Appendix A — Setting Up Python

This appendix covers how to install Python, choose an execution environment, and set up your development tools.

A.1 Interpreted vs. Compiled Languages

Python is an interpreted language, your code is executed line by line rather than being compiled into machine code first. This makes Python excellent for learning and prototyping because you get immediate feedback.

A.2 Ways to Run Python Code

A.2.1 The Python Interpreter

The simplest way to run Python is the interactive interpreter. Open a terminal and type:

python3

You will see a prompt (>>>) where you can type Python statements and see results immediately:

>>> 2 + 2
4
>>> print("Hello!")
Hello!
>>> exit()

A.2.2 IPython

IPython is an enhanced interactive shell with features like tab completion, syntax highlighting, and history search:

pip install ipython
ipython

Useful IPython features:

Tab completion: type the start of a name and press Tab
?: append to any object for documentation (print?)
%timeit: measure execution time of a statement

A.2.3 Python Scripts

For larger programs, save your code in .py files and run them:

python3 my_script.py

On macOS/Linux, you can make scripts directly executable:

#!/usr/bin/env python3
"""My first script."""
print("Hello from a script!")

chmod +x my_script.py
./my_script.py

A.2.4 Jupyter Notebooks

Jupyter Notebooks combine code, output, and documentation in one file. They are ideal for learning, data analysis, and experimentation:

pip install jupyter
jupyter notebook

This opens a browser-based interface where you can create and run notebooks.

A.2.5 Choosing the Right Environment

Environment	Best For
Interpreter	Quick calculations, testing ideas
IPython	Interactive exploration, debugging
Scripts	Reusable programs, automation
Jupyter	Learning, data analysis, sharing

A.3 Installing Python

A.3.1 Recommended: Miniconda

Miniconda provides Python plus the conda package manager in a lightweight install:

Download from docs.conda.io/en/latest/miniconda.html
Run the installer and follow the prompts
Verify the installation:

python --version
conda --version

A.3.2 Alternative: Official Python

Download from python.org if you prefer a minimal install without conda.

A.3.3 Managing Packages with pip

pip is Python’s standard package installer:

# Install a package
pip install requests

# Install a specific version
pip install pandas==2.0.0

# Install from a requirements file
pip install -r requirements.txt

# List installed packages
pip list

A.3.4 Virtual Environments

Virtual environments isolate each project’s dependencies:

# Create a virtual environment
python3 -m venv .venv

# Activate it
# macOS/Linux:
source .venv/bin/activate
# Windows:
.venv\Scripts\activate

# Install packages (isolated to this environment)
pip install pytest

# Deactivate when done
deactivate

Tip

Always use a virtual environment for each project. Add .venv/ to your .gitignore file.

A.4 Useful Libraries by Domain

Domain	Libraries
Data Science	pandas, numpy, matplotlib
Web Development	Flask, Django, FastAPI
Automation	requests, beautifulsoup4, selenium
Testing	pytest, coverage

Install what you need as you go, do not install everything at once.

A.5 Setting Up an IDE

VS Code is recommended for beginners:

Install from code.visualstudio.com
Install the Python extension
Open your project folder
Select your Python interpreter (bottom status bar)

PyCharm is a full-featured alternative with built-in Python support.

Jupyter Lab extends Jupyter Notebooks with a more complete IDE-like interface:

pip install jupyterlab
jupyter lab

A.6 Troubleshooting

“command not found” errors:

# macOS/Linux: add to your PATH
echo 'export PATH="$HOME/miniconda3/bin:$PATH"' \
    >> ~/.bashrc
source ~/.bashrc

# Windows (PowerShell as administrator):
$conda = "C:\Users\YourUsername\miniconda3"
$Env:Path = "$Env:Path;$conda;$conda\Scripts"

Package conflicts: Use virtual environments to isolate projects.

Permission errors:

# macOS/Linux
sudo chown -R $USER:$USER ~/miniconda3

Mixed Python versions: Use python3 explicitly rather than python to ensure you are using the correct version.

# Setting Up Python This appendix covers how to install Python, choose an execution environment, and set up your development tools. ## Interpreted vs. Compiled Languages Python is an *interpreted* language, your code is executed line by line rather than being compiled into machine code first. This makes Python excellent for learning and prototyping because you get immediate feedback. ## Ways to Run Python Code ### The Python Interpreter The simplest way to run Python is the interactive interpreter. Open a terminal and type: ```bash python3 ``` You will see a prompt (`>>>`) where you can type Python statements and see results immediately: ```text >>> 2 + 2 4 >>> print("Hello!") Hello! >>> exit() ``` ### IPython IPython is an enhanced interactive shell with features like tab completion, syntax highlighting, and history search: ```bash pip install ipython ipython ``` Useful IPython features: - **Tab completion**: type the start of a name and press Tab - **`?`**: append to any object for documentation (`print?`) - **`%timeit`**: measure execution time of a statement ### Python Scripts For larger programs, save your code in `.py` files and run them: ```bash python3 my_script.py ``` On macOS/Linux, you can make scripts directly executable: ```python #!/usr/bin/env python3 """My first script.""" print("Hello from a script!") ``` ```bash chmod +x my_script.py ./my_script.py ``` ### Jupyter Notebooks Jupyter Notebooks combine code, output, and documentation in one file. They are ideal for learning, data analysis, and experimentation: ```bash pip install jupyter jupyter notebook ``` This opens a browser-based interface where you can create and run notebooks. ### Choosing the Right Environment | Environment | Best For | |-------------|----------| | Interpreter | Quick calculations, testing ideas | | IPython | Interactive exploration, debugging | | Scripts | Reusable programs, automation | | Jupyter | Learning, data analysis, sharing | ## Installing Python ### Recommended: Miniconda Miniconda provides Python plus the `conda` package manager in a lightweight install: 1. Download from [docs.conda.io/en/latest/miniconda.html](https://docs.conda.io/en/latest/miniconda.html) 2. Run the installer and follow the prompts 3. Verify the installation: ```bash python --version conda --version ``` ### Alternative: Official Python Download from [python.org](https://www.python.org/downloads/) if you prefer a minimal install without conda. ### Managing Packages with pip pip is Python's standard package installer: ```bash # Install a package pip install requests # Install a specific version pip install pandas==2.0.0 # Install from a requirements file pip install -r requirements.txt # List installed packages pip list ``` ### Virtual Environments Virtual environments isolate each project's dependencies: ```bash # Create a virtual environment python3 -m venv .venv # Activate it # macOS/Linux: source .venv/bin/activate # Windows: .venv\Scripts\activate # Install packages (isolated to this environment) pip install pytest # Deactivate when done deactivate ``` ::: {.callout-tip} Always use a virtual environment for each project. Add `.venv/` to your `.gitignore` file. ::: ## Useful Libraries by Domain | Domain | Libraries | |--------|-----------| | Data Science | pandas, numpy, matplotlib | | Web Development | Flask, Django, FastAPI | | Automation | requests, beautifulsoup4, selenium | | Testing | pytest, coverage | Install what you need as you go, do not install everything at once. ## Setting Up an IDE **VS Code** is recommended for beginners: 1. Install from [code.visualstudio.com](https://code.visualstudio.com/) 2. Install the Python extension 3. Open your project folder 4. Select your Python interpreter (bottom status bar) **PyCharm** is a full-featured alternative with built-in Python support. **Jupyter Lab** extends Jupyter Notebooks with a more complete IDE-like interface: ```bash pip install jupyterlab jupyter lab ``` ## Troubleshooting **"command not found" errors:** ```bash # macOS/Linux: add to your PATH echo 'export PATH="$HOME/miniconda3/bin:$PATH"' \ >> ~/.bashrc source ~/.bashrc # Windows (PowerShell as administrator): $conda = "C:\Users\YourUsername\miniconda3" $Env:Path = "$Env:Path;$conda;$conda\Scripts" ``` **Package conflicts:** Use virtual environments to isolate projects. **Permission errors:** ```bash # macOS/Linux sudo chown -R $USER:$USER ~/miniconda3 ``` **Mixed Python versions:** Use `python3` explicitly rather than `python` to ensure you are using the correct version.