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Learn Python by Building Data Science Applications

Learn Python by Building Data Science Applications

By : Kats, Katz
2.8 (4)
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Learn Python by Building Data Science Applications

Learn Python by Building Data Science Applications

2.8 (4)
By: Kats, Katz

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)
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1
Section 1: Getting Started with Python
11
Section 2: Hands-On with Data
17
Section 3: Moving to Production

Setting up a simple model

Similar to how we built a REST API in Chapter 18, Serving Models with a RESTful API, let's start by serving median values from a JSON file. This will help us to set the model for working with Chalice:

  1. First of all, we need to load the JSON object:
import json

with open('./model.json', 'r') as f:
model = json.load(f)
  1. Now we will rename the route and define the last resource to map to the complaint type (in the same way we would for FastAPI, again!). We will also have to import a Response object:
from chalice import Response

@app.route('/predict/{complaint_type}', methods=['GET'])
def predict(complaint_type:str) -> Response:

  1. Finally, finalize the function by adding simple lookup logic; here, we decided to be nice and let our user know if they pass a wrong complaint type:
@app.route('/predict...
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