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Python Data Science Essentials
book

Python Data Science Essentials

by Alberto Boschetti
April 2015
Beginner content levelBeginner
258 pages
5h 48m
English
Packt Publishing
Content preview from Python Data Science Essentials

Working with categorical and textual data

Typically, you'll find yourself dealing with two main kinds of data: categorical and numerical. Numerical data, such as temperature, amount of money, days of usage, or house number, can be composed of either floating point numbers (like 1.0, -2.3, 99.99, …) or integers (like -3, 9, 0, 1, …). Each value that the data can assume has a direct relation with others since they're comparable. In other words, you can say that a feature with a value of 2.0 is greater (actually, it is double) than a feature that assumes a value of 1.0. This type of data is very well-defined and comprehensible, with binary operators such as equal, greater, and less.

The other type of data you might see in your career is the categorical ...

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Publisher Resources

ISBN: 9781785280429Supplemental Content