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Pandas 1.x Cookbook

You're reading from   Pandas 1.x Cookbook Practical recipes for scientific computing, time series analysis, and exploratory data analysis using Python

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Product type Paperback
Published in Feb 2020
Publisher Packt
ISBN-13 9781839213106
Length 626 pages
Edition 2nd Edition
Languages
Tools
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Authors (2):
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Theodore Petrou Theodore Petrou
Author Profile Icon Theodore Petrou
Theodore Petrou
Matthew Harrison Matthew Harrison
Author Profile Icon Matthew Harrison
Matthew Harrison
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Toc

Table of Contents (17) Chapters Close

Preface 1. Pandas Foundations 2. Essential DataFrame Operations FREE CHAPTER 3. Creating and Persisting DataFrames 4. Beginning Data Analysis 5. Exploratory Data Analysis 6. Selecting Subsets of Data 7. Filtering Rows 8. Index Alignment 9. Grouping for Aggregation, Filtration, and Transformation 10. Restructuring Data into a Tidy Form 11. Combining Pandas Objects 12. Time Series Analysis 13. Visualization with Matplotlib, Pandas, and Seaborn 14. Debugging and Testing Pandas 15. Other Books You May Enjoy
16. Index

Working with ZIP files

As was mentioned previously, CSV files are very common for sharing data. Because they are plain text files, they can get big. One solution for managing the size of CSV files is to compress them. In this recipe, we will look at loading files from ZIP files.

We will load a CSV file that is compressed as the only thing in the ZIP file. This is the behavior that you get if you were to right-click on a file in the Finder on Mac and click Compress beatles.csv. We will also look at reading a CSV file from a ZIP file with multiple files in it.

The first file is from the fueleconomy.gov website. It is a list of all car makes that have been available in the US market from 1984-2018.

The second file is a survey of users of the Kaggle website. It was intended to get information about the users, their background, and the tools that they prefer.

How to do it...

  1. If the CSV file is the only file in the ZIP file, you can just call the read_csv function...
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