In NumPy, the key is its array object class, which is quite similar to Python lists. However, arrays have a special rule: every element inside an array must be of the same type, like a number (float or integer). This unique rule allows arrays to work really fast with a lot of numbers, making them much quicker than lists for dealing with big sets of data.

The first step is always to import the NumPy library.

`1 >>> import numpy as np`

When using NumPy in Python, a common practice is to import it using the shorthand alias `np`

. This convention is widely adopted and helps save a bit of typing when referring to NumPy functions and objects.

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**How to Create a NumPy array?**

`2 >>> a = np.array([1,2,3,4,5]) #passing a list in array function to create 1d array`

3 >>> a

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

5 >>> type(a)

6 numpy.ndarray

In line 2, we use an array() function to create a NumPy array. We pass a list of numbers in the array function to create a 1D array. In line 3, we print the array and in line 5, we check the type of the array and we see that the array is numpy.ndarray. This is a 1-D array.

You can also use a tuple to create an array.

`7 >>> a = np.array((1,2,3,4,5))`

You can also create the arrays of different dimensions as follows:

`8 >>> a = np.array(1) # 0 dim`

9 >>> b = np.array([1, 2, 3, 4, 5]) # 1d Array

10 >>> c = np.array([[1, 2, 3], [4, 5, 6]]) #2d array

11 >>> d = np.array([[[1, 2, 3], [4, 5, 6]], [[7, 8, 9], [10, 11, 12]]]) #3d array

12 >>> e = np.array([1, 2, 3, 4, 5], nddim=5)13 >>> print(a)

1

14 >>> print(b)

[1 2 3 4 5]

15 >>> print(c)

[[1 2 3]

[4 5 6]]

16 >>> print(d)

[[[ 1 2 3]

[ 4 5 6]]

[[ 7 8 9]

[10 11 12]]]

17 >>> print(e)

[[[[[1 2 3 4 5]]]]]

You can also make an array of complex numbers.

`18 >>> a = np.array([1, 2, 3], dtype=complex)`

19 >>> a

array([ 1.+0.j, 2.+0.jā¦