An

Industrial Training Report

On
Python programming
In Partial Fulfillment for The Degree of
Bachelor of Technology
In
Computer Science & Engineering
From

Engineering College , Bikaner

Affiliated to
Bikaner Technical University , Rajasthan

(Session : 2024-25)
Submitted by: Submitted to:
Mr. Danish Sharma Dr. Maninder Singh Nehra
CSE-1(7th Semester) Assistant Professor
21EEBCS027 CSE Department
 Danish sharma

Python Programming Course

Powered by TCPDF (www.tcpdf.org)

 Acknowledgement

I would like to express my sincere gratitude to technophilia for providing me with an enriching
and comprehensive learning experience during the two-month internship program.This journey
has been incredibly rewarding, and I am immensely thankful for the knowledge, skills, and
insights gained throughout the program. The well- structured curriculum, hands-on projects, and
guidance from the experienced instructors have been instrumental in shaping my understanding
of python programming.. The well- structured curriculum, hands-on projects, and guidance from
the experienced instructors have been instrumental in shaping my understanding of python
programming.I would like to extend my appreciation to the entire technophilia team for their
dedication and support. The collaborative learning environment, regular feedback sessions, and
real-world application of concepts have significantly enhanced my proficiency in python
programming . Once again, thank you, technophilia , for providing me with this invaluable
opportunity.

Regards,
Danish Sharma
 Abstract

Python is a versatile, high-level programming language known for its readability, simplicity, and
extensive libraries, making it ideal for beginners and professionals alike. Initially developed in
the late 1980s, Python has grown significantly, becoming one of the most popular programming
languages for web development, data science, artificial intelligence, machine learning, and
automation. Its clean syntax and dynamic semantics encourage productivity and ease of
debugging, leading to reduced development time.Python supports multiple programming
paradigms, including procedural, object-oriented, and functional programming, giving
developers flexibility in coding styles. Its large community and ecosystem provide robust
libraries and frameworks, such as Django, Flask, Pandas, and TensorFlow, which facilitate
development across a wide array of applications. The language’s extensive use in data-driven
fields is further enhanced by its compatibility with other programming languages, scalability,
and continuous improvement through regular updates.Python’s simplicity, combined with its
powerful tools and libraries, enables rapid application development and seamless integration,
proving indispensable across various industries, including finance, healthcare, technology, and
education.

Keywords: Python programming, readability, simplicity, libraries, web development, data

science, artificial intelligence, machine learning, automation, programming paradigms,
productivity, debugging, development time, community, frameworks, data-driven applications,
scalability, industry applications.
 List of content

S.no Topic name

1 Introduction about python

2 Scripting language

3 Oops introduction

4 History about python

5 Variable type and operators

6 Tuple and list

7 Loops and conditional

8 Functions

9 Uses of python and scope

10 Projects

11 Conclusion

12 References
 Chapter 1

Introduction to Python Programming and Its Versions

Python is a high-level, interpreted programming language designed for simplicity and ease of
use. Its development was conceived by Guido van Rossum in the late 1980s and released for the
first time in 1991. Over the years, Python has grown into one of the most popular and versatile
programming languages in the world. It is used in diverse fields ranging from web development
and data science to machine learning and artificial intelligence. In this article, we will explore the
evolution of Python, its various versions, and its role in modern software development.

Guido van Rossum designed Python as a successor to the ABC programming language, which
was developed at the Centrum Wiskunde & Informatica in the Netherlands. ABC was a teaching
language, and while it was designed to be simple, it had many limitations. Van Rossum’s goal
with Python was to create a language that was intuitive, highly readable, and extensible. Python's
first version, Python 0.9.0, was released in February 1991. It included many of the features that
are still prominent in Python today, such as exception handling, functions, and the core data
types like strings, lists, and dictionaries.

Python is widely appreciated for its simplicity and readability, which makes it an excellent
choice for both beginners and experienced programmers. The key features of Python include:

• Readability: Python emphasizes code readability and clean syntax. It uses indentation to
define code blocks, which makes the structure of Python code easily understandable.
• Dynamic Typing: Python is dynamically typed, meaning variable types do not need to be
declared explicitly. This flexibility allows for faster prototyping and reduces boilerplate
code.
• Extensive Standard Library: Python comes with a vast standard library that provides
tools for file I/O, regular expressions, threading, databases, and more. This reduces the
need for external libraries in many applications.
 • Object-Oriented and Functional: Python supports multiple programming paradigms,
including object-oriented programming (OOP) and functional programming, enabling
developers to use the best features of each approach.
• Cross-Platform Compatibility: Python code can run on various platforms, such as
Windows, macOS, Linux, and more. This makes it an excellent choice for cross-platform
software development.
• Interpreted Language: Python code is executed by an interpreter, which allows for
interactive development. This means that developers can test and modify code quickly.

Python’s development can be broken down into several key milestones. Each new version
introduced new features, optimizations, and sometimes breaking changes. Let’s look at some of
the major Python versions and their impact.

Python 1.x

• First Release: Python 1.0 was released in January 1994, following the initial release of
version 0.9.0 in 1991. It had the core features of the language we know today, including
basic data types, functions, exception handling, and more.
• Python 1.5 (1997): Python 1.5 introduced several important features, including the long
integer type and the string module. It also laid the foundation for the integration of
modules and libraries that would make Python more versatile.

Python 2.x

• Python 2.0 (2000): This version marked a significant shift in Python’s development. The
language became more robust and had a more mature feature set. Python 2.x introduced
list comprehensions, garbage collection, and the ability to handle Unicode, among other
improvements.
• Python 2.7 (2010): The last release in the 2.x series, Python 2.7, was considered a long-
term support (LTS) release and continued to be widely used even after the introduction of
Python 3. Python 2.7 included improvements such as better integration with newer
libraries, optimizations, and minor syntax changes. Python 2.x versions, however, were
not compatible with Python 3, making the transition between the two series a significant
challenge for developers.
 Chapter 2

Scripting language introduction

A scripting language is a type of programming language that is primarily designed to automate

tasks that would otherwise be performed manually by a user. Unlike compiled languages, which
require a separate compilation step before execution, scripting languages are typically
interpreted. This means the code is executed line-by-line by an interpreter at runtime, making
scripting languages well-suited for tasks that require quick development and easy modification.

Scripting languages are often used for:

• Automating repetitive tasks: Such as file management, system administration, or data

processing.
• Glueing components together: By writing scripts that can integrate different systems,
programs, or services.
• Prototyping and rapid development: For quickly testing ideas and building smaller
applications.
• Controlling software environments: Often used for interacting with web servers,
databases, and other software systems in an automated manner.

Python is considered a scripting language due to its ability to automate tasks, its interpreted
nature, and its focus on ease of use and quick development.

Why Python is a Great Scripting Language

Python’s design philosophy emphasizes readability, simplicity, and flexibility, which makes it an
excellent choice for scripting tasks. Below are several reasons why Python is considered one of
the best scripting languages available today:

1. Readability and Ease of Use

Python's syntax is clean, straightforward, and highly readable, which makes it easy to learn and
use for both beginners and experienced developers. In Python, code often looks very close to
human language, which reduces the cognitive load on programmers when writing or debugging
scripts. For example, Python uses indentation to define code blocks, rather than curly braces or
keywords, which encourages clear and concise code structure.

Example of Python code for a simple script that reads and prints a file:

python
Copy code
with open("example.txt", "r") as file:
content = file.read()
print(content)

In this example, Python's simplicity allows us to read and print the contents of a file in just a few
lines of code, making it easy for a developer to quickly write scripts for automation tasks.

2. Cross-Platform Compatibility

Python is cross-platform, meaning that scripts written in Python can run on different operating
systems (Windows, macOS, Linux) without modification. This is particularly valuable for
system administration and automating tasks that need to run across multiple platforms.

For instance, a Python script that automates the management of files on a server can be written
on a developer’s local machine and then executed on a remote server regardless of the
underlying operating system.

3. Extensive Standard Library

Python comes with a vast standard library that includes modules for performing a wide variety of
tasks. Whether you need to interact with the file system, send HTTP requests, parse data, or work
with regular expressions, Python’s standard library provides pre-built solutions that make
scripting faster and easier.

For example, Python includes modules such as:

• os: For interacting with the operating system (e.g., file management).
• subprocess: For running system commands and managing processes.
Chapter 3

Introduction about oops

Object-Oriented Programming (OOP) is a programming paradigm that organizes software design

around data, or objects, rather than functions and logic. In OOP, the concept of objects—
representing real-world entities—is central to creating more modular, reusable, and organized
code. Python, as an object-oriented language, supports and encourages the use of OOP
principles.

Object-Oriented Programming (OOP) is a method of programming where the software is

structured around objects rather than actions. An object is an instance of a class, which is
essentially a blueprint or template that defines the structure (attributes) and behaviors (methods)
of the objects created from it.
The main principles of OOP include:

• Encapsulation
• Inheritance
• Polymorphism
• Abstraction
Core Concepts of OOP in Python
1. Class and Object

A class is a blueprint for creating objects. It defines the properties and behaviors of the objects
created from it. An object is an instance of a class. It has the attributes (variables) and methods
(functions) that are defined by the class.

In Python, a class is defined using the class keyword:

class Dog:

# The init method is the constructor that initializes the object

def init (self, name, age

self.name = name # Attribute for the dog's name

self.age = age # Attribute for the dog's age

# Method that defines behavior for the dog

def speak(self):

return f"{self.name} says Woof!"

# Creating an object (instance) of the Dog class

dog1 = Dog("Buddy", 5)

dog2 = Dog("Bella", 3)

print(dog1.speak()) # Output: Buddy says Woof!

print(dog2.speak()) # Output: Bella says Woof!

• The init method is a special method in Python classes. It is called a constructor,

and it is used to initialize the attributes of the object when it is created.
• The self parameter represents the instance of the class. It allows access to the
attributes and methods of the object.

2. Encapsulation: Encapsulation is the concept of bundling the data (attributes) and the methods
that operate on the data into a single unit called a class. It also involves restricting access to some
of the object's components, which can prevent the accidental modification of data. This is usually
done by making attributes private and providing public methods to access them.

In Python, you can encapsulate data by using private attributes (prefix with ) and then create
getter and setter methods:

class Account:
def init (self, owner, balance): self.owner = owner #
Public attribute
self. balance = balance # Private attribute

# Getter method to access the balance def

get_balance(self):
return self. balance

# Setter method to modify the balance

 Chapter 4

History about python

Python is one of the most popular and versatile programming languages in the world today,
known for its simplicity, readability, and vast ecosystem of libraries and frameworks. Created by
Guido van Rossum in the late 1980s, Python’s evolution has been marked by a combination of
visionary design choices and continuous development. Its history is a fascinating journey that
highlights both the technical aspects and the community-driven nature of its growth.
The Birth of Python

The story of Python begins in December 1989 when Guido van Rossum, a Dutch programmer,
began developing Python at the Centrum Wiskunde & Informatica (CWI) in the Netherlands. At
the time, van Rossum was working on a project called "ABC", a programming language aimed
at beginners that was developed at CWI. Although ABC was simple and powerful, it had
limitations that made it unsuitable for more advanced tasks. Van Rossum, wanting to improve
upon ABC, created Python as a successor.

Python was designed with the goal of being simple, easy to read, and highly extensible. Van
Rossum drew inspiration from ABC, but he also incorporated features from other languages such
as C, Modula-3, and Unix shell scripting. The name "Python" itself was chosen as a tribute to
Monty Python’s Flying Circus, a British comedy series that van Rossum enjoyed watching.
The name was intended to convey a sense of fun and creativity, reflecting the language's goal of
being both easy to use and powerful.

Early Development: Python 0.9.0 to 1.0

The first version of Python, Python 0.9.0, was released in February 1991. This early version
already contained many features that would become core elements of the language, including:

• Exception handling: Python introduced the ability to catch and handle errors in code
gracefully.
• Functions: Like other programming languages, Python supported functions as building
blocks of code.
• Core data types: The language included basic types like strings, lists, and dictionaries.

Van Rossum also released Python 0.9.0 with some familiar syntax that would make Python
unique, such as the whitespace-based indentation to define code blocks, which later became
one of Python's most defining features. This early version of Python was already structured in
such a way that readability would be a key selling point.

Python 1.0 was officially released in January 1994. By then, the language had already grown in
both features and user base. Python 1.0 introduced several important concepts, including:
 Chapter 5

Variables and operators

In Python, variables and operators are fundamental concepts that allow you to store and
manipulate data. Understanding how to work with variables and operators is essential for writing
effective and efficient Python code. This article will provide an overview of variables and
operators in Python, including how to use them in different contexts.

1. Variables in Python

What is a Variable?

A variable in Python is a symbolic name for a value. It acts as a reference to data stored in
memory, allowing you to perform operations on it. Python uses variables to store data of various
types, such as numbers, strings, lists, and more.

Declaring and Assigning Variables

In Python, you don't need to explicitly declare a variable type before using it. Python is a
dynamically typed language, which means the type of the variable is inferred from the value
assigned to it.

To create a variable, you simply assign a value to a name using the =operator.

python Copy
code
# Variable assignment x = 10
# Integer
name = "Alice" # String is_active =
True # Boolean pi = 3.14159 # Float

In the example above:

 • xstores an integer value.
• namestores a string.
• is_activestores a boolean.
• pistores a floating-point number.
Variable Naming Rules

Python has some basic rules for naming variables:

1. Valid variable names must start with a letter (A-Z or a-z) or an underscore (_).
2. The rest of the variable name can include letters, digits (0-9), and underscores.
3. Variable names are case-sensitive, meaning myVarand myvarare different.
4. You cannot use Python keywords (reserved words like True, False, None, if, else, etc.)
as variable names.

Examples of valid and invalid variable names:

python Copy
code
valid_name = "John" # Valid name_1 =
"Doe" # Valid
_name = "Alice" # Valid

1name = "invalid" # Invalid, starts with a digit

if = "keyword" # Invalid, 'if' is a reserved keyword

2. Operators in Python

Operators are symbols used to perform operations on variables and values. Python supports a
wide range of operators, including arithmetic, relational, logical, and more. Here's a breakdown
of different types of operators in Python:
CHAPTER 6

TUPLES AND LIST

In Python, tuples and lists are both sequence data types that can store collections of
items, but they have some key differences. Let’s go over the main concepts of
each.

1. Tuples

• Definition: A tuple is an ordered, immutable collection of items. Once a

tuple is created, its values cannot be changed.
• Syntax: Tuples are created by placing items inside parentheses ()separated
by commas.

python Copy
code
my_tuple = (1, 2, 3)

• Immutability: You cannot add, modify, or remove elements in a tuple after

it’s created.
• Accessing Elements: You can access tuple elements by index, starting from
0.

python Copy
code
print(my_tuple[0]) # Output: 1
 • Methods: Tuples have limited methods compared to lists. Key methods
include:
o count(item): Returns the number of times an item appears in the
tuple.
o index(item): Returns the index of the first occurrence of the item.
• Usage: Tuples are generally used when the data should not change
throughout the program, like coordinates, fixed configuration values, etc.

2. Lists

• Definition: A list is an ordered, mutable collection of items. You can modify

it after creation.
• Syntax: Lists are created by placing items inside square brackets [],
separated by commas.

python Copy
code
my_list = [1, 2, 3]

• Mutability: Lists allow for changes. You can add, remove, or modify
elements.
• Accessing Elements: Like tuples, list elements can also be accessed by
index.

python Copy
code
 CHAPTER 9

USE OF PYTHON AND SCOPE

Python is a versatile, high-level programming language with a wide range of applications. Here’s
an overview of some of the main uses of Python and the concept of scope in Python
programming.

Uses of Python

1. Web Development
a. Python has powerful frameworks like Django, Flask, and FastAPI that make
web development efficient and scalable.
b. These frameworks provide built-in libraries and tools for handling backend
development, data management, and security.

2. Data Science and Machine Learning

 a. Python is popular in data science for its extensive libraries, including Pandas,
NumPy, Matplotlib, scikit-learn, and TensorFlow.
b. These libraries allow for data manipulation, statistical analysis, visualization, and
machine learning model creation.
3. Scripting and Automation
a. Python is commonly used for writing scripts to automate repetitive tasks, like file
management, data scraping, and data processing.
b. Libraries such as Selenium and Beautiful Soup help with web scraping, and the
os and sys modules aid in automating file operations and system tasks.
4. Game Development
a. Libraries like Pygame allow developers to create 2D games with Python.
b. Python is also used for scripting and prototyping game logic before implementing
it in other, performance-intensive languages.
5. Embedded Systems and IoT
a. Python, particularly MicroPython and CircuitPython, is used in embedded
systems and IoT projects with platforms like Raspberry Pi and Arduino.
6. Artificial Intelligence and Deep Learning
a. Python frameworks such as TensorFlow, PyTorch, and Keras are widely used
for creating deep learning models.
b. It’s an ideal language for prototyping complex algorithms and AI-driven
applications.
7. Desktop GUI Development
a. Python offers libraries like Tkinter, PyQt, and Kivy for developing cross-
platform GUI applications.
8. Cybersecurity and Ethical Hacking
a. Python is widely used in cybersecurity for tasks like vulnerability scanning,
malware analysis, and network monitoring.
b. Libraries such as Scapy and Paramiko aid in these tasks.

Scope in Python
 Chapter 7
Loops and conditional statements

Loops and conditional statements are fundamental components in Python that allow you to
control the flow of your program. Let's go over the basics of each.

1. Conditional Statements
Conditional statements allow you to execute specific blocks of code based on certain conditions.
In Python, the main conditional statements are if, elif, and else.

Syntax:

python
Copy code
if condition:
# code to execute if condition is True
elif another_condition:
# code to execute if another_condition is True
else:
# code to execute if all previous conditions are False

Example:

python
Copy code
age = 18

if age < 18:

print("You are a minor.")
elif age == 18:
print("You just became an adult!")
else:
print("You are an adult.")

Explanation:

• If ageis less than 18, it will print "You are a minor."

• If ageequals 18, it will print "You just became an adult!"
 • Otherwise, it will print "You are an adult."

2. Loops
Loops allow you to repeat a block of code multiple times. Python has two main types of loops:
for loops and while loops. for

Loops

forloops are typically used to iterate over a sequence (like a list, tuple, dictionary, set, or string).

Syntax:

python
Copy code
for item in sequence:
# code to execute for each item in sequence

Example:

python Copy
code
fruits = ["apple", "banana", "cherry"]

for fruit in fruits: print(fruit)

Explanation:

This loop will print each fruit in the fruits list one by one. while

Loops

whileloops continue to execute a block of code as long as a specified condition is True.

Syntax:

python
Copy code
while condition:
# code to execute as long as condition is True

Example:

python
Copy code
count = 1

while count <= 5:

print(count)
count += 1 # Increment count to avoid infinite loop

Explanation:

This loop will print the numbers 1 through 5. The loop stops when count exceeds 5. Combining

Loops and Conditionals

You can also use conditional statements inside loops to create more complex flow control.

Example:

python Copy
code
for number in range(1, 11): if number
% 2 == 0:
print(f"{number} is even") else:
print(f"{number} is odd")

Explanation:

This loop goes through numbers 1 to 10, checking if each number is even or odd and printing the
result.

• if, elif, else: Control flow based on conditions.

• forloop: Iterate over items in a sequence.
• whileloop: Continue as long as a condition is true.
 Chapter 8
Functions in Python

Functions in Python are reusable blocks of code designed to perform a specific task. They make
code more modular, easier to read, and easier to debug.

• Defining and Using Functions

To define a function in Python, use the `def` keyword, followed by the function name,
parentheses (with any parameters), and a colon. The code inside the function is indented.

Syntax:
def function_name(parameters):
# code to execute
return result # optional, returns a value
```

- `function_name` is the name of the function.

- `parameters` are inputs (optional) that the function can take to perform its task.
- `return` statement (optional) outputs a result when the function is called.

Example: A Simple Function

Let's define a function that greets the user.

python
def greet ():
print ("Hello! Welcome to Python programming.")
To call this function, just write:
Python greet ()
Functions with Parameters
You can pass data to functions by using parameters.

Example:
def greet(name):
print(f"Hello, {name}! Welcome to Python programming.")
greet("Alice")

Output:
```
Hello, Alice! Welcome to Python programming.
```
➢ Functions with Return Values
Sometimes, functions need to return a value after processing.

Example:
def add(a, b):
return a + b
Calling this function:
result = add(5, 3)
print(result)
```
Chapter 10
Small project on cafe billing system

What is a Billing Software?

Billing Software are an important part of accounting and finance software
aggregates that will offer numerous services under your budget or even free of
cost.
Billing software can be any type of calculation or tracking-based software
designed for-
• Reducing long queues Saving time
• Track billing
• Customer Invoices
• Employee work hours
• Expenses related to Clients Immediate updates
• Swift notifications plus reminders Affordable and cost-effective
• Manages expenses record
• Depreciates Conflicts and Manual Errors Creates Brand Identification
• Simplifies statements Enables 24*7 service
• Builds brand professionalism Reduces Manual labour

❑ Aim of the project:

The businesses in café are now growing constantly. At the same time, the need
for managing its operations and tasks arises. The best way to optimize these activities
is growing the business online as well. Today’s generation encourages high-tech
services especially over the Internet.

The project “café Billing System” is implemented to reduce the manual work
and enhances the accuracy of work in a restaurant.
Café Billing system is Console Application to Café management. Hence the
project is developed proficiently to help café

. This system wakes to provide service facility to café and owners automate their
BILLING OPERATIONS.
 This system entirely reduces the unnecessary time waste inside the café as
well as it reduces unnecessary calculation.
Café Billing system project fully developed in python language which is
currently demanded in market using python GUI Tkinter.
Python is a programming language that lets you work quickly and integrate systems
more efficiently.

Python graphical user interfaces (GUIs) used in this Project:

Tkinter –
Tkinter is the Python interface to the Tk GUI toolkit shipped with Python. Tkinter is
the standard GUI library for Python. Python when combined with Tkinter provides a
fast and easy way to create GUI applications. Tkinter provides a powerful object-
oriented interface to the Tk GUI toolkit.
PYTHON TKINTER GUI

Flowchart:
Hardware Requirements:
• Processor – Intel i3 7th generation
• RAM – 4 GB
• System Type – 64-bit windows operating system
• Model – Lenovo Ideapad 310kb

Software Requirements:
• Technologies used - Python 3.6.Python Tkinter GUI
• Language used - Python
 Project code:

# import tkinter module from tkinter import *

# make a window window

= Tk()

# specify it's size window. Geometry

("700x600")

# take a image for background bg

= PhotoImage(file='bg.png')

# label it in the background label17

= Label(window, image=bg)

# position the image as well

label17.place(x=0, y=0)

# function to
calculate the # price
of all the orders def

calculate():

# dic for storing the # food quantity and price dic =

{'Espresso': [e1, 30],

'Chicken_Nugget': [e2, 50],

'pizza': [e3, 150],

'chilli_potato': [e4, 50],

'chowmein': [e5, 70],

'black_coffee': [e6, 35]

} total

= 0 for key, val in

dic.items():

if val[0].get() != "":

total += int(val[0].get())*val[1]

label16 = Label(window,text="Your Total Bill is -

"+str(total),font="times 16")

# position it

label16.place(x=20,

y=490) label16.after(3000,

label16.destroy)
 #window.after(1000, calculate)

label8 = Label(window,text="Sonam Cafe",font="times 28

bold",bg="purple")

label8.place(x=350, y=20, anchor="center")

label1 = Label(window,text="Menu",font="times 28
bold",bg="aqua")

label1.place(x=520, y=70)

label2 = Label(window,

text="Espresso \ Rs 30",

font="times 18",bg="brown")

label2.place(x=450, y=120)

label3 = Label(window, text="Chicken

Nugget \ Rs 50", font="times 18",bg="blue")

label3.place(x=450, y=150)
label4 = Label(window,

text="Pizza \ Rs 150", font="times

18",bg="yellow")

label4.place(x=450, y=180)

label5 = Label(window, text="Chilli Potato \

Rs 50", font="times 18",bg="pink") label5.place(x=450, y=210)

label6 = Label(window,

text="Chowmein \ Rs 70", font="times

18",bg="red")

label6.place(x=450, y=240)

label7 = Label(window, text="black

coffee \ Rs 35", font="times

18",bg="orange")

label7.place(x=450, y=270)

# billing section label9 = Label(window, text="Select the

items",font="times
18",bg="#f08080")

label9.place(x=115, y=70)

label10 = Label(window,text="Espresso",font="times

18",bg="brown") label10.place(x=20,
y=120)

e1 = Entry(window) e1.place(x=20,

y=150)

label11 = Label(window, text="Chicken Nugget",font="times

18",bg="blue")

label11.place(x=20, y=200)

e2 = Entry(window) e2.place(x=20,

y=230)

label12 = Label(window, text="Pizza",font="times 18",bg="yellow")

label12.place(x=20, y=280)

e3 = Entry(window)

e3.place(x=20,y=310)
label13 = Label(window,text="Chilli Potato",font="times

18",bg="pink") label13.place(x=20,

y=360)

e4 = Entry(window) e4.place(x=20,

y=390)

label14 = Label(window,text="Chowmein",font="times

18",bg="red") label14.place(x=250,

y=120)

e5 = Entry(window)

e5.place(x=250,

y=150) label15=

Label(window,text=

"black

coffee",font="times

18",bg="orange")

label15.place(x=250, y=200)

e6 = Entry(window)

e6.place(x=250 y=230)
# execute calculate function after 3 second

#window.after(1000, calculate)

b2=Button(window,bg='teal',text="bill",width=30,command=calculate)

b2.place(x=100,y=500)

# closing the main loop window.mainloop()

• Screenshot
Merits:
It will give curate accurate result as compared to manual calculation.
Affordable and cost-effective

User friendly: no expert knowledge required

Reducing long queues: customer don’t have to wait long

Reduce manual working: No need of manual calculation.

Time saving: User don’t need to look for menu card, they can directly see
on application.

Demerits:
It doesn’t include input of user details
It doesn’t give user information.

It doesn’t generate bill number

Backend is not included so it can’t save bills on database

It doesn’t generate printed invoice.

Future Aspect:
This project will Include user details form area.
It will generate bill.

Backend will be included where data will be stored.

It will have print invoice option.
CHAPTER 11

CONCLUSION

Practical knowledge means the visualization of the knowledge, which we read in our books. For
this, we perform experiments and get observations. Practical knowledge is very important in
every field. One must be familiar with the problems related to that field so that he may solve
them and become a successful person.

After achieving the proper goal in life, an engineer has to enter in professional life. According to
this life, he has to serve an industry, may be public or private sector or selfown. For the efficient
work in the field, he must be well aware of the practical knowledge as well as theoretical
knowledge.

Due to all above reasons and to bridge the gap between theory and practical, our Engineering
curriculum provides a practical training of 2 month. During this period a student work in the
industry and get well all type of experience and knowledge about the working of companies and
hardware and software tools.

I have undergone my 30 days summer training in 7 th sem at Edufever.pvt.ltd . This report is

based on the knowledge, which I acquired during my 30 days of summer training.
 Chapter 12
Reference

BOOK:
Think Python by Allen B. downey

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