{
"cells": [
{
"cell_type": "markdown",
"id": "228d57af-2bd1-458d-860b-e17b2fe7d445",
"metadata": {},
"source": [
"## Example: Bar Ordering\n",
"\n",
"Don't neglect the role of ordering when working with categorical variables. These provide an additional opportunity to emphasize or highlight.\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "279c3be4-53f3-4db5-a4e3-8f55019529af",
"metadata": {},
"outputs": [],
"source": [
"import altair as alt\n",
"import polars as pl"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "b071e9be-1fad-4852-87b8-38157be6de66",
"metadata": {},
"outputs": [],
"source": [
"countries = {\n",
" \"AA\": 13, \"AB\": 45, \"AC\": 30, \"AD\": 14, \"AE\": 21,\n",
" \"BA\": 17, \"BB\": 25, \"BC\": 29, \"BD\": 16, \"BE\": 21,\n",
" \"CA\": 20, \"CB\": 22, \"CC\": 28, \"CD\": 18, \"CE\": 24,\n",
" \"DA\": 40, \"DB\": 33, \"DC\": 30, \"DD\": 13, \"CE\": 28,\n",
" \"EA\": 16, \"EB\": 45, \"EC\": 27, \"ED\": 80, \"CE\": 33,\n",
" }"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "f9d54624-a50e-414e-a29c-c8371fe5b98f",
"metadata": {},
"outputs": [],
"source": [
"df = pl.DataFrame(countries).unpivot(variable_name=\"country\")"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "334fe526-2536-478c-9457-aad9e166eb1d",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"\n",
""
],
"text/plain": [
"alt.Chart(...)"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"alt.Chart(df).mark_bar().encode(x=\"country\", y=\"value\")"
]
},
{
"cell_type": "markdown",
"id": "e163a712-3674-48bf-b0d3-f1d279c5ba2b",
"metadata": {},
"source": [
"This alphabetical ordering may serve you well if you want people to quickly be able to find their country. But you can consider other orderings & groupings that might make your point better."
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "7883c59e-0bb4-4713-8772-aca9441e6800",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"\n",
""
],
"text/plain": [
"alt.Chart(...)"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# ordering by value to emphasize outliers\n",
"alt.Chart(df).mark_bar().encode(\n",
" alt.X(\"country\", sort=alt.EncodingSortField(field=\"value\", order=\"ascending\")\n",
" ),\n",
" y=\"value\",\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "1b965691-7e32-45d7-b1db-833e353c230d",
"metadata": {},
"outputs": [],
"source": [
"# adding a grouping column and using it for coloring/grouping\n",
"# using first letter of country for a pretend grouping -- \n",
"# in reality you could group by region/characteristics\n",
"df_grouped = df.with_columns(grouping=pl.col(\"country\").str.slice(0, 1))"
]
},
{
"cell_type": "code",
"execution_count": 24,
"id": "c7735212-e6aa-405d-9b76-87a92565e88b",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"\n",
""
],
"text/plain": [
"alt.Chart(...)"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"alt.Chart(df_grouped).mark_bar().encode(\n",
" alt.X(\"country\", sort=alt.EncodingSortField(field=\"group\", order=\"ascending\")),\n",
" y=\"value\",\n",
" color=\"grouping:N\",\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a721eddb-a9b1-457a-9648-e9606b62f044",
"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",
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