Code in Python using Jupyter and VS Code Explore the basics of coding – loops, variables, functions, and classes Deploy continuous integration with Git, Bash, and DVC Get to grips with Pandas, NumPy, and scikit-learn Perform data visualization with Matplotlib, Altair, and Datashader Create a package out of your code using poetry and test it with PyTest Make your machine learning model accessible to anyone with the web API 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. Learn the basics of developing applications with Python and deploy your first data application Take your first steps in Python programming by understanding and using data structures, variables, and loops Delve into Jupyter, NumPy, Pandas, SciPy, and sklearn to explore the data science ecosystem in Python Table of contents 1 Preparing the Workspace Technical requirements Installing Python Downloading materials for running the code Working with VS Code Beginning with Jupyter Pre-flight check Summary Questions Further reading 2 First Steps in Coding - Variables and Data Types Technical requirements Assigning variables Naming the variable Understanding data types Converting the data types Exercise Summary Questions Further reading 3 Functions Technical requirements Understanding a function Defining the function Refactoring the temperature conversion Understanding anonymous (lambda) functions Understanding recursion Summary Questions Further reading 4 Data Structures Technical requirements What are data structures? More data structures Using generators Useful functions to use with data structures Comprehensions Summary Questions Further reading 5 Loops and Other Compound Statements Technical requirements Understanding if, else, and elif statements Running code many times with loops Handling exceptions with try/except and try/finally Understanding the with statements Summary Questions Further reading 6 First Script – Geocoding with Web APIs Technical requirements Geocoding as a service Learning about web APIs Working with the Nominatim API Caching with decorators Reading and writing data Moving code to a separate module Collecting NYC Open Data from the Socrata service Summary Questions Further reading 7 Scraping Data from the Web with Beautiful Soup 4 Technical requirements When there is no API Scraping WWII battles Beyond Beautiful Soup Summary Questions Further reading 8 Simulation with Classes and Inheritance Technical requirements Understanding classes Using classes in simulation Summary Questions Further reading 9 Shell, Git, Conda, and More – at Your Command Technical requirements Shell Git Conda Make Cookiecutter Summary Questions 10 Python for Data Applications Technical requirements Introducing Python for data science Exploring NumPy Beginning with pandas Trying SciPy and scikit-learn Understanding Jupyter Summary Questions 11 Data Cleaning and Manipulation Technical requirements Getting started with pandas Working with real data Getting to know regular expressions Parsing locations Time Belligerents Understanding casualties Quality assurance Writing the file Summary Questions Further reading 12 Data Exploration and Visualization Technical requirements Exploring the dataset Declarative visualization with vega and altair Big data visualization with datashader Summary Questions Further reading 13 Training a Machine Learning Model Technical requirements Understanding the basics of ML Summary Questions Further reading 14 Improving Your Model – Pipelines and Experiments Technical requirements Understanding cross-validation Exploring feature engineering Optimizing the hyperparameters Tracking your data and metrics with version control Summary Questions Further reading 15 Packaging and Testing with Poetry and PyTest Technical requirements Building a package A few ways to build your package Testing the code so far Automating the process with CI services Generating documentation generation with sphinx Installing a package in editable mode Summary Questions Further reading 16 Data Pipelines with Luigi Technical requirements Introducing the ETL pipeline Building our first task in Luigi Understanding time-based tasks Exploring the different output formats Expanding Luigi with custom template classes Summary Questions Further reading 17 Let's Build a Dashboard Technical requirements Building a dashboard – three types of dashboard Understanding dynamic dashboards Summary Questions Further reading 18 Serving Models with a RESTful API Technical requirements What is a RESTful API? Building a basic API service Building a web page Speeding up with asynchronous calls Deploying and testing your API loads with Locust Summary Questions Further reading 19 Serverless API Using Chalice Technical requirements Understanding serverless Getting started with Chalice Setting up a simple model Building a serverless API for an ML model Building a serverless function as a data pipeline Summary Questions Further reading 20 Best Practices and Python Performance Technical requirements Speeding up your Python code Using best practices for coding in your project Beyond this book – packages and technologies to look out for Summary Questions Further reading