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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
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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

Generating tests with Hypothesis

The Hypothesis library is a third-party library for generating tests, or performing property-based testing. You create a strategy (an object that generates samples of data) and then run your code against the generated output of the strategy. You want to test an invariant, or something about your data that you presume to always hold true.

Again, there could be a book written solely about this type of testing, but in this section we will show an example of using the library.

We will show how to generate Kaggle survey data, then using that generated survey data, we will run it against the tweak_kag function and validate that the function will work on new data.

We will leverage the testing code found in the previous section. The Hypothesis library works with pytest, so we can use the same layout.

How to do it…

  1. Create a project data layout. If you had the code from the previous section, add a test_hypot.py file...
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