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 grouped boxplots to uncover unexpected values in a particular group

We saw in the previous recipe that boxplots are a great tool for examining the distribution of continuous variables. They can also be useful when we want to see if those variables are distributed differently for parts of our dataset: salaries for different age groups; number of children by marital status; litter size for different mammal species. Grouped boxplots are a handy and intuitive way to view differences in variable distribution by categories in our data.

Getting ready

We will work with the NLS and the Covid case data. You will need Matplotlib and Seaborn installed on your computer to run the code in this recipe.

How to do it...

We generate descriptive statistics of weeks worked by highest degree earned. We then use grouped boxplots to visualize the spread of the weeks worked distribution by degree, and of Covid cases by region:

  1. Import the pandas, matplotlib, and seaborn libraries...