{
"cells": [
{
"cell_type": "code",
"execution_count": 43,
"id": "4a015e87-cf20-47b0-b271-a9dc1c38f2b3",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"\n",
""
],
"text/plain": [
"alt.LayerChart(...)"
]
},
"execution_count": 43,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import altair as alt\n",
"from vega_datasets import data\n",
"\n",
"source = data.stocks()\n",
"source = source[source[\"date\"] > \"2005-01-01\"]\n",
"\n",
"lines = (\n",
" alt.Chart(source)\n",
" .mark_line()\n",
" .encode(x=\"date\", y=\"price\", color=\"symbol\")\n",
")\n",
"\n",
"xrule = (\n",
" alt.Chart()\n",
" .mark_rule(color=\"black\", strokeWidth=1)\n",
" .encode(x=alt.datum(alt.DateTime(year=2007, month=\"June\")))\n",
")\n",
"\n",
"label = (\n",
" alt.Chart()\n",
" .mark_text(color=\"black\", dx=-45, dy=-130)\n",
" .encode(\n",
" x=alt.datum(alt.DateTime(year=2007, month=\"June\")),\n",
" text=alt.datum(\"iPhone Released\"),\n",
" )\n",
")\n",
"\n",
"xrule2 = (\n",
" alt.Chart()\n",
" .mark_text(color=\"black\", dx=45, dy=60)\n",
" .encode(x=alt.datum(alt.DateTime(year=2008, month=\"November\")),\n",
" text=alt.datum(\"2008 Financial Crisis\"),\n",
" )\n",
"\n",
")\n",
"\n",
"label2 = (\n",
" alt.Chart()\n",
" .mark_rect(color=\"#cccccc33\")\n",
" .encode(\n",
" x=alt.datum(alt.DateTime(year=2008, month=\"January\")),\n",
" x2=alt.datum(alt.DateTime(year=2009, month=\"January\")),\n",
" text=alt.datum(\"2008 Financial Crisis\"),\n",
" )\n",
")\n",
"\n",
"\n",
"lines + label + xrule + xrule2 + label2"
]
},
{
"cell_type": "code",
"execution_count": 53,
"id": "44096965-f500-4ebf-81c5-c1e8df9d4ea1",
"metadata": {},
"outputs": [],
"source": [
"import math\n",
"import polars as pl"
]
},
{
"cell_type": "code",
"execution_count": 58,
"id": "f652c1e0-ed86-45cd-b299-4ab662f2437d",
"metadata": {},
"outputs": [],
"source": [
"rows = []\n",
"for pt in range(100):\n",
" rows.append({\"percentage\": pt, \"theta\":pt / 100 * 2 * math.pi, \"area\": (pt / 100 * 100**2) / (100**2)})\n",
"area_df = pl.DataFrame(rows)"
]
},
{
"cell_type": "code",
"execution_count": 59,
"id": "813f6626-9ff4-4d9f-ad60-6a2bf508490a",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"\n",
""
],
"text/plain": [
"alt.Chart(...)"
]
},
"execution_count": 59,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"alt.Chart(area_df).mark_point().encode(x=\"percentage\", y=\"area\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "abf756b4-3ccd-4ea3-972b-ac36926a39b3",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "b4c75d21-2a82-4326-87b0-8f4b788a3d38",
"metadata": {},
"outputs": [],
"source": []
}
],
"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": "3.10.15"
}
},
"nbformat": 4,
"nbformat_minor": 5
}