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
CodePudding user response:
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
CodePudding user response:
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