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tdhoppertdhopper
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Add Box Plot and Heatmap rows to the comparison
Box plot shows hwy by class from mpg; heatmap shows diamonds counts by cut and clarity. seaborn.objects is skipped for both: 0.13 has no boxplot mark and no rect mark, and pandas has no heatmap.
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‎Examples.ipynb‎

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@@ -698,6 +698,174 @@
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"sns.histplot(mpg, x='cty', binwidth=2);"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"#### Box Plot of a Continuous Value by Category"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"tags": [
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"ex",
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"name:box-plot",
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"package:ggplot"
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]
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},
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"outputs": [],
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"source": [
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"%%R -w 10 -h 10 -u in\n",
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"ggplot(data=mpg) +\n",
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" aes(x=class, y=hwy) +\n",
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" geom_boxplot()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"tags": [
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"ex",
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"name:box-plot",
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"package:pandas"
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]
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},
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"outputs": [],
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"source": [
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"mpg.boxplot(column='hwy', by='class',\n",
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" rot=45);"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"tags": [
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"ex",
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"name:box-plot",
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"package:plotnine"
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]
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},
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"outputs": [],
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"source": [
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"(ggplot(mpg) +\n",
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" aes(x=\"class\", y=\"hwy\") +\n",
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" geom_boxplot())"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"tags": [
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"ex",
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"name:box-plot",
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"package:lets-plot"
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]
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},
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"outputs": [],
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"source": [
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"(lp.ggplot(mpg) +\n",
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" lp.aes(x=\"class\", y=\"hwy\") +\n",
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" lp.geom_boxplot())"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"tags": [
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"ex",
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"name:box-plot",
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"package:plotly"
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]
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},
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"outputs": [],
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"source": [
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"px.box(\n",
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" mpg, x=\"class\", y=\"hwy\"\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"tags": [
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"ex",
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"name:box-plot",
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"package:altair"
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]
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},
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"outputs": [],
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"source": [
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"(\n",
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" alt.Chart(mpg)\n",
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" .mark_boxplot()\n",
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" .encode(x=\"class\", y=\"hwy\")\n",
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" .properties(width=400)\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"tags": [
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"ex",
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"name:box-plot",
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"package:hvplot"
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]
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},
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"outputs": [],
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"source": [
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"mpg.hvplot.box(y=\"hwy\", by=\"class\",\n",
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" rot=45)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"tags": [
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"ex",
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"name:box-plot",
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"package:matplotlib"
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]
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},
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"outputs": [],
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"source": [
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"\"\"\"`boxplot` takes a list of arrays, so the\n",
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"groups are split by hand.\n",
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"\"\"\"\n",
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"groups = mpg.groupby('class')['hwy']\n",
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"fig, ax = pyplot.subplots()\n",
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"ax.boxplot([v for _, v in groups],\n",
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" tick_labels=list(groups.groups))\n",
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"ax.set_xlabel('class')\n",
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"ax.set_ylabel('hwy')\n",
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"pyplot.xticks(rotation=45);"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"tags": [
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"ex",
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"name:box-plot",
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"package:seaborn"
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]
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},
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"outputs": [],
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"source": [
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"ax = sns.boxplot(mpg, x='class', y='hwy')\n",
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"ax.tick_params(axis='x', rotation=45);"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"sns.lineplot(ts, x='date', y='value');"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"#### Heatmap of Counts by Two Categories"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"tags": [
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"ex",
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"name:heatmap",
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"package:ggplot"
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]
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},
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"outputs": [],
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"source": [
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"%%R -w 10 -h 10 -u in\n",
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"ggplot(data=diamonds) +\n",
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" aes(x=cut, y=clarity) +\n",
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" geom_bin2d()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"tags": [
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"ex",
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"name:heatmap",
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"package:plotnine"
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]
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},
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"outputs": [],
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"source": [
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"(ggplot(diamonds) +\n",
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" aes(x=\"cut\", y=\"clarity\") +\n",
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" geom_bin2d())"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"tags": [
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"ex",
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"name:heatmap",
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"package:lets-plot"
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]
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},
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"outputs": [],
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"source": [
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"(lp.ggplot(diamonds) +\n",
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" lp.aes(x=\"cut\", y=\"clarity\") +\n",
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" lp.geom_bin2d())"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"tags": [
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"ex",
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"name:heatmap",
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"package:plotly"
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]
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},
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"outputs": [],
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"source": [
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"px.density_heatmap(\n",
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" diamonds, x=\"cut\", y=\"clarity\"\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"tags": [
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"ex",
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"name:heatmap",
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"package:altair"
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]
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},
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"outputs": [],
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"source": [
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"alt.data_transformers.disable_max_rows()\n",
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"(\n",
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" alt.Chart(diamonds)\n",
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" .mark_rect()\n",
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" .encode(\n",
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" x=\"cut\", y=\"clarity\",\n",
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" color=\"count()\",\n",
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" )\n",
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" .properties(width=400, height=400)\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"tags": [
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"ex",
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"name:heatmap",
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"package:hvplot"
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]
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},
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"outputs": [],
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"source": [
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"\"\"\"hvPlot needs the counts up front; `C` names\n",
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"the column that colors each tile.\n",
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"\"\"\"\n",
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"(diamonds\n",
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" .groupby([\"cut\", \"clarity\"])\n",
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" .size()\n",
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" .reset_index(name=\"count\")\n",
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" .hvplot.heatmap(x=\"cut\", y=\"clarity\",\n",
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" C=\"count\", rot=45))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"tags": [
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"ex",
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"name:heatmap",
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"package:matplotlib"
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]
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},
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"outputs": [],
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"source": [
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"\"\"\"Matplotlib has no heatmap function, so the\n",
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"counts are pivoted and drawn with `pcolormesh`.\n",
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"\"\"\"\n",
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"counts = (diamonds\n",
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" .groupby(['clarity', 'cut'])\n",
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" .size()\n",
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" .unstack())\n",
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"fig, ax = pyplot.subplots()\n",
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"mesh = ax.pcolormesh(counts.values)\n",
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"ax.set_xticks(\n",
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" np.arange(len(counts.columns)) + .5,\n",
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" counts.columns, rotation=45)\n",
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"ax.set_yticks(\n",
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" np.arange(len(counts.index)) + .5,\n",
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" counts.index)\n",
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"ax.set_xlabel('cut')\n",
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"ax.set_ylabel('clarity')\n",
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"fig.colorbar(mesh, label='count');"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"tags": [
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"ex",
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"name:heatmap",
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"package:seaborn"
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]
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},
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"outputs": [],
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"source": [
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"\"\"\"`heatmap` colors a matrix, so the counts are\n",
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"pivoted into one first.\n",
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"\"\"\"\n",
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"counts = (diamonds\n",
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" .groupby(['clarity', 'cut'])\n",
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" .size()\n",
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" .unstack())\n",
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"sns.heatmap(counts,\n",
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" cbar_kws=dict(label='count'));"
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]
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}
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],
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"metadata": {

‎render.py‎

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names = {
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"bar-counts": "Bar Chart",
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"simple-histogram": "Histogram",
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"box-plot": "Box Plot",
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"scatter-plot": "Scatter Plot",
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"timeseries": "Time Series",
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"scatter-plot-with-colors": "Scatter Plot with Faceted with Color",
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"stacked-bar-chart": "Stacked Bar Chart",
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"dodged-bar-chart": "Dodged Bar Chart",
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"stacked-kde": "Stacked KDE Plot",
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"heatmap": "Heatmap",
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}
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with open("INTRO.md", "r") as f:

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