Chapter 1: Python Basics

Overview

Python is a powerful, versatile programming language perfect for data science, web development, automation, and much more. This chapter introduces fundamental Python concepts: writing procedural code, working with variables and expressions, understanding built-in types, performing mathematical operations, and using classes and objects.

By mastering these basics, you'll build a solid foundation for data science and advanced Python programming.

Learning Objectives

By the end of this chapter, you will be able to:

• Write and execute procedural Python statements
• Use variables, expressions, and Python's built-in types (list, dict, set, tuple)
• Perform arithmetic operations and type conversions
• Print formatted output using f-strings
• Understand basic object-oriented concepts with classes and objects
• Use decorators and special class methods

1.1 Write Procedural Code

Procedural Statements

Procedural statements are commands executed one line at a time. These statements can run in:

• Jupyter Notebook
• IPython shell
• Python interpreter
• Python scripts
• This interactive browser terminal (powered by Pyodide!)

Let's start with a simple list assignment:

Multiple Procedural Statements

You can unpack lists into variables and use f-strings for formatted output:

Adding Numbers

Python handles numeric operations naturally:

Adding Strings

String concatenation combines text:

Complex Statements with Loops

More complex statements can iterate over data structures:

1.2 Use Simple Expressions and Variables

Assert Statement

The assert statement validates conditions. If the condition is False, it raises an AssertionError:

Pass Statement

The pass statement is a null operation - a placeholder that does nothing:

Del Statement

The del statement deletes objects:

Return Statement

Functions use return to send values back to the caller:

Yield Statement (Generators)

The yield statement creates generators - functions that produce values one at a time:

Generators are memory-efficient because they produce values on-demand rather than storing them all in memory.

Break Statement

The break statement exits loops early:

Continue Statement

The continue statement skips to the next loop iteration:

1.3 Work with Built-In Types

Dictionary (dict)

Dictionaries store key-value pairs:

List

Lists are ordered, mutable collections:

Set

Sets store unique, unordered elements:

Tuple

Tuples are ordered, immutable collections:

1.4 Printing

Basic Printing

Use print() to output text:

F-Strings (Formatted String Literals)

F-strings provide elegant string formatting:

Print Function with Separator

The sep parameter controls what appears between printed items:

1.5 Perform Basic Math Operations

Adding and Subtracting

Multiplication with Decimals (Floats)

Decimal Precision

For financial calculations or when precision matters, use the Decimal library:

Exponents

Python provides two ways to calculate exponents:

Converting Between Numerical Types

Rounding Numbers

Python provides multiple ways to round numbers:

1.6 Use Classes and Objects with Dot Notation

Special Class Methods (len)

Classes can implement special methods (also called "dunder methods" - double underscore):

@property Decorator

The @property decorator creates read-only attributes:

@staticmethod Decorator

The @staticmethod decorator creates methods that don't need self:

Immutability with Properties

Properties act like read-only attributes:

Summary

In this chapter, you learned Python basics:

• Procedural statements: Variables, loops, conditionals
• Control flow: break, continue, return, yield
• Built-in types: dict, list, set, tuple
• Math operations: Addition, multiplication, exponents, rounding
• Classes and objects: Properties, static methods, special methods

These fundamentals form the foundation for data science, web development, and automation.

Quiz

Next Steps

Now that you understand Python basics, you're ready to dive deeper into:

• Chapter 2: Working with Strings - manipulation, formatting, and regular expressions
• Chapter 3: Python Data Structures - advanced list, dict, set operations
• Chapter 4: Data Conversion Recipes - transforming data between types

Continue your learning journey to master Python for data science!