Reading Data from Different Text-Based (and Non-Text-Based) Sources

One of the most valued and widely used skills of a data wrangling professional is the ability to extract and read data from a diverse array of sources into a structured format. Modern analytics pipelines depend on their ability to scan and absorb a variety of data sources to build and analyze a pattern-rich model. Such a feature-rich, multi-dimensional model will have high predictive and generalization accuracy. It will be valued by stakeholders and end users alike for any data-driven product.

In the first topic of this chapter, we will go through various data sources and how they can be imported into pandas DataFrames, thus imbuing wrangling professionals with extremely valuable data ingestion knowledge.

Data Files Provided with This Chapter

Because this topic is about reading from various data sources, we will use small files of various types in the following exercises. All of the data files are provided along with the Jupyter...