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)
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Code to transform data

In this chapter, we will look at some code that analyzes survey data that Kaggle did in 2018. The survey queried Kaggle users about socio-economic information.

This section will present the survey data along with some code to analyze it. The subtitle for this data is "the most comprehensive dataset available on the state of machine learning and data science". Let's dig into this data and see what it has. The data was originally available at

How to do it…

  1. Load the data into a DataFrame:
    >>> import pandas as pd
    >>> import numpy as np
    >>> import zipfile
    >>> url = 'data/'
    >>> with zipfile.ZipFile(url) as z:
    ...     print(z.namelist())
    ...     kag = pd.read_csv('multipleChoiceResponses.csv'))
    ...     df = kag.iloc[1:]
    ['multipleChoiceResponses.csv', 'freeFormResponses...