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
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