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)

Finding missing values

Before starting any analysis, we need to have a good sense of the number of missing values for each variable, and why those values are missing. We also want to know which rows in our data frame are missing values for several key variables. We can get this information with just a couple of statements in pandas.

We also need good strategies for dealing with missing values before we begin statistical modeling, since those models do not typically handle missing values flexibly. We introduce imputation strategies in this recipe and go into more detail in subsequent recipes in this chapter.

Getting ready

We will work with cumulative data on coronavirus cases and deaths by country. The DataFrame has other relevant information, including population density, age, and GDP.


Our World in Data provides COVID-19 public use data at The data used in this recipe was downloaded on June 1, 2020. The Covid case...