Summary and Schedule

This is a new lesson built with The Carpentries Workbench.

The actual schedule may vary slightly depending on the topics and exercises chosen by the instructor.

This lesson uses JupyterLab and pandas to analyze and visualize data from an ecology dataset. It was adapted (and currently includes huge chunks of text lifted directly from) Data Management with SQL for Ecologists and covers similar material to Data Analysis and Visualization in Python for Ecologists. The idea was to create parallel lessons for SQL and Python for use by the Smithsonian Carpentries.

This lesson is designed to use JupyterLab, an interactive development environment widely used for doing data science with Python. The rest of this page provides instructions for setting up the software and data needed to complete this lesson. Please complete the setup prior to your scheduled lesson.

Setup

We will use a program called Miniconda to set up JupyterLab, so first we need to download and install Miniconda (64 bit). We recommend using the following installers:

  • Windows: Miniconda3 Windows 64-bit
  • macOS:
    • Apple M1: Miniconda3 macOS Apple M1 64-bit pkg
    • Intel: Miniconda3 macOS Intel x86 64-bit pkg

Using the command-line interface

A command-line interface, or CLI, is an application that can run commands supplied as text. Examples of command-line interfaces include the Windows command prompt and Unix shells, including bash. We’ll be using the CLI to install and run JupyterLab.

Each operating system has one or more command-line interfaces available. We recommend using the following applications for this lesson:

  • Windows: Use the Anaconda Prompt, which was installed as part of Miniconda. You can find it by searching for Anaconda Prompt in the search box on the Windows toolbar.
  • macOS: Use the Terminal. You can find it in the Applications/Utilities folder or by searching for Terminal using Spotlight.

The commands given below may not work if a different application is used, so we’d strongly encourage you to use the recommended ones.

Running commands

To run any of the commands presented here, copy-paste them into the CLI, then press enter to run the command.

Installing JupyterLab

Once the command-line interface is open, run the following commands to install the software needed for this course:

SH 

conda create --name python-ecology-lesson
conda activate python-ecology-lesson
conda install --channel conda-forge --yes altair jupyterlab pandas

Downloading data to the lesson folder

Next we’ll create the lesson folder and download the data needed for the lesson:

  1. Create a folder called python-ecology-lesson on your desktop
  2. Create a folder called data inside the folder created in step 1
  3. Download the following files into the data folder:

Alternatively, we can run the following commands to create the lesson folders and download the necessary data:

SH 

cd ~/Desktop
mkdir python-ecology-lesson/data
cd python-ecology-lesson/data
wget -O surveys.csv https://figshare.com/ndownloader/files/10717177
wget -O species.csv https://figshare.com/ndownloader/files/3299483

Note for Windows users

The commands used here assume that your desktop is in a standard location. If you are using a Windows computer with OneDrive enabled, your desktop may be in a different place. You can use the following command in place of cd ~/Desktop in the instructions on this page to get to your desktop no matter where it is:

POWERSHELL 

cd $([Environment]::GetFolderPath("Desktop"))

Running JupyterLab

Once JupyterLab has been installed, we can run it by opening the command-line interface and running the following commands:

SH 

conda activate python-ecology-lesson
cd ~/Desktop
cd python-ecology-lesson
jupyter-lab

JupyterLab should now open in a new browser window.

Test your installation

Try running this command before the scheduled lesson – does JupyterLab appear as expected?