diff --git a/examples/highlighting-example.ipynb b/examples/highlighting-example.ipynb
index eae69dd..61ec0ef 100644
--- a/examples/highlighting-example.ipynb
+++ b/examples/highlighting-example.ipynb
@@ -70,7 +70,7 @@
},
{
"cell_type": "code",
- "execution_count": 45,
+ "execution_count": 48,
"id": "17e6f86a-bd9f-44a7-af5f-917237c3393b",
"metadata": {},
"outputs": [
@@ -79,23 +79,23 @@
"text/html": [
"\n",
"\n",
- "\n",
+ "\n",
""
],
"text/plain": [
"alt.Chart(...)"
]
},
- "execution_count": 47,
+ "execution_count": 49,
"metadata": {},
"output_type": "execute_result"
}
@@ -356,7 +358,7 @@
" color=alt.Color(\"country:N\", scale=alt.Scale(\n",
" domain=list(countries_initial.keys()),\n",
" range=[color_mapping.get(c, color_mapping['default']) for c in countries_initial] \n",
- " )),\n",
+ " ), legend=None),\n",
" #detail=\"country\",\n",
") "
]
diff --git a/examples/sorting_and_grouping_bars.ipynb b/examples/sorting_and_grouping_bars.ipynb
new file mode 100644
index 0000000..e91f4c0
--- /dev/null
+++ b/examples/sorting_and_grouping_bars.ipynb
@@ -0,0 +1,378 @@
+{
+ "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",
+ "pygments_lexer": "ipython3",
+ "version": "3.10.15"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}