Python is arguably the most popular programming language in the world (depending on how you count). But it is definitely the most popular language with scientists and engineers. There are a few major reasons for this, but the sheer momentum Python has in our communities means if you want to do something science-y, there are probably already tools ready to solve your problems. And if you have questions, there are a million answers and high-quality tutorials online to help you.
You'll never find a programming language that frees you from the burden of clarifying your ideas.
Perhaps the reason Python originally got so popular was that it is easy to learn. Every programming language has syntax, the rules that make up the language. But Python's syntax design makes it one of the easiest languages in the world to learn. Especially for beginners.
That's the Python motto. The author of the first version of Python, Guido von Rossom, has started a culture in the Python community that code should be easy to use. To that end, Python comes with a large collection of standard libraries. These are tools to do the sorts of things that people frequently want to do with a computer programming language: complex math, random numbers, calendars, dates and times, dealing with *.zip files, communicating over the internet via HTTP, reading CSV files, you name it.
If you are a scientist or (non-software) engineer or some kind, you probably have tried other programming languages.
How does Python compare?
Well, first off, if you have experience with Matlab, LabView, JavaScript, or LaTeX the big difference is: Python is a general-purpose programming language. Unlike those languages, Python is used in the real world to just do practically (almost) everything you want to do with software.
If, in the past, you have experience with C, C++, Java, Fortran, or Rust, the first thing you'll notice is that Python is a lot easier to learn. And much faster to get really good with. The second thing you'll notice is that Python isn't a strongly-typed language, and that will be strange for you. Lastly, Python code will run slower than those other languages. The cost-benefit argument that Python makes is that sometimes you want to optimize for faster code, but sometimes you want to optimize for your developers time. And in that case, there's no comparison: Python wins.
Write code.
If you really want to learn the material in this class, there's only one solution: use what you learn.
As a starting point, there will be example problem sets at the end of each lecture. But don't stop there!
At the beginning of the class, maybe all you can do is replace your calculator with Python. As the class continues, you should be able to replace Excel with Python. Eventually, you should be able to do all your data analysis and scripting work in Python. But the trick is to start using what you learn every week at work, school, or home.
Practice.
Start using Python daily. Make a GitHub account and start a silly Python project, just for fun.
Python should be easy to install on basically all desktops and laptops. There are many different versions and flavors of Python. This class will be built around Python v3.9, but you will probably be fine using anything Python v3.7 to v3.11.
The installation procedure depends on what operating system you have installed. The Hitchhiker's Guide to Python has a great description of how to install for the three major operating systems:
NOTE: The above guide also explains how to install SetupTools, Pip, and VirtualEnv. While these are great tools worth learning, and I highly encourage it, they are not yet necessary for most of this course.
I strongly recommend installing Anaconda instead. Anaconda is Python packaged with hundreds of tools and libraries you will want.
For the beginning student, there are three major ways to execute Python code: in the interpreter, as a script/program, and in a Jupyter notebook. In this class, you will be free to use which of these you want. But we will cover the strengths and weaknesses of each, so you can chose wisely.
Perhaps the easiest way to execute Python code is to use the Python interpreter. This is a handy Python feature not available in most other languages.
To start the Python interpreter, go to the "Command Prompt" in Windows or the "Terminal" in Mac OS / Linux. (From here on out I will call this the "command line".) Now the easy part, type python:
The Python interpreter should start right up, and you should see a prompt (>>>) to start entering Python code:
You can now enter valid Python code at the prompt:
Finally, to exit out of the interpreter, type exit():
So, that's the interpreter. It's easy to use, comes standard with Python, and will be much the same on all computers.
PRO TIP: If you really want to learn Python, I recommend using the Python interpreter instead of a calculator, starting now. It will help you get used to the language basics. And it is vastly more powerful than any calculator.
The Python interpreter has one serious limitation: when you close the Python intepreter you lose all your work. What if you want to use your code again tomorrow? You will want to save your work. That is where Python scripts come into play.
To execute Python code as a script or a program, we first need to create a plain text file and give it the extension ".py". The classic first example in any language is to print "Hello, World!" to the screen. To do so, create a plain text file and add the text:
print("Hello, world!")
Then save the file with the name "hello_world.py":
Now I can run the program from my command line by typing python hello_world.py:
And that's it! You can now write Python code and save it off for later use. This is how Python is typically written.
NOTE: Some people call them Python "scripts" and other people call them Python "programs". Broadly speaking, if the script gets complicated enough you call it a program. Don't worry about it though; these are not important distinctions.
NOTE: If you're coming from a language like Fortran or C, you'll notice we didn't need to compile our code. Because of this, we call Python an interpreted language.
Lastly, in this class you might want to run Python code by using Jupyter Notebooks. This is a solution designed for the daily work of scientists and engineers, and you will probably find it highly convenient. However, Jupyter doesn't come installed standard with Python, you will have to download and install it separately. Alternatively, I recommend installing Anaconda, as it comes packaged with Jupyter.
Jupyter Notebooks are a flexible and easy-to-use Python interface for your web browser, designed more for our daily work: data exploration, statistics, quick plots, and tables. It is not ideally suited for large programs with many components. All of the code you write in Jupyter is saved in a special text file, like in a standard Python script.
To start a notebook, go to the command line and type jupyter notebook:
After a couple seconds your default web browser will open up and you will see a list of contents of your local directory:
Click "new" in the top-right corner and create a "notebook". This will give you an empty notebook to start working. Valid Python code can be entered in these cells:
Hit shift-and-enter to execute the code in your current cell. You will see a new cell to pop up below to continue your work:
To label your work, touch "Untitled" at the top of the page and you can give your Jupyter notebook a new file name:
If you close the tab in your web browser, or look at the first tab that was opened, you should see your new notebook file, with the extension .ipynb:
That's all you need to know to get started with Jupyter! But that's not all there is to know about Jupyter. It has a lot of handy tools to make your life easier. A popular tool is the ability to display tables and plots right in the notebook:
Jupyter is an ideal choice if you just want to do some quick thinking, data analysis, or plotting. It is also a friendly environment for a student to learn the language.
NOTE: Jupyter notebooks used to be called "iPython Notebooks". There was a name change because Jupyter notebooks now support more than just Python, but we won't worry about that in this class.
If you find this class useful, the best way you can repay me is to help me make it better. Make a GitHub account, and help me clarify and polish the course.
Things I would love from you?
- Is a lecture unclear? Open a ticket.
- Is there a grammatical error? Open a Pull Request.
- Did you find a great reference for the "further reading" section? Open a ticket.
- Do you have an idea to improve a lecture? Open a ticket.