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)

Functions for displaying summary statistics and frequencies

During the first few days of working with a DataFrame, we try to get a good sense of the distribution of continuous variables and counts for categorical variables. We also often do counts by selected groups. Although pandas and NumPy have many built-in methods for these purposes – describe, mean, valuecounts, crosstab, and so on – data analysts often have preferences for how they work with these tools. If, for example, an analyst finds that she usually needs to see more percentiles than those generated by describe, she can use her own function instead. We will create user-defined functions for displaying summary statistics and frequencies in this recipe.

Getting ready

We will be working with the basicdescriptives module again in this recipe. All of the functions we will define are saved in that module. We continue to work with the NLS data.

How to do it...

We will use functions we create to generate...