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)

Examining both the distribution shape and outliers with violin plots

Violin plots combine histograms and boxplots in one plot. They show the IQR, median, and whiskers, as well as the frequency of observations at all ranges of values. It is hard to visualize how that is possible without seeing an actual violin plot. We generate a few violin plots on the same data we used for boxplots in the previous recipe, to make it easier to grasp how they work.

Getting ready

We will work with the NLS and the Covid case data. You need Matplotlib and Seaborn installed on your computer to run the code in this recipe.

How to do it…

We do violin plots to view both the spread and shape of the distribution on the same graphic. We then do violin plots by groups:

  1. Load pandas, matplotlib, and seaborn, and the Covid case and NLS data:
    >>> import pandas as pd
    >>> import numpy as np
    >>> import matplotlib.pyplot as plt
    >>> import seaborn as sns