{
"cells": [
{
"cell_type": "markdown",
"id": "150dc00f",
"metadata": {},
"source": [
"# `geom_function()`"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "9b4af9d3",
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"from scipy.stats import norm\n",
"\n",
"from lets_plot import *"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "fc1b5d6d",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
" \n",
" \n",
" "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"LetsPlot.setup_html()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "8c7aada0",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
" \n",
" "
],
"text/plain": [
""
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ggplot() + geom_function(fun=norm.pdf, xlim=[-3, 3])"
]
},
{
"cell_type": "markdown",
"id": "24bc3634",
"metadata": {},
"source": [
"### 1. Comparison With Empirical Density"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "22fd1b16",
"metadata": {},
"outputs": [],
"source": [
"df = pd.read_csv(\"https://raw.githubusercontent.com/JetBrains/lets-plot-docs/master/data/mpg.csv\")\n",
"\n",
"xs = df[\"cty\"]\n",
"\n",
"loc, scale = xs.mean(), xs.std()\n",
"xmin, xmax = xs.min(), xs.max()\n",
"\n",
"fun = lambda t: norm.pdf(t, loc, scale)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "759eed16",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
" \n",
" "
],
"text/plain": [
""
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ggplot(df, aes(\"cty\")) + \\\n",
" geom_density() + \\\n",
" geom_function(fun=fun, xlim=[xmin, xmax], color=\"red\")"
]
},
{
"cell_type": "markdown",
"id": "955951bf",
"metadata": {},
"source": [
"### 2. Use Data to Autodetect `xlim`"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "216f5b42",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
" \n",
" "
],
"text/plain": [
""
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ggplot(df, aes(\"cty\")) + \\\n",
" geom_density() + \\\n",
" geom_function(aes(\"cty\"), data=df, fun=fun, color=\"red\")"
]
},
{
"cell_type": "markdown",
"id": "64f599ee",
"metadata": {},
"source": [
"### 3. Composition of Function and Statistic"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "e79810ce",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
" \n",
" "
],
"text/plain": [
""
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ggplot(df, aes(\"cty\")) + \\\n",
" geom_density() + \\\n",
" geom_function(fun=fun, xlim=[xmin, xmax], \\\n",
" stat='smooth', method='loess', span=.3, color=\"red\")"
]
},
{
"cell_type": "markdown",
"id": "f018193d",
"metadata": {},
"source": [
"### 4. Use Different Geometry"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "1cd6c684",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
" \n",
" "
],
"text/plain": [
""
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ggplot(df, aes(\"cty\")) + \\\n",
" geom_density() + \\\n",
" geom_function(aes(fill='y'), fun=fun, xlim=[xmin, xmax], n=30, \\\n",
" geom='histogram', alpha=.5) + \\\n",
" scale_fill_brewer(type='seq', palette='YlOrRd', trans='reverse')"
]
}
],
"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": "3.8.17"
}
},
"nbformat": 4,
"nbformat_minor": 5
}