IMTI

I’ve been distracted for over a year now, writing a (~500 page) end-to-end tutorial on constructing data-centric platforms with Kubernetes. The book is titled “Advanced Platform Development with Kubernetes: Enabling Data Management, the Internet of Things, Blockchain, and Machine Learning”

A little more than a year ago, Apress reached out and asked if I would write a book on Kubernetes for them, mirroring the wide range of projects I develop (and write about) for my clients. I have been building data-centric platforms for almost twenty years, spanning everything from my early days on the aggregation of massive volumes of international log files for Disney to fan-driven location data for Nine Inch Nails. And in the last decade, retailers with point-of-sale, logistics, and inventory systems, marketers leveraging social media metrics, fleet operators with demanding telematics platforms, and manufacturers with advanced IIoT (industrial internet of things) networks.

This series began with Linear Algebra: Vectors Crash Course for Python Programmers Part 1. Vectors are the building blocks of matrices, and a good foundational understanding of vectors is essential to understanding matrices.

There is an overwhelming number of options for developers needing to provide data visualization. The most popular library for data visualization in Python is Matplotlib, and built directly on top of Matplotlib is Seaborn. The Seaborn library is “tightly integrated with the PyData stack, including support for numpy and pandas data structures and statistical routines from scipy and statsmodels.”

This article is only intended to get you started with Matplotlib and Seaborn. Both libraries have extensive and mature documentation.

Pandas bring Python a data type equivalent to super-charged spreadsheets. Pandas add two highly expressive data structures to Python, Series and DataFrame. Pandas Series and DataFrames provide a performant analysis and manipulation of “relational” or “labeled” data similar to relational database tables like MySQL or the rows and columns of Excel. Pandas are great for working with time series data as well as arbitrary matrix data, and unlabeled data.

Pandas leverage NumPy and if you are not familiar with this fundamental library for working with numbers, then I suggest you take a look at Python Data Essentials - NumPy to get a decent footing.

Python is one of The Most Popular Languages for Data Science, and because of this adoption by the data science community, we have libraries like NumPy, Pandas and Matplotlib. NumPy at it’s core provides a powerful N-dimensional array objects in which we can perform linear algebra, Pandas give us data structures and data analysis tools, similar to working with a specialized database or powerful spreadsheets and finally Matplotlib to generate plots, histograms, power spectra, bar charts, error charts and scatterplots to name a few.