Learn Python Using Google Colab

Explore conditional statements in python, using if, elif, and else to control code flow based on boolean expressions. Learn examples, including even/odd checks and a login scenario, plus nested ifs.

Explore nested if statements in Python to check multiple conditions in a hierarchical way, with examples on driving eligibility, login validation, and reducing excessive nesting through end conditions.

Learn how for loops control flow in Python, including range with start, stop, and step, and while and nested loops, plus enumerate to access index and value.

Master the while loop by repeating code while a condition stays true, and contrast it with fixed-count for loops for counting and input validation, while avoiding infinite loops.

Discover how nested loops work in Python, with inner loops running fully for each outer loop iteration, and learn to build multiplication tables and patterns using for and while.

Master the break statement to exit loops in Python, whether in for or while loops, and apply it in nested loops and practical examples like password checks and sum thresholds.

Skip the rest of the current loop iteration using the continue statement, and move to the next iteration in for and while loops with Google Colab examples.

Learn how the pass statement in Python acts as a placeholder that does nothing, avoids syntax errors in if, for, and while blocks, and does not affect loop flow.

Define and use functions in Python on Google Colab, covering def keyword, parameters, return values, default and keyword arguments, and variable scope to write reusable, debuggable code.

Learn how to define functions with parameters and return values in Python on Google Colab, making code flexible, reusable, and capable of handling multiple inputs and outputs.

Learn how default arguments provide predefined values for optional parameters, reducing errors and boosting flexibility and readability with examples like a guest default name and powers as defaults.

Learn how keyword arguments in Python allow passing by name, improving readability and reducing errors with default values, as shown by name, age, and city examples.

Understand Python variable scope by distinguishing local scope inside functions from global scope outside, and learn how the global keyword updates global variables while avoiding pitfalls.

Explore lambda functions as anonymous, inline Python functions, learning their syntax and when to use them with map, filter, and sorted in a functional style.

Define and call lambda functions in Python using Google Colab, mastering their syntax, parameters, and one line expressions, including conditional expressions and multi-argument usage.

Learn lambda functions and their use cases in Python for sorting, filtering, and mapping data, with examples of sorting by a key, filtering evens, and doubling values.

Write a Python function named factorial to compute the factorial of a number using a loop in a notebook, returning the result and testing with sample inputs.

Write a Python function to check if a number is prime by testing divisibility up to half the number with modulus, returning false for zero, one, or negative inputs.

Develop a Python function to reverse a string in Google Colab, using length, a reverse loop with a negative step, and zero-based indexing to build the reversed result.

Create a single function that computes the area for circle, rectangle, and triangle using keyword arguments, with input validation and clear error messages.

Create lists with square brackets, index and slice them, and modify their elements. Learn that lists are ordered, mutable collections that hold multiple data types and support methods, comprehensions, iteration.

Access list elements using indexing and slicing in Python, applying zero-based and negative indices, and using start, stop, and step to slice or reverse lists with practical examples.

Learn to modify Python lists by adding, deleting, and updating elements with indexing; concatenate lists (not with strings or integers) and insert via slicing, noting original list changes.

Explore list methods such as append, insert, remove, pop, index, count, sort, and reverse to safely update lists in place, with pop returning the removed element.

Master list comprehensions in Python to create lists from an iterable, using an expression for item in iterable, range, and optional if filters, replacing loops.

Learn to iterate over lists in Python using for loops, range, and enumerate to access elements and their indices, print student scores, and synchronize multiple lists by index.

Explore tuples, a data structure similar to lists but immutable, defined with parentheses. Learn to create single-element tuples with a trailing comma, and why and where to use tuples.

Learn to access tuple elements using zero-based and negative indexing, and slice tuples with steps, mirroring lists; discuss the immutability of the tuples in the next lecture.

Explore how tuples are immutable and cannot be changed after creation. Learn why immutability boosts performance, supports data integrity, and allows only hashable keys in dictionaries.

Discover how tuples in Python offer immutability and memory efficiency for fixed data, enabling use as dictionary keys and storing coordinates.

Learn how to create and use dictionaries, a key-value data structure with unique and immutable keys and flexible value types for fast lookups and nested data.

Access dictionary values using keys with square brackets or the dot get method, handle missing keys with defaults, and navigate nested dictionaries and lists.

Learn to modify dictionaries in Python by adding, removing, and updating entries using keys; understand del usage and the update behavior when keys exist or new ones are created.

Learn Python dictionary methods such as keys, values, items, get, update, and pop in Google Colab, with examples showing how to fetch, update, and safely pop entries.

Master dictionary comprehensions to build dictionaries in one line using key and value expressions, with filtering, if-else, nested structures, and reversing dictionaries by swapping keys and values.

Learn to iterate over dictionaries in Python, using keys, values, and key-value pairs with for loops, and update values while printing informative messages.

Explore sets in Python: create with square or curly braces, note unordered, unique elements, and mutability. Use set() to convert lists or tuples and to build an empty set.

Learn how Python sets store only unique elements and automatically ignore duplicates when added. Convert lists to sets to remove duplicates, then convert back to lists for duplicate-free results.

Learn how Python sets support union, intersection, difference, and symmetric difference to combine or compare unique collections, with examples using colors and student lists.

Learn Python set methods like add, remove, discard, union, intersection, and difference with color examples; see how add avoids duplicates, remove raises errors, and discard handles missing items without error.

Practice building a dictionary-based data structure to store five students' information, including names, ages, and nested grades, using a list of dictionaries in Google Colab.

Use sets to find common elements between two lists by converting each to a set and intersecting. Convert the intersection back to a list and print the results.

Create a menu-driven shopping cart using a dictionary to track items and quantities, learn input handling, enumeration, and loop-based updates to add items and finish the order.

Find the most frequent element in a list with a Python program. Count occurrences of unique items (via a set) and track the max count to print the result.

Explore what Python libraries are and how to install and import them in Colab. Access pre-written code and specialized tools for data science with libraries like numpy and pandas.

Google Colab does not permanently store installed libraries; sessions reset on reconnect. Use pip in a code cell with an exclamation mark to install, upgrade, or uninstall libraries.

Learn to import libraries in Python using import, from, and aliases; import selective functions, keep imports at the top, and apply math functions like sqrt, ceil, and floor.

Explore numpy for numerical computing in Python on Google Colab, create and manipulate multi-dimensional arrays, perform vectorized math, linear algebra, and reproducible random generation with seeds.

Learn to use the pandas library for data analysis and manipulation with data frames and series, read and write CSV and Excel files, and perform cleaning, filtering, and grouping.

Explore Matplotlib for data visualization in Python, creating line, scatter, bar, and histogram plots in Google Colab, and customize labels, legends, colors, and figure size before saving figures.

Explore Seaborn, a Python visualization library built on matplotlib, offering simplified syntax, built-in aesthetics, and quick plots like scatter with regression, pair plots, and box plots.

Practice numpy to perform matrix operations - addition, subtraction, element-wise multiplication, and dot product - using Google Colab. Learn to create numpy arrays, import numpy as np, and print results.

Load a CSV into a Pandas DataFrame and perform basic analysis by computing mean, median, info, describe, and value counts on the California housing dataset.

Create a line plot and a bar chart with Matplotlib using the Seaborn flights data to visualize yearly maximum passengers from 1949 to 1960.

Learn to create a regression plot with Seaborn by visualizing iris sepal length versus petal length with a blue scatter and a red regression line.

Explore object oriented programming (OOP) by contrasting it with procedural coding, defining objects as classes and blueprints, and using constructors, instance variables, and methods to model world entities with encapsulation.

Explore the four basic principles of OOP—abstraction, encapsulation, inheritance, and polymorphism—with real-world examples like driving a car and bank accounts to show how these concepts shape objects and their behavior.

Learn how a class acts as a blueprint for creating objects, with attributes and methods describing real-world entities, illustrated by cars with unique names and colors.

Learn how the Python __init__ constructor initializes new objects, assigns attributes with self, and runs automatically during object creation.

Instance variables belong to each object and are defined with self, giving objects independent copies, while class variables are shared across all instances.

Explore how methods operate inside a class, including instance, class, and static methods, and how they access and modify both instance and class attributes with Python examples.

Discover inheritance in Python OOP, where child classes derive attributes from a parent class using super. See examples with animal, dog, fish, and dolphin to build hierarchical, reusable code.

Explore polymorphism in oop concepts, where objects from different classes share a common superclass and execute the same method with different behaviors, via overriding and overloading examples.

Explore encapsulation as a pillar of object-oriented programming in Python. Learn how single and double underscores control access and how getters and setters preserve data integrity via bank account example.

Explore abstraction in Python by hiding complex details and exposing a simplified interface, using abc to enforce blueprints for modular, secure bank account and car examples.

Explore Python methods, including instance, class, and static methods, and learn how self, class variables, and decorators enable methods to access and modify data inside a class.

Learn how packages organize Python code into modules within a directory, using __init__.py for initialization, and how to create, structure, and import packages in Google Colab.

Learn how to import modules and functions in Python, explore different import syntaxes, aliases, and package structures, and apply them to data frames with pandas.

Create a Python dog class in Google Colab with name, breed (private), and age attributes, plus bark and fetch methods, using getter and setter functions.

Create a cat class that inherits from a pet class, define name, age, and breed attributes with getters and setters, and demonstrate walk and meow.

Build a simple banking system in Python using classes for account, customer, and bank; implement deposit, withdraw, balance checks, and basic object-oriented concepts with a test demo.

Learn how to open files in Python using the open function, explore read, write, append, and exclusive create modes, and work with text, csv, and json files in Google Colab.

Learn to read text files in Python, loading data and configuration settings with read, readline, and readlines, then process lines efficiently with loops and strip newline characters.

Learn to write to files in Python using write and write lines, with overwrite and append modes, and manage newline characters to create text outputs and reports.

Learn how to properly close files in Python to prevent resource leaks and locked files, using the close method or the with statement that automatically closes after read or write.

Learn to read and write text, csv, and json files in Python using with statements, and the csv and json modules for practical data workflows.

Master exception handling by identifying syntax, runtime, and logical errors, and learn to use try-except blocks, raising and customizing exceptions to manage value, type, zero-division, and other errors in Python.

Learn how to use try-except-finally blocks in Python to handle errors gracefully, catch multiple exceptions, and ensure resources like files are closed reliably.

Raise exceptions in Python with the raise keyword and custom messages. Explore value error and zero division error, and implement age checks and custom exceptions for clearer error handling.

Create custom exceptions in Python by inheriting from the exception class and raising descriptive messages, illustrated with a custom invalid age error and a bank withdraw error.

Write a Python program in Google Colab to read a text file and count its words using open, read, and split, then print the result.

Learn to read a csv file in Python on Google Colab, use a dictionary reader to access a column, convert values to float, and compute the average.

Learn to build a Python program that handles numeric input errors, validates numbers between zero and 100 using try and except for value and type errors, and loops until valid.

Implement a custom exception for invalid file formats in Python, enforcing txt files only, using a try-except block, and printing the error message when an unsupported file is encountered.

Explore Python iterators in Google Colab, including lazy evaluation and memory efficiency, how to use iter and next, convert lists to iterators, and build custom iterators with a counter.

Explore how Python generators use the yield keyword to create memory-efficient iterators that generate values on demand. Compare generators and iterators: observe lazy evaluation and infinite sequences.

Learn how Python decorators wrap existing functions to modify behavior without changing code, enabling reusable, modular design and cross-cutting concerns like logging, timing, and access control.

Explore how decorators in Python wrap functions with a wrapper, using the @decorator syntax to apply additional behavior like uppercase or exclaim, and pass arguments to decorated functions.

Explore how decorators in Python modify or extend function behavior without altering source code, with practical use cases like timing functions, logging, authentication, and input validation.

Explore context managers and the with statement to define custom managers and use cases, managing resources like files or network connections, automatically closing them and preventing leaks or corruption.

Define custom context managers by implementing __enter__ and __exit__ to manage resources using the with statement. Explore exception handling in __exit__, including suppression or propagation.

Explore Python context managers to safely acquire and release resources with enter and exit, covering file handling, database and network connections via sqlite3 and with statements.

Create a Python generator that yields Fibonacci numbers starting from 0 and 1, updates two numbers each iteration, and demonstrates generating the sequence with next or a loop.

Learn to implement a Python decorator in Google Colab that logs function calls with timestamps, including function name, call time, and completion status.

Implement a context manager that ensures a file closes even if an error occurs, using enter and exit methods to open the file in a given mode and handle exceptions.

Discover regular expressions (regex) as patterns to search, match, replace, extract, and validate text in Python. Learn practical use cases such as phone numbers, email addresses, and log files.

Learn the basics of regular expressions in Python, including character classes, quantifiers, anchors, and groups, with practical examples using re.find_all to match words and digits.

Explore the re module to perform regular expression operations such as search, match, find all, and substitution, demonstrated with masking phone numbers and redacting emails.

Explore practical use cases of Python's re module for match, search, find all, and sub, including validating emails and phone numbers, extracting dates and urls, and censoring or replacing text.

Learn how REST APIs use HTTP methods and JSON or XML to access resources, emphasizing statelessness, client-server architecture, uniform interfaces, and caching.

Explore how to use the requests library in Python to send get, post, put, and delete requests, handle responses, status codes, and JSON data with automatic encoding.

Explore API keys and authentication methods such as basic, bearer token, and OAuth 2.0, and learn to securely store keys, embed them in requests, and test with NASA API.

Learn to validate email addresses with a Python regular expression by importing re, defining a validation pattern, creating a validate_email function, and testing multiple emails in a loop with results.

Learn to fetch data from a public api using requests in python, display a joke by extracting setup and punchline from the api response and handling a 200 status.

Master the requests library to authenticate with the Reddit rest api using client id, secret key, username, and password, obtain a token, and fetch data for pandas data frames.

Explore advanced pandas in Google Colab, focusing on data pre-processing, cleaning, transformation, and time series analysis with practical selections, filtering, sorting, renaming, and resampling.

Explore Plotly in Python for interactive 2D and 3D plots, animations, and dashboards, with easy customization and support for multiple data sources.

Install Plotly in Google Colab using pip install plotly and import plotly to verify the setup and version. Explore interactive visualizations, pandas compatibility, and easy sharing that Colab enables.

Explore basic plotting with Plotly Express by creating scatter plots, line charts, and bar charts from data using concise px functions, with an emphasis on examples and interactivity.

Learn to customize plots in Google Colab using Plotly in Python by adding a title, updating axis labels, configuring legends, and adding annotations.

Explore Plotly's interactive features in Python on Google Colab, including default zooming and panning, customizable hover data, and click events to trigger layout changes or color updates.

Explore advanced plots in Plotly, including 3d plots (scatter, surface, line), geographic maps (choropleth, scatter, mapbox), heat maps, and contour plots.

Clean and transform air quality data from UCI in pandas on Google Colab, removing unnamed columns, standardizing headers, converting dates, and handling missing values with a 50% threshold to finalize.

Learn to build interactive visualizations with Plotly to explore time-based concentrations of nitrogen dioxide and carbon monoxide, using line and scatter plots. Present findings with clear explanations.