Python Data Science Handbook, 2nd Edition (Fourth Early Release)

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
MatDescriptionlib 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