Posts tagged data-viz

Visualizing Dropped Video Frames

Welcome back, everyone! This week on Cameron’s Corner, I’m going to get a bit creative. I want to take you through my process for optimizing the (many) Python instruction videos I make.

But, first, I want to let you know about my upcoming seminar, “Arrow & In-Memory Data”! This seminar is designed to provide attendees with a comprehensive understanding of Arrow and its interface with PyArrow, a Python library for working with Arrow data structures.

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Dataviz Makeover

Hello, everyone! Two weeks ago, I re-created a data visualization I found online and I had so much fun that I decided to do it again! This week I’m recreating another visualization from Data is Beautiful on Reddit.

But, before we get started, I want to let you know about my seminar coming up next week, “Understanding Textual,” which is part of our Investigating the Hype seminar series! This series offers an in-depth exploration of different software that will help make your code more efficient. We’ll dive into Textual, DuckDB, Polars, and Apache Arrow and see if they’re really worth all the hype! I have some great things planned, so you won’t want to miss it!

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Business Jet Demand In North America

Hello, everyone! This week, I’m recreating a visualization from Data is Beautiful on Reddit.

Before I get started, I want to remind you of the final part of the Correctness seminar series, “How do I Check that my Data and Analyses are Correct?”. We’ll join James Powell as he unravels the art of performing data analysis with confidence in Python. Explore the challenges of data analysis pipelines and learn how to write robust analyses that have observable hooks. Discover methods for data cleaning and validation to avoid silent errors that can pollute your results.

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Time-series Alignment & Viz

Hey all, welcome back to Cameron’s Corner. This week, we are taking an even deeper dive into our use of Gantt charts to represent binary signals. We’ll certainly cover visualizing these data but I also want to get into some of the signal processing tricks we can apply to align multiple signals against each other.

Speaking of visualization, don’t forget to join me on August 17th for a FREE seminar, “Visualizations: Exploratory → Communicative,” where I’ll demonstrate how to harness the power of Matplotlib to create impactful data visualizations. From exploratory analysis to communicative visualizations, I’ll guide you through uncovering insights and effectively conveying your message. Discover the techniques to profile your audience, focus their attention, and deliver precise and compelling data visualizations.

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Gantt Charts in Matplotlib

Hey everyone! Welcome to this week’s entry into Cameron’s Corner. This week, I’ve been busy teaching courses, working on some exciting TOPS updates, and helping James prep for a FREE popup seminar coming up on August 10th, “Solving Uno… the Right Way!” I can’t wait for you to see what he in store.

For today’s post, I wanted to share a fun consulting project I’m working on which involves visualizing binary signals (on/off states) across multiple devices. These types of data are often visualized using stateful lines where they rapidly increase to a value of 1 to indicate an “on” state or drop to 0 to indicate an “off” state. However, for the volume of data that we are working with, the vertical lines become nearly impossible to track because there is no ramp-up in our signal.

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Fix those overlapping labels!

Hello, everyone! Welcome back to Cameron’s Corner! This week, I want to resolve a common frustration I encounter in Matplotlib: overlapping labels.

Ever since Matplotlib 3.4, we have had an easy Axes.bar_label to quickly introduce labels on top of our bars. The example is fairly straightforward and nicely highlights centered labels.

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Star Trader & Matplotlib: A Live-coded Session

Welcome to Cameron’s Corner! This week, I wanted to reflect a on a pop-up seminar I held where I demonstrated some live-coded Matplotlib data visualizations.

In this session, we talked about planning an effective data visualization. My biggest recommendation once you understand the data and have an idea of what you want to convey is to not jump straight into creating visualizations. But instead, plan out your visualization using simple drawing tools—in this case, I chose PowerPoint as it was already installed on my machine. This lets me easily plan and adjust a layout of multiple plots and iterate on my design.

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Bokeh: Interactive plots in static HTML

Welcome back, everyone! Before I get started, I want to let you know about an upcoming FREE seminar: “On the Spot, Live-Coded Data Visualizations,” where I’ll be live-coding data visualizations that YOU pick for me! You won’t want to miss it!

Last week, I shared a primer on Bokeh. This week, I wanted to take things up a notch and share some of the more powerful features Bokeh has beyond its core components. Sure, we can make figures and add Glyphs to them, but we can also make a completely responsive data visualization with just a few lines of JavaScript.

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