Book Image

Python Data Cleaning Cookbook

By : Michael Walker
Book Image

Python Data Cleaning Cookbook

By: Michael Walker

Overview of this book

Getting clean data to reveal insights is essential, as directly jumping into data analysis without proper data cleaning may lead to incorrect results. This book shows you tools and techniques that you can apply to clean and handle data with Python. You'll begin by getting familiar with the shape of data by using practices that can be deployed routinely with most data sources. Then, the book teaches you how to manipulate data to get it into a useful form. You'll also learn how to filter and summarize data to gain insights and better understand what makes sense and what does not, along with discovering how to operate on data to address the issues you've identified. Moving on, you'll perform key tasks, such as handling missing values, validating errors, removing duplicate data, monitoring high volumes of data, and handling outliers and invalid dates. Next, you'll cover recipes on using supervised learning and Naive Bayes analysis to identify unexpected values and classification errors, and generate visualizations for exploratory data analysis (EDA) to visualize unexpected values. Finally, you'll build functions and classes that you can reuse without modification when you have new data. By the end of this Python book, you'll be equipped with all the key skills that you need to clean data and diagnose problems within it.
Table of Contents (12 chapters)

Chapter 2: Anticipating Data Cleaning Issues when Importing HTML and JSON into pandas

This chapter continues our work on importing data from a variety of sources, and the initial checks we should do on the data after importing it. Gradually, over the last 25 years, data analysts have found that they increasingly need to work with data in non-tabular, semi-structured forms. Sometimes they even create and persist data in those forms themselves. We work with a common alternative to traditional tabular datasets in this chapter, JSON, but the general concepts can be extended to XML and NoSQL data stores such as MongoDB. We also go over common issues that occur when scraping data from websites.

In this chapter, we will work through the following recipes:

  • Importing simple JSON data
  • Importing more complicated JSON data from an API
  • Importing data from web pages
  • Persisting JSON data