Summary and Schedule

Python is a general purpose programming language that is useful for writing scripts to work effectively and reproducibly with data.

This is an introduction to Python designed for participants with no programming experience. These lessons can be taught in one and a half days (~ 10 hours). They start with some basic information about Python syntax, the Jupyter notebook interface, and move through how to import CSV files, using the pandas package to work with data frames, how to calculate summary information from a data frame, and a brief introduction to plotting. The last lesson demonstrates how to work with databases directly from Python.

Getting Started

Data Carpentry’s teaching is hands-on, so participants are encouraged to use their own computers to ensure the proper setup of tools for an efficient workflow.
These lessons assume no prior knowledge of the skills or tools.

To get started, follow the directions in the “Setup” tab to download data to your computer and follow any installation instructions.

Prerequisites

This lesson requires a working copy of Python.
To most effectively use these materials, please make sure to install everything before working through this lesson.

For Instructors

If you are teaching this lesson in a workshop, please see the Instructor notes.

	Setup Instructions	Download files required for the lesson
Duration: 00h 00m	1. Before we start	What is Python and why should I learn it?
Duration: 00h 30m	2. Short Introduction to Programming in Python	How do I program in Python? How can I represent my data in Python?
Duration: 01h 05m	3. Starting With Data	How can I import data in Python? What is Pandas? Why should I use Pandas to work with data?
Duration: 02h 05m	4. Indexing, Slicing and Subsetting DataFrames in Python	How can I access specific data within my data set? How can Python and Pandas help me to analyse my data?
Duration: 03h 05m	5. Data Types and Formats	What types of data can be contained in a DataFrame? Why is the data type important?
Duration: 03h 50m	6. Combining DataFrames with Pandas	Can I work with data from multiple sources? How can I combine data from different data sets?
Duration: 04h 35m	7. Data Workflows and Automation	Can I automate operations in Python? What are functions and why should I use them?
Duration: 06h 05m	8. Making Plots With plotnine	How can I visualize data in Python? What is ‘grammar of graphics’?
Duration: 07h 35m	9. Data Ingest and Visualization - Matplotlib and Pandas	What other tools can I use to create plots apart from ggplot? Why should I use Python to create plots?
Duration: 09h 20m	10. Accessing SQLite Databases Using Python and Pandas	What if my data are stored in an SQL database? Can I manage them with Python? How can I write data from Python to be used with SQL?
Duration: 10h 05m	Finish

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

Data

Data for this lesson is from the Portal Project Teaching Database. Specifically, we use the following eight data files:

• bouldercreek_09_2013.txt
• plots.csv
• portal_mammals.sqlite
• species.csv
• speciesSubset.csv
• surveys.csv
• surveys2001.csv
• surveys2002.csv

Please download them (by clicking on the corresponding links) and move them to the same directory, or download all the files as a zip which will give you everything in a single compressed file. You’ll need to unzip this file after downloading it.

Installing Python using Anaconda

Python is a popular language for scientific computing, and great for general-purpose programming as well. Installing all of the scientific packages we use in the lesson individually can be a bit cumbersome, and therefore recommend the all-in-one installer Anaconda.

Regardless of how you choose to install it, please make sure you install Python version 3.x (e.g., 3.10 is fine and will continue to receive security patches unitl 2026-OCT-04).

Installing Anaconda

Select your operating system from the options below.

Windows

1. Open https://www.anaconda.com/download/success in your web browser.

2. Download the Anaconda Python 3.12 installer for Windows.

3. Double-click the executable and install Python 3.12 using the recommended settings. Make sure that Register Anaconda as my default Python 3.12 option is checked – it should be in the latest version of Anaconda.

4. Verify the installation: click Start, search and select Anaconda Prompt from the menu. A window should pop up where you can now type commands such as checking your Conda installation with:

BASH

conda --help

MacOS

1. Visit https://www.anaconda.com/download/success in your web browser.

2. Download the Anaconda Python 3.12 installer for macOS. These instructions assume that you use the graphical installer .pkg file.

3. Follow the Anaconda Python 3.12 installation instructions. Make sure that the install location is set to “Install only for me” so Anaconda will install its files locally, relative to your home directory. Installing the software for all users tends to create problems in the long run and should be avoided.

4. Verify the installation: click the Launchpad icon in the Dock, type Terminal in the search field, then click Terminal. A window should pop up where you can now type commands such as checking your conda installation with:

BASH

conda --help

Linux

Note that the following installation steps require you to work from the terminal (shell). If you run into any difficulties, please request help before the workshop begins.

1. Open https://www.anaconda.com/download/success in your web browser.

2. Download the Anaconda Python 3.12 installer for Linux.

3. Install Anaconda using all of the defaults for installation.

• Open a terminal window.
• Navigate to the folder where you downloaded the installer.
• Type bash Anaconda3- and press Tab. The name of the file you just downloaded should appear.
• Press Return
• Follow the text-only prompts. When the license agreement appears (a colon will be present at the bottom of the screen) press Spacebar until you see the bottom of the text. Type yes and press Return to approve the license. Press Return again to approve the default location for the files. Type yes and press Return to prepend Anaconda to your PATH (this makes the Anaconda distribution your user’s default Python).

4. Verify the installation: this depends a bit on your Linux distribution, but often you will have an Applications listing in which you can select a Terminal icon you can click. A window should pop up where you can now type commands such as checking your conda installation with:

BASH

conda --help

Required Python Packages

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The following are packages needed for this workshop:

• Pandas
• Jupyter notebook
• Numpy
• Matplotlib
• Plotnine

All packages apart from plotnine will have automatically been installed with Anaconda and we can use Anaconda as a package manager to install the missing plotnine package: You need to open up a Terminal, if you are using Mac OSX, or Linux (see instructions above), or launch an anaconda-prompt, if you are using Windows. In your terminal window type the following:

BASH

conda install -y -c conda-forge plotnine

This will then install the latest version of plotnine into your conda environment.

Required packages: Miniconda

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Miniconda is a lightweight version of Anaconda. If you install Miniconda instead of Anaconda, you need to install required packages manually in the following way:

BASH

conda install -y numpy pandas matplotlib jupyter
conda install -c conda-forge plotnine

(Alternative) Installing required packages with environment file

Download the environment.yml file by right-clicking the link and selecting save as. In the directory where you downloaded the environment.yml file run:

BASH

conda env create -f environment.yml

Activate the new environment with:

BASH

conda activate python-ecology-lesson

You can deactivate the environment with:

BASH

conda deactivate

Launch JupyterLab

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After installing either Anaconda or Miniconda and the workshop packages, launch JupyterLab by typing this command into the terminal or anaconda-prompt:

BASH

jupyter lab

The JupyterLab interface should open automatically in your browser. If it does not or you wish to use a different browser, open this link: http://localhost:8888.

Leave terminal used to launch Jupyter open

Jupyter depends on a server running in the background associated with the window used to launch it. Closing that window will results in web interface errors in the web interface. When done, you can either close the terminal or shut down the server using CTRL+C and submitting y within 5 seconds if the terminal is needed for other tasks.

For a brief introduction to Jupyter Notebooks, please consult our Introduction to Jupyter Notebooks page.