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 Isolation Forest to find anomalies

Isolation Forest is a relatively new machine learning technique for identifying anomalies. It has quickly become popular, partly because its algorithm is optimized to find anomalies, rather than normal values. It finds outliers by successive partitioning of the data until a data point has been isolated. Points that require fewer partitions to be isolated receive higher anomaly scores. This process turns out to be fairly easy on system resources. In this recipe, we demonstrate how to use it to detect outlier COVID-19 cases and deaths.

Getting ready

You will need scikit-learn and Matplotlib to run the code in this recipe. You can install them by entering pip install sklearn and pip install matplotlib in the terminal or powershell (in Windows).

How to do it...

We will use Isolation Forest to find the countries whose attributes indicate that they are most anomalous:

  1. Load pandas, matplotlib, and the StandardScaler and IsolationForest...