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Python data science handbook: essential tools for working with data 2nd edition, jake...

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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.
\n \nWorking 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. \n \nWith this handbook, you'll learn how: \n

- IPython and Jupyter provide computational environments for scientists using Python
\n

- NumPy includes the ndarray for efficient storage and manipulation of dense data arrays
\n

- Pandas contains the DataFrame for efficient storage and manipulation of labeled/columnar data
\n

- Matplotlib includes capabilities for a flexible range of data visualizations
\n

- Scikit-learn helps you build efficient and clean Python implementations of the most important and established machine learning algorithms
\n

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