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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

Summary

We've done a lot in this chapter. First, we learned about geocoding in general, including geocoding services and their web APIs. We also discussed how you can interact with web APIs programmatically, from Python, using the requests library. Then, we experimented with a specific API from Nominatim and wrote a thin wrapper function that geocodes any arbitrary address. On top of that, we wrote another function to geocode addresses in bulk that keeps working even if a specific request fails or no location was found for some addresses. We used the built-in csv library both to read data from and write to CSV files. Finally, as the code we used seemed as though it might be useful in the future, we moved it from a notebook into a dedicated Python file, which can be used as a standalone script with its own interface or as a module to import functions from.

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