dataviz-solutions.ipynb 71.5 KB
Newer Older
George Mount's avatar
George Mount committed
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "In C:\\Users\\User\\Anaconda3\\lib\\site-packages\\matplotlib\\mpl-data\\stylelib\\_classic_test.mplstyle: \n",
      "The savefig.frameon rcparam was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
      "In C:\\Users\\User\\Anaconda3\\lib\\site-packages\\matplotlib\\mpl-data\\stylelib\\_classic_test.mplstyle: \n",
      "The verbose.level rcparam was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
      "In C:\\Users\\User\\Anaconda3\\lib\\site-packages\\matplotlib\\mpl-data\\stylelib\\_classic_test.mplstyle: \n",
      "The verbose.fileo rcparam was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n"
     ]
    },
    {
     "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>price</th>\n",
       "      <th>lotsize</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrms</th>\n",
       "      <th>stories</th>\n",
       "      <th>driveway</th>\n",
       "      <th>recroom</th>\n",
       "      <th>fullbase</th>\n",
       "      <th>gashw</th>\n",
       "      <th>airco</th>\n",
       "      <th>garagepl</th>\n",
       "      <th>prefarea</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>42000.0</td>\n",
       "      <td>5850</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>38500.0</td>\n",
       "      <td>4000</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>49500.0</td>\n",
       "      <td>3060</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>60500.0</td>\n",
       "      <td>6650</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>61000.0</td>\n",
       "      <td>6360</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     price  lotsize  bedrooms  bathrms  stories driveway recroom fullbase  \\\n",
       "0  42000.0     5850         3        1        2      yes      no      yes   \n",
       "1  38500.0     4000         2        1        1      yes      no       no   \n",
       "2  49500.0     3060         3        1        1      yes      no       no   \n",
       "3  60500.0     6650         3        1        2      yes     yes       no   \n",
       "4  61000.0     6360         2        1        1      yes      no       no   \n",
       "\n",
       "  gashw airco  garagepl prefarea  \n",
       "0    no    no         1       no  \n",
       "1    no    no         0       no  \n",
       "2    no    no         0       no  \n",
       "3    no    no         0       no  \n",
       "4    no    no         0       no  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "housing = pd.read_csv('data/housing.csv')\n",
    "housing.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## DRILLS\n",
    "\n",
    "For this exercise, analyze the `housing` dataset from the `data` folder: \n",
    "\n",
    "1. Plot a histogram of the `price` variable using 50 bins. \n",
    "2. Count the number of observations by number of `stories`.\n",
    "\n",
    "You can code this from scratch, or fill in the code below. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With a bar chart we will count up the observations of each value of a category. For example we could find how many observations are placed for each category. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x252dd672668>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "# Plot a histogram of price with 50 bins\n",
    "sns.distplot(housing['price'],bins=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x252dde80668>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEGCAYAAACKB4k+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAOxElEQVR4nO3df6xfd13H8eeLbgz56ZZ2c7QLbbQqRWXItQ4XFTYCFZQOIqRLIBWXlJhhmJLoZoigZhEioASBpMLYkLk5HbCpqCx1QFTcuMMJ3cakoZXV1vWOoRsQpi1v//iefnbpbtsv7T3fc388H8nN93s+55xvX/cku6+dn99UFZIkATxu6ACSpIXDUpAkNZaCJKmxFCRJjaUgSWpOGTrAyVi5cmWtXbt26BiStKjccccdD1TVqrnmLepSWLt2LdPT00PHkKRFJcl/HG2eh48kSY2lIElqLAVJUmMpSJIaS0GS1FgKkqTGUpAkNZaCJKmxFCRJzaK+o1mTtW7dnqEjLBi7d68dOoLUC/cUJEmNpSBJaiwFSVJjKUiSGktBktRYCpKkxlKQJDWWgiSpWfI3r3nD1aO84UrS8binIElqLAVJUmMpSJIaS0GS1FgKkqTGUpAkNZaCJKmxFCRJjaUgSWosBUlSYylIkhpLQZLUWAqSpMZSkCQ1loIkqbEUJEmNpSBJaiwFSVJjKUiSGktBktT0VgpJzklya5J7ktyV5A3d+BlJbknype719FnrXJFkV5J7k7y4r2ySpLn1uadwEHhjVT0TOA+4NMkG4HJgR1WtB3Z003TztgDPAjYB702yosd8kqQj9FYKVbW/qj7XvX8YuAdYDWwGrukWuwa4qHu/Gbi+qh6pqt3ALmBjX/kkSY81kXMKSdYCzwFuA86qqv0wKg7gzG6x1cB9s1bb240d+VnbkkwnmZ6ZmekztiQtO72XQpInAzcCl1XVQ8dadI6xesxA1faqmqqqqVWrVs1XTEkSPZdCklMZFcK1VfWRbvj+JGd3888GDnTje4FzZq2+BtjXZz5J0nfq8+qjAB8A7qmqd86adTOwtXu/Fbhp1viWJKclWQesB27vK58k6bFO6fGzzwdeA3whyZ3d2G8BbwVuSHIJ8BXglQBVdVeSG4C7GV25dGlVHeoxnyTpCL2VQlX9I3OfJwC48CjrXAlc2VcmSdKxeUezJKmxFCRJjaUgSWosBUlSYylIkhpLQZLUWAqSpMZSkCQ1loIkqbEUJEmNpSBJaiwFSVJjKUiSGktBktRYCpKkxlKQJDWWgiSpsRQkSY2lIElqLAVJUmMpSJIaS0GS1FgKkqTGUpAkNZaCJKmxFCRJjaUgSWosBUlSYylIkhpLQZLUWAqSpMZSkCQ1loIkqbEUJElNb6WQ5KokB5LsnDX2liT/meTO7ucls+ZdkWRXknuTvLivXJKko+tzT+FqYNMc439YVed2Px8HSLIB2AI8q1vnvUlW9JhNkjSH3kqhqj4NPDjm4puB66vqkaraDewCNvaVTZI0tyHOKbw+yee7w0und2OrgftmLbO3G3uMJNuSTCeZnpmZ6TurJC0rky6F9wHfD5wL7Afe0Y1njmVrrg+oqu1VNVVVU6tWreonpSQtUxMthaq6v6oOVdW3gT/h0UNEe4FzZi26Btg3yWySpAmXQpKzZ02+HDh8ZdLNwJYkpyVZB6wHbp9kNkkSnNLXBye5Dng+sDLJXuDNwPOTnMvo0NAe4HUAVXVXkhuAu4GDwKVVdaivbJKkufVWClV18RzDHzjG8lcCV/aVR5J0fN7RLElqLAVJUmMpSJIaS0GS1FgKkqRmrFJIsmOcMUnS4nbMS1KTPAF4IqN7DU7n0cdRPBV4es/ZJEkTdrz7FF4HXMaoAO7g0VJ4CHhPj7kkSQM4ZilU1buAdyX51ap694QySZIGMtYdzVX17iQ/BaydvU5VfainXJKkAYxVCkn+lNEjr+8EDj+TqABLQZKWkHGffTQFbKiqOb/jQJK0NIx7n8JO4Pv6DCJJGt64eworgbuT3A48cniwql7WSypJ0iDGLYW39BlCkrQwjHv10af6DiJJGt64Vx89zOhqI4DHA6cC36iqp/YVTJI0eePuKTxl9nSSi4CNvSSSJA3mhJ6SWlUfAy6Y5yySpIGNe/joFbMmH8fovgXvWZCkJWbcq49+Ydb7g8AeYPO8p5EkDWrccwqv7TuIJGl4437JzpokH01yIMn9SW5MsqbvcJKkyRr3RPMHgZsZfa/CauCvujFJ0hIybimsqqoPVtXB7udqYFWPuSRJAxi3FB5I8uokK7qfVwNf7TOYJGnyxi2FXwZeBfwXsB/4RcCTz5K0xIx7ServAVur6msASc4A3s6oLCRJS8S4ewo/drgQAKrqQeA5/USSJA1l3FJ4XJLTD090ewrj7mVIkhaJcf+wvwP45yR/yejxFq8CruwtlSRpEOPe0fyhJNOMHoIX4BVVdXevySRJEzf2IaCuBCwCSVrCTujR2ZKkpclSkCQ1vZVCkqu6B+jtnDV2RpJbknype519RdMVSXYluTfJi/vKJUk6uj73FK4GNh0xdjmwo6rWAzu6aZJsALYAz+rWeW+SFT1mkyTNobdSqKpPAw8eMbwZuKZ7fw1w0azx66vqkaraDezC74CWpImb9DmFs6pqP0D3emY3vhq4b9Zye7uxx0iyLcl0kumZmZlew0rScrNQTjRnjrE5vwO6qrZX1VRVTa1a5dO7JWk+TboU7k9yNkD3eqAb3wucM2u5NcC+CWeTpGVv0qVwM7C1e78VuGnW+JYkpyVZB6wHbp9wNkla9np7qF2S64DnAyuT7AXeDLwVuCHJJcBXgFcCVNVdSW5gdMf0QeDSqjrUVzZJ0tx6K4Wquvgosy48yvJX4kP2JGlQC+VEsyRpAbAUJEmNpSBJaiwFSVJjKUiSGktBktRYCpKkxlKQJDWWgiSpsRQkSY2lIElqLAVJUmMpSJIaS0GS1FgKkqTGUpAkNZaCJKmxFCRJjaUgSWosBUlSYylIkhpLQZLUWAqSpMZSkCQ1loIkqbEUJEmNpSBJaiwFSVJjKUiSGktBktRYCpKkxlKQJDWWgiSpsRQkSc0pQ/yjSfYADwOHgINVNZXkDODPgbXAHuBVVfW1IfJJ0nI15J7CC6rq3Kqa6qYvB3ZU1XpgRzctSZqghXT4aDNwTff+GuCiAbNI0rI0VCkU8IkkdyTZ1o2dVVX7AbrXM+daMcm2JNNJpmdmZiYUV5KWh0HOKQDnV9W+JGcCtyT54rgrVtV2YDvA1NRU9RVQkpajQfYUqmpf93oA+CiwEbg/ydkA3euBIbJJ0nI28VJI8qQkTzn8HngRsBO4GdjaLbYVuGnS2SRpuRvi8NFZwEeTHP73/6yq/i7JZ4EbklwCfAV45QDZJGlZm3gpVNWXgWfPMf5V4MJJ55EkPWohXZIqSRqYpSBJaoa6JFWS5tW6dXuGjrBg7N699oTXtRSkgfhH7FEn80dM88vDR5KkxlKQJDWWgiSpsRQkSY2lIElqLAVJUmMpSJIaS0GS1FgKkqTGUpAkNZaCJKmxFCRJjaUgSWosBUlSYylIkhpLQZLUWAqSpMZSkCQ1loIkqbEUJEmNpSBJaiwFSVJjKUiSGktBktRYCpKkxlKQJDWWgiSpsRQkSY2lIElqLAVJUmMpSJKaBVcKSTYluTfJriSXD51HkpaTBVUKSVYA7wF+DtgAXJxkw7CpJGn5WFClAGwEdlXVl6vqf4Hrgc0DZ5KkZeOUoQMcYTVw36zpvcBPzl4gyTZgWzf59ST3TijbyVgJPDB0iGToBPNm8O25hLYluD3n0+DbEsbans842oyFVgpz/Sr1HRNV24Htk4kzP5JMV9XU0DmWCrfn/HJ7zp+lsC0X2uGjvcA5s6bXAPsGyiJJy85CK4XPAuuTrEvyeGALcPPAmSRp2VhQh4+q6mCS1wN/D6wArqqquwaONR8W1eGuRcDtOb/cnvNn0W/LVNXxl5IkLQsL7fCRJGlAloIkqbEUepTkqiQHkuwcOstil+ScJLcmuSfJXUneMHSmxSzJE5LcnuTfuu35O0NnWgqSrEjyr0n+eugsJ8pS6NfVwKahQywRB4E3VtUzgfOAS30Eykl5BLigqp4NnAtsSnLewJmWgjcA9wwd4mRYCj2qqk8DDw6dYymoqv1V9bnu/cOM/sNbPWyqxatGvt5Nntr9eNXJSUiyBngp8P6hs5wMS0GLTpK1wHOA24ZNsrh1hzruBA4At1SV2/Pk/BHwG8C3hw5yMiwFLSpJngzcCFxWVQ8NnWcxq6pDVXUuoycHbEzyI0NnWqyS/DxwoKruGDrLybIUtGgkOZVRIVxbVR8ZOs9SUVX/DXwSz3+djPOBlyXZw+jpzhck+fCwkU6MpaBFIUmADwD3VNU7h86z2CVZleR7u/ffA7wQ+OKwqRavqrqiqtZU1VpGj+f5h6p69cCxToil0KMk1wGfAX4oyd4klwydaRE7H3gNo/8Du7P7ecnQoRaxs4Fbk3ye0TPHbqmqRXsZpeaPj7mQJDXuKUiSGktBktRYCpKkxlKQJDWWgiSpsRSk70KSy5I88QTWe78P8NNi4CWp0nehu2N1qqoe+C7WWVFVh/pLJc0f9xSko0jypCR/033nwM4kbwaezuimr1u7ZS5O8oVu/ttmrfv1JL+b5DbgeUk+mWSqm/eiJJ9J8rkkf9E9z4kkb01yd5LPJ3n7AL+yxClDB5AWsE3Avqp6KUCSpwGvBV5QVQ8keTrwNuC5wNeATyS5qKo+BjwJ2FlVv92tS/e6EngT8MKq+kaS3wR+PckfAy8Hfriq6vAjKKRJc09BOrovAC9M8rYkP11V/3PE/J8APllVM1V1ELgW+Jlu3iFGD+870nnABuCfusdWbwWeATwEfAt4f5JXAN+c/19HOj73FKSjqKp/T/Jc4CXA7yf5xBGL5Birf+so5xHC6DlDFz9mRrIRuJDRA9VeD1xwYsmlE+eegnQU3eGhb1bVh4G3Az8OPAw8pVvkNuBnk6xMsgK4GPjUcT72X4Dzk/xA9288MckPducVnlZVHwcuY/QVmdLEuacgHd2PAn+Q5NvA/wG/AjwP+Nsk+6vqBUmuAG5ltAfw8aq66VgfWFUzSX4JuC7Jad3wmxiVzU1JntB91q/18htJx+ElqZKkxsNHkqTGUpAkNZaCJKmxFCRJjaUgSWosBUlSYylIkpr/BxB+Z+Fq9qW6AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Count the number of observations by number of `stories`\n",
    "sns.countplot(housing['stories'], color='blue')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## DRILLS\n",
    "\n",
    "For this exercise, analyze the `housing` dataset from the `data` folder: \n",
    "\n",
    "1. Make a box plot of the distribution of `lotsize` for each value of the `prefarea` category.  \n",
    "2.  Make a scatterplot of `lotsize` versus `price`. Add a custom title to the plot.\n",
    "\n",
    "You can code this from scratch, or fill in the code below. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x252ddeed908>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "# Box plot of the distribution of `lotsize` for each value of the `prefarea` category.  \n",
    "sns.boxplot(x='prefarea', y='lotsize', data=housing)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Lot size versus sales price of houses')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "#  Make a scatterplot of `lotsize` versus `price`. Add a custom title to the plot.\n",
    "\n",
    "# First we need to call pyplot\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Now let's make our scatterplot\n",
    "sns.scatterplot(data=housing,x='lotsize',y='price')\n",
    "\n",
    "# Now let's add a custom title\n",
    "plt.title('Lot size versus sales price of houses')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "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": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}