{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"execution": {
"iopub.execute_input": "2024-04-17T07:32:12.895762Z",
"iopub.status.busy": "2024-04-17T07:32:12.895680Z",
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{
"data": {
"text/html": [
"\n",
" \n",
" \n",
" "
]
},
"metadata": {},
"output_type": "display_data"
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],
"source": [
"import numpy as np\n",
"from lets_plot import *\n",
"\n",
"LetsPlot.setup_html()"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"execution": {
"iopub.execute_input": "2024-04-17T07:32:13.234109Z",
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"name": "#%%\n"
}
},
"outputs": [
{
"data": {
"text/html": [
" \n",
" "
],
"text/plain": [
""
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.random.seed(12)\n",
"data = dict(\n",
" cond=np.repeat(['A','B'], 200),\n",
" rating=np.concatenate((np.random.normal(0, 1, 200), np.random.normal(1, 1.5, 200)))\n",
")\n",
"\n",
"ggplot(data, aes(x='rating', fill='cond')) + ggsize(500, 250) \\\n",
"+ geom_density(color='dark_green', alpha=.7) + scale_fill_brewer(type='seq')"
]
}
],
"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": "3.10.13"
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"nbformat": 4,
"nbformat_minor": 2
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