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Merge pull request #25 from tdhopper/add-seaborn-objects
Add seaborn.objects column to the plotting comparison
2 parents cc92ad4 + 61d6de7 commit 30bdf4f

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‎Examples.ipynb‎

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"execution_count": null,
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"metadata": {},
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"outputs": [],
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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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"source": [
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"%matplotlib inline\n",
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"\n",
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"import readline\n",
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"import altair as alt\n",
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"import pandas as pd\n",
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"import seaborn as sns\n",
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"from matplotlib import pyplot\n",
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"from plotnine import *\n",
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"import numpy as np\n",
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"\n",
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"from plotly import figure_factory\n",
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"from plotly import graph_objects\n",
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"import plotly.express as px\n",
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"from IPython.core.magic import Magics, magics_class, cell_magic\n",
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"\n",
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"from IPython.display import Image\n",
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"\n",
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"from pylab import rcParams\n",
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"\n",
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"size = 20\n",
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"params = {\n",
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" \"legend.fontsize\": size,\n",
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" \"figure.figsize\": (15, 5),\n",
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" \"axes.labelsize\": size,\n",
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" \"axes.titlesize\": size,\n",
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" \"xtick.labelsize\": size,\n",
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" \"ytick.labelsize\": size,\n",
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" \"axes.titlesize\": 1.5 * size,\n",
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" \"figure.figsize\": (12, 12),\n",
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"}\n",
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"rcParams.update(params)\n",
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"theme_update(\n",
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" figure_size=(9, 9),\n",
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" title=element_text(size=size),\n",
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" text=element_text(size=0.6 * size),\n",
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") # for plotnine\n",
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"\n",
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"\n",
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"import seaborn.objects as so\n",
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"\n",
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"# Match the other libraries: ~750x750 px output with scaled-up text\n",
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"so.Plot.config.display[\"scaling\"] = 1\n",
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"so.Plot.config.theme.update(\n",
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" {\n",
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" \"figure.figsize\": (8, 8),\n",
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" \"axes.labelsize\": size,\n",
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" \"axes.titlesize\": 1.5 * size,\n",
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" \"xtick.labelsize\": size,\n",
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" \"ytick.labelsize\": size,\n",
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" \"legend.fontsize\": size,\n",
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" \"legend.title_fontsize\": size,\n",
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" }\n",
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")\n",
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"\n",
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"import plotly.io as pio\n",
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"pio.renderers.default = \"png\"\n",
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"pio.renderers[\"png\"].width = 750\n",
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"pio.renderers[\"png\"].height = 750\n",
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"\n",
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"# Render Altair charts as PNG via vl-convert\n",
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"alt.renderers.enable(\"png\", scale_factor=2.0)\n",
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"\n",
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"import tempfile\n",
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"\n",
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"import lets_plot as lp\n",
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"from lets_plot.export import ggsave\n",
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"\n",
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"lp.LetsPlot.setup_html()\n",
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"lp.LetsPlot.set_theme(\n",
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" lp.theme(text=lp.element_text(size=16), title=lp.element_text(size=20))\n",
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")\n",
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"\n",
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"_LETS_PLOT_DIR = tempfile.mkdtemp(prefix=\"lets-plot-\")\n",
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"\n",
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"\n",
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"def _lets_plot_png(plot):\n",
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" \"\"\"Render a Lets-Plot spec to PNG so the cell output carries an image.\"\"\"\n",
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" path = ggsave(plot + lp.ggsize(750, 750), \"plot.png\", path=_LETS_PLOT_DIR)\n",
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" with open(path, \"rb\") as f:\n",
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" return f.read()\n",
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"\n",
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"\n",
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"get_ipython().display_formatter.formatters[\"image/png\"].for_type(\n",
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" lp.plot.core.PlotSpec, _lets_plot_png\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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")"
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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:seaborn-objects"
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]
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},
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"outputs": [],
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"source": [
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"\"\"\"seaborn.objects has no `coord_flip`, so the\n",
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"categorical variable is mapped to `y` instead.\n",
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"\"\"\"\n",
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"(so.Plot(mpg, y=\"manufacturer\")\n",
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" .add(so.Bar(), so.Count())\n",
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" .label(title=\"Number of Cars by Make\"))"
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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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")"
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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:seaborn-objects"
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]
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},
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"outputs": [],
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"source": [
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"(so.Plot(mpg, x=\"cty\")\n",
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" .add(so.Bars(), so.Hist(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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")"
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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:seaborn-objects"
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]
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},
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"outputs": [],
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"source": [
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"(so.Plot(mpg, x=\"displ\", y=\"hwy\")\n",
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" .add(so.Dot())\n",
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" .label(\n",
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" x=\"Engine Displacement in Liters\",\n",
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" y=\"Highway MPG\",\n",
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" title=\"Engine Displacement in Liters vs Highway MPG\"))"
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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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"));"
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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:seaborn-objects"
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]
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},
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"outputs": [],
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"source": [
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"(so.Plot(mpg, x=\"displ\", y=\"hwy\",\n",
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" color=\"class\")\n",
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" .add(so.Dot())\n",
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" .label(\n",
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" x=\"Engine Displacement in Liters\",\n",
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" y=\"Highway MPG\",\n",
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" title=\"Engine Displacement in Liters vs Highway MPG\"))"
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]
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},
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"cell_type": "code",
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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:seaborn-objects"
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]
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},
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"outputs": [],
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"source": [
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"(so.Plot(mpg, x=\"cty\", y=\"hwy\",\n",
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" pointsize=\"cyl\")\n",
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" .add(so.Dots(alpha=.5))\n",
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" .scale(pointsize=(4, 12))\n",
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" .label(x=\"City MPG\", y=\"Highway MPG\"))"
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]
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},
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"cell_type": "markdown",
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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:scatter-plot-with-facet",
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"package:seaborn-objects"
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]
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},
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"outputs": [],
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"source": [
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"(so.Plot(mpg, x=\"displ\", y=\"hwy\")\n",
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" .add(so.Dot())\n",
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" .facet(\"class\", wrap=4))"
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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:seaborn-objects"
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]
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},
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"outputs": [],
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"source": [
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"(so.Plot(mpg, x=\"displ\", y=\"hwy\")\n",
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" .add(so.Dot())\n",
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" .facet(col=\"cyl\", row=\"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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"metadata": {
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"tags": [
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"ex",
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"name:stacked-bar-chart",
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"package:seaborn-objects"
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]
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},
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"outputs": [],
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"source": [
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"(so.Plot(diamonds, x=\"cut\", color=\"clarity\")\n",
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" .add(so.Bar(), so.Count(), so.Stack()))"
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]
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},
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"cell_type": "code",
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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:seaborn-objects"
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]
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},
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"outputs": [],
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"source": [
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"(so.Plot(diamonds, x=\"cut\", color=\"clarity\")\n",
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" .add(so.Bar(), so.Count(), so.Dodge()))"
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]
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},
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"cell_type": "code",
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"outputs": [],
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"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);"
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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:seaborn-objects"
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]
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},
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"outputs": [],
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"source": [
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"\"\"\"`.limit()` only clips the axis; unlike ggplot2's\n",
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"`xlim()` it does not drop rows before the density is\n",
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"estimated.\n",
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"\"\"\"\n",
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"(so.Plot(diamonds, x=\"depth\", color=\"cut\")\n",
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" .add(so.Area(alpha=.1), so.KDE(gridsize=500))\n",
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" .limit(x=(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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" x=\"date\", y=\"value\"\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:timeseries",
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"package:seaborn-objects"
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]
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},
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"outputs": [],
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"source": [
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"(so.Plot(ts, x=\"date\", y=\"value\")\n",
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" .add(so.Line()))"
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]
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}
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],
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"metadata": {

‎INTRO.md‎

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"[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.
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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.
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#### Interactive Plotting Libraries
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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).

‎render.py‎

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"pandas": "Pandas",
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"matplotlib": "Matplotlib",
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"seaborn": "Seaborn",
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"seaborn-objects": "seaborn.objects",
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"plotnine": "plotnine",
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"lets-plot": "lets-plot",
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"plotly": "plotly",

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