{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Bivariate\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "import numpy as np\nimport seaborn as sns\nimport matplotlib.pyplot as plt\nsns.set_theme(style=\"dark\")\n\n# Simulate data from a bivariate Gaussian\nn = 10000\nmean = [0, 0]\ncov = [(2, .4), (.4, .2)]\nrng = np.random.RandomState(0)\nx, y = rng.multivariate_normal(mean, cov, n).T\n\n# Draw a combo histogram and scatterplot with density contours\nf, ax = plt.subplots(figsize=(6, 6))\nsns.scatterplot(x=x, y=y, s=5, color=\".15\")\nsns.histplot(x=x, y=y, bins=50, pthresh=.1, cmap=\"mako\")\nsns.kdeplot(x=x, y=y, levels=5, color=\"w\", linewidths=1)"
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8.10"
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  "nbformat": 4,
  "nbformat_minor": 0
}