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Arrays are mainly used for mathematical operations where lists are mainly used for data management.

NumPy can be imported into the Jupyter Notebook by using the given statement:

>>> import numpy # this will import the complete numpy
# package
OR
>>> import numpy as npy # this will import numpy and refer to it
# as npy
OR
>>> from numpy import array # this will import ONLY arrays module
# from whole numpy package
OR
>>> from numpy import array as ary # this will import ONLY
# arrays module and refer to it as ary
In NumPy, we can create n-dimensional arrays which are considered as an alternative to Python lists because
they allow faster access in reading and writing items effectively and efficiently.
Syntax for creating an array is:

>>> import numpy
marks = numpy.array ([34,23,41,42])
Syntax for creating a list is:
marks = [34, 23, 41, 42]
Creating an Array using NumPy

We can create different types of arrays using NumPy.

Example 1: Program to create one dimensional array.
import numpy
roll_no = numpy.array([1,2,3,4,5])
print(roll_no)
Output:
[1 2 3 4 5]

Example 2: Program to create 1D array with 4 random values.

import numpy as np
a=np.random.random(4)
print(a)

Output:
[0.49374929 0.86811999 0.48420151 0.44405495]

Example 3: Program to create 3 × 4 array with random integer values less than 10.
import numpy as np
a=np.random.randint(10, size = (3,4))
print(a)
Output:

[[4 8 3 6]
[7 9 0 8]
[5 1 2 2]]

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