docs/seaborn_demo_from_jakevdp.ipynb

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 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Seaborn demo per Jake VanderPlas below"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from __future__ import print_function, division\n",
    "\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.style.use('ggplot')\n",
    "x = np.linspace(0, 10, 1000)\n",
    "plt.plot(x, np.sin(x), x, np.cos(x));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "sns.set()\n",
    "plt.plot(x, np.sin(x), x, np.cos(x));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = np.random.multivariate_normal([0, 0], [[5, 2], [2, 2]], size=2000)\n",
    "data = pd.DataFrame(data, columns=['x', 'y'])\n",
    "\n",
    "for col in 'xy':\n",
    "    plt.hist(data[col], density=True, alpha=0.5)\n",
    "    # old Matplotlib would be    plt.hist(data[col], normed=True, alpha=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for col in 'xy':\n",
    "    sns.kdeplot(data[col], shade=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.distplot(data['x']);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.kdeplot(data.x, data.y); # formerly sns.kdeplot(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "with sns.axes_style('white'):\n",
    "    sns.jointplot(\"x\", \"y\", data, kind='kde');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "with sns.axes_style('white'):\n",
    "    sns.jointplot(\"x\", \"y\", data, kind='hex')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "iris = sns.load_dataset(\"iris\")\n",
    "iris.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tips = sns.load_dataset('tips')\n",
    "tips.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tips['tip_pct'] = 100 * tips['tip'] / tips['total_bill']\n",
    "\n",
    "grid = sns.FacetGrid(tips, row=\"sex\", col=\"time\", margin_titles=True)\n",
    "grid.map(plt.hist, \"tip_pct\", bins=np.linspace(0, 40, 15));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "with sns.axes_style(style='ticks'):\n",
    "    g = sns.factorplot(\"day\", \"total_bill\", \"sex\", data=tips, kind=\"box\")\n",
    "    g.set_axis_labels(\"Day\", \"Total Bill\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "with sns.axes_style('white'):\n",
    "    sns.jointplot(\"total_bill\", \"tip\", data=tips, kind='hex')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.jointplot(\"total_bill\", \"tip\", data=tips, kind='reg');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "planets = sns.load_dataset('planets')\n",
    "planets.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "with sns.axes_style('white'):\n",
    "    g = sns.factorplot(\"year\", data=planets, aspect=1.5)\n",
    "    g.set_xticklabels(step=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "with sns.axes_style('white'):\n",
    "    g = sns.factorplot(\"year\", data=planets, aspect=4.0,\n",
    "                       hue='method', order=range(2001, 2015), kind=\"count\")\n",
    "    g.set_ylabels('Number of Planets Discovered')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scikit-learn tutorial from pycon 2015 Jake VanderPlas [here](http://nbviewer.ipython.org/github/jakevdp/sklearn_pycon2015/blob/master/notebooks/Index.ipynb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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