Book Image

Learn Python by Building Data Science Applications

By : Philipp Kats, David Katz
Book Image

Learn Python by Building Data Science Applications

By: Philipp Kats, David 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.

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Learn Python by Building Data Science Applications
Philipp Kats | David Katz
Aug, 2019 | 482 pages
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Table of Contents (26 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Get in touch
Free Chapter
1
Section 1: Getting Started with Python
Section 1: Getting Started with Python
2
Preparing the Workspace
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
3
First Steps in Coding - Variables and Data Types
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
4
Functions
Functions
Technical requirements
Understanding a function
Defining the function
Refactoring the temperature conversion
Understanding anonymous (lambda) functions
Understanding recursion
Summary
Questions
Further reading
5
Data Structures
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
6
Loops and Other Compound Statements
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
7
First Script – Geocoding with Web APIs
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
8
Scraping Data from the Web with Beautiful Soup 4
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
9
Simulation with Classes and Inheritance
Simulation with Classes and Inheritance
Technical requirements
Understanding classes
Using classes in simulation
Summary
Questions
Further reading
10
Shell, Git, Conda, and More – at Your Command
Shell, Git, Conda, and More – at Your Command
Technical requirements
Shell
Git
Conda
Make
Cookiecutter
Summary
Questions
11
Section 2: Hands-On with Data
Section 2: Hands-On with Data
12
Python for Data Applications
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
13
Data Cleaning and Manipulation
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
14
Data Exploration and Visualization
Data Exploration and Visualization
Technical requirements
Exploring the dataset
Declarative visualization with vega and altair
Big data visualization with datashader
Summary
Questions
Further reading
15
Training a Machine Learning Model
Training a Machine Learning Model
Technical requirements
Understanding the basics of ML
Summary
Questions
Further reading
16
Improving Your Model – Pipelines and Experiments
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
17
Section 3: Moving to Production
Section 3: Moving to Production
18
Packaging and Testing with Poetry and PyTest
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
19
Data Pipelines with Luigi
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
20
Let's Build a Dashboard
Let's Build a Dashboard
Technical requirements
Building a dashboard – three types of dashboard
Understanding dynamic dashboards
Summary
Questions
Further reading
21
Serving Models with a RESTful API
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
22
Serverless API Using Chalice
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
23
Best Practices and Python Performance
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
24
Assessments
Assessments
Chapter 1
Chapter 2
Chapter 3
Chapter 4
Chapter 5
Chapter 6
Chapter 7
Chapter 8
Chapter 9
Chapter 10
Chapter 11
Chapter 12
Chapter 13
Chapter 14
Chapter 15
Chapter 16
Chapter 17
Chapter 18
Chapter 19
Chapter 20
25
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Downloading materials for running the code

All code in this book is also available as a separate archive of files—either Python scripts or Jupyter notebooks. You can download the full archive and follow along with the book using the relevant code from GitHub (https://github.com/PacktPublishing/Learn-Python-by-Building-Data-Science-Applications). Everything is stored on GitHub, which is an online service for code storage with version control. We will discuss both Git and GitHub in Chapter 9, Shell, Git, Conda, and More – at Your Command, but in this case, you won't need version control, so it is easier to download everything as an archive. Just use the Clone or download button on the right side (1), and select Download ZIP (2):

Once the download is complete, unzip the file and move it to a convenient location. This folder will be our main workspace throughout...

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