doc/docstrings/ecdfplot.ipynb

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    "Plot a univariate distribution along the x axis:"
   ]
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   "source": [
    "import seaborn as sns; sns.set()\n",
    "penguins = sns.load_dataset(\"penguins\")\n",
    "sns.ecdfplot(data=penguins, x=\"flipper_length_mm\")"
   ]
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    "Flip the plot by assigning the data variable to the y axis:"
   ]
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   "cell_type": "code",
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   "source": [
    "sns.ecdfplot(data=penguins, y=\"flipper_length_mm\")"
   ]
  },
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   "cell_type": "markdown",
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   "source": [
    "If neither `x` nor `y` is assigned, the dataset is treated as wide-form, and a histogram is drawn for each numeric column:"
   ]
  },
  {
   "cell_type": "code",
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   "source": [
    "sns.ecdfplot(data=penguins.filter(like=\"culmen_\", axis=\"columns\"))"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "You can also draw multiple histograms from a long-form dataset with hue mapping:"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.ecdfplot(data=penguins, x=\"culmen_length_mm\", hue=\"species\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The default distribution statistic is normalized to show a proportion, but you can show absolute counts instead:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.ecdfplot(data=penguins, x=\"culmen_length_mm\", hue=\"species\", stat=\"count\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's also possible to plot the empirical complementary CDF (1 - CDF):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.ecdfplot(data=penguins, x=\"culmen_length_mm\", hue=\"species\", complementary=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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