{ "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", "Plotapi Sankey supports a linked data table. This means as you hover over nodes and links in the Sankey diagram, a data table will be filtering in real-time to show more information.\n", "\n", "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": 2, "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200},\n", " {\"source\":\"Rank 2\", \"target\":\"Club C\", \"value\": 400},\n", " {\"source\":\"Rank 3\", \"target\":\"Withdrawn\", \"value\": 400},\n", " {\"source\":\"Club A\", \"target\":\"The Most Amazing Prize\", \"value\": 500},\n", "]" ] }, { "cell_type": "markdown", "metadata":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.\n", "\n", "To make use of the linked data table, we need to provide some data in CSV format. This could be loaded through a file, directly in a strong, or using a pandas DataFrame (`.to_csv(index=False)`)." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "data_table = '''Start,Destination,TV Rating,Image\n", "Group A,,9.0,\n", "Group A,Rank 1,6.0,\n", "Group A,Rank 1,5.0,'''" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Visualisation" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We'll enable the linked data table by passing data into the `data_table` parameter, and we'll modify the `data_table_column_width` by setting it to a smaller value of $80$.\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! Try hovering over `Group A` and its link to `Rank 1`.\n", "
" ] }, { "cell_type": "code", "execution_count": 4, "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", "" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "Sankey(links, width=400, link_numbers=False, \n", " horizontal_labels_at_shallowest=False, horizontal_labels_at_deepest=False,\n", " data_table=data_table, data_table_column_width=100).show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Data table mode includes the \"locking\" feature. Try clicking on the `Group A` node and you will see a padlock icon appear in the top right, and until you click somewhere again the current selection will persist." ] }, { "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 (ipykernel)", "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 }