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Numpy :A Hands On Guide For Beginners

This article was published as a part of the Data Science Blogathon

What is NumPy and Why NumPy is an important library to work with data?

NumPy is a Python library widely used to handle arrays with arrays. Numpy can handle oversized, multi-dimensional arrays and matrices, along with a large collection of mathematical operations to operate on these arrays. It stands for numerical python. NumPy can provide an array object that is  50 times faster than traditional Python lists.

An array occupies less memory and is extremely convenient to use as compared to python lists.  Additionally, it has a mechanism for specifying the data types. NumPy can operate on individual elements in the array without using loops and list comprehensions.

Now, before diving deep into the concept of NumPy arrays, it’s important to note that Python lists can very well perform all the actions that NumPy arrays perform; it is simply the fact that NumPy arrays are faster and more convenient that lists when it comes extensive computations, which make them extremely useful, especially when you are working with a large amount of data.

Installing NumPy in your System

$ pip install numpy

Getting started with NumPy

Once Numpy is successfully installed in your system, you can import NumPy simply by

import numpy as np

where np is an alias used for NumPy so that the NumPy package can be referred to as np instead of numpy.

There are two ways to create NumPy arrays, which are mentioned below:

  1. By converting the existing lists or tuples to arrays using np.array
  2. By initializing fixed-length arrays using the Numpy functions.

 

#Creating a 1D numpy array
arr = np.array([1,2,3,4,5])
#Creating a 2D array
arr = np.array( [  [1,2,3],
                   [4,5,6] ] )
# Creating array of ones
np.ones(5) #1D 
np.ones( (3,5) ) #2D
# Change the default dtype of an array 
np.ones( (5) ,dtype = np.int) # creating array of integers
np.ones( (5) ,dtype = np.float ) # creating array of floats
# Creating array of random numbers
np.random.random( [3,4] ) 
#creating an array of numbers 1 to 100 with step of 5
np.arange(1,100,5)
#Creating an array of length 25 between 1 to 10
np.linspace(1,10,25)  # It equally divides 1 to 100 in 25 parts
#creating a array of particular number
np.full( ( 3,3) , 5) #It will create an array of 3x3 of 5's
# To create an array of repeating sequence
np.tile( [1,2,3] , 3 ) # The op will look like [ 1,2,3,1,2,3,1,2,3 ]
# Creating an array of identity matrix
np.eye(3, dtype = int )   # it will create an 3x3 identity matrix
#create a matrix of random integer between a particular range
np.random.randint(0, 10, (3,3))  #3x3 array of random integers ranging from 0 to 9
# creating uninitialized array of specific shape and dtype
np.empty( [3,3] , dtype = int ) 
#create an array of specific shape filled with zeroes
np.zeros( [3,3] , dtype = int )
# Checking shape of an array
arr = np.array( [1,2,3] )
arr.shape
#checking dimensions of an array
arr.ndim
#checking shape of an array
arr.size
#checking the dtype of an array
np.dtype

 

Reshaping the NumPy arrays

arr = np.array( [1,2,3,4,5,6,7,8,9] ) 
arr.reshape( [3,3]) # convering above array in 3x3 matrix
#flatenning the array
y = np.array( [[1,1], [1,1] ])
y.flatten()  # output : [1,1,1,1]

Array Indexing

array_1d[2] # Third element 
array_1d[2:] # Third element onwards 
array_1d[:3] # First three elements 
array_1d[2:7] # Third to seventh elements 
array_1d[0::2] # Subset starting 0 at increment of 2
print(arr[0]) #it will print first row 
print(arr[1]) #it will print second row 
print(arr[0][1]) #it will print second element of first row 
arr=np.array([[81,19,46,74,94],[69,79,26,7,29],[21,45,12,80,72] ])
print(arr[2][1]) #it will print second element of third

Array Slicing

arr=np.array([0,1,2,3,4,5,6,7,8,9,10]) 
print(arr[:5]) #by default its startindex start from [0 1 2 3 4]
print(arr[5:]) #by default its stopindex go till la [ 5 6 7 8 9 10]
print(arr[4:10:2]) #output : [4 6 8]
print(arr[::]) #by default it startindex is 0 stopin [ 1 2 3 4 5 6 7 8 9 10]
arr=np.array([[1,2,3,4,5,6],[7,8,9,10,11,12],[13,14,15,16,17,18]]) 
print(arr[0,1:5]) #it select first row and element from 1  
#output : [2 3 4 5]
print(arr[1:4,2:4]) #it select row from first to third and  
# output : [[ 9 10] [15 16]]

Concatenation

Concatenate() function can join the sequence of arrays that we provide, along with the axis specified. If the axis is not explicitly given, it is taken as 0

Concatenating arrays along row:

concatenating arrays in numpy

 

#Concatenating 1D array
arr1=np.array([1,2,3])
arr2=np.array([4,5,6])
arr=np.concatenate((arr1,arr2),axis=0)
print(arr)
#output : [1 2 3 4 5 6]
#Concatenating @d array along axis 0
arr1=np.array([[1,1,1],[1,1,1]])
arr2=np.array([[9,9,9,],[9,9,9]])
arr=np.concatenate((arr1,arr2),axis=0)
print(arr)
 #output : [[1 1 1]
             [1 1 1]
             [9 9 9]
            [9 9 9]]

Concatenating along the column:

concatenating along columns | numpy
#concatenating along axsi 1
arr1=np.array([[1,1,1],[1,1,1]]) 
arr2=np.array([[9,9,9,],[9,9,9]]) 
arr=np.concatenate((arr1,arr2),axis=1) 
print(arr) 
#output : [[1 1 1 9 9 9] 
           [1 1 1 9 9 9]]

Joining array using Stack function

Stacking is the same as concatenation, the only difference is that stacking is done along a new axis.

np.hstack() :

 

join array using stack
#stacking two 1D arrays horizontally
arr1=np.array([0,0]) 
arr2=np.array([1,1])
arr=np.hstack((arr1,arr2))
# Stacking two 2D arrays horizontally
arr1=np.array([[0,0],[0,0]]) 
arr2=np.array([[1,1],[1,1]]) 
arr=np.hstack((arr1,arr2)) 
print(arr)
output : [[0 0 1 1] 
 [0 0 1 1]]

np.vstack() :

numpy vstack function
#Stacking 1D arrays vertically
arr1=np.array([0,0]) 
arr2=np.array([1,1]) 
arr=np.vstack((arr1,arr2)) 
print(arr) 
#output :[[0 0] 
          [1 1]]
# Stacking 2D arrays vertically
arr1=np.array([[0,0],[0,0]]) 
arr2=np.array([[1,1],[1,1]]) 
arr=np.vstack((arr1,arr2)) 
print(arr) 
#output : [[0 0] 
           [0 0] 
           [1 1] 
           [1 1]]

Sort the arrays

sorting means simply arranging elements in an ordered sequence.

arr=np.array([3,2,0,1]) 
print(np.sort(arr)) 
#output : [0 1 2 3]

Operations on  Arrays

Array Addition : 

array_1 + array_2

np.array([0,1]) + np.array([2,3])

Array Subtraction

array_1 – array_2

np.array([0,1]) + np.array([2,3])

Array Multiplication

array_1 * array_2

np.array([0,1]) * np.array([2,3])

Raising by a power :

some_array ** n  , where n is any real number

np.array([0,1])**2 #squaring
np.array([1,2]) ** 1.2 #raising power by 1.2

Trigonometric functions

NumPy provides the functions sin(), cos() and tan()

np.sin(some_array)

np.cos(some_array)

np.tan(some_array)

arr = np.array([1,2,3])
print( np.sin(arr) )
print( np.cos(arr) )
print( np.tan(arr) )

Exponential function :

np.exp(some_array)

np.exp(arr)

Logarithmic function :

np.log(some_array)

np.log( arr )

Applying Linear Algebra operations :

A = np.array([[6, 1, 1],
[4, -2, 5],
[2, 8, 7]])
# Rank of a matrix
print( np.linalg.matrix_rank(A) )
# Trace of matrix A
print( np.trace(A) )
# Determinant of a matrix
print(np.linalg.det(A) )
# Inverse of matrix A
print( np.linalg.inv(A) )
#raising power of each elt in matrix
print(np.linalg.matrix_power(A, 3))

eigen_val, eigen_vec = np.linalg.eig(some_array) # to find eigen value and eigen vectors

End Notes

Thanks for reading!

We did some hands-on NumPy.I hope you enjoyed the article.

Image source links :

Image1:https://cdn.educba.com/academy/wp-content/uploads/2019/04/What-is-NumPy-1.jpg.webp

Image 2: https://www.w3resource.com/w3r_images/numpy-manipulation-concatenate-function-image-1.png

Image 3: https://www.w3resource.com/w3r_images/python-numpy-image-exercise-58.png

Image 4: https://www.w3resource.com/w3r_images/numpy-manipulation-hstack-function-image-1.png

Image 5: https://www.w3resource.com/w3r_images/numpy-manipulation-vstack-function-image-1.png

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