Posts tagged bokeh

Edward Tufte’s NYC Weather In Bokeh

Hello, everyone! Welcome back to Cameron’s Corner. This week, I wanted to expand upon using Bokeh to visualize the weather by revisiting the Edward Tufte NYC Weather in 2003 visualization I recreated in Matplotlib. Except, this time, I want to see if Bokeh is up to the challenge.

All of the data & set up will be identical to the previous post from March, so we can gloss over those details. If you’re up to date, feel free to skip down to the Recreating Tufte in Bokeh section.

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Exploring Weather with Bokeh!

Hey everyone! This probably comes as a surprise, but I’m on another data-viz kick! This week, I wanted to share with you a way to interact with a few years of daily timeseries data.

We’ll be revisiting a fun dataset: daily temperature readings from New York City! This historical dataset has decades of data. However, for our purposes, I wanted to limit it to five years’ worth and visualize daily data (maximum and minimum temperatures) while allowing the ability to interactively to zoom in/out on any specific set of dates.

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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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Working with Long Labels In Bokeh

Hey all, I wanted to revisit a topic I discussed a few weeks ago and demonstrate how use deal with long labels in another one of my favorite plotting libraries in Python: bokeh.

In a previous post, I mentioned that I came across a fun blog post by Andrew Heiss covering how to work with long tick labels in R’s ggplot2. As I mentioned in my last post: “I couldn’t resist the urge to recreate the visualizations in and wanted to share with you how you can deal with long tick labels in Python!”

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