First Steps in Python

Download the PDF Python overview to get an overview about Python and a list of helpful resources (you need to download the file in order to use the embedded links).

First of all, install Anaconda - it’s a free and open-source distribution of the Python programming language that aims to simplify package management and deployment. It already contains Jupyter Notebook (see below) and other important data science modules.

• Install Anaconda (select the current version of Python 3). After installation, launch the Anaconda Navigator and start Jupyter Notebook or Jupyter Lab.

One important third-party tool for data science is Jupyter, an open-source web application that allows you to create and share documents that contain live code, equations, visualizations and narrative text. Learn why Jupyter is data scientists’ computational notebook of choice:

• Perkel, J. M. (2018). Why Jupyter is data scientists’ computational notebook of choice. Nature, 563(7729), p. 145.

• Pandey, P. (2018). Bringing the best out of Jupyter Notebooks for Data Science. Towards Data Science

• Pandey, P. (2019). Jupyter Lab: Evolution of the Jupyter Notebook. Towards Data Science

Now, let’s start with some code examples:

1. Import and save CSV-files with Pandas

2. Check for missing values

3. Change data type (level of measurment)

4. Descriptive statistics Colaboratory is a free Jupyter notebook environment that requires no setup and runs entirely in the cloud. With Colaboratory you can write and execute code, save and share your analyses, and access powerful computing resources, all for free from your browser:

5. Overview of the data science programming process

Recommended reading:

“A Whirlwind Tour of Python” is a free and fast-paced introduction to essential features of the Python language. The material is particularly designed for those who wish to use Python for data science and/or scientific programming:

• VanderPlas, J. (2016). Whirlwind Tour of Python. O’Reilly Media.