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 aggregating or combining data

Most data analysis projects require some reshaping of data. We may need to aggregate by group or combine data vertically or horizontally. We have to do similar tasks each time we prepare our data for this reshaping. We can routinize some of these tasks with functions, improving both the reliability of our code and our efficiency in getting the work done. We sometimes need to check for mismatches in merge-by columns before doing a merge, check for unexpected changes in values in panel data from one period to the next before aggregating, or concatenate a number of files at once and verify that data has been combined accurately.

These are just a few examples of the kind of data aggregation and combining tasks that might lend themselves to a more generalized coding solution. In this recipe, we define functions that can help with these tasks.

Getting ready

We will work with the Covid daily data in this recipe. This data comprises new...