{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "prescription-boost",
"metadata": {},
"outputs": [],
"source": [
"from lets_plot import *\n",
"from lets_plot.mapping import as_discrete"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "rural-denial",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
" \n",
" \n",
" "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"LetsPlot.setup_html()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "employed-browser",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
" \n",
" "
],
"text/plain": [
""
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df1 = {\n",
" 'x' : ['c', 'c', 'a', 'a', 'd', 'b', 'b', 'a']\n",
"}\n",
"\n",
"gggrid([\n",
" ggplot(df1) + geom_bar(aes('x')),\n",
" ggplot(df1) + geom_bar(aes(as_discrete('x', levels=['a','b','c','d']))),\n",
" ggplot(df1) + geom_bar(aes(as_discrete('x', levels=['a','d']))) # missed values will be added\n",
"])"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "trying-happiness",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
" \n",
" "
],
"text/plain": [
""
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df2 = {\n",
" 'x' : [3, 2, 1, 3, 3, 1, 4]\n",
"}\n",
"\n",
"gggrid([\n",
" ggplot(df2) + geom_bar(aes('x', fill = as_discrete('x'))),\n",
" ggplot(df2) + geom_bar(aes('x', fill = as_discrete('x', levels=[1,2,3,4])))\n",
"],ncol=1)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "inner-yacht",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
" \n",
" "
],
"text/plain": [
""
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ggplot(df2) + geom_bar(aes(as_discrete('x',order=-1), fill = as_discrete('x', levels=[1,2,3,4])))"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "understood-birmingham",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
" \n",
" "
],
"text/plain": [
""
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ggplot(df2) + geom_bar(aes(as_discrete('x',order=-1), fill = as_discrete('x', levels=[1,2])))"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "structured-anthony",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
" \n",
" "
],
"text/plain": [
""
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# https://github.com/JetBrains/lets-plot/issues/914\n",
"\n",
"data = dict(\n",
" cat = [\"B\", \"C\", \"A\"],\n",
" c = [\"blue\", \"cyan\", \"gray\"]\n",
")\n",
"\n",
"(ggplot(data) \n",
" + geom_point(aes(color=\"c\"), size=15) \n",
" + scale_color_identity() \n",
" + theme_void() + ggsize(400, 200)\n",
" + facet_wrap(\"cat\", ncol=3, order=0)\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "convinced-mouse",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
" \n",
" "
],
"text/plain": [
""
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Facet ordering corresponds to the levels (facet_wrap)\n",
"\n",
"(ggplot(data) \n",
" + geom_point(aes(color=as_discrete(\"cat\", levels=[\"C\",\"A\",\"B\"])), size=15)\n",
" + theme_void() + ggsize(400, 200)\n",
" + facet_wrap(\"cat\", ncol=3, order=0)\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "726ecdca",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
" \n",
" "
],
"text/plain": [
""
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Facet ordering corresponds to the levels (facet_grid)\n",
"\n",
"(ggplot(data) \n",
" + geom_point(aes(color=as_discrete(\"cat\", levels=[\"C\",\"A\",\"B\"])), size=15)\n",
" + theme_void() + ggsize(400, 200)\n",
" + facet_grid(\"cat\", x_order=0)\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "f517eafb",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
" \n",
" "
],
"text/plain": [
""
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# levels in reverse order\n",
"\n",
"(ggplot(data) \n",
" + geom_point(aes(color=as_discrete(\"cat\", levels=[\"C\",\"A\",\"B\"], order=-1)), size=15)\n",
" + theme_void() + ggsize(400, 200)\n",
" + facet_wrap(\"cat\", ncol=3, order=0)\n",
")"
]
}
],
"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": "3.7.12"
}
},
"nbformat": 4,
"nbformat_minor": 5
}