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)

Missing value imputation with K-nearest neighbor

KNN is a popular machine learning technique because it is intuitive and easy to run and yields good results when there is not a large number of features (variables) and observations. For the same reasons, it is often used to impute missing values. As its name suggests, KNN identifies the k observations whose features are most similar to each observation. When used to impute missing values, KNN uses the nearest neighbors to determine what fill values to use.

Getting ready

We will work with the National Longitudinal Survey data again in this recipe, and then try to impute reasonable values for the same school record data that we worked with in the preceding recipe.

You will need scikit-learn to run the code in this recipe. You can install it by entering pip install sklearn in a Terminal or Windows PowerShell.

How to do it…

In this recipe, we will use scikit-learn's KNNImputer module to fill in missing values...