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)

Selecting and organizing columns

We explore several ways to select one or more columns from your DataFrame in this recipe. We can select columns by passing a list of column names to the [] bracket operator, or by using the pandas-specific data accessors loc and iloc.

When cleaning data or doing exploratory or statistical analyses, it is helpful to focus on the variables that are relevant to the issue or analysis at hand. This makes it important to group columns according to their substantive or statistical relationships with each other, or to limit the columns we are investigating at any one time. How many times have we said to ourselves something like, "Why does variable A have a value of x when variable B has a value of y?" We can only do that when the amount of data we are viewing at a given moment does not exceed our perceptive abilities at that moment.

Getting ready…

We will continue working with the NLS data in this recipe.

How to do it…