Software Programming with Python II.pptx
- 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
- 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
- 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