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tdhopper merged 2 commits into from
Aug 5, 2026
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Add Lets-Plot column to the plotting comparison #24

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72e89b1
Add lets-plot examples for all 13 plots
tdhopper Aug 5, 2026
32d2273
Describe Lets-Plot in the site intro and project docs
tdhopper Aug 5, 2026
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4 changes: 2 additions & 2 deletions CLAUDE.md
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Expand Up @@ -4,7 +4,7 @@ This file provides guidance to Claude Code (claude.ai/code) when working with co

## Project Overview

pythonplot.com is a static website that provides a visual comparison of different Python plotting libraries (pandas, matplotlib, seaborn, plotnine, plotly, altair) and R's ggplot2 for exploratory data analysis. It serves as a "Rosetta Stone" showing how to create the same plots across different libraries.
pythonplot.com is a static website that provides a visual comparison of different Python plotting libraries (pandas, matplotlib, seaborn, plotnine, lets-plot, plotly, altair) and R's ggplot2 for exploratory data analysis. It serves as a "Rosetta Stone" showing how to create the same plots across different libraries.

## Architecture

Expand Down Expand Up @@ -115,7 +115,7 @@ All plots are rendered to static PNG images inside the executed notebook:

## Dependencies

Python dependencies are declared in `pyproject.toml` and locked in `uv.lock` (committed). Key libraries: pandas, matplotlib, seaborn, plotnine, plotly (+kaleido), altair (+vl-convert-python), statsmodels, rpy2, Jinja2 with jinja2-highlight.
Python dependencies are declared in `pyproject.toml` and locked in `uv.lock` (committed). Key libraries: pandas, matplotlib, seaborn, plotnine, lets-plot, plotly (+kaleido), altair (+vl-convert-python), statsmodels, rpy2, Jinja2 with jinja2-highlight.

R (system install) with ggplot2 and mgcv, installed by `setup_r.sh`.

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243 changes: 241 additions & 2 deletions Examples.ipynb
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Expand Up @@ -5,7 +5,7 @@
"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)"
"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)"
},
{
"cell_type": "code",
Expand Down Expand Up @@ -227,6 +227,28 @@
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"ex",
"name:bar-counts",
"package:lets-plot"
]
},
"outputs": [],
"source": [
"\"\"\"Lets-Plot mirrors the ggplot2 grammar. It is imported as `lp` here\n",
"so its names don't collide with plotnine's.\n",
"\"\"\"\n",
"(lp.ggplot(mpg) +\n",
" lp.aes(x=\"manufacturer\") +\n",
" lp.geom_bar() +\n",
" lp.coord_flip() +\n",
" lp.ggtitle(\"Number of Cars by Make\"))"
]
},
{
"cell_type": "code",
"execution_count": null,
Expand Down Expand Up @@ -344,6 +366,23 @@
" geom_histogram(binwidth=2))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"ex",
"name:simple-histogram",
"package:lets-plot"
]
},
"outputs": [],
"source": [
"(lp.ggplot(mpg) +\n",
" lp.aes(x=\"cty\") +\n",
" lp.geom_histogram(binwidth=2))"
]
},
{
"cell_type": "code",
"execution_count": null,
Expand Down Expand Up @@ -452,6 +491,26 @@
" ylab(\"Highway MPG\"))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"ex",
"name:scatter-plot",
"package:lets-plot"
]
},
"outputs": [],
"source": [
"(lp.ggplot(mpg) +\n",
" lp.aes(x=\"displ\", y=\"hwy\") +\n",
" lp.geom_point() +\n",
" lp.ggtitle(\"Engine Displacement in Liters vs Highway MPG\") +\n",
" lp.xlab(\"Engine Displacement in Liters\") +\n",
" lp.ylab(\"Highway MPG\"))"
]
},
{
"cell_type": "code",
"execution_count": null,
Expand Down Expand Up @@ -543,6 +602,24 @@
" geom_smooth(method=\"lm\"))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"ex",
"name:scatter-with-regression",
"package:lets-plot"
]
},
"outputs": [],
"source": [
"(lp.ggplot(mpg) +\n",
" lp.aes(\"displ\", \"hwy\") +\n",
" lp.geom_point() +\n",
" lp.geom_smooth(method=\"lm\"))"
]
},
{
"cell_type": "code",
"execution_count": null,
Expand Down Expand Up @@ -762,6 +839,26 @@
" ylab(\"Highway MPG\"))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"ex",
"name:scatter-plot-with-colors",
"package:lets-plot"
]
},
"outputs": [],
"source": [
"(lp.ggplot(mpg) +\n",
" lp.aes(x=\"displ\", y=\"hwy\", color=\"class\") +\n",
" lp.geom_point() +\n",
" lp.ggtitle(\"Engine Displacement in Liters vs Highway MPG\") +\n",
" lp.xlab(\"Engine Displacement in Liters\") +\n",
" lp.ylab(\"Highway MPG\"))"
]
},
{
"cell_type": "code",
"execution_count": null,
Expand Down Expand Up @@ -883,6 +980,23 @@
" geom_point(alpha=.5))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"ex",
"name:scatter-plot-with-size",
"package:lets-plot"
]
},
"outputs": [],
"source": [
"(lp.ggplot(mpg) +\n",
" lp.aes(x=\"cty\", y=\"hwy\", size=\"cyl\") +\n",
" lp.geom_point(alpha=.5))"
]
},
{
"cell_type": "code",
"execution_count": null,
Expand Down Expand Up @@ -989,6 +1103,24 @@
" facet_wrap(\" ~ c\", nrow = 2))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"ex",
"name:scatter-plot-with-facet",
"package:lets-plot"
]
},
"outputs": [],
"source": [
"(lp.ggplot(mpg) +\n",
" lp.aes(x=\"displ\", y=\"hwy\") +\n",
" lp.geom_point() +\n",
" lp.facet_wrap(facets=\"class\", nrow=2))"
]
},
{
"cell_type": "code",
"execution_count": null,
Expand Down Expand Up @@ -1097,6 +1229,24 @@
" facet_grid(\"drv ~ cyl\"))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"ex",
"name:scatter-plot-with-facets",
"package:lets-plot"
]
},
"outputs": [],
"source": [
"(lp.ggplot(mpg) +\n",
" lp.aes(x=\"displ\", y=\"hwy\") +\n",
" lp.geom_point() +\n",
" lp.facet_grid(x=\"cyl\", y=\"drv\"))"
]
},
{
"cell_type": "code",
"execution_count": null,
Expand Down Expand Up @@ -1214,6 +1364,26 @@
" ))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"ex",
"name:stacked-smooth-line-and-scatter",
"package:lets-plot"
]
},
"outputs": [],
"source": [
"(lp.ggplot(mpg) +\n",
" lp.aes(x=\"displ\", y=\"hwy\") +\n",
" lp.geom_point(lp.aes(color=\"class\")) +\n",
" lp.geom_smooth(data=mpg[mpg[\"class\"] == \"subcompact\"],\n",
" se=False,\n",
" method=\"loess\"))"
]
},
{
"cell_type": "code",
"execution_count": null,
Expand Down Expand Up @@ -1301,6 +1471,23 @@
" geom_bar())"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"ex",
"name:stacked-bar-chart",
"package:lets-plot"
]
},
"outputs": [],
"source": [
"(lp.ggplot(diamonds) +\n",
" lp.aes(x=\"cut\", fill=\"clarity\") +\n",
" lp.geom_bar())"
]
},
{
"cell_type": "code",
"execution_count": null,
Expand Down Expand Up @@ -1415,6 +1602,23 @@
" geom_bar(position = \"dodge\"))\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"ex",
"name:dodged-bar-chart",
"package:lets-plot"
]
},
"outputs": [],
"source": [
"(lp.ggplot(diamonds) +\n",
" lp.aes(x=\"cut\", fill=\"clarity\") +\n",
" lp.geom_bar(position=\"dodge\"))"
]
},
{
"cell_type": "code",
"execution_count": null,
Expand Down Expand Up @@ -1530,6 +1734,24 @@
" geom_density(alpha=0.1))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"ex",
"name:stacked-kde",
"package:lets-plot"
]
},
"outputs": [],
"source": [
"(lp.ggplot(diamonds) +\n",
" lp.aes(\"depth\", fill=\"cut\", color=\"cut\") +\n",
" lp.geom_density(alpha=0.1) +\n",
" lp.xlim(55, 70))"
]
},
{
"cell_type": "code",
"execution_count": null,
Expand Down Expand Up @@ -1614,6 +1836,23 @@
" + geom_line())"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"ex",
"name:timeseries",
"package:lets-plot"
]
},
"outputs": [],
"source": [
"(lp.ggplot(ts)\n",
" + lp.aes(\"date\", \"value\")\n",
" + lp.geom_line())"
]
},
{
"cell_type": "code",
"execution_count": null,
Expand Down Expand Up @@ -1686,4 +1925,4 @@
},
"nbformat": 4,
"nbformat_minor": 1
}
}
2 changes: 2 additions & 0 deletions INTRO.md
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Expand Up @@ -29,6 +29,8 @@ There are several tools that can make the kinds of plots described here. At pres

"[plotly](https://plot.ly/ "Plotly - Make charts and dashboards online")'s Python graphing library makes interactive, publication-quality graphs online. Examples of how to make line plots, scatter plots, area charts, bar charts, error bars, box plots, histograms, heatmaps, subplots, multiple-axes, polar charts, and bubble charts." I provide plotly examples rendered as static images.

"[Lets-Plot](https://lets-plot.org/ "Lets-Plot: an open-source plotting library for statistical data") is an open-source plotting library for statistical data," written by JetBrains and modeled on the grammar of graphics. Its Python API tracks ggplot2 closely enough that most of the examples below translate line for line. I provide Lets-Plot examples rendered as static images.

"[Bokeh](http://bokeh.pydata.org/en/latest/ "Python interactive visualization library") is a Python interactive visualization library that targets modern web browsers for presentation."

"[bqplot](https://github.com/bloomberg/bqplot) is a Grammar of Graphics-based interactive plotting framework for the Jupyter notebook."
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1 change: 1 addition & 0 deletions pyproject.toml
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Expand Up @@ -18,6 +18,7 @@ dependencies = [
"matplotlib>=3.7",
"seaborn>=0.13",
"plotnine>=0.13",
"lets-plot>=4.11",
"plotly>=5.24",
"altair>=5.0",
"vl-convert-python>=1.0", # Required for Altair PNG export
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1 change: 1 addition & 0 deletions render.py
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Expand Up @@ -21,6 +21,7 @@
"matplotlib": "Matplotlib",
"seaborn": "Seaborn",
"plotnine": "plotnine",
"lets-plot": "lets-plot",
"plotly": "plotly",
"altair": "Altair",
"ggplot": "ggplot2 (R)",
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