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 linear regression to identify data points with significant influence

The remaining recipes in this chapter use statistical modeling to identify outliers. The advantage of these techniques is that they are less dependent on the distribution of the variable of concern, and take more into account than can be revealed in either univariate or bivariate analyses. This allows us to identify outliers that are not otherwise apparent. On the other hand, by taking more factors into account, multivariate techniques may provide evidence that a previously suspect value is actually within an expected range, and provides meaningful information.

In this recipe, we use linear regression to identify observations (rows) that have an outsized influence on models of a target or dependent variable. This can indicate that one or more values for a few observations are so extreme that they compromise model fit for all of the other observations.

Getting ready

The code in this recipe requires the...