Search icon CANCEL
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Conferences
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
Python Data Cleaning Cookbook

You're reading from   Python Data Cleaning Cookbook Prepare your data for analysis with pandas, NumPy, Matplotlib, scikit-learn, and OpenAI

Arrow left icon
Product type Paperback
Published in May 2024
Publisher Packt
ISBN-13 9781803239873
Length 486 pages
Edition 2nd Edition
Languages
Arrow right icon
Author (1):
Arrow left icon
Michael Walker Michael Walker
Author Profile Icon Michael Walker
Michael Walker
Arrow right icon
View More author details
Toc

Table of Contents (14) Chapters Close

Preface 1. Anticipating Data Cleaning Issues When Importing Tabular Data with pandas 2. Anticipating Data Cleaning Issues When Working with HTML, JSON, and Spark Data FREE CHAPTER 3. Taking the Measure of Your Data 4. Identifying Outliers in Subsets of Data 5. Using Visualizations for the Identification of Unexpected Values 6. Cleaning and Exploring Data with Series Operations 7. Identifying and Fixing Missing Values 8. Encoding, Transforming, and Scaling Features 9. Fixing Messy Data When Aggregating 10. Addressing Data Issues When Combining DataFrames 11. Tidying and Reshaping Data 12. Automate Data Cleaning with User-Defined Functions, Classes, and Pipelines 13. Index

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/nls97f.csv",
    low_memory=False)...
lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at €18.99/month. Cancel anytime