Extracting and Exploring Data with Singer and pandas

It is a capital mistake to theorize before one has data.

– Sherlock Holmes in “A Study in Scarlet,” by Arthur Conan Doyle

Usually, in introductory analysis books, the data extraction job is assumed or left as an afterthought. Unfortunately, you will spend a big piece of your time either attempting to extract data or cleaning and preparing it for further analysis.

To bridge that gap, we will spend this chapter going through some data extraction and cleaning basics. We’ll provide a brief introduction to data extraction, commonly referred to as the E in extract, transform, and load (ETL), and how to extract and handle marketing data ingestion. After that, we will dive into exploratory data analysis (EDA). We will explore standard measures of descriptive statistics, such as measures of central tendency and variability, and how to verify common issues such as the normality of data.

In this chapter...