Numpy (part-1)

What is Numpy?

• Numpy is a python library helps in mathematical, scientific, engineering, and data science programming.
• It is a very useful library to perform mathematical and statistical operations in Python.
• NumPy is a programming language that deals with multi-dimensional arrays and matrices.
• Numpy is Numeric python or Numerical python.

Why to use Numpy?

• NumPy is memory efficiency and it can handle the vast amount of data.
• Numpy is fast as it uses algorithm that are written in C language.
• Clear code without loops.

How to install numpy?

• To install numpy use command:

pip install numpy

• To install numpy on poetry environment

poetry add numpy

How to import numpy and check version?

• To import and check version use:

import numpy as np
print(np.__version__)

It will print version of numpy: for me it’s 1.23.5.

What is numpy array?

• NumPy arrays are a bit like Python lists, but still very much different at the same time.
• And it’s a central data structure of the numpy library.

How to create numpy array from python list?

To create numpy array use np.array function:

list_ = [10, 20, 30, 40]
numpy_list = np.array(list_)
print(f'Type of variable: {type(numpy_list)}')
print(f'Numpy Array: {numpy_list}')
#output:
# Type of variable: <class 'numpy.ndarray'>
# Numpy Array: [10 20 30 40]

Basic operation on numpy:

To add constant value to full numpy array use:

numpy_list = np.array([10, 20, 30, 40])
print(f'Array before adding constant value: {numpy_list}')
constant_add_array = numpy_list + 10
print(f'New array after adding constant: {constant_add_array}')
#Output:
# Array before adding constant value: [10 20 30 40]
# New array after adding constant: [20 30 40 50]

Add two numpy array of same size:

• To add two numpy array use:

numpy_array_a = np.array([10, 20, 30, 40])
numpy_array_b = np.array([10, 20, 30, 40]) + 10
numpy_array_c = numpy_array_a + numpy_array_b
print(f'Numpy array a: {numpy_array_a}')
print(f'Numpy array b: {numpy_array_b}')
print(f'Numpy array c after sum of a and b array: {numpy_array_c}')
# Output: 
# Numpy array a: [10 20 30 40]
# Numpy array b: [20 30 40 50]
# Numpy array c after sum of a and b array: [30 50 70 90]

How to get shape of array and type of array?

To get array shape use shape function of numpy:

numpy_array_a = np.array([x for x in range(10, 60, 10)])
print(f'Numpy array a: {numpy_array_a}')
print(f'Numpy array shape: {numpy_array_a.shape}')
print(f'Variable a type {type(numpy_array_a)}')
# Output: 
# Numpy array a: [10 20 30 40 50]
# Numpy array shape: (5,)
# Variable a type <class 'numpy.ndarray'>

How to create 2 Dimensional array and 3 dimensional array?

To create 2-d and 3-d array:

array_2_dim = np.array(
[
[10, 20, 30],
[40, 50, 60]
])
print(f'Two dimensional array shape: {np.shape(array_2_dim)}')
array_3_dim = np.array([[[10, 20, 30], [40, 50, 60]], [[10, 20, 30], [40, 50, 60]],])
print(f'Three dimensional array: {np.shape(array_3_dim)}')
# Output: 
# Two dimensional array shape: (2, 3). Three dimensional array: (2, 2, 3)

How to create a matrix with all values as 0 in numpy?

• To create 2D zero value array use

np.zeros(shape=(3,3))

• To create 3D zero value array use

np.zeros(shape=(2,2, 2))

How to create matrix with all values as 1 in numpy?

• To create 2D one value array use

np.ones(shape=(3,3))

• To create 3D one value array use

np.ones(shape=(2,2, 2))

• By default, it creates float type matrix but we can define type using

np.zeros(shape=(2,2), dtype=np.int64)

Youtube link.