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)

Generating summary statistics for continuous variables

Pandas has a good number of tools we can use to get a sense of the distribution of continuous variables. We will focus on the splendid functionality of describe in this recipe and demonstrate the usefulness of histograms for visualizing variable distributions.

Before doing any analysis with a continuous variable it is important to have a good understanding of how it is distributed – its central tendency, its spread, and its skewness. This understanding greatly informs our efforts to identify outliers and unexpected values. But it is also crucial information in and of itself. I do not think it overstates the case to say that we understand a particular variable well if we have a good understanding of how it is distributed, and any interpretation without that understanding will be incomplete or flawed in some way.

Getting ready…

We will work with the COVID totals data in this recipe. You will need Matplotlib...