Both R and Python possess libraries for using SQL statements to interact with data frames. While both languages have native facilities for manipulating data, the sqldf and pandasql provide a simple and elegant interface for conducting tasks using an intuitive framework that’s widely used by analysts. R and sqldf sqldf(“SELECT COUNT(*) FROM … Continue reading Turning Data Into Awesome With sqldf and pandasql
Anyone who has regularly worked with Google Trends data has had to deal with the slightly tedious task of grabbing keyword level data and reformatting the spreadsheet provided by Google. After looking for a seamless way to pull the data, I came upon the PyTrends library on GitHub, and sought to put together some quick … Continue reading Extract Google Trends Data with Python
There are plenty of instances where analysts are regularly forwarded xls spreadsheets and tasked with summarizing the data. In many cases, these scenarios can be automated through fairly simple Python scripts. In the following code, I take an Excel spreadsheet with two sheets, summarize each sheet using a pivot table, and add those results to … Continue reading A Few Days of Python: Automating Tasks Involving Excel Files
Using R Functions in Python
Using the csv module Using the pandas module
Not too long ago, I was on the job market looking for work as an applied statistician or data scientist within the the online marketing industry. One thing I’ve come to expect with almost every company is some sort of homework assignment or challenge where a spreadsheet would be presented along with some guidelines on … Continue reading Homework during the hiring process…no thanks!
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