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)

Showing summary statistics for a pandas series

There are a large number of pandas series methods for generating summary statistics. We can easily get the mean, median, maximum, or minimum values for a series with the mean, median, max, and min methods, respectively. The incredibly handy describe method will return all of these statistics, as well as several others. We can also get the series value at any percentile using quantile. These methods can be used across all values for a series, or just for selected values. This will be demonstrated in this recipe.

Getting ready

We will continue working with the overall GPA column from the NLS.

How to do it...

Let's take a good look at the distribution of the overall GPA for the DataFrame and for the selected rows. To do this, follow these steps:

  1. Import pandas and numpy and load the NLS data:
    >>> import pandas as pd
    >>> import numpy as np
    >>> nls97 = pd.read_csv("data/nls97b.csv&quot...