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)

Identifying outliers with one variable

The concept of an outlier is somewhat subjective but is closely tied to the properties of a particular distribution; to its central tendency, spread, and shape. We make assumptions about whether a value is expected or unexpected based on how likely we are to get that value given the variable's distribution. We are more inclined to view a value as an outlier if it is multiple standard deviations away from the mean and it is from a distribution that is approximately normal; one that is symmetrical (has low skew) and has relatively skinny tails (low kurtosis).

This becomes clear if we imagine trying to identify outliers from a uniform distribution. There is no central tendency and there are no tails. Each value is equally likely. If, for example, Covid cases per country were uniformly distributed, with a minimum of 1 and a maximum of 10,000,000, neither 1 nor 10,000,000 would be considered an outlier.

We need to understand how a variable...