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 k-nearest neighbor to find outliers

Unsupervised machine learning tools can help us identify observations that are unlike others when we have unlabeled data; that is, when there is no target or dependent variable. (In the previous recipe, we used total cases per million as the dependent variable.) Even when selecting targets and factors is relatively straightforward, it might be helpful to identify outliers without making any assumptions about relationships between variables. We can use k-nearest neighbor to find observations that are most unlike others, those where there is the greatest difference between their values and their nearest neighbors' values.

Getting ready

You will need PyOD (Python outlier detection) and scikit-learn to run the code in this recipe. You can install both by entering pip install pyod and pip install sklearn in the terminal or powershell (in Windows).

How to do it…

We will use k-nearest neighbor to identify countries whose attributes...