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 line plots to examine trends in continuous variables

A typical way to visualize values for a continuous variable over regular intervals of time is through a line plot, though sometimes bar charts are used for small numbers of intervals. We will use line plots in this recipe to display variable trends, and examine sudden deviations in trends and differences in values over time by groups.

Getting ready

We will work with daily Covid case data in this recipe. In previous recipes, we have used totals by country. The daily data provides us with the number of new cases and new deaths each day by country, in addition to the same demographic variables we used in other recipes. You will need Matplotlib installed to run the code in this recipe.

How to do it…

We use line plots to visualize trends in daily coronavirus cases and deaths. We create line plots by region, and stacked plots to get a better sense of how much one country can drive the number of cases for a whole...