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.
Table of Contents (26 chapters)
Free Chapter
1
Section 1: Getting Started with Python
11
Section 2: Hands-On with Data
17
Section 3: Moving to Production

Data Cleaning and Manipulation

Before we dive into data analysis, data needs to be properly prepared and structured. Some datasets, for example, structured computer logs, are ready to go from the start, but, most of the time, the majority of the time is spent preparing data properly. This process inevitably requires certain decisions that depend on the specifics of the task.

In this chapter, we will learn how to prepare the data with pandas, using the dataset we collected from Wikipedia in Chapter 7, Scraping Data from the Web with Beautiful Soup 4, as an example.

We will cover the following topics in the chapter:

  • Quick start with pandas
  • Working with real data
  • Regular expressions
  • Using custom functions with pandas dataframes
  • Writing the file