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)

Importing SPSS, Stata, and SAS data

We will use pyreadstat to read data from three popular statistical packages into pandas. The key advantage of pyreadstat is that it allows data analysts to import data from these packages without losing metadata, such as variable and value labels.

The SPSS, Stata, and SAS data files we receive often come to us with the data issues of CSV and Excel files and SQL databases having been resolved. We do not typically have the invalid column names, changes in data types, and unclear missing values that we can get with CSV or Excel files, nor do we usually get the detachment of data from business logic, such as the meaning of data codes, that we often get with SQL data. When someone or some organization shares a data file from one of these packages with us, they have often added variable labels and value labels for categorical data. For example, a hypothetical data column called presentsat has the variable label overall satisfaction with presentation...