Python for Data Analysis: Data Wrangling with Pandas NumPy and IPython

Python for Data Analysis: Data Wrangling with Pandas NumPy and IPython

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Reseña del editor Get complete instructions for manipulating processing cleaning and crunching datasets in Python. Updated for Python 3.6 the second edition of this hands-on guide is packed with practical case studies that show you how to solve a broad set of data analysis problems effectively. You'll learn the latest versions of pandas NumPy IPython and Jupyter in the process. Written by Wes McKinney the creator of the Python pandas project this book is a practical modern introduction to data science tools in Python. It's ideal for analysts new to Python and for Python programmers new to data science and scientific computing. Data files and related material are available on GitHub. Use the IPython shell and Jupyter notebook for exploratory computing Learn basic and advanced features in NumPy (Numerical Python) Get started with data analysis tools in the pandas library Use flexible tools to load clean transform merge and reshape data Create informative visualizations with matplotlib Apply the pandas groupby facility to slice dice and summarize datasets Analyze and manipulate regular and irregular time series data Learn how to solve real-world data analysis problems with thorough detailed examples Biografía del autor Wes McKinney is the main author of pandas the popular open sourcePython library for data analysis. Wes is an active speaker andparticipant in the Python and open source communities. He worked as a quantitative analyst at AQR Capital Management and Python consultant before founding DataPad a data analytics company in 2013. He graduated from MIT with an S.B. in Mathematics.

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