Seaborn_Matplotlib_Solutions_and_Demos.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Matplotlib and Seaborn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Handy hints "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Some of the plotting libraries we use need to communicate a lot of data to the browser. Depending on which version of Jupyter you are running, you may need to launch this notebook with a higher data rate limit: `jupyter notebook --NotebookApp.iopub_data_rate_limit=10000000`\n",
    "\n",
    "* In general, we are using plotting libraries that return objects encapsulating the plot. You can check the type of these returned objects with `type()`. Jupyter's tools for exploring objects and methods will also be useful: the `?` and `??` operators, and tab autocompletion."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-14T13:44:21.920488Z",
     "start_time": "2018-01-14T13:44:20.823184Z"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-14T13:44:22.374371Z",
     "start_time": "2018-01-14T13:44:21.925250Z"
    }
   },
   "outputs": [],
   "source": [
    "# This causes Jupyter to display any matplotlib plots directly in the notebook\n",
    "# It also works for seaborn, since seaborn uses matplotlib to render plots\n",
    "%matplotlib inline\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Be aware that Seaborn automatically changes Matplotlib's defaults *on import*. Not only your Seaborn plots, but also your Matplotlib plots, will look different once Seaborn is imported. If you don't want this behaviour, you can call `sns.reset_orig()` after import."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-14T13:44:23.219284Z",
     "start_time": "2018-01-14T13:44:22.377459Z"
    }
   },
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Housing data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-14T14:20:04.384919Z",
     "start_time": "2018-01-14T14:20:04.279317Z"
    }
   },
   "outputs": [],
   "source": [
    "sales = pd.read_csv(\"housing-data-10000.csv\", \n",
    "                    usecols=['id','date','price','zipcode','lat','long', 'bedrooms',\n",
    "                             'waterfront','view','grade','sqft_living','sqft_lot'],\n",
    "                    parse_dates=['date'], \n",
    "                    dtype={'zipcode': 'category',\n",
    "                           'waterfront': 'bool'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-14T13:44:23.385768Z",
     "start_time": "2018-01-14T13:44:23.354728Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>date</th>\n",
       "      <th>price</th>\n",
       "      <th>sqft_living</th>\n",
       "      <th>sqft_lot</th>\n",
       "      <th>waterfront</th>\n",
       "      <th>view</th>\n",
       "      <th>grade</th>\n",
       "      <th>zipcode</th>\n",
       "      <th>lat</th>\n",
       "      <th>long</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1446800660</td>\n",
       "      <td>2015-03-16</td>\n",
       "      <td>276500.0</td>\n",
       "      <td>1400</td>\n",
       "      <td>6650</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>98168</td>\n",
       "      <td>47.4888</td>\n",
       "      <td>-122.332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1453601502</td>\n",
       "      <td>2015-02-26</td>\n",
       "      <td>303697.0</td>\n",
       "      <td>2520</td>\n",
       "      <td>7334</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>98125</td>\n",
       "      <td>47.7263</td>\n",
       "      <td>-122.291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9523103990</td>\n",
       "      <td>2014-12-08</td>\n",
       "      <td>611000.0</td>\n",
       "      <td>1850</td>\n",
       "      <td>5000</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>98103</td>\n",
       "      <td>47.6727</td>\n",
       "      <td>-122.351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7308600040</td>\n",
       "      <td>2014-07-23</td>\n",
       "      <td>769995.0</td>\n",
       "      <td>3360</td>\n",
       "      <td>12080</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>98011</td>\n",
       "      <td>47.7757</td>\n",
       "      <td>-122.173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1562200240</td>\n",
       "      <td>2014-09-18</td>\n",
       "      <td>550000.0</td>\n",
       "      <td>2160</td>\n",
       "      <td>15360</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>98007</td>\n",
       "      <td>47.6232</td>\n",
       "      <td>-122.138</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           id       date     price  sqft_living  sqft_lot  waterfront  view  \\\n",
       "0  1446800660 2015-03-16  276500.0         1400      6650       False     0   \n",
       "1  1453601502 2015-02-26  303697.0         2520      7334       False     0   \n",
       "2  9523103990 2014-12-08  611000.0         1850      5000       False     0   \n",
       "3  7308600040 2014-07-23  769995.0         3360     12080       False     0   \n",
       "4  1562200240 2014-09-18  550000.0         2160     15360       False     0   \n",
       "\n",
       "   grade zipcode      lat     long  \n",
       "0      6   98168  47.4888 -122.332  \n",
       "1      7   98125  47.7263 -122.291  \n",
       "2      7   98103  47.6727 -122.351  \n",
       "3      9   98011  47.7757 -122.173  \n",
       "4      8   98007  47.6232 -122.138  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sales.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-14T13:44:24.377402Z",
     "start_time": "2018-01-14T13:44:24.370046Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                      int64\n",
       "date           datetime64[ns]\n",
       "price                 float64\n",
       "sqft_living             int64\n",
       "sqft_lot                int64\n",
       "waterfront               bool\n",
       "view                    int64\n",
       "grade                   int64\n",
       "zipcode              category\n",
       "lat                   float64\n",
       "long                  float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sales.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that as well as specifying that the `date` field should be parsed as a date, we specified that certain variables are categorical (as opposed to integers). Some plotting commands understand pandas DataFrames and will treat categorical variables differently to numerical variables."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Toy data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-14T13:44:28.764201Z",
     "start_time": "2018-01-14T13:44:28.723301Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>number</th>\n",
       "      <th>engine_type</th>\n",
       "      <th>colour</th>\n",
       "      <th>wheels</th>\n",
       "      <th>top_speed_mph</th>\n",
       "      <th>weight_tons</th>\n",
       "      <th>size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Thomas</td>\n",
       "      <td>1</td>\n",
       "      <td>Tank</td>\n",
       "      <td>Blue</td>\n",
       "      <td>6</td>\n",
       "      <td>40</td>\n",
       "      <td>52.00</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Edward</td>\n",
       "      <td>2</td>\n",
       "      <td>Tender</td>\n",
       "      <td>Blue</td>\n",
       "      <td>14</td>\n",
       "      <td>70</td>\n",
       "      <td>41.00</td>\n",
       "      <td>Small</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Henry</td>\n",
       "      <td>3</td>\n",
       "      <td>Tender</td>\n",
       "      <td>Green</td>\n",
       "      <td>18</td>\n",
       "      <td>90</td>\n",
       "      <td>72.20</td>\n",
       "      <td>Big</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Gordon</td>\n",
       "      <td>4</td>\n",
       "      <td>Tender</td>\n",
       "      <td>Blue</td>\n",
       "      <td>18</td>\n",
       "      <td>100</td>\n",
       "      <td>91.35</td>\n",
       "      <td>Big</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>James</td>\n",
       "      <td>5</td>\n",
       "      <td>Tender</td>\n",
       "      <td>Red</td>\n",
       "      <td>14</td>\n",
       "      <td>70</td>\n",
       "      <td>46.00</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Percy</td>\n",
       "      <td>6</td>\n",
       "      <td>Tank</td>\n",
       "      <td>Green</td>\n",
       "      <td>4</td>\n",
       "      <td>40</td>\n",
       "      <td>22.85</td>\n",
       "      <td>Small</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Toby</td>\n",
       "      <td>7</td>\n",
       "      <td>Tank</td>\n",
       "      <td>Brown</td>\n",
       "      <td>6</td>\n",
       "      <td>20</td>\n",
       "      <td>27.00</td>\n",
       "      <td>Small</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Emily</td>\n",
       "      <td>12</td>\n",
       "      <td>Tender</td>\n",
       "      <td>Green</td>\n",
       "      <td>8</td>\n",
       "      <td>85</td>\n",
       "      <td>45.00</td>\n",
       "      <td>Small</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Rosie</td>\n",
       "      <td>37</td>\n",
       "      <td>Tank</td>\n",
       "      <td>Purple</td>\n",
       "      <td>6</td>\n",
       "      <td>65</td>\n",
       "      <td>37.00</td>\n",
       "      <td>Small</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Hiro</td>\n",
       "      <td>51</td>\n",
       "      <td>Tender</td>\n",
       "      <td>Black</td>\n",
       "      <td>20</td>\n",
       "      <td>55</td>\n",
       "      <td>76.80</td>\n",
       "      <td>Big</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     name  number engine_type  colour  wheels  top_speed_mph  weight_tons  \\\n",
       "0  Thomas       1        Tank    Blue       6             40        52.00   \n",
       "1  Edward       2      Tender    Blue      14             70        41.00   \n",
       "2   Henry       3      Tender   Green      18             90        72.20   \n",
       "3  Gordon       4      Tender    Blue      18            100        91.35   \n",
       "4   James       5      Tender     Red      14             70        46.00   \n",
       "5   Percy       6        Tank   Green       4             40        22.85   \n",
       "6    Toby       7        Tank   Brown       6             20        27.00   \n",
       "7   Emily      12      Tender   Green       8             85        45.00   \n",
       "8   Rosie      37        Tank  Purple       6             65        37.00   \n",
       "9    Hiro      51      Tender   Black      20             55        76.80   \n",
       "\n",
       "     size  \n",
       "0  Medium  \n",
       "1   Small  \n",
       "2     Big  \n",
       "3     Big  \n",
       "4  Medium  \n",
       "5   Small  \n",
       "6   Small  \n",
       "7   Small  \n",
       "8   Small  \n",
       "9     Big  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from io import StringIO\n",
    "\n",
    "data_string = \"\"\"name\tnumber\tengine_type\tcolour\twheels\ttop_speed_mph\tweight_tons\n",
    "Thomas\t1\tTank\tBlue\t6\t40\t52\n",
    "Edward\t2\tTender\tBlue\t14\t70\t41\n",
    "Henry\t3\tTender\tGreen\t18\t90\t72.2\n",
    "Gordon\t4\tTender\tBlue\t18\t100\t91.35\n",
    "James\t5\tTender\tRed\t14\t70\t46\n",
    "Percy\t6\tTank\tGreen\t4\t40\t22.85\n",
    "Toby\t7\tTank\tBrown\t6\t20\t27\n",
    "Emily\t12\tTender\tGreen\t8\t85\t45\n",
    "Rosie\t37\tTank\tPurple\t6\t65\t37\n",
    "Hiro\t51\tTender\tBlack\t20\t55\t76.8\"\"\"\n",
    "\n",
    "trains = pd.read_table(StringIO(data_string))\n",
    "trains['size'] = pd.cut(trains['weight_tons'], 3, labels=['Small','Medium','Big'])\n",
    "\n",
    "trains"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Matplotlib "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Matplotlib is the oldest and still the fundamental plotting library in Python. It has a huge range of capabilities. Many other libraries (including Seaborn) use Matplotlib as a back-end renderer.\n",
    "\n",
    "Today we're focussing on plotting tabular data. We won't touch on all Matplotlib's capabilities. If you want to see more of the range of things Matplotlib can do, you can look through the [Matplotlib gallery](https://matplotlib.org/gallery.html.), or try out this excellent [Matplotlib tutorial](https://www.labri.fr/perso/nrougier/teaching/matplotlib/)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "An example Matplotlib plot with legend and annotation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-14T05:36:41.975324Z",
     "start_time": "2018-01-14T05:36:41.805467Z"
    },
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "x = [1,2,3,4,5]\n",
    "y = [2,5,10,17,26]\n",
    "y2 = [1,4,9,11,9]\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(x, y, c='blue', label='Projected')\n",
    "ax.scatter(x, y2, c='red', label='Actual')\n",
    "fig.legend()\n",
    "ax.annotate(\"where it all went wrong\", \n",
    "                                 xy=(3,10), xytext=(1,12),\n",
    "                                 arrowprops={'width':2})\n",
    "\n",
    "fig.savefig('example_matplotilb.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Seaborn "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Seaborn builds on Matplotlib. Some nice features are:\n",
    "\n",
    "- works directly with Pandas dataframes, concise syntax\n",
    "- lots of plot types, including some more advanced options\n",
    "- statistical plotting: many plots do summary statistics for you\n",
    "- good default aesthetics and easy control of aesthetics\n",
    "- using Matplotlib gives benefits of Matplotlib - many backends, lots of control\n",
    "- underlying Matplotlib objects are easy to tweak directly"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For completeness, here's the plot we made before:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame({\n",
    "    'Time': [1,2,3,4,5],\n",
    "    'Projected': [2,5,10,17,26],\n",
    "    'Actual': [1,4,9,11,9]\n",
    "})\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "sns.scatterplot(data=df, x='Time', y='Actual', color='red', ax=ax)\n",
    "sns.lineplot(data=df, x='Time', y='Projected', color='blue', ax=ax)\n",
    "\n",
    "ax.set_ylabel('Huge profits')\n",
    "\n",
    "ax.annotate(\"where it all went wrong\", \n",
    "                                 xy=(3,10), xytext=(1,12),\n",
    "                                 arrowprops={'width':2})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that we can add changes like annotations in exactly the same way, as we have Matplotlib Figure and Axes objects."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Seaborn and Pandas "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can use Seaborn the way we used Matplotlib, passing in lists (or arrays or series) directly:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-14T13:44:44.926516Z",
     "start_time": "2018-01-14T13:44:44.392053Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x112aa8908>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112a737b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=['A','B','C'], y=[33,44,65])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "However Seaborn is aware of Pandas and it is very common to use Seaborn directly with DataFrames. Plotting functions can take a DataFrame as their `data` parameter and then refer directly to column names:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-14T13:44:49.482670Z",
     "start_time": "2018-01-14T13:44:49.294246Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x112d37160>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c23e630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(data=trains, x='engine_type', y='top_speed_mph')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here Seaborn has interpreted the `x` and `y` arguments as field names in the supplied DataFrame. Notice also that Seaborn has performed the summary statistics for us - in this case, using the default `estimator`, which is `mean()`. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice also what happens if we simply swap the `x` and `y` parameters. Seaborn will automatically deduce that the categorical or string-like variable must be the bar labels, and the numeric variable must be the numeric axis:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-11T11:06:03.518992Z",
     "start_time": "2018-01-11T11:06:03.183177Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1d728cc0>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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9Rw+H/vHDJHsBN1XVwlXtMyptQe8398cD/1lVjwPuYIXuoFFpi+5eywvpBe/DgZlJ9h27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\nmjFkJEnNGDKSpGYMGUlSM4aMJKmZ/wektK8JyGeEgQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1d809940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(data=trains, x='top_speed_mph', y='engine_type')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise:** create a (vertical) bar plot using the `sales` data, showing how house prices vary with the value of the property `grade`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-14T13:45:47.839058Z",
     "start_time": "2018-01-14T13:45:47.048466Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10d7edef0>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10d660ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(data=sales, x='grade', y='price')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bar plots are often deplored as a way of showing statistical estimates, as only the top of the bar is really important, and the bar itself is a visual distraction. A point plot is an alternative, and plots like box plots can show more information. Several other plot types also show distributional information within categories.\n",
    "\n",
    "**Exercise:** reproduce the plot you just made, using instead each of the Seaborn functions:\n",
    "\n",
    "- pointplot()\n",
    "- boxplot()\n",
    "- violinplot()  (try the `scale` parameter)\n",
    "- boxenplot()\n",
    "- stripplot() [SEE WARNING]  (try the `jitter` parameter)\n",
    "- swarmplot() [SEE WARNING]\n",
    "\n",
    "Note what sort of information about the distribution is shown by each.\n",
    "\n",
    "WARNING: `stripplot()` and `swarmplot()` will plot individual data points. There are too many house sales to easily display in this way - you may want to subsample the dataframe with e.g.  `data=sales.sample(100)`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-14T13:47:11.857483Z",
     "start_time": "2018-01-14T13:47:11.434988Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1d5a0c50>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1d51c438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.swarmplot(data=sales.sample(100), x='grade', y='price')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's try making a horizontal bar plot of `price` against `grade` by putting `grade` on the y axis:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-11T10:50:06.555318Z",
     "start_time": "2018-01-11T10:46:53.661003Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1146887b8>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Z4jkjiRjOPCNVrIdZO2D0DZ9M39xjNdS0suSA4TjPzUJfL8yTIMaBYg6aqPQl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\nIiJBKdCIiEhQ/x/l2cxmdGM7QQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a18e20d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# WARNING: \n",
    "# Do not rerun this cell without modifying it!\n",
    "# It will take a long time to run.\n",
    "sns.barplot(data=sales, x='price', y='grade')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is obviously not what we want.\n",
    "\n",
    "**Questions:**\n",
    "* What went wrong here?\n",
    "* Can you fix it? (There are at least two sensible approaches.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-14T13:47:41.839504Z",
     "start_time": "2018-01-14T13:47:41.132250Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1d64cdd8>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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zH4BPFkvVq7VUJfBpYAZwR0Q8EBGfq6p/SVJ36i5VeU1V/UmSeuOTxZLUciYCSWo5E4EktVxf1COYOv9A7x6RpIo4I5CkljMRSFLLmQgkqeX64hrBzie38+RV9zYdhjSQDv/Qa5sOQQ1zRiBJLWcikKSWMxFIUsvVXbP4z8p6xQ9ExO0RcURV/UuSulN3zeJPZOZrMvNkYCXwkQr7lyR1oe6axds6Pk4DLDgjSQ1rokLZx4ELgK3Am+ruX/3pz79zDZt2bGk6jIE05b6pTYegUdRRZ7v2RJCZVwBXRMQy4D8AHx1pu86axUfNtrxg223asYUntj/ddBiDaXvTAahpTT5Q9mXg6+wjEXTWLD7pFa/yFFLLzT94dtMhDKwps5wRTGZ11Nmuu2bx4sxcV348B3iozv7Vv5YtfV/TIQwsnyxWZYmgrFn8RmBeRKyn+Mv/rIg4AXgR+Cnw/qr6lyR1x5rFktRyPlksSS1nIpCkljMRSFLL9UU9ggMPn+adDZJUEWcEktRykTn5n9WKiCHg4abjqNA84Kmmg6jYoI9x0McHgz/GQRzfMZk5f38b9cWpIeDhzFzSdBBViYhVgzw+GPwxDvr4YPDHOOjjG42nhiSp5UwEktRy/ZIIljcdQMUGfXww+GMc9PHB4I9x0Me3T31xsViSVJ1+mRFIkioyqRNBRLwtIh6OiEci4vKm49klIj4fERsjYk1H25yIuCMi1pWvs8v2iIhPlmP4XkSc0vE7F5bbr4uICzvafzUivl/+zicjInrto8fxvSIi/iEi1kbEDyLi0kEaY0RMjYh7I+LBcnx/WrYfGxH3lH3fEBEHle0vLz8/Un6/qGNfy8r2hyPitzraRzx2e+ljHOOcEhH3R8TKAR3fo+Ux9EBErCrbBuIYrV1mTsofYArwY+A44CDgQeDVTcdVxvZ64BRgTUfblcDl5fvLgb8o358F3AoEcBpwT9k+B/hJ+Tq7fD+7/O5eYGn5O7cCZ/bSxzjGtxA4pXw/A/gR8OpBGWO5j+nl+wOBe8p9fgV4d9n+OeAD5fsPAp8r378buKF8/+ryuHw5cGx5vE4Z7dgdax/j/Hf8TxQFoFb20ncfjO9RYN6wtoE4Ruv+aTyAUf6RlwK3dXxeBixrOq6OeBaxdyJ4GFhYvl9I8ewDwNXA+cO3A84Hru5ov7psWwg81NG+e7ux9jGBY70FeMsgjhE4BLgP+DWKh4kOGH78AbcBS8v3B5TbxfBjctd2+zp2y98ZUx/jGNdRwJ3AbwIre+l7Mo+v3M+jvDQRDNwxWsfPZD41dCTws47P68u2yerwzHwcoHw9rGzf1zhGa18/QnsvfYxbOYX/FYq/mgdmjOVpkweAjcAdFH/hPpOZL4yw/919l99vBebuZ3wjtc/toY9eXQV8mKIIFD32PZnHB5DA7RGxOooa5zBAx2idJvOTxTFCWz/e4rSvcYy1vZc+xiUipgM3AR/KzG3lKdKx9D9px5iZvwBOjohZwM3Aq0bZ/1jHMdIfWPsb94SNLyLOBjZm5uqIeGMX+++r8XU4PTMfi4jDgDsiYrTSt313jNZpMs8I1gOv6Ph8FPBYQ7F048mIWAhQvm4s2/c1jtHajxqhvZc+ehYRB1Ikgesy82s99j+pxwiQmc8A36Q4pzsrInb9cdS5/919l98fCmweJaZ9tT/VQx+9OB04JyIeBf6W4vTQVQM0PgAy87HydSNFMn8tA3iM1mEyJ4LvAovLuxAOorjAtKLhmEazAth1x8GFFOfVd7VfUN5RcBqwtZxO3ga8NSJml3cdvJXifOrjwFBEnFbepXDBsH2NpY+elP1eA6zNzP81aGOMiPnlTICIOBh4M7AW+AfgvH30vSum84C/z+Ik8Arg3eUdMccCiykuMI547Ja/M9Y+xiwzl2XmUZm5qOz77zPzPYMyPoCImBYRM3a9pzi21jAgx2jtmr5IMdoPxVX4H1Gcv72i6Xg64roeeBzYSfFXwPsoznfeCawrX+eU2wbwV+UYvg8s6djPRcAj5c8fdrQvoTiofwx8mj0P/o25jx7H9zqKKe33gAfKn7MGZYzAa4D7y/GtAT5Sth9H8R/dI8BXgZeX7VPLz4+U3x/Xsa8rypgepryrZLRjt5c+xvlv+Ub23DU0MOMr+3mw/PnBrhgG5Rit+8cniyWp5SbzqSFJUg1MBJLUciYCSWo5E4EktZyJQJJazkQg9SgiPhYRb246Dmm8vH1U6kFETMlimQqp7zkjkIaJiEUR8VBEfLFcV/7GiDgkivXvPxIR/wS8KyK+EBHnlb9zakT8cxQ1Du6NiBnlwnafiIjvlvv544aHJo3IRCCN7ARgeWa+BthGsZ4+wM8z83WZ+be7NiyXWbgBuDQzT6JYsmIHxRPnWzPzVOBU4I/KpRqkScVEII3sZ5n57fL9tRTLbkDxH/5wJwCPZ+Z3ATJzWxZLLb+VYu2ZByiW8Z5LsV6PNKlM5mWopSYNv3i26/P2EbaNEbbf1X5JZt42kYFJE80ZgTSyoyNiafn+fOCfRtn2IeCIiDgVoLw+cADFypYfKJf0JiL+TblSpjSpmAikka0FLoyI71HUs/3svjbMzOeB3wM+FREPUlQ8mwr8DfBD4L6IWENRytBZuCYdbx+VhomiPOfKzDyx4VCkWjgjkKSWc0YgSS3njECSWs5EIEktZyKQpJYzEUhSy5kIJKnlTASS1HL/Hy2nDZ6EV/FSAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1d21c588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(data=sales, x='price', y='grade', orient='h')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-14T13:48:47.862671Z",
     "start_time": "2018-01-14T13:48:46.962037Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1dfc4fd0>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1dfc46a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sales_fixed = sales.copy()\n",
    "sales_fixed['grade'] = sales['grade'].astype('category')\n",
    "\n",
    "sns.barplot(data=sales_fixed, x='price', y='grade')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hue "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Many Seaborn plotting functions take a `hue` parameter. This colours the plot elements by some categorical variable, but more than this, summary statistics are calculated for each level of the hue variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-12T04:32:17.095524Z",
     "start_time": "2018-01-12T04:32:14.953775Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1a1275fe80>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Gw3nnnbdj6dKlC970pjctyGx7//vfv7u1tbVjypQpffudO3du97XXXjt1/vz5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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1275f160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# It appears that my hypothesis that more wheels make you faster is flawed\n",
    "sns.lmplot(data=trains, x='wheels', y='top_speed_mph', \n",
    "           hue='engine_type', palette=['red', 'blue'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercises:** \n",
    "* Create an `lmplot` of house price against living area. Do this without a `hue` parameter, then add in `waterfront` as the `hue` parameter. What information is the hue giving in this case?\n",
    "* Try adding the `hue` parameter to one of your previous plots of some other type - for instance, a box plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-14T14:13:21.674010Z",
     "start_time": "2018-01-14T14:13:20.164591Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1a204bc390>"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a203c0048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot(data=sales, x='sqft_living', y='price', hue='waterfront')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-14T14:13:56.858387Z",
     "start_time": "2018-01-14T14:13:55.848985Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x113746780>"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1e5f8080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(data=sales, x='grade', y='price', hue='waterfront')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we'd wanted to colour scatter points by some continuous variable, `hue` can be made to work, but doesn't really make sense as it is intended for discrete values. In this specific case, we could pass our colour variable down to the underlying Matplotlib scatter call via the `scatter_kws` parameter. We'll look more at this later. Or, if we're not trying to fit a linear model, we could just use Seaborn aesthetics around a Matplot scatter plot:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-14T14:14:47.685114Z",
     "start_time": "2018-01-14T14:14:46.732761Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a204ff160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sample = sales.sample(200)\n",
    "\n",
    "with sns.axes_style('white'):\n",
    "    fig, ax = plt.subplots()\n",
    "    #ax.set_facecolor(sns.xkcd_rgb['very pale green'])\n",
    "    ax.scatter(x=sales['long'], y=sales['lat'], c=sales['price'].apply(np.log), \n",
    "               alpha=0.5, cmap='Reds')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For discrete color palettes, as needed by the `hue` parameter, Seaborn has a `color_palette()` function to generate a useful range of palettes. You can find [a tutorial on choosing color palettes here](https://seaborn.pydata.org/tutorial/color_palettes.html). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compound plots "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Seaborn has some plotting functions which create more complex figures made of multiple subplots. These include `pairplot()`, `catplot()`, `jointplot()`, `lmplot()` and `clustermap()`. Let's see a few examples:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-13T23:59:59.565941Z",
     "start_time": "2018-01-13T23:59:58.642601Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x1a1437fc88>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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K2/xZE6irrenxvMRgl6ye5iOKVKdMe4pTgT+a2Yvh+SHAJjPbQLQt4tF5aZ1I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\nIiISKCiKiIgECooiIiKBgqKIiEjw/wF2JPhrzEMb0gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1437f630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# jointplot shows a scatter or density plot, with marginal distributions\n",
    "sns.jointplot(data=sales, x='sqft_living', y='price') #, kind='reg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-13T23:55:07.448709Z",
     "start_time": "2018-01-13T23:55:05.689426Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x109dbe128>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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P7EBskiSpplpKICLij4F/T3M0ToCfj4hnZOZLOhaZtEWM7X1vy/POXvmsDkbS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\nBJtAAACbQAAAm0AAAFtJ2x5T0LZZaQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x109dbe898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# pairplot shows pairwise relationships between variables\n",
    "# Note that a variable like engine_type would be ignored as it is not numeric\n",
    "sns.pairplot(data=trains[['wheels', 'top_speed_mph', 'weight_tons']])\n",
    "\n",
    "# TODO: maybe demo with sales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-14T00:11:44.991361Z",
     "start_time": "2018-01-14T00:11:44.563340Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1a15036358>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a154eb668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# catplot conditions different subplots on different variable values\n",
    "# we map variables to row and column of a grid of plots (as well as to hue)\n",
    "# in this example, we just use columns, and so have only one row\n",
    "sns.catplot(data=trains, kind='bar',\n",
    "               x='size', y='top_speed_mph', \n",
    "               col='engine_type')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-14T14:19:48.429448Z",
     "start_time": "2018-01-14T14:19:48.402116Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['id', 'date', 'price', 'sqft_living', 'sqft_lot', 'waterfront', 'view',\n",
       "       'grade', 'zipcode', 'lat', 'long'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sales.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-11T11:39:43.169894Z",
     "start_time": "2018-01-11T11:39:43.013278Z"
    }
   },
   "source": [
    "**Exercise:** design a plot using sns.catplot, to show the relationship between house price and (at least): grade, waterfront, and view. Available channels of information are:\n",
    "- x and y coordinates\n",
    "- hue\n",
    "- row and column of subplot (`row` and `col`)\n",
    "\n",
    "You do not have to use all of these channels - in fact your plot may be difficult to take in if you do.\n",
    "\n",
    "You can set the `kind` parameter to the kind of plot you want to make: point, bar, count, box, violin, and strip.\n",
    "\n",
    "You can control the size of the overall figure with `size` and `aspect`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-14T14:31:02.672952Z",
     "start_time": "2018-01-14T14:31:00.907170Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1a24a6fe80>"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a23e16630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# One option\n",
    "sns.catplot(data=sales, y='price', x='grade', row='view', hue='waterfront', kind='violin',\n",
    "               kwargs={'scale':'width'}, size=2, aspect=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Colour and Palettes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Seaborn has good colour options. There are a few ways we could want to use access colours:\n",
    "\n",
    "* Specify an individual colour for some plot element. Matplotlib named colours can be used, or rgb values specified. Also check out the `sns.xkcd_rgb` dictionary of 954 named colours from the XKCD colour survey - for instance, `sns.xkcd['fire engine red']` is a colour.\n",
    "* Specify a colormap, for mapping a continuous value to colour. All Matplotlib colormaps can be used by name. You can see these under the `plt.cm` module. Seaborn's `light_palette()` and `dark_palette()` functions can also generate custom colourmaps easily.\n",
    "* Specify a discrete colour palette (a list of colours), for mapping a discrete or categorical variable to colour. In Seaborn, there is a distinction between colour palettes and colormaps. In general, you can create a colour palette by explicitly listing some colours, or by selecting a series of colours along some colormap. There are several functions, including `color_palette()`, `light_palette()`, `dark_palette()`, `diverging_palette()` and `xkcd_palette()`, which can produce many discrete colour palettes of whatever size you need. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-14T13:53:33.683766Z",
     "start_time": "2018-01-14T13:53:33.531585Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1e1fdfd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# An example discrete colour palette of 7 colours, based on the XKCD colour \"denim blue\"\n",
    "# palplot is a function to visualise a palette\n",
    "palette = sns.light_palette(\"denim blue\", n_colors=7, input='xkcd')\n",
    "sns.palplot(palette)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-12T05:15:15.360791Z",
     "start_time": "2018-01-12T05:15:15.190540Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "#3b5b92\n",
      "[array([ 0.92878049,  0.94439024,  0.97121951,  1.        ]), array([ 0.8139133 ,  0.84762101,  0.90555614,  1.        ]), array([ 0.69631117,  0.74854774,  0.83832935,  1.        ]), array([ 0.57870905,  0.64947448,  0.77110256,  1.        ]), array([ 0.46384186,  0.55270524,  0.70543918,  1.        ]), array([ 0.34623974,  0.45363198,  0.63821239,  1.        ]), array([ 0.23137255,  0.35686275,  0.57254902,  1.        ])]\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c548320>"
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    }
   ],
   "source": [
    "# Equivalently (to illustrate that we can use an rgb value directly)\n",
    "denim_blue = sns.xkcd_rgb[\"denim blue\"]\n",
    "print(denim_blue)\n",
    "palette = sns.light_palette(denim_blue, n_colors=7)\n",
    "print(palette)\n",
    "sns.palplot(palette)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise:** Try out the Seaborn function `choose_diverging_palette()` in your notebook (it requires no arguments). You can assign the result to a variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-14T13:54:03.680225Z",
     "start_time": "2018-01-14T13:54:03.327115Z"
    }
   },
   "outputs": [
    {
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      },
      "text/html": [
       "<p>Failed to display Jupyter Widget of type <code>interactive</code>.</p>\n",
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       "  If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
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       "interactive(children=(IntSlider(value=220, description='h_neg', max=359), IntSlider(value=10, description='h_pos', max=359), IntSlider(value=74, description='s', max=99), IntSlider(value=50, description='l', max=99), IntSlider(value=10, description='sep', max=50, min=1), IntSlider(value=9, description='n', max=16, min=2), Dropdown(description='center', options=('light', 'dark'), value='light'), Output()), _dom_classes=('widget-interact',))"
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       "[array([0.25199714, 0.49873371, 0.57516028, 1.        ]),\n",
       " array([0.43026136, 0.62000665, 0.67878019, 1.        ]),\n",
       " array([0.60852558, 0.74127959, 0.7824001 , 1.        ]),\n",
       " array([0.7867898 , 0.86255253, 0.88602001, 1.        ]),\n",
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     "execution_count": 37,
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   "source": [
    "sns.choose_diverging_palette()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's try a heatmap. Unlike most Seaborn functions, heatmaps take data in wide form. We'll need to pivot our long-form data to get a table of numbers, indexed by two variables. The heatmap function will then transform each number to a colour via a continuous colourmap.\n",
    "\n",
    "**Exercise:** Try to use `sales.pivot_table()` to produce a table showing average house prices. The x-axis (column headers) should be the values of the `grade` variable, and the rows (index) should be the values of the `view` variable. If you're new to Pandas, check the example below that uses the `trains` toy dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-14T13:58:21.164169Z",
     "start_time": "2018-01-14T13:58:21.117549Z"
    }
   },
   "outputs": [
    {
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       "      <th>grade</th>\n",
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       "      <th>6</th>\n",
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       "      <th>10</th>\n",
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       "      <th>12</th>\n",
       "      <th>13</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>view</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <td>3.650000e+06</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>443333.333333</td>\n",
       "      <td>415916.666667</td>\n",
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       "      <th>3</th>\n",
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       "      <td>1.673342e+06</td>\n",
       "      <td>1.478750e+06</td>\n",
       "      <td>7700000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>456633.333333</td>\n",
       "      <td>437098.666667</td>\n",
       "      <td>691218.750000</td>\n",
       "      <td>1.037782e+06</td>\n",
       "      <td>1.098339e+06</td>\n",
       "      <td>1.605336e+06</td>\n",
       "      <td>2.164386e+06</td>\n",
       "      <td>3.072364e+06</td>\n",
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      ],
      "text/plain": [
       "grade        1              4              5              6              7   \\\n",
       "view                                                                          \n",
       "0      142000.0  194866.666667  242826.258929  293428.914414  399605.945441   \n",
       "1           NaN  217000.000000  335000.000000  414500.714286  512973.488372   \n",
       "2           NaN            NaN  443333.333333  415916.666667  487865.806818   \n",
       "3           NaN            NaN  290000.000000  424387.777778  484809.677419   \n",
       "4           NaN            NaN  456633.333333  437098.666667  691218.750000   \n",
       "\n",
       "grade            8             9             10            11            12  \\\n",
       "view                                                                          \n",
       "0      5.218487e+05  7.329931e+05  9.751164e+05  1.327405e+06  2.027127e+06   \n",
       "1      7.032118e+05  9.666998e+05  1.326138e+06  2.007900e+06  3.650000e+06   \n",
       "2      6.650691e+05  9.078050e+05  1.067558e+06  1.631918e+06  2.099375e+06   \n",
       "3      7.619189e+05  9.861143e+05  1.351980e+06  1.673342e+06  1.478750e+06   \n",
       "4      1.037782e+06  1.098339e+06  1.605336e+06  2.164386e+06  3.072364e+06   \n",
       "\n",
       "grade         13  \n",
       "view              \n",
       "0      3222500.0  \n",
       "1            NaN  \n",
       "2      2385000.0  \n",
       "3      7700000.0  \n",
       "4            NaN  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "price_table = sales.pivot_table(index='view', columns='grade', values='price', aggfunc=np.mean)\n",
    "price_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-14T13:56:59.868319Z",
     "start_time": "2018-01-14T13:56:59.788080Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>size</th>\n",
       "      <th>Small</th>\n",
       "      <th>Medium</th>\n",
       "      <th>Big</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>engine_type</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Tank</th>\n",
       "      <td>41.666667</td>\n",
       "      <td>40.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tender</th>\n",
       "      <td>77.500000</td>\n",
       "      <td>70.0</td>\n",
       "      <td>81.666667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "size             Small  Medium        Big\n",
       "engine_type                              \n",
       "Tank         41.666667    40.0        NaN\n",
       "Tender       77.500000    70.0  81.666667"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Here's an example using the toy dataset\n",
    "# Avoid looking at the details first if you want to solve the above without hints!\n",
    "\n",
    "speed_table = trains.pivot_table(index='engine_type', columns='size', \n",
    "                                 values='top_speed_mph', \n",
    "                                 aggfunc=np.mean)\n",
    "speed_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-11T15:26:46.137623Z",
     "start_time": "2018-01-11T15:26:45.639782Z"
    }
   },
   "outputs": [
    {
     "data": {
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V1NRg2rRpCAsLw6RJkxATE6P/DYRMjyPgP/Tp0wcfffQRBg8ejJCQEISFhaFn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\nJmAiIpkwARMRyYQJmIhIJv8HX+sKCjQ39rIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a2be6e978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# we'll use a dark-background style so we can easily \n",
    "# distinguish the (transparent) null values from the default colourmap\n",
    "with sns.axes_style('dark'):\n",
    "    # vmin=0 starts our colour scale from zero, which makes sense for speeds\n",
    "    sns.heatmap(speed_table, vmin=0, annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercises:** \n",
    "* If you haven't already, produce a heatmap with the `sales` data as described above, i.e. average price with view against grade. You will probably want to leave off the annotations unless your plot is very large. \n",
    "* Specify a different colourmap using the `cmap` parameter to `heatmap`. An ascending (not diverging) colourmap is appropriate for prices that are all positive, and in the Jupyter notebook, it might be nice to pick a colourmap that is reversed so that the whiter colours are closer to zero. Matplotlib colourmaps ending in \"_r\" are reversed.\n",
    "* House prices have a skewed distribution, and so our heatmap doesn't really show detail for the lower end of the scale. Try to plot a heatmap where colour is based on the *log* of the price. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-14T13:59:15.113502Z",
     "start_time": "2018-01-14T13:59:14.880902Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1eecfda0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with sns.axes_style('dark'):\n",
    "    sns.heatmap(price_table.apply(np.log), cmap='Greens')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Seaborn and Matplotlib "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Seaborn plotting functions call Matplotlib plotting functions, and we can pass arguments down to these underlying functions.\n",
    "\n",
    "For instance, `lmplot()` calls `scatter()` and `plot()` to draw points and lines. We can pass arguments down using `scatter_kws` and `line_kws`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-14T01:11:06.474444Z",
     "start_time": "2018-01-14T01:11:05.344908Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1a16e444a8>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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uxrs+Hby+//hrn8e7PlGrmmrZ2taHiIMIFNwSQ/ki1/psnN5YHSPXN0i9lyf5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\nACulVEj+f9rqJzcyYxYUAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a16e442b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot(data=sales, x='grade', y='price',\n",
    "           scatter_kws={'alpha': 0.3},\n",
    "           line_kws={'linestyle': 'dashed', 'color': 'red'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise:** Which underlying plotting function is called by your `catplot` above? You can try passing arguments to it with catplot's `kwargs`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's look at the object returned by a Seaborn plot function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-14T08:33:06.896426Z",
     "start_time": "2018-01-14T08:33:06.240935Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1064b4438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.countplot(trains['engine_type'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-14T08:33:07.630374Z",
     "start_time": "2018-01-14T08:33:07.623376Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "matplotlib.axes._subplots.AxesSubplot"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(g)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is a Matplotlib `Axes` object. We can use all the usual `Axes` methods to tweak the plot. What's more, if we have an existing `Axes` object, we can ask Seaborn to draw the plot onto it by passing it in as the `ax` parameter:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-14T08:33:09.048211Z",
     "start_time": "2018-01-14T08:33:08.831279Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x104dd51d0>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x104dcbc88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, myaxes = plt.subplots(figsize=(7,5), facecolor=sns.xkcd_rgb['pale pink'])\n",
    "\n",
    "sns.countplot(trains['engine_type'], color='purple', ax=myaxes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And the plot is still attached to `fig`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-14T08:33:33.810072Z",
     "start_time": "2018-01-14T08:33:33.729930Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x104dcbc88>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The simpler Seaborn plotting functions return `Axes` objects, and can take an `Axes` as a parameter. More complex functions like `jointplot()` and `catplot()` need to make multiple subplots. What do they return?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-14T14:25:00.720438Z",
     "start_time": "2018-01-14T14:24:58.888428Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a2060cef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.pairplot(data=trains[['wheels', 'top_speed_mph', 'weight_tons']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-14T14:25:00.729135Z",
     "start_time": "2018-01-14T14:25:00.723374Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "seaborn.axisgrid.PairGrid"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(g)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-14T14:25:00.743034Z",
     "start_time": "2018-01-14T14:25:00.732074Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "matplotlib.figure.Figure"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(g.fig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-14T14:25:00.755729Z",
     "start_time": "2018-01-14T14:25:00.746634Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x1a20612128>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x11304f908>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x113054898>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x1a20a2f8d0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1a20a5a860>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1a20a6a128>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x1a20aada58>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1a20acab00>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1a20afc6d8>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g.axes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-14T14:25:01.178785Z",
     "start_time": "2018-01-14T14:25:00.758961Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a2060cef0>"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g.fig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see, these more complex functions produce entire Matplotlib `Figure` objects, which can contain multiple `Axes`. The `Figure`, however, comes wrapped in a Seaborn class, which provides some convenient functions to manipulate the figure properties."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-14T14:25:07.207291Z",
     "start_time": "2018-01-14T14:25:05.865066Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a2060cef0>"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# g.set sets a property on all Axes in the Figure\n",
    "# set x-axis to log scale:\n",
    "g.set(xscale='log')\n",
    "g.fig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can create custom plots by instantiating these classes yourself, though we won't do that here. The main reuseable classes  for these compound plots are:\n",
    "\n",
    "- JointGrid : a central bivariate plot with two marginal univariate plots. Used by `jointplot()`. \n",
    "- PairGrid : a grid of subplots for plotting pairwise relationships. Has convenience functions for mapping plots onto diagonal and non-diagonal elements. Used by `pairplot()`.\n",
    "- FacetGrid : a grid of subplots showing the same relationship, conditioned on some variable across different subplots. Designed to map fields of a Pandas DataFrame to different rows, columns, and hues. Used by `catplot()` and `lmplot()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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