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)

Persisting tabular data

We persist data, copy it from memory to local or remote storage, for several reasons: to be able to access the data without having to repeat the steps we used to generate it; to share the data with others; or to make it available for use with different software. In this recipe, we save data that we have loaded into a pandas data frame as different file types (CSV, Excel, pickle, and feather).

Another important, but sometimes overlooked, reason to persist data is to preserve some segment of our data that needs to be examined more closely; perhaps it needs to be scrutinized by others before our analysis can be completed. For analysts who work with operational data in medium- to large-sized organizations, this process is part of the daily data cleaning workflow.

In addition to these reasons for persisting data, our decisions about when and how to serialize data are shaped by several other factors: where we are in terms of our data analysis projects, the hardware...