Learn Core Python, Numpy and Pandas

Set up a free Jupyter notebook on Google Colab, sign in, upload a notebook, and run code to start learning core Python, NumPy, and Pandas.

Learn to use Jupyter notebook to write and run Python in a browser, with text blocks and code blocks, then execute and view results.

Explain data types and variables in Python, highlighting dynamic typing, assigning numbers or strings, and naming rules with letters, numbers, underscores, and not starting with numbers.

Discover how Python handles integer data types, including long integers of infinite size, and how arithmetic works across integers, floats, and complex numbers.

Explore creating strings in Python with single, double, and triple quotes, including multi-line strings. Learn operations such as concatenation, slicing, escaping quotes with backslashes, and basic int conversion to numbers.

Learn how to take input in python using the input function, convert string inputs to numbers, sum them, and print the data types involved.

Explore Python boolean data type with true and false values, and use comparisons such as == and != in numeric and string contexts, including <, >, and other boolean operators.

Explore Python blocks and control flow, using if statements to execute code when conditions are true and showing how indentation defines blocks of code.

Explore how if-else statements control flow by executing a single code block based on a condition, illustrated with comparing two numbers.

Learn how to use if, elif, and else in Python to handle conditions, using a number example to show how each condition executes and how else runs when none apply.

Explore boolean logic and its operators and, or, not, to evaluate true and false conditions, study truth tables, and learn operator precedence for building complex conditions.

Explore how the while loop repeats a block based on a condition, using break and continue to control flow. See examples printing 0–9 and stopping at 5 to yield 0–4.

Explore Python lists that store items of different types, including strings and numbers. Learn 0-based indexing, access nested lists, print lists, and modify items with assignment and multiplication.

Explore python list operations including membership checks, append, insert at index, index for first occurrences, removing elements, and computing max and min.

Master Python range by understanding start inclusive and end exclusive, and by using positive steps to generate sequences for loops, with examples like range(10) and range(1, 10, 2).

Define python functions with def, supply arguments, and return values; assign them to variables, call with positional or named arguments, and use docstrings for documentation.

Learn how to pass variable numbers of arguments to functions in Python, using positional arguments, *args, and **kwargs, and see how keyword labels map to values.

Explore how Python modules organize related functions, import full modules or individual functions, use aliases, and leverage standard library and third-party packages to boost productivity.

Python exceptions occur when errors halt code execution, as shown by zero division or invalid indexing. Use try-except and finally to handle errors and provide descriptive messages.

Master Python file handling by opening text files, reading lines, and writing with write or append modes. Use the with statement to ensure automatic closing and preserve data when appending.

Learn how the None data type in Python represents the absence of a value, like null, and how a function without a return yields None.

Learn how dictionaries map keys to values in Python, access data with square brackets, and use get and in to handle missing keys and updates.

Tuples are immutable lists created with parentheses; once set, elements cannot change, offering memory efficiency and enabling use as dictionary keys, while simplifying returning multiple arguments from a function.

Learn how Python list slicing uses start, end, and step, with end not included. See how defaults and negative steps create forward and reversed selections, illustrated by square numbers.

Learn how list comprehensions in Python create lists by generating squares from 0 to 4 using range(5). Optionally filter values that satisfy a condition before squaring.

Learn Python string functions, including formatting with positional and named arguments, joining lists, replacing substrings, and checks like starts with and ends with, plus splitting and applying lower to strings.

Explore Python list functions such as min, max, sum, and abs, and demonstrate the any function through numbers, strings, and empty lists.

Explore how the Python all function evaluates lists, returning true when all elements are true and false when any element is false, including examples with numbers, strings, and empty lists.

Explore object oriented programming by creating classes with attributes and an __init__ method. Learn how instances like Russell and Panini hold separate data, illustrating that each object has its state.

Explore object oriented programming by creating a class instance, invoking methods, and understanding self as an implicit parameter; learn about class level attributes to count instances.

Explore inheritance by deriving Greek philosopher from philosopher, sharing base attributes like name and country; call super to initialize the base constructor and access details across classes.

Explore Python magic methods, also called dunders, which implement operator overloading for addition, subtraction, multiplication, and division, and enable container-like behavior with get item and len.

Explore Python object lifecycle from creation to destruction by examining how __new__ and __init__ control instantiation and constructor behavior in a philosopher example.

Explore how Python manages memory via reference counting and garbage collection, showing how object destruction frees memory when reference counts drop to zero.

Learns data hiding by using public, private, and protected concepts, with single underscore signaling weak attributes and double underscore triggering name mangling; explore magic methods for string representation.

Learn about instance, class, and static methods in Python; instance methods use self, class methods use cls to access class attributes, while static methods avoid class and instance data.