How to Store Different Datatypes In One Numpy Array

Numpy Array :-

In python, a numpy array is a data structure provided by the Numpy library. Where we can store same data type.

• A NumPy array is a way to store numbers in Python.
• It is faster and smaller than a normal list.
• You can do math on all the numbers at once.
• It has tools to cut, resize, and manage data

Storing Different Datatypes In One Numpy Array :- 

We can archive the storing of diffrent datatype in one numpy array by following methods.

1. Using Structured Arrays.
2. Using the object dtype.

1.Using Structured Arrays. :-

Structured arrays allow you to define a custom data type with multiple fields, each having its own specific data type.For example, if you want to store information about people with Name (string), Age (integer), and Height (float), a structured array lets you store them together in a single array.

import numpy as np
dtype = [('name', 'U10'), ('age', 'i4'), ('height', 'f8')]
data = np.array([('OM', 25, 1.65), ('Raju', 30, 1.80)], dtype=dtype)
print(data)
print(data['name'])
print(data['age'])

Output :

[(‘OM’, 25, 1.65) (‘Raju’, 30, 1.8 )]
[‘OM’ ‘Raju’]
[25 30]

2. Using the Object dtype :

You can store different types by setting the dtype = Object This approach is less memory-efficient and slower since it behaves like a Python list.

This approach allows you to store arbitrary Python objects in the array,

import numpy as np
data = np.array([1, 'hello', 3.14], dtype=object)
print(data)

output :

[1 'hello' 3.14]