Data-Analytics using python (Module 4).pptx

Web Scrapping & Numerical
Analysis
Module 3
MCA Dept,
Dr Mahantesh Sajjan
KLE Institutue of Technology

Chapter
Contents
2
Data acquisition by scrapping web
applications
Submitting a Form, Fetching web pages
Downloading web pages through form submission
CSS selectors
Numpy Essentials: TheNumPy

Data Acquisition by Scrapping Web Application
Suppose we want to get some information from a website?
• Let’s say an article from website or some news article, what will you do?
• The first thing that may come in your mind is to copy and paste the
information into your local media.
• But what if you want a large amount of data on a daily basis and as quickly as
possible. In such situations, copy and paste will not work and that’s where
you’ll need web scraping.
• In this article, we will discuss how to perform web scraping using the requests
library and beautifulsoup library in Python.
Requests Module
• Requests library is used for making HTTP requests to a specific URL and returns
the response. Python requests provide inbuilt functionalities for managing
both the request and response.
3

4

Installation: Requests installation depends on the type of operating system, the basic
command anywhere would be to open a command terminal and run,
pip install requests
Making a Request: Python requests module has several built-in methods to make HTTP
requests to specified URI using GET, POST, PUT, PATCH, or HEAD requests. A HTTP request is
meant to either retrieve data from a specified URI or to push data to a server. It works as a
request-response protocol between a client and a server. Here we will be using the GET
request.
GET method is used to retrieve information from the given server using a given URI. The GET
method sends the encoded user information appended to the page request.
import requests
# Making a GET request
r = requests.get('https://www.geeksforgeeks.org/python-programming-language/')
# check status code for response received
# success code - 200
print(r)
# print content of request
print(r.content) 5

O/p:
Response object:
• When one makes a request to a URI, it returns a response. This Response object in terms of
python is returned by requests.method(), method being – get, post, put, etc.
• Response is a powerful object with lots of functions and attributes that assist in normalizing
data or creating ideal portions of code.
• For example, response.status_code returns the status code from the headers itself, and one can
check if the request was processed successfully or not.Response objects can be used to imply
lots of features, methods, and functionalities.
6

Facebook Graph API:
We will use this API to retrieve profile pictures of users on Facebook. This API can be used to
get basic information about their profile and bio (if added).
Query Format: URL = “http://graph.facebook.com/ <user_id(numeric)> /picture?type=large"
• The first and most important parameter that we have to integrate into our URL is the user
id. There is a unique numeric user id that is assigned to every user on Facebook.
7

• In order to get this id, we first have to go to that person’s profile and then copy the
URL (as shown in the image) and use this website to get the user id for that profile.
• We will be using the requests library for scraping images using the graph API. The
requests library is one of the most popular Python libraries for making HTTP requests,
it is a wrapper over the urllib library and works well with python 3.x.
import requestsurl = "http://graph.facebook.com/4/picture?type=large"
r = requests.get(url)
print(r)
print(type(r.content))
We used user_id = 4, that is the official profile of Mark Zuckerberg.
O/p:
<Response [200]>
<class 'bytes'>
• As we can see we got a response for our query with response code 200 which means
a successful API request, to study more about
• We got the image but in byte format hence we will first save it and then visualize it
using OpenCV and matplotlib.
8

#This will create a jpeg file from the binary content we scraped
with open("./Data/sample_pic.jpeg", 'wb') as f: # 'wb' stands for write-binary
f.write(r.content)
# We will visualize image using opencv and matplotlib
import cv2
import matplotlib.pyplot as pltimg = cv2.imread("./Data/sample_pic.jpeg")
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
plt.imshow(img)
plt.axis("off")
plt.show()
O/p:
• We have successfully scraped the profile picture using the requests library and
some basic code. 9

BeautifulSoup Library : is used extract information from the HTML and XML
files. It provides a parse tree and the functions to navigate, search or modify this
parse tree.
• Beautiful Soup is a Python library used to pull the data out of HTML and XML
files for web scraping purposes. It produces a parse tree from page source
code that can be utilized to drag data hierarchically and more legibly.
• It was first presented by Leonard Richardson, who is still donating to this
project, and this project is also supported by Tide lift (a paid subscription tool
for open-source supervision).
• Beautiful soup3 was officially released in May 2006, Latest version released by
Beautiful Soup is 4.9.2, and it supports Python 3 and Python 2.4 as well.
10

Features of Beautiful Soup: Beautiful Soup is a Python library developed for quick
reversal projects like screen-scraping. Three features make it powerful:
1. Beautiful Soup provides a few simple methods and Pythonic phrases for guiding,
searching, and changing a parse tree: a toolkit for studying a document and removing
what you need. It doesn’t take much code to document an application.
2. Beautiful Soup automatically converts incoming records to Unicode and outgoing
forms to UTF-8. You don’t have to think about encodings unless the document doesn’t
define an encoding, and Beautiful Soup can’t catch one. Then you just have to choose
the original encoding.
3. Beautiful Soup sits on top of famous Python parsers like LXML and HTML, allowing
you to try different parsing strategies or trade speed for flexibility.
To install Beautifulsoup on Windows, Linux, or any operating system, one would need
pip package. To check how to install pip on your operating system, check out – PIP
Installation – Windows || Linux. Now run the below command in the terminal.
pip install beautifulsoup4
11

Acquring Data with Python
Reading a CSV File Format in Python:
import csv
# opening the CSV file
with open('Giants.csv', mode ='r')as file:
# reading the CSV file
csvFile = csv.reader(file)
# displaying the contents of the CSV file
for lines in csvFile:
print(lines)
Output:
['Organiztion', 'CEO', 'Established']
['Alphabet', 'Sundar Pichai', '02-Oct-15']
['Microsoft', 'Satya Nadella', '04-Apr-75']
['Aamzon', 'Jeff Bezos', '05-Jul-94'] 12

Reading a CSV File Format Using pandas.read_csv() method:
• It is very easy and simple to read a CSV file using pandas library functions.
Here read_csv() method of pandas library is used to read data from CSV files.
• Example: This code uses the pandas library to read and display the contents of
a CSV file named ‘Giants.csv.’
• It reads the CSV file and stores it as a DataFrame using pandas.read_csv()
function. Finally, it prints the entire DataFrame, which provides a structured
and tabular representation of the CSV data.
• This is a common approach when working with tabular data in Python,
as pandas offers powerful tools for data manipulation and analysis.
import pandas
csvFile = pandas.read_csv('Giants.csv')
print(csvFile)
13

Reading a CSV File Format Using pandas.read_csv() method:
import csv
# opening the CSV file
with open(‘Students.csv', mode ='r')as file:
# reading the CSV file
csvFile = csv.reader(file)
# displaying the contents of the CSV file
for lines in csvFile:
print(lines)
o/p:
[['Steve', 13, 'A'],
['John', 14, 'F'],
['Nancy', 14, 'C'],
['Ravi', 13, 'B']] 14

Writing to CSV File Format in Python:
csv.writer class is used to insert data to the CSV file. This class returns a writer
object which is responsible for converting the user’s data into a delimited string.
A CSV file object should be opened with newline=” otherwise, newline
characters inside the quoted fields will not be interpreted correctly.
• Syntax:
• csv.writer(csvfile, dialect='excel', **fmtparams)
• csv.writer class provides two methods for writing to CSV. They
are writerow() and writerows().
• writerow(): This method writes a single row at a time. Field row can be written
using this method.
• writerows(): This method is used to write multiple rows at a time. This can be
used to write rows list.
15

Python program to demonstrate # writing to CSV
import csv
# field names
fields = ['Name', 'Branch', 'Year', 'CGPA']
# data rows of csv file
rows = [ ['Nikhil', 'COE', '2', '9.0'], ['Sanchit', 'COE', '2', '9.1'], ['Aditya', 'IT', '2', '9.3'], ['Sagar', 'SE', '1', '9.5'],
['Prateek', 'MCE', '3', '7.8'], ['Sahil', 'EP', '2', '9.1']]
# name of csv file
filename = "university_records.csv“ O/p:
# writing to csv file
with open(filename, 'w') as csvfile:
# creating a csv writer object
csvwriter = csv.writer(csvfile)
# writing the fields
csvwriter.writerow(fields)
# writing the data rows
csvwriter.writerows(rows)
16

Downloading files from web using Python
Requests : is a versatile HTTP library in python with various applications. One of its applications is to download a file from
web using the file URL. Installation: First of all, you would need to download the requests library.
You can install it using pip command: pip install requests
Downloading files:
# imported the requests library
import requests
image_url = "https://www.python.org/static/community_logos/python-logo-master-v3-TM.png"
# URL of the image to be downloaded is defined as image_url
r = requests.get(image_url) # create HTTP response object
# send a HTTP request to the server and save , HTTP response in a response object called r
with open("python_logo.png",'wb') as f:
# Saving received content as a png file in binary format write the contents of the response (r.content)
# to a new file in binary mode.
f.write(r.content)
Aboveof code will download the f image from the web. Now check your local directory(the folder where this script resides),
and you will find this image: All we need is the URL of the image source. (You can get the URL of image source by right-
clicking on the image and selecting the View Image option.)
17

• Downloading Videos: In this example, we are downloading all the video lectures available on
this web-page. All the archives of this lecture are available , we first scrape the webpage to extract
all video links and then download the videos one by one.
import requests
from bs4 import BeautifulSoup
# specify the URL of the archive here
archive_url = http://www-personal.umich.edu/~csev/books/py4inf/media/
def get_video_links():
# create response object
r = requests.get(archive_url)
# create beautiful-soup object
soup = BeautifulSoup(r.content,'html5lib')
# find all links on web-page
links = soup.findAll('a')
# filter the link sending with .mp4
video_links = [archive_url + link['href'] for link in links if link['href'].endswith('mp4')]
return video_links
18

def download_video_series(video_links):
for link in video_links:
# obtain filename by splitting url and getting # last string
file_name = link.split('/')[-1]
print( "Downloading file:%s"%file_name)
# download started
with open(file_name, 'wb') as f:
for chunk in r.iter_content(chunk_size = 1024*1024):
if chunk:
f.write(chunk)
print( "%s downloaded!n"%file_name )
print ("All videos downloaded!")
return
if __name__ == "__main__":
# getting all video links
video_links = get_video_links()
# download all videos
download_video_series(video_links) 19

• Advantages of using Requests library to download web files are:One can easily
download the web directories by iterating recursively through the website!
• This is a browser-independent method and much faster!
• One can simply scrape a web page to get all the file URLs on a webpage and
hence, download all files in a single command-Implementing Web Scraping in
Python with BeautifulSoup
20

Opening Web pages/URLs with Python
• When you execute the program, it will open the trial file, read its contents into
a Python string called webContent and then print the first three hundred
characters of the string to the “Command Output” pane.
• # open-webpage.py
• import urllib.request, urllib.error, urllib.parse
• url = 'http://www.oldbaileyonline.org/browse.jsp?id=t17800628-
33&div=t17800628-33‘
• response = urllib.request.urlopen(url) webContent =
response.read().decode('UTF-8')
• print(webContent[0:300])
21

Python - WebForm Submission
• In the below example we use the sign up form of a website by supplying the userid
and password value. After the submission of the values we print the response.
• import requests
• ID_USERNAME = 'signup-user-name‘
• ID_PASSWORD = 'signup-user-password'
• USERNAME = 'username' PASSWORD = 'yourpassword'
• SIGNUP_URL = 'http://codepad.org/login'
• def submit_form(): """Submit a form"""
• payload = {ID_USERNAME : USERNAME, ID_PASSWORD : PASSWORD,}
• resp = requests.get(SIGNUP_URL)
• print "Response to GET request: %s" %resp.content
• resp = requests.post(SIGNUP_URL, payload)
• print "Headers from a POST request response: %s" %resp.headers #print "HTML
Response: %s" %resp.read()
• if __name__ == '__main__': submit_form()
22

Python - WebForm Submission
• When we run the above program, we get the following output −
• Response to GET request: <!DOCTYPE html>
• <html> <head> <meta http-equiv="content-type" content="text/html;
charset=UTF-8"> <meta HTTP-EQUIV="Pragma" CONTENT="no-cache"> <meta
HTTP-EQUIV="Expires" CONTENT="-1">
• <title>Login - codepad</title>
• <link href="/main.css" media="screen" rel="stylesheet" type="text/css" />
<style type="text/css"> </style> <script
src='https://www.google.com/recaptcha/api.js'></script> <script>
• function onRecaptcha(token) { document.getElementById("editor-
form").submit(); } </script> </head>
• <body > ..................... .....................
23

CSS Selectors in Python
• Python has several popular packages that can parse HTML using CSS selectors.
The most popular one is BeautifulSoup which can execute CSS selectors
through the select() and select_one() methods:
• from bs4 import BeautifulSoup
• soup = BeautifulSoup("""
• <a>link 1</a>
• <a>link 2</a> """)
• print(soup.select_one('a'))
• "<a>link 1</a>"
• print(soup.select('a'))
• ["<a>link 1</a>", "<a>link 2</a>"]
24

Accessing SQL Databases
• Step 5: Test MySQL Connector
• To check if the installation was successful, or if you already installed “MySQL Connector”, go
to your IDE and run the given below code :
• import mysql.connector
• If the above code gets executed with no errors, “MySQL Connector” is ready to be used.
• Step 6: Create Connection 25

# Importing module
import mysql.connector
# Creating connection object
mydb = mysql.connector.connect( host = "localhost",
user = "yourusername", password = "your_password")
# Printing the connection object
print(mydb)
26

27
Creating MySQL Database:
To create a database, we will use CREATE DATABASE database_name statement and we will execute this statement by
creating an instance of the ‘cursor’ class.

28
Creating MySQL Database: To create a database, we will use CREATE DATABASE database_name statement and
we will execute this statement by creating an instance of the ‘cursor’ class.
mydb = mysql.connector.connect(
host = "localhost",
user = "yourusername",
password = "your_password"
)
# Creating an instance of 'cursor' class
# which is used to execute the 'SQL'
# statements in 'Python'
cursor = mydb.cursor()
# Creating a database with a name
# 'geeksforgeeks' execute() method
# is used to compile a SQL statement
# below statement is used to create
# the 'geeksforgeeks' database
cursor.execute("CREATE DATABASE geeksforgeeks")

29
Creating MySQL Database:If the database with the name ‘geeksforgeeks’ already exists then you will get an
error, otherwise no error. So make sure that the new database that you are creating does not have the same
name as the database already you created or exists previously. Now to check the databases that you created,
use “SHOW DATABASES” – SQL statement i.e. cursor.execute(“SHOW DATABASES”)
host = "localhost",
user = "root",
password = "1234"
)
# Creating an instance of 'cursor' class
# which is used to execute the 'SQL'
# statements in 'Python'
# Show database
cursor.execute("SHOW DATABASE")
for x in cursor:
print(x)

30
Creating Tables
Now to create tables in a database, first, we have to select a database and for that, we will pass database =
“NameofDatabase” as your fourth parameter in connect() function. Since we have created a database with the
name ‘geekforgeeks’ above, so we will use that and create our tables. We will use CREATE TABLE gfg
(variableName1 datatype, variableName2 datatype) statement to create our table with the name ‘gfg’.
host = "localhost",
user = "yourusername",
password = "your_password",
database = "geeksforgeeks"
)
# Creating a table called 'gfg' in the
# 'geeksforgeeks' database
cursor.execute("CREATE TABLE gfg (name VARCHAR(255), user_name VARCHAR(255))")
If the table with the name ‘gfg’ already exists, you will get an error, otherwise no error. Now to check tables that
you created, use “SHOW TABLES” – SQL statement i.e. cursor.execute(“SHOW TABLES”).

31
host = "localhost",
user = "root
password = "1234",
database = "geeksforgeeks"
)
# Show existing tables
cursor.execute("SHOW TABLES")
for x in cursor:
print(x)

32
Note:
mysql.connector allows Python programs to access MySQL databases.
connect() method of the MySQL Connector class with the arguments will connect to MySQL
and would return a MySQLConnection object if the connection is established successfully.
user = “yourusername”, here “yourusername” should be the same username as you set
during MySQL installation.
password = “your_password”, here “your_password” should be the same password as you
set during MySQL installation.
cursor() is used to execute the SQL statements in Python.
execute() method is used to compile a SQL statement.

Cleaning the Data with Python
Missing data is always a problem in real life scenarios. Areas like machine
learning and data mining face severe issues in the accuracy of their model
predictions because of poor quality of data caused by missing values. In these
areas, missing value treatment is a major point of focus to make their models
more accurate and valid.
When and Why Is Data Missed?
• Let us consider an online survey for a product. Many a times, people do not
share all the information related to them.
• Few people share their experience, but not how long they are using the
product; few people share how long they are using the product, their
experience but not their contact information.
• Thus, in some or the other way a part of data is always missing, and this is very
common in real time.
33

Let us now see how we can handle missing values (say NA or NaN) using Pandas.
# import the pandas library
import pandas as pd
import numpy as np
df = pd.DataFrame(np.random.randn(5, 3), index=['a', 'c', 'e', 'f', 'h'],columns=['one', 'two', 'three'])
df = df.reindex(['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h'])
print df
O/p:
one two three
a 0.077988 0.476149 0.965836
b NaN NaN NaN
c -0.390208 -0.551605 -2.301950
d NaN NaN NaN
e -2.000303 -0.788201 1.510072
f -0.930230 -0.670473 1.146615
g NaN NaN NaN
h 0.085100 0.532791 0.887415
Using reindexing, we have created a DataFrame with missing values.
In the output, NaN means Not a Number. 34

• Check for Missing Values
• To make detecting missing values easier (and across different array dtypes), Pandas provides
the isnull() and notnull() functions, which are also methods on Series and DataFrame
objects −
import pandas as pd
import numpy as np
df = pd.DataFrame(np.random.randn(5, 3),
index=['a', 'c', 'e', 'f', 'h'], columns=['one', 'two', 'three'])
df = df.reindex(['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h'])
print df['one'].isnull()
O/p: a False b True c False d True
e False f False g True h False
Name: one, dtype: bool
• Cleaning / Filling Missing Data: Pandas provides various methods for cleaning the missing
values. The fillna function can “fill in” NA values with non-null data in a couple of ways,
35

Stripping Extra Data
d ={'apple': ['15.8', '356,2', '51.36', '17986,8','6.0tY', 'Null'],
'banana': ['27.84883300816733U', 'Z44.64197389840307Y', '?????',
'13.3T', 'p17.6', '6.1Q'],
'cheese': ['27.68303400840678O', '39.93121897299962W', '???', '9.4J',
'7.2K','6.0Y'],
'egg': ['???', '7.2K', '66.0p','23.77814972104277T', '2396,7', 'None']}
36

df = pd.DataFrame(d)
print (df)
apple banana cheese egg
0 15.8 27.84883300816733U 27.68303400840678O ???
1 356,2 Z44.64197389840307Y 39.93121897299962W 7.2K
2 2 51.36 ????? ??? 66.0p
3 17986,8 13.3T 9.4J 23.77814972104277T
4 6.0tY p17.6 7.2K 2396,7
5 Null 6.1Q 6.0Y None
#https://stackoverflow.com/a/28832504/2901002
pat = r"(d+.*d*)"
df = df.apply(lambda x: x.str.replace(',','.').str.extract(pat, expand=False))
print (df) 37

apple banana cheese egg
0 15.8 27.84883300816733 27.68303400840678 NaN
1 356.2 44.64197389840307 39.93121897299962 7.2
2 51.36 NaN NaN 66.0
3 3 17986.8 13.3 9.4 23.77814972104277
4 6.0 17.6 7.2 2396.7
5 NaN 6.1 6.0 NaN
38

Normalizing the Data
Data Normalization with Pandas:
• Pandas: Pandas is an open-source library that’s built on top of NumPy library. it is a
Python package that provides various data structures and operations for
manipulating numerical data and statistics. It’s mainly popular for importing and
analyzing data much easier. Pandas is fast and it’s high-performance & productive for
users.
• Data Normalization: Data Normalization could also be a typical practice in machine
learning which consists of transforming numeric columns to a standard scale. In
machine learning, some feature values differ from others multiple times. The
features with higher values will dominate the learning process.
• Steps Needed
• Here, we will apply some techniques to normalize the data and discuss these with
the help of examples. For this, let’s understand the steps needed for data
normalization with Pandas.
• Import Library (Pandas)
• Import / Load / Create data.
• Use the technique to normalize the data.
39

# importing packages
import pandas as pd
# create data
df = pd.DataFrame([
[180000, 110, 18.9, 1400],
[360000, 905, 23.4, 1800],
[230000, 230, 14.0, 1300],
[60000, 450, 13.5, 1500]], O/p:
columns=['Col A', 'Col B',
'Col C', 'Col D'])
# view data
display(df)
40

import matplotlib.pyplot as plt
df.plot(kind = 'bar')
41

• Apply normalization techniques Using The maximum absolute scaling:
• The maximum absolute scaling rescales each feature between -1 and 1 by dividing
every observation by its maximum absolute value. We can apply the maximum
absolute scaling in Pandas using the .max() and .abs() methods, as shown below.
# copy the data
df_max_scaled = df.copy()
# apply normalization techniques
for column in df_max_scaled.columns:
df_max_scaled[column] = df_max_scaled[column] /
df_max_scaled[column].abs().max() O/p:
# view normalized data
display(df_max_scaled)
42

43
df_max_scaled.plot(kind = 'bar')

• Using The min-max feature scaling:The min-max approach (often called
normalization) rescales the feature to a hard and fast range of [0,1] by subtracting
the minimum value of the feature then dividing by the range. We can apply the min-
max scaling in Pandas using the .min() and .max() methods.
# copy the data
df_min_max_scaled = df.copy()
# apply normalization techniques
for column in df_min_max_scaled.columns:
df_min_max_scaled[column] = (df_min_max_scaled[column] -
df_min_max_scaled[column].min()) / (df_min_max_scaled[column].max() -
df_min_max_scaled[column].min())
# view normalized data
print(df_min_max_scaled)
44

45
df_min_max_scaled.plot(kind = 'bar')

Formatting the Data
• Pandas DataFrame is two-dimensional size-mutable, potentially heterogeneous
tabular data structure with labelled axes (rows and columns). A Data frame is a two-
dimensional data structure, i.e., data is aligned in a tabular fashion in rows and
columns. We can join, merge, and concat dataframe using different methods.
• In Dataframe df.merge(),df.join(), and df.concat() methods help in joining, merging
and concating different dataframe.
• In order to concat dataframe, we use concat() function which helps in concatenating
a dataframe. We can concat a dataframe in many different ways, they are:
• Concatenating DataFrame using .concat()
• Concatenating DataFrame by setting logic on axes
• Concatenating DataFrame using .append()
• Concatenating DataFrame by ignoring indexes
• Concatenating DataFrame with group keys
• Concatenating with mixed ndims 46

Concatenating DataFrame
• Concatenating DataFrame using .concat() :
In order to concat a dataframe, we use .concat() function this function concat a dataframe and returns a new dataframe.
# importing pandas module
import pandas as pd
# Define a dictionary containing employee data
data1 = {'Name':['Jai', 'Princi', 'Gaurav', 'Anuj'],
'Age':[27, 24, 22, 32],
'Address':['Nagpur', 'Kanpur', 'Allahabad', 'Kannuaj'],
'Qualification':['Msc', 'MA', 'MCA', 'Phd']}
data2 = {'Name':['Abhi', 'Ayushi', 'Dhiraj', 'Hitesh'],
'Age':[17, 14, 12, 52],
'Qualification':['Btech', 'B.A', 'Bcom', 'B.hons']}
# Convert the dictionary into DataFrame
df = pd.DataFrame(data1,index=[0, 1, 2, 3])
df1 = pd.DataFrame(data2, index=[4, 5, 6, 7])
print(df, "nn", df1)
47

• Now we apply .concat function in order to concat two dataframe:
# using a .concat() method
frames = [df, df1]
res1 = pd.concat(frames)
Res1
Output :
As shown in the output image,
we have created two
dataframe after concatenating
we get one dataframe
48

• Concatenating DataFrame by setting logic on axes :
In order to concat dataframe, we have to set different logic on axes. We can set axes
in the following three ways: Taking the union of them all, join='outer'. This is the
default option as it results in zero information loss.
• Taking the intersection, join='inner'. Use a specific index, as passed to
the join_axes argument.
• # importing pandas module
• import pandas as pd
•
• # Define a dictionary containing employee data
• data1 = {'Name':['Jai', 'Princi', 'Gaurav', 'Anuj'],
• 'Age':[27, 24, 22, 32],
• 'Address':['Nagpur', 'Kanpur', 'Allahabad', 'Kannuaj'],
• 'Qualification':['Msc', 'MA', 'MCA', 'Phd'],
• 'Mobile No': [97, 91, 58, 76]}
•
49

• Concatenating DataFrame by setting logic on axes :
data2 = {'Name':['Gaurav', 'Anuj', 'Dhiraj', 'Hitesh'],
'Age':[22, 32, 12, 52],
'Address':['Allahabad', 'Kannuaj', 'Allahabad', 'Kannuaj'],
'Qualification':['MCA', 'Phd', 'Bcom', 'B.hons'],
'Salary':[1000, 2000, 3000, 4000]}
Now we set axes join = inner for intersection of dataframe:
# applying concat with axes
# join = 'inner'
res2 = pd.concat([df, df1], axis=1, join='inner')
res2
50

Concatenating DataFrame using .append()
In order to concat a dataframe, we use .append() function this function concatenate along axis=0, namely the
index. This function exist before .concat.
import pandas as pd
data1 = {'Name':['Jai', 'Princi', 'Gaurav', 'Anuj'],
'Age':[27, 24, 22, 32],
'Qualification':['Msc', 'MA', 'MCA', 'Phd']}
data2 = {'Name':['Abhi', 'Ayushi', 'Dhiraj', 'Hitesh'],
'Age':[17, 14, 12, 52],
print(df, "nn", df1) 52

Concatenating DataFrame using .append()
Now we apply .append() function inorder to concat to dataframe
# using append function
res = df.append(df1)
Res
53

Merging DataFrame
• Pandas have options for high-performance in-memory merging and
joining. When we need to combine very large DataFrames, joins serve
as a powerful way to perform these operations swiftly.
• Joins can only be done on two DataFrames at a time, denoted as left
and right tables. The key is the common column that the two
DataFrames will be joined on.
• It’s a good practice to use keys which have unique values throughout
the column to avoid unintended duplication of row values. Pandas
provide a single function, merge(), as the entry point for all standard
database join operations between DataFrame objects.
• There are four basic ways to handle the join (inner, left, right, and
outer), depending on which rows must retain their data.
54

Merging DataFrame
• Merging a dataframe with one unique key combination
• # importing pandas module
• import pandas as pd
• data1 = {'key': ['K0', 'K1', 'K2', 'K3'],
• 'Name':['Jai', 'Princi', 'Gaurav', 'Anuj'],
• 'Age':[27, 24, 22, 32],}
• data2 = {'key': ['K0', 'K1', 'K2', 'K3'],
• 'Address':['Nagpur', 'Kanpur', 'Allahabad', 'Kannuaj'],
• 'Qualification':['Btech', 'B.A', 'Bcom', 'B.hons']}
• # Convert the dictionary into DataFrame
• df = pd.DataFrame(data1)
• # Convert the dictionary into DataFrame
• df1 = pd.DataFrame(data2)
• print(df, "nn", df1)
56

Merging DataFrame
• Now we are using .merge() with one unique key combination
# using .merge() function
res = pd.merge(df, df1, on='key')
res
57

Merging DataFrame
Merging dataframe using multiple join keys.
import pandas as pd
data1 = {'key': ['K0', 'K1', 'K2', 'K3'],
'key1': ['K0', 'K1', 'K0', 'K1'],
'Name':['Jai', 'Princi', 'Gaurav', 'Anuj'],
'Age':[27, 24, 22, 32],}
data2 = {'key': ['K0', 'K1', 'K2', 'K3'],
'key1': ['K0', 'K0', 'K0', 'K0'],
df = pd.DataFrame(data1)
df1 = pd.DataFrame(data2)
58

Merging DataFrame
Now we merge dataframe using multiple keys
# merging dataframe using multiple keys
res1 = pd.merge(df, df1, on=['key', 'key1'])
res1
59

Reshape data in Python
• Pandas use various methods to reshape the dataframe and series. Reshaping a Pandas DataFrame
is a common operation to transform data structures for better analysis and visualization.
The stack method pivots columns into rows, creating a multi-level index Series.
• Reshape DataFrame in Pandas: Below three methods that used to reshape the layout of tables
in Pandas: Using Pandas stack() method,Using unstack() method, Using melt() method
• Reshape the Layout of Tables in Pandas Using stack() method: The stack() method works with the
MultiIndex objects in DataFrame, it returns a DataFrame with an index with a new inner-most level
of row labels. It changes the wide table to a long table.
import pandas module
import pandas as pd
# making dataframe
df = pd.read_csv("nba.csv")
# reshape the dataframe using stack() method
df_stacked = df.stack()
print(df_stacked.head(26))
60

Reshape in Python
• O/p: 0 Name Avery Bradley
Team Boston Celtics
Number 0.0
Position PG
Age 25.0
Height 6-2
Weight 180.0
College Texas
Salary 7730337.0
• 1. Name Jae Crowder
Team Boston Celtics
Number 99.0
Position SF
Age 25.0
Height 6-6
Weight 235.0
College Marquette
Salary 6796117.0
•
2 Name John Holland
Team Boston Celtics
Number 30.0
Position SG
Age 27.0
Height 6-5
Weight 205.0
College Boston University
dtype: object
61

Reshape data in Python
Reshape a Pandas DataFrame Using unstack() method: The unstack() is similar
to stack method, It also works with multi-index objects in dataframe, producing
a reshaped DataFrame with a new inner-most level of column labels.
# import pandas module
import pandas as pd
# making dataframe
# unstack() method
df_unstacked = df_stacked.unstack()
print(df_unstacked.head(10))
62

Reshape in Python
Name Team Number Position Age Height Weight College Salary
Avery B Boston 0.0 PG 25.0 6-2 180.0 Texas 7730337.0
Jae C Boston 99.0 SF 25.0 6-6 235.0 Marquette 6796117.0
John H Boston 30.0 SG 27.0 6-5 205.0 Boston University NaN
R.J. Hunter Boston 28.0 SG 22.0 6-5 185.0 Georgia State 1148640.0
Jonas J Boston 8.0 PF 29.0 6-10 231.0 NaN 5000000.0
Amir J Boston 90.0 PF 29.0 6-9 240.0 NaN 12000000.0
63

Reshape in Python
• Reshape the Layout of Tables in Pandas Using melt() method: The melt() in Pandas
reshape dataframe from wide format to long format. It uses the “id_vars[‘col_names’]” to
melt the dataframe by column names.
# import pandas module
import pandas as pd
# making dataframe
# it takes two columns "Name" and "Team"
df_melt = df.melt(id_vars=['Name', 'Team'])
print(df_melt.head(10))
64

Pivot Tables in Python
• Data reshaping can simplify the process of data computing/analyzing. It's very easy to turn
categorical columns into rows and vice versa with Pandas.
• Automatically collected data (for example, a log of user actions or an aircraft flight
recorder) usually have a flat structure, also known as "stacked" or "long". It often has an
ordinal index, and every column reflects an attribute of a described object/process. That's
why the word "features" in Machine Learning is used. The columns contain feature values.
This data is easy to process, but hard to evaluate at first glance. 65

• Let's start with the very basic reshaping. When you need to quickly transform
your data into a readable (and office-management favorite) format, use
the .pivot() method:DataFrame.pivot
• Let's consider a simple example below. The Flights dataset shows how many
people traveled by air from 1949 to 1960 by month. It can be imported from
GitHub with one line (requires internet connection):
import pandas as pd
df = pd.read_csv('https://raw.githubusercontent.com/mwaskom/seaborn-
data/master/flights.csv')
df.head()
66

• This dataset contains 144 rows. It's quite difficult to look at them at once. We
can apply the following:
• df.pivot(index='year', columns='month', values='passengers')
67

• As you can see there are a couple of changes:
• Index and columns now have names: "year" and "month". The "year" column is
now the index. The "month" has been transformed into columns. Each
passenger value is now located where a year and a month collide.
DataFrame.pivot_table:
• This method allows you to build pivot tables with data aggregation. If
every index/columns pair corresponds to only one value then the results are
similar to the .pivot() method.
• For example, we take a bit more complex dataset called "mpg" (miles per
gallon) that contains cars from the past century and their specs.
df = pd.read_csv('https://raw.githubusercontent.com/mwaskom/seaborn-
data/master/mpg.csv')
df.head()
68

Reshape & Pivot Tables in Python
69

Regular expressions and string manipulation in
Python
• Regular expressions and string manipulation are powerful tools in
Python for searching, matching, and manipulating text patterns
within strings. Regular expressions, also known as regex, provide a
concise and flexible way to define patterns and perform pattern
matching operations on strings.
• Python's built-in re module provides functions and methods to work
with regular expressions. Here's a brief introduction to regular
expressions and string manipulation in Python:
Pattern Matching
Matching Functions
Regex Patterns
String Manipulation
70

Python
• Pattern Matching: Regular expressions allow you to define patterns and
search for specific patterns within strings. For example, you can search for
email addresses, phone numbers, URLs, or any other specific pattern you
define.
• Matching Functions: Python's re module provides various functions to
match patterns, such as re.search(), re.match(), and re.findall(). These
functions search for a pattern in a given string and return the first match,
match at the beginning of the string, or all matches, respectively.
• Regex Patterns: Regular expressions use special characters and sequences
to define patterns. For instance, . matches any character, d matches a
digit, w matches a word character, and so on. You can also use quantifiers
like *, +, or {} to specify the number of occurrences.
• String Manipulation: In addition to pattern matching, regular expressions
enable powerful string manipulation capabilities. You can use regex to
replace patterns, split strings based on patterns, or extract specific parts of
a string. 71

Python
• Here's a simple example that demonstrates the use of regular
expressions in Python for pattern matching:
import re
# Search for a pattern in a string
text = "Hello, my email address is example@example.com"
pattern = r'w+@w+.w+'
match = re.search(pattern, text)
if match: print("Found email address:", match.group())
# Replace a pattern in a string
replaced_text = re.sub(pattern, "REDACTED", text)
print("Replaced text:", replaced_text)
72

Python
• In the above example, we search for an email address pattern using a
regular expression. If a match is found, we print the matched email
address. Then, we use the re.sub() function to replace the email
address with the string "REDACTED".
• Regular expressions and string manipulation provide a powerful
toolkit for working with text patterns in Python. They can be used for
tasks like data validation, data extraction, data cleaning, and more.
• By exploring and mastering regular expressions and string
manipulation techniques, you can efficiently process and manipulate
text data according to specific patterns and requirements.
73

Python
• Common Python Regex Patterns
• The pattern we used with re.findall() above contains a fully spelled-out out
string, "From:". This is useful when we know precisely what we're looking
for, right down to the actual letters and whether or not they're upper or
lower case. If we don't know the exact format of the strings we want, we'd
be lost. Fortunately, regex has basic patterns that account for this scenario.
Let's look at the ones we use in this tutorial:
• w matches alphanumeric characters, which means a-z, A-Z, and 0-9. It also
matches the underscore, _, and the dash, -.
• d matches digits, which means 0-9.
• s matches whitespace characters, which include the tab, new line, carriage
return, and space characters.
• S matches non-whitespace characters.
• . matches any character except the new line character n.
• With these regex patterns in hand, you'll quickly understand our code
above as we go on to explain it.

Examples and Use Cases in Regular Expressions
• Email validation is crucial to ensure that communication with users is
possible. A simple RegEx for email validation could be ^[a-zA-Z0-
9._%+-]+@[a-zA-Z0-9.-]+.[a-zA-Z]{2,}$. This checks if the email has
the general format of text@text.text.
• Extracting URLs from a block of text is a common task, especially in
web scraping or reading through logs. A basic RegEx for this could
be (http|https)://[a-zA-Z0-9./-]+.
• RegEx can be used to find and replace patterns in a text. For example,
to replace all occurrences of “dog” with “cat” in a text, you could use
the RegEx dog and the replacement string cat.
• CSV files are commonly used for data storage and analysis. RegEx can
be used to parse these files and extract data. A simple RegEx for this
could be ([^,]+), which matches any character except a comma, until it
hits a comma.

Examples and Use Cases in Regular Expressions
• Cleaning text data is a common task in Natural Language Processing
(NLP). RegEx can be used to remove unwanted characters, such as
punctuation. A simple RegEx for this could be [^a-zA-Z ] which
matches anything that is not a letter or a space.
• Extracting Dates and Times:Dates and times often come in various
formats. Regular expressions can be used to extract these from a text.
A simple RegEx for a date could be d{2}/d{2}/d{4} which matches a
date in the format MM/DD/YYYY.
• Validating Password Strength:RegEx can be used to enforce password
policies. For example, a RegEx for a strong password could
be ^(?=.*[a-z])(?=.*[A-Z])(?=.*d)[a-zA-Zd]{8,}$ which requires at
least one lowercase letter, one uppercase letter, one digit, and a
minimum length of 8 characters.

How to Use RegEx in Python?
• $ – Dollar
• Dollar($) symbol matches the end of the string i.e checks whether the string ends with the given
character(s) or not. For example-
• s$ will check for the string that ends with a such as geeks, ends, s, etc.
• ks$ will check for the string that ends with ks such as geeks, geeksforgeeks, ks, etc.
• Example:
• This code uses a regular expression to check if the string ends with “World!”. If a match is found,
it prints “Match found!” otherwise, it prints “Match not found”.
import re
string = "Hello World!"
pattern = r"World!$"
match = re.search(pattern, string)
if match:
print("Match found!")
else:
print("Match not found.")
O/p: Match found!
77

Formatting Strings
Python String center() Method: The center() method is a built-in method
in Python‘s str class that returns a new string that is centered within a string
of a specified width.
string = “KLEIT MCA!"
width = 30
centered_string = string.center(width)
print(centered_string)
KLEITMCA!
• f-strings are faster and better than both %-formatting and str.format(). f-
strings expressions are evaluated at runtime, can also embed expressions
inside f-string, using a very simple and easy syntax.
• The expressions inside the braces are evaluated in runtime and then put
together with the string part of the f-string and then the final string is
returned. 79

Reverse the words in the given string
# Python code To reverse words in a given string input string
string = “guys quiz practice code"
# reversing words in a given string
s = string.split()[::-1]
l = []
for i in s:
# appending reversed words to l
l.append(i)
#printing reverse words
print(" ".join(l))
• Separate each word in a given string using split() method of string data type in
python.
• Reverse the word separated list.
• Print words of the list, in string form after joining each word with space using ”
“.join() method in python.
80

String Deletion
String1 = "Hello, I'm a Geek"
print("Initial String: ")
print(String1)
# Deleting a String
# with the use of del
del String1
print("nDeleting entire String: ")
print(String1)
81

Formatting of Strings
Python Program for
# Formatting of Strings
# Default order
String1 = "{} {} {}".format('Geeks', 'For', 'Life')
print("Print String in default order: ")
print(String1)
# Positional Formatting
String1 = "{1} {0} {2}".format('Geeks', 'For', 'Life')
print("nPrint String in Positional order: ")
print(String1)
# Keyword Formatting
String1 = "{l} {f} {g}".format(g='Geeks', f='For', l='Life')
print("nPrint String in order of Keywords: ")
print(String1)
82

Python String Operators
83
+ It is known as concatenation operator used to join the strings given either side of the
operator.
* It is known as repetition operator. It concatenates the multiple copies of the same
string.
[] It is known as slice operator. It is used to access the sub-strings of a particular string.
[:] It is known as range slice operator. It is used to access the characters from the
specified range.
in It is known as membership operator. It returns if a particular sub-string is present in
the specified string.
not in It is also a membership operator and does the exact reverse of in. It returns true if a
particular substring is not present in the specified string.
r/R It is used to specify the raw string. Raw strings are used in the cases where we need
to print the actual meaning of escape characters such as "C://python". To define any
string as a raw string, the character r or R is followed by the string.
% It is used to perform string formatting. It makes use of the format specifiers used in C
programming like %d or %f to map their values in python. We will discuss how
formatting is done in python.

Python Lists
What will we Learn:
Python Lists, list methods in Python, list operations in Python with
examples, lists in Python examples, and how to create a list in Python.
• Python Lists are just like dynamically sized arrays, declared in other
languages (vector in C++ and ArrayList in Java). In simple language, a
list is a collection of things, enclosed in [ ] and separated by commas.
• A list is a versatile data structure in Python that may hold a collection
of objects of various data types.It is a mutable data type in Python,
which means that its elements can be changed after they are created.
84

Python Lists
Creating a List in Python
• In Python, creating a list is as simple as containing a sequence of
things within square brackets ([]) and separating each item with a
comma.
• Lists are adaptable data structures that may store a wide range of
elements such as integers, texts, floating-point values, and even other
lists.
fruits = ["apple", "banana", "cherry"]
print(fruits)
O/p:
['apple', 'banana', 'cherry']
85

Python Lists
Accessing list elements in Python
• With indexing, the developer can access each list element quickly and easily by
specifying its position in the list.
• This can be done using integers or negative numbers. Integers start from 0 and move up
to the length of the list minus one, while negative numbers start from -1 and move down
to -len(list).
• Knowing this, accessing individual values or even slices of a list becomes a breeze by
accessing the right index or range of indexes.
• Using accessing list elements in Python enables greater control over data manipulation
within the code.
# Accessing the first element (index 0) in the list
first_fruit = fruits[0]
print(first_fruit)
# Output: apple
# Accessing the second element (index 1) in the list
second_fruit = fruits[1] print(second_fruit)
# Output: banana
86

Python Lists
• Update List in Python: List updates allow the programmer to easily
move, alter, and modify data stored in the list such as strings and
values.
• This process helps make programming more time-efficient and can
simplify coding tasks.
• List updates are closely related to array-based operations, but provide
an even greater level of control over the representation and use of
any given list.
# Updating the second element (index 1) in the list
fruits[1] = "orange“
# Printing the updated list print(fruits)
# Output: ['apple', 'orange', 'cherry']
87

Python Lists
Remove element from a list in Python:
• Delete List Elements in Python is easy to do.using the del() method or the pop() method
the developer can eliminate values from a given list.
• The del() method will allow the user to completely remove an element from a list, while
pop() allows its users to temporarily delete an element and store its value in another
variable if desired.
• When using either of these methods it is important to consider their indexing system,
which starts at 0, so depending on how many elements a list has will affect the indexes
available.
• Delete List Elements in Python gives the convenience of having complete control over
what elements are still part of a list, when changes need to be made it can be done
quickly and without complication.
# Removing the second element (index 1) from the list
removed_fruit = fruits.pop(1)
# Printing the updated list and the removed element
print(fruits)
# Output: ['apple', 'cherry'] print(removed_fruit)
# Output: banana
88

Python Lists
• Reversing a List
• In Python, reversing a list involves flipping its elements' positions so
that the final element becomes the first and vice versa.
• The 'reverse()' method of a list or the '[::-1]' slicing notation, which
both produce a reversed duplicate of the original list, can be used to
do this.
• There are two methods used for reversing a list:
• A list can be reversed by using the reverse() method in Python.
• Using the reversed() function:
# Reversing the order of elements in the list
fruits.reverse()
# Printing the reversed list print(fruits)
# Output: ['cherry', 'banana', 'apple'] 89

Python Lists
• Basic List Operations
• Working with basic lists in Python is a great way to start introducing more advanced coding
concepts.
• It allows the developer to manipulate data and create basic structures that are essential for
solving programming challenges.
• Basic list operations in Python include performing basic arithmetic on the numbers
contained within a list, accessing elements of an existing list, replacing elements of a list,
rearranging elements of a list, concatenating multiple lists together, and duplicating specific
entries in a list.
90
Python Expression Results Description
len([1, 2, 3]) 3 Length
[1, 2, 3] + [4, 5, 6] [1, 2, 3, 4, 5, 6] Concatenation
['Hi!'] * 4 ['Hi!', 'Hi!', 'Hi!', 'Hi!'] Repetition
3 in [1, 2, 3] True Membership
for x in [1, 2, 3]: print x, 1 2 3 Iteration

Python Lists
• List Length
• The term "list length" in Python describes the quantity of objects
(elements) that make up a list.
• The len() function, which accepts a list as an input and returns an
integer corresponding to the list's length, can be used to determine it.
• fruits = ["apple", "banana", "cherry"]
# Finding the length of the list
length_of_fruits = len(fruits)
# Printing the length of the list
print(length_of_fruits)
# Output: 3
91

Python Lists
The list() Constructor
• To build a new list object in Python, use the built-in list() constructor
function.
• There are various methods to utilize it to make a list.
# Using the list() constructor to create a list
numbers = list([1, 2, 3, 4, 5])
# Printing the created list
print(numbers)
# Output: [1, 2, 3, 4, 5]
The list() constructor is called in this example with an iterable (in this
case, a list [1, 2, 3, 4, 5]), and it constructs a new list named numbers.
The list that results is then printed. 92

Python Lists
• List Comprehension
• Python's list comprehension feature is a clear and easy approach to
building lists.
• By applying an expression to each item in an existing iterable (such as
a list, tuple, or range) and optionally filtering the elements based on a
condition, it enables you to construct a new list.
• List construction with list comprehensions is more readable and
effective than using conventional for loops.
• # Python program to demonstrate list comprehension in Python
# below list contains square of all
# odd numbers from range 1 to 10
odd_square = [x ** 2 for x in range(1, 11) if x % 2 == 1]
print(odd_square) 93

Python Lists
• Using append() method: Elements can be added to the List by using
the built-in append() function. Only one element at a time can be
added to the list by using the append() method, for the addition of
multiple elements with the append() method, loops are used. Tuples
can also be added to the list with the use of the append method
because tuples are immutable. Unlike Sets, Lists can also be added to
the existing list with the use of the append() method.
94

Python Lists
# Creating a List
List = []
print("Initial blank List: ")
print(List)
# Addition of Elements # in the List
List.append(1)
List.append(2)
List.append(4)
print("nList after Addition of Three elements: ")
print(List)
# Adding elements to the List # using Iterator
for i in range(1, 4):
List.append(i)
print("nList after Addition of elements from 1-3: ")
print(List)
95

Python Lists
# Adding Tuples to the List
List.append((5, 6))
print("nList after Addition of a Tuple: ")
print(List)
# Addition of List to a List
List2 = ['For', 'Geeks']
List.append(List2)
print("nList after Addition of a List: ")
print(List)
96

Python Lists
• # Python program to demonstrate Addition of elements in a List
# Creating a List
List = [1, 2, 3, 4]
print("Initial List: ")
print(List)
# Addition of multiple elements to the List at the end (using Extend
Method)
List.extend([8, 'Geeks', 'Always'])
print("nList after performing Extend Operation: ")
print(List)
O/p: Initial List: [1, 2, 3, 4]
List after performing Extend Operation:
[1, 2, 3, 4, 8, 'Geeks', 'Always']
97

Python Lists
• Using insert() method
• append() method only works for the addition of elements at the end of the List,
for the addition of elements at the desired position, insert() method is used.
Unlike append() which takes only one argument, the insert() method requires two
arguments(position, value).
• # Creating a List
List = [1,2,3,4]
print("Initial List: ")
print(List)
# Addition of Element at
# specific Position
# (using Insert Method)
List.insert(3, 12)
List.insert(0, 'Geeks')
print("nList after performing Insert Operation: ")
print(List) 98

Python Lists
Taking Input of a Python List:
• We can take the input of a list of elements as string, integer, float, etc.
But the default one is a string.
#Python program to take space separated input as a string split and
store it to a list and print the string list input the list as string
string = input("Enter elements (Space-Separated): ")
# split the strings and store it to a list
lst = string.split()
print('The list is:', lst) # printing the list
O/p:
Enter elements: KLE IT MCA DEPT The list is: [‘KLEIT', ‘MCA', ‘DEPT'] 99

Python Lists
100
# input size of the list
n = int(input("Enter the size of list : "))
# store integers in a list using map,
# split and strip functions
lst = list(map(int, input("Enter the integer
elements:").strip().split()))[:n]
# printing the list
print('The list is:', lst)
O/p:
Enter the size of list : 4
Enter the integer elements: 6 3 9 10
The list is: [6, 3, 9, 10]

Python Lists Indexing & Slicing
• In Python, every list with elements has a position or index. Each element of
the list can be accessed or manipulated by using the index number.
They are two types of indexing −
• Positive Indexing, Negative Indexing,
• In positive the first element of the list is at an index of 0 and the following
elements are at +1 and as follows.
• In the below figure, we can see how an element is associated with its index
or position.
• Example:
list= [5,2,9,7,5,8,1,4,3]
print(list[2])
print(list[5])
O/p: 9 8 101

Python Lists (Indexing & Slicing)
• In negative indexing, the indexing of elements starts from the end of the
list. That is the last element of the list is said to be at a position at -1 and
the previous element at -2 and goes on till the first element.
• In the below figure, we can see how an element is associated with its index
or position.
• The following is an example code to show the negative indexing of lists.
list= [5,2,9,7,5,8,1,4,3]
print(list[-2])
print(list[-8])
O/p: 4 2 102

Python Lists: Slicing
• List slicing is a frequent practice in Python, and it is the most prevalent
technique used by programmers to solve efficient problems. Consider a
Python list. You must slice a list in order to access a range of elements in it.
One method is to utilize the colon as a simple slicing operator (:).
• The slice operator allows you to specify where to begin slicing, where to
stop slicing, and what step to take. List slicing creates a new list from an old
one.
List[Start : Stop : Stride]
list= [5,2,9,7,5,8,1,4,3]
print(list[0:6])
print(list[1:9:2])
print(list[-1:-5:-2])
O/p:
[5, 2, 9, 7, 5, 8]
[2, 7, 8, 4]
[3, 1]
103

Python Indexing
Python's sequence types are list, tuple, string, range, byte, and byte
arrays. And indexing and slicing are applicable to all of these types.
• The term "indexing" refers to refers to an element of an iterable
based on its position inside the iterable.
• The indexing begins from 0. The first element in the sequence is
represented by index 0.
• Negative indexing begins from -1. The last element in the sequence is
represented by index -1.
• Each character in a string corresponds to an index number, and each
character can be accessed by its index number. There are two ways to
access characters in a String
• Accessing string characters using positive indexing
• Accessing string characters using negative indexing 104

Python Indexing Vs Slicing
• The term "slicing" refers to obtaining a subset of elements from an
iterable based on their indices.
• We create a substring by slicing a string, which is effectively a string
that exists within another string. We utilize slicing when we only need
a portion of the string and not the entire string.
Syntax:
string[start : end : step]
• Start - index from where to start
• end - ending index
• step - numbers of jumps/increment to take between i.e stepsize
105

Python Indexing Vs Slicing
• # input string
inputString = ""Hello tutorialspoint python"
print("First 4 characters of the string:",
inputString[: 4])
print("Alternate characters from 1 to 10 index(excluded):",inputString[1 : 10 :
2])
print("Alternate characters in reverse order from 1 to 10 index(excluded):",
inputString[-1 : -10 : -2])
('First 4 characters of the string:', 'Hell')
('Alternate characters from 1 to 10 index(excluded):', 'el uo')
('Alternate characters in reverse order from 1 to 10 index(excluded):', 'nhy n')
O/p:
First 4 characters of the string: Hell Alternate characters from 1 to 10
index(excluded): el uo Alternate characters in reverse order from 1 to 10
index(excluded): nhy n
106

Python Tuple Slicing
• Tuple Slicing
• We can use tuple slicing. It is similar to how we use strings and lists.
Tuple slicing is used to obtain a variety of items. We also use the
slicing operator to perform tuple slicing. The slicing operator can be
represented by the syntax
• [start:stop:step]
107

Python Tuple Slicing
• # Input tuple
givenTuple = ("Welcome", "this", "is", "TutorialsPoint", "Website", 10)
# Slicing with start and stop values(indices)
print('Tuple slicing from index 1 to index 6 :', givenTuple[1:6])
# Slicing with only stop values(indices)
print("Tuple slicing till index 7: ", givenTuple[:7])
# Slicing with only start value(indices)
print("Tuple slicing from index 2 is:", givenTuple[2:])
# Slicing without any start and stop values
print("Tuple slicing without any start and stop values:", givenTuple[:])
# Slicing in reverse order
print("Tuple slicing in reverse order:",
givenTuple[::-1]) ('Tuple slicing from index 1 to index 6 :', ('this', 'is', 'TutorialsPoint', 'Website', 10))
('Tuple slicing till index 7: ', ('Welcome', 'this', 'is', 'TutorialsPoint', 'Website', 10))
('Tuple slicing from index 2 is:', ('is', 'TutorialsPoint', 'Website', 10)) ('Tuple slicing without any start and stop
values:',
('Welcome', 'this', 'is', 'TutorialsPoint', 'Website', 10))
('Tuple slicing in reverse order:', (10, 'Website', 'TutorialsPoint', 'is', 'this', 'Welcome')) 108

Python Dictonary
• A dictionary in Python is a data structure that stores the value in value:key pairs.
• Example:
As you can see from the example, data is stored in key:value pairs in dictionaries, which makes it easier to find
values.
Syntax:
dict_var = {key1 : value1, key2 : value2, …..}
Dict = {1: 'Geeks', 2: 'For', 3: 'Geeks'}
print("nDictionary with the use of Integer Keys: ")
print(Dict)
Dict = {'Name': 'Geeks', 1: [1, 2, 3, 4]}
print("nDictionary with the use of Mixed Keys: ")
print(Dict)
O/p:
Dictionary with the use of Integer Keys:
{1: 'Geeks', 2: 'For', 3: 'Geeks'}
Dictionary with the use of Mixed Keys:
{'Name': 'Geeks', 1: [1, 2, 3, 4]}
109

Python Nested Dictonary
110
Dict = {1: 'Geeks', 2: 'For',
3: {'A': 'Welcome', 'B': 'To', 'C': 'Geeks'}}
print(Dict)
{1: 'Geeks', 2: 'For', 3: {'A': 'Welcome', 'B': 'To', 'C': 'Geeks'}}

Python Dictonary
• Add Items to a Python Dictionary with Different DataTypesDict = {}
print("Empty Dictionary: ")
print(Dict)
Dict[0] = 'Geeks'
Dict[2] = 'For'
Dict[3] = 1
print("nDictionary after adding 3 elements: ")
print(Dict)
Dict['Value_set'] = 2, 3, 4
print("nDictionary after adding 3 elements: ")
print(Dict)
Dict[2] = 'Welcome'
print("nUpdated key value: ")
print(Dict)
Dict[5] = {'Nested': {'1': 'Life', '2': 'Geeks'}}
print("nAdding a Nested Key: ")
print(Dict)
111

Python Dictonary
• Empty Dictionary:
{}
Dictionary after adding 3 elements:
{0: 'Geeks', 2: 'For', 3: 1}
Dictionary after adding 3 elements:
{0: 'Geeks', 2: 'For', 3: 1, 'Value_set': (2, 3, 4)}
Updated key value:
{0: 'Geeks', 2: 'Welcome', 3: 1, 'Value_set': (2, 3, 4)}
Adding a Nested Key:
{0: 'Geeks', 2: 'Welcome', 3: 1, 'Value_set': (2, 3, 4), 5:
{'Nested': {'1': 'Life', '2': 'Geeks'}}}
112

Reading and Writing to text files in Python
• Python provides built-in functions for creating, writing, and reading files. Two
types of files can be handled in Python, normal text files and binary files
(written in binary language, 0s, and 1s).
• Text files: In this type of file, Each line of text is terminated with a special
character called EOL (End of Line), which is the new line character (‘n’) in
Python by default.
• Binary files: In this type of file, there is no terminator for a line, and the data is
stored after converting it into machine-understandable binary language.
• will focus on opening, closing, reading, and writing data in a text file. we will
also see how to get Python output in text file.
113

• File Access Modes
• Access modes govern the type of operations possible in the opened file. It
refers to how the file will be used once its opened. These modes also define
the location of the File Handle in the file. The file handle is like a cursor, which
defines from where the data has to be read or written in the file and we can
get Python output in text file.
There are 6 access modes in Python:
• Read Only (‘r’)
• Read and Write (‘r+’)
• Write Only (‘w’)
• Write and Read (‘w+’)
• Append Only (‘a’)
• Append and Read (‘a+’)
114

• Read Only (‘r’) : Open text file for reading. The handle is positioned at the beginning
of the file. If the file does not exist, raises the I/O error. This is also the default mode
in which a file is opened.
• Read and Write (‘r+’): Open the file for reading and writing. The handle is positioned
at the beginning of the file. Raises I/O error if the file does not exist.
• Write Only (‘w’) : Open the file for writing. For the existing files, the data is truncated
and over-written. The handle is positioned at the beginning of the file. Creates the
file if the file does not exist.
• Write and Read (‘w+’) : Open the file for reading and writing. For an existing file,
data is truncated and over-written. The handle is positioned at the beginning of the
file.
• Append Only (‘a’): Open the file for writing. The file is created if it does not exist. The
handle is positioned at the end of the file. The data being written will be inserted at
the end, after the existing data.
• Append and Read (‘a+’) : Open the file for reading and writing. The file is created if it
does not exist. The handle is positioned at the end of the file. The data being written
will be inserted at the end, after the existing data.
115

• There are two kinds of memory in a computer i.e. Primary and Secondary every file that you
saved or anyone saved is on secondary memory cause any data in primary memory is
deleted when the computer is powered off.
• So when you need to change any text file or just to work with them in python you need to
load that file into primary memory. Python interacts with files loaded in primary memory or
main memory through “file handlers” ( This is how your operating system gives access to
python to interact with the file you opened by searching the file in its memory if found it
returns a file handler and then you can work with the file ).
• Opening a Text File in Python
• It is done using the open() function. No module is required to be imported for this function.
• File_object = open(r"File_Name","Access_Mode")
• The file should exist in the same directory as the python program file else, the full address of
the file should be written in place of the filename. Note: The r is placed before the filename
to prevent the characters in the filename string to be treated as special characters.
• The r makes the string raw, that is, it tells that the string is without any special characters.
The r can be ignored if the file is in the same directory and the address is not being placed.
116

# Opening and Closing a file "MyFile.txt for object name file1.
file1 = open("MyFile.txt","a")
file1.close()
• Writing to a file in Python
• There are two ways to write in a file:
• Using write()
• Using writelines()
117

• # Program to show various ways to read and # write data in a file.
file1 = open("myfile.txt", "w")
L = ["This is Delhi n", "This is Paris n", "This is London n"]
# n is placed to indicate EOL (End of Line)
file1.write("Hello n")
file1.writelines(L)
file1.close() # to change file access modes
file1 = open("myfile.txt", "r+")
print("Output of Read function is ")
print(file1.read())
print()
# seek(n) takes the file handle to the nth # byte from the beginning.
file1.seek(0)
print("Output of Readline function is ")
print(file1.readline())
print()
file1.seek(0)
118

# To show difference between read and readline
print("Output of Read(9) function is ")
print(file1.read(9))
print()
file1.seek(0)
print("Output of Readline(9) function is ")
print(file1.readline(9))
file1.seek(0)
# readlines function
print("Output of Readlines function is ")
print(file1.readlines())
print()
file1.close()
119

Output of Read function is
Hello
This is Delhi
This is Paris
This is London
Output of Readline function is
Hello
Output of Read(9) function is
Hello
Th
Output of Readline(9) function is
Hello
Output of Readlines function is
['Hello n', 'This is Delhi n', 'This is Paris n', 'This is London n']
120

• Appending To a File in Python
• In this example, a file named “myfile.txt” is initially opened in write mode ("w")
to write lines of text. The file is then reopened in append mode ("a"), and
“Today” is added to the existing content. The output after appending is
displayed using readlines. Subsequently, the file is reopened in write mode,
overwriting the content with “Tomorrow”. The final output after writing is
displayed using readlines.
121

# Python program to illustrate Append vs write mode
file1 = open("myfile.txt","w")
L = ["This is Delhi n","This is Paris n","This is London n"]
file1.writelines(L)
file1.close()
# Append-adds at last
file1 = open("myfile.txt","a")#append mode
file1.write("Today n")
file1.close()
file1 = open("myfile.txt","r")
print("Output of Readlines after appending")
print()
file1.close()
122

# Write-Overwrites
file1 = open("myfile.txt","w")#write mode
file1.write("Tomorrow n")
file1.close()
file1 = open("myfile.txt","r")
print("Output of Readlines after writing")
print()
file1.close()
Output of Readlines after appending
['This is Delhi n', 'This is Paris n', 'This is London n', 'Today n']
Output of Readlines after writing
['Tomorrow n']
123

Python Class Definition
• Python is an object oriented programming language.Almost everything in
Python is an object, with its properties and methods. A Class is like an object
constructor, or a "blueprint" for creating objects.
• To create a class, use the keyword class:
Create a class named MyClass, with a property named x:
class MyClass:
x = 5
• Create Object,Now we can use the class named MyClass to create objects:
• Create an object named p1, and print the value of x:
p1 = MyClass()
print(p1.x)
124

• The __init__() Function
• To understand the meaning of classes we have to understand the built-in __init__()
function. All classes have a function called __init__(), which is always executed when
the class is being initiated.
• Use the __init__() function to assign values to object properties, or other operations
that are necessary to do when the object is being created:
• Example: Create a class named Person, use the __init__() function to assign values
for name and age:
class Person:
def __init__(self, name, age):
self.name = name
self.age = age
p1 = Person("John", 36)
print(p1.name)
print(p1.age)
Note: The __init__() function is called automatically every time the class is being used
to create a new object.
125

• The __str__() Function: controls what should be returned when the class object is
represented as a string. If the __str__() function is not set, the string representation of the
object is returned:
• Example: The string representation of an object WITHOUT the __str__() function:
class Person:
self.name = name
self.age = age
print(p1)
• Example:The string representation of an object WITH the __str__() function:
class Person:
self.name = name
self.age = age
def __str__(self):
return f"{self.name}({self.age})"
print(p1) 126

• Objects can also contain methods. Methods in objects are functions that
belong to the object.
• Let us create a method in the Person class: Insert a function that prints a
greeting, and execute it on the p1 object:
• class Person:
self.name = name
self.age = age
def myfunc(self):
print("Hello my name is " + self.name)
p1.myfunc()
127

• The self parameter is a reference to the current instance of the class, and is used to access variables that belongs to the
class.
• It does not have to be named self , you can call it whatever you like, but it has to be the first parameter of any function in
the class:
• Example: Use the words mysillyobject and abc instead of self:
• class Person:
def __init__(mysillyobject, name, age):
mysillyobject.name = name
mysillyobject.age = age
def myfunc(abc):
print("Hello my name is " + abc.name)
p1.myfunc()
• You can modify properties on objects like this: Example :
Set the age of p1 to 40:
p1.age = 40
• Delete the age property from the p1 object: del p1.age
• class definitions cannot be empty, but if you for some reason have a class definition with no content, put in
the pass statement to avoid getting an error.
class Person:
pass
128

Python Constructors
• Constructors are generally used for instantiating an object. The task of constructors is
to initialize(assign values) to the data members of the class when an object of the
class is created. In Python the __init__() method is called the constructor and is
always called when an object is created.
• Syntax of constructor declaration :
def __init__(self): # body of the constructor
• Types of constructors :
• default constructor: The default constructor is a simple constructor which doesn’t
accept any arguments. Its definition has only one argument which is a reference to
the instance being constructed.
• parameterized constructor: constructor with parameters is known as parameterized
constructor. The parameterized constructor takes its first argument as a reference to
the instance being constructed known as self and the rest of the arguments are
provided by the programmer.
129

Python Constructors
• class KLEIT:
# default constructor
def __init__(self):
self.KLEIT = “KLEIT mca Dept"
# a method for printing data members
def print_KLEIT(self):
print(self.KLEIT)
# creating object of the class
obj = kleitmca()
# calling the instance method using the object obj
obj.print_kleitmca()
130

Python parameterized constructor
class Addition:
first = 0
second = 0
answer = 0
# parameterized constructor
def __init__(self, f, s):
self.first = f
self.second = s
def display(self): Creating &invoke parameterized constructor
print("First number = " + str(self.first)) obj1 = Addition(1000, 2000)
print("Second number = " + str(self.second)) obj2 = Addition(10, 20)
print("Addition of two numbers = " + str(self.answer)) obj1.calculate()
obj2.calculate()
def calculate(self): obj1.display(), obj2.display()
self.answer = self.first + self.second Addition of two numbers = 3000
Addition of two numbers = 30
131

Inheritance in Python
• It is a mechanism that allows you to create a hierarchy of classes that share a
set of properties and methods by deriving a class from another class.
Inheritance is the capability of one class to derive or inherit the properties
from another class.
The benefits of Inheritance in Python are as follows:
• It represents real-world relationships well.
• It provides the reusability of a code. We don’t have to write the same code
again and again. Also, it allows us to add more features to a class without
modifying it.
• It is transitive in nature, which means that if class B inherits from another class
A, then all the subclasses of B would automatically inherit from class A.
• Inheritance offers a simple, understandable model structure.
• Less development and maintenance expenses result from an inheritance. 132

A Python program to demonstrate inheritance
class Person(object):
# Constructor
def __init__(self, name, id):
self.name = name
self.id = id
# To check if this person is an employee
def Display(self):
print(self.name, self.id)
# Driver code
emp = Person(“KLEIT", 02) # An Object of Person
emp.Display()
O/p: KLEIT 02
133

A child class is a class that drives the properties from its parent class. Here Emp is
another class that is going to inherit the properties of the Person class(base class).
class Emp(Person):
def Print(self):
print("Emp class called")
Emp_details = Emp(“MCA", 103)
# calling parent class function
Emp_details.Display()
# Calling child class function
Emp_details.Print()
O/p: Mayank 103 Emp class called
134

Over Loading in Python
What is Overloding(Polymorphism): The word polymorphism means having
many forms. In programming, polymorphism means the same function name
(but different signatures) being used for different types. The key difference is the
data types and number of arguments used in function.
# A simple Python function to demonstrate Polymorphism
def add(x, y, z = 0):
return x + y+z
# Driver code
print(add(2, 3))
print(add(2, 3, 4))
135

Over Loading in Python
Operator Overloading : gives extended meaning forpredefined operational meaning.
For operator + is used to add two integers also to join two strings and merge two lists.
Here ‘+’ operator is overloaded by int class and str class. You might have noticed that
the same built-in operator or function shows different behavior for objects of different
classes, this is called Operator Overloading.
# Python program to show use of + operator for different purposes.
print(1 + 2)
# concatenate two strings
print(“KLEIT"+“MCA")
# Product two numbers
print(3 * 4)
# Repeat the String
print(“KLEIT"*3)
136

Data-Analytics using python (Module 4).pptx

More Related Content

Similar to Data-Analytics using python (Module 4).pptx (20)

More from DRSHk10 (6)

Recently uploaded (20)

Data-Analytics using python (Module 4).pptx