What you need for this book

Pandas is a third-party package for the Python programming language and, as of the printing of this book, is on version 0.20. Currently, Python has two major supported releases, versions 2.7 and 3.6. Python 3 is the future, and it is now highly recommended that all scientific computing users of Python use it, as Python 2 will no longer be supported in 2020. All examples in this book have been run and tested with pandas 0.20 on Python 3.6.

In addition to pandas, you will need to have the matplotlib version 2.0 and seaborn version 0.8 visualization libraries installed. A major dependence for pandas is the NumPy library, which forms the basis of most of the popular Python scientific computing libraries.

There are a wide variety of ways in which you can install pandas and the rest of the libraries mentioned on your computer, but by far the simplest method is to install the Anaconda distribution. Created by Continuum Analytics, it packages together all the popular libraries for scientific computing in a single downloadable file available on Windows, Mac OSX, and Linux. Visit the download page to get the Anaconda distribution (https://www.anaconda.com/download).

In addition to all the scientific computing libraries, the Anaconda distribution comes with Jupyter Notebook, which is a browser-based program for developing in Python, among many other languages. All of the recipes for this book were developed inside of a Jupyter Notebook and all of the individual notebooks for each chapter will be available for you to use.

It is possible to install all the necessary libraries for this book without the use of the Anaconda distribution. For those that are interested, visit the pandas Installation page (http://pandas.pydata.org/pandas-docs/stable/install.html).

Running a Jupyter Notebook

The suggested method to work through the content of this book is to have a Jupyter Notebook up and running so that you can run the code while reading through the recipes. This allows you to go exploring on your own and gain a deeper understanding than by just reading the book alone.

Assuming that you have installed the Anaconda distribution on your machine, you have two options available to start the Jupyter Notebook:

• Use the program Anaconda Navigator
• Run the jupyter notebook command from the Terminal/Command Prompt

The Anaconda Navigator is a GUI-based tool that allows you to find all the different software provided by Anaconda with ease. Running the program will give you a screen like this:

As you can see, there are many programs available to you. Click Launch to open the Jupyter Notebook. A new tab will open in your browser, showing you a list of folders and files in your home directory:

Instead of using the Anaconda Navigator, you can launch Jupyter Notebook by opening up your Terminal/Command Prompt and running the jupyter notebook command like this:

It is not necessary to run this command from your home directory. You can run it from any location, and the contents in the browser will reflect that location.

Although we have now started the Jupyter Notebook program, we haven't actually launched a single individual notebook where we can start developing in Python. To do so, you can click on the New button on the right-hand side of the page, which will drop down a list of all the possible kernels available for you to use. If you just downloaded Anaconda, then you will only have a single kernel available to you (Python 3). After selecting the Python 3 kernel, a new tab will open in the browser, where you can start writing Python code:

You can, of course, open previously created notebooks instead of beginning a new one. To do so, simply navigate through the filesystem provided in the Jupyter Notebook browser home page and select the notebook you want to open. All Jupyter Notebook files end in .ipynb. For instance, when you navigate to the location of the notebook files for this book, you will see all of them like this: