While the word "python" might immediately make you think of a snake, that's not all it is! Python refers to a programming language. There are a lot of different programming languages that are used for a variety of tasks, but what they all have in common is that they tell your computer what to do. Behind all of your favorite applications is a software developer (or, more often, several developers) who have written the instructions for the computer in a programming language. Python is a particularly versatile language that can be used for web development, data science, machine learning, and more.

So why should you consider Python for your data analysis? There are a few reasons:

• Python is designed to be human-readable. Creating human-readable code is great for research because your analyses will be more reproducible (if you know exactly what you did, you can reproduce it) and transparent (writing the methods section of a paper is much easier when there is clear language about what was done and why).

• Python is free to download and use; it is also open source, meaning that updates and innovations can happen more quickly than with proprietary software.

• Because Python is quite popular and fairly mature, there are quite a lot of resources out there to help you get started learning, from free online tutorials and cheatsheets to semester-long courses and everything in between.

What are some reasons to consider using Python for data analysis? Select all that apply.

[[X]] Python is free and open-source. [[ ]] Python is simple point-and-click software. [[X]] Python is human-readable. [[X]] The Python community has lots of resources to help you get started.

Python can be written in several ways:

• You can write Python code interactively using your computer's Terminal program or Command Line (if you're not sure where that is or how to use it, the Bash/Command Line 101 module will help you get started).
• You can write scripts that include Python code.
• You can create Python notebooks using Jupyter or online using Google Colaboratory (or "Colab" for short).

In most cases, you'll need to start with downloading Python to your computer (the exception is when using cloud-based solutions like Google Colab); which option you choose will come down to personal preference and your specific goals.

If you have Python installed on your computer, you can use your computer's Command Line or Terminal program to write Python code interactively using a Python interpreter by typing python3:

Click on the image to play the demo.

To exit the Python interpreter, type the command exit() and then press the Enter key. Alternatively, you can simply close the interpreter window.

Using Python interactively in the terminal can be useful if you want to quickly test out short pieces of code. However, there are a couple of reasons why it's not useful to code interactively in the interpreter for longer or more complex code:

• If you want to re-run a line of code, because you want to change the parameters or accidentally included a typo, you'll need to re-type the code again with your edits. There are some shortcuts you can use to scroll through previous lines of code in the Terminal/Command Line (the up arrow on your keyboard will usually do it) but it can still be time-consuming if the code is longer than a line or two.

• The code you write in the interpreter will disappear once you exit.

Because of these reasons, you'll usually either be creating Python scripts or using a notebook instead.

For more complex tasks, it often makes sense to write a python script instead of writing instructions one at a time. Scripts allow you to write out a series of instructions ahead of time and then run in order with one command.

Scripts are often written using special text editors that are designed for writing code (such as VS Code or Sublime) or full-featured integrated development environments (IDEs) such as PyCharm or Spyder, which have more debugging and automation capabilities than text editors.

Here is a short script written in the Spyder IDE:

In the above example, our script asks for input of a user's name using the input() function, stores that input in a variable we've called name, and prints out a welcome message to the screen that includes the name the user entered using the code print("Welcome to Intro to Python, "+name+"!").

In the script above you can also see lines of text marked with a pound sign/hash mark # at the beginning. This symbol indicates the start of a comment, which is anything you write in a script that you don't want to run as code. Comments are a good way to briefly describe or explain aspects of your script, and can also be used to "hide" lines of code as you experiment (an alternative to deleting code that you may want to add back later).

The Spyder IDE has a console where you can see the output of your script without having to run it in the Command Prompt/Terminal. As you can see in the screenshot below, all we had to do was create the file one time and then run that file instead of typing in the code interactively. This can save you time and effort if you have a task you know you'll want to perform repeatedly. In the Spyder IDE there is a "run" button (a green arrow in menu at the top) but you could also run the same file in your Terminal/Command Line by typing the command python followed by the file name of your Python script.

Click on the image to play the demo.

In this case, when you run the code we wrote before in the Spyder console, you'll be asked for an input value (I've supplied the name "Pythonista"). If you hit "Enter", the output of the second line of code will appear: Welcome to Intro to Python, Pythonista!.

Python scripts are saved with the file extension .py.
To run the script in the example above using the Terminal, you would use Terminal to navigate to the correct directory(the folder where you've saved your file) and type python intro-to-python.py. If you are in the right place, you should see the output of your code!

Which of the following is not a recommended way to write Python code?

[( )] in the Python interpreter in the command line. [(X)] as a script a Google document. [( )] as a script in an IDE. [( )] online in a notebook.

If you are interested in using Python for data analysis, you might want to write Python in a Jupyter notebook (which was previously called an IPython notebook; this is where its file extension .ipynb comes from). There are a couple of reasons to consider using Jupyter notebooks:

• Notebooks provide the best of both worlds between interactive and scripted code: code is written in chunks that can be run individually, with any output displayed beneath the code chunk.

• You can intersperse sections of text, written in Markdown language, that can provide explanations and context for your code. This is similar to a scientific notebook, and is especially valuable for data analysis.

What are the components of a Jupyter notebook? Select all that apply.

[[X]] Code cells [[X]] Text cells written in Markdown [[ ]] A console where you can write lines of code interactively

There are two primary ways to use Python: on your own computer, or in the cloud! Here are instructions for a way to get set up no matter which one you prefer.

Your computer most likely will not have Python already installed. To check, you can open your Command Line/Terminal and type python --version. The version you have will be displayed if you have Python installed. If Python is not installed, you have a few options:

• You can install Python using Anaconda (recommended), a toolkit that gives you not only Python itself but also many other data science tools to help with coding, analysis, and visualization. The individual version of Anaconda is also free to download!

  Once you've downloaded Anaconda, you can open the Anaconda Navigator. When you do, you'll see the the window below. Now you have access to lots of tools for working with Python!

  

• You can also download just the latest version of Python directly at python.org. If you also want Jupyter or any IDEs, you will need to download those separately as well. You can install Jupyter here and here is a list of IDEs where you can write Python code.

If you prefer to continue practicing in the cloud instead of downloading Python, Google Colaboratory, or Google Colab, provides an online Jupyter notebook environment that is completely free (though you do need a Google account). The introduction is actually written as a Colab notebook itself, so you can jump right in practicing. While it won't look exactly like the Jupyter notebook we used for this module, it will be a very similar experience, with a mix of code cells and text cells written in Markdown.

You can even see what this module's notebook looks like in Google Colab! Once you've worked through the introduction above:

1. Click "File" in the upper left-hand corner and then "Open notebook" in the drop-down menu:

   

2. In the window that appears, one of the options at the top is GitHub. Click that, and you'll see where you can type in a GitHub URL.

3. In that space, copy and paste the repository where these modules live: https://github.com/arcus/virtual_library.

4. Click the magnifying glass to the far right to search. You should see a list of the Python notebooks in that repository.

5. When you find the one you want, the button on the far right (an upward-slanting arrow in a box) will open the notebook file as a Colab notebook in another tab.

 <img src="proxy.php?url=https%3A%2F%2Fgithub.com%2Farcus%2Fvirtual_library%2Fblob%2Fmain%2Fdemystifying_python%2Fmedia%2Fgithub_repo_colab.png%3Fraw%3Dtrue" height="500" width="800" alt="Finding a GitHub repository in Google Colab following the above instructions." data-alt="https://github.com/arcus/virtual_library/blob/main/demystifying_python/media/github_repo_colab.gif?raw=true">

Click on the image to play the demo.

How can you access Python to begin practicing on your own?

[( )] By downloading the latest version at https://www.python.org/downloads/ [( )] By downloading the latest Anaconda distribution [( )] By using cloud-based tools, such as Google Colab [(X)] All of the above

• python.org is a great resource for documentation, FAQs, and tutorials for beginners, as well as information about what is happening in the wider Python community. Check it out and explore!

• If you're interested in practicing more with Google Colab, check out this notebook looking at statistics.

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