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 scatter plots to view bivariate relationships

My sense is that there are few plots that data analysts rely more on than scatter plots, with the possible exception of histograms. We are all very used to looking at relationships that can be illustrated in two dimensions. Scatter plots capture important real-world phenomena (the relationship between variables) and are quite intuitive for most people. This makes them a valuable addition to our visualization toolkit.

Getting ready

You will need Matplotlib and Seaborn for this recipe. We will be working with the landtemps dataset, which provides the average temperature in 2019 for 12,095 weather stations across the world.

How to do it...

We level up our scatter plot skills from the previous chapter and visualize more complicated relationships. We display the relationship between average temperature, latitude, and elevation by showing multiple scatter plots on one chart, creating 3D scatter plots, and showing multiple regression...