Book Image

Python Real-World Projects

By : Steven F. Lott
5 (1)
Book Image

Python Real-World Projects

5 (1)
By: Steven F. Lott

Overview of this book

In today's competitive job market, a project portfolio often outshines a traditional resume. Python Real-World Projects empowers you to get to grips with crucial Python concepts while building complete modules and applications. With two dozen meticulously designed projects to explore, this book will help you showcase your Python mastery and refine your skills. Tailored for beginners with a foundational understanding of class definitions, module creation, and Python's inherent data structures, this book is your gateway to programming excellence. You’ll learn how to harness the potential of the standard library and key external projects like JupyterLab, Pydantic, pytest, and requests. You’ll also gain experience with enterprise-oriented methodologies, including unit and acceptance testing, and an agile development approach. Additionally, you’ll dive into the software development lifecycle, starting with a minimum viable product and seamlessly expanding it to add innovative features. By the end of this book, you’ll be armed with a myriad of practical Python projects and all set to accelerate your career as a Python programmer.

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Table of Contents (20 chapters)
Preface
Preface
Who this book is for
What this book covers
A note on skills required
To get the most out of this book
Conventions used
Get in touch
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1
Chapter 1: Project Zero: A Template for Other Projects
Chapter 1: Project Zero: A Template for Other Projects
1.1 On quality
1.2 Suggested project sprints
1.3 List of deliverables
1.4 Development tool installation
1.5 Project 0 – Hello World with test cases
1.6 Summary
1.7 Extras
Free Chapter
2
Chapter 2: Overview of the Projects
Chapter 2: Overview of the Projects
2.1 General data acquisition
2.2 Acquisition via Extract
2.3 Inspection
2.4 Clean, validate, standardize, and persist
2.5 Summarize and analyze
2.6 Statistical modeling
2.7 Data contracts
2.8 Summary
3
Chapter 3: Project 1.1: Data Acquisition Base Application
Chapter 3: Project 1.1: Data Acquisition Base Application
3.1 Description
3.2 Architectural approach
3.3 Deliverables
3.4 Summary
3.5 Extras
4
Chapter 4: Data Acquisition Features: Web APIs and Scraping
Chapter 4: Data Acquisition Features: Web APIs and Scraping
4.1 Project 1.2: Acquire data from a web service
4.2 Project 1.3: Scrape data from a web page
4.3 Summary
4.4 Extras
5
Chapter 5: Data Acquisition Features: SQL Database
Chapter 5: Data Acquisition Features: SQL Database
5.1 Project 1.4: A local SQL database
5.2 Project 1.5: Acquire data from a SQL extract
5.3 Summary
5.4 Extras
6
Chapter 6: Project 2.1: Data Inspection Notebook
Chapter 6: Project 2.1: Data Inspection Notebook
6.1 Description
6.2 Approach
6.3 Deliverables
6.4 Summary
6.5 Extras
7
Chapter 7: Data Inspection Features
Chapter 7: Data Inspection Features
7.1 Project 2.2: Validating cardinal domains — measures, counts, and durations
7.1.1 Description
7.1.2 Approach
7.1.3 Deliverables
7.2 Project 2.3: Validating text and codes — nominal data and ordinal numbers
7.2.1 Description
7.2.2 Approach
7.2.3 Deliverables
7.3 Project 2.4: Finding reference domains
7.4 Summary
7.5 Extras
8
Chapter 8: Project 2.5: Schema and Metadata
Chapter 8: Project 2.5: Schema and Metadata
8.1 Description
8.2 Approach
8.3 Deliverables
8.4 Summary
8.5 Extras
9
Chapter 9: Project 3.1: Data Cleaning Base Application
Chapter 9: Project 3.1: Data Cleaning Base Application
9.1 Description
9.2 Approach
9.3 Deliverables
9.4 Summary
9.5 Extras
10
Chapter 10: Data Cleaning Features
Chapter 10: Data Cleaning Features
10.1 Project 3.2: Validate and convert source fields
10.2 Project 3.3: Validate text fields (and numeric coded fields)
10.3 Project 3.4: Validate references among separate data sources
10.4 Project 3.5: Standardize data to common codes and ranges
10.5 Project 3.6: Integration to create an acquisition pipeline
10.6 Summary
10.7 Extras
11
Chapter 11: Project 3.7: Interim Data Persistence
Chapter 11: Project 3.7: Interim Data Persistence
11.1 Description
11.2 Overall approach
11.3 Deliverables
11.4 Summary
11.5 Extras
12
Chapter 12: Project 3.8: Integrated Data Acquisition Web Service
Chapter 12: Project 3.8: Integrated Data Acquisition Web Service
12.1 Description
12.2 Overall approach
12.3 Deliverables
12.4 Summary
12.5 Extras
13
Chapter 13: Project 4.1: Visual Analysis Techniques
Chapter 13: Project 4.1: Visual Analysis Techniques
13.1 Description
13.2 Overall approach
13.3 Deliverables
13.4 Summary
13.5 Extras
14
Chapter 14: Project 4.2: Creating Reports
Chapter 14: Project 4.2: Creating Reports
14.1 Description
14.2 Overall approach
14.3 Deliverables
14.4 Summary
14.5 Extras
15
Chapter 15: Project 5.1: Modeling Base Application
Chapter 15: Project 5.1: Modeling Base Application
15.1 Description
15.2 Approach
15.3 Deliverables
15.4 Summary
15.5 Extras
16
Chapter 16: Project 5.2: Simple Multivariate Statistics
Chapter 16: Project 5.2: Simple Multivariate Statistics
16.1 Description
16.2 Approach
16.3 Deliverables
16.4 Summary
16.5 Extras
17
Chapter 17: Next Steps
Chapter 17: Next Steps
17.1 Overall data wrangling
17.2 The concept of “decision support”
17.3 Concept of metadata and provenance
17.4 Next steps toward machine learning
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18
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19
Index
Index

1.3 List of deliverables

We’ll take another look at the project, this time from the view of what files will be created. This will parallel the outline of the activities shown in the previous section.

The following outline shows many of the files in a completed project:

  • The documentation in the docs directory. There will be other files in there, but you’ll be focused on the following files:

    • The Sphinx index.rst starter file with references to overview and API sections.

    • An overview.rst section with a summary of the project.

    • An api.rst section with .. automodule:: commands to pull in documentation from the application.

  • A set of test cases in the tests directory.

    • Acceptance tests aimed at Behave (or the pytest-bdd plug-in for Gherkin). When using Behave, there will be two sub-directories: a features directory and a steps directory. Additionally, there will be an environment.py file.

    • Unit test modules written with the pytest framework. These all have a name that starts...

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