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 more complicated aggregation functions with groupby

In the previous recipe, we created a groupby DataFrame object and used it to run summary statistics by groups. We use chaining in this recipe to create the groups, choose the aggregation variable(s), and select the aggregation function(s), all in one line. We also take advantage of the flexibility of the groupby object, which allows us to choose the aggregation columns and functions in a variety of ways.

Getting ready

We will work with the National Longitudinal Survey of Youth (NLS) data in this recipe.

Data note

The NLS, administered by the United States Bureau of Labor Statistics, are longitudinal surveys of individuals who were in high school in 1997 when the surveys started. Participants were surveyed each year through 2018. The surveys are available for public use at

How to do it…

We do more complicated aggregations with groupby than we did in the previous recipe, taking advantage...