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)

Evaluating and cleaning string series data

There are many string cleaning methods in Python and pandas. This is a good thing. Given the great variety of data stored in strings, it is important to have a wide range of tools to call upon when performing string evaluation and manipulation: when selecting fragments of a string by position, when checking whether a string contains a pattern, when splitting a string, when testing a string's length, when joining two or more strings, when changing the case of a string, and so on. We'll explore some of the methods that are used most frequently for string evaluation and cleaning in this recipe.

Getting ready

We will work with the National Longitudinal Survey data in this recipe. (The NLS data was actually a little too clean for this recipe. To illustrate working with strings with trailing spaces, I added trailing spaces to the maritalstatus column values.)

How to do it...

In this recipe, we will perform some common string...