{
"cells": [
{
"cell_type": "markdown",
"id": "021b0473-978d-4f33-9fbc-15ddb7d02046",
"metadata": {},
"source": [
"## Legend Wrap\n",
"\n",
"When the legend contains many items, it automatically wraps to prevent overlap - \n",
"up to 15 rows for vertical legends and 5 columns for horizontal ones."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "688c06d9-401f-4bfc-a5c9-8e8b2fda7caa",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
" \n",
" \n",
" "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import random\n",
"import colorsys\n",
"import pandas as pd\n",
"from lets_plot import *\n",
"\n",
"LetsPlot.setup_html()"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "67c03745-047e-4a2a-b3e6-fbcaf5156150",
"metadata": {},
"outputs": [],
"source": [
"# Prepare a dataset\n",
"\n",
"def distinct_palette(n, s=0.72, l=0.53):\n",
" # Evenly spaced hues in HSL -> hex\n",
" vals = []\n",
" for i in range(n):\n",
" h = i / n\n",
" r, g, b = colorsys.hls_to_rgb(h, l, s)\n",
" vals.append('#{:02X}{:02X}{:02X}'.format(int(r*255), int(g*255), int(b*255)))\n",
" return vals\n",
"\n",
"random.seed(0)\n",
"n_cats = 36\n",
"pts_per_cat = 13\n",
"cats = [f\"C{i:02d}\" for i in range(1, n_cats + 1)]\n",
"\n",
"rows = []\n",
"for i, c in enumerate(cats):\n",
" for _ in range(pts_per_cat):\n",
" x = i % 6 + random.uniform(-0.4, 0.4)\n",
" y = i // 6 + random.uniform(-0.4, 0.4)\n",
" rows.append({\"x\": x, \"y\": y, \"cat\": c})\n",
"\n",
"df = pd.DataFrame(rows)\n",
"\n",
"palette = distinct_palette(n_cats)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "8acb5638-8484-4058-a31f-248b0ba005ca",
"metadata": {},
"outputs": [],
"source": [
"base = (\n",
" ggplot(df, aes(\"x\", \"y\", color=\"cat\")) +\n",
" geom_point(size=3, alpha=0.9) +\n",
" scale_color_discrete(name=f\"Categories ({n_cats})\") +\n",
" scale_color_manual(values=palette, name=f\"Categories ({n_cats})\") +\n",
" ggsize(800, 460)\n",
")\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "064b9fb2-7383-4bc8-a1fd-26f7ceec5d88",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
" \n",
" "
],
"text/plain": [
""
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# The default legend layout (vertical)\n",
"base"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "512dc5bc-d4e9-492a-b489-757160fa3e41",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
" \n",
" "
],
"text/plain": [
""
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# The horizontal case\n",
"base + theme(legend_position=\"bottom\")"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "54f0fba8-8297-4bfb-ac8a-6a73e75e0972",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
" \n",
" "
],
"text/plain": [
""
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# You can still adjust the number of legend rows or columns manually.\n",
"\n",
"base + theme(legend_position=\"bottom\") + guides(color=guide_legend(ncol=10, byrow=True))"
]
}
],
"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": "3.9.23"
}
},
"nbformat": 4,
"nbformat_minor": 5
}