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)

Generating a heat map based on a correlation matrix

The correlation between two variables is a measure of how much they move together. A correlation of 1 means that the two variables are perfectly positively correlated. As one variable increases in size, so does the other. A value of -1 means that they are perfectly negatively correlated. As one variable increases in size, the other decreases. Correlations of 1 or -1 only rarely happen, but correlations above 0.5 or below -0.5 might still be meaningful. There are several tests that can tell us whether the relationship is statistically significant (such as Pearson, Spearman, and Kendall). Since this is a chapter on visualizations, we will focus on viewing important correlations.

Getting ready

You will need Matplotlib and Seaborn installed to run the code in this recipe. Both can be installed by using pip, with the pip install matplotlib and pip install seaborn commands.

How to do it…

We first show part of a correlation...