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Basic Python Programming

This tutorial covers fundamental Python programming concepts essential for robotics and SLAM development.

Topics Covered

File	Topics
01_basics.py	Variables, data types, operators, control flow, loops
02_data_structures.py	Lists, tuples, dictionaries, sets, comprehensions
03_functions.py	Functions, lambdas, decorators, generators
04_classes.py	Classes, inheritance, magic methods, dataclasses
05_file_io.py	File I/O, JSON, CSV, exceptions, context managers
06_modules_venv.py	Modules, packages, virtual environments (venv)
07_rustlike_python.py	Rust-inspired patterns: type hints, dataclasses, ADTs

---

How to Run

Using Virtual Environment (Recommended)

# Create virtual environment
python3 -m venv .venv

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

# Activate (Windows)
# .venv\Scripts\activate

# Install dependencies
pip install numpy opencv-python

# Run examples
cd examples
python3 01_basics.py
python3 02_data_structures.py
# ... etc

# Deactivate when done
deactivate

Without Virtual Environment

cd examples
python3 01_basics.py

---

Key Concepts

1. Variables and Types

# Numbers
x = 42          # int
y = 3.14        # float

# Strings
name = "Robot"
msg = f"Name: {name}"  # f-string

# Collections
items = [1, 2, 3]       # list (mutable)
point = (1.0, 2.0)      # tuple (immutable)
config = {"speed": 2.0}  # dict

2. Control Flow

# Conditional
if score >= 90:
    grade = "A"
elif score >= 80:
    grade = "B"
else:
    grade = "C"

# Loop
for i in range(10):
    print(i)

# List comprehension
squares = [x**2 for x in range(10)]

3. Functions

def process_data(data: list, threshold: float = 0.5) -> list:
    """Process data with threshold filter."""
    return [x for x in data if x > threshold]

# Lambda
square = lambda x: x ** 2

4. Classes

from dataclasses import dataclass

@dataclass
class Point:
    x: float
    y: float
    z: float = 0.0

class Robot:
    def __init__(self, name: str):
        self.name = name

    def move(self, dx: float, dy: float):
        # ...

5. File I/O

# JSON
import json

with open("config.json", "r") as f:
    config = json.load(f)

# CSV
import csv

with open("data.csv", "r") as f:
    reader = csv.DictReader(f)
    for row in reader:
        print(row)

6. Virtual Environment (venv)

# Create
python3 -m venv .venv

# Activate
source .venv/bin/activate

# Install packages
pip install numpy scipy opencv-python

# Save dependencies
pip freeze > requirements.txt

# Install from requirements
pip install -r requirements.txt

---

Common Packages for Robotics

Package	Description
numpy	Numerical computing
scipy	Scientific computing
opencv-python	Computer vision
matplotlib	Plotting
rerun-sdk	3D visualization
transforms3d	3D transformations

---

Best Practices

1. Use type hints for better code documentation

   def add(a: float, b: float) -> float:
       return a + b

2. Use virtual environments for every project

   • Never install packages globally
   • Keep dependencies isolated

3. Never use conda (per course guidelines)

   • Use venv + pip instead

4. Use f-strings for formatting

   print(f"Position: ({x:.2f}, {y:.2f})")

5. Use context managers for resources

   with open("file.txt") as f:
       data = f.read()