Chapter 2. Introductory Examples
This book teaches you the Python tools to work productively with data. While readers may have many different end goals for their work, the tasks required generally fall into a number of different broad groups:
- Interacting with the outside world
Reading and writing with a variety of file formats and databases.
Cleaning, munging, combining, normalizing, reshaping, slicing and dicing, and transforming data for analysis.
Applying mathematical and statistical operations to groups of data sets to derive new data sets. For example, aggregating a large table by group variables.
- Modeling and computation
Connecting your data to statistical models, machine learning algorithms, or other computational tools
Creating interactive or static graphical visualizations or textual summaries
In this chapter I will show you a few data sets and some things we can
do with them. These examples are just intended to pique your interest and
thus will only be explained at a high level. Don’t worry if you have no
experience with any of these tools; they will be discussed in great detail
throughout the rest of the book. In the code examples you’ll see input and
output prompts like
In :; these are
from the IPython shell.
To follow along with these examples, you should run IPython in Pylab
mode by running
ipython --pylab at the command
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