Python Data Science Handbook
Jake VanderPlas has been using and developing with Python for a long time. He is currently an interdisciplinary research director at the University of Washington, where he also performs his own astronomical research and advises and consults with local scientists from a variety of subjects.
Python is a first-rate tool for many academics, owing to its libraries for storing, manipulating, and getting insight from data. There are several resources for individual components of this data science stack, but only the Python Data Science Handbook has all of them—IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools.
Working scientists and data crunchers who are comfortable reading and writing Python code will find this comprehensive desk reference useful for dealing with day-to-day issues such as manipulating, transforming, and cleaning data, visualizing various types of data, and using data to build statistical or machine learning models. Simply put, this is the essential reference for scientific computing in Python.
Python Data Science Handbook will teach you 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
Author: Jake VanderPlas
Link to buy: https://www.amazon.com/Python-Data-Science-Handbook-Essential/dp/1491912057/
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