Get started with Python

Welcome to your beginner’s guide to Python! This document is designed to get you up and running with Python programming. By the end of this tutorial, you’ll have Python installed and ready to code. Let’s get started and enjoy learning Python!

What is Python

Python is a high-level general-purpose programming language. Its design philosophy emphasizes code readability with the use of significant indentation. This means that the code we are writing should be readable by humans (us) and the empty space (idents such as tab) modify the code. Python is dynamically typed. Dynamic typing means we do not have to declare the type of data we are working with beforehand based on the characteristics of the data. It supports multiple programming paradigms including structured object-oriented and functional programming. (source: Wikipedia)

The basics

A popular programming language

• Python is one of the most popular programming languages in the world known for its readability and simplicity. It is widely used by both beginners and experienced developers.

Free and open source

• Accessibility: Python is free to use and its source code is open to the public. It is developed under an OSI-approved open-source license.

• Community Support: Being open source Python has a large and active community. This community contributes to a vast array of libraries and tools making finding help and resources for your projects easier. The Python Package Index (PyPI) hosts thousands of third-party modules for Python. Both Python’s standard library and the community-contributed modules allow for endless possibilities.

Wide range of applications

• Data Analysis

• Machine Learning

• Software Development

• Web Development

• Education

The technical stuff

High-level language Like C++ and Java, Python provides a higher level of abstraction than low-level languages like Assembly which are closer to machine code meaning Python abstracts away many of the complex details of the computer’s hardware. This makes it easier to write and understand code.

Interpreted and Dynamically Typed Python is an interpreted language meaning you can run your code directly without the need for a separate compilation step. Python can be used interactively running code line by line in a Python shell or an integrated development environment (IDE) like Jupyter Notebook. This allows for quick testing and debugging.

Impact on Performance While Python’s high-level nature and dynamic typing can lead to slower performance compared to some compiled languages like C++, the difference is often negligible for many applications. You can use libraries written in faster languages or optimize your code for performance-critical tasks.

Optionally object-oriented but doesn’t force you to be (unlike Java) Python supports object-oriented programming (OOP) which is useful for organizing and managing larger codebases and a good choice for developing large-scale software applications.

Why use Python

The basics

Free, open-source, and cross-platform

• Cost-Effective: Python is free to download and use making it accessible to everyone. There are no licensing fees or costs associated with it.

• Open Source: Being open source, Python’s source code is publicly available. You can inspect, modify, and distribute it fostering a collaborative environment.

• Cross-Platform: Python works on various operating systems including Windows, macOS, and Linux. This cross-platform compatibility means you can run your Python programs on any device without modifications.

Scientific Libraries and Community

• Scientific Libraries: Python boasts a plethora of scientific libraries such as NumPy, SciPy, Pandas, and Matplotlib. These libraries simplify complex mathematical computations, data manipulation, and visualization tasks.

• Community Support: Python has a large and passionate user community especially in scientific and academic circles. This community contributes to a wealth of resources, tutorials, and forums where you can seek help and share knowledge.

Popularity in Data Science and Machine Learning Python is a go-to language for data science due to its comprehensive library ecosystem. Python offers a wide range of libraries and frameworks specifically designed for machine learning and data science making it easier for developers to implement ML algorithms. Such as NumPy, Pandas, PyTorch, Scikit-learn, TensorFlow, and Keras.

Low learning curve Python’s straightforward syntax and readability make learning easier than other programming languages. Beginners can quickly pick up the basics and start coding.

The Technical Stuff

Advanced Data Structures Python includes advanced data structures such as dictionaries (key-value pairs) and tuples (immutable sequences). These structures are efficient and easy to use which helps in organizing and managing.

Flexibility You can use Python for scripting small tasks, developing complete applications, or anything in between.

Human-Readable Syntax Python’s syntax is designed to read and write easily. It uses indentation to define code blocks which enforces clean and readable code.

Reproducible Python is free to use and Python code can be easily shared across different platforms and machines. This is especially useful in collaborative environments fostering transparency and collaboration.

Summary

Learning Python is beneficial because it is free, open-source, and cross-platform making it accessible and versatile for a wide range of applications. Its extensive libraries, strong community support, straightforward syntax, and powerful data science and machine learning capabilities make it an ideal choice for beginners and experts.

Where to use Python at Tufts

Python is available in most computer labs at Tufts University.

What is that called? Terminology related to Python

Kernel
In the context of computing and programming, a kernel is the core part of an operating system or an interactive computing environment. In Python, especially in environments like Jupyter notebooks, the kernel is the process that runs the user’s code. It receives input, executes it, and sends the output back to the front-end interface.

Virtual Environment
A virtual environment is an isolated environment created to manage dependencies for different projects. Each virtual environment can have its own set of installed packages separate from the global Python environment which helps avoid conflicts between project dependencies.

Python Distribution
A Python distribution is a package that includes the Python interpreter, the standard library, and often additional libraries and tools to help you get started with Python development. Examples include Anaconda and the official CPython distribution.

Python Interpreter
The Python interpreter is the software that reads and executes Python code. It converts Python code into machine code that the computer’s hardware can understand. Common interpreters include CPython, PyPy, Jython, and IronPython.

Python Module
A Python module is a single file containing Python code. Modules allow you to organize your Python code into manageable sections and they can be imported and used in other Python programs.

Python Package
A Python package is a collection of modules organized in directories. Each package in Python contains a special __init__.py file making it easy to group related modules together.

Python Library
A Python library is a collection of related modules and packages that provide specific functionalities making it easier to perform common tasks. Libraries are often broader and more comprehensive than individual packages.

Pip (pip install)
Pip is the package installer for Python. It allows you to install and manage additional libraries and dependencies that are not included in the standard Python distribution. The command pip install is used to install a package from the Python Package Index (PyPI).

Conda (conda install)
Conda is an open-source package management and environment management system that runs on Windows, macOS, and Linux. It can be used to install and manage software packages and dependencies including those outside the Python ecosystem. The command conda install is used to install packages from the Anaconda repository.

File format: .py
A .py file is a plain text file containing Python code. It is the standard file extension for Python scripts.

File format: .ipynb
An .ipynb file is an interactive Python notebook file used by Jupyter Notebooks. It allows you to combine executable code, narratives, visualizations, and other multimedia resources into a single document.

How to get started with Python:

How to install Python

One option: Directly download Python to the computer Installing Python – Download Python

Best option: Anaconda – a Python distribution

• Free and cross-platform

• Simple graphical interface

• Contains most packages

• Package manager

• Virtual environments

• Jupyter Notebook

• Spyder

How to run Python script

Option 1 - Command Line Interactive and text-based: the original way to control the computer and interact with your computer using text commands.

Operating system:

• Windows: Command Prompt and PowerShell

• Linux and macOS: Terminal

Also called a console – a common term for the command line interface. Anaconda has an Anaconda Prompt, a command line interface provided by the Anaconda distribution of Python.

Option 2 - Write & Run – code editor (file extension: .py) Get a good text editor to write your Python code; some popular choices include:

• Sublime Text

• Atom

• Notepad++

• VS Code

Install Python extension within the software (you need to have Python installed on your local computer). Write your code in a text editor and save it with a .py extension. Execute the command in your command line interface (Command Prompt, Terminal, or Anaconda Prompt): python filename.py or run it directly within the editor like VS Code. It connects the terminal as well; the Conda will still work.

Option 3 - Integrated Development Environment (IDE) All-in-one tool for programming providing a comprehensive environment for coding, helping with syntax, debugging, and more. Examples of IDE:

• Spyder: Comes with Anaconda and is great for data science.

• PyCharm: Powerful IDE used in data science and general Python development.

• Thonny: Designed for beginners providing a simple and clear interface.

• Visual Studio Code: This is a very powerful and versatile IDE for many types of code including Python. Additionally, you can use notebooks in this IDE which we discuss below.

Option 4 – Notebook Best option for data analysis and visualization. Notebooks are ideal for keeping a record of your work and integrating notes, code, and output all in one place. You can run and re-run code snippets individually. These code snippets are referred to as cells.

Options for Notebooks:

• Jupyter Notebook: The original Python notebook ships with Anaconda and runs on a local server allowing access to local storage.

  • Runs on a local server

  • Access local storage

  • Available on the Tufts HPC

• Google Colab: A web-based notebook that runs on Google servers providing an easy start with many pre-loaded libraries and integration with GitHub and Google Drive. 100% ONLINE: colab.research.google.com

  • No Python installation needed

  • Most libraries pre-loaded

  • GPU and TPU acceleration available

  • Easier to collaborate and start off with

Drawbacks:

• Google-dependent

• Must upload all files

• Resources are capped

• Can only use pip for installing packages

A summary of comparing the four options

Feature	Command Line	Text Editor	IDE	Notebook
Description	Direct interaction with the system’s shell or terminal	Simple text-based code editing	Integrated development environment with comprehensive tools	Interactive environment for writing and running code in chunks
Ease of Use	Basic, requires knowledge of commands	Simple, user-friendly	Advanced, user-friendly	Highly user-friendly, especially for data analysis
Setup Complexity	Low, Python must be installed	Low, text editor installation	High, IDE installation and setup	Medium, installation of Jupyter or equivalent
Features	Limited to command-line tools	Basic syntax highlighting, minimal features	Code completion, debugging, version control, project management	Inline code execution, visualization, markdown support
Best For	Quick scripts, command-line tasks	Simple scripts, small projects	Large projects, full software development lifecycle	Data science, machine learning, exploratory data analysis
Example Tools	Python Shell, Command Prompt, Terminal	Notepad++, Sublime Text, VS Code (as text editor)	PyCharm, VS Code (as IDE), Spyder	Jupyter Notebook, Google Colab
Code Execution	Sequential, one command at a time	Sequential via command line or terminal	Integrated, can run, debug, and test within the environment	Interactive, allows running code in cells, supports re-running individual cells
Debugging	Basic, print statements	Basic, print statements	Advanced, built-in debugging tools	Basic, some support through print statements or magic commands
Version Control	External tools (e.g. Git)	External tools (e.g. Git)	Integrated, built-in support for Git	External tools (e.g. Git)
Visualization	Limited, via external commands (e.g. matplotlib plots in separate windows)	Limited, via external commands	Good, integrated support for visualization libraries	Excellent, inline visualization directly in the notebook
Documentation	Limited, help commands	Limited, external documentation	Integrated, documentation and help features	Excellent, can include markdown and rich text alongside code

For all our Python tutorials, we will use Notebooks to organize the text, code, and results. We use this format as it allows us to step through notes, annotations, and code clearly with results being shown for each cell.