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 4: Identifying Missing Values and Outliers in Subsets of Data

Outliers and unexpected values may not be errors. They often are not. Individuals and events are complicated and surprise the analyst. Some people really are 7'4" tall and some really have $50 million salaries. Sometimes, data is messy because people and situations are messy; however, extreme values can have an outsized impact on our analysis, particularly when we are using parametric techniques that assume a normal distribution.

These issues may become even more apparent when working with subsets of data. That is not just because extreme or unexpected values have more weight in smaller samples. It is also because they may make less sense when bivariate and multivariate relationships are considered. When the 7'4" person, or the person making $50 million, is 10 years old, the red flag gets even redder. We take these complications into account in this chapter when considering strategies for detecting...