Explore array(), type() and astype() functions

What is ndarray in Numpy?

"ndarray" stands for n-dimensional array. Numpy creates an object for an array in this type.

How to create an array in Numpy?

We can create an array using the built-in Numpy function called numpy.array() function.

Example

import numpy as np
array_1 = np.array([1,2,3,4,5,6], dtype='i', ndmin=0)
print(array_1)        

Output

[1 2 3 4 5 6]        

In the above example, we create an array with list of object, integer datatype and number of dimensions.

• object - It can used to pass the elements of list or tuple in an array.
• dtype - It is used to define the datatype of an array.
• ndmin - It is used to define the higher dimensions of an array.

type() function

It is used to show the type of the object passed in it.

Example

print(type(array_1))        

Output

<class 'numpy.ndarray'>        

astype() function

It is used to create a copy of an particular array with specified datatype.

Example

array_2 = array_1.astype(dtype='f')
print(array_2)        

Output

[1. 2. 3. 4. 5. 6.]        

Note: Some of the datatypes are integer('i'), float('f'), boolean('b') and string('S').

Some of the attributes of Numpy

• dtype - It is used to check the datatype of an element present in an array.
• ndim - It is used to check the number of dimensions of an array.
• nbytes - It is used to check the number of bytes of an array.

Example

print("Array 1 - dtype, ndim, nbytes")
print(array_1.dtype)
print(array_1.ndim)
print(array_1.nbytes)


print("Array 2 - dtype, ndim, nbytes")
print(array_2.dtype)
print(array_2.ndim)
print(array_2.nbytes)        

Output

Array 1 - dtype, ndim, nbytes
int32
1
24

Array 2 - dtype, ndim, nbytes
float32
1
24        

Pro tip: Google Colaboratory is a cloud platform makes us easy to use and run anywhere.

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