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)
Free Chapter
About the Authors
Advertisement Page
Connect with Dummies
End User License Agreement

Chapter 2

Introducing Python’s Capabilities and Wonders


Bullet Delving into why Python came about

Bullet Getting a quick start with Python

Bullet Considering Python’s special features

Bullet Defining and exploring the power of Python for the data scientist

All computers run on just one language — machine code. However, unless you want to learn how to talk like a computer in 0s and 1s, machine code isn’t particularly useful. You’d never want to try to define data science problems using machine code. It would take an entire lifetime (if not longer) just to define one problem. Higher-level languages make it possible to write a lot of code that humans can understand quite quickly. The tools used with these languages make it possible to translate the human-readable code into machine code that the machine understands. Therefore, the choice of languages depends on the human need, not the machine need. With this in mind, this chapter introduces you to the capabilities...