Python for Data Analysis by Wes McKinney deals with the nuts and bolts of manipulating, processing, cleansing, and crunching data in Python. Additionally it is a practical, fashionable introduction to scientific computing in Python, tailored for data-intensive applications. This is a book concerning the parts of the Python language and libraries you’ll successfully resolve a broad set of information analysis problems.
This book will not be an exposition on analytical methods using Python as the implementation language. Written by Wes McKinney, the primary creator of the pandas library, this arms-on book is full of sensible circumstances studies. It’s splendid for analysts new to Python and for Python programmers new to scientific computing.
You should utilize the IPython interactive shell as your major growth setting, be taught basic and advanced NumPy (Numerical Python) features, get began with data analysis tools within the pandas library, use excessive-performance tools to load, clear, remodel, merge, and reshape knowledge, create scatter plots and static or interactive visualizations with mat plot lib and apply the pandas group by facility to slice, cube, and summarize datasets.
You can measure data by points in time, whether it’s specific instances, fixed periods, or intervals. This book shows how to solve problems in web analytics, social sciences, finance, and economics, through detailed examples. Wes McKinney is the main author of pandas, the popular open source Python library for data analysis. Wes is an active speaker and participant 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.
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