You're reading from Python Data Science Essentials. - Third Edition

Product typeBook

Published inSep 2018

Reading LevelIntermediate

PublisherPackt

ISBN-139781789537864

Edition3rd Edition

Languages

Python

Tools

Scikit-learn

Concepts

Data Science

Author (1)

Alberto Boschetti

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 (such as 1.0, -2.3, 99.99, and so on) or integers (such as -3, 9, 0, 1, and so on). 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 to, greater than, and less than.

The other type of data you might see in your career is the categorical type. A categorical datum expresses an attribute that cannot be measured and assumes...

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Python Data Science Essentials. - Third Edition

Published in: Sep 2018Publisher: PacktISBN-13: 9781789537864

Author (1)

Alberto Boschetti

Alberto Boschetti is a data scientist with expertise in signal processing and statistics. He holds a Ph.D. in telecommunication engineering and currently lives and works in London. In his work projects, he faces challenges ranging from natural language processing (NLP) and behavioral analysis to machine learning and distributed processing. He is very passionate about his job and always tries to stay updated about the latest developments in data science technologies, attending meet-ups, conferences, and other events.
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