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)

Functions for identifying outliers and unexpected values

If I had to pick one data cleaning area where I find reusable code most beneficial, it would be in the identification of outliers and unexpected values. This is because our prior assumptions often lead us to the central tendency of a distribution, rather than to the extremes. Quickly – think of a cat. Unless you were thinking about a particular cat in your life, an image of a generic feline between 8 and 10 pounds probably came to mind; not one that is 6 pounds or 22 pounds.

We often need to be more deliberate to elevate extreme values to consciousness. This is where having a standard set of diagnostic functions to run on our data is very helpful. We can run these functions even if nothing in particular triggers us to run them. This recipe provides examples of functions that we can use regularly to identify outliers and unexpected values.

Getting ready

We will create two files in this recipe, one with the functions...