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tdhopper merged 3 commits into from
Aug 5, 2026
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Add Box Plot and Heatmap rows to the comparison #31

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a7409d0
Add Box Plot and Heatmap rows to the comparison
tdhopper Aug 5, 2026
48bbc09
Update the intro's per-library coverage counts for the new rows
tdhopper Aug 5, 2026
84f2022
Draw the heatmap row with geom_tile on precomputed counts
tdhopper Aug 5, 2026
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360 changes: 360 additions & 0 deletions Examples.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -698,6 +698,174 @@
"sns.histplot(mpg, x='cty', binwidth=2);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Box Plot of a Continuous Value by Category"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"ex",
"name:box-plot",
"package:ggplot"
]
},
"outputs": [],
"source": [
"%%R -w 10 -h 10 -u in\n",
"ggplot(data=mpg) +\n",
" aes(x=class, y=hwy) +\n",
" geom_boxplot()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"ex",
"name:box-plot",
"package:pandas"
]
},
"outputs": [],
"source": [
"mpg.boxplot(column='hwy', by='class',\n",
" rot=45);"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"ex",
"name:box-plot",
"package:plotnine"
]
},
"outputs": [],
"source": [
"(ggplot(mpg) +\n",
" aes(x=\"class\", y=\"hwy\") +\n",
" geom_boxplot())"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"ex",
"name:box-plot",
"package:lets-plot"
]
},
"outputs": [],
"source": [
"(lp.ggplot(mpg) +\n",
" lp.aes(x=\"class\", y=\"hwy\") +\n",
" lp.geom_boxplot())"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"ex",
"name:box-plot",
"package:plotly"
]
},
"outputs": [],
"source": [
"px.box(\n",
" mpg, x=\"class\", y=\"hwy\"\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"ex",
"name:box-plot",
"package:altair"
]
},
"outputs": [],
"source": [
"(\n",
" alt.Chart(mpg)\n",
" .mark_boxplot()\n",
" .encode(x=\"class\", y=\"hwy\")\n",
" .properties(width=400)\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"ex",
"name:box-plot",
"package:hvplot"
]
},
"outputs": [],
"source": [
"mpg.hvplot.box(y=\"hwy\", by=\"class\",\n",
" rot=45)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"ex",
"name:box-plot",
"package:matplotlib"
]
},
"outputs": [],
"source": [
"\"\"\"`boxplot` takes a list of arrays, so the\n",
"groups are split by hand.\n",
"\"\"\"\n",
"groups = mpg.groupby('class')['hwy']\n",
"fig, ax = pyplot.subplots()\n",
"ax.boxplot([v for _, v in groups],\n",
" tick_labels=list(groups.groups))\n",
"ax.set_xlabel('class')\n",
"ax.set_ylabel('hwy')\n",
"pyplot.xticks(rotation=45);"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"ex",
"name:box-plot",
"package:seaborn"
]
},
"outputs": [],
"source": [
"ax = sns.boxplot(mpg, x='class', y='hwy')\n",
"ax.tick_params(axis='x', rotation=45);"
]
},
{
"cell_type": "markdown",
"metadata": {},
Expand Down Expand Up @@ -2855,6 +3023,198 @@
"source": [
"sns.lineplot(ts, x='date', y='value');"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Heatmap of Counts by Two Categories"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"ex",
"name:heatmap",
"package:ggplot"
]
},
"outputs": [],
"source": [
"%%R -w 10 -h 10 -u in\n",
"counts <- as.data.frame(\n",
" table(cut=diamonds$cut,\n",
" clarity=diamonds$clarity),\n",
" responseName=\"count\")\n",
"ggplot(data=counts) +\n",
" aes(x=cut, y=clarity, fill=count) +\n",
" geom_tile()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"ex",
"name:heatmap",
"package:plotnine"
]
},
"outputs": [],
"source": [
"counts = (diamonds\n",
" .groupby([\"cut\", \"clarity\"])\n",
" .size()\n",
" .reset_index(name=\"count\"))\n",
"(ggplot(counts) +\n",
" aes(x=\"cut\", y=\"clarity\", fill=\"count\") +\n",
" geom_tile())"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"ex",
"name:heatmap",
"package:lets-plot"
]
},
"outputs": [],
"source": [
"counts = (diamonds\n",
" .groupby([\"cut\", \"clarity\"])\n",
" .size()\n",
" .reset_index(name=\"count\"))\n",
"(lp.ggplot(counts) +\n",
" lp.aes(x=\"cut\", y=\"clarity\",\n",
" fill=\"count\") +\n",
" lp.geom_tile())"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"ex",
"name:heatmap",
"package:plotly"
]
},
"outputs": [],
"source": [
"px.density_heatmap(\n",
" diamonds, x=\"cut\", y=\"clarity\"\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"ex",
"name:heatmap",
"package:altair"
]
},
"outputs": [],
"source": [
"alt.data_transformers.disable_max_rows()\n",
"(\n",
" alt.Chart(diamonds)\n",
" .mark_rect()\n",
" .encode(\n",
" x=\"cut\", y=\"clarity\",\n",
" color=\"count()\",\n",
" )\n",
" .properties(width=400, height=400)\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"ex",
"name:heatmap",
"package:hvplot"
]
},
"outputs": [],
"source": [
"\"\"\"hvPlot needs the counts up front; `C` names\n",
"the column that colors each tile.\n",
"\"\"\"\n",
"(diamonds\n",
" .groupby([\"cut\", \"clarity\"])\n",
" .size()\n",
" .reset_index(name=\"count\")\n",
" .hvplot.heatmap(x=\"cut\", y=\"clarity\",\n",
" C=\"count\", rot=45))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"ex",
"name:heatmap",
"package:matplotlib"
]
},
"outputs": [],
"source": [
"\"\"\"Matplotlib has no heatmap function, so the\n",
"counts are pivoted and drawn with `pcolormesh`.\n",
"\"\"\"\n",
"counts = (diamonds\n",
" .groupby(['clarity', 'cut'])\n",
" .size()\n",
" .unstack())\n",
"fig, ax = pyplot.subplots()\n",
"mesh = ax.pcolormesh(counts.values)\n",
"ax.set_xticks(\n",
" np.arange(len(counts.columns)) + .5,\n",
" counts.columns, rotation=45)\n",
"ax.set_yticks(\n",
" np.arange(len(counts.index)) + .5,\n",
" counts.index)\n",
"ax.set_xlabel('cut')\n",
"ax.set_ylabel('clarity')\n",
"fig.colorbar(mesh, label='count');"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"ex",
"name:heatmap",
"package:seaborn"
]
},
"outputs": [],
"source": [
"\"\"\"`heatmap` colors a matrix, so the counts are\n",
"pivoted into one first.\n",
"\"\"\"\n",
"counts = (diamonds\n",
" .groupby(['clarity', 'cut'])\n",
" .size()\n",
" .unstack())\n",
"ax = sns.heatmap(\n",
" counts, cbar_kws=dict(label='count'))\n",
"ax.tick_params(axis='y', rotation=0);"
]
}
],
"metadata": {
Expand Down
4 changes: 2 additions & 2 deletions INTRO.md
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Expand Up @@ -14,11 +14,11 @@ Put the Matplotlib and ggplot2 versions of the two-variable faceted scatter plot

#### Matplotlib-Based Libraries

[Pandas plotting](https://pandas.pydata.org/docs/user_guide/visualization.html "pandas user guide: Chart Visualization") provides "the basics ... to easily create decent looking plots" from data frames. That is about 70% of what I do day-to-day. It has no faceting, no categorical color mapping, and no smoothing, so five of the examples below have no pandas column.
[Pandas plotting](https://pandas.pydata.org/docs/user_guide/visualization.html "pandas user guide: Chart Visualization") provides "the basics ... to easily create decent looking plots" from data frames. That is about 70% of what I do day-to-day. It has no faceting, no categorical color mapping, no smoothing, and no heatmap, so six of the examples below have no pandas column.

Seaborn calls itself "[statistical data visualization](https://seaborn.pydata.org/ "seaborn: statistical data visualization")." Its classic interface is a set of named functions (`histplot`, `scatterplot`, `countplot`, `lmplot`, `kdeplot`) plus [FacetGrid](http://seaborn.pydata.org/tutorial/axis_grids.html), which I use for faceting more than anything else in the library. It covers every plot below, once you know which function to reach for.

Seaborn 0.12 added [seaborn.objects](https://seaborn.pydata.org/tutorial/objects_interface.html), a second interface built on the grammar of graphics. It composes a plot from marks and statistical transforms instead of dispatching to a named plotting function. The interface has no loess smoother and no regression confidence band, so those two examples are missing.
Seaborn 0.12 added [seaborn.objects](https://seaborn.pydata.org/tutorial/objects_interface.html), a second interface built on the grammar of graphics. It composes a plot from marks and statistical transforms instead of dispatching to a named plotting function. The interface has no loess smoother, no regression confidence band, and neither a box-plot nor a rectangle mark, so four of the examples are missing.

"[plotnine](https://plotnine.org/) is a data visualization package for Python based on the grammar of graphics." It tracks ggplot2 closely enough that most R code translates line for line, down to the `+` for layering. I reach for it when I want ggplot2 semantics without leaving Python.

Expand Down
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