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)

Using boxplots to identify outliers for continuous variables

Boxplots are essentially a graphical representation of our work in the Identifying outliers with one variable recipe in Chapter 4, Identifying Missing Values and Outliers in Subsets of Data. There, we used the concept of interquartile range (IQR)—the distance between the value at the first quartile and the value at the third quartile—to determine outliers. Any value greater than (1.5 * IQR) + the third quartile value, or less than the first quartile value – (1.5 * IQR), was considered an outlier. That is precisely what is revealed in a boxplot.

Getting ready

We will work with cumulative data on coronavirus cases and deaths by country, and the National Longitudinal Surveys (NLS) data. You will need the Matplotlib library to run the code on your computer.

How to do it…

We use boxplots to show the shape and spread of Scholastic Assessment Test (SAT) scores, weeks worked, and Covid cases...