Book Image

Python Data Science Essentials

By : Alberto Boschetti, Luca Massaron
Book Image

Python Data Science Essentials

By: Alberto Boschetti, Luca Massaron

Overview of this book

The book starts by introducing you to setting up your essential data science toolbox. Then it will guide you across all the data munging and preprocessing phases. This will be done in a manner that explains all the core data science activities related to loading data, transforming and fixing it for analysis, as well as exploring and processing it. Finally, it will complete the overview by presenting you with the main machine learning algorithms, the graph analysis technicalities, and all the visualization instruments that can make your life easier in presenting your results. In this walkthrough, structured as a data science project, you will always be accompanied by clear code and simplified examples to help you understand the underlying mechanics and real-world datasets.
Table of Contents (13 chapters)

A peek into Natural Language Processing (NLP)

This section is not strictly related to machine learning, but it contains some machine learning results in the area of Natural Language Processing. Python has many toolkits to process text data, but the most powerful and complete toolkit is NLTK, the Natural Language Tool Kit.

In the following sections, we'll explore its core functionalities. We will work on the English language; for other languages, you will first need to download the language corpora (note that sometimes, languages have no free open source corpora for NLTK).

Word tokenization

Tokenization is the action of splitting the text in words. Chunking the whitespace seems very easy, but it's not, because text contains punctuation and contractions. Let's start with an example:

In: my_text = "The coolest job in the next 10 years will be statisticians. People think I'm joking, but who would've guessed that computer engineers would've been the coolest job of the 1990s?"
simple_tokens = my_text...