software programming python

1. Software
Programming with
Python II
Harnessing the Power of Python for Software Development
By
Gevita Chinnaiah

2. Agenda
• Introduction to Python
• Python Data Structures
• Python Libraries and Frameworks
• Object-Oriented Programming (OOP) in Python
• Error Handling and Testing in Python
• Python for Web Development
• Python for Data Science and Machine Learning
• Best Practices in Python Development
• Conclusion

3. Introduction to Python
• Python is a high-level, interpreted programming language
known for its simplicity, readability, and versatility.
• Created by Guido van Rossum and first released in 1991,
Python has since become one of the most popular
programming languages worldwide

4. Why Python is Popular for
Software Development
• Simplicity
• Python's syntax is designed to be straightforward and easy to
understand
• Readability
• Python code is highly readable, thanks to its use of meaningful
whitespace and English-like syntax
• Versatility
• Python's versatility allows it to be used in various domains,
including web development, data science, machine learning,
artificial intelligence, scientific computing, automation, and
more

5. Examples of Python's
Versatility
• Web Development
• Django, flask
• Data Science and Machine Learning
• Numpy, pandas, ,matplotlib,
• Scripting and Automation
• Scientific Computing

6. Python Data Structures
• Python provides a rich set of built-in data structures
• 1. Lists
• 2. Tuples
• 3. Dictionaries

7. Lists
• A list in Python is an ordered collection of items which can be
of mixed types.
• Lists are mutable.
• Storing a series of items, iterating over data for processing, or
keeping elements for later processing

8. Example: List

9. Tuples
• A tuple in Python is similar to a list in that it is an ordered
collection of items
• Tuples are immutable
• Tuples can contain mixed data types

10. Example: Tuple

11. Dictionaries
• A dictionary in Python is an unordered collection of items
• Each item in a dictionary is stored as a key-value pair
• Dictionaries are mutable
• Dictionaries are incredibly useful for mappings

12. Example: Dictionaries

13. Data Manipulation Examples
• List Comprehension
• Dictionary Access and Iteration

14. Python Libraries and
Frameworks
• Python, with its vast ecosystem, hosts an array of libraries and
frameworks designed to streamline software development
across various domains such as web development, data
analysis, machine learning, and more
• Popular Python libraries and frameworks
• e.g., NumPy, Pandas, Django

15. NumPy
• NumPy is the cornerstone library for numerical computing in
Python.
• It provides support for large, multi-dimensional arrays and
matrices
• Collection of mathematical functions to operate on these
arrays

16. Pandas
• Pandas is a powerful, flexible data manipulation and analysis
library.
• It offers data structures like DataFrame and Series, making it
easy to clean, analyze, and visualize data

17. Django
• Django is a high-level Python web framework that encourages
rapid development and clean, pragmatic design.
• It follows the DRY (Don't Repeat Yourself) principle and comes
with many features to handle common web development
tasks.

18. Streamlining Software
Development Tasks
• Libraries and frameworks greatly reduce the time and effort
required for developing software by providing pre-written
code that developers can use to solve common programming
tasks.
• Helps in maintaining a high level of code quality and reliability
• Efficiency
• Security
• Scalability

19. Real-World Applications
• Finance
• Pandas and NumPy are used for quantitative analysis, algorithmic
trading, and risk management
• Science and Engineering
• Scientific research and engineering projects leverage NumPy for
its mathematical capabilities
• Web Development
• Django and Flask (another popular Python web framework)
power many web applications

20. Object-Oriented Programming
(OOP) in Python
• Object-Oriented Programming (OOP) is a programming
paradigm that organizes software design around objects and
data, rather than actions and logic.
• OOP concepts
• (classes, objects, inheritance, polymorphism)

21. Classes and Objects
• A class is a blueprint for creating objects. It defines attributes
(data) and methods (functions) that operate on those
attributes.
• An object is an instance of a class. It represents a specific
instance of the class, with its own unique data.

22. Inheritance
• Inheritance allows a class (child class) to inherit attributes and
methods from another class (parent class).
• It promotes code reusability and establishes an "is-a"
relationship between classes.

23. Polymorphism
• Polymorphism allows objects of different classes to be treated
as objects of a common superclass.
• It enables flexibility and code extensibility by using a unified
interface.

24. How Python Supports OOP
Principles
• Built-in features such as classes, inheritance, polymorphism,
encapsulation (data hiding), and abstraction
• It has a clear and readable syntax
• Python's dynamic typing and flexibility allow for dynamic
object creation and modification during runtime.

25. Benefits of Using OOP in
Software Development
• Modularity
• Code Reusability
• Encapsulation
• Flexibility and Scalability
• Abstraction

26. Error Handling Techniques in
Python
• Try-Except Blocks
• Raising Exceptions
• Handling Specific Exceptions

27. Importance of Testing in Software
Development
• Quality Assurance
• Reliability and Robustness
• Regression Testing
• Customer Satisfaction

28. Python Testing Frameworks
• unittest:
• This is Python's built-in testing framework, inspired by Java's
JUnit.
• pytest:
• pytest is a popular third-party testing framework that simplifies
writing and executing tests.
• nose2:
• nose2 is a successor to the nose testing framework. It provides an
extended feature set and improved test discovery mechanisms.

29. Best Practices in Python
Development
• Coding conventions and style guides
• (e.g., PEP 8)
• Writing clean, maintainable code
• Importance of documentation and code comments

30. Coding Conventions and Style
Guides
• Follow PEP 8
• Use descriptive names
• Maintain consistent indentation
• Limit line length
• Use blank lines sparingly

31. Writing Clean, Maintainable
Code
• Keep functions small and focused
• Avoid unnecessary complexity
• Use meaningful comments
• Write modular and reusable code

32. Importance of Documentation
and Code Comments
• Write docstrings
• Comment where necessary
• Update documentation and comments

33. Conclusion
• In conclusion, we have covered several key points in today's
presentation about Python in software development. We
discussed Python's versatility as a programming language, its
readability and simplicity which make it ideal for both
beginners and experienced developers, as well as its extensive
library support and community-driven ecosystem.
• I want to encourage you all to further explore Python in your
software development journey. Whether you are interested in
web development, data science, machine learning, or
automation, Python has a wide range of applications that can
benefit your projects and career growth.

34. THANK YOU