Название книги: Python Data Science Handbook Essential Tools for Working with Data
Год: 2016
Автор: Дж. Вандер Плас
Язык: Английский
Формат: pdf
Размер: 18.9 MB

Описание книги «Python Data Science Handbook Essential Tools for Working with Data»:

This is a book about doing data science with Python, which immediately begs the question: what is data science? It’s a surprisingly hard definition to nail down, especially given how ubiquitous the term has become. Vocal critics have variously dismissed the term as a superfluous label (after all, what science doesn’t involve data?) or a simple buzzword that only exists to salt résumés and catch the eye of verzealous tech recruiters.

Who Is This Book For?

The book is not meant to be an introduction to Python or to programming in general; The reader has familiarity with the Python language, including defining functions, assigning variables, calling methods of objects, controlling the flow of a program, and other basic tasks. Instead, it is meant to help Python users learn to use Python’s data science stack—libraries such as IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and related tools—to effectively store, manipulate, and gain insight from data.

Why Python?

Python has emerged over the last couple decades as a first-class tool for scientific computing tasks, including the analysis and visualization of large datasets. This may have come as a surprise to early proponents of the Python language: the language itself was not specifically designed with data analysis or scientific computing in mind.

The usefulness of Python for data science stems primarily from the large and active ecosystem of third-party packages: NumPy for manipulation of homogeneous arraybased data, Pandas for manipulation of heterogeneous and labeled data, SciPy for common scientific computing tasks, Matplotlib for publication-quality visualizations, IPython for interactive execution and sharing of code and many more tools that will be mentioned in the following pages.


Chapter 1: IPython: Beyond Normal Python

Chapter 2: Introduction to NumPy

Chapter 3: Data Manipulation with Pandas

Chapter 4: Visualization with Matplotlib

Chapter 5: Machine Learning

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