Foundations for Analytics with Python

This book PDF is perfect for those who love Business & Economics genre, written by Clinton W. Brownley and published by "O'Reilly Media, Inc." which was released on 16 August 2016 with total hardcover pages 351. You could read this book directly on your devices with pdf, epub and kindle format, check detail and related Foundations for Analytics with Python books below.

Foundations for Analytics with Python
Author : Clinton W. Brownley
File Size : 53,8 Mb
Publisher : "O'Reilly Media, Inc."
Language : English
Release Date : 16 August 2016
ISBN : 9781491922507
Pages : 351 pages
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Foundations for Analytics with Python by Clinton W. Brownley Book PDF Summary

If you’re like many of Excel’s 750 million users, you want to do more with your data—like repeating similar analyses over hundreds of files, or combining data in many files for analysis at one time. This practical guide shows ambitious non-programmers how to automate and scale the processing and analysis of data in different formats—by using Python. After author Clinton Brownley takes you through Python basics, you’ll be able to write simple scripts for processing data in spreadsheets as well as databases. You’ll also learn how to use several Python modules for parsing files, grouping data, and producing statistics. No programming experience is necessary. Create and run your own Python scripts by learning basic syntax Use Python’s csv module to read and parse CSV files Read multiple Excel worksheets and workbooks with the xlrd module Perform database operations in MySQL or with the mysqlclient module Create Python applications to find specific records, group data, and parse text files Build statistical graphs and plots with matplotlib, pandas, ggplot, and seaborn Produce summary statistics, and estimate regression and classification models Schedule your scripts to run automatically in both Windows and Mac environments

Foundations for Analytics with Python

If you’re like many of Excel’s 750 million users, you want to do more with your data—like repeating similar analyses over hundreds of files, or combining data in many files for analysis at one time. This practical guide shows ambitious non-programmers how to automate and scale the processing

Get Book
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