Class 12 Ip Whole Text Book Preeti Arora

SULTAN CHAND & SONS (P) LTD
Educational Publishers
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ISBN: 978-93-89174-54-0
First Edition 2019
Second Thoroughly Revised Edition 2020
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Programming is important for learning to innovate and create eco-friendly solutions
to global problems. Programming is also important in our day-to-day life to enhance
the power of computers and internet. A significant step towards learning innovative
programming solutions is through Python programming which this book has at its core.
This thoroughly revised Informatics Practices with Python for Class XII provides
an in-depth understanding of the Informatics Practices (065) curriculum and strictly
adheres to the guidelines laid down by the CBSE. The book deals with detailed
concepts of Python Pandas, NumPy, Data Visualization, Django, Relational Database
Management System (SQL), Python-MySQL Connectivity, Basic Software Engineering
and Cyber Ethics.
Python is a popular object-oriented language used both for stand-alone programs
and scripting applications in a variety of domains. This book adopts a contemporary
approach to the most popular Python library, Pandas, with stress on principles of good
programming, such as clarity, legibility and efficiency in program design. Thus, an
interactive programming style has been emphasized/expressed throughout the book.
The hallmark of this book is that it teaches Python Pandas concepts in detail and
usage of several other Python libraries, such as plotting graphs and charts using
Python Pyplot, and establishing Python-MySQL Connectivity. Ample case studies
to understand the basic concepts of Software Engineering with a student-centred
approach have also been provided in the book. With easy-to-understand examples,
practical implementations and other tools, the student will learn how to create and
implement Python series and dataframes, and develop GUI applications based on it.
The text of the book has been presented in a friendly and easy-to-comprehend language.
The book contains example programs that are concise and practical besides diagrams
and examples from real-life applications. Each chapter provides tested, debugged and
error-free codes with screenshots. Based on the CBSE curriculum, the book has been
divided into four units:
Unit I: Data Handling (DH-2) – Chapters 1 to 3
This unit contains three chapters covering fundamentals of Pandas including Series
and Dataframes. Advanced concepts of Pandas such as Pivoting, Sorting, Aggregation,
Function Applications, Reindexing and Quantiles have been explained in detail with
ample examples and associated codes.
Apart from the above, detailed concepts of NumPy (ndarrays) and their
implementation, Data Visualization using Pyplot in terms of Line chart, Bar chart,
Scatter plot, Histograms, Frequency Polygons and Boxplot have also been thoroughly
discussed.
Unit II: Basic Software Engineering (BSE) – Chapters 4 and 5
Thisunithasbeendividedintotwochapters—4and5—andcoversSoftwareEngineering
concepts such as Software process models, Delivery models, Agile methods, business-
use diagrams and some practical aspects and their implementation.
PREFACE

Unit III: Data Management (DM-2) – Chapters 6 to 8
This unit covers development of Django web application, Interface Python with an
SQL database and SQL commands, Aggregation functions along with important SQL
clauses such as group by, having and order by.
Unit IV: Society, Law and Ethics (SLE-2) – Chapter 9
This unit deals with intellectual property rights, plagiarism, digital rights
management, licensing, open source and standards, privacy laws, frauds and
cybercrimes such as phishing, illegal downloading, child pornography, cyber scams
and frauds, cyber forensics, IT Act, 2000, and Unique ID and Biometrics. It also
explains related concepts of technology and society, e-waste management, and gender
and disability issues while teaching and using computers and the role of new media
in society with case studies.
The book has two appendices containing a Sample Question Paper (Solved) and a
Model Test Paper (Unsolved).
As part of our Web Support, Presentation on detailed concepts of Python Pandas,
Chapter-wise Program Codes, Projects based on Python-MySQL Connectivity using
Pandas, Practical File, Sample Papers, Model Test Papers for practice, Installation
Guide to Django and Viva Voce questions are available online and can be accessed
at sultan-chand.com/ws/ipp12. Guide to Django Installation is also available in
QR Code. Besides, exam-related updates, if any, will be made available online in
due course.
I am confident that students and teachers will benefit immensely by making the best
use of this book.
Your feedback is important to me. Any suggestions for the improvement of this book
will be highly appreciated and duly acknowledged.
My special thanks are due to Ms Rinku Kumari and Ms Payal Bhattacharjee for
their valuable suggestions during the course of my writing this book.
Last but not the least, I express my deep gratitude to my esteemed publishers,
Sultan Chand & Sons (P) Ltd, for their patience, guidance and support.
AUTHOR

1. NumPy 1.1–1.42
1.1 Introduction . . . 1.1
1.2 What is NumPy . . . 1.1
1.3 Working with NumPy . . . 1.3
1.4 How to Create a NumPy Array . . . 1.4
1.5 Operations on NumPy Array . . . 1.8
1.5.1 Array Slicing . . . 1.8
1.5.2 Joins in Arrays . . . 1.10
1.5.3 Array Subsets . . . 1.12
1.6 Arithmetic Operations on Arrays . . . 1.13
1.7 Applications of NumPy Arrays . . . 1.16
1.7.1 Covariance . . . 1.16
1.7.2 Correlation . . . 1.21
1.7.3 Linear Regression . . . 1.23
2. Data Visualization Using Pyplot 2.1–2.60
2.2 Matplotlib . . . 2.2
2.3 NumPy . . . 2.3
2.4 Installing Matplotlib . . . 2.3
2.5 Types of Visualization . . . 2.4
2.6 Basic Visualization Rules . . . 2.5
2.7 Basic Nomenclature of a Plot . . . 2.5
2.8 Line Plot/Chart . . . 2.6
2.8.1 Multiple Plots . . . 2.9
2.8.2 Multiple Views . . . 2.10
2.9 Scatter Chart . . . 2.18
2.10 Bar Plot/Chart . . . 2.22
2.11 Histograms . . . 2.25
2.12 Saving Plots to File . . . 2.31
2.13 Frequency Polygons . . . 2.32
2.14 Box Plot . . . 2.34
3. Python Pandas 3.1–3.95
3.2 Pandas . . . 3.2
3.2.1 Features of Pandas . . . 3.2
3.3 Installing Pandas . . . 3.3
3.4 Data Structures in Pandas . . . 3.5
3.4.1 Series . . . 3.5
3.4.2 Creation of Series . . . 3.6
3.4.3 Creating an Empty Series using Series() Method . . . 3.6
3.4.4 Creating a Series using Series() with Arguments . . . 3.6
3.4.5 Creating a Series from Dictionary . . . 3.13
3.4.6 Creating a Series using a Mathematical Expression/Function . . . 3.15
3.5 Series Object Attributes . . . 3.16
3.5.1 Retrieving Values from a Series using head() and tail() functions . . . 3.16
3.6 Mathematical Operations on Series . . . 3.17
3.7 Vector Operations on Series . . . 3.18
3.8 Retrieving Values Using Conditions . . . 3.18
3.9 Deleting Elements from a Series . . . 3.19
3.10 Dataframes . . . 3.19
3.11 Binary Operations . . . 3.30
3.12 Matching and Broadcasting Operation . . . 3.32
3.13 Missing Data and Filling Values . . . 3.34
3.14 Comparing the Series . . . 3.35
3.15 Combining Dataframes . . . 3.37
3.16 Boolean Reduction . . . 3.38
3.17 Descriptive Statistics with Pandas . . . 3.39
3.17.1 max() . . . 3.40
3.17.2 min() . . . 3.41
3.17.3 sum() . . . 3.42
3.17.4 count() . . . 3.42
CONTENTS

3.17.5 mode(), mean(), median() . . . 3.43
3.17.6 quantile . . . 3.47
3.17.7 var() . . . 3.52
3.18 Advanced Operations on Dataframes . . . 3.53
3.19 Sorting . . . 3.62
3.20 Creating Histogram . . . 3.67
3.21 Function Application . . . 3.69
3.21.1 pipe() . . . 3.69
3.21.2 apply() . . . 3.71
3.21.3 applymap() . . . 3.72
3.21.4 groupby() in Pandas . . . 3.73
3.21.5 transform() . . . 3.75
3.22 Reindexing and Altering Labels . . . 3.77
4. Introduction to Software Engineering 4.1–4.22
4.2 What is Software Engineering . . . 4.1
4.2.1 Need for Software Engineering . . . 4.2
4.3 Software Process . . . 4.3
4.4 Software Process Activities . . . 4.3
4.4.1 Software Specification . . . 4.4
4.4.2 Software Design and Development . . . 4.4
4.4.3 Software Validation (Testing) . . . 4.5
4.4.4 Software Evolution/Evaluation . . . 4.6
4.5 Software Process Models . . . 4.6
4.5.1 Waterfall Model . . . 4.7
4.5.2 Evolutionary Model . . . 4.9
4.5.3 Component-based Model . . . 4.14
4.6 Delivery Models . . . 4.14
4.6.1 Incremental Delivery Model . . . 4.15
4.6.2 Spiral Delivery Model . . . 4.16
5. Agile Methods and Practical Aspects of Software Engineering 5.1–5.30
5.2 What is Agile Software Development . . . 5.2
5.3 Pair Programming . . . 5.4
5.4 Scrum . . . 5.6
5.4.1 Scrum Team—Roles and Responsibilities . . . 5.6
5.4.2 Scrum Events . . . 5.7
5.5 Version Control System . . . 5.9
5.5.1 Significance of Using Version Control System . . . 5.9
5.5.2 Types of Version Control System . . . 5.10
5.5.3 Why Use a Version Control System . . . 5.11
5.6 GIT—A Distributed Version Control System . . . 5.11
5.7 Business Use-Case Diagram . . . 5.12
5.7.1 What is a Use-Case Diagram . . . 5.13
5.7.2 Case Study and Use-Case Diagram for Result Management System . . . 5.15
5.7.3 Use-Case Diagram of a Software System—“Shopping App” Use-Case Diagram . . . 5.18
5.7.4 Use Case Diagram of a Software System—“Banking App” Use-Case Diagram . . . 5.19
6. Web Development with Django 6.1–6.32
6.2 What is a Framework . . . 6.1
6.3 What is Django . . . 6.2
6.4 Django Web Framework . . . 6.3
6.5 How Django Works . . . 6.4
6.6 Django Installation . . . 6.5
6.7 Web Server . . . 6.5
6.8 Creating Projects . . . 6.6
6.9 Creating Apps of a Django Project . . . 6.15
6.10 GET and POST Methods . . . 6.17
6.10.1 Difference Between GET and POST Methods . . . 6.17
6.10.2 Minimal Django-Based Web Application that Parses a GET . . . 6.20
6.10.3 Minimal Django-Based Web Application that Parses a POST . . . 6.23
6.11 Working with Flat Files and CSV Files . . . 6.24
6.11.1 Write the Fields to a Flat File . . . 6.25
6.11.2 Write the Fields to a CSV File . . . 6.25
6.11.3 Read the Fields from a CSV File . . . 6.26

7. Interface Python with SQL 7.1–7.28
7.2 Python-MySQL Connectivity . . . 7.2
7.3 Why Python . . . 7.2
7.4 Installing MySQL Connector . . . 7.3
7.4.1 MySQLdb . . . 7.4
7.4.2 Steps for Creating Database Connectivity Applications . . . 7.6
7.5 Establishing Connection . . . 7.7
7.6 Creating Cursor Object . . . 7.8
7.7 Creating a Database . . . 7.9
7.8 Closing Cursor and Connection . . . 7.21
7.9 Operations on a Table in a Nutshell . . . 7.21
8. More on SQL 8.1–8.32
8.2 Functions in MySQL . . . 8.1
8.3 Aggregate Functions in SQL . . . 8.3
8.4 Sorting in SQL—Order By . . . 8.9
8.5 Group By . . . 8.10
8.5.1 Having Clause . . . 8.10
8.6 Aggregate Functions & Conditions on Groups (Having Clause) . . . 8.12
9. Society, Law and Ethics 9.1–9.36
9.2 Intellectual Property Rights . . . 9.2
9.3 Plagiarism . . . 9.2
9.4 Digital Rights Management . . . 9.3
9.5 Licensing . . . 9.5
9.6 Open Source and Open Data . . . 9.8
9.7 Privacy Laws . . . 9.9
9.8 Cybercrime . . . 9.10
9.8.1 Phishing . . . 9.11
9.8.2 Illegal Downloading . . . 9.11
9.8.3 Child Pornography . . . 9.12
9.8.4 Cyber Scams and Frauds . . . 9.12
9.8.5 Cyber Forensics . . . 9.13
9.9 Information Technology Act, 2000 . . . 9.14
9.10 Unique IDs and Biometrics . . . 9.15
9.11 Impact of Technology Change on Society . . . 9.15
9.12 E-Waste Management . . . 9.16
9.13 Gender and Disability Issues while Teaching and Using Computers . . . 9.19
9.14 Role of New Media in Society . . . 9.20
9.14.1 Online Campaigns . . . 9.20
9.14.2 Crowdsourcing . . . 9.21
9.15 Issues with the Internet . . . 9.23
9.15.1 Net Neutrality . . . 9.24
9.15.2 Internet as an Echo Chamber . . . 9.24
9.15.3 Internet Addiction . . . 9.25
9.16 Role of New Media—Case Studies . . . 9.26
9.16.1 Case Study 1: Arab Spring . . . 9.26
9.16.2 Case Study 2:WikiLeaks . . . 9.26
9.16.3 Case Study 3: Bitcoins . . . 9.27
APPENDICES A.1–A.10
Appendix A: Sample Question Paper (Solved) . . . A.1–A.7
Appendix B: Model Test Paper (Unsolved) . . . A.8–A.10

To My Parents
Shri Gulshan Kumar Arora
and
Smt. Kamlesh Arora

2.1 INTRODUCTION
“A picture is worth a thousand words.”
We all know that images or visuals are a powerful form of communication. We often use them to
understand a situation better or to condense pieces of information into a graphical representation.
Visualization is the easiest way to analyze and absorb information. It is the first step for any kind
of data analysis work. Visuals or better called Data Visualization help us to easily understand a
complex problem and see certain patterns. They also help in identifying patterns, relationships
and outliers in data and in understanding business problems better and quickly. Insights gathered
from the visuals help in building strategies for businesses. For this reason, data visualization
techniques have gained popularity. Data visualization basically refers to the graphical or visual
representation of information and data using visual elements like charts, graphs, maps, etc.
Finance Time Management Process Control
SALES
Fig. 2.1: Significance of Graphs in Various Applications
Data Visualization
Using Pyplot
2

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Python is an interpreted language with a strong core functions basis and a powerful modular
aspect which allows us to expand the language with external modules that offer new
functionalities. Thus, we have an extensible language with tools to accomplish a single task
in the best possible way. Modules are often organized in packages. A package is a structured
collection of modules that has the same purpose. Data visualization in Python can be done via
many packages. One example of a package is Matplotlib.
Matplotlib package can be used in Python scripts, Jupyter notebook and web application
servers. In Python, we can use two exclusive libraries for visualization, commonly known as
matplotlib and seaborn. However, we shall be restricting ourselves to the usage of matplotlib only.
CTM: Data Visualization refers to the graphical or visual representation of information and data using
visual elements like charts, graphs, maps, etc.
2.2 MATPLOTLIB
Matplotlib is a 2D plotting library that helps in
visualizing figures. Matplotlib is used in Python as it
is a robust, free and easy library for data visualization.
It is easy to learn and understand.
Data visualization is an important part of business activities as organizations nowadays collect
a huge amount of data. Sensors all over the world are collecting climate data, user data through
clicks, car data for prediction of steering wheels, etc. All this data collected holds key insights
for businesses and visualizations make these insights easy to interpret. Data is only as good
as it is presented.
Matplotlib is the most popular plotting library for Python. It gives us control over every aspect
of a figure. It supports interactive and non-interactive plotting and can save images in several
output formats (PNG, PS and others). It was originally written by J.D.Hunter and is actively
being developed. It is distributed under a BSD-Style Licence.
Matplotlib is the whole Python package/library used to create 2D graphs and plots by using
Python scripts. Pyplot is a module in matplotlib which supports a very wide variety of graphs
and plots, namely histogram, bar charts, powerspectra, error charts, etc. It is used along with
NumPy to provide an environment for MatLab. It supports interactive and non-interactive
plotting and can save images in several output formats (PNG, PS and others).
CTM: Matplotlib is a Python 2D plotting library which produces publication-quality figures. Pyplot is a
module of matplotlib library (of Python) containing collection of methods which allows a user to create
2D plots and graphs easily and interactively.
It is a programming platform, designed specifically for engineers and scientists, which allows
the most natural expression of computational mathematics.
Pyplot provides the state-machine interface to the plotting library in matplotlib. It means
that figures and axes are implicitly and automatically created to achieve the desired plot.
For example, calling plot() from pyplot will automatically create the necessary figure and axes to
achieve the desired plot. Setting a title will then automatically set that title to the current object
axes. The pyplot interface is generally preferred for non-interactive plotting (i.e., scripting).

Data
Visualization
Using
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2.3
Each pyplot function makes some changes to a figure, like creating a figure, creating a plotting
area in a figure, plotting some lines in a plotting area, decorating the plot with labels, etc.
CTM: A plot is a graphical representation technique for representing a dataset, usually as a graph,
showing the relationship between two or more variables.
2.3 NumPy
Another library which helps in the process of plotting graphs/
charts using pyplot is NumPy. NumPy stands for numerical
Python. NumPy is the core library for scientific computing
in Python. It provides a high-performance multidimensional
array object and tools for working with these arrays.
Using NumPy, a developer can perform the following operations:
 Mathematical and logical operations on arrays.
 Fourier transforms and routines for shape manipulation.
 Operations related to linear algebra. NumPy has in-built functions for linear algebra and
random number generation.
We can install NumPy using the popular Python package installer, pip. Type the following
command at the command prompt—
>C:pip install numpy
NumPy will get installed onto your system and will be ready to be used.
2.4 INSTALLING MATPLOTLIB
Before we start plotting graphs in matplotlib, it needs to be installed first. For the installation
of Matplotlib, follow the steps listed below:
Step 1: Open cmd (command prompt) and run command prompt as an Administrator.
The following window gets displayed.

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Step 2: Type cd to move to the root directory.

Step 3: Type: pip install matplotlib (with internet connection) as shown in the above window.
Note: Please ensure that the system has Python Shell installed before installing matplotlib
Library.
Step 4: Installation of Matplotlib will start.
After the installation is successfully done on the system, an appropriate message shall be
displayed as shown in the window given below:

 After the successful installation of the above Library package (Matplotlib), we can plot
various types of graphs in Python shell using pyplot methods.
POINT TO REMEMBER
Internet connection is required only at the time of installation of Matplotlib. After installation gets
completed, there is no such requirement.
2.5 TYPES OF VISUALIZATION
Matplotlib can be used to explore the basic plotting capabilities for single or multiple lines.
We can add information to the plots such as legends, axis labels and titles. It also provides the
capability to save a plot to a file.

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2.5
There are many types of visualizations available with Matplotlib. Some of the most famous are:
line plot, scatter plot, histogram, box plot, bar chart and pie chart.
In this chapter, we shall discuss line chart, bar chart, histogram, frequency polygons, box plots
and scatter plots as per the CBSE curriculum.
2.6 BASIC VISUALIZATION RULES
Before we look at some of the plots, let us introduce some basic rules. These rules help us to
make nice and informative plots instead of confusing ones.
Before you plot/create any chart or graph type, make sure to import the matplotlib.pyplot
library by giving the command:
import matplotlib.pyplot
OR import matplotlib.pyplot as plt
Along with pyplot, if you are using NumPy functionality,
make sure to import it also using the command as:
import numpy as np
The next step is to choose an appropriate plot type. If there are various options, we should
compare them and choose the one that fits our model the best.
Third, when we choose the type of plot, one of the most important things is to label the axis.
If we don’t do this, the plot is not informative enough. When there are no axis labels, we can
try to look at the code to see what data is used and, if we’re lucky, we’ll understand the plot.
Fourth, we can add a title to make our plot more informative.
Fifth, add labels for different categories when needed.
Sixth, optionally we can add a text or an arrow at relevant data points.
Seventh, in some cases, we can use some sizes and colours of the data to make the plot more
informative.
For drawing simple charts, the line charts and scatter charts are almost similar. The only
difference is the presence/absence of the lines connecting the points. Also, using plot()
function of pyplot, you can create both of these basic charts. However, the scatter charts
can also be created using scatter() function, which we shall be learning in the subsequent
topics.
Let us begin with line chart first.
2.7 BASIC NOMENCLATURE OF A PLOT
Pyplot provides the state-machine interface to the plotting library in matplotlib. It means
that figures and axes are implicitly and automatically created to achieve the desired plot.
For example, calling plot from pyplot will automatically create the necessary figure and
axes to achieve the desired plot. Setting a title will then automatically set that title to
the current axes object. The pyplot interface is generally preferred for non-interactive
plotting (i.e., scripting).
Here, plt is an alias name for pyplot, so
now you can use plt for typing pyplot
commands as plt. <command>

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YLABEL
XLABEL
TITLE
LEGEND
Scores by PLAYER
Y-axis
X-axis
0
A B C D
PLAYER
SACHIN
RAHUL
Scores
10
20
30
40
50
60
70
80
90
Fig. 2.2: Basic Components of a Graph/Chart
A Matplotlib figure can be categorized into several parts as below:
• Figure: It is a whole figure which may contain one or more than one axes (plots). You can
think of a Figure as a canvas which contains plots.
• Axes: It is what we generally think of as a plot. A Figure can contain many Axes. It contains
two or three (in the case of 3D) Axis objects. Each Axes has a title, an x-label and a y-label.
• Axis: They are the number line like objects and take care of generating the graph limits.
• Artist: Everything which one can see on the figure is an artist like Text objects, Line2D
objects, collection objects. Most Artists are tied to Axes.
• Labels: To manage the axes dimensions of a plot, another important piece of information to
add to a plot is the axes labels, since they usually specify what kind of data we are plotting.
• Title: Just like in a book or a paper, the title of a graph describes what it is. Matplotlib
provides a simple function, plt.title(), to add a title to an image.
• Legend: Legends are used to explain what each line means in the current figure.
2.8 LINE PLOT/CHART
Line plot/chart is a type of plot which displays information as a series of data points called
“markers” connected by straight lines. In this type of plot, we need the measurement points
to be ordered (typically by their X-axis values). This type of plot is often used to visualize a
trend in data over intervals of time—a time series.
The line chart is represented by a series of data points connected by a straight line. Generally,
line charts are used to display trends over time. A line chart or line graph can be created using
the plot() function available in pyplot library. We can not only just plot a line but also explicitly
define the grid, the X-axis and Y-axis scale and labels, title and display options.

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2.7
To make a line plot with matplotlib, we call plt.plot(). The first argument is used for the data
on the horizontal axis, and the second is used for the data on the vertical axis. This function
generates your plot but it doesn’t display it. To display the plot, we need to call the plt.show()
function.
Markers and Line Styles
In the Practical Implementation examples that we are going to implement in successive
subtopics, all the plots are made of points with lines joining them. The points are the
pairs (x,y) from the X and Y input lists we pass to plot(); lines are the straight segments
connecting any two adjacent points.
Points are almost invisible, if not for the edges in the graph. However, they are the real
generators of the plot because points mark positions. As a result, they are called markers in
matplotlib terminology.
By default, Matplotlib draws markers as a single dot and lines as straight thin segments; there
are situations where we would like to change either the marker style (to clearly identify them
in the plot) or the line style.
CTM: A line chart or line graph is a type of chart which displays information as a series of data points
called ‘markers’ connected by straight line segments.
In order to draw a line plot, the steps to be followed are as under:
Steps:
1. Importing matplotlib.
2. plt.plot(x, y, color, others) Plot y versus x as lines and/or markers.
3. plt.xlabel(“Your Text”) Set the X-axis label of the current axes.
4. plt.ylabel(“Your Text”) Set the Y-axis label of the current axes.
5. plt.set_title(“Your Title”) Set a title of the current axes.
6. plt.show() Display a figure.
This can be better understood through the Practical Implementation that follows.
Practical Implementation–1
To plot a simple line chart using two lists.

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Explanation:
For plotting a line plot, the first thing to be done is importing matplotlib using the statement:
import matplotlib.pyplot as plt
Here, plt is a short name or an alias name and a standard. Similarly, NumPy is imported as np,
which we will be using for successive charts. These short names make the code more clear and
easy to understand. In the next statement, plt is used to call plot() method, which plots the
graph between the given set of values.
plt.plot([1,2,3],[5,7,4])
This statement describes two lists as the arguments to be plotted in the graph. Since everything
is drawn in the background first, it is brought and displayed in the front using the command
plt.show().
plt.show() is used to display the graph.
Interactive Navigation Toolbar
When using matplotlib.pyplot, the toolbar is enabled by default
for every figure. It provides basic elaboration and manipulation
functions for interactive plotting. At the bottom of the window, we can find the navigation
toolbar. A description of each of its buttons (from left to right) follows:
Apart from the plot area, we have a few options displayed at the bottom-left corner of the
window.
Reset Original Window: We can resize the plot window as per our requirement. It can be
reset to the original window size using this option.
Back to Previous View: It takes us to the previous view from the current view.
Forward to Next View: It takes us to the next view from the current view.
Pan Axis with Left Mouse, Zoom with Right: Used to zoom any section of the plotted
figure.

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• Pan: Click on the left mouse button and hold it to pan the figure, dragging it to a new
position. Once you are happy with the position, release the mouse button. While
panning, if we press (or hold) the x or y key, then the panning is limited to the
selected axis.
• Zoom: Click on the right mouse button and hold it to zoom the figure, dragging it to a
new position. Movement to the right or to the left generates a proportional zoom in
or out of the X-axis of the figure. The same holds true for the up or down movement
of the Y-axis. The point where we click the mouse remains still so that we are able
to zoom around a given point in the figure. The x and y keys work in the same way
as mentioned earlier, but now we can press the Ctrl key to preserve the aspect ratio.
Zoom to Rectangle: Enabling this mode, we can draw a rectangle on the figure (hold the
left mouse button while drawing it) and the view will be zoomed to that rectangle.
Configure Subplots: When we click on this button, a window pops up that allows us to
configure the various spaces that surround the figure (left, right, up, button, between).
Save the Figure: This option is used to save the figure drawn to the hard disk by giving a
proper name to it. Click on this button and a save file dialog box will pop up that allows
us to save the current figure.
This is useful because we might want to add some additional customizations to our plot before
we display it. For example, we might want to add labels to the axis and title for the plot.
2.8.1 Multiple Plots
If we want to plot multiple lines in one chart, we can simply call the plot() function multiple
times.
To add legends, titles and labels to a line plot with multiple lines.
CTM: Legends can be dynamically changed.

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Legend Title Plot Area
Explanation:
In the above program, we have drawn two lines using
line chart with proper titles given along X-axis and
Y-axis. Also, a new term has been used, i.e., legend. If
we look at the line graphs of our previous examples, we
realize that we have to look into our code to understand what kind of function is depicted. This
information should be available in the diagram for the sake of convenience. Legends are used
for this purpose. So, legend is the text or string that “has to be read” to understand the graph.
Legends are used in line graphs to explain the function or the values underlying the different
lines of the graph.
2.8.2 Multiple Views
In case we want to plot legends in different views in the same window, we can use the subplot()
function as shown in Practical Implementation–3:
To plot two lines in two different views of the same window.
Learning Tip: We can draw as many lines
as required by calling plot() function
multiple times with suitable arguments.

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In the above program, the plt.subplot() statement is used. The subplot() command specifies
numrows, numcols and fignum. In the above program, we have imported NumPy library through
the statement—import numpy as np.
Practical Implementation–3A
To plot two lines in two different views of the same window by adjusting the space between
the subplots. (Modification of Practical Implementation–3)
In the above program, we have modified the code for Practical Implementation-3.
subplots_adjust() method is used for providing horizontal spaces (hspace) and width-wise
spaces (wspace) between two subplots so that their respective titles or any other subcomponents
don’t overlap or collide with each other and, hence, the output is so obtained.

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arange()
One more function that we have used in the above code is arange(). The NumPy arange function
(sometimes called np.arange) is a tool for creating numeric sequences in Python. It returns
evenly spaced numeric values within an interval, stored as a NumPy array or we can say a list
(i.e., an ndarray object).
The syntax for arange() is:
The function name
The data type
(optional)
np.arange([start,]stop[,step,][,dtype])
The start of
the interval
(optional)
The end of
the interval
The “step”
between values
(optional)
Here,
 start (optional)
The start parameter indicates the beginning value of the range.
This parameter is optional, so if you omit it, it will automatically default to 0.
 stop (required)
The stop parameter indicates the end of the range. Keep in mind that like all Python indexing,
this value will not be included in the resulting range.
 step (optional)
The step parameter specifies the spacing between values in the sequence.
This parameter is optional. If you don’t specify a step value, by default, the step value will be 1.

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 dtype (optional)
The dtype parameter specifies the data type.
For example, to create a range of values from 0 to 8, in increments of 2, we will use the start
position of 0 and a stop position of 8. To increment in steps of 2, we’ll set the step parameter
to 2. Hence, the statement shall be:
np.arange(start = 0, stop = 8, step = 2)
This command shall create a sequence of numbers as shown:

0 2 4 6
The last value should be 8 but as per the syntax it is to be excluded and, hence, 6 will be taken
as the last number to be displayed for the given range.
Program to plot frequency of marks using line chart.

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Program to plot a sine wave using line chart.
In the program given on the previous page, we have used another method arange(). It is used
to provide a range of points to be displayed as a grid, with first argument as the starting point
followed by end point/value, and the third argument constituting the increment/step value.
Sine wave is formed by using sin() method and passing the values on the X-axis as the parameter.
Program to plot an algebraic expression: 10x + 14 using line chart.

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Program to plot a quadratic equation using dashed line chart.

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For plotting any equation, NumPy library is required to be imported along with matplotlib.
With the plot(), we have given an argument as ‘b––’ which denotes that the line which will be
displayed shall be of dashed type with blue colour as given with the starting letter ‘b’.
Changing Line Colours and Styles
We have already seen that in a multiline plot, Matplotlib automatically chooses different
colours for different lines. We are also free to choose them by ourselves. Matplotlib provides
different styles and colours for line(s) we are plotting. To understand this better, it has been
implemented in Practical Implementation–8.
To plot multiple lines with different colours defined explicitly.
In the preceding code, we specify colour as the last argument (in this case, with an implicit
Y-axis)—to draw yellow, magenta and cyan lines (from bottom to top).

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Here is a table of the abbreviations used to select colours:
Colour
abbreviation
Colour name
b blue
c cyan
g green
k black
m magenta
r red
w white
y yellow
Apart from using colour abbreviations, we can also
give complete names for colours like yellow, red,
blue, etc., and hence, the output shown below is
obtained.
Also, the line styles can also be changed as per
the needs of the user. Matplotlib allows us to use
different line styles. All the available styles are listed
in the following table:
Style
abbreviation
Style
- solid line
-- dashed line
-. dash-dot line
: dotted line
All the lines seen until now were proper ones without any dots or dashes. Matplotlib allows us
to use different line styles which are implemented in Practical Implementation–9.
To plot lines with different styles using plot() function.
Learning Tip: Even if you skip the colour
information in plot(), Python will plot
multiple lines in the same plot with
different colours but these colours are
decided internally by matplotlib (python).

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This code snippet generates a blue dashed line, a green dash-dotted line, and a red dotted
line.
2.9 SCATTER CHART
A scatter plot is a two-dimensional data visualization that uses dots to represent the values
obtained for two different variables—one plotted along the X-axis and the other plotted along
the Y-axis. The data visualization is done as a collection of points not connected by lines. Each of
them has its coordinates determined by the value of the variables (one variable determines the
X position, the other the Y position). A scatter plot is often used to identify potential association
between two variables.
To plot a Scatter chart for given heights and weights of 15 students.

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In the above plot, the scatter chart is simple and displayed as per the default settings. We can
decorate the chart by using some of the following keyword arguments:
• s: This stands for the size of the markers in pixel*pixel. It can be a single value (to be used
for all the points) or an array of the same size of X and Y (so that each point will have its
own size).
• c: This is the points colour. It can be a single value or a list of colours (that will be cycled
on the points plotted) eventually of the same size of X and Y.
• marker: This specifies the marker to be used to plot the points; the available values
are:
Marker value Description
s Square
o Circle
^ Triangle up
v Triangle down
> Triangle right
< Triangle left
d Diamond
p Pentagon
h Hexagon
8 Octagon
+ Plus
x Cross

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Modification of Practical Implementation-10.
In the previous implementation, the plot exhibited no special attributes and the attributes
like colour and shape were decided by matplotlib. You can also select the marker value and
its associated colour displayed as per your choice by explicitly defining ‘c’ and ‘marker’ with
scatter() function.
As it is observed from the output window, the font size for title, x-label and y-label has been
displayed as given in the command. The colour and marker style was taken as “red” and star
“*” symbol as the marker value and, hence, the scatter plot is obtained accordingly.
To generate a Scatter plot on the basis of dataset generated randomly (use of randn() function
from Python random library).
In this question, we shall be importing numpy library for randn() function. Using the randn()
NumPy function to generate the datasets.

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As it is observed from the output window, there are ‘n’ number of datasets generated using the
randn() from NumPy library. Since we have not mentioned any colour for marker points, it will
display the points in blue colour by default.
To generate a Scatter plot on the basis of dataset generated randomly and marker colours and
size are different and explicitly defined.

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2.10 BAR PLOT/CHART
A bar chart represents categorical data with rectangular bars. Each bar has a height which
corresponds to the value it represents. It is useful when we want to compare a given numeric
value on different categories. It can also be used with two data series. The bars can be plotted
vertically or horizontally.
A bar chart/bar graph is a very commonly-used two-dimensional data visualization made up
of rectangular bars, each for a specific category, with its length representing the value of that
category.
Additionally, we can also configure other characteristics for the chart, like width of the bars,
colour, etc., among others. The X-axis will be a range with the same quantity of items as the
Y-axis. Let us take a simple example where we will store the configurations we want in variables
and then will pass them to the bar() function:
To make a bar chart with matplotlib, we need to use the plt.bar() function.
To plot a simple bar chart with orange in colour.

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Matplotlib charts can be horizontal. To create a horizontal bar chart, type the following code
as shown in Practical Implementation–15.
To plot a bar chart horizontally.

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To plot the elements of two lists using a bar chart.
To plot a bar chart showing the favourite movie of different sets of people.

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2.11 HISTOGRAMS
A histogram is a powerful technique in data visualization. It is an accurate graphical
representation of the distribution of numerical data. It was first introduced by Karl Pearson. It
is an estimate of the distribution of a continuous variable (quantitative variable). It is similar
to a bar graph.
In other words, we can say that histogram charts are a graphical display of frequencies,
represented as bars. They show what portion of the dataset falls into each category, usually
specified as non-overlapping intervals called bins.
To construct a histogram, the first step is to “bin” the range of values, i.e., divide the entire
range of values into a series of intervals and then count how many values fall into each interval.
The bins are usually specified as consecutive, non-overlapping intervals of a variable. The bins
(intervals) must be adjacent, and are often (but are not required to be) of equal size.
 Difference between a bar chart/graph and a histogram
A bar chart majorly represents categorical data (data that has some labels associated
with it); they are usually represented using rectangular bars with lengths proportional
to the values that they represent. Histograms, on the other hand, are used to describe
distributions. Given a set of data, what are their distributions.

Dhoni Shikhar Rohit Virat Sachin
10
20
30
40
50
60
0
Bar Graph
Gaps
100
10
20
30
40
50
0
150 200 250 300 350
Histogram
No Gaps
Fig. 2.3: Bar Chart vs Histogram

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Drawing a histogram in Python is quite simple. hist() function is called for creating a histogram.
Before we start drawing a histogram, a few concepts should be clear in your mind. The
components of a histogram plot constitute:
 Title: To display heading of the histogram.
 Colour: To show the colour of the bar.
 Axis: Y-axis and X-axis.
 Data: The data can be represented as an array.
 Height and width of bars: This is determined based on the analysis. The width of the bar
is called bin or intervals.
 Border colour: To display border colour of the bar.
To plot a histogram using randomly generated datasets.
Histogram plots group up values into bins of values. By default, hist() uses a bin value of
10 (so only ten categories, or bars, are computed), but we can customize it, either by
passing an additional parameter, for example, in hist(y, <bins>), or using the bin keyword
argument as hist(y, bin=<bins>).

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The normal or Gaussian distribution is a continuous probability distribution characterized by a
symmetric bell-shaped curve. A normal distribution is defined by its centre (mean) and spread
(standard deviation.). The bulk of the observations generated from a normal distribution lie
near the mean, which lies at the exact centre of the distribution. As a rule of thumb, about 68%
of the data lies within 1 standard deviation of the mean, 95% lies within 2 standard deviations
and 99.7% lies within 3 standard deviations.
The normal distribution is perhaps the most important distribution in all of statistics. It turns
out that many real world phenomena, like IQ test scores and human heights, roughly follow a
normal distribution, so it is often used to model random variables. Many common statistical
tests assume distributions are normal.
The above-plotted histogram is known as Normal Distribution curve, which is bell shaped.
Normal distribution has a bell-shaped density curve described by its mean μμ and standard
deviation σσ. The density curve is symmetrical, centered about its mean, with its spread
determined by its standard deviation showing that data near the mean are more frequent in
occurrence than data far from the mean.
The normal distribution is a form of presenting data by arranging the probability distribution
of each value in the data. Most values remain around the mean value making the arrangement
symmetric.
Replotting the previous dataset, but with bin=25

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This results in a set of finer grained bars.
As evident from the above output, the edges of bars are not clear. This can be done by using
attribute “edgecolor” in hist() function, as shown in the next implementation.
Histograms are a very common type of plots when we are looking at data like height and weight,
stock prices, waiting time for a customer, etc., which are continuous in nature. Histogram’s data
is plotted within a range against its frequency.
To display a histogram with well-defined edges. (Modification of Implementation-19)
Basically, histograms are used to represent data given in the form of some groups. X-axis is
about bin ranges where Y-axis talks about frequency. So, if you want to represent age-wise
population in the form of a graph, then histogram suits well as it tells you how many exist in
certain group range or bin, if you talk in the context of histograms. This we will now implement
in Practical Implementation–21 for plotting a histogram for displaying the number of students
having the same weight.

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To generate a histogram for displaying the number of students having the same weights.
In the above program, the histogram shall display the number of students lying in the same
weight range, by inputting the student data as data_students. To plot this dataset, hist()
function is used. The first argument passed to hist() is the position of (x, y) coordinates where
the value of weight of each student shall be displayed. The second argument calculates the bins
or range of intervals on the basis of data_students. The third argument holds the respective
weights.
One thing is to be kept in mind that the number of coordinates, i.e., the first argument must
match with the third argument, i.e., number of weights to be displayed; else it will generate
an error.
With show() function, the histogram generated gets displayed as shown in the output
obtained.
Weight
Bins/Intervals
Now, the question may arise—can we modify the above program in any way? The answer is, yes
we can. In case we don’t wish to plot a particular range in continuity, it can be skipped while
plotting the histogram. This shall become more clear in Practical Implementation-22.

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To generate a histogram for displaying the number of students having the same weights,
assuming there is no student having weight in the range of 40 to 50 kgs. (Modification of
Practical Implementation-21)
As it is observed from the output window there is no value for the range 40 to 50. This is because
in the source code, we have placed two values as 15 for the coordinates. At interval(bin) 40 to
50, no bar is displayed because we have not mentioned position from 40 to 50 in first argument
(list) of hist() method, whereas in interval 10 to 20 width is being displayed as 16 (10+6 both
weights are added) because 15 is twice in the first argument. As a result, one bar is skipped.
 Changing the Look of the Histogram
By default, bars of histogram are displayed in blue colour but you can change it as per your
colour choice with the following code:
plt.hist([1,11,21,31,41,51],bins=[0,10,20,30,40,50,60],
weights=[10,1,0,33,6,8], facecolor='y',edgecolor="red")

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In the above code, we are passing ‘y’ as face colour means yellow colour to be displayed in bars
with edges in red colour.
To give a name to the histogram, given statement is to be added before calling show()
plt.title("HistogramHeading")
For x and y labels, type the given code:
plt.xlabel('Value')
plt.ylabel('Frequency')
This will generate a histogram accordingly as shown above.
2.12 SAVING PLOTS TO FILE
The active figure can be saved to file using plt.savefig() method.
The histogram created in the above implementation can be saved by clicking on the Save button
on the GUI panel. It can also be done using savefig() and typing the following code:
plt.savefig(“student.png")

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Also, the following code will save the histogram as a PNG image.
This statement shall save the histogram with the name as “student” and “png” extension.
On execution of the above-added code, the histogram or for that matter any plot shall be saved
on the hard disk in the parent folder for Python (where Python has been installed) as shown
in the screenshot given below:
2.13 FREQUENCY POLYGONS
Frequency polygons are a graphical
device for understanding the shapes
of distributions. They serve the
same purpose as histograms, but are
especially helpful for comparing sets
of data. Frequency polygons are also a
good choice for displaying cumulative
frequency distributions.
In a frequency polygon, the number of
observations is marked with a single
point at the midpoint of an interval.
A straight line then connects each
set of points. Frequency polygons
make it easy to compare two or more
distributions on the same set of axes.
0
1
First data point connected
to mid-point of previous
interval on X-axis
Last data point connected
to mid-point of following
interval on X-axis
Midpoints of intervals
are connected for a
frequency polygon
Frequency Polygon
Histogram
2 3 4 5 6 7 8 9
5
10
15
Week of Onset of Illness
Number
of
Cases
Number of reported cases of influenza-like illness by week of onset
Source: CDC 1998, p.241
Fig. 2.4: Frequency Polygon

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Let’s look at an example of a frequency polygon. Creating a frequency polygon for the number
of influenza cases per week.
Notice the dotted outline of a histogram for the same data. A frequency polygon smoothes out
the abrupt changes that may appear in a histogram, and is useful for demonstrating continuity
in the variables being studied. In this example, the number of reported cases of influenza-like
illness peaked during week 4 after the onset of illness.
Like a histogram, frequency polygons are used to display the entire frequency distribution
(counts) of a continuous variable. They must be closed at both ends because the area under
the curve represents all of the data. By contrast, an arithmetic-scale line graph represents a
series of observed data points (counts or rates), usually over time—it simply plots data points.
To generate a frequency polygon with reference to the histogram created in Practical
Implementation–22.

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2.14 BOX PLOT
Box plots are descriptive diagrams that help compare the distribution of different series of
data. They are descriptive because they show measures (e.g., the median) which do not assume
an underlying probability distribution.
A Box plot is the visual representation
of the statistical five-number summary
of a given dataset.
A Five-Number Summary includes:
• Minimum
• First Quartile (Q1)
• Median (Second Quartile-Q2)
• Third Quartile (Q3)
• Maximum
 What is a Box plot
A box plot, also called a box and whisker plot, is a way to show the spread and centres of a
dataset. Measures of spread include the interquartile range and the mean of the dataset.
Measures of centre include the mean or average and median (the middle of a dataset).
The box and whisker chart shows you how your data is spread out.
Five pieces of information (the “five-number summary”) are generally included in the chart:
• The minimum (the smallest number
in the dataset). The minimum is
shown at the far left of the chart, at
the end of the left “whisker.”
• First quartile, Q1, is the far left of
the box (or the far right of the left
whisker).
• The median is shown as a line in the
centre of the box.
• Third quartile, Q3, shown at the far
right of the box (at the far left of the
right whisker).
• The maximum (the largest number in
the dataset), shown at the far right
of the box.
Minimum
Medium
Maximum
Third quartile
Interquartile range
First quartile
Daily
log
returns
(%)
extreme data points
bottom whisker
1st quartile
2nd quartile (median)
3rd quartile
top whisker
extreme data points
–3
–2
–1
0
1
2
3
Fig. 2.5: Representation of a Quartile

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Thus, in a nutshell, the various components can be explained as:
"Minimum"
(Q1 – 1.5*IQR)
Interquartile Range
(IQR)
Q1
(25th Percentile)
Q3
(75th Percentile)
Outliers
Median
–4 –3 –2 –1 0 1 2 3 4
Outliers
"Maximum"
(Q3 + 1.5*IQR)
• median (Q2/50th Percentile): the middle value of the dataset.
• first quartile (Q1/25th Percentile): the middle number between the smallest number (not
the “minimum”) and the median of the dataset.
• third quartile (Q3/75th Percentile): the middle value between the median and the highest
value (not the “maximum”) of the dataset.
• interquartile range (IQR): 25th to the 75th percentile.
• whiskers (shown in blue)
• outliers (shown as green circles)
• “maximum”: Q3 + 1.5 * IQR
• “minimum”: Q1 – 1.5 * IQR
 What is Quartile
In the above description, an important term which is to be discussed most importantly is
“quartile” which has been derived or taken from the word “quantile”.
The word “quantile” finds its origin in the word quantity which means a quantile is where a
sample is divided into equal-sized subgroups (that’s why it’s sometimes called a “fractile”). It
can also refer to dividing a probability distribution into areas of equal probability.
The median is a kind of quantile; the median is placed in a probability distribution at the
centre so that exactly half of the data is lower than the median and half of the data is above
the median. The median cuts a distribution into two equal parts and that is why sometimes it
is called 2-quantile.
So we can say that quartiles are the quantiles which divide the distribution of data into four
equal parts. Deciles are quantiles that divide a distribution into 10 equal parts while percentiles
are quantiles that divide a distribution into 100 equal parts.
 How to Find Quartiles
Q1: Find the number in the following set of data where 30 per cent of values fall below it and
70 per cent fall above it:
2 4 5 7 9 11 12 17 19 21 22 31 35 36 45 44 55 68 79 80 81 88 90 91 92 100 112 113 114 120
121 132 145 148 149 152 157 170 180 190

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Step 1: Order the data from smallest to largest. The data in question is already in ascending
order.
Step 2: Count how many observations you have in your dataset. This particular dataset has
40 items.
Step 3: Convert any percentage to a decimal for “q”. We are looking for a number where
30 per cent of the values fall below it, so convert that to .3.
Step 4: Insert your values into the formula:
ith
observation = q (n + 1)
ith
observation = .3 (40 + 1) = 12.3
Answer: The ith
observation is at 12.3, so we round off to 12 (remembering that this
formula is an estimate). The 12th
number in the set is 31, which is the number below
which 30 per cent of the values fall.
In a nutshell, it can be interpreted as:
• The minimum and the maximum are just
the min and max values from our data.
• The median is the value that separates the
higher half of a data from the lower half. It
is calculated by the following steps: order
your values and find the middle one. In
a case when our count of values is even,
we actually have 2 middle numbers, so
the median here is calculated by summing
these 2 numbers and dividing the sum
by 2. For example, if we have the numbers
1, 2, 5, 6, 8, 9, the median will be
(5 + 6) / 2 = 5.5.
• The first quartile is the median of the
data values to the left of the median
in our ordered values. For example, if
we have the numbers 1, 3, 4, 7, 8, 8, 9,
the first quartile is the median from the
1, 3, 4 values, so it is 3.
• The third quartile is the median of the data values to the right of the median in our
ordered values. For example, if we use the numbers 1, 3, 4, 7, 8, 8, 9 again, the third
quartile is the median from the 8, 8, 9 values, so it is 8.
• The IQR (Interquartile Range) approximates the amount of spread in the middle 50% of
the data. The formula is the third quartile – the first quartile.
• This type of plot can also show outliers. An outlier is a data value that lies outside the
overall pattern. They are visualized as circles. When we have outliers, the minimum and
the maximum are visualized as the min and the max values from the values which aren’t
outliers. There are many ways to identify what an outlier is. A commonly used rule says
that a value is an outlier if it’s less than the first quartile – 1.5 * IQR or higher than the
third quartile + 1.5 * IQR.
third quartile
IQR
first quartile
minimum
maximum
median
Fig. 2.6: Components of a Box Plot

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 How to Read a Box Plot
A box plot is a way to show a five-number summary in a chart. The main part of the chart (the
“box”) shows where the middle portion of the data is: the interquartile range. The Interquartile
Range or IQR is the distance between the Upper and Lower Quartile. At the ends of the
box, you find the first quartile (the 25% mark) and the third quartile (the 75% mark). The far
left of the chart (at the end of the left “whisker”) is the minimum (the smallest number in the
set) and the far right is the maximum (the largest number in the set). Finally, the median is
represented by a vertical bar in the centre of the box. All these components to be read can be
described as an “artist”.
For example, given below is a box plot. It can be read and summarized as:
1
100 200 300 400 500 600 700
Min
1
100 200 300 400 500 600 700
Max
Q1 Q3
Med
The above image shows a box and whiskers chart with the following information:
• Minimum: 20
• Q1
: 160
• Median: 200
• Q3
: 330
• Maximum: 590
Box plots aren’t used that much in real life. However, they can be a useful tool for getting a
quick summary of data.
The box plot is generated by using boxplot() method, with the data set values, to be passed
as an argument to it.
To plot a simple box plot by passing list of lists.
list1 = [43,76,34,63,56,82,87,55,64,87,95,23,14,65,67,25,23,85]
list2 = [34,45,34,23,43,76,26,18,24,74,23,56,23,23,34,56,32,23]

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To plot a box plot for the frequency of marks obtained in each of the four courses offered by
“Vidhyarthi University”.
value1=[72,76,24,40,57,62,75,78,31,32]
value2=[62,5,91,25,36,32,96,95,30,90]
value3=[23,89,12,78,72,89,25,69,68,86]
value4=[99,73,70,16,81,61,88,98,10,87]

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• In the above code, four lists are passed to another list variable named box_plot_data and it
is plotted with boxplot function. boxplot() function takes the data array to be plotted as
input in the first argument, the second argument vert=1 signifies that it is a vertical plot;
the third argument patch_artist=True fills the box plot and the fourth argument takes
the label to be plotted.
A patch is a 2D artist with a face color and an edge color. When we set the attribute patch_
artist as True, it fills the box plot with colour, else if it is set to False, it will produce boxes
with a 2DLine without any colour fill.
The next statement defines a list of colours to be filled in the respective boxes. The number
of colours in the list should match the number of boxes to be filled. The next statement
(for loop) has been given for iterations to be carried for filling each box of the box plot.
• Colors list takes up four different colours and is passed to four different boxes of the box
plot with patch.set_facecolor() function. This argument sets the patch face color.
Note: if vert=0 in boxplot() is set, then horizontal box plots will be drawn.

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In the above code, if we add a new argument “notch”, then it will result in the creation of
another type of box plot known as “Notch plot”.
As can be observed from the code, boxplot() function takes the data array to be plotted as input
in the first argument and the second argument notch=‘True’ creates the notch format of the
box plot. The third argument patch_artist=True fills the box plot with colour while the fourth
argument takes the label to be plotted and, as a result, the output in the form of a “NOTCH”
type box plot is obtained as shown on the next page.

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NOTCH is a logical attribute for boxplot() method. NOTCH means narrowing of the box around
the median. If TRUE, it creates a notched box plot. The notch displays a confidence interval
around the median which is normally based on the median ( +/– 1.58 * IQR/sqrt(n)). Notches
are used to compare groups; if the notches of two boxes do not overlap, this signifies that the
medians differ. The width of the notches is proportional to the Interquartile Range (IQR) of the
sample and inversely proportional to the square root of the size of the sample.
In the above program code, by setting the argument for NOTCH attribute as True, it will narrow
down the range of given values (inputted list) towards the median value and, hence, a notch-
shaped box plot is created.
MEMORY BYTES
 In Python, we can use two exclusive libraries for visualization, commonly known as matplotlib and seaborn.
 Matplotlib is a Python package for 2D plotting that generates production-quality graphs.
 The aim of matplotlib is to generate graphs.
 We can add information to the plots such as legends, axis labels and titles.
 It is required to import the main matplotlib sub-module for plotting pyplot.
 A plot is a graphical technique for representing a data set, usually as a graph, showing the relationship between
two or more variables.
 Matplotlib is a package with a collection of command style functions that makes it work. It is a programming
platform designed specifically for engineers and scientists which allows the most natural expression of
computational mathematics.
 Each pyplot function makes some change to a figure, e.g., creating a figure, creating a plotting area in a figure,
plotting some lines in a plotting area, decorating the plot with labels, etc.
 plot() is a versatile command and takes an arbitrary number of arguments.
 In bar charts, each column represents a group defined by a categorical variable.

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 Seaborn is a library for creating informative and attractive statistical graphics in Python. This library is based
on Matplotlib. Seaborn offers various features such as built-in themes, color palettes, functions and tools to
visualize univariate, bivariate, linear regression, matrices of data, statistical time series, etc., which let us build
complex visualizations.
 A line chart or line graph is a type of chart which displays information as a series of data points called ‘markers’
connected by straight line segments.
 For the bar chart, we use the bar() function, where we define the position of the bars on the X-axis and their
height on the Y-axis.
 You can change line colour, width, line-style, marker-type, marker-colour, marker-size in plot() function.
 The scatter chart is a graph of plotted points on two axes that show the relationship between two sets of data.
 You can create scatter charts using either plot() function or scatter() function.
 The axes can be labelled using xlabel() and ylabel() functions.
 The title() function adds title to the plot.
 Using legend() function, one can add legends to a plot where multiple data ranges have been plotted, but before
that the data ranges must have their label argument defined in plot() or bar() function.
 A histogram provides a visual interpretation of numerical data by showing the number of data points that fall
within a specified range of values (called bins).
 Pyplot module’s hist() lets you create histograms.
 A frequency polygon is a type of frequency distribution graph.
 In a frequency polygon, the number of observations is marked with a single point at the midpoint of an interval.
 The box plot is used to show the range and the middle half of the ranked data.
 The boxplot() of plyplot lets you draw box plots.
OBJECTIVE TYPE QUESTIONS
1. Fill in the blanks.
(a) ................. refers to the graphical or visual representation of information and data using visual
elements like charts, graphs and maps, etc.
(b) ................. is a collection of methods with matplotlib library which allows the user to construct 2D
plots easily and interactively.
(c) The ................. chart is a graph of plotted points on two axes that shows the relationship between
two sets of data.
(d) ................. is the text that appears on the top of the plot and defines what the chart is about.
(e) The axes of a plot can be labelled using ................. and ................. functions.
(f) A ................. is a summarization tool for discrete or continuous data.
(g) Pyplot module’s ................. lets you create histograms.
(h) In a ................., the number of observations is marked with a single point at the midpoint of an
interval.
(i) The ................. is used to show the range and the middle half of the ranked data.
(j) The ................. module of Pyplot lets you draw box plots.
(k) Two functions of Pyplot library used to create scatter charts are ................ and ................ .
(l) Barh() function is used to create ................. bar chart.
(m) The area on which actual plot will appear is defined by ................. .
(n) ................. describe the number of data points that fall within a specified range of values.
(o) To change the orientation of the histogram, we can use ................. argument with hist().

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Answers: (a) Data Visualization (b) Pyplot (c) scatter
(d) Title (e) xlabel(), ylabel() (f) histogram
(g) hist() (h) frequency polygon (i) box plot
(j) boxplot() (k) plot(), scatter() (l) horizontal
(m) axes (n) Bins (o) orientation
2. State whether the following statements are True or False.
(a) The matplotlib is a Python interface.
(b) To save the plot, we have to use save graph() function.
(c) Frequency polygons are drawn with respect to the histogram created.
(d) The box plot is also described as five-number summary plot.
(e) In box plot, the highest and lowest scores are not joined to the box by straight lines.
(f) Plot can be saved in a pdf format.
(g) We can specify different colours for different bars of a bar chart.
(h) To use Pyplot for data visualization, we have to import it by giving import command for matplotlib.
pyplot.
(i) Pyplot is a Python library.
(j) Frequency polygon is a type of frequency distribution graph.
(k) To specify a common width for all bars in a bar graph, we have to use thick argument.
(l) To add a Title to the plot, we have to call function header().
(m) Markers are data points in the graphs.
(n) Line style argument of plot() function is not required in scatter chart.
(o) When we don’t specify X or Y limits for a plot, then Pyplot does not automatically decide limits as
per values being plotted.

Answers: (a) False (b) False (c) True (d) True (e) False (f) True
(g) True (h) True (i) False (j) True (k) False (l) False
(m) True (n) True (o) False
3. Multiple Choice Questions (MCQs)
(a) Which Python package is used for 2D graphics?
(i) matplotlib.pyplot (ii) matplotlib.pip
(iii) matplotlib.numpy (iv) matplotlib.plt
(b) The most popular data visualization library in Python is:
(i) pip (ii) matinfolib (iii) matplotlib (iv) matpiplib
(c) Matplotlib allows you to create:
(i) table (ii) charts (iii) maps (iv) infographics
(d) Which of the following is not a visualization under matplotlib?
(i) Scatter plot (ii) Histogram (iii) Box plot (iv) Table plot
(e) Which plot displays the distribution of data based on the five-number summary?
(i) Scatter plot (ii) Line plot (iii) Box plot (iv) Chart plot
(f) Which of the following commands is used to install matplotlib for coding?
(i) import plt.matplotlib as plot (ii) import plot.matplotlib as pt
(iii) import matplotlib.plt as plot (iv) import matplotlib.pyplot as plt
(g) Which of the following methods should be employed in the code to display a plot()?
(i) show() (ii) display() (iii) execute() (iv) plot()
(h) Which of the following statements is used to create a histogram of ‘step’ type with 20 bins?
(i) plt.hist(x, bins=20,histype=“barstacked”) (ii) plt.hist(x, bins=20)
(iii) plt.hist(x, bins=20,histype=“step”) (iv) plt.hist(x, bins=20,histype=hist()
(i) Which of the following plots makes it easy to compare two or more distributions on the same set of
axes?
(i) Box plot (ii) Histogram (iii) Frequency Polygon (iv) Bar chart

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(j) The part of chart which identifies different sets of data plotted on plot by using different colours is
called:
(i) legends (ii) title (iii) axes (iv) figure
(k) Which of the following is an incorrect example of savefig() function?
(i) plt.savefig(“bar1.pdf”) (ii) plt.savefig(“bar1.png”)
(iii) plt.savefig(“bar1.eps”) (iv) plt.savefig(“bar1.ppt”)
(l) Which of the following plots makes it easy to compare two or more distributions on the same set of
axes?
(i) Box plot (ii) Histogram (iii) Frequency polygon (iv) Bar chart
(m) Consider the snippet given below:
#arr1,arr2 defined here
colors=[‘r’,‘b’,‘k’,‘g’,‘m’]
sizes=[50,120,220,150,80]
plt.scatter(arr1,arr2, c=colors, s=sizes, marker=“s”)
With reference to the above code, what will be the shape of marker?
(i) square (ii) circle (iii) star (iv) diamond
(n) With reference to the code in (m), what will be the colour of the last point?
(i) Red (ii) Blue (iii) Black (iv) Magenta
(o) The scatter() function:
(i) is a powerful method of creating scatter plots than plot() function
(ii) can create line graph
(iii) can create bar graph
(iv) None of the above

Answers: (a) (i) (b) (iii) (c) (ii) (d) (iv) (e) (iii) (f) (iv)
(g) (i) (h) (iii) (i) (iii) (j) (i) (k) (iv) (l) (iii)
(m) (i) (n) (iv) (o) (i)
SOLVED QUESTIONS
1. What is Python matplotlib?
Ans. matplotlib.pyplot is a plotting library used for 2D graphics in Python programming language. It can be
used in Python scripts, shell, web application servers and other graphical user interface toolkits.
2. How can we install matplotlib?
Ans. It is easy to install Python matplotlib library with pip statement – pip install matplotlib.
3. What are the various types of plots offered by matplotlib?
Ans. Matplotlib offers several types of plots:
• Line Graph
• Bar Graph
• Histogram
• Scatter Plot
• Area Plot
• Pie Chart
4. What is data visualization? What is its significance?
Ans. Data visualization is a general term that describes any effort to help people understand the significance
of data by placing it in a visual context. In simple words, Data visualization is the process of displaying
data/information in graphical charts, figures and bars.
5. Name the functions you will use to create a (i) line chart, (ii) bar chart, (iii) scatter chart.
Ans. (i) matplotlib.pyplot.plot()
(ii) matplotliblpyplot.bar()
(iii) matplotlib.pyplot.plot() and matplotlib.pyplot.scatter()

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6. Mr. Sanjay wants to plot a bar graph for the given set of values of subject on x-axis and number of students
who opted for that subject on y-axis. [CBSE Sample Paper 2020]
Complete the code to perform the following:
(i) To plot the bar graph in statement 1
(ii) To display the graph in statement 2
x=['Hindi', 'English', 'Science', 'SST']
y=[10,20,30,40]
_____________________ Statement 1
_____________________ Statement 2
Ans. (i) plt.bar(x, y)
(ii) plt.show()
7. Mr. Harry wants to draw a line chart using a list of elements named LIST. Complete the code to perform
the following operations:
(i) To plot a line chart using the given LIST,
(ii) To give a y-axis label to the line chart named “Sample Numbers”.
import matplotlib.pyplot as PLINE
LIST=[10,20,30,40,50,60]
_____________________ Statement 1
_____________________ Statement 2
PLINE.show()
Ans. (i) PLINE.plot(LIST)
(ii) PLINE.ylabel(Sample Numbers)
8. Write a code to plot the speed of a passenger train as shown in the figure given below:

Ans. import matplotlib.pyplot as plt
import numpy as np
x = np.arange(1, 5)
plt.plot(x, x*1.5, label='Normal')
plt.plot(x, x*3.0, label='Fast')
plt.plot(x, x/3.0, label='Slow')
plt.legend()
plt.show()

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9. Considerthecodegivenbelow(allrequiredlibraries
are imported) and the output produced by it. Why
is the chart showing one bar only while we are
plotting four values on the chart?
a = [3, 6, 9, 12]
b = [30, 48, 54, 48]
plt.xlim(-3, 5)
plt.bar(a, b)
plt.show()
Ans. The given chart is showing a single bar as the limits
of X-axis have been set as –3 to 5. On this range,
only one value from the data range being plotted
falls, i.e., only a[0] and b[0] fall on this range. Thus,
only a single value b[0], i.e., 30 is plotted against
a[0], i.e., 3.
10. What changes will you make to the code of previous question so that the bars are visible for all four
points? But do keep in mind that the X-axis must begin from the point –3.
Ans. If we change the limits of X-axis so that all the points being plotted fall in the range of limits, all values
will show. Thus, we have changed the limits from =3 to 15, in place of –3 to 5.
plt.xlim(-3, 15)
plt.bar(a, b)
plt.show()
11. Why is the following code not producing any result? Why is it giving error? (Note that all required libraries
have been imported and are available)
a = range(10, 50, 12)
b = range(90, 200, 20)
matplotlib.pyplot.plot(a, b)
Ans. The above code is producing error because the two sequences being plotted, i.e., a and b do not match
in shape. While sequence ‘a’ contains 4 elements (10, 22, 34 and 46), sequence ‘b’ contains 6 elements
(90, 110, 130, 150, 170 and 190). For plotting, it is necessary that the two sequences being plotted must
match in their shape.
12. What modification is required to rectify the error in previous question code?
Ans. Since both the sequences being plotted must match in their shape, we can achieve this either by adding
two elements to sequence a so that it has the same shape as sequence b (i.e., 6 elements) or by removing
two elements from sequence b so that it matches the shape of sequence a (i.e., 4 elements)
For instance,
a = range(10, 50, 12)
b = range(90, 160, 20)
matplotlib.pyplot.plot(a, b)
13. What is a scatter chart? How is it different from a line chart?
Ans. The scatter chart is a graph of plotted points that show the relationship between two sets of data. With
a scatter plot a mark or marker (usually a dot or small circle), represents a single data point. With one
mark (point) for every data point a visual distribution of the data can be seen. Depending on how tightly
the points cluster together, you may be able to discern a clear trend in the data.
The difference is that with a scatter plot, the decision is made from the data points such that the individual
points should not be connected directly together with a line but, instead express a trend.
14. What is a histogram? How is it useful?
Ans. A histogram is a statistical tool used to summarize discrete or continuous data. It provides a visual
interpretation of numerical data by showing the number of data points that fall within a specified range
of values (called “bins”).

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15. Given below is the data about several airlines and flight arrival delay.
Filghts_arr_delay Name
0 11.0 Air India
1 20.0 SpiceJet Ltd.
2 33.0 Jet Airways
3 –18.0 Indigo
4 –25.0 Air India
5 12.0 GoAir
6 19.0 JetAirways
7 –14.0 SpiceJet Ltd.
8 –8.0 Air India
9 8.0 GoAir
The flight arrival delays are in minutes and negative values mean the flights came early. There are over
300,000 flights with a minimum delay of –60 minutes and a maximum delay of 120 minutes. The other
column is the name of the airline which we can use for comparisons.
Ans. For the plot calls, we specify the binwidth by the number of bins. For this plot, we will use bins that are
5 minutes in length, which means that the number of bins will be the range of the data (from –60 to 120
minutes) divided by the binwidth, i.e., 5 minutes (bins = int(180/5)).
# Import the libraries
flights_arr_delay=[-25.0,-18.0,-14.0,
-8.0,8.0,11.0,12.0,
19.0,20.0,33.0]
# matplotlib histogram
plt.hist(flights_arr_delay, color =
'blue', edgecolor = 'black',

bins = int(180/5))
# Add labels
plt.title('Histogram of Arrival Delays')
plt.xlabel('Delay (min)')
plt.ylabel('Flights')
plt.show()
16. Create a box plot from the following set of data:
34, 18, 100, 27, 54, 52, 93, 59, 61, 87, 68, 85, 78,
82, 91
a = [34, 18, 100, 27, 54, 93, 59, 61,
87, 68, 85, 78, 82, 91]
plt.boxplot(a)
plt.show()

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17. Create the same box plot as previous question,
but change the orientation to horizontal.
a = [34, 18, 100, 27, 54, 52, 93, 59,
61, 87, 68, 85, 78, 82, 91]
plt.boxplot(a, vert=False)
plt.show()
18. How are bar charts represented using matplotlib?
Ans. Bar charts display rectangular bars (either vertical or horizontal) with their length proportional to the
values they represent. They are commonly used to visually compare two or more values. The bar()
function is used to generate bar charts in Matplotlib. The function expects two lists of values: the X
coordinates that are the positions of the bar’s left margin and the height of the bars.
19. What is the purpose of plot function?
Ans. plot() is a versatile command and takes an arbitrary number of arguments. For example, plot (x, y) to plot x
versus y.
20. Which function is required to plot a bar graph?
Ans. Bar in place of plot(), i.e., bar() function is used to plot a bar graph.
21. List the methods used with pyplot.
Ans. Various methods used with pyplot object are:
• plot()
• show()
• title()
• xlabel()
• ylabel()
• explode()
• bar()
• hist()
• scatter()
• box plot()
22. Given below are the sugar levels for men and women in a city. Compare the sugar levels amongst them
through a histogram.
Men: [113,85,90,150,149,88,93,115,135,80,77,82,129]
Women: [67,98,89,120,133,150,84,69,89,79,120,112,100]
blood_sugar_men = [113,85,90,150,149,88,93,
115,135,80,77,82,129]
blood_sugar_women = [67,98,89,120,133,150,84,
69,89,79,120,112,100]
plt.xlabel('sugar range')
plt.ylabel('Total no. of patients')
plt.title('Blood sugar analysis')
plt.hist([blood_sugar_men, blood_sugar_women], bins=[80,100,125,150],
rwidth=0.95, color=['green','orange'], label=['men','women'])
plt.legend()
plt.show()

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23. Modify the above code for displaying the histogram horizontally.
Ans. This can be done by using attribute “orientation” in hist() function and setting it to ‘horizontal’.

blood_sugar_men = [113,85,90,150,149,88,93,
115,135,80,77,82,129]
blood_sugar_women = [67,98,89,120,133,150,84,
69,89,79,120,112,100]
plt.xlabel('sugar range')
plt.ylabel('Total no. of patients')
plt.title('Blood sugar analysis')
plt.hist([blood_sugar_men, blood_sugar_women],
bins=[80,100,125,150],
rwidth=0.95,
color=['green','orange'],
label=['men','women'],
orientation='horizontal')
plt.legend()
plt.show()
24. The data below represents the number of pages each student in class 12C read during reading time.
16,16,16,20,21,21,23,25,26,26,28,28
Which box plot given below correctly summarizes the data?

15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
A
15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
B
15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
C
Ans. B is the correct answer.
The following box plot correctly summarizes the data:

15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30

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16,16,16,20,21,21,23,25,26,26,28,28
Min = 16
Median =
21 + 23
2
= 22
Max = 28
16,16,16,20,21,21,23,25,26,26,28,28

Q1
=
16 + 20
2
= 18

Q3
=
16 + 20
2
= 26
25. Which dataset could be represented by the box plot shown below?

0 2 4 6 8 10 12 14 16 18 20

A 3,4,8,9,9,10,12,13,13,16,18

B 3,4,7,9,9,10,12,13,13,16,18

C 3,4,8,9,9,12,12,13,13,16,18

D 2,4,7,9,9,10,12,13,13,16,18
Ans.

0 2 4 6 8 10 12 14 16 18 20
min max
Q1 Q3
median
The minimum of the box plot is 3.
We can eliminate one of the datasets because it does not have a minimum of 3.
2,4,7,9,9,10,12,13,13,16,18
The median of the box plot is 10.
We can eliminate one of the datasets because it does not have a median of 10.
3,4,8,9,9,12,12,13,13,16,18
The first quartile of the box plot is 7.
We can eliminate one of the datasets because it does not have a first quartile of 7.
3,4,8,9,9,12,12,13,13,16,18
The following dataset could be represented by the box plot.
3,4,7,9,9,10,12,13,13,16,18

min max
Q1 Q3
median
0 2 4 6 8 10 12 14 16 18 20

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26. Suppose that the box-and-whisker plots below represent quiz scores out of 25 points for Quiz 1 and
Quiz 2 for the same class. What do these box-and-whisker plots show about how the class did on
test #2 compared to test #1?

0 2 4 6 8 10 12 14 16 18 20 22 24
Test 1
Test 2
Ans. These box-and-whisker plots show that the lowest score, the highest score and Q3
are all the same for
both the exams. So performance on the two exams was quite similar. However, the movement Q1
up
from a score of 6 to a score of 9, indicates that there was an overall improvement. On the first test,
approximately 75% of the students scored at or above a score of 6. On the second test, the same number
of students (75%) scored at or above a score of 9.
27. Which module needs to be imported for showing data in a chart form?
Ans. Matplot
28. Plot a line chart to depict a comparison of population between India and Pakistan.
Ans. #Population comparison between India and Pakistan
from matplotlib import pyplot as plt
year = [1960, 1970, 1980, 1990, 2000, 2010]
popul_pakistan = [44.91, 58.09, 78.07, 107.7, 138.5, 170.6]
popul_india = [449.48, 553.57, 696.783, 870.133, 1000.4, 1309.1]
plt.plot(year, popul_pakistan, color='green')
plt.plot(year, popul_india, color='orange')
plt.xlabel('Countries')
plt.ylabel('Population in million')
plt.title('India v/s Pakistan Population till 2010')
plt.show()

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2.52
29. Plot list elements using line chart.
Ans. #To plot list elements using Line Chart
#in Python pyplot
list2 = [1, 2, 3, 4, 5, 6, 7]
plt.plot(list2)
plt.ylabel('some numbers')
plt.show()

30. Write a program to plot list elements between X-axis and Y-axis.
Ans. #Program to plot a simple Line chart holding list elements
#against x-axis and y-axis respectively
plt.plot([1,2,3,4],[1,4,9,16])
plt.xlabel(label for X axis)
plt.ylabel(label for Y axis)
plt.show()

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31. Plot a bar chart to depict the popularity of various programming languages.
Ans. #Program to plot a Bar chart on the basis of popularity of Programming Languages
import numpy as np
objects = ('DotNet', 'C++', 'Java', 'Python', 'C', 'CGI/PERL')
y_pos = np.arange(len(objects))
performance = [8,10,9,20,4,1]
plt.bar(y_pos, performance, align='center', color='blue')
plt.xticks(y_pos, objects) #set location and label
plt.ylabel('Usage')
plt.title('Programming language usage')
plt.show()

32. Write a Python program to draw a line with a suitable label in the X-axis and Y-axis, and a title. The code
snippet gives the output shown in the following screenshot:

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X = range(1, 50)
Y = [value * 3 for value in X]
print(Values of X:)
print(range(1,50))
print(Values of Y (thrice of X):)
print(Y)
# Plot lines and/or markers to the Axes.
plt.plot(X, Y)
# Set the x axis label of the current axis.
plt.xlabel('x – axis')
# Set the y axis label of the current axis.
plt.ylabel('y – axis')
# Set a title
plt.title('Draw a line.')
# Display the figure.
plt.show()

33. Write a Python program to draw a line using given axis values with a suitable label in the X-axis and Y-axis,
and a title. The code snippet gives the output as shown in the following screenshot:

# x axis values
x = [1,2,3]
# y axis values

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y = [2,4,1]
plt.plot(x, y)
plt.xlabel('X – axis')
plt.ylabel('Y – axis')
# Set a title
plt.title('Sample graph!')
# Display a figure.
plt.show()
34. Write a Python program to plot two or more lines with legends, different widths and colours.

# line 1 points
x1 = [10,20,30]
y1 = [20,40,10]
# line 2 points
x2 = [10,20,30]
y2 = [40,10,30]
# Set a title
plt.title('Two or more lines with different widths and colors with suitable
legends')
# Display the figure.
plt.plot(x1,y1, color='blue', linewidth = 3, label = 'line1-width-3')
plt.plot(x2,y2, color='red', linewidth = 5, label = 'line2-width-5')
# show a legend on the plot
plt.legend()
plt.show()

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35. Write a Python program to plot two or more lines with different styles (dotted lines).
Ans.

# line 1 points
x1 = [10,20,30]
y1 = [20,40,10]
# line 2 points
x2 = [10,20,30]
y2 = [40,10,30]
plt.plot(x1,y1, color='blue', linewidth = 3, label = 'line1-dotted',
linestyle='dotted')
plt.plot(x2,y2, color='red', linewidth = 5, label = 'line2-dashed',
linestyle='dashed')
# Set a title
plt.title(Plot with two or more lines with different styles)
# show a legend on the plot
plt.legend()
# function to show the plot
plt.show()
36. Write a Python program to create a bar plot of scores by group and gender. Use multiple X values on
the same chart for men and women.
Sample Data:
Means (men) = (22, 30, 35, 35, 26)
Means (women) = (25, 32, 30, 35, 29)
Ans. import numpy as np
# data to plot
n_groups = 5
men_means = (22, 30, 35, 35, 26)
women_means = (25, 32, 30, 35, 29)
y_pos = np.arange(len(men_means))
plt.bar(2*y_pos,women_means,color='g',label=Women's score)
plt.bar(2*y_pos+1,men_means,color= 'b',label=Men's score)
plt.xlabel('Person')
plt.ylabel('Scores')

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plt.title('Scores by person')
plt.legend()
plt.show()

37. Plot a square using grid() in pyplot.
Ans. #To plot a square through function using pyplot
def square(x, y):
'''
Objective: To plot a square
Input Parameters: x, y - lists of x coordinates and y
coordinates respectively
Return Value: None
'''
if (x[1] - x[0]) = 0:
return
plt.plot(x, y, 'ro--')
return square([x[0]+1, x[1]-1, x[2]-1, x[3]+1, x[4]+1],[y[0]+1, y[1]+1,

y[2]-1, y[3]-1, y[4]+1])
def main():
'''
Objective: To plot a square based on user input
Input Parameter: None
Return Value: None
'''
size = int(input('Enter size of the square: '))
x = [0, size, size, 0, 0]
y = [0, 0, size, size, 0]
square(x, y)
plt.title('Square')
plt.axis([min(x)-1, max(x)+1, min(y)-1, max(y)+1])
plt.grid()
plt.show()
if __name__ == '__main__':
main()

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38. Depict the relationship between unemployment Rate and Stock Index Price through a scatter plot.
Unemployment_Rate Stock_Index_Price
6.1 1500
5.8 1520
5.7 1525
5.7 1523
5.8 1515
5.6 1540
5.5 1545
5.3 1560
5.2 1555
5.2 1565
Unemployment_Rate = [6.1,5.8,5.7,5.7,5.8,5.6,5.5,5.3,5.2,5.2]
Stock_Index_Price = [1500,1520,1525,1523,1515,1540,1545,1560,1555,1565]
plt.scatter(Unemployment_Rate, Stock_Index_Price, color='green')
plt.title('Unemployment Rate Vs Stock Index Price', fontsize=14)
plt.xlabel('Unemployment Rate', fontsize=14)
plt.ylabel('Stock Index Price', fontsize=14)
plt.grid(True)
plt.show()

Unemployment Rate vs Stock Index Price
Unemployment Rate
Stock
Index
Price
1560
1550
1540
1530
1520
1510
1500
5.2 5.4 5.6 5.8 6.0

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UNSOLVED QUESTIONS
1. Plot a line chart for depicting the population for the last 5 years as per the specifications given below:
• plt.title(“My Title”) will add a title “My Title” to your plot.
• plt.xlabel(“Year”) will add a label “Year” to your X-axis.
• plt.ylabel(“Population”) will add a label “Population” to your Y-axis.
• plt.xticks([1, 2, 3, 4, 5]) set the numbers on the X-axis to be 1, 2, 3, 4, 5. Pass it and label as a second
argument. For example, if we use this code plt.xticks([1, 2, 3, 4, 5], [1M, 2M, 3M, 4M, 5M]),
it will set the labels 1M, 2M, 3M, 4M, 5M on the X-axis.
• plt.yticks() — works the same as plt.xticks(), but for the Y-axis.
2. What is matplotlib?
3. What do you mean by pyplot?
4. How do we update pip?
5. Why do we update pip?
6. How many types of graphs are plotted using pyplot?
7. Explain the utility of explode().
8. How is a pie chart different from a bar graph?
9. Which function is used to show the graph?
10. Write a Python program to draw a line with a suitable label in the X-axis and Y-axis, and a title.
11. Write a Python program to plot two or more lines with legends, different widths and colours.
12. Write a Python program to plot two or more lines and set the line markers.
13. Write a Python program to create a pie chart with the title of the Stream and percentage of Students.
Make multiple wedges of the pie.
Sample data:
Stream : Science, Commerce, Humanities, Vocational, FMM
Strengths: 29%, 30%, 21%, 13%, 7%
14. Write a Python program to display a bar chart of the number of students in a class. Use different colours
for each bar.
Sample data:
Class : I,II,III,IV,V,VI,VII,VIII,IX, X
Strengths: 40,43,45,47,49,38,50,37,43,39
15. Write a Python program to display a horizontal bar chart of the number of students in a class.
Sample data:
Class : I,II,III,IV,V,VI,VII,VIII,IX, X
Strengths: 40,43,45,47,49,38,50,37,43,39
16. Plot a pie chart of a class test of 40 students based on random sets of marks obtained by the students
(MM=100).
17. Plot a line graph for: y2
=4*x
18. Write a Python program to plot the function y = x2
using the pyplot or matplotlib libraries.
19. What are the various types of graphs?
20. Name the various methods used with pyplot object.
21. Write the specific purpose of the following functions used in plotting:
(i) shape() (ii) legend()
22. Write a Python program to plot the function y=x2
.
23. Plot a histogram of a class test of 40 students based on random sets of marks obtained by the students
(MM=100).
24. A list namely temp contains average temperature for seven days of last week. You want to see how the
temperature changes in last seven days. Which chart type will you plot for the same and why?

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25. Write the code to practically produce a chart of previous question (i) using line chart, (ii) using scatter
chart.
26. What is a histogram? How do you create histograms in Python?
27. What are the various types of histograms that can be created through hist() function?
28. When should you create histograms and when should you create bar charts to present data visually?
29. What is a frequency polygon? How do you create it?
30. What is Box plot? How do you create it in Pyplot?
31. Given the following set of data:
Weight measurements for 14 values of muffins (in grams)
78, 72, 69, 81, 63, 67, 65
79, 74, 71, 83, 71, 79, 80
(a) Create a simple histogram from the above data.
(b) Create a horizontal histogram from the above data.
(c) Create a step type of histogram from the above data.
(d) Create a cumulative histogram from the above data.
32. Create/draw frequency polygon from the data used in the above question.
33. From the following ordered set of data:
63, 65, 67, 69, 71, 71, 72, 74, 75, 78, 79, 79, 80, 81, 83
(a) Create a horizontal box plot
(b) Create a vertical box plot
34. Kritika was asked to write the names of a few libraries in Python used for data analysis and one method
of each. Help her write at least 3 libraries and their methods.
35. The data below represents the number of workouts each member of ABC Gym attended last month.
4, 5, 7, 7, 7, 8, 10, 11, 11, 13, 13, 14
Which box plot correctly summarizes the data?

0 2 4 6 8 10 12 14 16
0 2 4 6 8 10 12 14 16
0 2 4 6 8 10 12 14 16
A
B
C
36. The following amounts (in `) were the hourly collection from BIG MARKET at a local store in one day in
December:
19, 26, 25, 37, 32, 28, 22, 23, 29, 34, 39 and 31.
Construct the box-and-whisker plot for the amount collected.

Class 12 Ip Whole Text Book Preeti Arora

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