{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Preamble" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "from plotapi import Sankey\n", "\n", "Sankey.set_license(\"your username\", \"your license key\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Introduction\n", "\n", "Our first Plotapi Sankey Diagram! As we can see, we have set our license details in the preamble with `Sankey.set_license()`." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Dataset" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Plotapi Sankey expects a `list` of `dictionary` items, these will define the flow between a `source` and a `target`." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "links = [\n", " {\"source\":\"Group A\", \"target\":\"Rank 1\", \"value\": 1000},\n", " {\"source\":\"Group B\", \"target\":\"Rank 1\", \"value\": 300},\n", " {\"source\":\"Group B\", \"target\":\"Rank 2\", \"value\": 600},\n", " {\"source\":\"Group B\", \"target\":\"Rank 3\", \"value\": 400},\n", " {\"source\":\"Rank 1\", \"target\":\"Club A\", \"value\": 700},\n", " {\"source\":\"Rank 1\", \"target\":\"Club B\", \"value\": 400},\n", " {\"source\":\"Rank 1\", \"target\":\"Club C\", \"value\": 200},\n", " {\"source\":\"Rank 2\", \"target\":\"Club B\", \"value\": 200},\n", " {\"source\":\"Rank 2\", \"target\":\"Club C\", \"value\": 400},\n", " {\"source\":\"Rank 3\", \"target\":\"Withdrawn\", \"value\": 400},\n", " {\"source\":\"Club A\", \"target\":\"The Most Amazing Prize\", \"value\": 500},\n", "]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can add many `source`'s and `target`'s in any arrangement." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Visualisation" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Creating our first Sankey Diagram is as easy as calling Plotapi with our one input.\n", "\n", "Here we're using `.show()` which outputs to a Jupyter Notebook cell, however, we may want to output to an HTML file with `.to_html()` instead. More on the different output methods later!\n", "\n", "Be sure to interact with the visualisation to see what the default settings can do!\n", "
" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "Plotapi - Sankey Diagram\n", "\n", "\n", " \n", " \n", " \n", "\n", "\n", "\n", "
\n", " \n", " \n", "\n", " \n", "\n", "\n", "\n", "" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "Sankey(links).show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here we can see the default behaviour of Plotapi Chord. This includes:\n", "\n", "- Horizontal node labels at the shallowest and deepest depths of our diagram, with vertical labels in-between.\n", "- An arrangement of nodes that are _left_ aligned.\n", "- Text-wrapping, e.g. \"The Most Amazing Prize\" as seen above.\n", "- Links labelled with their quantity.\n", "- Node and Link tooltips.\n", "- Animations when hovering over a node." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can do so much more than what's presented in this example, and we'll cover this in later sections. If you want to see the full list of growing features, check out the Plotapi Documentation." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.1" } }, "nbformat": 4, "nbformat_minor": 4 }