week-1-datasaurus-dozen.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Example loading, viewing, exploring, plotting and saving plots.\n",
    "\n",
    "### AMAV Webinar 1\n",
    "\n",
    "##### *'...make both calculations and graphs. Both sorts of output should be studied; each will contribute to understanding.' -- F. J. Anscombe, 1973*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Load libraires \n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import os\n",
    "\n",
    "# Make it easier to show plots\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv('./data/DatasaurusDozen.tsv', sep='\\t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dataset</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>dino</td>\n",
       "      <td>55.3846</td>\n",
       "      <td>97.1795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>dino</td>\n",
       "      <td>51.5385</td>\n",
       "      <td>96.0256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>dino</td>\n",
       "      <td>46.1538</td>\n",
       "      <td>94.4872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>dino</td>\n",
       "      <td>42.8205</td>\n",
       "      <td>91.4103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>dino</td>\n",
       "      <td>40.7692</td>\n",
       "      <td>88.3333</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "  dataset        x        y\n",
       "0    dino  55.3846  97.1795\n",
       "1    dino  51.5385  96.0256\n",
       "2    dino  46.1538  94.4872\n",
       "3    dino  42.8205  91.4103\n",
       "4    dino  40.7692  88.3333"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "star          142\n",
       "dino          142\n",
       "v_lines       142\n",
       "slant_down    142\n",
       "wide_lines    142\n",
       "slant_up      142\n",
       "high_lines    142\n",
       "h_lines       142\n",
       "circle        142\n",
       "x_shape       142\n",
       "bullseye      142\n",
       "away          142\n",
       "dots          142\n",
       "Name: dataset, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['dataset'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data = df[df['dataset']=='dino']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(142, 3)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Helper function to get a dataset\n",
    "def get_dataset(name):\n",
    "    return df[df['dataset']==name]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>dataset</th>\n",
       "      <th>x</th>\n",
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       "      <td>25.538008</td>\n",
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       "      <th>1151</th>\n",
       "      <td>circle</td>\n",
       "      <td>26.320711</td>\n",
       "      <td>62.396281</td>\n",
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       "    <tr>\n",
       "      <th>1152</th>\n",
       "      <td>circle</td>\n",
       "      <td>26.163387</td>\n",
       "      <td>62.026815</td>\n",
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       "    <tr>\n",
       "      <th>1153</th>\n",
       "      <td>circle</td>\n",
       "      <td>25.213790</td>\n",
       "      <td>60.273399</td>\n",
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       "      <th>1154</th>\n",
       "      <td>circle</td>\n",
       "      <td>26.859530</td>\n",
       "      <td>63.141835</td>\n",
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       "    <tr>\n",
       "      <th>1155</th>\n",
       "      <td>circle</td>\n",
       "      <td>31.760669</td>\n",
       "      <td>69.797655</td>\n",
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       "      <td>39.793291</td>\n",
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       "      <td>45.617439</td>\n",
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       "      <td>circle</td>\n",
       "      <td>53.771461</td>\n",
       "      <td>79.302932</td>\n",
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       "      <th>1159</th>\n",
       "      <td>circle</td>\n",
       "      <td>56.451434</td>\n",
       "      <td>79.247107</td>\n",
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       "      <th>1160</th>\n",
       "      <td>circle</td>\n",
       "      <td>66.093720</td>\n",
       "      <td>77.017818</td>\n",
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       "      <th>1161</th>\n",
       "      <td>circle</td>\n",
       "      <td>56.925839</td>\n",
       "      <td>79.206319</td>\n",
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       "      <th>1162</th>\n",
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       "      <td>58.988254</td>\n",
       "      <td>78.917259</td>\n",
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       "      <th>1163</th>\n",
       "      <td>circle</td>\n",
       "      <td>57.902266</td>\n",
       "      <td>79.080868</td>\n",
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       "      <th>1164</th>\n",
       "      <td>circle</td>\n",
       "      <td>64.812867</td>\n",
       "      <td>77.486939</td>\n",
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       "      <th>1165</th>\n",
       "      <td>circle</td>\n",
       "      <td>60.349794</td>\n",
       "      <td>78.729446</td>\n",
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       "      <th>1248</th>\n",
       "      <td>circle</td>\n",
       "      <td>56.365153</td>\n",
       "      <td>16.326546</td>\n",
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       "      <th>1249</th>\n",
       "      <td>circle</td>\n",
       "      <td>43.046183</td>\n",
       "      <td>18.402559</td>\n",
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       "      <th>1250</th>\n",
       "      <td>circle</td>\n",
       "      <td>52.081756</td>\n",
       "      <td>16.453875</td>\n",
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       "    <tr>\n",
       "      <th>1251</th>\n",
       "      <td>circle</td>\n",
       "      <td>42.879037</td>\n",
       "      <td>18.489411</td>\n",
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       "    <tr>\n",
       "      <th>1252</th>\n",
       "      <td>circle</td>\n",
       "      <td>51.566995</td>\n",
       "      <td>16.485714</td>\n",
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       "    <tr>\n",
       "      <th>1253</th>\n",
       "      <td>circle</td>\n",
       "      <td>58.839125</td>\n",
       "      <td>16.637635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1254</th>\n",
       "      <td>circle</td>\n",
       "      <td>60.755789</td>\n",
       "      <td>17.033232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1255</th>\n",
       "      <td>circle</td>\n",
       "      <td>54.761156</td>\n",
       "      <td>16.366189</td>\n",
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       "      <th>1256</th>\n",
       "      <td>circle</td>\n",
       "      <td>53.569905</td>\n",
       "      <td>16.339713</td>\n",
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       "      <th>1257</th>\n",
       "      <td>circle</td>\n",
       "      <td>48.914618</td>\n",
       "      <td>16.798637</td>\n",
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       "    <tr>\n",
       "      <th>1258</th>\n",
       "      <td>circle</td>\n",
       "      <td>52.646137</td>\n",
       "      <td>16.384836</td>\n",
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       "    <tr>\n",
       "      <th>1259</th>\n",
       "      <td>circle</td>\n",
       "      <td>49.838493</td>\n",
       "      <td>16.676330</td>\n",
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       "    <tr>\n",
       "      <th>1260</th>\n",
       "      <td>circle</td>\n",
       "      <td>52.684066</td>\n",
       "      <td>16.407963</td>\n",
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       "    <tr>\n",
       "      <th>1261</th>\n",
       "      <td>circle</td>\n",
       "      <td>65.807219</td>\n",
       "      <td>18.140765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1262</th>\n",
       "      <td>circle</td>\n",
       "      <td>60.673921</td>\n",
       "      <td>17.051802</td>\n",
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       "    <tr>\n",
       "      <th>1263</th>\n",
       "      <td>circle</td>\n",
       "      <td>63.394713</td>\n",
       "      <td>17.700535</td>\n",
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       "    <tr>\n",
       "      <th>1264</th>\n",
       "      <td>circle</td>\n",
       "      <td>64.802009</td>\n",
       "      <td>18.216879</td>\n",
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       "    <tr>\n",
       "      <th>1265</th>\n",
       "      <td>circle</td>\n",
       "      <td>65.025940</td>\n",
       "      <td>18.287825</td>\n",
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       "    <tr>\n",
       "      <th>1266</th>\n",
       "      <td>circle</td>\n",
       "      <td>65.755552</td>\n",
       "      <td>18.516216</td>\n",
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       "    <tr>\n",
       "      <th>1267</th>\n",
       "      <td>circle</td>\n",
       "      <td>69.967734</td>\n",
       "      <td>20.538989</td>\n",
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       "    <tr>\n",
       "      <th>1268</th>\n",
       "      <td>circle</td>\n",
       "      <td>68.892784</td>\n",
       "      <td>20.032449</td>\n",
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       "    <tr>\n",
       "      <th>1269</th>\n",
       "      <td>circle</td>\n",
       "      <td>61.826547</td>\n",
       "      <td>17.269655</td>\n",
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       "    <tr>\n",
       "      <th>1270</th>\n",
       "      <td>circle</td>\n",
       "      <td>60.787970</td>\n",
       "      <td>17.046205</td>\n",
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       "    <tr>\n",
       "      <th>1271</th>\n",
       "      <td>circle</td>\n",
       "      <td>61.915610</td>\n",
       "      <td>17.282191</td>\n",
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       "    <tr>\n",
       "      <th>1272</th>\n",
       "      <td>circle</td>\n",
       "      <td>45.028998</td>\n",
       "      <td>17.724447</td>\n",
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       "    <tr>\n",
       "      <th>1273</th>\n",
       "      <td>circle</td>\n",
       "      <td>39.921363</td>\n",
       "      <td>19.701850</td>\n",
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       "    <tr>\n",
       "      <th>1274</th>\n",
       "      <td>circle</td>\n",
       "      <td>84.794278</td>\n",
       "      <td>55.568650</td>\n",
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       "    <tr>\n",
       "      <th>1275</th>\n",
       "      <td>circle</td>\n",
       "      <td>55.662959</td>\n",
       "      <td>83.356480</td>\n",
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       "      <th>1276</th>\n",
       "      <td>circle</td>\n",
       "      <td>50.492248</td>\n",
       "      <td>78.997532</td>\n",
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       "    <tr>\n",
       "      <th>1277</th>\n",
       "      <td>circle</td>\n",
       "      <td>51.467101</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>142 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     dataset          x          y\n",
       "1136  circle  55.993030  79.277264\n",
       "1137  circle  50.032254  79.013071\n",
       "1138  circle  51.288459  82.435940\n",
       "1139  circle  51.170537  79.165294\n",
       "1140  circle  44.377915  78.164628\n",
       "1141  circle  45.010270  77.880863\n",
       "1142  circle  48.559817  78.788371\n",
       "1143  circle  42.142268  76.880635\n",
       "1144  circle  41.026972  76.409588\n",
       "1145  circle  34.575309  72.724841\n",
       "1146  circle  31.168601  69.245421\n",
       "1147  circle  32.644254  70.732555\n",
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       "1161  circle  56.925839  79.206319\n",
       "1162  circle  58.988254  78.917259\n",
       "1163  circle  57.902266  79.080868\n",
       "1164  circle  64.812867  77.486939\n",
       "1165  circle  60.349794  78.729446\n",
       "...      ...        ...        ...\n",
       "1248  circle  56.365153  16.326546\n",
       "1249  circle  43.046183  18.402559\n",
       "1250  circle  52.081756  16.453875\n",
       "1251  circle  42.879037  18.489411\n",
       "1252  circle  51.566995  16.485714\n",
       "1253  circle  58.839125  16.637635\n",
       "1254  circle  60.755789  17.033232\n",
       "1255  circle  54.761156  16.366189\n",
       "1256  circle  53.569905  16.339713\n",
       "1257  circle  48.914618  16.798637\n",
       "1258  circle  52.646137  16.384836\n",
       "1259  circle  49.838493  16.676330\n",
       "1260  circle  52.684066  16.407963\n",
       "1261  circle  65.807219  18.140765\n",
       "1262  circle  60.673921  17.051802\n",
       "1263  circle  63.394713  17.700535\n",
       "1264  circle  64.802009  18.216879\n",
       "1265  circle  65.025940  18.287825\n",
       "1266  circle  65.755552  18.516216\n",
       "1267  circle  69.967734  20.538989\n",
       "1268  circle  68.892784  20.032449\n",
       "1269  circle  61.826547  17.269655\n",
       "1270  circle  60.787970  17.046205\n",
       "1271  circle  61.915610  17.282191\n",
       "1272  circle  45.028998  17.724447\n",
       "1273  circle  39.921363  19.701850\n",
       "1274  circle  84.794278  55.568650\n",
       "1275  circle  55.662959  83.356480\n",
       "1276  circle  50.492248  78.997532\n",
       "1277  circle  51.467101  79.201845\n",
       "\n",
       "[142 rows x 3 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_dataset('circle')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Discussion point 1:\n",
    "#### What do we think we can conlcude about the datasets from the summary stats?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>142.000000</td>\n",
       "      <td>142.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>54.263273</td>\n",
       "      <td>47.832253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>16.765142</td>\n",
       "      <td>26.935403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>22.307700</td>\n",
       "      <td>2.948700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>44.102600</td>\n",
       "      <td>25.288450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>53.333300</td>\n",
       "      <td>46.025600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>64.743600</td>\n",
       "      <td>68.525675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>98.205100</td>\n",
       "      <td>99.487200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                x           y\n",
       "count  142.000000  142.000000\n",
       "mean    54.263273   47.832253\n",
       "std     16.765142   26.935403\n",
       "min     22.307700    2.948700\n",
       "25%     44.102600   25.288450\n",
       "50%     53.333300   46.025600\n",
       "75%     64.743600   68.525675\n",
       "max     98.205100   99.487200"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Descriptive statistics\n",
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dataset</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>away</th>\n",
       "      <td>54.266100</td>\n",
       "      <td>47.834721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bullseye</th>\n",
       "      <td>54.268730</td>\n",
       "      <td>47.830823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>circle</th>\n",
       "      <td>54.267320</td>\n",
       "      <td>47.837717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dino</th>\n",
       "      <td>54.263273</td>\n",
       "      <td>47.832253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dots</th>\n",
       "      <td>54.260303</td>\n",
       "      <td>47.839829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>h_lines</th>\n",
       "      <td>54.261442</td>\n",
       "      <td>47.830252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>high_lines</th>\n",
       "      <td>54.268805</td>\n",
       "      <td>47.835450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>slant_down</th>\n",
       "      <td>54.267849</td>\n",
       "      <td>47.835896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>slant_up</th>\n",
       "      <td>54.265882</td>\n",
       "      <td>47.831496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>star</th>\n",
       "      <td>54.267341</td>\n",
       "      <td>47.839545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v_lines</th>\n",
       "      <td>54.269927</td>\n",
       "      <td>47.836988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wide_lines</th>\n",
       "      <td>54.266916</td>\n",
       "      <td>47.831602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>x_shape</th>\n",
       "      <td>54.260150</td>\n",
       "      <td>47.839717</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    x          y\n",
       "dataset                         \n",
       "away        54.266100  47.834721\n",
       "bullseye    54.268730  47.830823\n",
       "circle      54.267320  47.837717\n",
       "dino        54.263273  47.832253\n",
       "dots        54.260303  47.839829\n",
       "h_lines     54.261442  47.830252\n",
       "high_lines  54.268805  47.835450\n",
       "slant_down  54.267849  47.835896\n",
       "slant_up    54.265882  47.831496\n",
       "star        54.267341  47.839545\n",
       "v_lines     54.269927  47.836988\n",
       "wide_lines  54.266916  47.831602\n",
       "x_shape     54.260150  47.839717"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Descriptive statistics\n",
    "df.groupby('dataset').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dataset</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>away</th>\n",
       "      <td>16.769825</td>\n",
       "      <td>26.939743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bullseye</th>\n",
       "      <td>16.769239</td>\n",
       "      <td>26.935727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>circle</th>\n",
       "      <td>16.760013</td>\n",
       "      <td>26.930036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dino</th>\n",
       "      <td>16.765142</td>\n",
       "      <td>26.935403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dots</th>\n",
       "      <td>16.767735</td>\n",
       "      <td>26.930192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>h_lines</th>\n",
       "      <td>16.765898</td>\n",
       "      <td>26.939876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>high_lines</th>\n",
       "      <td>16.766704</td>\n",
       "      <td>26.939998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>slant_down</th>\n",
       "      <td>16.766759</td>\n",
       "      <td>26.936105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>slant_up</th>\n",
       "      <td>16.768853</td>\n",
       "      <td>26.938608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>star</th>\n",
       "      <td>16.768959</td>\n",
       "      <td>26.930275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v_lines</th>\n",
       "      <td>16.769959</td>\n",
       "      <td>26.937684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wide_lines</th>\n",
       "      <td>16.770000</td>\n",
       "      <td>26.937902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>x_shape</th>\n",
       "      <td>16.769958</td>\n",
       "      <td>26.930002</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    x          y\n",
       "dataset                         \n",
       "away        16.769825  26.939743\n",
       "bullseye    16.769239  26.935727\n",
       "circle      16.760013  26.930036\n",
       "dino        16.765142  26.935403\n",
       "dots        16.767735  26.930192\n",
       "h_lines     16.765898  26.939876\n",
       "high_lines  16.766704  26.939998\n",
       "slant_down  16.766759  26.936105\n",
       "slant_up    16.768853  26.938608\n",
       "star        16.768959  26.930275\n",
       "v_lines     16.769959  26.937684\n",
       "wide_lines  16.770000  26.937902\n",
       "x_shape     16.769958  26.930002"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('dataset').std()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Discussion point2 :\n",
    "\n",
    "#### Has our viewpoint changed now we have plotted the data?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## Create a function to show a scatterplot of the data\n",
    "\n",
    "def show_scatter(df, dataset_name, xlim=(-5, 105), ylim=(-5, 105), color=\"black\", marker=\"o\", reg_fit=False):\n",
    "    sns.regplot(\"x\", y=\"y\", data=df, ci=None, fit_reg=reg_fit, marker=marker,\n",
    "           scatter_kws={\"s\": 50, \"alpha\": 0.7, \"color\":color},\n",
    "                line_kws={\"linewidth\":4, \"color\":\"red\"})\n",
    "    plt.title(dataset_name)\n",
    "    plt.xlim(xlim)\n",
    "    plt.ylim(ylim)\n",
    "    plt.tight_layout()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_scatter(data, 'dino')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "away (142, 3)\n",
      "bullseye (142, 3)\n",
      "circle (142, 3)\n",
      "dino (142, 3)\n",
      "dots (142, 3)\n",
      "h_lines (142, 3)\n",
      "high_lines (142, 3)\n",
      "slant_down (142, 3)\n",
      "slant_up (142, 3)\n",
      "star (142, 3)\n",
      "v_lines (142, 3)\n",
      "wide_lines (142, 3)\n",
      "x_shape (142, 3)\n"
     ]
    }
   ],
   "source": [
    "for dataset_name, data in df.groupby('dataset'):\n",
    "    print(dataset_name, data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for dataset_name, data in df.groupby('dataset'):\n",
    "    show_scatter(data, dataset_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## Saving a plot\n",
    "\n",
    "# Creating a plotting function\n",
    "\n",
    "def save_scatter(df, name, dp=72):\n",
    "    show_scatter(df, name)\n",
    "    plt.savefig(str(name) + \".png\", dpi=dp)\n",
    "    plt.clf()\n",
    "    plt.cla()\n",
    "    plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Saving plot 1\n",
    "save_scatter(get_dataset('dino'),\"dino\",dp =300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Discussion point 3:\n",
    "\n",
    "#### But are statisticans so easily fooled?\n",
    "\n",
    "#### What additional information do we glean from some further summary stats?\n",
    "\n",
    "#### How else could we arrive at this information?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset 1\n",
      "Kurtosis X = -0.25\n",
      "Kurtosis Y = -1.04\n",
      "Skew X = 0.28\n",
      "Skew Y = 0.25\n"
     ]
    }
   ],
   "source": [
    "from scipy.stats import kurtosis\n",
    "from scipy.stats import skew\n",
    "\n",
    "## Skewedness tell you he amount and direction of skew, which is departure from horziontal symetery\n",
    "## Kurtosis tells you how tall and sharp the central peak is relative to a standard bell curve\n",
    "df1 = get_dataset('dino')\n",
    "\n",
    "print('Dataset 1')\n",
    "print('Kurtosis X =', round(kurtosis(df1.x),2))\n",
    "print('Kurtosis Y =', round(kurtosis(df1.y),2))\n",
    "print('Skew X =', round(skew(df1.x),2))\n",
    "print('Skew Y =', round(skew(df1.y),2))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset 2\n",
      "Kurtosis X = -1.16\n",
      "Kurtosis Y = -1.34\n",
      "Skew X = 0.08\n",
      "Skew Y = 0.24\n"
     ]
    }
   ],
   "source": [
    "df2 = get_dataset('star')\n",
    "\n",
    "print('Dataset 2')\n",
    "print('Kurtosis X =', round(kurtosis(df2.x),2))\n",
    "print('Kurtosis Y =', round(kurtosis(df2.y),2))\n",
    "print('Skew X =', round(skew(df2.x),2))\n",
    "print('Skew Y =', round(skew(df2.y),2))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Discussion point 4:\n",
    "\n",
    "#### What new information do we aquire when we plot a histogram?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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aURC0jq6qO0bYvyQJdw1JUueNaouggMuSFPChqlo1dYEkK4GVAEuXLh1yeZK2ZNnpl466hKHbcMYJoy5hoEa1RfCMqjoEOB54TZKjpi5QVauqakVVrZiYmBh+hZLUESMJgqr6Xvu4CfgMcNgo6pAkjSAIkuyaZPfJ58CzgXXDrkOS1BjFMYLFwGeSTPb/sar6/AjqkCQxgiCoqluApwy7X0nS9Dx9VJI6bpQXlEnSvDDKU2aHceqqWwSS1HEGgSR1nEEgSR1nEEhSxxkEktRxBoEkdZxBIEkdZxBIUscZBJLUcQaBJHWcQSBJHWcQSFLHGQSS1HEGgSR1nEEgSR1nEEhSxxkEktRxBoEkdZxBIEkdZxBIUscZBJLUcQaBJHWcQSBJHWcQSFLHjSQIkhyX5MYkNyc5fRQ1SJIaQw+CJDsAHwCOBw4ETkxy4LDrkCQ1RrFFcBhwc1XdUlW/AD4OvGAEdUiSgB1H0OfewHd7pm8DnjZ1oSQrgZXt5L1JbpzmtfYC7pjzCueO9W0f69s+414fjH+NI68v75hx9mz1Pb6fPkYRBH2pqlXAqpmWSbK6qlYMqaStZn3bx/q2z7jXB+NfY1fqG8Wuoe8B+/ZM79O2SZJGYBRB8H+BJyXZL8nDgZcBF4+gDkkSI9g1VFX3JXkt8AVgB+Ccqrp+G19uxl1HY8D6to/1bZ9xrw/Gv8ZO1JeqmovXkSTNU15ZLEkdZxBIUsfNiyBIsm+SK5PckOT6JK9r2/dMcnmSm9rHR4+wxl2SXJ3k622Nb2vb90tyVTucxifaA+SjqnGHJNcmuWTcamvr2ZBkbZLrkqxu28bpM16U5FNJvplkfZIjxqW+JAe079vkz91JTh2X+toa/6z93ViX5Pz2d2ZsvoNJXtfWdn2SU9u2kb1/Sc5JsinJup62aetJ473t+/iNJIdsTV/zIgiA+4DTqupA4HDgNe2wFKcDV1TVk4Ar2ulR+TlwTFU9BVgOHJfkcOAdwJlVtT/wY+CUEdb4OmB9z/Q41Tbp6Kpa3nNu9Dh9xn8DfL6qngw8hea9HIv6qurG9n1bDhwK/CvwmXGpL8newJ8CK6rqIJoTRV7GmHwHkxwEvIpm5IOnAM9Lsj+jff8+Ahw3pW1L9RwPPKn9WQmctVU9VdW8+wEuAp4F3AgsaduWADeOura2lkcC19BcMX0HsGPbfgTwhRHVtE/7xTkGuATIuNTWU+MGYK8pbWPxGQN7AN+mPcFi3OqbUtOzga+OU308OKLAnjRnK14CPGdcvoPAS4Cze6b/EvjPo37/gGXAutm+b8CHgBOnW66fn/myRfArSZYBBwNXAYuramM76wfA4hGVBfxq18t1wCbgcuBbwJ1VdV+7yG00vxCj8Nc0X+wH2unHMD61TSrgsiRr2iFGYHw+4/2AzcD/anev/W2SXceovl4vA85vn49FfVX1PeBdwHeAjcBdwBrG5zu4DvidJI9J8kjguTQXvo7F+9djS/VMN3RP3+/lvAqCJLsBnwZOraq7e+dVE4MjPRe2qu6vZtN8H5pNzCePsp5JSZ4HbKqqNaOuZRbPqKpDaDZzX5PkqN6ZI/6MdwQOAc6qqoOBnzBlN8E4fAfbfezPBz45dd4o62v3Zb+AJlAfB+zKQ3d7jExVrafZTXUZ8HngOuD+KcuM/PPtNZf1zJsgSLITTQicV1UXts23J1nSzl9C85f4yFXVncCVNJu6i5JMXrg3quE0jgSen2QDzWivx9Ds7x6H2n6l/auRqtpEs3/7MMbnM74NuK2qrmqnP0UTDONS36TjgWuq6vZ2elzq+13g21W1uap+CVxI870cm+9gVZ1dVYdW1VE0xyv+H+Pz/k3aUj3bNXTPvAiCJAHOBtZX1Xt6Zl0MnNw+P5nm2MFIJJlIsqh9/giaYxjraQLhxe1iI6mxqt5YVftU1TKa3QZfqqqXj0Ntk5LsmmT3yec0+7nXMSafcVX9APhukgPapmOBGxiT+nqcyIO7hWB86vsOcHiSR7a/z5Pv3zh9Bx/bPi4Ffg/4GOPz/k3aUj0XAye1Zw8dDtzVswtpdqM4MLMNB0yeQbMJ9A2aTbbraPbhPYbmAOhNwBeBPUdY428D17Y1rgPe0rY/AbgauJlmc33nEb+XzwQuGbfa2lq+3v5cD7y5bR+nz3g5sLr9jP8P8Ogxq29X4IfAHj1t41Tf24Bvtr8fHwV2HrPv4D/RhNPXgWNH/f7RBPpG4Jc0W6SnbKkempM/PkBzXHItzdlZffflEBOS1HHzYteQJGlwDAJJ6jiDQJI6ziCQpI4zCCSp4wwCLVhpRqx9zpS2U5PMOCBXknsHXNdEO+LmtUl+p6d9h3Z4jaN62i5L8pJB1iMZBFrIzqe5gK5X7zg8o3IssLaqDq6qf5psrKr7gVcD70+yU5ITgQeq6iHDRUhzyesItGAl2ZPmAqZ9quoX7YCFXwYeT3Px1UU0F4XtBPyXqrqoXe/eqtotyTOBv6iq57Xt7wdWV9VHkhwKvAfYjWYEzX9fU67kbPs7B9iLZsC6V9KMvnkx8AiaIQCOqKqfTlnvQ+1r/gHwrKq6ee7eFemh3CLQglVVP6K5avX4tullwAXV/PXzM+BF1QxydzTw7nbog1m14169D3hxVR1K85/9X02z6PuAc6vqt4HzgPdW1XXAW4BPVHP/gJ9Os94bgVOBjxkCGoYdZ19Emtcmdw9d1D5O3vgkwNvb/fEP0AzZu5hmaN/ZHAAcBFzeZscONEMBTHUEzZg10Ayp8M4+az6KZpjmg/pcXtoubhFoobsIOLa9dd8j68GhuF8OTACHVjN0+O3ALlPWvY9f/x2ZnB/g+vYv+uVV9VtV9ey5KLYdcO+dNCPEPjbJc+fidaWZGARa0KrqXpoRLs/h1w8S70Fzj4ZfJjma5rjBVLcCBybZuR1Z9ti2/UZgIskR0OwqSvKb06z/zzx4sPrlNIOazeYtNLuvvklz4PjMJFMDSppTBoG64Hya+9D2BsF5wIoka4GTaA4q/5qq+i5wAc1omRfQjC5LVf2CZujkdyT5Os1ouE+fpt8/AV6Z5BvAK2juGb1FbZi8iPZ4Q1VdC3wBeEO//1BpW3jWkCR1nFsEktRxBoEkdZxBIEkdZxBIUscZBJLUcQaBJHWcQSBJHff/AUNc+RTwubDWAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(df1.x)\n",
    "plt.title(\"Data set 1\")\n",
    "plt.xlabel(\"Value of X\")\n",
    "plt.ylabel(\"Count of X\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(df2.x)\n",
    "plt.title(\"Data set 2\")\n",
    "plt.xlabel(\"Value of X\")\n",
    "plt.ylabel(\"Count of X\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Over to you:\n",
    "\n",
    "<li> 1) Download the dataset: https://www.autodeskresearch.com/sites/default/files/SameStatsDataAndImages.zip </li>\n",
    "<li> 2) Have a go at making some of your own scatter plots and histograms </li>\n",
    "<li> 3) If you like email any you are particulary proud of (image and code) and we can start putting a gallery together which we can disucss as a group. </li>\n",
    "<li> 4) Any questions from today?</li>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}