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)

Using histograms to examine the distribution of continuous variables

The go-to visualization tool for statisticians trying to understand how single variables are distributed is the histogram. Histograms plot a continuous variable on the x axis, in bins determined by the researcher, and the frequency of occurrence on the y axis.

Histograms provide a clear and meaningful illustration of the shape of a distribution, including central tendency, skewness (symmetry), excess kurtosis (relatively fat tails), and spread. This matters for statistical testing, as many tests make assumptions about a variable's distribution. Moreover, our expectation of what data values to expect should be guided by our understanding of the distribution's shape. For example, a value at the 90th percentile has very different implications when it comes from a normal distribution rather than from a uniform distribution.

One of the first tasks I ask introductory statistics students to do is construct...