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Python Real-World Projects

You're reading from  Python Real-World Projects

Product type Book
Published in Sep 2023
Publisher Packt
ISBN-13 9781803246765
Pages 478 pages
Edition 1st Edition
Languages
Author (1):
Steven F. Lott Steven F. Lott
Profile icon Steven F. Lott

Table of Contents (20) Chapters

Preface 1. Chapter 1: Project Zero: A Template for Other Projects 2. Chapter 2: Overview of the Projects 3. Chapter 3: Project 1.1: Data Acquisition Base Application 4. Chapter 4: Data Acquisition Features: Web APIs and Scraping 5. Chapter 5: Data Acquisition Features: SQL Database 6. Chapter 6: Project 2.1: Data Inspection Notebook 7. Chapter 7: Data Inspection Features 8. Chapter 8: Project 2.5: Schema and Metadata 9. Chapter 9: Project 3.1: Data Cleaning Base Application 10. Chapter 10: Data Cleaning Features 11. Chapter 11: Project 3.7: Interim Data Persistence 12. Chapter 12: Project 3.8: Integrated Data Acquisition Web Service 13. Chapter 13: Project 4.1: Visual Analysis Techniques 14. Chapter 14: Project 4.2: Creating Reports 15. Chapter 15: Project 5.1: Modeling Base Application 16. Chapter 16: Project 5.2: Simple Multivariate Statistics 17. Chapter 17: Next Steps 18. Other Books You Might Enjoy 19. Index

16.5 Extras

Here are some ideas for you to add to this project.

16.5.1 Use pandas to compute basic statistics

The pandas package offers a robust set of tools for doing data analysis. The core concept is to create a DataFrame that contains the relevant samples. The pandas package needs to be installed and added to the requirements.txt file.

There are methods for transforming a sequence of SeriesSample objects into a DataFrame. The best approach is often to convert each of the pydantic objects into a dictionary, and build the dataframe from the list of dictionaries.

The idea is something like the following:

import pandas as pd

df = pd.DataFrame([dict(s) for s in series_data])

In this example, the value of series_data is a sequence of SeriesSample instances.

Each column in the resulting dataframe will be one of the variables of the sample. Given this object, methods of the DataFrame object produce useful statistics.

The corr() function, for example, computes the correlation values...

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