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tdhopper merged 3 commits into from
Aug 5, 2026
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Add seaborn.objects column to the plotting comparison #25

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d0a32ae
Register seaborn.objects as a comparison package
tdhopper Aug 5, 2026
e68081c
Add seaborn.objects examples for eleven plots
tdhopper Aug 5, 2026
61d6de7
Describe seaborn.objects in the site intro
tdhopper Aug 5, 2026
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287 changes: 286 additions & 1 deletion Examples.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -5,7 +5,93 @@
"execution_count": null,
"metadata": {},
"outputs": [],
"source": "%matplotlib inline\n\nimport readline\nimport altair as alt\nimport pandas as pd\nimport seaborn as sns\nfrom matplotlib import pyplot\nfrom plotnine import *\nimport numpy as np\n\nfrom plotly import figure_factory\nfrom plotly import graph_objects\nimport plotly.express as px\nfrom IPython.core.magic import Magics, magics_class, cell_magic\n\nfrom IPython.display import Image\n\nfrom pylab import rcParams\n\nsize = 20\nparams = {\n \"legend.fontsize\": size,\n \"figure.figsize\": (15, 5),\n \"axes.labelsize\": size,\n \"axes.titlesize\": size,\n \"xtick.labelsize\": size,\n \"ytick.labelsize\": size,\n \"axes.titlesize\": 1.5 * size,\n \"figure.figsize\": (12, 12),\n}\nrcParams.update(params)\ntheme_update(\n figure_size=(9, 9),\n title=element_text(size=size),\n text=element_text(size=0.6 * size),\n) # for plotnine\n\n\nimport plotly.io as pio\npio.renderers.default = \"png\"\npio.renderers[\"png\"].width = 750\npio.renderers[\"png\"].height = 750\n\n# Render Altair charts as PNG via vl-convert\nalt.renderers.enable(\"png\", scale_factor=2.0)\n\nimport tempfile\n\nimport lets_plot as lp\nfrom lets_plot.export import ggsave\n\nlp.LetsPlot.setup_html()\nlp.LetsPlot.set_theme(\n lp.theme(text=lp.element_text(size=16), title=lp.element_text(size=20))\n)\n\n_LETS_PLOT_DIR = tempfile.mkdtemp(prefix=\"lets-plot-\")\n\n\ndef _lets_plot_png(plot):\n \"\"\"Render a Lets-Plot spec to PNG so the cell output carries an image.\"\"\"\n path = ggsave(plot + lp.ggsize(750, 750), \"plot.png\", path=_LETS_PLOT_DIR)\n with open(path, \"rb\") as f:\n return f.read()\n\n\nget_ipython().display_formatter.formatters[\"image/png\"].for_type(\n lp.plot.core.PlotSpec, _lets_plot_png\n)"
"source": [
"%matplotlib inline\n",
"\n",
"import readline\n",
"import altair as alt\n",
"import pandas as pd\n",
"import seaborn as sns\n",
"from matplotlib import pyplot\n",
"from plotnine import *\n",
"import numpy as np\n",
"\n",
"from plotly import figure_factory\n",
"from plotly import graph_objects\n",
"import plotly.express as px\n",
"from IPython.core.magic import Magics, magics_class, cell_magic\n",
"\n",
"from IPython.display import Image\n",
"\n",
"from pylab import rcParams\n",
"\n",
"size = 20\n",
"params = {\n",
" \"legend.fontsize\": size,\n",
" \"figure.figsize\": (15, 5),\n",
" \"axes.labelsize\": size,\n",
" \"axes.titlesize\": size,\n",
" \"xtick.labelsize\": size,\n",
" \"ytick.labelsize\": size,\n",
" \"axes.titlesize\": 1.5 * size,\n",
" \"figure.figsize\": (12, 12),\n",
"}\n",
"rcParams.update(params)\n",
"theme_update(\n",
" figure_size=(9, 9),\n",
" title=element_text(size=size),\n",
" text=element_text(size=0.6 * size),\n",
") # for plotnine\n",
"\n",
"\n",
"import seaborn.objects as so\n",
"\n",
"# Match the other libraries: ~750x750 px output with scaled-up text\n",
"so.Plot.config.display[\"scaling\"] = 1\n",
"so.Plot.config.theme.update(\n",
" {\n",
" \"figure.figsize\": (8, 8),\n",
" \"axes.labelsize\": size,\n",
" \"axes.titlesize\": 1.5 * size,\n",
" \"xtick.labelsize\": size,\n",
" \"ytick.labelsize\": size,\n",
" \"legend.fontsize\": size,\n",
" \"legend.title_fontsize\": size,\n",
" }\n",
")\n",
"\n",
"import plotly.io as pio\n",
"pio.renderers.default = \"png\"\n",
"pio.renderers[\"png\"].width = 750\n",
"pio.renderers[\"png\"].height = 750\n",
"\n",
"# Render Altair charts as PNG via vl-convert\n",
"alt.renderers.enable(\"png\", scale_factor=2.0)\n",
"\n",
"import tempfile\n",
"\n",
"import lets_plot as lp\n",
"from lets_plot.export import ggsave\n",
"\n",
"lp.LetsPlot.setup_html()\n",
"lp.LetsPlot.set_theme(\n",
" lp.theme(text=lp.element_text(size=16), title=lp.element_text(size=20))\n",
")\n",
"\n",
"_LETS_PLOT_DIR = tempfile.mkdtemp(prefix=\"lets-plot-\")\n",
"\n",
"\n",
"def _lets_plot_png(plot):\n",
" \"\"\"Render a Lets-Plot spec to PNG so the cell output carries an image.\"\"\"\n",
" path = ggsave(plot + lp.ggsize(750, 750), \"plot.png\", path=_LETS_PLOT_DIR)\n",
" with open(path, \"rb\") as f:\n",
" return f.read()\n",
"\n",
"\n",
"get_ipython().display_formatter.formatters[\"image/png\"].for_type(\n",
" lp.plot.core.PlotSpec, _lets_plot_png\n",
")"
]
},
{
"cell_type": "code",
Expand Down Expand Up @@ -291,6 +377,26 @@
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"ex",
"name:bar-counts",
"package:seaborn-objects"
]
},
"outputs": [],
"source": [
"\"\"\"seaborn.objects has no `coord_flip`, so the\n",
"categorical variable is mapped to `y` instead.\n",
"\"\"\"\n",
"(so.Plot(mpg, y=\"manufacturer\")\n",
" .add(so.Bar(), so.Count())\n",
" .label(title=\"Number of Cars by Make\"))"
]
},
{
"cell_type": "markdown",
"metadata": {},
Expand Down Expand Up @@ -423,6 +529,22 @@
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"ex",
"name:simple-histogram",
"package:seaborn-objects"
]
},
"outputs": [],
"source": [
"(so.Plot(mpg, x=\"cty\")\n",
" .add(so.Bars(), so.Hist(binwidth=2)))"
]
},
{
"cell_type": "markdown",
"metadata": {},
Expand Down Expand Up @@ -558,6 +680,26 @@
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"ex",
"name:scatter-plot",
"package:seaborn-objects"
]
},
"outputs": [],
"source": [
"(so.Plot(mpg, x=\"displ\", y=\"hwy\")\n",
" .add(so.Dot())\n",
" .label(\n",
" x=\"Engine Displacement in Liters\",\n",
" y=\"Highway MPG\",\n",
" title=\"Engine Displacement in Liters vs Highway MPG\"))"
]
},
{
"cell_type": "markdown",
"metadata": {},
Expand Down Expand Up @@ -819,6 +961,27 @@
"));"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"ex",
"name:scatter-plot-with-colors",
"package:seaborn-objects"
]
},
"outputs": [],
"source": [
"(so.Plot(mpg, x=\"displ\", y=\"hwy\",\n",
" color=\"class\")\n",
" .add(so.Dot())\n",
" .label(\n",
" x=\"Engine Displacement in Liters\",\n",
" y=\"Highway MPG\",\n",
" title=\"Engine Displacement in Liters vs Highway MPG\"))"
]
},
{
"cell_type": "code",
"execution_count": null,
Expand Down Expand Up @@ -1017,6 +1180,25 @@
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"ex",
"name:scatter-plot-with-size",
"package:seaborn-objects"
]
},
"outputs": [],
"source": [
"(so.Plot(mpg, x=\"cty\", y=\"hwy\",\n",
" pointsize=\"cyl\")\n",
" .add(so.Dots(alpha=.5))\n",
" .scale(pointsize=(4, 12))\n",
" .label(x=\"City MPG\", y=\"Highway MPG\"))"
]
},
{
"cell_type": "markdown",
"metadata": {},
Expand Down Expand Up @@ -1085,6 +1267,23 @@
");"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"ex",
"name:scatter-plot-with-facet",
"package:seaborn-objects"
]
},
"outputs": [],
"source": [
"(so.Plot(mpg, x=\"displ\", y=\"hwy\")\n",
" .add(so.Dot())\n",
" .facet(\"class\", wrap=4))"
]
},
{
"cell_type": "code",
"execution_count": null,
Expand Down Expand Up @@ -1211,6 +1410,23 @@
");"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"ex",
"name:scatter-plot-with-facets",
"package:seaborn-objects"
]
},
"outputs": [],
"source": [
"(so.Plot(mpg, x=\"displ\", y=\"hwy\")\n",
" .add(so.Dot())\n",
" .facet(col=\"cyl\", row=\"drv\"))"
]
},
{
"cell_type": "code",
"execution_count": null,
Expand Down Expand Up @@ -1546,6 +1762,22 @@
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"ex",
"name:stacked-bar-chart",
"package:seaborn-objects"
]
},
"outputs": [],
"source": [
"(so.Plot(diamonds, x=\"cut\", color=\"clarity\")\n",
" .add(so.Bar(), so.Count(), so.Stack()))"
]
},
{
"cell_type": "code",
"execution_count": null,
Expand Down Expand Up @@ -1659,6 +1891,22 @@
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"ex",
"name:dodged-bar-chart",
"package:seaborn-objects"
]
},
"outputs": [],
"source": [
"(so.Plot(diamonds, x=\"cut\", color=\"clarity\")\n",
" .add(so.Bar(), so.Count(), so.Dodge()))"
]
},
{
"cell_type": "code",
"execution_count": null,
Expand Down Expand Up @@ -1715,6 +1963,27 @@
"outputs": [],
"source": "(sns\n .FacetGrid(diamonds, \n hue=\"cut\", \n height=10, \n xlim=(55, 70))\n .map(sns.kdeplot, 'depth', fill=True)\n .add_legend()\n);"
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"ex",
"name:stacked-kde",
"package:seaborn-objects"
]
},
"outputs": [],
"source": [
"\"\"\"`.limit()` only clips the axis; unlike ggplot2's\n",
"`xlim()` it does not drop rows before the density is\n",
"estimated.\n",
"\"\"\"\n",
"(so.Plot(diamonds, x=\"depth\", color=\"cut\")\n",
" .add(so.Area(alpha=.1), so.KDE(gridsize=500))\n",
" .limit(x=(55, 70)))"
]
},
{
"cell_type": "code",
"execution_count": null,
Expand Down Expand Up @@ -1901,6 +2170,22 @@
" x=\"date\", y=\"value\"\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"ex",
"name:timeseries",
"package:seaborn-objects"
]
},
"outputs": [],
"source": [
"(so.Plot(ts, x=\"date\", y=\"value\")\n",
" .add(so.Line()))"
]
}
],
"metadata": {
Expand Down
2 changes: 2 additions & 0 deletions INTRO.md
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Expand Up @@ -20,6 +20,8 @@ Many excellent plotting tools are built on top of Matplotlib.

"[Seaborn](https://seaborn.pydata.org/ "Seaborn: statistical data visualization") is a Python visualization library based on matplotlib. It provides a high-level interface for drawing attractive statistical graphics." Seaborn makes beautiful plots but is geared toward specific statistical plots, not general purpose plotting. It does have a powerful [faceting utility function](http://seaborn.pydata.org/tutorial/axis_grids.html) that I use regularly.

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, so it covers far more of the examples below than the classic interface does. It has no loess smoother and no regression confidence band, so those two examples are missing.

#### Interactive Plotting Libraries

There are several tools that can make the kinds of plots described here. At present, I have little experience with them. If anyone would like to help add examples, please [get in touch](https://github.com/tdhopper/pythonplot.com).
Expand Down
1 change: 1 addition & 0 deletions render.py
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Expand Up @@ -20,6 +20,7 @@
"pandas": "Pandas",
"matplotlib": "Matplotlib",
"seaborn": "Seaborn",
"seaborn-objects": "seaborn.objects",
"plotnine": "plotnine",
"lets-plot": "lets-plot",
"plotly": "plotly",
Expand Down
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