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Add lets-plot examples for all 13 plots
Lets-Plot renders to PNG through ggsave; an IPython image/png formatter registered on PlotSpec keeps the example cells free of export boilerplate. It is imported as lp so its ggplot2-style names do not shadow plotnine's.
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‎Examples.ipynb‎

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"execution_count": null,
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"metadata": {},
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"outputs": [],
8-
"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)"
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"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)"
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},
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{
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"cell_type": "code",
@@ -227,6 +227,28 @@
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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:bar-counts",
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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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"\"\"\"Lets-Plot mirrors the ggplot2 grammar. It is imported as `lp` here\n",
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"so its names don't collide with plotnine's.\n",
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"\"\"\"\n",
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"(lp.ggplot(mpg) +\n",
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" lp.aes(x=\"manufacturer\") +\n",
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" lp.geom_bar() +\n",
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" lp.coord_flip() +\n",
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" lp.ggtitle(\"Number of Cars by Make\"))"
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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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" geom_histogram(binwidth=2))"
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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:simple-histogram",
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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=\"cty\") +\n",
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" lp.geom_histogram(binwidth=2))"
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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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" ylab(\"Highway MPG\"))"
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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:scatter-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=\"displ\", y=\"hwy\") +\n",
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" lp.geom_point() +\n",
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" lp.ggtitle(\"Engine Displacement in Liters vs Highway MPG\") +\n",
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" lp.xlab(\"Engine Displacement in Liters\") +\n",
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" lp.ylab(\"Highway MPG\"))"
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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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" geom_smooth(method=\"lm\"))"
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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:scatter-with-regression",
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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(\"displ\", \"hwy\") +\n",
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" lp.geom_point() +\n",
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" lp.geom_smooth(method=\"lm\"))"
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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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" ylab(\"Highway MPG\"))"
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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:scatter-plot-with-colors",
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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=\"displ\", y=\"hwy\", color=\"class\") +\n",
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" lp.geom_point() +\n",
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" lp.ggtitle(\"Engine Displacement in Liters vs Highway MPG\") +\n",
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" lp.xlab(\"Engine Displacement in Liters\") +\n",
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" lp.ylab(\"Highway MPG\"))"
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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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" geom_point(alpha=.5))"
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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:scatter-plot-with-size",
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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=\"cty\", y=\"hwy\", size=\"cyl\") +\n",
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" lp.geom_point(alpha=.5))"
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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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" facet_wrap(\" ~ c\", nrow = 2))"
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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:scatter-plot-with-facet",
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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=\"displ\", y=\"hwy\") +\n",
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" lp.geom_point() +\n",
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" lp.facet_wrap(facets=\"class\", nrow=2))"
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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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" facet_grid(\"drv ~ cyl\"))"
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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:scatter-plot-with-facets",
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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=\"displ\", y=\"hwy\") +\n",
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" lp.geom_point() +\n",
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" lp.facet_grid(x=\"cyl\", y=\"drv\"))"
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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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" ))"
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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:stacked-smooth-line-and-scatter",
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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=\"displ\", y=\"hwy\") +\n",
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" lp.geom_point(lp.aes(color=\"class\")) +\n",
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" lp.geom_smooth(data=mpg[mpg[\"class\"] == \"subcompact\"],\n",
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" se=False,\n",
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" method=\"loess\"))"
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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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" geom_bar())"
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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:stacked-bar-chart",
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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\", fill=\"clarity\") +\n",
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" lp.geom_bar())"
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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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" geom_bar(position = \"dodge\"))\n"
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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:dodged-bar-chart",
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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\", fill=\"clarity\") +\n",
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" lp.geom_bar(position=\"dodge\"))"
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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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" geom_density(alpha=0.1))"
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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:stacked-kde",
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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(\"depth\", fill=\"cut\", color=\"cut\") +\n",
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" lp.geom_density(alpha=0.1) +\n",
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" lp.xlim(55, 70))"
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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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" + geom_line())"
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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:timeseries",
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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(ts)\n",
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" + lp.aes(\"date\", \"value\")\n",
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" + lp.geom_line())"
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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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},
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"nbformat": 4,
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"nbformat_minor": 1
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}
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}

‎pyproject.toml‎

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"matplotlib>=3.7",
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"seaborn>=0.13",
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"plotnine>=0.13",
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"lets-plot>=4.11",
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"plotly>=5.24",
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"altair>=5.0",
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"vl-convert-python>=1.0", # Required for Altair PNG export

‎render.py‎

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"matplotlib": "Matplotlib",
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"seaborn": "Seaborn",
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"plotnine": "plotnine",
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"lets-plot": "lets-plot",
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"plotly": "plotly",
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"altair": "Altair",
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"ggplot": "ggplot2 (R)",

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