd.DataFrame(altlist)
Out[281]: 
       0   1
0   John  50
1   Alex  60
2  Smith  70

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