Python Data Cleaning Cookbook

Discover how to describe your data in detail, identify data issues, and find out how to solve them using commonly used techniques and tips and tricks

▶Book Description
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.

▶What You Will Learn
-Find out how to read and analyze data from a variety of sources
-Produce summaries of the attributes of data frames, columns, and rows
-Filter data and select columns of interest that satisfy given criteria
-Address messy data issues, including working with dates and missing values
-Improve your productivity in Python pandas by using method chaining
-Use visualizations to gain additional insights and identify potential data issues
-Enhance your ability to learn what is going on in your data
-Build user-defined functions and classes to automate data cleaning

▶Key Features
-Get well-versed with various data cleaning techniques to reveal key insights
-Manipulate data of different complexities to shape them into the right form as per your business needs
-Clean, monitor, and validate large data volumes to diagnose problems before moving on to data analysis

▶Who This Book Is For
This book is for anyone looking for ways to handle messy, duplicate, and poor data using different Python tools and techniques. The book takes a recipe-based approach to help you to learn how to clean and manage data. Working knowledge of Python programming is all you need to get the most out of the book.

▶What this book covers
- Chapter 1, Anticipating Data Cleaning Issues when Importing Tabular Data into pandas, explores tools for loading CSV files, Excel files, relational database tables, SAS, SPSS, and Stata files, and R files into pandas DataFrames.

- Chapter 2, Anticipating Data Cleaning Issues when Importing HTML and JSON into pandas, discusses techniques for reading and normalizing JSON data, and for web scraping.

- Chapter 3, Taking the Measure of Your Data, introduces common techniques for navigating around a DataFrame, selecting columns and rows, and generating summary statistics.

- Chapter 4, Identifying Missing Values and Outliers in Subsets of Data, explores a wide range of strategies to identify missing values and outliers across a whole DataFrame and by selected groups.

- Chapter 5, Using Visualizations for the Identification of Unexpected Values, demonstrates the use of matplotlib and seaborn tools to visualize how key variables are distributed, including with histograms, boxplots, scatter plots, line plots, and violin plots.

- Chapter 6, Cleaning and Exploring Data with Series Operations, discusses updating pandas series with scalars, arithmetic operations, and conditional statements based on the values of one or more series.

- Chapter 7, Fixing Messy Data when Aggregating, demonstrates multiple approaches to aggregating data by group, and discusses when to choose one approach over the others.

- Chapter 8, Addressing Data Issues when Combining DataFrames, examines different strategies for concatenating and merging data, and how to anticipate common data challenges when combining data.

- Chapter 9, Tidying and Reshaping Data, introduces several strategies for de-duplicating, stacking, melting, and pivoting data.

- Chapter 10, User-Defined Functions and Classes to Automate Data Cleaning, examines how to turn many of the techniques from the first nine chapters into reusable code.