Book Image

Pandas 1.x Cookbook - Second Edition

By : Matt Harrison, Theodore Petrou
Book Image

Pandas 1.x Cookbook - Second Edition

By: Matt Harrison, Theodore Petrou

Overview of this book

The pandas library is massive, and it's common for frequent users to be unaware of many of its more impressive features. The official pandas documentation, while thorough, does not contain many useful examples of how to piece together multiple commands as one would do during an actual analysis. This book guides you, as if you were looking over the shoulder of an expert, through situations that you are highly likely to encounter. This new updated and revised edition provides you with unique, idiomatic, and fun recipes for both fundamental and advanced data manipulation tasks with pandas. Some recipes focus on achieving a deeper understanding of basic principles, or comparing and contrasting two similar operations. Other recipes will dive deep into a particular dataset, uncovering new and unexpected insights along the way. Many advanced recipes combine several different features across the pandas library to generate results.
Table of Contents (17 chapters)
15
Other Books You May Enjoy
16
Index

Selecting the smallest of the largest

This recipe can be used to create catchy news headlines such as Out of the Top 100 Universities, These 5 have the Lowest Tuition, or From the Top 50 Cities to Live, these 10 are the Most Affordable.

During analysis, it is possible that you will first need to find a grouping of data that contains the top n values in a single column and, from this subset, find the bottom m values based on a different column.

In this recipe, we find the five lowest budget movies from the top 100 scoring movies by taking advantage of the convenience methods: .nlargest and .nsmallest.

How to do it…

  1. Read in the movie dataset, and select the columns: movie_title, imdb_score, and budget:
    >>> movie = pd.read_csv("data/movie.csv")
    >>> movie2 = movie[["movie_title", "imdb_score", "budget"]]
    >>> movie2.head()
       movie_title  imdb_score       budget
    0       Avatar    ...