Aaron Schlegel's Notebook of Interesting Things

In this example, we walk through a sample use case of extracting data from a database using an API and then structuring that data in a cohesive manner that allows us to create a relational database that we can then query with SQL statements. The database we will create with the extracted data will use Postgresql. The Python libraries that will be used in this example are poetpy, a Python wrapper for the PoetryDB API written by yours truly, pandas for transforming and cleansing the data as needed, and sqlalchemy for handling the SQL side of things.

R and SQL make excellent complements for analyzing data due to their respective strengths. The sqldf package provides an interface for working with SQL in R by querying data from a database into an R data.frame. This post will demonstrate how to query and analyze data using the sqldf package in conjunction with the graphing libraries plotly and ggplot2 as well as some other packages that provide useful statistical tests and other functions.

The consumer complaints database is a collection of complaints received by the Bureau of Consumer Financial Protection related to financial products and services. This post explores creating a database file using SQLite and analyzing the data with Pandas and Plotly.