Matplotlib_and_tidy_data.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Matplotlib and tidy data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Handy hints "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 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": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-14T05:15:13.992926Z",
     "start_time": "2018-01-14T05:15:12.952701Z"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-14T05:15:14.344614Z",
     "start_time": "2018-01-14T05:15:13.995373Z"
    },
    "scrolled": true
   },
   "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",
    "# There is an alterative `%matplotlib notebook` magic for interactivity, which we won't use today\n",
    "# - we will use other libraries for interactive plots\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-14T05:15:14.352667Z",
     "start_time": "2018-01-14T05:15:14.347446Z"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# pyplot (as plt) is the module we'll primarily use to instantiate matplotlib plot objects\n",
    "# We wouldn't usually need to import matplotlib itself, but we'll use it to explore the library\n",
    "import matplotlib\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Toy data "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's create a toy dataset to use for illustration and problem-posing purposes. It's very small, so you we can easily see what's going on. In most cases we'll ask you to do exercises using the more complex housing data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-14T05:15:22.134037Z",
     "start_time": "2018-01-14T05:15:22.070408Z"
    },
    "scrolled": true
   },
   "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>count</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>1</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>1</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>1</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>1</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>1</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>1</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>1</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>1</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>1</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>1</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",
       "   count    size  \n",
       "0      1  Medium  \n",
       "1      1   Small  \n",
       "2      1     Big  \n",
       "3      1     Big  \n",
       "4      1  Medium  \n",
       "5      1   Small  \n",
       "6      1   Small  \n",
       "7      1   Small  \n",
       "8      1   Small  \n",
       "9      1     Big  "
      ]
     },
     "execution_count": 6,
     "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 intro: the pyplot interface "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Matplotlib comes with an interface of convenience functions, available under `pyplot`. It was originally designed to mimic MATLAB's plotting interface."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-14T05:36:41.975324Z",
     "start_time": "2018-01-14T05:36:41.805467Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x119c8cf98>]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10bb87ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = [1,2,3,4,5]\n",
    "y = [2,5,10,17,26]\n",
    "\n",
    "plt.plot(x,y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`pyplot` has a concept of the \"current\" axes and figure, so you don't generally have to tell functions which plot to act on - they will automatically operate on the most-recent, currently open plot. This is often convenient, but sometimes too restrictive. \n",
    "\n",
    "In Jupyter, each input cell will create a new plot, and every command in the cell will operate on that plot:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-14T05:36:44.353067Z",
     "start_time": "2018-01-14T05:36:44.012833Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x119c96630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y2 = [1,4,9,11,9]\n",
    "\n",
    "plt.plot(x, y, c='blue', label='Projected')\n",
    "plt.scatter(x, y2, c='red', label='Actual')\n",
    "plt.legend()\n",
    "plt.title(\"An example plot\")\n",
    "\n",
    "# Save the plot to a file\n",
    "plt.savefig(\"example_matplotlib.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We saved the plot to a PNG file. If we don't specify the format to `savefig()`, it is deduced from the filename. One of Matplotlib's strengths is that it supports a wide range of plotting backends, including many image formats."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's very common to use Numpy arrays and Pandas Series with Matplotlib."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-14T05:36:48.636277Z",
     "start_time": "2018-01-14T05:36:48.278668Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x119b080f0>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x119aa7358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.linspace(0, 100, 200)  # 200 points from 0 to 100\n",
    "wavelength = 20\n",
    "y = np.sin(2*np.pi*x/wavelength) * 1/np.sqrt(x+1)\n",
    "y_noisy = y + 0.05*np.random.randn(len(y))\n",
    "\n",
    "plt.plot(x, y, c='red')\n",
    "plt.scatter(x, y_noisy, c='green', s=8)  # s sets size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise:** Using the toy `trains` dataset, create a scatter plot of `top_speed_mph` against   `weight_tons`.\n",
    "\n",
    "For those new to Pandas, you can get vector-like Pandas Series of values with `trains['top_speed_mph']` and `trains['wheels']`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are many plot types. You can find a gallery at https://matplotlib.org/gallery.html.\n",
    "\n",
    "Let's try a bar plot showing the average speed of tender engines vs tank engines. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-14T05:29:43.282699Z",
     "start_time": "2018-01-14T05:29:43.133337Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "engine_type\n",
      "Tank      41.250000\n",
      "Tender    78.333333\n",
      "Name: top_speed_mph, dtype: float64\n",
      "engine_type\n",
      "Tank      18.427787\n",
      "Tender    16.329932\n",
      "Name: top_speed_mph, dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Container object of 2 artists>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11030d400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "average_top_speeds = trains.groupby('engine_type')['top_speed_mph'].aggregate(np.mean)\n",
    "speed_stderrs = trains.groupby('engine_type')['top_speed_mph'].aggregate(np.std)\n",
    "print(average_top_speeds)\n",
    "print(speed_stderrs)\n",
    "\n",
    "# this is a horizontal bar plot; plt.bar would give a vertical plot\n",
    "plt.barh(y=[0,1], width=average_top_speeds, xerr=speed_stderrs, \n",
    "         tick_label=average_top_speeds.index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that Matplotlib bar plots expect us to input the length of the bars directly, so we needed to calculate these averages using Pandas.\n",
    "\n",
    "A [boxplot](https://matplotlib.org/api/_as_gen/matplotlib.axes.Axes.boxplot.html) is actually a little easier in terms of summary statistics: we provide a list of arrays/series/lists, each one of which is the set of datapoints for one box. Here's some data transformed with Pandas to use:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "start_time": "2018-01-14T08:49:12.754Z"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "data = [series for (groupname, series) in list(trains.groupby('engine_type')['top_speed_mph'])]\n",
    "label_names = [groupname for (groupname, series) in list(trains.groupby('engine_type')['top_speed_mph'])]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise:** Create a boxplot using the above data and labels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Interlude: tidy data "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before doing this section, we'll have a discussion in the workshop. [Here is a link](https://docs.google.com/spreadsheets/d/1tnLVYxd6k5oGWVHsiF6D78ds1nOztU3Ynri_Bx0oOjE/edit?usp=sharing) to a spreadsheet that we'll look at."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here are two \"wide\" datasets to follow along with the demo, or to try to solve yourself:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-14T06:02:21.527421Z",
     "start_time": "2018-01-14T06:02:21.466340Z"
    }
   },
   "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>property_id</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>date1</th>\n",
       "      <th>date2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5631500400</td>\n",
       "      <td>2</td>\n",
       "      <td>2015-02-25</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6021501535</td>\n",
       "      <td>3</td>\n",
       "      <td>2014-07-25</td>\n",
       "      <td>2014-12-23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7129300520</td>\n",
       "      <td>3</td>\n",
       "      <td>2014-10-13</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   property_id  bedrooms      date1      date2\n",
       "0   5631500400         2 2015-02-25        NaT\n",
       "1   6021501535         3 2014-07-25 2014-12-23\n",
       "2   7129300520         3 2014-10-13        NaT"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sales_wide1 = pd.read_csv('housing-data-wide1.csv', parse_dates=['date1','date2'])\n",
    "sales_wide1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-14T06:02:32.226902Z",
     "start_time": "2018-01-14T06:02:32.200949Z"
    }
   },
   "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>property_id</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>date1</th>\n",
       "      <th>price1</th>\n",
       "      <th>date2</th>\n",
       "      <th>price2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5631500400</td>\n",
       "      <td>2</td>\n",
       "      <td>2015-02-25</td>\n",
       "      <td>180000.0</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6021501535</td>\n",
       "      <td>3</td>\n",
       "      <td>2014-07-25</td>\n",
       "      <td>430000.0</td>\n",
       "      <td>2014-12-23</td>\n",
       "      <td>700000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7129300520</td>\n",
       "      <td>3</td>\n",
       "      <td>2014-10-13</td>\n",
       "      <td>221900.0</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   property_id  bedrooms      date1    price1      date2    price2\n",
       "0   5631500400         2 2015-02-25  180000.0        NaT       NaN\n",
       "1   6021501535         3 2014-07-25  430000.0 2014-12-23  700000.0\n",
       "2   7129300520         3 2014-10-13  221900.0        NaT       NaN"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sales_wide2 = pd.read_csv('housing-data-wide2.csv', parse_dates=['date1','date2'])\n",
    "sales_wide2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "start_time": "2018-01-14T10:29:52.650Z"
    }
   },
   "outputs": [],
   "source": [
    "# A first step for the second problem:\n",
    "price_date_table = sales_wide2[['date1', 'date2', 'price1', 'price2']]\n",
    "price_date_table.index = sales_wide2['property_id']\n",
    "price_date_table.columns = pd.MultiIndex.from_product([['date','price'],['sale1','sale2']], \n",
    "                                                        names=('variable', 'salenum'))\n",
    "price_date_table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dataset in the next section is a spoiler! Before looking at it, make sure you're done with the \"tidy data\" exercise."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Housing data "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's read in the data we will use for the rest of the workshop. This is house sales data from Seattle in 2014-2015. To simplify things, we'll read in only the fields we'll use, but you can read in the rest of the fields to play later if you want more variables.\n",
    "\n",
    "First, a quick look at the raw data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-13T05:40:05.729365Z",
     "start_time": "2018-01-13T05:40:05.536798Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "id,date,price,bedrooms,bathrooms,sqft_living,sqft_lot,floors,waterfront,view,condition,grade,sqft_above,sqft_basement,yr_built,yr_renovated,zipcode,lat,long\r\n",
      "1446800660,2015-03-16,276500.0,4,1.75,1400,6650,1.5,0,0,4,6,1400,0,1942,0,98168,47.4888,-122.33200000000001\r\n",
      "1453601502,2015-02-26,303697.0,4,2.0,2520,7334,1.0,0,0,3,7,1600,920,1955,0,98125,47.7263,-122.291\r\n"
     ]
    }
   ],
   "source": [
    "!head -n 3 housing-data-10000.csv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This data is pretty tidy already. We have one sale per row, one column per variable, and just one table of observations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-14T10:33:55.993647Z",
     "start_time": "2018-01-14T10:33:55.971791Z"
    }
   },
   "outputs": [],
   "source": [
    "sales = pd.read_csv(\"housing-data-10000.csv\", \n",
    "                    usecols=['id','date','price','zipcode','lat','long',\n",
    "                             'waterfront','view','grade','sqft_living','sqft_lot'],\n",
    "                    parse_dates=['date'], \n",
    "                    dtype={'zipcode': 'category',\n",
    "                           'waterfront': 'bool'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-11T03:03:24.803054Z",
     "start_time": "2018-01-11T03:03:24.736592Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <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",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7129300520</td>\n",
       "      <td>2014-10-13</td>\n",
       "      <td>221900.0</td>\n",
       "      <td>1180</td>\n",
       "      <td>5650</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>98178</td>\n",
       "      <td>47.5112</td>\n",
       "      <td>-122.257</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6414100192</td>\n",
       "      <td>2014-12-09</td>\n",
       "      <td>538000.0</td>\n",
       "      <td>2570</td>\n",
       "      <td>7242</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>98125</td>\n",
       "      <td>47.7210</td>\n",
       "      <td>-122.319</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5631500400</td>\n",
       "      <td>2015-02-25</td>\n",
       "      <td>180000.0</td>\n",
       "      <td>770</td>\n",
       "      <td>10000</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>98028</td>\n",
       "      <td>47.7379</td>\n",
       "      <td>-122.233</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2487200875</td>\n",
       "      <td>2014-12-09</td>\n",
       "      <td>604000.0</td>\n",
       "      <td>1960</td>\n",
       "      <td>5000</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>98136</td>\n",
       "      <td>47.5208</td>\n",
       "      <td>-122.393</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1954400510</td>\n",
       "      <td>2015-02-18</td>\n",
       "      <td>510000.0</td>\n",
       "      <td>1680</td>\n",
       "      <td>8080</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>98074</td>\n",
       "      <td>47.6168</td>\n",
       "      <td>-122.045</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           id       date     price  sqft_living  sqft_lot  waterfront  view  \\\n",
       "0  7129300520 2014-10-13  221900.0         1180      5650       False     0   \n",
       "1  6414100192 2014-12-09  538000.0         2570      7242       False     0   \n",
       "2  5631500400 2015-02-25  180000.0          770     10000       False     0   \n",
       "3  2487200875 2014-12-09  604000.0         1960      5000       False     0   \n",
       "4  1954400510 2015-02-18  510000.0         1680      8080       False     0   \n",
       "\n",
       "   grade zipcode      lat     long  count  \n",
       "0      7   98178  47.5112 -122.257      1  \n",
       "1      7   98125  47.7210 -122.319      1  \n",
       "2      6   98028  47.7379 -122.233      1  \n",
       "3      7   98136  47.5208 -122.393      1  \n",
       "4      8   98074  47.6168 -122.045      1  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sales.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-11T05:39:16.953619Z",
     "start_time": "2018-01-11T05:39:16.480326Z"
    },
    "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",
       "count                   int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 8,
     "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": [
    "## Matplotlib  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Figures and Axes "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Under the hood, Matplotlib is an object-oriented library. I recommend using the object-oriented interface directly rather than the pyplot interface, for full control over your plots. \n",
    "\n",
    "Libraries built on Matplotlib can also return Matplotlib objects - we'll see this later with Seaborn. Understanding a bit of the structure of Matplotlib objects will let you tweak your Seaborn plots.\n",
    "\n",
    "Here's how to make the plot we made above, using method calls instead of the shortcut `plt.scatter`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-14T05:37:39.957390Z",
     "start_time": "2018-01-14T05:37:39.660521Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x119ace160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# we could be even more explicit here and instead use\n",
    "# fig = plt.figure()\n",
    "# ax = fig.add_subplot(1,1,1)\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "x = [1,2,3,4,5]\n",
    "y = [2,5,10,17,26]\n",
    "y2 = [1,4,9,11,9]\n",
    "\n",
    "ax.plot(x, y, c='blue', label='Projected')\n",
    "ax.scatter(x, y2, c='red', label='Actual')\n",
    "ax.legend()\n",
    "ax.set_title(\"An example plot\")\n",
    "\n",
    "fig.savefig(\"example_matplotlib.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's look at the objects we created in the first step:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-14T05:37:58.133080Z",
     "start_time": "2018-01-14T05:37:58.128261Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(matplotlib.figure.Figure, matplotlib.axes._subplots.AxesSubplot)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(fig), type(ax)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, `fig` is a Matplotlib `Figure` class instance representing the figure we've drawn. The `Axes` are a property of the figure. So, we still have access to our plot, and can display and modify it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-14T05:37:58.872780Z",
     "start_time": "2018-01-14T05:37:58.758877Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x119ace160>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-14T05:37:59.340630Z",
     "start_time": "2018-01-14T05:37:59.334899Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.axes._subplots.AxesSubplot at 0x119aceba8>]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fig.axes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-14T05:38:02.238795Z",
     "start_time": "2018-01-14T05:38:02.231188Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(1,12,'where it all went wrong')"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "the_same_axes_as_before = fig.axes[0]\n",
    "\n",
    "the_same_axes_as_before.annotate(\"where it all went wrong\", \n",
    "                                 xy=(3,10), xytext=(1,12),\n",
    "                                 arrowprops={'width':2})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-14T05:38:03.444532Z",
     "start_time": "2018-01-14T05:38:03.327100Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x119ace160>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`ax` is a Matplotlib `Axes` class instance. It represents an area of a figure with coordinates, on which we can plot. You can see that most of our plotting methods are actually methods of the `Axes` class, e.g. `Axes.scatter()`, and they add plot elements to the axes they're called on. `plt.scatter()` is just a wrapper around `Axes.scatter()`. Try running `??plt.scatter` to see the source code of this wrapper.\n",
    "\n",
    "If you've been working in the pyplot interface and need to retrieve the currently-active Figure or Axes objects, you can use the `plt.gcf()` (get current figure) and `plt.gca()` (get current axes) functions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise:** Create a scatter plot of house sales, using the latitude as the y-coordinate, and the longitude as the x-coordinate. Try to use object-oriented method calls for everything except creating the initial object(s). Give your figure a title, like \"House sales in Seattle\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Subplots and more Axes "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A figure can have more than one set of axes. This is how we create subplots.\n",
    "\n",
    "`Figure.add_subplot(nrows, ncols, n)` will create a grid of `nrows x ncols` subplots, and return the nth axes object from the list. `plt.subplot()` is a wrapper around this method.\n",
    "\n",
    "`plt.subplots(nrows, ncols)` will create a new Figure object and then add a grid of subplots, and return a tuple of the Figure and the array of all Axes. It's a commonly-used utility function. You will often see it used even when we only want a single plot on our figure, as we did above.\n",
    "\n",
    "Extra arguments to `plt.subplots()` are passed through to the `plt.figure()` call, so we can specify e.g. the figure size this way."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-14T05:38:07.667329Z",
     "start_time": "2018-01-14T05:38:07.297620Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,0.98,'Really useful engines')"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a6a0470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(nrows=2, ncols=1, sharex=True, figsize=(6,8))\n",
    "\n",
    "axes[0].scatter(x=trains['wheels'], y=trains['top_speed_mph'])\n",
    "axes[0].set_ylabel(\"Top speed\")\n",
    "\n",
    "axes[1].scatter(x=trains['wheels'], y=trains['weight_tons'])\n",
    "axes[1].set_ylabel(\"Weight\")\n",
    "axes[1].set_xlabel(\"Wheels\")\n",
    "\n",
    "# This sets the minimum of the x-axis to zero, and leaves the maximum to auto\n",
    "# It affects both subplots since we set sharex=True\n",
    "axes[0].set_xlim(0,None)\n",
    "\n",
    "# This sets the y-axis, but each call only affects one subplot\n",
    "# Set max weight to 200 just to illustrate\n",
    "axes[0].set_ylim(0,None)\n",
    "axes[1].set_ylim(0,200)\n",
    "\n",
    "# Add a title to the entire figure, not one of the axes\n",
    "fig.suptitle(\"Really useful engines\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "NB: the `matplotlib.gridspec.GridSpec` class can be used to make more complicated layouts of subplots. We won't use this today."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can add arbitrary axes to our figure, which is useful for making an inset plot:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-14T08:23:30.831122Z",
     "start_time": "2018-01-14T08:23:30.562123Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Weight distribution')"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11add99b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "ax.scatter(x=trains['weight_tons'], y=trains['top_speed_mph'])\n",
    "ax.set_ylabel(\"Top speed\")\n",
    "ax.set_xlabel(\"Weight\")\n",
    "\n",
    "# the first argument should be a list or tuple (left, bottom, width, height)\n",
    "# coordinates are fractions of figure width and height\n",
    "inset_axes = fig.add_axes([0.57, 0.15, 0.3, 0.2])\n",
    "inset_axes.set_xticklabels([])  # no labels on inset axes\n",
    "inset_axes.set_yticklabels([]) \n",
    "inset_axes.hist(trains['weight_tons'], bins=5)\n",
    "inset_axes.set_title(\"Weight distribution\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise:** Create two subplots, one above the other. In the top plot, plot a boxplot of house price across the `waterfront` variable (i.e. `price` on y axis, `waterfront` on x axis). In the bottom plot, plot a barplot of the *count* of the number of waterfront and non-waterfront sales.\n",
    "\n",
    "The positions of the box plots along the x-axis will default to `[1,2]` (this is the default `positions` argument), so you can set the `x` parameter in the bar plot to match if you want your plots to line up nicely.\n",
    "\n",
    "Use `?plt.bar()` and `?plt.boxplot()` to easily see documentation on the plotting functions, but try to use the Axes methods to actually plot them.\n",
    "\n",
    "<details closed style=\"border: 1px solid darkgreen; background: #f0fff0; padding: 5px; margin: 5px\">\n",
    "<summary>Hint: making a box plot</summary>\n",
    "<p>\n",
    "You need to pass the `boxplot()` function's `x` parameter a list of two vectors: one vector of numbers for non-waterfront prices, and one for waterfront prices. You could make these with\n",
    "</p><p>\n",
    "```\n",
    "nonwaterfront_prices = sales.loc[~sales['waterfront'], 'price']\n",
    "waterfront_prices = sales.loc[sales['waterfront'], 'price']\n",
    "price_lists = [nonwaterfront_prices, waterfront_prices]\n",
    "```\n",
    "</p><p>\n",
    "Here `sales['waterfront']` is already a boolean variable, so `sales['waterfront']` is equivalent to `sales['waterfront']==True` and `~sales['waterfront']` is equivalent to `sales['waterfront']==False`.\n",
    "</p><p>\n",
    "More efficient is to use `groupby()`:\n",
    "</p><p>\n",
    "```\n",
    "price_groups = list(sales.groupby('waterfront')['price'])\n",
    "price_lists = [series for (label, series) in price_groups]\n",
    "```\n",
    "</p>\n",
    "\n",
    "```\n",
    "\n",
    "```\n",
    "\n",
    "</details>\n",
    "\n",
    "<details closed style=\"border: 1px solid darkgreen; background: #f0fff0; padding: 5px; margin: 5px\">\n",
    "<summary>Hint: summing counts for the bar plot</summary>\n",
    "\n",
    "You can sum counts with Pandas `Series.value_counts()`.\n",
    "\n",
    "Specifically, if \n",
    "</p><p>\n",
    "```counts = sales['waterfront'].value_counts()```\n",
    "</p><p>\n",
    "then the heights of the bars shoud be set to `counts`, and the `tick_label` parameter can be set to `counts.index`.\n",
    "</p><p>\n",
    "It's also possible to use `groupby()` and then `count()` or `aggregate(np.sum)`.\n",
    "</p>\n",
    "</details>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Styles"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Matplotlib now has style sheets and context managers, which allow you to change the parameters for a plot. Context managers have an effect within the scope of a `with` block. For instance:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-14T05:38:35.632382Z",
     "start_time": "2018-01-14T05:38:35.435848Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x119ace6d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with plt.style.context('ggplot'):\n",
    "    fig, ax = plt.subplots()\n",
    "    ax.scatter(x=trains['weight_tons'], y=trains['top_speed_mph'])\n",
    "    ax.set_xlabel('Weight')\n",
    "    ax.set_ylabel('Top speed')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A few example style sheets are:\n",
    "\n",
    "- default : the Matplotlib 2 default\n",
    "- ggplot : like R's ggplot\n",
    "- seaborn : like Seaborn\n",
    "- classic : what Matplotlib looked like once apon a time\n",
    "\n",
    "Styles can be set permanently, instead of in a context, using `plt.style.use()`. \n",
    "\n",
    "Stylesheets are essentially sets of parameters, and you can define your own. You can find out more about the `style` package [here](https://matplotlib.org/users/style_sheets.html). \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Colours "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Matplotlib has a good range of colours. We'll look a bit at colour functionality, and there are some more convenience functions when we get up to Seaborn.\n",
    "\n",
    "Colours can be specified by name (e.g. \"red\", \"blue\", \"palegoldenrod\"....) - a list of named colours is [here](https://matplotlib.org/examples/color/named_colors.html)\n",
    "\n",
    "Colours can be specified by RGB or RGBA value, as hex (e.g. \"#ffffa0\" or \"#ffffa088\") or as tuples, e.g. (1,1,0.6) or (1,1,0.6,0.5).\n",
    "\n",
    "_Colormaps_ map continuous variables to colours. This is done by normalising the variable into the [0,1] range, then mapping this to a defined range of colours. The `plt.cm` module contains built-in colourmaps, and these can also be retrieved with `plt.get_cmap(name)`.\n",
    "\n",
    "There's some Matplotlib documentation on [Choosing Colormaps](https://matplotlib.org/users/colormaps.html), which illustrates available built-in colormaps."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-13T22:51:26.488443Z",
     "start_time": "2018-01-13T22:51:26.272986Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x11353a588>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAATYAAAA+CAYAAAChmDdwAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nTNK28Y4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11353a7b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Show the plt.cm.Greens_r colormap. Replace with other colormaps to see them. \n",
    "# Greens_r is a sequential colormap of greens of differing intensities.\n",
    "# _r means it is reversed (dark to light)\n",
    "\n",
    "colourmap = plt.get_cmap(\"Greens_r\")\n",
    "\n",
    "fix, ax = plt.subplots(figsize=(5,0.5))\n",
    "\n",
    "# Make an image which is two rows of points with a gradient from 0 to 1\n",
    "gradient = np.linspace(0, 1, 256)\n",
    "image = np.vstack([gradient, gradient])\n",
    "\n",
    "ax.imshow(image, aspect='auto', cmap=colourmap)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To colour a scatter plot with a continuous variable, we can just set the `c` parameter to the colour variable, and optionally, select a colormap using the `cmap` parameter. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-14T05:38:40.607111Z",
     "start_time": "2018-01-14T05:38:40.425297Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Top speed')"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a699160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "ax.scatter(x=trains['weight_tons'], y=trains['top_speed_mph'], \n",
    "           c=trains['wheels'],\n",
    "           cmap='Reds',\n",
    "           vmin=0)   # Set the lightest colour to map to zero. Default is to the smallest value.\n",
    "ax.set_xlabel('Weight')\n",
    "ax.set_ylabel('Top speed')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also add a colour bar to show the variable mapping."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-13T23:41:46.137551Z",
     "start_time": "2018-01-13T23:41:45.837617Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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9kh6U9IV0+1RJiyU9mj5OqXVsZmaDqpMJ3PM4hW0FToqIw4EjgFMkHQNcBCyJ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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1149c6828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "points = ax.scatter(x=trains['weight_tons'], y=trains['top_speed_mph'],\n",
    "                    c=trains['wheels'], \n",
    "                    cmap='Reds')\n",
    "ax.set_xlabel('Weight')\n",
    "ax.set_ylabel('Top speed')\n",
    "\n",
    "# Create a colorbar showing the colour mapping for the points\n",
    "cbar = fig.colorbar(points)\n",
    "cbar.set_label('Wheels')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we want to colour points by some discrete category, and get an appropriate legend, we do this by drawing each set of points separately."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-14T05:38:43.518759Z",
     "start_time": "2018-01-14T05:38:43.294939Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x119dc8630>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a0e5cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "engine_groups = trains.groupby('engine_type')\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "for (label,df) in engine_groups:\n",
    "    ax.scatter(x=df['weight_tons'], y=df['top_speed_mph'], label=label)\n",
    "    ax.set_xlabel('Weight')\n",
    "    ax.set_ylabel('Top speed')\n",
    "\n",
    "ax.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# If you're not familiar with what groupby does, have a look at the resulting groups here\n",
    "list(engine_groups)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "**Exercise:** \n",
    "\n",
    "For these exercises, you may want to subsample your data with `sample = sales.sample(2000)` or similar.\n",
    "\n",
    "* Colour your scatter plot of house sale locations (`long` & `lat`) by the `waterfront` variable. \n",
    "* Colour your scatter plot of house sale locations (`long` & `lat`) by `price`. Try to choose a colourmap so that higher prices are more noticeable or intense than lower."
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