Variables, Data Types & Type System

Duration: 5 min

Visual: Type Hierarchy

┌─────────────────────────────────┐
│      Python Type System         │
├─────────────────────────────────┤
│                                 │
│  Immutable:  int, float, str    │
│  Mutable:    list, dict, set    │
│  Special:    None, bool         │
│  Callable:   function, class    │
│                                 │
└─────────────────────────────────┘

Key Concepts Table

Installing Python

Download Python 3.9 or later from python.org. We recommend Python 3.10 or 3.11 for the best balance of features and library support.

Installation Steps:

1. Go to python.org
2. Download the installer for your OS
3. Run the installer
4. IMPORTANT (Windows): Check 'Add Python to PATH'
5. Complete installation

# Verify Python installation
python --version
# or
python3 --version

# Check pip (package manager)
pip --version

Try it in Google Colab:

Python 3.10.7
pip 21.0.1 from /usr/lib/python3.10/site-packages/pip

Understanding Virtual Environments

A virtual environment is an isolated Python installation for each project. Think of it as a sandbox where each project has its own set of libraries.

Why is this important?

Imagine you have two projects:

• Project A needs TensorFlow 2.8
• Project B needs TensorFlow 2.10

Without virtual environments, you can only have one version installed globally. Virtual environments solve this by letting each project have its own Python installation with its own libraries.

Benefits:

• No conflicts between projects
• Easy to share projects (requirements.txt)
• Prevents 'works on my machine' problems
• Easy to clean up (just delete the folder)

# Create a virtual environment named 'venv'
python -m venv venv

# Activate it (macOS/Linux)
source venv/bin/activate

# Activate it (Windows)
venv\Scripts\activate

# You should see (venv) at the start of your terminal line
# Now install packages - they go into venv only
pip install numpy pandas

# Deactivate when done
deactivate

Important: Always activate your virtual environment before installing packages. Never use sudo with pip in a venv.

Managing Dependencies

# Save all installed packages to a file
pip freeze > requirements.txt

# This creates a file like:
# numpy==1.21.0
# pandas==1.3.0
# scikit-learn==0.24.2

# Share this file with others
# They can install all packages with:
pip install -r requirements.txt

💡 Tip: Always use virtual environments. It's a best practice that will save you hours of debugging.

Dynamic Typing in Python

Python uses dynamic typing, meaning you don't declare variable types explicitly. The type is inferred from the value assigned. This is different from statically-typed languages like Java where you must declare types upfront.

Learn more: https://docs.python.org/3/tutorial/

# Dynamic typing - type is inferred
x = 42              # x is an int
print(type(x))      # <class 'int'>

x = "Hello"         # x is now a string
print(type(x))      # <class 'str'>

x = 3.14            # x is now a float
print(type(x))      # <class 'float'>

# Type checking
if isinstance(x, float):
    print("x is a float")

# Type conversion
num_str = "123"
num_int = int(num_str)  # Convert string to int
print(f"Converted: {num_int}, type: {type(num_int)}")

<class 'int'>
<class 'str'>
<class 'float'>
x is a float
Converted: 123, type: <class 'int'>

💡 Tip: Use type() to check a variable's type, or isinstance() for more flexible type checking. Type conversion functions like int(), str(), float() are commonly used.

Practice Quizzes

Quiz 1: Which is mutable?

• str
• tuple
• [✓] list
• int

Quiz 2: What is type casting?

• Changing variable name
• [✓] Converting between types
• Deleting variables
• Creating new types

Quiz 3: When would you use a tuple instead of a list?

• Always
• When you need mutability
• [✓] When data shouldn't change
• Never