Intro to Python Coding

The following tutorial is an introduction to writing and running Python code in the futurecoder.io and Google Colab/Jupyter environments, loosely based on swcarpentry.github.io/python-novice-gapminder/01-run-quit.html, (but more streamlined - not covering topics like shortcut keys).

In this tutorial, we will look at three ways of running Python code inside JupyterLite:

• The Python console
• Python scripts
• Python notebooks

The Python Console

• The Python console is great for running short snippets of Python code
• Use it to experiment and play with Python to learn more about how it works
• It’s also useful as a general purpose calculator

Open a Python Console in Futurecoder:

• Visit futurecoder.io
• Click the link that says “Just code”
• You will see the Console on the right-side of the screen, indicated by the >>> (three angle brackets) called the prompt.
• Click anywhere in the Console to be able to type into it.

Enter the following code, then press the enter key to run it:

print('Hello world')

Congratulations! You’ve just run your first Python program!

Note: Casing is important in Python, so make sure you write print in lower-case.

We can also use Python to perform maths; try running the following:

1 + 1

5 - 3

Note: The spaces around mathematical operators are optional, but they are important to make the code more readable!

It is important to remember that any work you do in the console is lost when you close the console (or refresh the futurecoder webpage).

Tip: You can press the “up arrow” key in the Console to bring up lines you’ve previously run (saving you from typing them again).

Python scripts

Python scripts are ideal for creating re-usable programs.

• You can write a Python script in the left-hand side of the screen of the futurecoder interface.
• Click on the left-hand side of the screen to be able to type your script into it.
• Write the following code in your script:

print('Hello world')

• Press the Run button to run your script
• You should see Hello world printed again in the console.
• Now try adding a new line to your script with the following code:

1 + 1

• Now run it again.
• When you run your script, you won’t see the result of 1 + 1.
• This is because Python scripts only output what we explicitly tell Python to print!
• To print the result of 1 + 1, change the line in your script to be:

print(1 + 1)

• Run the script again and you should see the following output:

Hello world
2

• If you refresh or close and re-open futurecoder, the last script you wrote will be saved.
• To save your scripts for future use, copy and save them save them somewhere else.

Running python scripts locally: You can use a plain text editor (e.g. Notepad, Notepad++, Visual Studio Code) to create a Python script file that ends in a .py extension. If you have Python installed on your computer, you can run your script from the Terminal/Command Prompt with python my_script.py (assuming you have used cd commands to move your terminal session into the same folder as my_script.py). It is is important to remember to save changes to your script files before you re-run them!

Python Notebooks

• You may have heard of Jupyter notebooks, as they are a very popular platform for Python coding, particularly for data analysis and visualisation.
• We will create Jupyter notebooks in Google Colab, which is a service that will allow us to run Jupyter notebooks without installing Python or Jupyter on your own computer.
• Note: Because your notebooks will run on Google’s servers, you shouldn’t upload any company data or code you are not authorised to use outside of company-approved systems.
• Jupyter notebooks can also be run on your own desktop or server with a piece of software called JupyterLab - a popular format for writing Python code that we will be using in this course.
• Python notebooks are:
  • Interactive like the Console
  • Re-usable like scripts
  • Support rich formatting of notes and outputs

To create a new notebook in Google Colab:

• Open colab.google in your web browser
• Click the New Notebook link
• Click on the title at the top of the page, and rename it pynoon_starter_1.ipynb - helpful names are important!

In the “cell” prompt at the at the top of your notebook, enter the following code, and run it with shift + enter (like at the Console):

print('Hello world')

The result is printed and a new “cell” appears below.

Run this code in the new cell:

1.5 + 10

Each time you press shift+enter, a new cell is created

You can edit and re-run cells; update your previous cell to have the following content, then re-run it:

(1.5 * 10) / (5 - 2)

Note that we can use * for multiplication, / for division, and group operations with parentheses()`.

You can add non-code cells for formatted notes:

• Click the + Text button at the top of the notebook
• Add the following content:

My First Notebook

• Press shift+enter, and see the text displayed.

You can add formatting in Markdown cells:

• Double-click My First Notebook to edit the cell
• Add a # at the beginning of the line:

# My First Notebook

• Press shift-enter and see it formatted as a heading

Markdown code is similar to shortcuts supported by software like Slack and Teams

For example, add a bullet list with:

* item 1
* item 2
* item 3

• To see what else is possible with Markdown, see: www.markdownguide.org/

You can re-order notebook cells

• Click on a cell you want to move up or down in the notebook
• Click on the up/down arrows that appear in the top-right of the cell.

Notebooks also support rich output from Python code

• Run a cell with the following code (you don’t need to understand the code yet):

%pip install plotly nbformat pandas
import plotly.express as px
px.bar(x=['a', 'b', 'c'], y=[1, 2, 3])

You should see an interactive plot

• Click and drag to zoom to a selection
• Double click to reset zoom
• Find the camera button in the plot header to download the plot as an image

Try changing y=[1, 2, 3] in your cell to use different numbers, then re-execute the cell.

• Notebooks are really good for presenting the results of your code - they are very popular in data science and just for data exploration
• We’ll primarily use notebooks in this course so that you can save the commands you run and add notes

Note: Your notebooks will all be saved to your Google Drive account, you can find previous notebooks from the File -> Open notebook menu.