01-03: Python Platforms and IDEs¶

Python can be written and run in many different environments. The right choice depends on the intended workflow — scripting, data science, web development, or quick experimentation.

IDLE — Python's Built-in Editor¶

IDLE (Integrated Development and Learning Environment) ships with every Python installation. It is minimal but requires zero setup.

Launching IDLE¶

Windows: Search "IDLE" in Start menu, or run python -m idlelib in terminal
Mac/Linux: Run idle3 in terminal

Two Modes¶

Mode	Description
Shell	Interactive REPL — type and run one line at a time
Editor	Write and save `.py` files, run with `F5`

IDLE Shell (interactive)¶

>>> 2 + 2
4
>>> name = "Alice"
>>> print(f"Hello, {name}!")
Hello, Alice!

IDLE Editor¶

File → New File (or Ctrl+N)
Write the code
File → Save (Ctrl+S)
Run → Run Module (F5)

Pros and Cons¶

Pros	Cons
Comes with Python — no install needed	Very limited features
Good for absolute beginners	No extensions/plugins
Syntax highlighting	No debugger integration
Lightweight	Not used professionally

Best for: Complete beginners running their first Python scripts.

VS Code — Recommended General-Purpose IDE¶

Visual Studio Code is a free, lightweight editor by Microsoft with powerful Python support via extensions. (Covered in detail in notes 01-02 and 01-03.)

Key Features¶

Python extension by Microsoft (IntelliSense, linting, debugging)
Integrated terminal
Git integration
Jupyter notebook support
Remote development (SSH, containers)
Huge extension marketplace

Extensions to install:
- Python (by Microsoft)       — core support
- Pylance                     — fast type checking
- Jupyter                     — notebook support
- GitLens                     — enhanced git
- Black Formatter / autopep8  — auto-formatting

Best for: General Python development, scripts, web apps, automation.

PyCharm — Professional Python IDE¶

PyCharm by JetBrains is a full-featured Python IDE. It comes in two editions:

Edition	Cost	Use Case
Community	Free	General Python, scripts
Professional	Paid (free for students)	Web (Django/Flask), databases, remote dev

Key Features¶

Deep code intelligence (refactoring, navigation, auto-import)
Built-in debugger with breakpoints
Database tools (Professional)
Django/Flask/FastAPI support (Professional)
Virtual environment management
Code inspections and quick-fixes

Getting Started¶

Download from jetbrains.com/pycharm
Open/create a project
PyCharm auto-detects or allows configuration of a Python interpreter
Run with Shift+F10, debug with Shift+F9

Best for: Large Python projects, professional development teams, Django/Flask web development.

Anaconda — Python for Data Science¶

Anaconda is a Python distribution bundled with 250+ data science packages and tools. It installs Python + NumPy + Pandas + Matplotlib + Jupyter and more in one click.

What Anaconda Includes¶

Python interpreter
conda — package and environment manager
Jupyter Notebook and JupyterLab
Spyder IDE
Navigator (graphical launcher)
Pre-installed: NumPy, Pandas, Matplotlib, Scikit-learn, SciPy, etc.

Install¶

Download from anaconda.com — choose the version matching the OS.

conda vs pip¶

	`pip`	`conda`
Package source	PyPI	Anaconda repository
Environment management	`venv`	`conda env`
Binary packages	Sometimes problematic	Pre-compiled, reliable
Non-Python deps	No	Yes (C libraries, etc.)

conda Environments¶

# Create a new environment
conda create -n myenv python=3.11

# Activate it
conda activate myenv

# Install packages
conda install numpy pandas matplotlib

# List environments
conda env list

# Deactivate
conda deactivate

# Export environment
conda env export > environment.yml

# Re-create from file
conda env create -f environment.yml

Best for: Data science, machine learning, scientific computing. Especially good on Windows where binary packages are hard to compile.

Spyder — Scientific Python IDE¶

Spyder comes bundled with Anaconda. It is designed for data science workflows and resembles MATLAB's interface.

Key Features¶

Variable Explorer — inspect arrays, dataframes visually
IPython console — interactive with inline plots
Editor with cell-based execution (# %% to define cells)
Debugger and profiler
Help pane showing docstrings

# In Spyder, use # %% to create "cells"
# %% Section 1
import numpy as np
x = np.linspace(0, 10, 100)

# %% Section 2
import matplotlib.pyplot as plt
plt.plot(x, np.sin(x))
plt.show()

Run only a cell with Ctrl+Enter, or run the entire file with F5.

Best for: Scientific computing and data analysis; users coming from MATLAB.

Jupyter Notebook¶

Jupyter Notebook is a browser-based interactive environment that mixes code, output, text (Markdown), equations (LaTeX), and visualizations in a single .ipynb file.

Starting Jupyter¶

# After installing (pip or conda)
pip install notebook
jupyter notebook

# Opens browser at http://localhost:8888

Notebook Structure¶

A notebook is made of cells:

Cell type	Content
Code	Python code — run with `Shift+Enter`
Markdown	Text, headings, equations, images
Raw	Plain text (not rendered)

Key Shortcuts¶

Shortcut	Action
`Shift+Enter`	Run cell and move to next
`Ctrl+Enter`	Run cell, stay
`A` (command mode)	Insert cell above
`B` (command mode)	Insert cell below
`DD` (command mode)	Delete cell
`M`	Change to Markdown
`Y`	Change to Code
`Esc`	Enter command mode
`Enter`	Enter edit mode

Magic Commands¶

# Time a single expression
%timeit sum(range(1000))

# Time a block
%%timeit
total = 0
for i in range(1000):
    total += i

# Run a shell command
!pip install numpy
!ls

# Show matplotlib plots inline
%matplotlib inline

# Load external file into cell
%load script.py

# Run an external file
%run script.py

# Display all variables
%whos

Pros and Cons¶

Pros	Cons
Great for exploration and storytelling	Not ideal for large production code
Inline visualizations	No traditional debugging
Share as HTML/PDF	Execution order can cause confusion
Reproducible analysis reports	Version control (git) is awkward

Best for: Data analysis, machine learning experiments, teaching, sharing results.

JupyterLab — Next-Gen Jupyter¶

JupyterLab is the modern successor to Jupyter Notebook. Same .ipynb format but with a richer interface.

pip install jupyterlab
jupyter lab

Additional Features over Notebook¶

Multiple tabs (notebooks, terminals, editors)
Drag-and-drop cells
File browser sidebar
Extension system
Better table-of-contents support

Best for: Same as Jupyter but with a more complete IDE feel.

Google Colab — Jupyter in the Cloud¶

Google Colaboratory (Colab) runs Jupyter notebooks entirely in the browser with no local setup, backed by Google's servers.

Access¶

Go to colab.research.google.com — free with a Google account.

Key Features¶

Free GPU/TPU access — critical for deep learning
Stored in Google Drive as .ipynb
Pre-installed: TensorFlow, PyTorch, Pandas, Matplotlib, etc.
Share like a Google Doc
Mount Google Drive to access files

# Mount Google Drive
from google.colab import drive
drive.mount('/content/drive')

# Access your files
import pandas as pd
df = pd.read_csv('/content/drive/MyDrive/data.csv')

# Install packages
!pip install some-package

# Check GPU availability
import tensorflow as tf
print("GPU:", tf.config.list_physical_devices('GPU'))

# Use magic commands (same as Jupyter)
%matplotlib inline

Colab-Specific Widgets¶

# File upload
from google.colab import files
uploaded = files.upload()

# File download
files.download('output.csv')

Free vs. Colab Pro¶

Feature	Free	Colab Pro
GPU	T4 (limited)	A100/V100
Session timeout	~12 hours	Longer
RAM	~12 GB	~25 GB
Background execution	No	Yes

Best for: Deep learning, sharing notebooks publicly, working without local setup, accessing free GPUs.

Kaggle Kernels — Data Science Notebooks¶

Kaggle (owned by Google) offers free Jupyter-like notebooks (called "Kernels" or "Notebooks") with:

Free GPU and TPU
Direct access to Kaggle datasets
Public notebook sharing and competitions

Access at kaggle.com/code

Replit — Browser-Based Coding¶

Replit is an online IDE that runs in the browser — no installation needed.

replit.com
Supports 50+ languages including Python
Collaborative real-time editing
Hosts web apps directly
Good for sharing runnable code snippets

AI-Integrated Development Platforms¶

Modern development increasingly integrates AI directly into the coding workflow:

GitHub Copilot¶

AI pair programmer integrated into VS Code, JetBrains, Neovim
Autocompletes entire functions based on comments or context
Tab to accept suggestions, Alt+[ / Alt+] to cycle alternatives
github.com/features/copilot

# Just write a comment — Copilot generates the function
# Function to calculate the nth Fibonacci number
def fibonacci(n):
    # Copilot suggests the full body here

Cursor — AI-Native Code Editor¶

VS Code fork with deep AI integration
Inline editing: Select code → Ctrl+K → describe change in English
Chat: Ask questions about the codebase
Agent mode: Multi-file edits with one instruction
cursor.com

Claude Code (by Anthropic)¶

Terminal-based AI coding agent
Can read, write, and execute code across an entire project
Understands full codebase context
Run: claude in the terminal
claude.ai/code

Windsurf (by Codeium)¶

AI-native IDE based on VS Code
"Cascade" agent for multi-step coding tasks
Free tier available
codeium.com/windsurf

Amazon CodeWhisperer / Q Developer¶

AWS's AI code assistant
Deep integration with AWS services
Free tier for individual developers

JetBrains AI Assistant¶

Built into all JetBrains IDEs (PyCharm, IntelliJ, etc.)
Code completion, explanation, refactoring, test generation
Subscription required

Platform Comparison Summary¶

Platform	Type	Best For	Cost
IDLE	Desktop IDE	Absolute beginners	Free
VS Code	Desktop IDE	All-purpose development	Free
PyCharm	Desktop IDE	Professional Python projects	Free/Paid
Spyder	Desktop IDE	Scientific/data analysis	Free
Anaconda	Distribution + tools	Data science environment setup	Free/Paid
Jupyter Notebook	Browser (local)	Exploratory analysis, teaching	Free
JupyterLab	Browser (local)	Same as Jupyter, richer UI	Free
Google Colab	Cloud	Free GPU, sharing, no setup	Free/Paid
Kaggle	Cloud	Data competitions, datasets	Free
Replit	Cloud	Quick experiments, sharing	Free/Paid
Cursor	Desktop IDE	AI-assisted development	Free/Paid
GitHub Copilot	Extension	AI autocomplete in any IDE	Paid
Claude Code	Terminal agent	Full-project AI coding	Usage-based

Choosing the Right Tool¶

Just starting out?
└── IDLE (zero setup) OR VS Code (best long-term)

Data science / machine learning?
└── Anaconda + Jupyter Lab / Google Colab (for GPU)

Professional Python development?
└── VS Code + Python extension  OR  PyCharm Community

Want AI assistance?
└── Cursor (free, AI-native) or VS Code + GitHub Copilot

No local install / quick share?
└── Google Colab or Replit

Scientific computing (MATLAB-like)?
└── Spyder (comes with Anaconda)

⬅️ Previous: 01-02: IDE ➡️ Next: 01-04: Environment Setup