Wes mckinney pandas book pdf
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pandas adopts significant Updated for latest pandas () Revamped intro chapters including abridged Python language tutorial, and introductions to Jupyter and IPython. Conventions Used in This Book The following typographical conventions are used in this book: Italic Includes index. Table of ContentsWhat Is This Book Programming booksPython、 Machine-Learning、 Deep-Learning、 NLPEvanLi/programming-book-3 Written by Wes McKinney, the creator of the Python pandas project, this book is a practical, modern introduction to data science tools in Python. Wes McKinney BeijingpandasmatplotlibIPythonSciPyInstallation and SetupWindowsApple OS XNumPy, IPython, matplotlib, and pandas) had also matured Updates for the latest versions of the pandas library in A new chapter on some more advanced pandas tools, and some other usage tips A brief introduction to using Data Wrangling with Pandas, NumPy, andIPython Wes McKinney Beijing Boston Farnham Sebastopol Tokyo O’REILLY. New Advanced pandas chapter. It's ideal for analysts new to embark on this writing project. Reorganized pandas content to be digested more easily. New Intro to Modeling Tools chapter This book is not an exposition on analytical methods using Python as the implementation n by Wes McKinney, the main author of the pandas library, this hands-on book is PreliminariesIntroductory examplesIPython: an interactive computing and development environmentNumPy basics: arrays and vectorized computationGetting started with pandasData loading, storage, and file formatsData wrangling: clean, transform, merge, reshapePlotting and visualizationData It contains data structures and data manipulation tools designed to make data cleaning and analysis fast and convenient in Python. This is the book that I wish existed when I started using Python for data analysis in I hope you find it useful and are able to apply these tools productively in your work. pandas is often used in tandem with numerical computing tools like NumPy and SciPy, analytical libraries like statsmodels and scikit-learn, and data visualization libraries like matplotlib.