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 groupby to organize data by groups

At a certain point in most data analysis projects, we have to generate summary statistics by groups. While this can be done using the approaches in the previous recipe, in most cases the pandas DataFrame groupby method is a better choice. If groupby can handle an aggregation task—and it usually can—it is likely the most efficient way to accomplish that task. We make good use of groupby in the remaining recipes in this chapter. We go over the basics in this recipe.

Getting ready

We will work with the COVID-19 daily data in this recipe.

How to do it…

We will create a pandas groupby DataFrame and use it to generate summary statistics by group:

  1. Import pandas and numpy, and load the Covid case daily data:
    >>> import pandas as pd
    >>> import numpy as np
    >>> coviddaily = pd.read_csv("data/coviddaily720.csv", parse_dates=["casedate"])
  2. Create a pandas groupby DataFrame...