Data Analysis with Rust Notebooks

A practical book on Data Analysis with Rust Notebooks that teaches you the concepts and how they’re implemented in practice.

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Plotting with Plotly


In [2]:
:dep plotly = {version = "0.4.0"}
extern crate plotly;

use plotly::{Plot, Scatter};
use plotly::common::{Mode};
use std::fs;

Plotly for Visualisation

In my other book, Practical Evolutionary Algorithms, I relied on the Plotly graphic libraries to generate visualisations throughout each notebook. When I started writing Rust Notebooks a Plotly solution was not available, however, I found Plotters to be a suitable alternative for rendering visualisations. Less than 24 hours after making that decision, a plotting library for Rust powered by Plotly.js was posted on Reddit and caught my attention.

At the time of writing this section, there is no documented support for rendering within Jupyter Notebook cells, however, it is possible to use the .to_html() function to save to a HTML file, and then load and print that HTML file with Rust. We'll store this in a file named temp_plot.html.

In [3]:
let plotly_file = "temp_plot.html";

Let's demonstrate this with the first code example listed on the Plotly with Rust README.

In [4]:
let trace1 = Scatter::new(vec![1, 2, 3, 4], vec![10, 15, 13, 17])
let trace2 = Scatter::new(vec![2, 3, 4, 5], vec![16, 5, 11, 9])
let trace3 = Scatter::new(vec![1, 2, 3, 4], vec![12, 9, 15, 12]).name("trace3");

let mut plot = Plot::new();

Next, we will save this to a file using the .to_html() function, read it to plotly_contents using Rust, print it using println!(), and finally delete the file created by .to_html() as we don't need it after it is embedded.

In [5]:
let plotly_contents = fs::read_to_string(plotly_file).unwrap();
println!("EVCXR_BEGIN_CONTENT text/html\n{}\nEVCXR_END_CONTENT", plotly_contents);