Introduction

Since data science and analytics have become key parts of our lives, the role of a data scientist has become even more important. Finding the source of data is an essential part of data science; however, it is the science part that makes you – the practitioner – truly valuable.

To practice high-quality science with data, you need to make sure it is properly sourced, cleaned, formatted, and pre-processed. This book will teach you the most essential basics of this invaluable component of the data science pipeline: data wrangling. In short, data wrangling is the process that ensures that the data is being presented in a way that is clean, accurate, formatted, and ready to be used for data analysis.

A prominent example of data wrangling with a large amount of data is the analysis conducted at the Supercomputer Center of the University of California San Diego (UCSD) every year. Wildfires are very common in California and are caused mainly by the dry weather and extreme heat, especially during the summers. Data scientists at the UCSD Supercomputer Center run an analysis every year and gather data to predict the nature and spread direction of wildfires in California. The data comes from diverse sources, such as weather stations, sensors in the forest, fire stations, satellite imagery, and Twitter feeds. However, this data might be incomplete or missing.

After collecting the data from various sources, if it is not cleaned and formatted using ways including scaling numbers and removing unwanted characters in strings, it could result in erroneous data. In cases where we might get a flawed analysis, we might need to reformat the data from JavaScript Object Notation (JSON) into Comma Separated Value (CSV); we may also need the numbers to be normalized, that is, centered and scaled with relation to themselves. Processing data in such a way might be required when we feed data to certain machine learning models.

This is an example of how data wrangling and data science can prove to be helpful and relevant. This chapter will discuss the fundamentals of data wrangling. Let's get started.