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: IPython and Jupyter provide computational environments for scientists using Python NumPy
includes the ndarray for efficient storage and manipulation of dense data arrays Pandas
contains the DataFrame for efficient storage and manipulation of labeled columnar data
Matplotlib includes capabilities for a flexible range of data visualizations Scikit-learn helps
you build efficient and clean Python implementations of the most important and established
machine learning algorithms
