Want to wrangle Pandas data like you would SQL using Python? This post serves as an introduction to pandasql, and details how to get it up and running inside of Rodeo.
This post originally appeared on the Yhat blog. Yhat is a Brooklyn based company whose goal is to make data science applicable for developers, data scientists, and businesses alike. Yhat provides a software platform for deploying and managing predictive algorithms as REST APIs, while eliminating the painful engineering obstacles associated with production environments like testing, versioning, scaling and security.
One of my favorite things about Python is that users get the benefit of observing the R community and then emulating the best parts of it. I'm a big believer that a language is only as helpful as its libraries and tools.
This post is about , a Python package we (Yhat) wrote that emulates the R package sqldf. It's a small but mighty library comprised of just 358 lines of code. The idea of is to make Python speak SQL. For those of you who come from a SQL-first background or still "think in SQL", is a nice way to take advantage of the strengths of both languages.
In this introduction, we'll show you to get up and running with inside of Rodeo, the integrated development environment (IDE) we built for data exploration and analysis. Rodeo is an open source and completely free tool. If you're an R user, its a comparable tool with a similar feel to RStudio. As of today, Rodeo can only run Python code, but last week we added syntax highlighting for a bunch of other languages to the editor (markdown, JSON, julia, SQL, markdown). As you may have read or guessed, we've got big plans for Rodeo, including adding SQL support so that you can run your SQL queries right inside of Rodeo, even without our handy little . More on that in the next week or two!
Start by downloading Rodeo for Mac, Windows or Linux from the Rodeo page on the Yhat website.
ps If you download Rodeo and encounter a problem or simply have a question, we monitor our discourse forum 24/7 (okay, almost).
Behind the scenes, uses the module to transfer data between and SQLite databases. Operations are performed in SQL, the results returned, and the database is then torn down.
Chief Analytics Officer Spring 2017
15% off with code MP15
Big Data and Analytics for Healthcare Philadelphia
$200 off with code DATA200
10% off with code 7WDATASMX
Data Science Congress 2017
20% off with code 7wdata_DSC2017
20% off with code AIP17-7WDATA-20