Summary and Setup

This lesson is an introduction to programming in Python requiring little or no previous programming experience. It uses examples that are relevant to a range of basic data exploration use cases, and is designed as a prerequisite for other Python lessons that could be offered in the future. The lesson uses the Google Colab computing environment and Python 3.

Prerequisites

  1. Learners need to understand what files and directories are and what a working directory is.

  2. Learners must have a Google account capable of accessing Google Colab.

Please see setup instructions below for details.

This lesson is designed to be run on a personal computer. All of the software and data used in this lesson are freely available online, and instructions on how to obtain them are provided below.

Setup

To participate in this lesson, we will use Colab notebooks. Colab is a cloud-based environment where you can run scripting languages such as Python, create small segments of code, annotate them with notes and rich text, and return to your work later.

We can import our own resources into Colab, such as our simulated data. To do this, one option is to use Google drive. Because we would need to grant Colab access to our Google Drive data, we strongly recommend creating an ad-hoc (“throwaway”) Gmail account. That way, we do not risk sharing or altering any sensitive data that we might have in the Google accounts we regularly use.

For our workshop, we will download directly to the Colab machine to avoid Google Drive permission concerns.

  1. Download this zip file and save it to your Desktop.
  2. Unzip the data.zip file, which should create a new folder called data.
  3. In a private browsing window, log into Google Drive using the Gmail account you created for this course.
  4. Upload the lesson content by clicking “+ New” followed by “Folder upload.” Select and upload the data folder.

You should see a data folder in “My Drive”.

Using data in Google Drive with Colab

To access you had uploaded to Google Drive, we will need to import a module from the google.colab library that will let us access files in Google Drive. Run the following code to enable Drive access.

PYTHON 

from google.colab import drive
drive.mount('/content/drive')

OUTPUT 

Mounted at /content/drive

This command sets up Colab to find data you’ve uploaded to Google Drive at “/content/drive/My Drive/” (note that in Colab “/drive/My Drive/” also works)

In the workshop content, wherever you see a filename (for example, ‘data/2011_circ.csv’), you will want to use add the path to Google Drive to the filename (for example, “drive/MyDrive/data/2011_circ.csv”)

We chose Google colab to ensure Python setup and everyone’s Jupyter interface would be consistent. For your personal use, you may prefer not to use colab. Below are colab alternatives that you might consider post-workshop.

Python interfaces

To start working with Python, we need to launch a program that will interpret and execute our Python commands. For our purposes today, we will use Colab, so you do not need to read further right now. However, if you want to do more with Python, you have a variety of options. It is a good idea to try out several of these interfaces to get a sense of which you prefer.

Jupyter Notebook

A Jupyter Notebook provides a browser-based interface for working with Python. Colab is one example of this. If you install Anaconda, you can launch a notebook in two ways:

NOTE: In 2020, Anaconda modified the Terms of Service. Free use of Anaconda for educational and non-profit organizations is now narrowly scoped. Understand the potential financial obligations of using Anaconda before proceeding. 1. Launch Anaconda Navigator. It might ask you if you’d like to send anonymized usage information to Anaconda developers: Anaconda Navigator first launch Make your choice and click “Ok, and don’t show again” button. 2. Find the “Notebook” tab and click on the “Launch” button: Anaconda Navigator Notebook launch Anaconda will open a new browser window or tab with a Notebook Dashboard showing you the contents of your Home (or User) folder. 3. Navigate to the data directory by clicking on the directory names leading to it: Desktop, swc-python, then data: Anaconda Navigator Notebook directory 4. Launch the notebook by clicking on the “New” button and then selecting “Python 3”: Anaconda Navigator Notebook directory

1. Navigate to the data directory:

If you’re using a Unix shell application, such as Terminal app in macOS, Console or Terminal in Linux, or Git Bash on Windows, execute the following command:

BASH 

cd ~/Desktop/swc-python/data

On Windows, you can use its native Command Prompt program. The easiest way to start it up is pressing Windows Logo Key+R, entering cmd, and hitting Return. In the Command Prompt, use the following command to navigate to the data folder:

cd /D %userprofile%\Desktop\swc-python\data

2. Start Jupyter server

BASH 

jupyter notebook
python -m notebook

3. Launch the notebook by clicking on the “New” button on the right and selecting “Python 3” from the drop-down menu: Anaconda Navigator Notebook directory

 

IPython interpreter

IPython is an alternative solution situated somewhere in between the plain-vanilla Python interpreter and Jupyter Notebook. It provides an interactive command-line based interpreter with various convenience features and commands. You should have IPython on your system if you installed Anaconda.

To start using IPython, execute:

ipython

 

plain-vanilla Python interpreter

To launch a plain-vanilla Python interpreter, execute:

python

If you are using Git Bash on Windows, you have to call Python via winpty:

winpty python