Advanced Python Programming with 5 Real Projects

Explore what makes an advanced Python developer: clean, modular code using decorators, generators, type hints, oop, testing, and essential libraries like pandas, numpy, scikit-learn, matplotlib, and seaborn.

Explore how advanced Python powers AI with machine learning, deep learning, and neural networks, guiding data cleaning, transformation, and model training using pandas, numpy, scikit-learn, TensorFlow, and PyTorch.

Learn Python basics, its 1991 origin, and its role as a general-purpose, high-level, interpreted language with easy syntax and powerful libraries for AI, data science, and web development.

Learn to check python installation with cmd, confirm a version like 3.8.0, then download from python.org and add python to path during installation. Also choose an IDE to start coding.

Explore variables in Python, including declaration, initialization, and the distinction between global and local scopes, with printing examples and undefined variable concepts.

Learn Python variable rules by avoiding spaces or starting with a number, using underscores or camel case, avoiding special characters, respecting case sensitivity, and not using reserved words.

Discover how Python lists act as a mutable, array-like data type that stores multiple types, supports nesting, and enables indexing, concatenation, repetition, and membership checks with in and not in.

Explore the tuple data structure in Python for advanced programming, covering immutability, indexing, nested tuples, concatenation, repetition, membership tests, and iteration across diverse data types.

Master the dictionary data structure in Python, a mutable key-value store accessed by keys, defined with curly braces, and characterized by item order and no duplicate keys.

Learn Python set data structure, unordered collection of immutable elements you can add to or remove from. Create sets with curly braces or set(), and perform union, intersection, and difference.

Explore how to implement Python if statements by evaluating conditions and using indentation. Learn about colon syntax, relational and equality operators, and practical examples like marks and names.

Learn python if else statements to decide actions based on a condition, using the syntax if condition: and else:, with true or false outcomes.

Explore how Python nested if statements work, with an inner if inside an outer one and modulus checks for divisibility by two and three.

Learn how to create and call functions in Python using def, understand parameters and arguments, and see real-world examples that illustrate code reuse and readability.

Explore for loop in Python and how it iterates over any iterable: lists, tuples, dictionaries, strings, using range to generate sequences, including even/odd examples.

Explore how the while loop in Python works, emphasizing condition-driven repetition, increment and decrement, and how it differs from for loops, with practical examples and common pitfalls.

This chapter introduces functional programming in Python, covering pure functions, higher-order functions, lambda styles, map, filter, reduce, generators and iterators with yield and next, and decorators.

Explore functional programming in Python by defining pure functions with no side effects using def, returning consistent outputs for given inputs, and examining examples like square, add, and string concatenation.

Explore higher order functions in Python that take or return functions as values. See how these patterns create flexible, reusable code with examples like add and multiply.

Learn lambda functions in advanced Python: anonymous, inline, single-expression functions and their syntax. See examples with one to three arguments and using lambdas as parameters for add, multiply, and average.

Explore how the Python map function applies a function to every element of an iterable, returning iterable; use a lambda function to square numbers and convert results to a list.

Explore how to use the map function in Python to convert list elements to uppercase. See examples for string lengths and map with multiple iterables using lambda.

Explore how Python's filter function applies a condition to each iterable element and returns those that satisfy it, using lambda examples to filter even numbers.

Explore advanced Python programming with practical filter function examples, including filtering strings by substring, filtering negative and positive numbers, removing empty strings, and applying multiple conditions with lambda.

Apply a function to elements of an iterable using reduce to produce a single value, optionally with an initializer, as shown with sum, product, and combining strings in python.

Learn how generator functions in Python yield values, create iterators, and use next to retrieve data, with finite, infinite, and square or even number examples.

Explore decorator functions in Python and how they extend another function's behavior at runtime. See how a wrapper intercepts calls, enabling functionality enhancement without modifying the original source code.

Delve into object oriented programming in this advanced Python chapter, covering classes and objects, data members, constructors, inheritance, super, polymorphism, and access specifiers.

Explore object oriented programming basics by defining objects and classes, and understanding properties, behavior, methods, encapsulation, inheritance, polymorphism, abstraction, and binding.

Learn how to define a Python class with data members and methods, instantiate objects, and access members using dot notation and self.

Discover that an object is an instance of a class with attributes and methods. Learn to create objects and access data members and member functions using dot notation.

Learn how a class uses data members and member functions, creates objects, accesses and updates data members via objects, and applies the self keyword for methods.

Explore how the self keyword references the current instance in Python, access and modify instance variables, and call methods within a class in object-oriented programming.

Master Python's constructor and destructor in object-oriented programming, learn about non-parameterized and parameterized constructors, and understand automatic invocation and memory cleanup through garbage collection.

Explore how destructor works in Python by contrasting it with __init__ constructor, showing that __del__ runs when an object is destroyed and memory is deallocated, with a VS Code example.

Explore inheritance in Python and object oriented programming, where a child class inherits attributes and methods from a base class, with vehicle and car examples.

Explore single inheritance in Python, showing how a child class inherits data members and methods from a single parent class and accesses them through objects.

Discover how multiple inheritance works in Python by deriving a child from multiple parent classes, accessing inherited methods and data members, and adding its own method.

Explore how multi-level inheritance in Python enables a class to inherit from a chain of superclasses, letting objects access methods from grandparent, parent, and child classes.

explains hierarchical inheritance in python, where a single superclass provides methods to multiple subclasses. demonstrates how each child accesses the parent methods, while siblings do not access each other's methods.

Explore how to use the super keyword in Python to access parent class methods and constructors from a child class, including resolving overridden methods.

Explore polymorphism in Python and object oriented programming by examining method overloading and overriding, using default parameters and super to unify behavior across classes.

Explore private, protected, and public access specifiers in object oriented programming, focusing on Python conventions and how underscores control data members and methods within the class, child class, and outside.

Explore protected and public access specifiers in Python, showing underscore-based protected members and unrestricted public members, and demonstrate access from within the class, in child classes, and from outside.

Explore chapter four of the advanced Python course with an introduction to regular expressions, essential patterns, and the four methods findall, split, and sub, plus key quantifiers and sequences.

Explore regular expressions, or regex, sequences of characters forming search patterns used for text processing, data validation, and search-and-replace, with Python's re module and examples like emails and URLs.

Discover how regular expressions in Python enable data validation, including emails, URLs, usernames, and phone numbers, and apply search and replace, web scraping, and text processing across programming languages.

Explore practical spatial sequences in regex, including \d, \D, \s, \S, \w, \W, dot, anchors ^ and $, plus \b and \B word boundaries and case-insensitive matching.

Master python regex with \d and \D for digits and non-digits, and quantifiers such as zero or more, one or more, zero or one, and {n},{n,},{n,m}.

Explore regex word characters with \w and non-word with \W, defining alphanumeric and underscore, using quantifiers, and applying these concepts in Python examples.

Learn how the lowercase s matches all whitespace—spaces, tabs, and newlines—and how uppercase S negates them in regex. See quantifiers and escapes like \n and \d used to control matching.

Explore the dot pattern in regex, showing that the dot matches any character except newline, and use quantifiers to match between 3 and 5 characters or at most five.

Explore how the caret symbol in regular expressions checks a string's starting position. Learn the dollar symbol for end matches and the (?i) notation for case-insensitive matching.

Explore the word boundary concept in Python regex using the \\b anchor to match whole words and its negation, with case-insensitive matching shown in code examples.

Explore sets in regular expressions using square brackets to define character classes and ranges. See caret negation exclude characters and sequences like \d, \w, \s match digits, words, or spaces.

Explore the findall method in Python's re module to search all pattern matches in a string and return a list, with digits, specific words, and email addresses as examples.

Explore how the Python regex search function finds the first occurrence of a pattern in a string, returns a match object, and reveals the start index and groups.

Explore Python's regex split function that divides text by a pattern, with optional maximum splits and flags. Split by whitespace, commas, or sentence-ending dots, and see practical examples.

Explore the Python regex sub function, learning how to search a pattern in a string, replace matches with a replacement string, and control count and flags with practical examples.

Outline for chapter five covers connecting to databases, creating databases and tables, and performing CRUD operations with Python using libraries across Oracle, MySQL, and SQL Server.

Explore the basics of databases and database management systems, with key examples such as Oracle, MySQL, SQL Server, and MongoDB, and core operations like create, insert, update, delete.

Explore python database libraries like psycopg2 and pymysql to connect to MySQL, PostgreSQL, Oracle, and SQLite, create tables, insert records, and set up your environment for database interaction.

Install the Pi MySQL Python base library with pip, import pi MySQL in Python, and run a quick test in VSCode to verify installation.

Learn to set up a MySQL environment with Xampp, create a database via phpMyAdmin, and connect from Python using PyMySQL to run queries.

Learn to create a MySQL database from Python by connecting to the server, executing a create database statement with a cursor, and committing changes before closing the connection.

Learn to create a database table from Python using a cursor's execute method, defining columns, primary key, and data types, then commit and close the connection.

Create database tables with sql using python by executing cursor queries, committing changes, and closing connections; explore users, task, and books examples and complete the practice assignment.

Learn to perform create, read, update, and delete operations on a database, and insert records from Python into a MySQL table using a cursor, execute, and commit.

Master parameterized inserts using the execute method and a values tuple to securely add records. Learn to insert single and multiple rows into a products table (name, price) with commit.

Master select queries in Python to read data from database tables, using cursor execution, fetch all, and parameterized queries to prevent SQL injection, with where clauses and limits.

Apply update queries to modify existing records in a database, updating single or multiple columns with conditions, prepared statements, and commit in MySQL.

Master deleting data from a database with delete from statements, where clauses, and commit operations, illustrated through Python PyMySQL and parameterized queries.

Explore how to use the limit clause in SQL with Python and PyMySQL to cap results, apply offset, and sort with order by, illustrated through practical queries.

Learn how to use the where clause in SQL to filter rows and drive select, update, delete, and insert queries with conditions such as greater than values from another table.

Explore advanced input/output operations in Python by reading and writing data to Excel, JSON, and CSV files, and learn how to handle inputs and outputs across these formats.

Explore basic Python input and output, using the input and print functions, converting strings to integers with int(), and outputting results to console or csv, json, or excel.

Learn to perform csv input and output using pandas, read csv into a dataframe, explore with head, describe, and dtypes, and write dataframes to csv with to_csv.

learn to read and write excel files with pandas, using read_excel, excel writer, and dataframe operations. explore input and output operations, head, columns, and describe on dataframe.

Learn how to read json data into a pandas data frame, using read_json, and view json input from a file in Python.

Learn how to write pandas data frames to json files using to_json with orient='records', including creating or overwriting files in a Python project.