Book Image

Python for Data Science For Dummies - Second Edition

By : John Paul Mueller, Luca Massaron
Book Image

Python for Data Science For Dummies - Second Edition

By: John Paul Mueller, Luca Massaron

Overview of this book

Python is a general-purpose programming language created in the late 1980s — and named after Monty Python — that's used by thousands of people to do things from testing microchips at Intel to powering Instagram to building video games with the PyGame library. The book begins by discussing how Python can make data science easy. You’ll learn how to work with the Anaconda tool suite that makes coding in Python easy. You’ll also learn to write code using Google Colab. As you progress, you'll discover how to perform interesting calculations and data manipulations using various Python libraries, such as pandas and NumPy. You’ll learn how to create data visualizations with MatPlotLib. While learning the advanced concepts, you’ll learn how to wrangle data by using techniques, such as hierarchical clustering. Finally, you’ll learn how to work with decision trees and use machine learning to make predictions. By the end of the book, you’ll have the skills and the knowledge that’s needed to write code in Python and extract information from data.
Table of Contents (13 chapters)
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Chapter 12

Stretching Python’s Capabilities


Bullet Understanding how Scikit-learn works with classes

Bullet Using sparse matrices and the hashing trick

Bullet Testing performances and memory consumption

Bullet Saving time with multicore algorithms

If you’ve gone through the previous chapters, by this point you’ve dealt with all the basic data loading and manipulation methods offered by Python. Now it’s time to start using some more complex instruments for data wrangling (or munging) and for machine learning. The final step of most data science projects is to build a data tool able to automatically summarize, predict, and recommend directly from your data.

Before taking that final step, you still have to process your data by enforcing transformations that are even more radical. That’s the data wrangling or data munging part, where sophisticated transformations are followed by visual and statistical explorations, and then again by further transformations. In...