In Chapter 12, Data Exploration and Visualization, Chapter 16, Data Pipelines with Luigi, Chapter 17, Let's Build a Dashboard, Chapter 18, Serving Models with a RESTful API, and Chapter 19, Serverless API Using Chalice, we'll be working with the New York City 311 complaints (a non-urgent version of the 911 service) dataset. This data is available via a public portal (https://data.cityofnewyork.us/Social-Services/311-Service-Requests-from-2010-to-Present/erm2-nwe9), both via a web interface and programmatically via an API. The code for pulling this data via the API is rather dull and similar to what we've written already, so we won't cover it in detail. In Chapter 16, Data Pipelines with Luigi, we'll discuss how to pull this dataset systematically and on a scheduled basis. If you want, however, feel free...
Learn Python by Building Data Science Applications
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Learn Python by Building Data Science Applications
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Overview of this book
Python is the most widely used programming language for building data science applications. Complete with step-by-step instructions, this book contains easy-to-follow tutorials to help you learn Python and develop real-world data science projects. The “secret sauce” of the book is its curated list of topics and solutions, put together using a range of real-world projects, covering initial data collection, data analysis, and production.
This Python book starts by taking you through the basics of programming, right from variables and data types to classes and functions. You’ll learn how to write idiomatic code and test and debug it, and discover how you can create packages or use the range of built-in ones. You’ll also be introduced to the extensive ecosystem of Python data science packages, including NumPy, Pandas, scikit-learn, Altair, and Datashader. Furthermore, you’ll be able to perform data analysis, train models, and interpret and communicate the results. Finally, you’ll get to grips with structuring and scheduling scripts using Luigi and sharing your machine learning models with the world as a microservice.
By the end of the book, you’ll have learned not only how to implement Python in data science projects, but also how to maintain and design them to meet high programming standards.
Table of Contents (26 chapters)
Preface
Preparing the Workspace
First Steps in Coding - Variables and Data Types
Functions
Data Structures
Loops and Other Compound Statements
First Script – Geocoding with Web APIs
Scraping Data from the Web with Beautiful Soup 4
Simulation with Classes and Inheritance
Shell, Git, Conda, and More – at Your Command
Section 2: Hands-On with Data
Python for Data Applications
Data Cleaning and Manipulation
Data Exploration and Visualization
Training a Machine Learning Model
Improving Your Model – Pipelines and Experiments
Section 3: Moving to Production
Packaging and Testing with Poetry and PyTest
Data Pipelines with Luigi
Let's Build a Dashboard
Serving Models with a RESTful API
Serverless API Using Chalice
Best Practices and Python Performance
Assessments
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