Month: May 2020

This Python Numpy tutorial for beginners talks about Numpy basic concepts, practical examples, and real-world Numpy use cases related to machine learning and data science

What is NumPy?

NumPy in python is a general-purpose array-processing package. It stands for Numerical Python. NumPy helps to create arrays (multidimensional arrays), with the help of bindings of C++. Therefore, it is quite fast. There are in-built functions of NumPy as well. It is the fundamental package for scientific computing with Python.

The NumPy library also contains a multidimensional array and matrix data structures. It provides ndarray, a homogeneous n-dimensional array object, with methods to efficiently operate on it. 

It is one of the very important libraries used in the field of Data Science & Machine Learning.

Here is an interesting search trend for NumPy for the last five years.

Why do we need NumPy?

Does a question arise that why do we need a NumPy array when we have python lists?

The answer is we can perform operations on all the elements of a NumPy array at once, which are not possible with python lists.

For example, we can’t multiply two lists directly we will have to do it element-wise. This is where the role of NumPy comes into play.

Example:

list1 = [2, 4, 6, 7, 8]
list2 = [3, 4, 6, 1, 5]

print(list1*list2)

Output:

TypeError: can't multiply sequence by non-int of type 'list'

Where the same thing can be done easily with NumPy arrays. Here is how NumPy works.

Numpy Array Example:

import numpy as np

list1 = [2, 4, 6, 7, 8]
list2 = [3, 4, 6, 1, 5]

arr1 = np.array(list1)
arr2 = np.array(list2)

print(arr1*arr2)

Output:

[ 6 16 36 7 40]

Python Numpy Tutorial

In this section, we will start with the installation of the key concepts involved in numPy. Also, we will go through some of the practical examples of NumPy library.

Installing NumPy In Python

Before you use NumPy you must install the NumPy library as a prerequisite. It can be installed with conda, with pip, or with a package manager.

If you use conda, you can install it with:

conda install numpy

If you use pip, you can install it with:

pip install numpy

Arrays in NumPy

Array in NumPy is a table of elements, all of the same type, indexed by a tuple of positive integers. In NumPy, the number of dimensions of the array is called the rank of the array. A tuple of integers giving the size of the array along each dimension is known as the shape of the array. An array class in NumPy is called as ndarray.

Example

1. First of all import the numpy library

import numpy as np

1. Creating an array object using np.array()

array = np.array([
    [1, 2, 3, 5, 6],
    [2, 1, 5, 6, 7]
])

1. Printing the array dimension using array.ndim

print("No. of dimensions of the array: ", array.ndim)

Output:

No. of dimensions of the array: 2

1. Printing the shape of the array using array.shape

print("Shape of the array: ", array.shape)

Output:

Shape of the array: (2, 5)

1. Printing the size of the array using array.size. The size of the array means nothing but the total number of elements in the given array.

print("Size of the array: ", array.size)

Output:

Size of the array: 10

Creating a NumPy Array

Arrays in NumPy can be created in multiple ways, with various number of Ranks, defining the size of the Array. Arrays can also be created with the use of various data types such as lists, tuples, etc.

Example:

1. First of all import the numpy library

import numpy as np

1. Creating a rank 1 array by passing one python list

list = [1, 2, 3, 5, 6]
array = np.array(list)
print(array)

Output:

[1 2 3 5 6]

1. Creating a rank 2 array by passing two python lists

list1 = [1, 2, 3, 5, 6]
list2 = [3, 1, 4, 5, 1]
array = np.array(
    [list1, list2]
)
print(array)

Output:

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

1. Creating array by passing a python tuple

tuple = (1, 2, 3, 5, 6)
array = np.array(tuple)
print(array)

Output:

[1 2 3 5 6]

1. Creating a 3×4 array with all zeros using np.zeros( )

array = np.zeros((3, 4))
print(array)

Output:

[[0. 0. 0. 0.]
[0. 0. 0. 0.]
[0. 0. 0. 0.]]

1. Creating a constant value array of complex type using np.full( )

array = np.full((3, 3), 6, dtype='complex')
print(array)

Output:

[[6.+0.j 6.+0.j 6.+0.j]
[6.+0.j 6.+0.j 6.+0.j]
[6.+0.j 6.+0.j 6.+0.j]]

NumPy Array Indexing

Indexing can be done in NumPy by using an array as an index. In case of slice, a view of the array is returned but in index array a copy of the original array is returned. Note that the first element is indexed by 0 second first by 1 and so on, whereas the last element is indexed by -1 second last by -2 and so on.

Example:

1. Creating a rank 1 array by passing one python list

list = [1, 2, 3, 5, 6]
array = np.array(list)
print(array)

Output:

[1 2 3 5 6]

1. Accessing elements and creating new array by passing indices

newArray = array[
    np.array([2, 0, 1, 4])
]
print(newArray)

Output:

[3 1 2 6]

1. Index values can be negative

newArray = array[
    np.array([2, 0, -1, -4])
]
print(newArray)

Output:

[3 1 6 2]

1. Basic slicing occurs when an object is a slice object that is of the form [start: stop: step]. Note that stop position is not included in slicing.

newArray = array[1:-2:1]
print(newArray)

Output:

[2 3]

Basic slicing and indexing in a multidimensional array

Slicing and indexing in a multidimensional array are a little bit tricky compared to slicing and indexing in a one-dimensional array.

import numpy as np

array = np.array([
    [2, 4, 5, 6],
    [3, 1, 6, 9],
    [4, 5, 1, 9],
    [2, 9, 1, 7]
])
print(array)

# Slicing and indexing in 4x4 array
# Print first two rows and first two columns
print("\n", array[0:2, 0:2])

# Print all rows and last two columns
print("\n", array[:, 2:4])

# Print all column but middle two rows
print("\n", array[1:3, :])

Output:

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

[[2 4]
[3 1]]

[[5 6]
[6 9]
[1 9]
[1 7]]

[[3 1 6 9]
[4 5 1 9]]

NumPy Operations on Array

In NumPy, arrays allow various operations that can be performed on a particular array or a combination of Arrays. These operations may include some basic Mathematical operations as well as Unary and Binary operations.

Example:

1. Creating two different two dimensional arrays

array1 = np.array([
    [2, 4, 5],
    [3, 1, 6]
])

array2 = np.array([
    [1, 5, 9],
    [10, 32, 78]
])

1. Adding 5 to every element in array1

newArray = array1 + 5
print(newArray)

Output:

[[ 7 9 10]
[ 8 6 11]]

1. Adding 5 to every element in array1

newArray = array1 + 5
print(newArray)

Output:

[[ 7 9 10]
[ 8 6 11]]

1. Multiplying 2 to every element in array2

newArray = array2 * 2
print(newArray)

Output:

[[ 2 10 18]
[ 20 64 156]]

1. Sum of every element in array2

newArray = array2.sum()
print(newArray)

Output:

135

1. Adding two arrays

newArray = array1 + array2
print(newArray)

Output:

[[ 3 9 14]
[13 33 84]]

String Operations using NumPy

This module is used to perform vectorized string operations for arrays of dtype numpy.string_ or numpy.unicode_.

1. numpy.upper( )

Returns the uppercased string from the given string. It converts all lowercase characters to uppercase. If no lowercase characters exist, it returns the original string.

Example:

import numpy as np

string = "devopscube"
print(np.char.upper(string))

Output:

DEVOPSCUBE

Similary one can use numpy.lower( ) to convert all uppercase characters to lowercase.

1. numpy.split( )

Returns a list of strings after breaking the given string by the specified separator.

Example:

import numpy as np

string = "devopscube.com"
print(np.char.split(string, sep='.'))

Output:

['devopscube', 'com']

1. numpy.title( )

It is used to convert the first character in each word to Uppercase and remaining characters to Lowercase in the string and returns a new string.

Example:

import numpy as np

string = "devopsCube is An amaZing pOrtal"
print(np.char.title(string))

Output:

Devopscube Is An Amazing Portal

1. numpy.equal( )

This function checks for string1 == string2 elementwise and return a boolean value true or false.

Example:

import numpy as np

string1 = "Devopscube"
string2 = "Devopscube.com"
print(np.char.equal(string1, string2))

Output:

False

To know more string functions in NumPy refer String operations.

Mathematical Functions in NumPy

NumPy contains a large number of various mathematical operations. NumPy provides standard trigonometric functions, functions for arithmetic operations, handling complex numbers, etc.

1. numpy.sin( )

This mathematical function helps the user to calculate trigonometric sine for given values.

Example:

import numpy as np

x = 0
print(np.sin(x))

x = np.pi / 2
print(np.sin(x))

x = np.pi / 4
print(np.sin(x))

Output:

0.0
1.0
0.7071067811865475

Similarly one can use numpy.cos( ), numpy.tan( ), numpy.arcsin( ), numpy.arccos( ), numpy.arctan( ) to calculate trignometric cosine (cos), tangent (tan), cosecant (csc), secant (sec), and cotangent (cot) respectively for given values.

1. numpy.round_( )

This mathematical function round an array to the given number of decimals.

Example:

import numpy as np

arr = [.33345, .1234, 1.456789]
roundOffValues = np.round_(arr, decimals=3)
print(roundOffValues)

Output:

[0.333 0.123 1.457]

1. numpy.log( )

This mathematical function helps users to calculate Natural logarithm of all elements in the input array.

Example:

import numpy as np

arr = [1, 3, 50]
logValues = np.log(arr)
print(logValues)

Output:

[0. 1.09861229 3.91202301]

1. numpy.exp( )

This mathematical function helps users to calculate the exponential of all elements in the input array.

Example:

import numpy as np

arr = [1, 3, 50]
expValues = np.exp(arr)
print(expValues)

Output:

[2.71828183e+00 2.00855369e+01 5.18470553e+21]

To know more mathematical functions in NumPy refer Mathematical functions.

NumPy Use Cases in Data Science & Machine Learning

NumPy is a very popular Python library for large multi-dimensional array and matrix processing. With the help of a large collection of high-level mathematical functions it is very useful for fundamental scientific computations in Machine Learning.

It is particularly useful for,

1. Linear Algebra
2. Fourier Transform
3. Random Number Generations

High-end libraries like TensorFlow uses NumPy internally for manipulation of Tensors.

NumPy Linear Algebra Examples

Lots of ML concepts are tied up with linear algebra. It helps in

1. To understand PCA(Principal Component Analysis),
2. To build better ML algorithms from scratch,
3. For processing Graphics in ML,
4. It helps to understand Matrix factorization.

In fact, it could be said that ML completely uses matrix operations.

The Linear Algebra module of NumPy offers various methods to apply linear algebra on any NumPy array. One can find:

1. Rank, determinant, transpose, trace, inverse, etc. of an array.
2. Eigenvalues and eigenvectors of the given matrices
3. The dot product of two scalar values, as well as vector values.
4. Solve a linear matrix equation and much more!

Lets looks at some NumPy sample exercises

1. Find rank, determinant, transpose, trace, inverse, etc. of an array using Numpy

Example:

1. Creating a 3×3 NumPy array

array = np.array([
    [6, 1, 1],
    [4, -2, 5],
    [2, 8, 7]
])

1. Calculating rank of array

rank = np.linalg.matrix_rank(array)
print(rank)

Output:

3

1. Calculating determinant of array

determinant = np.linalg.det(array)
print(determinant)

Output:

-306.0

1. Calculating trace of array

trace = np.trace(array)
print(trace)

Output:

11

1. Calculating transpose of array

transpose = np.transpose(array)
print(transpose)

Output:

[[ 6 4 2]
[ 1 -2 8]
[ 1 5 7]]

1. Calculating inverse of array

inverse = np.linalg.inv(array)
print(inverse)

Output:

[[ 0.17647059 -0.00326797 -0.02287582]
[ 0.05882353 -0.13071895 0.08496732]
[-0.11764706 0.1503268 0.05228758]]

2. Find eigenvalues and eigenvectors of the given matrices using NumPy

import numpy as np

array = np.array([
    [1, 2, 3],
    [4, 5, 6],
    [7, 8, 9]
])

eigenVal, eigenVec = np.linalg.eig(array)
print(eigenVal)
print(eigenVec)

Output:

[ 1.61168440e+01 -1.11684397e+00 -1.30367773e-15]
[[-0.23197069 -0.78583024 0.40824829]
[-0.52532209 -0.08675134 -0.81649658]
[-0.8186735 0.61232756 0.40824829]]

3. Find the dot product of two scalar values and vector values using NumPy

import numpy as np

scalarProduct = np.dot(6, 9)
print("Dot Product of scalar values  : ", scalarProduct)

vector_a = 4 + 3j
vector_b = 2 + 6j

vectorProduct = np.dot(vector_a, vector_b)
print("Dot Product of vector values  : ", vectorProduct)

Output:

Dot Product of scalar values : 54
Dot Product of vector values : (-10+30j)

4. Solve a linear matrix equation using Numpy

import numpy as np

A = np.array([
    [1, 3],
    [2, 4]
])

b = np.array([
    [7],
    [10]
])

x = np.linalg.solve(A, b)
print(x)

Output:

[[1.]
[2.]]

NumPy Fourier Transform Examples

In mathematics, a Fourier transform (FT) is a mathematical transform that decomposes a function (often a function of time, or a signal) into its constituent frequencies. Some of the important applications of the FT include:

1. Fast large-integer and polynomial multiplication,
2. Efficient matrix-vector multiplication for Toeplitz, circulant and other structured matrices,
3. Filtering algorithms,
4. Fast algorithms for discrete cosine or sine transform (e.g. Fast Discrete Cosine Transform used for JPEG and MPEG/MP3 encoding and decoding),
5. Solving difference equations.

Example:

1. Using np.fft( ), get the 1D  Fourier Transform

import numpy as np

A = np.array([2, 4, 6, 8, 9])
result = np.fft.fft(A)
print(result)

Output:

[29. +0.j -5.30901699+5.93085309j -4.19098301+1.03681323j
-4.19098301-1.03681323j -5.30901699-5.93085309j]

1. Using np.fft2( ), get the 2D  Fourier Transform

import numpy as np

A = np.array([
    [2, 4, 6, 8, 9],
    [3, 1, 6, -2, -4]
])
result = np.fft.fft2(A)
print(result)

Output:

[[ 33. +0.j -6.47213595 -3.52671151j
2.47213595 +5.7063391j 2.47213595 -5.7063391j
-6.47213595 +3.52671151j]
[ 25. +0.j -4.14589803+15.38841769j
-10.85410197 -3.63271264j -10.85410197 +3.63271264j
-4.14589803-15.38841769j]]

1. Using np.fftn( ), get the N-D  Fourier Transform

import numpy as np

A = np.array([
    [2.3, 4.1, 6.5, 8, 9],
    [3, -1.2, 6, -2, -4]
])
result = np.fft.fftn(A)
print(result)

Output:

[[ 31.7 +0.j -7.22558014 -1.82338546j
4.62558014 +7.41621639j 4.62558014 -7.41621639j
-7.22558014 +1.82338546j]
[ 28.1 +0.j -3.53966744+12.90709507j
-12.26033256 -4.50909046j -12.26033256 +4.50909046j
-3.53966744-12.90709507j]]

NumPy Random Number Generations

Using numpy.random.rand(d0, d1, …., dn ) creates an array of specified shape and fills it with random values, where d0, d1, …., dn are dimensions of the returned array. This function returns an array of defined shape and filled with random values.

Example:

1. Randomly constructing 1D array

import numpy as np

A = np.random.rand(7)
print(A)

Output:

[0.3126664 0.99492257 0.73164575 0.77857217 0.94840314 0.10833222
0.14896065]

1. Randomly constructing 2D array

import numpy as np

A = np.random.rand(3, 4)
print(A)

Output:

[[0.22751116 0.09730939 0.97083485 0.67629309]
[0.94896123 0.96087311 0.8725199 0.48835455]
[0.86496409 0.32296315 0.72891428 0.27729306]]

Conclusion

In this python Numpy tutorial, I have covered the key concepts with some practical examples pertaining to Machine learning use cases.

If you want to have a look at more Numpy practical examples, you can check out the practical NumPy examples.

Now, I would like to hear from you. What Numpy use cases are you currently working on?

This article talks about python web scrapping techniques using python libraries.

One of the most important things in the field of Data Science is the skill of getting the right data for the problem you want to solve. Data Scientists don’t always have a prepared database to work on but rather have to pull data from the right sources. For this purpose, APIs and Web Scraping are used.

1. API (Application Program Interface): An API is a set of methods and tools that allows one’s to query and retrieve data dynamically. Reddit, Spotify, Twitter, Facebook, and many other companies provide free APIs that enable developers to access the information they store on their servers; others charge for access to their APIs.
2. Web Scraping: A lot of data isn’t accessible through data sets or APIs but rather exists on the internet as Web pages. So, through web-scraping, one can access the data without waiting for the provider to create an API.

What’s Web Scraping?

Web scraping is a technique to fetch data from websites. While surfing on the web, many websites don’t allow the user to save data for private use.

One way is to manually copy-paste the data, which both tedious and time-consuming.

Web Scraping is the automatic process of data extraction from websites. This process is done with the help of web scraping software known as web scrapers.

They automatically load and extract data from the websites based on user requirements. These can be custom built to work for one site or can be configured to work with any website.

Why Python for Web Scrapping?

There are a number of web scraping tools out there to perform the task and various languages too, having libraries that support web scraping.

Among all these languages, Python is considered as one of the best for Web Scraping because of features like – a rich library, easy to use, dynamically typed, etc.

Here are some most commonly used python3 web Scraping libraries.

Now discuss the steps involved in web scraping using the implementation of Web Scraping in Python with Beautiful Soup.

Building Web Scraper Using Python

In this section, we will look at the step by step guide on how to build a basic web scraper using python Beautiful Soup module.

1. First of all, to get the HTML source code of the web page, send an HTTP request to the URL of that web page one wants to access. The server responds to the request by returning the HTML content of the webpage. For doing this task, one will use a third-party HTTP library called requests in python.
2. After accessing the HTML content, the next task is parsing the data. Though most of the HTML data is nested, so it’s not possible to extract data simply through string processing. So there is a need for a parser that can create a nested/tree structure of the HTML data. Ex. html5lib, lxml, etc.
3. The last task is navigating and searching the parse tree that was created using the parser. For this task, we will be using another third-party python library called Beautiful Soup. It is a very popular Python library for pulling data from HTML and XML files.

Step 1: Import required third party libraries

Before starting with the code, import some required third party libraries to your Python IDE.

pip install requests
pip install lxml
pip install bs4

Step 2: Get the HTML content from the web page

To get the HTML source code from the web page using the request library and to do this we have to write this code. I am taking this webpage.

source = requests.get('https://devopscube.com/project-management-software').text

Step 3: Parsing the HTML content

Parse the HTML file into the Beautiful Soup and one also needs to specify his/her parser. Here we are taking lxml parser.

soup = BeautifulSoup(source, 'lxml')

To print the visual representation of the parse tree created from the raw HTML content write down this code.

print(soup.prettify())

Step 4: Navigating and searching the parse tree

Now, we would like to extract some useful data from the HTML content. The soup object contains all the data in a nested structure that could be programmatically extracted. In our example, we are scraping a web page contains a headline and its corresponding website.

We can start parsing out the information that we want now just like before. Let’s start by grabbing the headline and its official website.

So to grab the first headline and its official website for the first post on this page let’s inspect this web page and see if we can figure out what the structure is.

From the above diagram, you can see that the whole content including the headline and the official website is under the article tag. So let’s start off by first grabbing this entire first article that contains all of this information.

article = soup.find('article')

Now let’s grab the headline. So if we look in the HTML source code, we have our <div> tag and within that <h3> tag the headline is present. So the code for grabbing the headline is

headline = article.div.h3.text
print(headline)

Output:

Backlog.com

Next, let’s grab the website. So if we look in the HTML source code, we have our <div> tag with its class = “entry-content” and inside that, we have a link inside <a> tag and the text of that link contains the official website. So the code for grabbing the website is

offcialWebsite = article.find('div', class_='entry-content').a.text
print(offcialWebsite)

Output:

www.backlog.com

The complete python web scrapping code is given below.

# Python program to illustrate web Scraping

import requests
from bs4 import BeautifulSoup
import lxml

source = requests.get('https://devopscube.com/project-management-software').text
soup = BeautifulSoup(source, 'lxml')

article = soup.find('article')
headline = article.div.h3.text
print(headline)
offcialWebsite = article.find('div', class_='entry-content').a.text
print(offcialWebsite)

Output:

Backlog.com
www.backlog.com

Realworld Python Web Scrapping Projects

Here are some real world project ideas you can try for web scrapping using python.

1. Price monitoring in e-commerce websites
2. News syndication from multiple news websites and blogs.
3. Competitor content analysis
4. Social media analysis for trending contents.
5. COVID-9 data tracker

Also look at some of the python web scrapping examples from Github.

Note: Web scraping is not considered good practice if you try to scrape web pages without the website owner’s consent. It may also cause your IP to be blocked permanently by a website.

Web Scrapping Courses

If you want to learn full-fledged web scraping techniques, you can try the following on-demand courses.

1. Web Scraping in Python [Datacamp]
2. Web scrapping courses [Udemy]
3. Using Python to Access Web Data [Coursera]

Conclusion

So, in this python web scraping tutorial we learned how to create a web scraper. I hope you got a basic idea about web scraping and understand this simple example. 

From here, you can try to scrap any other website of your choice.