this post was submitted on 23 Nov 2023
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Visualising data analysis in org-mode (alien.top)
submitted 11 months ago by akuszyk@alien.top to c/emacs@communick.news
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Hello r/emacs! First-time poster here!

I recently wanted to visualise some data in org-mode using a Python source block to generate an image, which was then rendered directly in the buffer.

It took my a couple of goes to work out how to do it.

Given some data like this:

#+begin_src bash :results output file :file data.njson
echo '{"name": "Spock", "editor": "Emacs"}
{"name": "James Kirk", "editor": "Vim"}
{"name": "Dr McCoy", "editor": "Vim"}
{"name": "Scotty", "editor": "Emacs"}
{"name": "Worf", "editor": "ed"}
{"name": "Geordi LaForge", "editor": "Emacs"}
{"name": "Data", "editor": "Emacs"}
{"name": "Jean-luc Picard", "editor": "VS Code"}
{"name": "Wesley Crusher", "editor": "VS Code"}
{"name": "William Riker", "editor": "Vim"}
'
#+end_src

A visualisation can be rendered as follows:

#+begin_src python :results output file :file usage.png
import pandas as pd
import seaborn as sns
import sys

df = pd.read_json("data.njson", lines=True)
axes = sns.histplot(df, x="editor")
axes.get_figure().savefig(sys.stdout.buffer)
#+end_src

The main trick here is to set the :results output file header argument to write the output to a file, and to save the figure to sys.stdout.buffer from Python.

I've written about this on my blog too, where you can see the [unsurprising] results of the analysis!

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[–] acow@alien.top 1 points 11 months ago

I highly recommend nix-direnv along with the direnv emacs package. I do everything with flakes, so I'd have a flake.nix that defines a shell with the inputs I need, then I'd have a .envrc with the contents use flake in the same directory. With those in place, you may need to run direnv allow in that directory, and then you can edit a .org file in the same directory as the flake and the python environment will have what you need.

Here's a flake I quickly made using a minimal template.

{
  description = "Python environment for plotting with Seaborn";
  inputs = {
    nixpkgs.url = github:nixos/nixpkgs/nixpkgs-23.05-darwin;
    flake-utils.url = github:numtide/flake-utils;
  };

  outputs = { self, nixpkgs, flake-utils }: 
    flake-utils.lib.eachDefaultSystem (system: 
      let pkgs = import nixpkgs { inherit system; };
          python = pkgs.python3.withPackages (ps: [
            ps.pandas
            ps.seaborn
          ]);
      in {
        devShell = pkgs.mkShell {
          buildInputs = [ python ];
        };
      }
    );
}

And then here's the .org file I tested with,

Example of plotting from this [[https://andykuszyk.github.io/2023-11-18-using-emacs-org-mode-as-a-jupyter-notebook.html][blog post]].

Some data to work with,

#+begin_src javascript :tangle data.njson
{"name": "Spock", "editor": "Emacs"}
{"name": "James Kirk", "editor": "Vim"}
{"name": "Dr McCoy", "editor": "Vim"}
{"name": "Scotty", "editor": "Emacs"}
{"name": "Worf", "editor": "ed"}
{"name": "Geordi LaForge", "editor": "Emacs"}
{"name": "Data", "editor": "Emacs"}
{"name": "Jean-luc Picard", "editor": "VS Code"}
{"name": "Wesley Crusher", "editor": "VS Code"}
{"name": "William Riker", "editor": "Vim"}
#+end_src

And now we plot,

#+begin_src python :results output file :file usage.png
import pandas as pd
import seaborn as sns
import sys

df = pd.read_json("data.njson", lines=True)
axes = sns.histplot(df, x="editor")
axes.get_figure().savefig(sys.stdout.buffer)
#+end_src

#+RESULTS:
[[file:usage.png]]