This book is a reference book for science, research, and computational and statistical methods centered on the deep needs of data. This book has five chapters, each of which introduces one or two key toolkits for Python data science. It starts with IPython and Jupyter, which provide the computing environment needed by data scientists; Chapter 2 explains NumPy, which provides ndarray objects, which can be used to efficiently store and manipulate large arrays in Python; Chapter 3 focuses on Pandas, which provides DataFrame objects, which can be used to efficiently store and manipulate labeled/column data in Python; Chapter 4 is the protagonist of Matplotlib, which provides many data visualization functions for Python; Chapter 5 focuses on Scikit-Learn, a library that provides efficient and clean Python implementations of the most important machine learning algorithms. This book is suitable for data science researchers who have a programming background and intend to use open source Python tools as tools for analyzing, manipulating, visualizing, and learning data.
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