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

About the Author

Clinton Brownley, Ph.D., is a data scientist at Facebook, where he is responsible for a wide variety of data pipelining, statistical modeling, and data visualization projects that inform data-driven decisions about large-scale infrastructure. Clinton is a Past-President of the San Francisco Bay Area Chapter of the American Statistical Association and a Council member for the Section on Practice of the Institute for Operations Research and the Management Sciences. Clinton received degrees from Carnegie Mellon University and American University.

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