Python Data Science Handbook
Essential Tools for Working with Data
by Jake VanderPlas
DescriptionTable of ContentsDetailsHashtagsReport an issue
Working scientists and data crunchers familiar with reading and writing Python code will find this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python.
With this handbook, you'll learn how to use:
- IPython and Jupyter: provide computational environments for data scientists using Python;
- NumPy: includes the ndarray for efficient storage and manipulation of dense data arrays in Python;
- Pandas: features the DataFrame for efficient storage and manipulation of labeled/columnar data in Python;
- Matplotlib: includes capabilities for a flexible range of data visualizations in Python;
- Scikit-Learn: for efficient and clean Python implementations of the most important and established machine learning algorithms. 
Book Description
For many researchers, Python is a first-class tool mainly because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them all - IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools.Working scientists and data crunchers familiar with reading and writing Python code will find this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python.
With this handbook, you'll learn how to use:
- IPython and Jupyter: provide computational environments for data scientists using Python;
- NumPy: includes the ndarray for efficient storage and manipulation of dense data arrays in Python;
- Pandas: features the DataFrame for efficient storage and manipulation of labeled/columnar data in Python;
- Matplotlib: includes capabilities for a flexible range of data visualizations in Python;
- Scikit-Learn: for efficient and clean Python implementations of the most important and established machine learning algorithms.
This open book is licensed under a Creative Commons License (CC BY-NC-ND). Free download in PDF format is not available. You can read Python Data Science Handbook book online for free.
Table of Contents
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
Book Details
Title
Python Data Science Handbook
Subject
Computer Science
Publisher
O'Reilly Media
Published
2018
Pages
548
Edition
1
Language
English
ISBN13 Digital
9781491912058
ISBN10 Digital
1491912057
License
Related Books
We've all heard it: according to Hal Varian, statistics is the next sexy job. Five years ago, in What is Web 2.0, Tim O'Reilly said that "data is the next Intel Inside." But what does that statement mean? Why do we suddenly care about statistics and about data? This report examines the many sides of data science - the technologi...
This engaging and clearly written textbook/reference provides a must-have introduction to the rapidly emerging interdisciplinary field of data science. It focuses on the principles fundamental to becoming a good data scientist and the key skills needed to build systems for collecting, analyzing, and interpreting data. The Data Science Design Manual...
This thoroughly revised guide demonstrates how the flexibility of the command line can help you become a more efficient and productive data scientist. You'll learn how to combine small yet powerful command-line tools to quickly obtain, scrub, explore, and model your data. To get you started, author Jeroen Janssens provides a Docker image packe...
R is one of the most popular, powerful data analytics languages and environments in use by data scientists. Actionable business data is often stored in Relational Database Management Systems (RDBMS), and one of the most widely used RDBMS is Microsoft SQL Server. Much more than a database server, it's a rich ecostructure with advanced analytic ...
To tap into the power of Python's open data science stack - including NumPy, Pandas, Matplotlib, Scikit-Learn, and other tools - you first need to understand the syntax, semantics, and patterns of the Python language. This report provides a brief yet comprehensive introduction to Python for engineers, researchers, and data scientists who are a...
When you combine the sheer scale and range of digital information now available with a journalist's "nose for news" and her ability to tell a compelling story, a new world of possibility opens up. With The Data Journalism Handbook, you'll explore the potential, limits, and applied uses of this new and fascinating field.
This ...