Book Image

Data Analysis with Python

By : David Taieb
Book Image

Data Analysis with Python

By: David Taieb

Overview of this book

Data Analysis with Python offers a modern approach to data analysis so that you can work with the latest and most powerful Python tools, AI techniques, and open source libraries. Industry expert David Taieb shows you how to bridge data science with the power of programming and algorithms in Python. You'll be working with complex algorithms, and cutting-edge AI in your data analysis. Learn how to analyze data with hands-on examples using Python-based tools and Jupyter Notebook. You'll find the right balance of theory and practice, with extensive code files that you can integrate right into your own data projects. Explore the power of this approach to data analysis by then working with it across key industry case studies. Four fascinating and full projects connect you to the most critical data analysis challenges you’re likely to meet in today. The first of these is an image recognition application with TensorFlow – embracing the importance today of AI in your data analysis. The second industry project analyses social media trends, exploring big data issues and AI approaches to natural language processing. The third case study is a financial portfolio analysis application that engages you with time series analysis - pivotal to many data science applications today. The fourth industry use case dives you into graph algorithms and the power of programming in modern data science. You'll wrap up with a thoughtful look at the future of data science and how it will harness the power of algorithms and artificial intelligence.
Table of Contents (16 chapters)
Data Analysis with Python
Contributors
Preface
Other Books You May Enjoy
3
Accelerate your Data Analysis with Python Libraries
Index

Chapter 5. Python and PixieDust Best Practices and Advanced Concepts

"In God we Trust, all others bring data."

W. Edwards Deming

In the remaining chapters of this book, we will do a deep dive into the architecture of industry use cases, including the implementation of sample data pipelines, heavily applying the techniques we've learned so far. Before we start looking at the code, let's complete our toolbox with a few best practices and advanced PixieDust concepts that will be useful in the implementation of our sample applications:

  • Calling third-party Python libraries with @captureOutput decorator

  • Increasing modularity and code reuse of your PixieApp

  • PixieDust support of streaming data

  • Adding dashboard drill-downs with PixieApp events

  • Extending PixieDust with a custom display renderer

  • Debugging:

    • Line-by-line Python code debugging running on the Jupyter Notebook using pdb

    • Visual debugging with PixieDebugger

    • Using the PixieDust logging framework to troubleshoot issues...