Summary and Schedule

The best way to learn how to program is to do something useful, so this introduction to Python is built around a common scientific task: data analysis.

Scenario: Analysing GDP data from countries around the world

We’ve got a set of files containing GDP data from countries around the world, separated into CSV files per continent. Each CSV file contain one row per country and multiple columns per years when the GDP were recorded.

We need to analyse it to see if we understand some global trends across the years.

To do so we would like to:

  1. Calculate the minimum, maximum and average GDP per continent per year.
  2. Plot the result to discuss and share with colleagues.

Data Format

The data sets are stored in comma-separated values (CSV) format:

  • each row holds information for a single country,
  • columns represent years when the GDP were recorded.

The first three rows of our first file look like this, first line contains the header of the file:

country,1952,1957,1962,1967,1972,1977,1982,1987,1992,1997,2002,2007
Algeria,2449.008185,3013.976023,2550.81688,3246.991771,4182.663766,4910.416756,5745.160213,5681.358539,5023.216647,4797.295051,5288.040382,6223.367465
Angola,3520.610273,3827.940465,4269.276742,5522.776375,5473.288005,3008.647355,2756.953672,2430.208311,2627.845685,2277.140884,2773.287312,4797.231267

Each number represents the GDP per capita for that particular country on the given year.

For example, value “3008.647355” at row 3 column 7 of the data set above means that Angola had a GDP per capita of approximately $3,008.65 in 1977.

In order to analyze this data and report to our colleagues, we’ll have to learn a little bit about programming.

Prerequisites

You need to understand the concepts of files and directories and how to start a Python interpreter before tackling this lesson. This lesson sometimes references Jupyter Lab although you can use any Python interpreter mentioned in the [Setup][lesson-setup].

The commands in this lesson pertain to any officially supported Python version, currently Python 3.7+. Newer versions usually have better error printouts, so using newer Python versions is recommend if possible.

Getting Started

To get started, follow the directions on the “[Setup][lesson-setup]” page to download data and install a Python interpreter.

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

Overview

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.

Install Python

In this lesson, we will be using Python 3 with some of its most popular scientific libraries. Although one can install a plain-vanilla Python and all required libraries by hand, we recommend installing Anaconda, a Python distribution that comes with everything we need for the lesson. Detailed installation instructions for various operating systems can be found on The Carpentries template website for workshops and in Anaconda documentation.

Obtain lesson materials

  1. Download python-novice-gapminder-data.zip and python-novice-gapminder-code.zip.
  2. Create a folder called swc-python on your Desktop.
  3. Move downloaded files to swc-python.
  4. Unzip the files.

You should see two folders called data and code in the swc-python directory on your Desktop.

Launch Python interface

To start working with Python, we need to launch a program that will interpret and execute our Python commands. Below we list several options. If you don’t have a preference, proceed with the top option in the list that is available on your machine. Otherwise, you may use any interface you like.

Option A: Jupyter Notebook

A Jupyter Notebook provides a browser-based interface for working with Python. If you installed Anaconda, you can launch a notebook in two ways:

  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 “JupyterLab” 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 lab
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

 

Option B: 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

 

Option C: 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