Python is a first-class tool for many researchers, primarily 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 new edition of 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 the second edition of 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: <ul> <li>IPython and Jupyter provide computational environments for scientists using Python</li> <li>NumPy includes the ndarray for efficient storage and manipulation of dense data arrays</li> <li>Pandas contains the DataFrame for efficient storage and manipulation of labeled/columnar data</li> <li>Matplotlib includes capabilities for a flexible range of data visualizations</li> <li>Scikit-learn helps you build efficient and clean Python implementations of the most important and established machine learning algorithms</li> </ul>

Python Data Science Handbook: Essential Tools for Working with Data

Computers & Technology

VanderPlas, Jake

Programming

O'Reilly Media

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:
<ul>
<li>
IPython and Jupyter: provide computational environments for data scientists using Python</li>
<li>
NumPy: includes the ndarray for efficient storage and manipulation of dense data arrays in Python</li>
<li>
Pandas: features the DataFrame for efficient storage and manipulation of labeled/columnar data in Python</li>
<li>
Matplotlib: includes capabilities for a flexible range of data visualizations in Python</li>
<li>
Scikit-Learn: for efficient and clean Python implementations of the most important and established machine learning algorithms</li>
</ul>

Python pour la Data Science - Les meilleures outils pour travailler avec les données

FIRST INTERACT

Data Science mit Python: Das Handbuch für den Einsatz von IPython, Jupyter, NumPy, Pandas, Matplotlib und Scikit-Learn

Programming Languages

MITP Verlags GmbH

See the best price to sell, buy, or rent books by VanderPlas, Jake. BookScouter helps to compare book prices from 25+ online bookstores and 30+ buyback vendors with a single search.

Books by VanderPlas, Jake