├── crime.csv
├── data-exploration-exercise.ipynb
└── data-exploration-with-solution.ipynb


/crime.csv:
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https://raw.githubusercontent.com/mochen862/explore-data-python/25e0ead56adf9aacf9517bd554320b04cbae82c7/crime.csv


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/data-exploration-exercise.ipynb:
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   1 | {
   2 |  "cells": [
   3 |   {
   4 |    "attachments": {},
   5 |    "cell_type": "markdown",
   6 |    "id": "96009edc",
   7 |    "metadata": {},
   8 |    "source": [
   9 |     "### Import all the necessary libraries"
  10 |    ]
  11 |   },
  12 |   {
  13 |    "cell_type": "code",
  14 |    "execution_count": 1,
  15 |    "id": "eb61f6f6",
  16 |    "metadata": {},
  17 |    "outputs": [],
  18 |    "source": [
  19 |     "import numpy as np\n",
  20 |     "import pandas as pd\n",
  21 |     "import matplotlib.pyplot as plt\n",
  22 |     "import seaborn as sns\n",
  23 |     "from encodings.aliases import aliases # Python has a file containing a dictionary of encoding names and associated aliases\n",
  24 |     "\n",
  25 |     "# the matplotlib plots will appear directly below the cell in which the plot function was called.\n",
  26 |     "%matplotlib inline"
  27 |    ]
  28 |   },
  29 |   {
  30 |    "attachments": {},
  31 |    "cell_type": "markdown",
  32 |    "id": "a1013c3a",
  33 |    "metadata": {},
  34 |    "source": [
  35 |     "### Read in the csv files and remove duplicates"
  36 |    ]
  37 |   },
  38 |   {
  39 |    "cell_type": "code",
  40 |    "execution_count": 2,
  41 |    "id": "a454bb34",
  42 |    "metadata": {},
  43 |    "outputs": [
  44 |     {
  45 |      "name": "stdout",
  46 |      "output_type": "stream",
  47 |      "text": [
  48 |       "successful iso8859_15\n",
  49 |       "successful cp864\n",
  50 |       "successful big5hkscs\n",
  51 |       "successful mac_latin2\n",
  52 |       "successful cp1252\n",
  53 |       "successful cp500\n",
  54 |       "successful iso8859_13\n",
  55 |       "successful iso8859_10\n",
  56 |       "successful koi8_r\n",
  57 |       "successful cp1251\n",
  58 |       "successful iso8859_11\n",
  59 |       "successful mac_turkish\n",
  60 |       "successful utf_16_be\n",
  61 |       "successful mac_iceland\n",
  62 |       "successful utf_16_le\n",
  63 |       "successful cp1254\n",
  64 |       "successful cp860\n",
  65 |       "successful cp1256\n",
  66 |       "successful cp1140\n",
  67 |       "successful mac_roman\n",
  68 |       "successful mac_cyrillic\n",
  69 |       "successful iso8859_6\n",
  70 |       "successful cp850\n",
  71 |       "successful iso8859_9\n",
  72 |       "successful cp949\n",
  73 |       "successful cp862\n",
  74 |       "successful latin_1\n",
  75 |       "successful cp866\n",
  76 |       "successful iso8859_5\n",
  77 |       "successful cp1125\n",
  78 |       "successful cp1258\n",
  79 |       "successful cp1253\n",
  80 |       "successful cp1257\n",
  81 |       "successful cp869\n",
  82 |       "successful kz1048\n",
  83 |       "successful iso8859_3\n",
  84 |       "successful cp932\n",
  85 |       "successful hp_roman8\n",
  86 |       "successful cp865\n",
  87 |       "successful cp1250\n",
  88 |       "successful iso8859_8\n",
  89 |       "successful cp037\n",
  90 |       "successful cp855\n",
  91 |       "successful cp273\n",
  92 |       "successful cp861\n",
  93 |       "successful cp858\n",
  94 |       "successful iso8859_14\n",
  95 |       "successful mac_greek\n",
  96 |       "successful cp775\n",
  97 |       "successful iso8859_16\n",
  98 |       "successful mbcs\n",
  99 |       "successful iso8859_7\n",
 100 |       "successful gbk\n",
 101 |       "successful ptcp154\n",
 102 |       "successful cp863\n",
 103 |       "successful iso8859_4\n",
 104 |       "successful iso8859_2\n",
 105 |       "successful cp1255\n",
 106 |       "successful gb18030\n",
 107 |       "successful cp437\n",
 108 |       "successful cp1026\n",
 109 |       "successful cp852\n",
 110 |       "successful cp857\n"
 111 |      ]
 112 |     }
 113 |    ],
 114 |    "source": [
 115 |     "# To find encodings that work\n",
 116 |     "\n",
 117 |     "# Below line creates a set of all available encodings\n",
 118 |     "alias_values = set(aliases.values())\n",
 119 |     "\n",
 120 |     "for encoding in set(aliases.values()):\n",
 121 |     "    try:\n",
 122 |     "        df=pd.read_csv(\"crime.csv\", nrows=10, encoding=encoding) # read in only 10 lines for faster read\n",
 123 |     "        print('successful', encoding)\n",
 124 |     "    except:\n",
 125 |     "        pass"
 126 |    ]
 127 |   },
 128 |   {
 129 |    "cell_type": "code",
 130 |    "execution_count": 3,
 131 |    "id": "20ee872c",
 132 |    "metadata": {},
 133 |    "outputs": [],
 134 |    "source": [
 135 |     "# Read in the crime.csv file and use the timestamp as a datetime index\n",
 136 |     "crime = pd.read_csv(\"crime.csv\", encoding=\"ISO-8859-11\")"
 137 |    ]
 138 |   },
 139 |   {
 140 |    "cell_type": "code",
 141 |    "execution_count": 4,
 142 |    "id": "6b786b8c",
 143 |    "metadata": {},
 144 |    "outputs": [
 145 |     {
 146 |      "data": {
 147 |       "text/html": [
 148 |        "<div>\n",
 149 |        "<style scoped>\n",
 150 |        "    .dataframe tbody tr th:only-of-type {\n",
 151 |        "        vertical-align: middle;\n",
 152 |        "    }\n",
 153 |        "\n",
 154 |        "    .dataframe tbody tr th {\n",
 155 |        "        vertical-align: top;\n",
 156 |        "    }\n",
 157 |        "\n",
 158 |        "    .dataframe thead th {\n",
 159 |        "        text-align: right;\n",
 160 |        "    }\n",
 161 |        "</style>\n",
 162 |        "<table border=\"1\" class=\"dataframe\">\n",
 163 |        "  <thead>\n",
 164 |        "    <tr style=\"text-align: right;\">\n",
 165 |        "      <th></th>\n",
 166 |        "      <th>INCIDENT_NUMBER</th>\n",
 167 |        "      <th>OFFENSE_CODE</th>\n",
 168 |        "      <th>OFFENSE_CODE_GROUP</th>\n",
 169 |        "      <th>OFFENSE_DESCRIPTION</th>\n",
 170 |        "      <th>DISTRICT</th>\n",
 171 |        "      <th>REPORTING_AREA</th>\n",
 172 |        "      <th>SHOOTING</th>\n",
 173 |        "      <th>OCCURRED_ON_DATE</th>\n",
 174 |        "      <th>YEAR</th>\n",
 175 |        "      <th>MONTH</th>\n",
 176 |        "      <th>DAY_OF_WEEK</th>\n",
 177 |        "      <th>HOUR</th>\n",
 178 |        "      <th>UCR_PART</th>\n",
 179 |        "      <th>STREET</th>\n",
 180 |        "      <th>Lat</th>\n",
 181 |        "      <th>Long</th>\n",
 182 |        "      <th>Location</th>\n",
 183 |        "    </tr>\n",
 184 |        "  </thead>\n",
 185 |        "  <tbody>\n",
 186 |        "    <tr>\n",
 187 |        "      <th>0</th>\n",
 188 |        "      <td>I182070945</td>\n",
 189 |        "      <td>619</td>\n",
 190 |        "      <td>Larceny</td>\n",
 191 |        "      <td>LARCENY ALL OTHERS</td>\n",
 192 |        "      <td>D14</td>\n",
 193 |        "      <td>808</td>\n",
 194 |        "      <td>NaN</td>\n",
 195 |        "      <td>2018-09-02 13:00:00</td>\n",
 196 |        "      <td>2018</td>\n",
 197 |        "      <td>9</td>\n",
 198 |        "      <td>Sunday</td>\n",
 199 |        "      <td>13</td>\n",
 200 |        "      <td>Part One</td>\n",
 201 |        "      <td>LINCOLN ST</td>\n",
 202 |        "      <td>42.357791</td>\n",
 203 |        "      <td>-71.139371</td>\n",
 204 |        "      <td>(42.35779134, -71.13937053)</td>\n",
 205 |        "    </tr>\n",
 206 |        "    <tr>\n",
 207 |        "      <th>1</th>\n",
 208 |        "      <td>I182070943</td>\n",
 209 |        "      <td>1402</td>\n",
 210 |        "      <td>Vandalism</td>\n",
 211 |        "      <td>VANDALISM</td>\n",
 212 |        "      <td>C11</td>\n",
 213 |        "      <td>347</td>\n",
 214 |        "      <td>NaN</td>\n",
 215 |        "      <td>2018-08-21 00:00:00</td>\n",
 216 |        "      <td>2018</td>\n",
 217 |        "      <td>8</td>\n",
 218 |        "      <td>Tuesday</td>\n",
 219 |        "      <td>0</td>\n",
 220 |        "      <td>Part Two</td>\n",
 221 |        "      <td>HECLA ST</td>\n",
 222 |        "      <td>42.306821</td>\n",
 223 |        "      <td>-71.060300</td>\n",
 224 |        "      <td>(42.30682138, -71.06030035)</td>\n",
 225 |        "    </tr>\n",
 226 |        "    <tr>\n",
 227 |        "      <th>2</th>\n",
 228 |        "      <td>I182070941</td>\n",
 229 |        "      <td>3410</td>\n",
 230 |        "      <td>Towed</td>\n",
 231 |        "      <td>TOWED MOTOR VEHICLE</td>\n",
 232 |        "      <td>D4</td>\n",
 233 |        "      <td>151</td>\n",
 234 |        "      <td>NaN</td>\n",
 235 |        "      <td>2018-09-03 19:27:00</td>\n",
 236 |        "      <td>2018</td>\n",
 237 |        "      <td>9</td>\n",
 238 |        "      <td>Monday</td>\n",
 239 |        "      <td>19</td>\n",
 240 |        "      <td>Part Three</td>\n",
 241 |        "      <td>CAZENOVE ST</td>\n",
 242 |        "      <td>42.346589</td>\n",
 243 |        "      <td>-71.072429</td>\n",
 244 |        "      <td>(42.34658879, -71.07242943)</td>\n",
 245 |        "    </tr>\n",
 246 |        "    <tr>\n",
 247 |        "      <th>3</th>\n",
 248 |        "      <td>I182070940</td>\n",
 249 |        "      <td>3114</td>\n",
 250 |        "      <td>Investigate Property</td>\n",
 251 |        "      <td>INVESTIGATE PROPERTY</td>\n",
 252 |        "      <td>D4</td>\n",
 253 |        "      <td>272</td>\n",
 254 |        "      <td>NaN</td>\n",
 255 |        "      <td>2018-09-03 21:16:00</td>\n",
 256 |        "      <td>2018</td>\n",
 257 |        "      <td>9</td>\n",
 258 |        "      <td>Monday</td>\n",
 259 |        "      <td>21</td>\n",
 260 |        "      <td>Part Three</td>\n",
 261 |        "      <td>NEWCOMB ST</td>\n",
 262 |        "      <td>42.334182</td>\n",
 263 |        "      <td>-71.078664</td>\n",
 264 |        "      <td>(42.33418175, -71.07866441)</td>\n",
 265 |        "    </tr>\n",
 266 |        "    <tr>\n",
 267 |        "      <th>4</th>\n",
 268 |        "      <td>I182070938</td>\n",
 269 |        "      <td>3114</td>\n",
 270 |        "      <td>Investigate Property</td>\n",
 271 |        "      <td>INVESTIGATE PROPERTY</td>\n",
 272 |        "      <td>B3</td>\n",
 273 |        "      <td>421</td>\n",
 274 |        "      <td>NaN</td>\n",
 275 |        "      <td>2018-09-03 21:05:00</td>\n",
 276 |        "      <td>2018</td>\n",
 277 |        "      <td>9</td>\n",
 278 |        "      <td>Monday</td>\n",
 279 |        "      <td>21</td>\n",
 280 |        "      <td>Part Three</td>\n",
 281 |        "      <td>DELHI ST</td>\n",
 282 |        "      <td>42.275365</td>\n",
 283 |        "      <td>-71.090361</td>\n",
 284 |        "      <td>(42.27536542, -71.09036101)</td>\n",
 285 |        "    </tr>\n",
 286 |        "  </tbody>\n",
 287 |        "</table>\n",
 288 |        "</div>"
 289 |       ],
 290 |       "text/plain": [
 291 |        "  INCIDENT_NUMBER  OFFENSE_CODE    OFFENSE_CODE_GROUP   OFFENSE_DESCRIPTION  \\\n",
 292 |        "0      I182070945           619               Larceny    LARCENY ALL OTHERS   \n",
 293 |        "1      I182070943          1402             Vandalism             VANDALISM   \n",
 294 |        "2      I182070941          3410                 Towed   TOWED MOTOR VEHICLE   \n",
 295 |        "3      I182070940          3114  Investigate Property  INVESTIGATE PROPERTY   \n",
 296 |        "4      I182070938          3114  Investigate Property  INVESTIGATE PROPERTY   \n",
 297 |        "\n",
 298 |        "  DISTRICT REPORTING_AREA SHOOTING     OCCURRED_ON_DATE  YEAR  MONTH  \\\n",
 299 |        "0      D14            808      NaN  2018-09-02 13:00:00  2018      9   \n",
 300 |        "1      C11            347      NaN  2018-08-21 00:00:00  2018      8   \n",
 301 |        "2       D4            151      NaN  2018-09-03 19:27:00  2018      9   \n",
 302 |        "3       D4            272      NaN  2018-09-03 21:16:00  2018      9   \n",
 303 |        "4       B3            421      NaN  2018-09-03 21:05:00  2018      9   \n",
 304 |        "\n",
 305 |        "  DAY_OF_WEEK  HOUR    UCR_PART       STREET        Lat       Long  \\\n",
 306 |        "0      Sunday    13    Part One   LINCOLN ST  42.357791 -71.139371   \n",
 307 |        "1     Tuesday     0    Part Two     HECLA ST  42.306821 -71.060300   \n",
 308 |        "2      Monday    19  Part Three  CAZENOVE ST  42.346589 -71.072429   \n",
 309 |        "3      Monday    21  Part Three   NEWCOMB ST  42.334182 -71.078664   \n",
 310 |        "4      Monday    21  Part Three     DELHI ST  42.275365 -71.090361   \n",
 311 |        "\n",
 312 |        "                      Location  \n",
 313 |        "0  (42.35779134, -71.13937053)  \n",
 314 |        "1  (42.30682138, -71.06030035)  \n",
 315 |        "2  (42.34658879, -71.07242943)  \n",
 316 |        "3  (42.33418175, -71.07866441)  \n",
 317 |        "4  (42.27536542, -71.09036101)  "
 318 |       ]
 319 |      },
 320 |      "execution_count": 4,
 321 |      "metadata": {},
 322 |      "output_type": "execute_result"
 323 |     }
 324 |    ],
 325 |    "source": [
 326 |     "crime.head() # Quick check on how the dataset looks like"
 327 |    ]
 328 |   },
 329 |   {
 330 |    "cell_type": "code",
 331 |    "execution_count": 5,
 332 |    "id": "78dac45e",
 333 |    "metadata": {},
 334 |    "outputs": [
 335 |     {
 336 |      "data": {
 337 |       "text/plain": [
 338 |        "(319073, 17)"
 339 |       ]
 340 |      },
 341 |      "execution_count": 5,
 342 |      "metadata": {},
 343 |      "output_type": "execute_result"
 344 |     }
 345 |    ],
 346 |    "source": [
 347 |     "crime.shape # Checking the shape of the data. It has 319,073 rows and 17 columns"
 348 |    ]
 349 |   },
 350 |   {
 351 |    "cell_type": "code",
 352 |    "execution_count": 6,
 353 |    "id": "93f474f9",
 354 |    "metadata": {},
 355 |    "outputs": [
 356 |     {
 357 |      "data": {
 358 |       "text/plain": [
 359 |        "23"
 360 |       ]
 361 |      },
 362 |      "execution_count": 6,
 363 |      "metadata": {},
 364 |      "output_type": "execute_result"
 365 |     }
 366 |    ],
 367 |    "source": [
 368 |     "crime.duplicated().sum() # Counting the duplicate rows"
 369 |    ]
 370 |   },
 371 |   {
 372 |    "cell_type": "code",
 373 |    "execution_count": 7,
 374 |    "id": "148a2f42",
 375 |    "metadata": {},
 376 |    "outputs": [],
 377 |    "source": [
 378 |     "crime.drop_duplicates(inplace=True) # Dropping the duplicate rows"
 379 |    ]
 380 |   },
 381 |   {
 382 |    "cell_type": "code",
 383 |    "execution_count": 8,
 384 |    "id": "1c0279ac",
 385 |    "metadata": {},
 386 |    "outputs": [
 387 |     {
 388 |      "data": {
 389 |       "text/plain": [
 390 |        "(319050, 17)"
 391 |       ]
 392 |      },
 393 |      "execution_count": 8,
 394 |      "metadata": {},
 395 |      "output_type": "execute_result"
 396 |     }
 397 |    ],
 398 |    "source": [
 399 |     "crime.shape # Checking the shape again to see if dropping the duplicate rows worked"
 400 |    ]
 401 |   },
 402 |   {
 403 |    "attachments": {},
 404 |    "cell_type": "markdown",
 405 |    "id": "f0823dc0",
 406 |    "metadata": {},
 407 |    "source": [
 408 |     "### Explore the dataset"
 409 |    ]
 410 |   },
 411 |   {
 412 |    "cell_type": "code",
 413 |    "execution_count": 9,
 414 |    "id": "5ad86c4d",
 415 |    "metadata": {},
 416 |    "outputs": [
 417 |     {
 418 |      "data": {
 419 |       "text/html": [
 420 |        "<div>\n",
 421 |        "<style scoped>\n",
 422 |        "    .dataframe tbody tr th:only-of-type {\n",
 423 |        "        vertical-align: middle;\n",
 424 |        "    }\n",
 425 |        "\n",
 426 |        "    .dataframe tbody tr th {\n",
 427 |        "        vertical-align: top;\n",
 428 |        "    }\n",
 429 |        "\n",
 430 |        "    .dataframe thead th {\n",
 431 |        "        text-align: right;\n",
 432 |        "    }\n",
 433 |        "</style>\n",
 434 |        "<table border=\"1\" class=\"dataframe\">\n",
 435 |        "  <thead>\n",
 436 |        "    <tr style=\"text-align: right;\">\n",
 437 |        "      <th></th>\n",
 438 |        "      <th>INCIDENT_NUMBER</th>\n",
 439 |        "      <th>OFFENSE_CODE</th>\n",
 440 |        "      <th>OFFENSE_CODE_GROUP</th>\n",
 441 |        "      <th>OFFENSE_DESCRIPTION</th>\n",
 442 |        "      <th>DISTRICT</th>\n",
 443 |        "      <th>REPORTING_AREA</th>\n",
 444 |        "      <th>SHOOTING</th>\n",
 445 |        "      <th>OCCURRED_ON_DATE</th>\n",
 446 |        "      <th>YEAR</th>\n",
 447 |        "      <th>MONTH</th>\n",
 448 |        "      <th>DAY_OF_WEEK</th>\n",
 449 |        "      <th>HOUR</th>\n",
 450 |        "      <th>UCR_PART</th>\n",
 451 |        "      <th>STREET</th>\n",
 452 |        "      <th>Lat</th>\n",
 453 |        "      <th>Long</th>\n",
 454 |        "      <th>Location</th>\n",
 455 |        "    </tr>\n",
 456 |        "  </thead>\n",
 457 |        "  <tbody>\n",
 458 |        "    <tr>\n",
 459 |        "      <th>0</th>\n",
 460 |        "      <td>I182070945</td>\n",
 461 |        "      <td>619</td>\n",
 462 |        "      <td>Larceny</td>\n",
 463 |        "      <td>LARCENY ALL OTHERS</td>\n",
 464 |        "      <td>D14</td>\n",
 465 |        "      <td>808</td>\n",
 466 |        "      <td>NaN</td>\n",
 467 |        "      <td>2018-09-02 13:00:00</td>\n",
 468 |        "      <td>2018</td>\n",
 469 |        "      <td>9</td>\n",
 470 |        "      <td>Sunday</td>\n",
 471 |        "      <td>13</td>\n",
 472 |        "      <td>Part One</td>\n",
 473 |        "      <td>LINCOLN ST</td>\n",
 474 |        "      <td>42.357791</td>\n",
 475 |        "      <td>-71.139371</td>\n",
 476 |        "      <td>(42.35779134, -71.13937053)</td>\n",
 477 |        "    </tr>\n",
 478 |        "    <tr>\n",
 479 |        "      <th>1</th>\n",
 480 |        "      <td>I182070943</td>\n",
 481 |        "      <td>1402</td>\n",
 482 |        "      <td>Vandalism</td>\n",
 483 |        "      <td>VANDALISM</td>\n",
 484 |        "      <td>C11</td>\n",
 485 |        "      <td>347</td>\n",
 486 |        "      <td>NaN</td>\n",
 487 |        "      <td>2018-08-21 00:00:00</td>\n",
 488 |        "      <td>2018</td>\n",
 489 |        "      <td>8</td>\n",
 490 |        "      <td>Tuesday</td>\n",
 491 |        "      <td>0</td>\n",
 492 |        "      <td>Part Two</td>\n",
 493 |        "      <td>HECLA ST</td>\n",
 494 |        "      <td>42.306821</td>\n",
 495 |        "      <td>-71.060300</td>\n",
 496 |        "      <td>(42.30682138, -71.06030035)</td>\n",
 497 |        "    </tr>\n",
 498 |        "    <tr>\n",
 499 |        "      <th>2</th>\n",
 500 |        "      <td>I182070941</td>\n",
 501 |        "      <td>3410</td>\n",
 502 |        "      <td>Towed</td>\n",
 503 |        "      <td>TOWED MOTOR VEHICLE</td>\n",
 504 |        "      <td>D4</td>\n",
 505 |        "      <td>151</td>\n",
 506 |        "      <td>NaN</td>\n",
 507 |        "      <td>2018-09-03 19:27:00</td>\n",
 508 |        "      <td>2018</td>\n",
 509 |        "      <td>9</td>\n",
 510 |        "      <td>Monday</td>\n",
 511 |        "      <td>19</td>\n",
 512 |        "      <td>Part Three</td>\n",
 513 |        "      <td>CAZENOVE ST</td>\n",
 514 |        "      <td>42.346589</td>\n",
 515 |        "      <td>-71.072429</td>\n",
 516 |        "      <td>(42.34658879, -71.07242943)</td>\n",
 517 |        "    </tr>\n",
 518 |        "    <tr>\n",
 519 |        "      <th>3</th>\n",
 520 |        "      <td>I182070940</td>\n",
 521 |        "      <td>3114</td>\n",
 522 |        "      <td>Investigate Property</td>\n",
 523 |        "      <td>INVESTIGATE PROPERTY</td>\n",
 524 |        "      <td>D4</td>\n",
 525 |        "      <td>272</td>\n",
 526 |        "      <td>NaN</td>\n",
 527 |        "      <td>2018-09-03 21:16:00</td>\n",
 528 |        "      <td>2018</td>\n",
 529 |        "      <td>9</td>\n",
 530 |        "      <td>Monday</td>\n",
 531 |        "      <td>21</td>\n",
 532 |        "      <td>Part Three</td>\n",
 533 |        "      <td>NEWCOMB ST</td>\n",
 534 |        "      <td>42.334182</td>\n",
 535 |        "      <td>-71.078664</td>\n",
 536 |        "      <td>(42.33418175, -71.07866441)</td>\n",
 537 |        "    </tr>\n",
 538 |        "    <tr>\n",
 539 |        "      <th>4</th>\n",
 540 |        "      <td>I182070938</td>\n",
 541 |        "      <td>3114</td>\n",
 542 |        "      <td>Investigate Property</td>\n",
 543 |        "      <td>INVESTIGATE PROPERTY</td>\n",
 544 |        "      <td>B3</td>\n",
 545 |        "      <td>421</td>\n",
 546 |        "      <td>NaN</td>\n",
 547 |        "      <td>2018-09-03 21:05:00</td>\n",
 548 |        "      <td>2018</td>\n",
 549 |        "      <td>9</td>\n",
 550 |        "      <td>Monday</td>\n",
 551 |        "      <td>21</td>\n",
 552 |        "      <td>Part Three</td>\n",
 553 |        "      <td>DELHI ST</td>\n",
 554 |        "      <td>42.275365</td>\n",
 555 |        "      <td>-71.090361</td>\n",
 556 |        "      <td>(42.27536542, -71.09036101)</td>\n",
 557 |        "    </tr>\n",
 558 |        "  </tbody>\n",
 559 |        "</table>\n",
 560 |        "</div>"
 561 |       ],
 562 |       "text/plain": [
 563 |        "  INCIDENT_NUMBER  OFFENSE_CODE    OFFENSE_CODE_GROUP   OFFENSE_DESCRIPTION  \\\n",
 564 |        "0      I182070945           619               Larceny    LARCENY ALL OTHERS   \n",
 565 |        "1      I182070943          1402             Vandalism             VANDALISM   \n",
 566 |        "2      I182070941          3410                 Towed   TOWED MOTOR VEHICLE   \n",
 567 |        "3      I182070940          3114  Investigate Property  INVESTIGATE PROPERTY   \n",
 568 |        "4      I182070938          3114  Investigate Property  INVESTIGATE PROPERTY   \n",
 569 |        "\n",
 570 |        "  DISTRICT REPORTING_AREA SHOOTING     OCCURRED_ON_DATE  YEAR  MONTH  \\\n",
 571 |        "0      D14            808      NaN  2018-09-02 13:00:00  2018      9   \n",
 572 |        "1      C11            347      NaN  2018-08-21 00:00:00  2018      8   \n",
 573 |        "2       D4            151      NaN  2018-09-03 19:27:00  2018      9   \n",
 574 |        "3       D4            272      NaN  2018-09-03 21:16:00  2018      9   \n",
 575 |        "4       B3            421      NaN  2018-09-03 21:05:00  2018      9   \n",
 576 |        "\n",
 577 |        "  DAY_OF_WEEK  HOUR    UCR_PART       STREET        Lat       Long  \\\n",
 578 |        "0      Sunday    13    Part One   LINCOLN ST  42.357791 -71.139371   \n",
 579 |        "1     Tuesday     0    Part Two     HECLA ST  42.306821 -71.060300   \n",
 580 |        "2      Monday    19  Part Three  CAZENOVE ST  42.346589 -71.072429   \n",
 581 |        "3      Monday    21  Part Three   NEWCOMB ST  42.334182 -71.078664   \n",
 582 |        "4      Monday    21  Part Three     DELHI ST  42.275365 -71.090361   \n",
 583 |        "\n",
 584 |        "                      Location  \n",
 585 |        "0  (42.35779134, -71.13937053)  \n",
 586 |        "1  (42.30682138, -71.06030035)  \n",
 587 |        "2  (42.34658879, -71.07242943)  \n",
 588 |        "3  (42.33418175, -71.07866441)  \n",
 589 |        "4  (42.27536542, -71.09036101)  "
 590 |       ]
 591 |      },
 592 |      "execution_count": 9,
 593 |      "metadata": {},
 594 |      "output_type": "execute_result"
 595 |     }
 596 |    ],
 597 |    "source": [
 598 |     "crime.head() # Quick check of the beginning of the dataframe"
 599 |    ]
 600 |   },
 601 |   {
 602 |    "cell_type": "code",
 603 |    "execution_count": 10,
 604 |    "id": "9f2eca52",
 605 |    "metadata": {},
 606 |    "outputs": [
 607 |     {
 608 |      "data": {
 609 |       "text/html": [
 610 |        "<div>\n",
 611 |        "<style scoped>\n",
 612 |        "    .dataframe tbody tr th:only-of-type {\n",
 613 |        "        vertical-align: middle;\n",
 614 |        "    }\n",
 615 |        "\n",
 616 |        "    .dataframe tbody tr th {\n",
 617 |        "        vertical-align: top;\n",
 618 |        "    }\n",
 619 |        "\n",
 620 |        "    .dataframe thead th {\n",
 621 |        "        text-align: right;\n",
 622 |        "    }\n",
 623 |        "</style>\n",
 624 |        "<table border=\"1\" class=\"dataframe\">\n",
 625 |        "  <thead>\n",
 626 |        "    <tr style=\"text-align: right;\">\n",
 627 |        "      <th></th>\n",
 628 |        "      <th>INCIDENT_NUMBER</th>\n",
 629 |        "      <th>OFFENSE_CODE</th>\n",
 630 |        "      <th>OFFENSE_CODE_GROUP</th>\n",
 631 |        "      <th>OFFENSE_DESCRIPTION</th>\n",
 632 |        "      <th>DISTRICT</th>\n",
 633 |        "      <th>REPORTING_AREA</th>\n",
 634 |        "      <th>SHOOTING</th>\n",
 635 |        "      <th>OCCURRED_ON_DATE</th>\n",
 636 |        "      <th>YEAR</th>\n",
 637 |        "      <th>MONTH</th>\n",
 638 |        "      <th>DAY_OF_WEEK</th>\n",
 639 |        "      <th>HOUR</th>\n",
 640 |        "      <th>UCR_PART</th>\n",
 641 |        "      <th>STREET</th>\n",
 642 |        "      <th>Lat</th>\n",
 643 |        "      <th>Long</th>\n",
 644 |        "      <th>Location</th>\n",
 645 |        "    </tr>\n",
 646 |        "  </thead>\n",
 647 |        "  <tbody>\n",
 648 |        "    <tr>\n",
 649 |        "      <th>319068</th>\n",
 650 |        "      <td>I050310906-00</td>\n",
 651 |        "      <td>3125</td>\n",
 652 |        "      <td>Warrant Arrests</td>\n",
 653 |        "      <td>WARRANT ARREST</td>\n",
 654 |        "      <td>D4</td>\n",
 655 |        "      <td>285</td>\n",
 656 |        "      <td>NaN</td>\n",
 657 |        "      <td>2016-06-05 17:25:00</td>\n",
 658 |        "      <td>2016</td>\n",
 659 |        "      <td>6</td>\n",
 660 |        "      <td>Sunday</td>\n",
 661 |        "      <td>17</td>\n",
 662 |        "      <td>Part Three</td>\n",
 663 |        "      <td>COVENTRY ST</td>\n",
 664 |        "      <td>42.336951</td>\n",
 665 |        "      <td>-71.085748</td>\n",
 666 |        "      <td>(42.33695098, -71.08574813)</td>\n",
 667 |        "    </tr>\n",
 668 |        "    <tr>\n",
 669 |        "      <th>319069</th>\n",
 670 |        "      <td>I030217815-08</td>\n",
 671 |        "      <td>111</td>\n",
 672 |        "      <td>Homicide</td>\n",
 673 |        "      <td>MURDER, NON-NEGLIGIENT MANSLAUGHTER</td>\n",
 674 |        "      <td>E18</td>\n",
 675 |        "      <td>520</td>\n",
 676 |        "      <td>NaN</td>\n",
 677 |        "      <td>2015-07-09 13:38:00</td>\n",
 678 |        "      <td>2015</td>\n",
 679 |        "      <td>7</td>\n",
 680 |        "      <td>Thursday</td>\n",
 681 |        "      <td>13</td>\n",
 682 |        "      <td>Part One</td>\n",
 683 |        "      <td>RIVER ST</td>\n",
 684 |        "      <td>42.255926</td>\n",
 685 |        "      <td>-71.123172</td>\n",
 686 |        "      <td>(42.25592648, -71.12317207)</td>\n",
 687 |        "    </tr>\n",
 688 |        "    <tr>\n",
 689 |        "      <th>319070</th>\n",
 690 |        "      <td>I030217815-08</td>\n",
 691 |        "      <td>3125</td>\n",
 692 |        "      <td>Warrant Arrests</td>\n",
 693 |        "      <td>WARRANT ARREST</td>\n",
 694 |        "      <td>E18</td>\n",
 695 |        "      <td>520</td>\n",
 696 |        "      <td>NaN</td>\n",
 697 |        "      <td>2015-07-09 13:38:00</td>\n",
 698 |        "      <td>2015</td>\n",
 699 |        "      <td>7</td>\n",
 700 |        "      <td>Thursday</td>\n",
 701 |        "      <td>13</td>\n",
 702 |        "      <td>Part Three</td>\n",
 703 |        "      <td>RIVER ST</td>\n",
 704 |        "      <td>42.255926</td>\n",
 705 |        "      <td>-71.123172</td>\n",
 706 |        "      <td>(42.25592648, -71.12317207)</td>\n",
 707 |        "    </tr>\n",
 708 |        "    <tr>\n",
 709 |        "      <th>319071</th>\n",
 710 |        "      <td>I010370257-00</td>\n",
 711 |        "      <td>3125</td>\n",
 712 |        "      <td>Warrant Arrests</td>\n",
 713 |        "      <td>WARRANT ARREST</td>\n",
 714 |        "      <td>E13</td>\n",
 715 |        "      <td>569</td>\n",
 716 |        "      <td>NaN</td>\n",
 717 |        "      <td>2016-05-31 19:35:00</td>\n",
 718 |        "      <td>2016</td>\n",
 719 |        "      <td>5</td>\n",
 720 |        "      <td>Tuesday</td>\n",
 721 |        "      <td>19</td>\n",
 722 |        "      <td>Part Three</td>\n",
 723 |        "      <td>NEW WASHINGTON ST</td>\n",
 724 |        "      <td>42.302333</td>\n",
 725 |        "      <td>-71.111565</td>\n",
 726 |        "      <td>(42.30233307, -71.11156487)</td>\n",
 727 |        "    </tr>\n",
 728 |        "    <tr>\n",
 729 |        "      <th>319072</th>\n",
 730 |        "      <td>142052550</td>\n",
 731 |        "      <td>3125</td>\n",
 732 |        "      <td>Warrant Arrests</td>\n",
 733 |        "      <td>WARRANT ARREST</td>\n",
 734 |        "      <td>D4</td>\n",
 735 |        "      <td>903</td>\n",
 736 |        "      <td>NaN</td>\n",
 737 |        "      <td>2015-06-22 00:12:00</td>\n",
 738 |        "      <td>2015</td>\n",
 739 |        "      <td>6</td>\n",
 740 |        "      <td>Monday</td>\n",
 741 |        "      <td>0</td>\n",
 742 |        "      <td>Part Three</td>\n",
 743 |        "      <td>WASHINGTON ST</td>\n",
 744 |        "      <td>42.333839</td>\n",
 745 |        "      <td>-71.080290</td>\n",
 746 |        "      <td>(42.33383935, -71.08029038)</td>\n",
 747 |        "    </tr>\n",
 748 |        "  </tbody>\n",
 749 |        "</table>\n",
 750 |        "</div>"
 751 |       ],
 752 |       "text/plain": [
 753 |        "       INCIDENT_NUMBER  OFFENSE_CODE OFFENSE_CODE_GROUP  \\\n",
 754 |        "319068   I050310906-00          3125    Warrant Arrests   \n",
 755 |        "319069   I030217815-08           111           Homicide   \n",
 756 |        "319070   I030217815-08          3125    Warrant Arrests   \n",
 757 |        "319071   I010370257-00          3125    Warrant Arrests   \n",
 758 |        "319072       142052550          3125    Warrant Arrests   \n",
 759 |        "\n",
 760 |        "                        OFFENSE_DESCRIPTION DISTRICT REPORTING_AREA SHOOTING  \\\n",
 761 |        "319068                       WARRANT ARREST       D4            285      NaN   \n",
 762 |        "319069  MURDER, NON-NEGLIGIENT MANSLAUGHTER      E18            520      NaN   \n",
 763 |        "319070                       WARRANT ARREST      E18            520      NaN   \n",
 764 |        "319071                       WARRANT ARREST      E13            569      NaN   \n",
 765 |        "319072                       WARRANT ARREST       D4            903      NaN   \n",
 766 |        "\n",
 767 |        "           OCCURRED_ON_DATE  YEAR  MONTH DAY_OF_WEEK  HOUR    UCR_PART  \\\n",
 768 |        "319068  2016-06-05 17:25:00  2016      6      Sunday    17  Part Three   \n",
 769 |        "319069  2015-07-09 13:38:00  2015      7    Thursday    13    Part One   \n",
 770 |        "319070  2015-07-09 13:38:00  2015      7    Thursday    13  Part Three   \n",
 771 |        "319071  2016-05-31 19:35:00  2016      5     Tuesday    19  Part Three   \n",
 772 |        "319072  2015-06-22 00:12:00  2015      6      Monday     0  Part Three   \n",
 773 |        "\n",
 774 |        "                   STREET        Lat       Long                     Location  \n",
 775 |        "319068        COVENTRY ST  42.336951 -71.085748  (42.33695098, -71.08574813)  \n",
 776 |        "319069           RIVER ST  42.255926 -71.123172  (42.25592648, -71.12317207)  \n",
 777 |        "319070           RIVER ST  42.255926 -71.123172  (42.25592648, -71.12317207)  \n",
 778 |        "319071  NEW WASHINGTON ST  42.302333 -71.111565  (42.30233307, -71.11156487)  \n",
 779 |        "319072      WASHINGTON ST  42.333839 -71.080290  (42.33383935, -71.08029038)  "
 780 |       ]
 781 |      },
 782 |      "execution_count": 10,
 783 |      "metadata": {},
 784 |      "output_type": "execute_result"
 785 |     }
 786 |    ],
 787 |    "source": [
 788 |     "crime.tail() # Quick check of the end of the dataframe"
 789 |    ]
 790 |   },
 791 |   {
 792 |    "cell_type": "code",
 793 |    "execution_count": 11,
 794 |    "id": "33f581e1",
 795 |    "metadata": {},
 796 |    "outputs": [],
 797 |    "source": [
 798 |     "pd.options.display.max_rows = 10"
 799 |    ]
 800 |   },
 801 |   {
 802 |    "cell_type": "code",
 803 |    "execution_count": 12,
 804 |    "id": "9ba67772",
 805 |    "metadata": {},
 806 |    "outputs": [
 807 |     {
 808 |      "data": {
 809 |       "text/html": [
 810 |        "<div>\n",
 811 |        "<style scoped>\n",
 812 |        "    .dataframe tbody tr th:only-of-type {\n",
 813 |        "        vertical-align: middle;\n",
 814 |        "    }\n",
 815 |        "\n",
 816 |        "    .dataframe tbody tr th {\n",
 817 |        "        vertical-align: top;\n",
 818 |        "    }\n",
 819 |        "\n",
 820 |        "    .dataframe thead th {\n",
 821 |        "        text-align: right;\n",
 822 |        "    }\n",
 823 |        "</style>\n",
 824 |        "<table border=\"1\" class=\"dataframe\">\n",
 825 |        "  <thead>\n",
 826 |        "    <tr style=\"text-align: right;\">\n",
 827 |        "      <th></th>\n",
 828 |        "      <th>INCIDENT_NUMBER</th>\n",
 829 |        "      <th>OFFENSE_CODE</th>\n",
 830 |        "      <th>OFFENSE_CODE_GROUP</th>\n",
 831 |        "      <th>OFFENSE_DESCRIPTION</th>\n",
 832 |        "      <th>DISTRICT</th>\n",
 833 |        "      <th>REPORTING_AREA</th>\n",
 834 |        "      <th>SHOOTING</th>\n",
 835 |        "      <th>OCCURRED_ON_DATE</th>\n",
 836 |        "      <th>YEAR</th>\n",
 837 |        "      <th>MONTH</th>\n",
 838 |        "      <th>DAY_OF_WEEK</th>\n",
 839 |        "      <th>HOUR</th>\n",
 840 |        "      <th>UCR_PART</th>\n",
 841 |        "      <th>STREET</th>\n",
 842 |        "      <th>Lat</th>\n",
 843 |        "      <th>Long</th>\n",
 844 |        "      <th>Location</th>\n",
 845 |        "    </tr>\n",
 846 |        "  </thead>\n",
 847 |        "  <tbody>\n",
 848 |        "    <tr>\n",
 849 |        "      <th>0</th>\n",
 850 |        "      <td>I182070945</td>\n",
 851 |        "      <td>619</td>\n",
 852 |        "      <td>Larceny</td>\n",
 853 |        "      <td>LARCENY ALL OTHERS</td>\n",
 854 |        "      <td>D14</td>\n",
 855 |        "      <td>808</td>\n",
 856 |        "      <td>NaN</td>\n",
 857 |        "      <td>2018-09-02 13:00:00</td>\n",
 858 |        "      <td>2018</td>\n",
 859 |        "      <td>9</td>\n",
 860 |        "      <td>Sunday</td>\n",
 861 |        "      <td>13</td>\n",
 862 |        "      <td>Part One</td>\n",
 863 |        "      <td>LINCOLN ST</td>\n",
 864 |        "      <td>42.357791</td>\n",
 865 |        "      <td>-71.139371</td>\n",
 866 |        "      <td>(42.35779134, -71.13937053)</td>\n",
 867 |        "    </tr>\n",
 868 |        "    <tr>\n",
 869 |        "      <th>1</th>\n",
 870 |        "      <td>I182070943</td>\n",
 871 |        "      <td>1402</td>\n",
 872 |        "      <td>Vandalism</td>\n",
 873 |        "      <td>VANDALISM</td>\n",
 874 |        "      <td>C11</td>\n",
 875 |        "      <td>347</td>\n",
 876 |        "      <td>NaN</td>\n",
 877 |        "      <td>2018-08-21 00:00:00</td>\n",
 878 |        "      <td>2018</td>\n",
 879 |        "      <td>8</td>\n",
 880 |        "      <td>Tuesday</td>\n",
 881 |        "      <td>0</td>\n",
 882 |        "      <td>Part Two</td>\n",
 883 |        "      <td>HECLA ST</td>\n",
 884 |        "      <td>42.306821</td>\n",
 885 |        "      <td>-71.060300</td>\n",
 886 |        "      <td>(42.30682138, -71.06030035)</td>\n",
 887 |        "    </tr>\n",
 888 |        "    <tr>\n",
 889 |        "      <th>2</th>\n",
 890 |        "      <td>I182070941</td>\n",
 891 |        "      <td>3410</td>\n",
 892 |        "      <td>Towed</td>\n",
 893 |        "      <td>TOWED MOTOR VEHICLE</td>\n",
 894 |        "      <td>D4</td>\n",
 895 |        "      <td>151</td>\n",
 896 |        "      <td>NaN</td>\n",
 897 |        "      <td>2018-09-03 19:27:00</td>\n",
 898 |        "      <td>2018</td>\n",
 899 |        "      <td>9</td>\n",
 900 |        "      <td>Monday</td>\n",
 901 |        "      <td>19</td>\n",
 902 |        "      <td>Part Three</td>\n",
 903 |        "      <td>CAZENOVE ST</td>\n",
 904 |        "      <td>42.346589</td>\n",
 905 |        "      <td>-71.072429</td>\n",
 906 |        "      <td>(42.34658879, -71.07242943)</td>\n",
 907 |        "    </tr>\n",
 908 |        "    <tr>\n",
 909 |        "      <th>3</th>\n",
 910 |        "      <td>I182070940</td>\n",
 911 |        "      <td>3114</td>\n",
 912 |        "      <td>Investigate Property</td>\n",
 913 |        "      <td>INVESTIGATE PROPERTY</td>\n",
 914 |        "      <td>D4</td>\n",
 915 |        "      <td>272</td>\n",
 916 |        "      <td>NaN</td>\n",
 917 |        "      <td>2018-09-03 21:16:00</td>\n",
 918 |        "      <td>2018</td>\n",
 919 |        "      <td>9</td>\n",
 920 |        "      <td>Monday</td>\n",
 921 |        "      <td>21</td>\n",
 922 |        "      <td>Part Three</td>\n",
 923 |        "      <td>NEWCOMB ST</td>\n",
 924 |        "      <td>42.334182</td>\n",
 925 |        "      <td>-71.078664</td>\n",
 926 |        "      <td>(42.33418175, -71.07866441)</td>\n",
 927 |        "    </tr>\n",
 928 |        "    <tr>\n",
 929 |        "      <th>4</th>\n",
 930 |        "      <td>I182070938</td>\n",
 931 |        "      <td>3114</td>\n",
 932 |        "      <td>Investigate Property</td>\n",
 933 |        "      <td>INVESTIGATE PROPERTY</td>\n",
 934 |        "      <td>B3</td>\n",
 935 |        "      <td>421</td>\n",
 936 |        "      <td>NaN</td>\n",
 937 |        "      <td>2018-09-03 21:05:00</td>\n",
 938 |        "      <td>2018</td>\n",
 939 |        "      <td>9</td>\n",
 940 |        "      <td>Monday</td>\n",
 941 |        "      <td>21</td>\n",
 942 |        "      <td>Part Three</td>\n",
 943 |        "      <td>DELHI ST</td>\n",
 944 |        "      <td>42.275365</td>\n",
 945 |        "      <td>-71.090361</td>\n",
 946 |        "      <td>(42.27536542, -71.09036101)</td>\n",
 947 |        "    </tr>\n",
 948 |        "    <tr>\n",
 949 |        "      <th>...</th>\n",
 950 |        "      <td>...</td>\n",
 951 |        "      <td>...</td>\n",
 952 |        "      <td>...</td>\n",
 953 |        "      <td>...</td>\n",
 954 |        "      <td>...</td>\n",
 955 |        "      <td>...</td>\n",
 956 |        "      <td>...</td>\n",
 957 |        "      <td>...</td>\n",
 958 |        "      <td>...</td>\n",
 959 |        "      <td>...</td>\n",
 960 |        "      <td>...</td>\n",
 961 |        "      <td>...</td>\n",
 962 |        "      <td>...</td>\n",
 963 |        "      <td>...</td>\n",
 964 |        "      <td>...</td>\n",
 965 |        "      <td>...</td>\n",
 966 |        "      <td>...</td>\n",
 967 |        "    </tr>\n",
 968 |        "    <tr>\n",
 969 |        "      <th>319068</th>\n",
 970 |        "      <td>I050310906-00</td>\n",
 971 |        "      <td>3125</td>\n",
 972 |        "      <td>Warrant Arrests</td>\n",
 973 |        "      <td>WARRANT ARREST</td>\n",
 974 |        "      <td>D4</td>\n",
 975 |        "      <td>285</td>\n",
 976 |        "      <td>NaN</td>\n",
 977 |        "      <td>2016-06-05 17:25:00</td>\n",
 978 |        "      <td>2016</td>\n",
 979 |        "      <td>6</td>\n",
 980 |        "      <td>Sunday</td>\n",
 981 |        "      <td>17</td>\n",
 982 |        "      <td>Part Three</td>\n",
 983 |        "      <td>COVENTRY ST</td>\n",
 984 |        "      <td>42.336951</td>\n",
 985 |        "      <td>-71.085748</td>\n",
 986 |        "      <td>(42.33695098, -71.08574813)</td>\n",
 987 |        "    </tr>\n",
 988 |        "    <tr>\n",
 989 |        "      <th>319069</th>\n",
 990 |        "      <td>I030217815-08</td>\n",
 991 |        "      <td>111</td>\n",
 992 |        "      <td>Homicide</td>\n",
 993 |        "      <td>MURDER, NON-NEGLIGIENT MANSLAUGHTER</td>\n",
 994 |        "      <td>E18</td>\n",
 995 |        "      <td>520</td>\n",
 996 |        "      <td>NaN</td>\n",
 997 |        "      <td>2015-07-09 13:38:00</td>\n",
 998 |        "      <td>2015</td>\n",
 999 |        "      <td>7</td>\n",
1000 |        "      <td>Thursday</td>\n",
1001 |        "      <td>13</td>\n",
1002 |        "      <td>Part One</td>\n",
1003 |        "      <td>RIVER ST</td>\n",
1004 |        "      <td>42.255926</td>\n",
1005 |        "      <td>-71.123172</td>\n",
1006 |        "      <td>(42.25592648, -71.12317207)</td>\n",
1007 |        "    </tr>\n",
1008 |        "    <tr>\n",
1009 |        "      <th>319070</th>\n",
1010 |        "      <td>I030217815-08</td>\n",
1011 |        "      <td>3125</td>\n",
1012 |        "      <td>Warrant Arrests</td>\n",
1013 |        "      <td>WARRANT ARREST</td>\n",
1014 |        "      <td>E18</td>\n",
1015 |        "      <td>520</td>\n",
1016 |        "      <td>NaN</td>\n",
1017 |        "      <td>2015-07-09 13:38:00</td>\n",
1018 |        "      <td>2015</td>\n",
1019 |        "      <td>7</td>\n",
1020 |        "      <td>Thursday</td>\n",
1021 |        "      <td>13</td>\n",
1022 |        "      <td>Part Three</td>\n",
1023 |        "      <td>RIVER ST</td>\n",
1024 |        "      <td>42.255926</td>\n",
1025 |        "      <td>-71.123172</td>\n",
1026 |        "      <td>(42.25592648, -71.12317207)</td>\n",
1027 |        "    </tr>\n",
1028 |        "    <tr>\n",
1029 |        "      <th>319071</th>\n",
1030 |        "      <td>I010370257-00</td>\n",
1031 |        "      <td>3125</td>\n",
1032 |        "      <td>Warrant Arrests</td>\n",
1033 |        "      <td>WARRANT ARREST</td>\n",
1034 |        "      <td>E13</td>\n",
1035 |        "      <td>569</td>\n",
1036 |        "      <td>NaN</td>\n",
1037 |        "      <td>2016-05-31 19:35:00</td>\n",
1038 |        "      <td>2016</td>\n",
1039 |        "      <td>5</td>\n",
1040 |        "      <td>Tuesday</td>\n",
1041 |        "      <td>19</td>\n",
1042 |        "      <td>Part Three</td>\n",
1043 |        "      <td>NEW WASHINGTON ST</td>\n",
1044 |        "      <td>42.302333</td>\n",
1045 |        "      <td>-71.111565</td>\n",
1046 |        "      <td>(42.30233307, -71.11156487)</td>\n",
1047 |        "    </tr>\n",
1048 |        "    <tr>\n",
1049 |        "      <th>319072</th>\n",
1050 |        "      <td>142052550</td>\n",
1051 |        "      <td>3125</td>\n",
1052 |        "      <td>Warrant Arrests</td>\n",
1053 |        "      <td>WARRANT ARREST</td>\n",
1054 |        "      <td>D4</td>\n",
1055 |        "      <td>903</td>\n",
1056 |        "      <td>NaN</td>\n",
1057 |        "      <td>2015-06-22 00:12:00</td>\n",
1058 |        "      <td>2015</td>\n",
1059 |        "      <td>6</td>\n",
1060 |        "      <td>Monday</td>\n",
1061 |        "      <td>0</td>\n",
1062 |        "      <td>Part Three</td>\n",
1063 |        "      <td>WASHINGTON ST</td>\n",
1064 |        "      <td>42.333839</td>\n",
1065 |        "      <td>-71.080290</td>\n",
1066 |        "      <td>(42.33383935, -71.08029038)</td>\n",
1067 |        "    </tr>\n",
1068 |        "  </tbody>\n",
1069 |        "</table>\n",
1070 |        "<p>319050 rows × 17 columns</p>\n",
1071 |        "</div>"
1072 |       ],
1073 |       "text/plain": [
1074 |        "       INCIDENT_NUMBER  OFFENSE_CODE    OFFENSE_CODE_GROUP  \\\n",
1075 |        "0           I182070945           619               Larceny   \n",
1076 |        "1           I182070943          1402             Vandalism   \n",
1077 |        "2           I182070941          3410                 Towed   \n",
1078 |        "3           I182070940          3114  Investigate Property   \n",
1079 |        "4           I182070938          3114  Investigate Property   \n",
1080 |        "...                ...           ...                   ...   \n",
1081 |        "319068   I050310906-00          3125       Warrant Arrests   \n",
1082 |        "319069   I030217815-08           111              Homicide   \n",
1083 |        "319070   I030217815-08          3125       Warrant Arrests   \n",
1084 |        "319071   I010370257-00          3125       Warrant Arrests   \n",
1085 |        "319072       142052550          3125       Warrant Arrests   \n",
1086 |        "\n",
1087 |        "                        OFFENSE_DESCRIPTION DISTRICT REPORTING_AREA SHOOTING  \\\n",
1088 |        "0                        LARCENY ALL OTHERS      D14            808      NaN   \n",
1089 |        "1                                 VANDALISM      C11            347      NaN   \n",
1090 |        "2                       TOWED MOTOR VEHICLE       D4            151      NaN   \n",
1091 |        "3                      INVESTIGATE PROPERTY       D4            272      NaN   \n",
1092 |        "4                      INVESTIGATE PROPERTY       B3            421      NaN   \n",
1093 |        "...                                     ...      ...            ...      ...   \n",
1094 |        "319068                       WARRANT ARREST       D4            285      NaN   \n",
1095 |        "319069  MURDER, NON-NEGLIGIENT MANSLAUGHTER      E18            520      NaN   \n",
1096 |        "319070                       WARRANT ARREST      E18            520      NaN   \n",
1097 |        "319071                       WARRANT ARREST      E13            569      NaN   \n",
1098 |        "319072                       WARRANT ARREST       D4            903      NaN   \n",
1099 |        "\n",
1100 |        "           OCCURRED_ON_DATE  YEAR  MONTH DAY_OF_WEEK  HOUR    UCR_PART  \\\n",
1101 |        "0       2018-09-02 13:00:00  2018      9      Sunday    13    Part One   \n",
1102 |        "1       2018-08-21 00:00:00  2018      8     Tuesday     0    Part Two   \n",
1103 |        "2       2018-09-03 19:27:00  2018      9      Monday    19  Part Three   \n",
1104 |        "3       2018-09-03 21:16:00  2018      9      Monday    21  Part Three   \n",
1105 |        "4       2018-09-03 21:05:00  2018      9      Monday    21  Part Three   \n",
1106 |        "...                     ...   ...    ...         ...   ...         ...   \n",
1107 |        "319068  2016-06-05 17:25:00  2016      6      Sunday    17  Part Three   \n",
1108 |        "319069  2015-07-09 13:38:00  2015      7    Thursday    13    Part One   \n",
1109 |        "319070  2015-07-09 13:38:00  2015      7    Thursday    13  Part Three   \n",
1110 |        "319071  2016-05-31 19:35:00  2016      5     Tuesday    19  Part Three   \n",
1111 |        "319072  2015-06-22 00:12:00  2015      6      Monday     0  Part Three   \n",
1112 |        "\n",
1113 |        "                   STREET        Lat       Long                     Location  \n",
1114 |        "0              LINCOLN ST  42.357791 -71.139371  (42.35779134, -71.13937053)  \n",
1115 |        "1                HECLA ST  42.306821 -71.060300  (42.30682138, -71.06030035)  \n",
1116 |        "2             CAZENOVE ST  42.346589 -71.072429  (42.34658879, -71.07242943)  \n",
1117 |        "3              NEWCOMB ST  42.334182 -71.078664  (42.33418175, -71.07866441)  \n",
1118 |        "4                DELHI ST  42.275365 -71.090361  (42.27536542, -71.09036101)  \n",
1119 |        "...                   ...        ...        ...                          ...  \n",
1120 |        "319068        COVENTRY ST  42.336951 -71.085748  (42.33695098, -71.08574813)  \n",
1121 |        "319069           RIVER ST  42.255926 -71.123172  (42.25592648, -71.12317207)  \n",
1122 |        "319070           RIVER ST  42.255926 -71.123172  (42.25592648, -71.12317207)  \n",
1123 |        "319071  NEW WASHINGTON ST  42.302333 -71.111565  (42.30233307, -71.11156487)  \n",
1124 |        "319072      WASHINGTON ST  42.333839 -71.080290  (42.33383935, -71.08029038)  \n",
1125 |        "\n",
1126 |        "[319050 rows x 17 columns]"
1127 |       ]
1128 |      },
1129 |      "execution_count": 12,
1130 |      "metadata": {},
1131 |      "output_type": "execute_result"
1132 |     }
1133 |    ],
1134 |    "source": [
1135 |     "crime"
1136 |    ]
1137 |   },
1138 |   {
1139 |    "cell_type": "code",
1140 |    "execution_count": 13,
1141 |    "id": "4413e055",
1142 |    "metadata": {
1143 |     "scrolled": true
1144 |    },
1145 |    "outputs": [
1146 |     {
1147 |      "name": "stdout",
1148 |      "output_type": "stream",
1149 |      "text": [
1150 |       "<class 'pandas.core.frame.DataFrame'>\n",
1151 |       "Int64Index: 319050 entries, 0 to 319072\n",
1152 |       "Data columns (total 17 columns):\n",
1153 |       " #   Column               Non-Null Count   Dtype  \n",
1154 |       "---  ------               --------------   -----  \n",
1155 |       " 0   INCIDENT_NUMBER      319050 non-null  object \n",
1156 |       " 1   OFFENSE_CODE         319050 non-null  int64  \n",
1157 |       " 2   OFFENSE_CODE_GROUP   319050 non-null  object \n",
1158 |       " 3   OFFENSE_DESCRIPTION  319050 non-null  object \n",
1159 |       " 4   DISTRICT             317285 non-null  object \n",
1160 |       " 5   REPORTING_AREA       319050 non-null  object \n",
1161 |       " 6   SHOOTING             1019 non-null    object \n",
1162 |       " 7   OCCURRED_ON_DATE     319050 non-null  object \n",
1163 |       " 8   YEAR                 319050 non-null  int64  \n",
1164 |       " 9   MONTH                319050 non-null  int64  \n",
1165 |       " 10  DAY_OF_WEEK          319050 non-null  object \n",
1166 |       " 11  HOUR                 319050 non-null  int64  \n",
1167 |       " 12  UCR_PART             318960 non-null  object \n",
1168 |       " 13  STREET               308179 non-null  object \n",
1169 |       " 14  Lat                  299052 non-null  float64\n",
1170 |       " 15  Long                 299052 non-null  float64\n",
1171 |       " 16  Location             319050 non-null  object \n",
1172 |       "dtypes: float64(2), int64(4), object(11)\n",
1173 |       "memory usage: 43.8+ MB\n"
1174 |      ]
1175 |     }
1176 |    ],
1177 |    "source": [
1178 |     "crime.info() # Summary information about the dataframe"
1179 |    ]
1180 |   },
1181 |   {
1182 |    "cell_type": "code",
1183 |    "execution_count": 14,
1184 |    "id": "b73455f0",
1185 |    "metadata": {},
1186 |    "outputs": [],
1187 |    "source": [
1188 |     "# Changing the data type from object to datetime for the OCCURRED_ON_DATE column\n",
1189 |     "crime.OCCURRED_ON_DATE = pd.to_datetime(crime.OCCURRED_ON_DATE)"
1190 |    ]
1191 |   },
1192 |   {
1193 |    "cell_type": "code",
1194 |    "execution_count": 15,
1195 |    "id": "f36de48b",
1196 |    "metadata": {},
1197 |    "outputs": [
1198 |     {
1199 |      "name": "stdout",
1200 |      "output_type": "stream",
1201 |      "text": [
1202 |       "<class 'pandas.core.frame.DataFrame'>\n",
1203 |       "Int64Index: 319050 entries, 0 to 319072\n",
1204 |       "Data columns (total 17 columns):\n",
1205 |       " #   Column               Non-Null Count   Dtype         \n",
1206 |       "---  ------               --------------   -----         \n",
1207 |       " 0   INCIDENT_NUMBER      319050 non-null  object        \n",
1208 |       " 1   OFFENSE_CODE         319050 non-null  int64         \n",
1209 |       " 2   OFFENSE_CODE_GROUP   319050 non-null  object        \n",
1210 |       " 3   OFFENSE_DESCRIPTION  319050 non-null  object        \n",
1211 |       " 4   DISTRICT             317285 non-null  object        \n",
1212 |       " 5   REPORTING_AREA       319050 non-null  object        \n",
1213 |       " 6   SHOOTING             1019 non-null    object        \n",
1214 |       " 7   OCCURRED_ON_DATE     319050 non-null  datetime64[ns]\n",
1215 |       " 8   YEAR                 319050 non-null  int64         \n",
1216 |       " 9   MONTH                319050 non-null  int64         \n",
1217 |       " 10  DAY_OF_WEEK          319050 non-null  object        \n",
1218 |       " 11  HOUR                 319050 non-null  int64         \n",
1219 |       " 12  UCR_PART             318960 non-null  object        \n",
1220 |       " 13  STREET               308179 non-null  object        \n",
1221 |       " 14  Lat                  299052 non-null  float64       \n",
1222 |       " 15  Long                 299052 non-null  float64       \n",
1223 |       " 16  Location             319050 non-null  object        \n",
1224 |       "dtypes: datetime64[ns](1), float64(2), int64(4), object(10)\n",
1225 |       "memory usage: 43.8+ MB\n"
1226 |      ]
1227 |     }
1228 |    ],
1229 |    "source": [
1230 |     "# Check if it worked\n",
1231 |     "crime.info()"
1232 |    ]
1233 |   },
1234 |   {
1235 |    "cell_type": "code",
1236 |    "execution_count": 16,
1237 |    "id": "8e6d7208",
1238 |    "metadata": {},
1239 |    "outputs": [
1240 |     {
1241 |      "data": {
1242 |       "text/plain": [
1243 |        "0         2018\n",
1244 |        "1         2018\n",
1245 |        "2         2018\n",
1246 |        "3         2018\n",
1247 |        "4         2018\n",
1248 |        "          ... \n",
1249 |        "319068    2016\n",
1250 |        "319069    2015\n",
1251 |        "319070    2015\n",
1252 |        "319071    2016\n",
1253 |        "319072    2015\n",
1254 |        "Name: OCCURRED_ON_DATE, Length: 319050, dtype: int64"
1255 |       ]
1256 |      },
1257 |      "execution_count": 16,
1258 |      "metadata": {},
1259 |      "output_type": "execute_result"
1260 |     }
1261 |    ],
1262 |    "source": [
1263 |     "# easily extract datetime information frome the OCCURRED_ON_DATE column\n",
1264 |     "crime.OCCURRED_ON_DATE.dt.year"
1265 |    ]
1266 |   },
1267 |   {
1268 |    "cell_type": "code",
1269 |    "execution_count": 17,
1270 |    "id": "a16d5dd8",
1271 |    "metadata": {},
1272 |    "outputs": [
1273 |     {
1274 |      "data": {
1275 |       "text/plain": [
1276 |        "0         9\n",
1277 |        "1         8\n",
1278 |        "2         9\n",
1279 |        "3         9\n",
1280 |        "4         9\n",
1281 |        "         ..\n",
1282 |        "319068    6\n",
1283 |        "319069    7\n",
1284 |        "319070    7\n",
1285 |        "319071    5\n",
1286 |        "319072    6\n",
1287 |        "Name: OCCURRED_ON_DATE, Length: 319050, dtype: int64"
1288 |       ]
1289 |      },
1290 |      "execution_count": 17,
1291 |      "metadata": {},
1292 |      "output_type": "execute_result"
1293 |     }
1294 |    ],
1295 |    "source": [
1296 |     "crime.OCCURRED_ON_DATE.dt.month"
1297 |    ]
1298 |   },
1299 |   {
1300 |    "cell_type": "code",
1301 |    "execution_count": 18,
1302 |    "id": "260527b7",
1303 |    "metadata": {},
1304 |    "outputs": [
1305 |     {
1306 |      "name": "stderr",
1307 |      "output_type": "stream",
1308 |      "text": [
1309 |       "C:\\Users\\moche\\AppData\\Local\\Temp\\ipykernel_124320\\2588699237.py:1: FutureWarning: Series.dt.weekofyear and Series.dt.week have been deprecated. Please use Series.dt.isocalendar().week instead.\n",
1310 |       "  crime.OCCURRED_ON_DATE.dt.week\n"
1311 |      ]
1312 |     },
1313 |     {
1314 |      "data": {
1315 |       "text/plain": [
1316 |        "0         35\n",
1317 |        "1         34\n",
1318 |        "2         36\n",
1319 |        "3         36\n",
1320 |        "4         36\n",
1321 |        "          ..\n",
1322 |        "319068    22\n",
1323 |        "319069    28\n",
1324 |        "319070    28\n",
1325 |        "319071    22\n",
1326 |        "319072    26\n",
1327 |        "Name: OCCURRED_ON_DATE, Length: 319050, dtype: int64"
1328 |       ]
1329 |      },
1330 |      "execution_count": 18,
1331 |      "metadata": {},
1332 |      "output_type": "execute_result"
1333 |     }
1334 |    ],
1335 |    "source": [
1336 |     "crime.OCCURRED_ON_DATE.dt.week"
1337 |    ]
1338 |   },
1339 |   {
1340 |    "cell_type": "code",
1341 |    "execution_count": 19,
1342 |    "id": "5e6502e9",
1343 |    "metadata": {},
1344 |    "outputs": [
1345 |     {
1346 |      "data": {
1347 |       "text/plain": [
1348 |        "0         13\n",
1349 |        "1          0\n",
1350 |        "2         19\n",
1351 |        "3         21\n",
1352 |        "4         21\n",
1353 |        "          ..\n",
1354 |        "319068    17\n",
1355 |        "319069    13\n",
1356 |        "319070    13\n",
1357 |        "319071    19\n",
1358 |        "319072     0\n",
1359 |        "Name: OCCURRED_ON_DATE, Length: 319050, dtype: int64"
1360 |       ]
1361 |      },
1362 |      "execution_count": 19,
1363 |      "metadata": {},
1364 |      "output_type": "execute_result"
1365 |     }
1366 |    ],
1367 |    "source": [
1368 |     "crime.OCCURRED_ON_DATE.dt.hour"
1369 |    ]
1370 |   },
1371 |   {
1372 |    "cell_type": "code",
1373 |    "execution_count": 20,
1374 |    "id": "0ef6d738",
1375 |    "metadata": {},
1376 |    "outputs": [
1377 |     {
1378 |      "data": {
1379 |       "text/plain": [
1380 |        "0          0\n",
1381 |        "1          0\n",
1382 |        "2         27\n",
1383 |        "3         16\n",
1384 |        "4          5\n",
1385 |        "          ..\n",
1386 |        "319068    25\n",
1387 |        "319069    38\n",
1388 |        "319070    38\n",
1389 |        "319071    35\n",
1390 |        "319072    12\n",
1391 |        "Name: OCCURRED_ON_DATE, Length: 319050, dtype: int64"
1392 |       ]
1393 |      },
1394 |      "execution_count": 20,
1395 |      "metadata": {},
1396 |      "output_type": "execute_result"
1397 |     }
1398 |    ],
1399 |    "source": [
1400 |     "crime.OCCURRED_ON_DATE.dt.minute"
1401 |    ]
1402 |   },
1403 |   {
1404 |    "cell_type": "code",
1405 |    "execution_count": 21,
1406 |    "id": "c03863c6",
1407 |    "metadata": {
1408 |     "scrolled": true
1409 |    },
1410 |    "outputs": [
1411 |     {
1412 |      "data": {
1413 |       "text/html": [
1414 |        "<div>\n",
1415 |        "<style scoped>\n",
1416 |        "    .dataframe tbody tr th:only-of-type {\n",
1417 |        "        vertical-align: middle;\n",
1418 |        "    }\n",
1419 |        "\n",
1420 |        "    .dataframe tbody tr th {\n",
1421 |        "        vertical-align: top;\n",
1422 |        "    }\n",
1423 |        "\n",
1424 |        "    .dataframe thead th {\n",
1425 |        "        text-align: right;\n",
1426 |        "    }\n",
1427 |        "</style>\n",
1428 |        "<table border=\"1\" class=\"dataframe\">\n",
1429 |        "  <thead>\n",
1430 |        "    <tr style=\"text-align: right;\">\n",
1431 |        "      <th></th>\n",
1432 |        "      <th>OFFENSE_CODE</th>\n",
1433 |        "      <th>YEAR</th>\n",
1434 |        "      <th>MONTH</th>\n",
1435 |        "      <th>HOUR</th>\n",
1436 |        "      <th>Lat</th>\n",
1437 |        "      <th>Long</th>\n",
1438 |        "    </tr>\n",
1439 |        "  </thead>\n",
1440 |        "  <tbody>\n",
1441 |        "    <tr>\n",
1442 |        "      <th>count</th>\n",
1443 |        "      <td>319050.000000</td>\n",
1444 |        "      <td>319050.000000</td>\n",
1445 |        "      <td>319050.000000</td>\n",
1446 |        "      <td>319050.000000</td>\n",
1447 |        "      <td>299052.000000</td>\n",
1448 |        "      <td>299052.000000</td>\n",
1449 |        "    </tr>\n",
1450 |        "    <tr>\n",
1451 |        "      <th>mean</th>\n",
1452 |        "      <td>2317.516957</td>\n",
1453 |        "      <td>2016.560674</td>\n",
1454 |        "      <td>6.609622</td>\n",
1455 |        "      <td>13.118176</td>\n",
1456 |        "      <td>42.214373</td>\n",
1457 |        "      <td>-70.908260</td>\n",
1458 |        "    </tr>\n",
1459 |        "    <tr>\n",
1460 |        "      <th>std</th>\n",
1461 |        "      <td>1185.308921</td>\n",
1462 |        "      <td>0.996312</td>\n",
1463 |        "      <td>3.273677</td>\n",
1464 |        "      <td>6.294258</td>\n",
1465 |        "      <td>2.159845</td>\n",
1466 |        "      <td>3.493746</td>\n",
1467 |        "    </tr>\n",
1468 |        "    <tr>\n",
1469 |        "      <th>min</th>\n",
1470 |        "      <td>111.000000</td>\n",
1471 |        "      <td>2015.000000</td>\n",
1472 |        "      <td>1.000000</td>\n",
1473 |        "      <td>0.000000</td>\n",
1474 |        "      <td>-1.000000</td>\n",
1475 |        "      <td>-71.178674</td>\n",
1476 |        "    </tr>\n",
1477 |        "    <tr>\n",
1478 |        "      <th>25%</th>\n",
1479 |        "      <td>1001.000000</td>\n",
1480 |        "      <td>2016.000000</td>\n",
1481 |        "      <td>4.000000</td>\n",
1482 |        "      <td>9.000000</td>\n",
1483 |        "      <td>42.297438</td>\n",
1484 |        "      <td>-71.097135</td>\n",
1485 |        "    </tr>\n",
1486 |        "    <tr>\n",
1487 |        "      <th>50%</th>\n",
1488 |        "      <td>2907.000000</td>\n",
1489 |        "      <td>2017.000000</td>\n",
1490 |        "      <td>7.000000</td>\n",
1491 |        "      <td>14.000000</td>\n",
1492 |        "      <td>42.325538</td>\n",
1493 |        "      <td>-71.077524</td>\n",
1494 |        "    </tr>\n",
1495 |        "    <tr>\n",
1496 |        "      <th>75%</th>\n",
1497 |        "      <td>3201.000000</td>\n",
1498 |        "      <td>2017.000000</td>\n",
1499 |        "      <td>9.000000</td>\n",
1500 |        "      <td>18.000000</td>\n",
1501 |        "      <td>42.348624</td>\n",
1502 |        "      <td>-71.062467</td>\n",
1503 |        "    </tr>\n",
1504 |        "    <tr>\n",
1505 |        "      <th>max</th>\n",
1506 |        "      <td>3831.000000</td>\n",
1507 |        "      <td>2018.000000</td>\n",
1508 |        "      <td>12.000000</td>\n",
1509 |        "      <td>23.000000</td>\n",
1510 |        "      <td>42.395042</td>\n",
1511 |        "      <td>-1.000000</td>\n",
1512 |        "    </tr>\n",
1513 |        "  </tbody>\n",
1514 |        "</table>\n",
1515 |        "</div>"
1516 |       ],
1517 |       "text/plain": [
1518 |        "        OFFENSE_CODE           YEAR          MONTH           HOUR  \\\n",
1519 |        "count  319050.000000  319050.000000  319050.000000  319050.000000   \n",
1520 |        "mean     2317.516957    2016.560674       6.609622      13.118176   \n",
1521 |        "std      1185.308921       0.996312       3.273677       6.294258   \n",
1522 |        "min       111.000000    2015.000000       1.000000       0.000000   \n",
1523 |        "25%      1001.000000    2016.000000       4.000000       9.000000   \n",
1524 |        "50%      2907.000000    2017.000000       7.000000      14.000000   \n",
1525 |        "75%      3201.000000    2017.000000       9.000000      18.000000   \n",
1526 |        "max      3831.000000    2018.000000      12.000000      23.000000   \n",
1527 |        "\n",
1528 |        "                 Lat           Long  \n",
1529 |        "count  299052.000000  299052.000000  \n",
1530 |        "mean       42.214373     -70.908260  \n",
1531 |        "std         2.159845       3.493746  \n",
1532 |        "min        -1.000000     -71.178674  \n",
1533 |        "25%        42.297438     -71.097135  \n",
1534 |        "50%        42.325538     -71.077524  \n",
1535 |        "75%        42.348624     -71.062467  \n",
1536 |        "max        42.395042      -1.000000  "
1537 |       ]
1538 |      },
1539 |      "execution_count": 21,
1540 |      "metadata": {},
1541 |      "output_type": "execute_result"
1542 |     }
1543 |    ],
1544 |    "source": [
1545 |     "crime.describe() # summary information on the numeric columns"
1546 |    ]
1547 |   },
1548 |   {
1549 |    "cell_type": "code",
1550 |    "execution_count": 22,
1551 |    "id": "6dc94fdd",
1552 |    "metadata": {},
1553 |    "outputs": [
1554 |     {
1555 |      "data": {
1556 |       "text/html": [
1557 |        "<div>\n",
1558 |        "<style scoped>\n",
1559 |        "    .dataframe tbody tr th:only-of-type {\n",
1560 |        "        vertical-align: middle;\n",
1561 |        "    }\n",
1562 |        "\n",
1563 |        "    .dataframe tbody tr th {\n",
1564 |        "        vertical-align: top;\n",
1565 |        "    }\n",
1566 |        "\n",
1567 |        "    .dataframe thead th {\n",
1568 |        "        text-align: right;\n",
1569 |        "    }\n",
1570 |        "</style>\n",
1571 |        "<table border=\"1\" class=\"dataframe\">\n",
1572 |        "  <thead>\n",
1573 |        "    <tr style=\"text-align: right;\">\n",
1574 |        "      <th></th>\n",
1575 |        "      <th>INCIDENT_NUMBER</th>\n",
1576 |        "      <th>OFFENSE_CODE_GROUP</th>\n",
1577 |        "      <th>OFFENSE_DESCRIPTION</th>\n",
1578 |        "      <th>DISTRICT</th>\n",
1579 |        "      <th>REPORTING_AREA</th>\n",
1580 |        "      <th>SHOOTING</th>\n",
1581 |        "      <th>DAY_OF_WEEK</th>\n",
1582 |        "      <th>UCR_PART</th>\n",
1583 |        "      <th>STREET</th>\n",
1584 |        "      <th>Location</th>\n",
1585 |        "    </tr>\n",
1586 |        "  </thead>\n",
1587 |        "  <tbody>\n",
1588 |        "    <tr>\n",
1589 |        "      <th>count</th>\n",
1590 |        "      <td>319050</td>\n",
1591 |        "      <td>319050</td>\n",
1592 |        "      <td>319050</td>\n",
1593 |        "      <td>317285</td>\n",
1594 |        "      <td>319050</td>\n",
1595 |        "      <td>1019</td>\n",
1596 |        "      <td>319050</td>\n",
1597 |        "      <td>318960</td>\n",
1598 |        "      <td>308179</td>\n",
1599 |        "      <td>319050</td>\n",
1600 |        "    </tr>\n",
1601 |        "    <tr>\n",
1602 |        "      <th>unique</th>\n",
1603 |        "      <td>282517</td>\n",
1604 |        "      <td>67</td>\n",
1605 |        "      <td>244</td>\n",
1606 |        "      <td>12</td>\n",
1607 |        "      <td>879</td>\n",
1608 |        "      <td>1</td>\n",
1609 |        "      <td>7</td>\n",
1610 |        "      <td>4</td>\n",
1611 |        "      <td>4657</td>\n",
1612 |        "      <td>18194</td>\n",
1613 |        "    </tr>\n",
1614 |        "    <tr>\n",
1615 |        "      <th>top</th>\n",
1616 |        "      <td>I162030584</td>\n",
1617 |        "      <td>Motor Vehicle Accident Response</td>\n",
1618 |        "      <td>SICK/INJURED/MEDICAL - PERSON</td>\n",
1619 |        "      <td>B2</td>\n",
1620 |        "      <td></td>\n",
1621 |        "      <td>Y</td>\n",
1622 |        "      <td>Friday</td>\n",
1623 |        "      <td>Part Three</td>\n",
1624 |        "      <td>WASHINGTON ST</td>\n",
1625 |        "      <td>(0.00000000, 0.00000000)</td>\n",
1626 |        "    </tr>\n",
1627 |        "    <tr>\n",
1628 |        "      <th>freq</th>\n",
1629 |        "      <td>13</td>\n",
1630 |        "      <td>37132</td>\n",
1631 |        "      <td>18783</td>\n",
1632 |        "      <td>49940</td>\n",
1633 |        "      <td>20250</td>\n",
1634 |        "      <td>1019</td>\n",
1635 |        "      <td>48489</td>\n",
1636 |        "      <td>158537</td>\n",
1637 |        "      <td>14192</td>\n",
1638 |        "      <td>19998</td>\n",
1639 |        "    </tr>\n",
1640 |        "  </tbody>\n",
1641 |        "</table>\n",
1642 |        "</div>"
1643 |       ],
1644 |       "text/plain": [
1645 |        "       INCIDENT_NUMBER               OFFENSE_CODE_GROUP  \\\n",
1646 |        "count           319050                           319050   \n",
1647 |        "unique          282517                               67   \n",
1648 |        "top         I162030584  Motor Vehicle Accident Response   \n",
1649 |        "freq                13                            37132   \n",
1650 |        "\n",
1651 |        "                  OFFENSE_DESCRIPTION DISTRICT REPORTING_AREA SHOOTING  \\\n",
1652 |        "count                          319050   317285         319050     1019   \n",
1653 |        "unique                            244       12            879        1   \n",
1654 |        "top     SICK/INJURED/MEDICAL - PERSON       B2                       Y   \n",
1655 |        "freq                            18783    49940          20250     1019   \n",
1656 |        "\n",
1657 |        "       DAY_OF_WEEK    UCR_PART         STREET                  Location  \n",
1658 |        "count       319050      318960         308179                    319050  \n",
1659 |        "unique           7           4           4657                     18194  \n",
1660 |        "top         Friday  Part Three  WASHINGTON ST  (0.00000000, 0.00000000)  \n",
1661 |        "freq         48489      158537          14192                     19998  "
1662 |       ]
1663 |      },
1664 |      "execution_count": 22,
1665 |      "metadata": {},
1666 |      "output_type": "execute_result"
1667 |     }
1668 |    ],
1669 |    "source": [
1670 |     "crime.describe(include='object') # summary information on the non-numeric columns"
1671 |    ]
1672 |   },
1673 |   {
1674 |    "cell_type": "code",
1675 |    "execution_count": 23,
1676 |    "id": "0e30db63",
1677 |    "metadata": {},
1678 |    "outputs": [
1679 |     {
1680 |      "data": {
1681 |       "text/plain": [
1682 |        "Index(['INCIDENT_NUMBER', 'OFFENSE_CODE', 'OFFENSE_CODE_GROUP',\n",
1683 |        "       'OFFENSE_DESCRIPTION', 'DISTRICT', 'REPORTING_AREA', 'SHOOTING',\n",
1684 |        "       'OCCURRED_ON_DATE', 'YEAR', 'MONTH', 'DAY_OF_WEEK', 'HOUR', 'UCR_PART',\n",
1685 |        "       'STREET', 'Lat', 'Long', 'Location'],\n",
1686 |        "      dtype='object')"
1687 |       ]
1688 |      },
1689 |      "execution_count": 23,
1690 |      "metadata": {},
1691 |      "output_type": "execute_result"
1692 |     }
1693 |    ],
1694 |    "source": [
1695 |     "crime.columns"
1696 |    ]
1697 |   },
1698 |   {
1699 |    "cell_type": "code",
1700 |    "execution_count": 24,
1701 |    "id": "9772d819",
1702 |    "metadata": {},
1703 |    "outputs": [
1704 |     {
1705 |      "data": {
1706 |       "text/plain": [
1707 |        "Index(['DISTRICT', 'SHOOTING', 'UCR_PART', 'STREET', 'Lat', 'Long'], dtype='object')"
1708 |       ]
1709 |      },
1710 |      "execution_count": 24,
1711 |      "metadata": {},
1712 |      "output_type": "execute_result"
1713 |     }
1714 |    ],
1715 |    "source": [
1716 |     "crime.columns[np.sum(crime.isnull()) != 0]"
1717 |    ]
1718 |   },
1719 |   {
1720 |    "cell_type": "code",
1721 |    "execution_count": 25,
1722 |    "id": "58e96c07",
1723 |    "metadata": {},
1724 |    "outputs": [
1725 |     {
1726 |      "data": {
1727 |       "text/plain": [
1728 |        "Index(['DISTRICT', 'SHOOTING', 'UCR_PART', 'STREET', 'Lat', 'Long'], dtype='object')"
1729 |       ]
1730 |      },
1731 |      "execution_count": 25,
1732 |      "metadata": {},
1733 |      "output_type": "execute_result"
1734 |     }
1735 |    ],
1736 |    "source": [
1737 |     "# Checking for columns with missing values\n",
1738 |     "crime.columns[np.sum(crime.isnull()) != 0]"
1739 |    ]
1740 |   },
1741 |   {
1742 |    "cell_type": "code",
1743 |    "execution_count": 26,
1744 |    "id": "906c0994",
1745 |    "metadata": {},
1746 |    "outputs": [
1747 |     {
1748 |      "data": {
1749 |       "text/plain": [
1750 |        "Index(['INCIDENT_NUMBER', 'OFFENSE_CODE', 'OFFENSE_CODE_GROUP',\n",
1751 |        "       'OFFENSE_DESCRIPTION', 'REPORTING_AREA', 'OCCURRED_ON_DATE', 'YEAR',\n",
1752 |        "       'MONTH', 'DAY_OF_WEEK', 'HOUR', 'Location'],\n",
1753 |        "      dtype='object')"
1754 |       ]
1755 |      },
1756 |      "execution_count": 26,
1757 |      "metadata": {},
1758 |      "output_type": "execute_result"
1759 |     }
1760 |    ],
1761 |    "source": [
1762 |     "# Checking for columns with no missing values\n",
1763 |     "crime.columns[np.sum(crime.isnull()) == 0]"
1764 |    ]
1765 |   },
1766 |   {
1767 |    "cell_type": "code",
1768 |    "execution_count": 27,
1769 |    "id": "887b29bf",
1770 |    "metadata": {},
1771 |    "outputs": [
1772 |     {
1773 |      "name": "stdout",
1774 |      "output_type": "stream",
1775 |      "text": [
1776 |       "INCIDENT_NUMBER has 282517 unique values\n",
1777 |       "OFFENSE_CODE has 222 unique values\n",
1778 |       "OFFENSE_CODE_GROUP has 67 unique values\n",
1779 |       "OFFENSE_DESCRIPTION has 244 unique values\n",
1780 |       "DISTRICT has 12 unique values\n",
1781 |       "REPORTING_AREA has 879 unique values\n",
1782 |       "SHOOTING has 1 unique values\n",
1783 |       "OCCURRED_ON_DATE has 233229 unique values\n",
1784 |       "YEAR has 4 unique values\n",
1785 |       "MONTH has 12 unique values\n",
1786 |       "DAY_OF_WEEK has 7 unique values\n",
1787 |       "HOUR has 24 unique values\n",
1788 |       "UCR_PART has 4 unique values\n",
1789 |       "STREET has 4657 unique values\n",
1790 |       "Lat has 18178 unique values\n",
1791 |       "Long has 18178 unique values\n",
1792 |       "Location has 18194 unique values\n"
1793 |      ]
1794 |     }
1795 |    ],
1796 |    "source": [
1797 |     "# Checking for the number of unique values in each column\n",
1798 |     "for col in crime.columns:\n",
1799 |     "    unique_count = crime[col].nunique()\n",
1800 |     "    print(col + \" has \" + str(unique_count) + \" unique values\")"
1801 |    ]
1802 |   },
1803 |   {
1804 |    "attachments": {},
1805 |    "cell_type": "markdown",
1806 |    "id": "7e12d2c7",
1807 |    "metadata": {},
1808 |    "source": [
1809 |     "### Let's answer some questions"
1810 |    ]
1811 |   },
1812 |   {
1813 |    "cell_type": "code",
1814 |    "execution_count": 28,
1815 |    "id": "5ce85da1",
1816 |    "metadata": {},
1817 |    "outputs": [
1818 |     {
1819 |      "data": {
1820 |       "text/plain": [
1821 |        "Motor Vehicle Accident Response              37132\n",
1822 |        "Larceny                                      25935\n",
1823 |        "Medical Assistance                           23540\n",
1824 |        "Investigate Person                           18749\n",
1825 |        "Other                                        18073\n",
1826 |        "                                             ...  \n",
1827 |        "HUMAN TRAFFICKING                                7\n",
1828 |        "INVESTIGATE PERSON                               4\n",
1829 |        "Biological Threat                                2\n",
1830 |        "HUMAN TRAFFICKING - INVOLUNTARY SERVITUDE        2\n",
1831 |        "Burglary - No Property Taken                     2\n",
1832 |        "Name: OFFENSE_CODE_GROUP, Length: 67, dtype: int64"
1833 |       ]
1834 |      },
1835 |      "execution_count": 28,
1836 |      "metadata": {},
1837 |      "output_type": "execute_result"
1838 |     }
1839 |    ],
1840 |    "source": [
1841 |     "# What are the most common crimes in terms of offense group?\n",
1842 |     "crime.OFFENSE_CODE_GROUP.value_counts()"
1843 |    ]
1844 |   },
1845 |   {
1846 |    "cell_type": "code",
1847 |    "execution_count": 29,
1848 |    "id": "da8409f8",
1849 |    "metadata": {},
1850 |    "outputs": [
1851 |     {
1852 |      "data": {
1853 |       "text/plain": [
1854 |        "Motor Vehicle Accident Response    37132\n",
1855 |        "Larceny                            25935\n",
1856 |        "Medical Assistance                 23540\n",
1857 |        "Investigate Person                 18749\n",
1858 |        "Other                              18073\n",
1859 |        "Drug Violation                     16545\n",
1860 |        "Simple Assault                     15826\n",
1861 |        "Vandalism                          15414\n",
1862 |        "Verbal Disputes                    13099\n",
1863 |        "Towed                              11287\n",
1864 |        "Name: OFFENSE_CODE_GROUP, dtype: int64"
1865 |       ]
1866 |      },
1867 |      "execution_count": 29,
1868 |      "metadata": {},
1869 |      "output_type": "execute_result"
1870 |     }
1871 |    ],
1872 |    "source": [
1873 |     "crime.OFFENSE_CODE_GROUP.value_counts()[:10]"
1874 |    ]
1875 |   },
1876 |   {
1877 |    "cell_type": "code",
1878 |    "execution_count": 30,
1879 |    "id": "dfdc9687",
1880 |    "metadata": {},
1881 |    "outputs": [
1882 |     {
1883 |      "data": {
1884 |       "text/plain": [
1885 |        "319050"
1886 |       ]
1887 |      },
1888 |      "execution_count": 30,
1889 |      "metadata": {},
1890 |      "output_type": "execute_result"
1891 |     }
1892 |    ],
1893 |    "source": [
1894 |     "crime.shape[0]"
1895 |    ]
1896 |   },
1897 |   {
1898 |    "cell_type": "code",
1899 |    "execution_count": 31,
1900 |    "id": "0ba23067",
1901 |    "metadata": {
1902 |     "scrolled": true
1903 |    },
1904 |    "outputs": [
1905 |     {
1906 |      "data": {
1907 |       "text/plain": [
1908 |        "Motor Vehicle Accident Response    0.116383\n",
1909 |        "Larceny                            0.081288\n",
1910 |        "Medical Assistance                 0.073782\n",
1911 |        "Investigate Person                 0.058765\n",
1912 |        "Other                              0.056646\n",
1913 |        "Drug Violation                     0.051857\n",
1914 |        "Simple Assault                     0.049604\n",
1915 |        "Vandalism                          0.048312\n",
1916 |        "Verbal Disputes                    0.041056\n",
1917 |        "Towed                              0.035377\n",
1918 |        "Name: OFFENSE_CODE_GROUP, dtype: float64"
1919 |       ]
1920 |      },
1921 |      "metadata": {},
1922 |      "output_type": "display_data"
1923 |     },
1924 |     {
1925 |      "data": {
1926 |       "image/png": 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",
1927 |       "text/plain": [
1928 |        "<Figure size 640x480 with 1 Axes>"
1929 |       ]
1930 |      },
1931 |      "metadata": {},
1932 |      "output_type": "display_data"
1933 |     }
1934 |    ],
1935 |    "source": [
1936 |     "offense_group_vals = crime.OFFENSE_CODE_GROUP.value_counts()[:10]\n",
1937 |     "\n",
1938 |     "display(offense_group_vals / crime.shape[0])\n",
1939 |     "\n",
1940 |     "# Creating a bar chart of the Top 10 offense groups\n",
1941 |     "(offense_group_vals / crime.shape[0]).plot(kind='bar');\n",
1942 |     "plt.title('Top 10 Offense Groups (as % of all crimes)');"
1943 |    ]
1944 |   },
1945 |   {
1946 |    "cell_type": "code",
1947 |    "execution_count": 32,
1948 |    "id": "e15c0db4",
1949 |    "metadata": {},
1950 |    "outputs": [
1951 |     {
1952 |      "data": {
1953 |       "text/plain": [
1954 |        "Burglary - No Property Taken                  2\n",
1955 |        "HUMAN TRAFFICKING - INVOLUNTARY SERVITUDE     2\n",
1956 |        "Biological Threat                             2\n",
1957 |        "INVESTIGATE PERSON                            4\n",
1958 |        "HUMAN TRAFFICKING                             7\n",
1959 |        "Gambling                                      8\n",
1960 |        "Manslaughter                                  8\n",
1961 |        "Explosives                                   27\n",
1962 |        "Phone Call Complaints                        31\n",
1963 |        "Aircraft                                     36\n",
1964 |        "Name: OFFENSE_CODE_GROUP, dtype: int64"
1965 |       ]
1966 |      },
1967 |      "execution_count": 32,
1968 |      "metadata": {},
1969 |      "output_type": "execute_result"
1970 |     }
1971 |    ],
1972 |    "source": [
1973 |     "# What are the least common offense groups?\n",
1974 |     "crime.OFFENSE_CODE_GROUP.value_counts().sort_values(ascending=True)[:10]"
1975 |    ]
1976 |   },
1977 |   {
1978 |    "cell_type": "code",
1979 |    "execution_count": 33,
1980 |    "id": "d269207d",
1981 |    "metadata": {},
1982 |    "outputs": [],
1983 |    "source": [
1984 |     "# Question 1 - What are the most common offense descriptions?\n",
1985 |     "# Hint - Use value_counts() to print out the values"
1986 |    ]
1987 |   },
1988 |   {
1989 |    "cell_type": "code",
1990 |    "execution_count": 34,
1991 |    "id": "da299784",
1992 |    "metadata": {
1993 |     "scrolled": true
1994 |    },
1995 |    "outputs": [],
1996 |    "source": [
1997 |     "# Question 2 - Now try and create a bar chart of the Top 10 Offense Descriptions as a % of total crimes"
1998 |    ]
1999 |   },
2000 |   {
2001 |    "cell_type": "code",
2002 |    "execution_count": 35,
2003 |    "id": "f79b9fbf",
2004 |    "metadata": {},
2005 |    "outputs": [
2006 |     {
2007 |      "data": {
2008 |       "text/plain": [
2009 |        "YEAR\n",
2010 |        "2015     53371\n",
2011 |        "2016     99110\n",
2012 |        "2017    100884\n",
2013 |        "2018     65685\n",
2014 |        "Name: INCIDENT_NUMBER, dtype: int64"
2015 |       ]
2016 |      },
2017 |      "execution_count": 35,
2018 |      "metadata": {},
2019 |      "output_type": "execute_result"
2020 |     }
2021 |    ],
2022 |    "source": [
2023 |     "crime.groupby('YEAR').count()['INCIDENT_NUMBER']"
2024 |    ]
2025 |   },
2026 |   {
2027 |    "cell_type": "code",
2028 |    "execution_count": 36,
2029 |    "id": "004ed113",
2030 |    "metadata": {},
2031 |    "outputs": [
2032 |     {
2033 |      "data": {
2034 |       "image/png": 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",
2035 |       "text/plain": [
2036 |        "<Figure size 640x480 with 1 Axes>"
2037 |       ]
2038 |      },
2039 |      "metadata": {},
2040 |      "output_type": "display_data"
2041 |     }
2042 |    ],
2043 |    "source": [
2044 |     "# In which year were the most crimes committed?\n",
2045 |     "crime.groupby('YEAR').count()['INCIDENT_NUMBER'].plot(kind='bar');\n",
2046 |     "plt.title('Number of crimes');"
2047 |    ]
2048 |   },
2049 |   {
2050 |    "cell_type": "code",
2051 |    "execution_count": 37,
2052 |    "id": "88ba7a14",
2053 |    "metadata": {},
2054 |    "outputs": [],
2055 |    "source": [
2056 |     "# Question 3 - Are there more crimes committed on specific days?"
2057 |    ]
2058 |   },
2059 |   {
2060 |    "cell_type": "code",
2061 |    "execution_count": 38,
2062 |    "id": "880134ba",
2063 |    "metadata": {},
2064 |    "outputs": [
2065 |     {
2066 |      "data": {
2067 |       "image/png": 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",
2068 |       "text/plain": [
2069 |        "<Figure size 640x480 with 1 Axes>"
2070 |       ]
2071 |      },
2072 |      "metadata": {},
2073 |      "output_type": "display_data"
2074 |     }
2075 |    ],
2076 |    "source": [
2077 |     "# Are there more crimes during specific hours?\n",
2078 |     "crime.groupby('HOUR').count()['INCIDENT_NUMBER'].plot(kind='bar');"
2079 |    ]
2080 |   },
2081 |   {
2082 |    "cell_type": "code",
2083 |    "execution_count": 39,
2084 |    "id": "8731c724",
2085 |    "metadata": {},
2086 |    "outputs": [],
2087 |    "source": [
2088 |     "# On what days and during which hours are the most crimes committed?"
2089 |    ]
2090 |   },
2091 |   {
2092 |    "cell_type": "code",
2093 |    "execution_count": 40,
2094 |    "id": "6b1da8af",
2095 |    "metadata": {},
2096 |    "outputs": [
2097 |     {
2098 |      "data": {
2099 |       "text/html": [
2100 |        "<div>\n",
2101 |        "<style scoped>\n",
2102 |        "    .dataframe tbody tr th:only-of-type {\n",
2103 |        "        vertical-align: middle;\n",
2104 |        "    }\n",
2105 |        "\n",
2106 |        "    .dataframe tbody tr th {\n",
2107 |        "        vertical-align: top;\n",
2108 |        "    }\n",
2109 |        "\n",
2110 |        "    .dataframe thead th {\n",
2111 |        "        text-align: right;\n",
2112 |        "    }\n",
2113 |        "</style>\n",
2114 |        "<table border=\"1\" class=\"dataframe\">\n",
2115 |        "  <thead>\n",
2116 |        "    <tr style=\"text-align: right;\">\n",
2117 |        "      <th>DAY_OF_WEEK</th>\n",
2118 |        "      <th>Friday</th>\n",
2119 |        "      <th>Monday</th>\n",
2120 |        "      <th>Saturday</th>\n",
2121 |        "      <th>Sunday</th>\n",
2122 |        "      <th>Thursday</th>\n",
2123 |        "      <th>Tuesday</th>\n",
2124 |        "      <th>Wednesday</th>\n",
2125 |        "    </tr>\n",
2126 |        "    <tr>\n",
2127 |        "      <th>HOUR</th>\n",
2128 |        "      <th></th>\n",
2129 |        "      <th></th>\n",
2130 |        "      <th></th>\n",
2131 |        "      <th></th>\n",
2132 |        "      <th></th>\n",
2133 |        "      <th></th>\n",
2134 |        "      <th></th>\n",
2135 |        "    </tr>\n",
2136 |        "  </thead>\n",
2137 |        "  <tbody>\n",
2138 |        "    <tr>\n",
2139 |        "      <th>0</th>\n",
2140 |        "      <td>2161</td>\n",
2141 |        "      <td>2000</td>\n",
2142 |        "      <td>2612</td>\n",
2143 |        "      <td>2400</td>\n",
2144 |        "      <td>2039</td>\n",
2145 |        "      <td>1897</td>\n",
2146 |        "      <td>1997</td>\n",
2147 |        "    </tr>\n",
2148 |        "    <tr>\n",
2149 |        "      <th>1</th>\n",
2150 |        "      <td>1275</td>\n",
2151 |        "      <td>1058</td>\n",
2152 |        "      <td>1855</td>\n",
2153 |        "      <td>2043</td>\n",
2154 |        "      <td>1077</td>\n",
2155 |        "      <td>1017</td>\n",
2156 |        "      <td>942</td>\n",
2157 |        "    </tr>\n",
2158 |        "    <tr>\n",
2159 |        "      <th>2</th>\n",
2160 |        "      <td>952</td>\n",
2161 |        "      <td>846</td>\n",
2162 |        "      <td>1827</td>\n",
2163 |        "      <td>1855</td>\n",
2164 |        "      <td>774</td>\n",
2165 |        "      <td>641</td>\n",
2166 |        "      <td>798</td>\n",
2167 |        "    </tr>\n",
2168 |        "    <tr>\n",
2169 |        "      <th>3</th>\n",
2170 |        "      <td>532</td>\n",
2171 |        "      <td>583</td>\n",
2172 |        "      <td>957</td>\n",
2173 |        "      <td>1119</td>\n",
2174 |        "      <td>526</td>\n",
2175 |        "      <td>460</td>\n",
2176 |        "      <td>412</td>\n",
2177 |        "    </tr>\n",
2178 |        "    <tr>\n",
2179 |        "      <th>4</th>\n",
2180 |        "      <td>441</td>\n",
2181 |        "      <td>386</td>\n",
2182 |        "      <td>672</td>\n",
2183 |        "      <td>704</td>\n",
2184 |        "      <td>436</td>\n",
2185 |        "      <td>399</td>\n",
2186 |        "      <td>370</td>\n",
2187 |        "    </tr>\n",
2188 |        "    <tr>\n",
2189 |        "      <th>...</th>\n",
2190 |        "      <td>...</td>\n",
2191 |        "      <td>...</td>\n",
2192 |        "      <td>...</td>\n",
2193 |        "      <td>...</td>\n",
2194 |        "      <td>...</td>\n",
2195 |        "      <td>...</td>\n",
2196 |        "      <td>...</td>\n",
2197 |        "    </tr>\n",
2198 |        "    <tr>\n",
2199 |        "      <th>19</th>\n",
2200 |        "      <td>2564</td>\n",
2201 |        "      <td>2606</td>\n",
2202 |        "      <td>2301</td>\n",
2203 |        "      <td>2114</td>\n",
2204 |        "      <td>2510</td>\n",
2205 |        "      <td>2768</td>\n",
2206 |        "      <td>2724</td>\n",
2207 |        "    </tr>\n",
2208 |        "    <tr>\n",
2209 |        "      <th>20</th>\n",
2210 |        "      <td>2307</td>\n",
2211 |        "      <td>2319</td>\n",
2212 |        "      <td>2131</td>\n",
2213 |        "      <td>2109</td>\n",
2214 |        "      <td>2349</td>\n",
2215 |        "      <td>2369</td>\n",
2216 |        "      <td>2265</td>\n",
2217 |        "    </tr>\n",
2218 |        "    <tr>\n",
2219 |        "      <th>21</th>\n",
2220 |        "      <td>2089</td>\n",
2221 |        "      <td>2003</td>\n",
2222 |        "      <td>2077</td>\n",
2223 |        "      <td>1902</td>\n",
2224 |        "      <td>2070</td>\n",
2225 |        "      <td>1925</td>\n",
2226 |        "      <td>2043</td>\n",
2227 |        "    </tr>\n",
2228 |        "    <tr>\n",
2229 |        "      <th>22</th>\n",
2230 |        "      <td>2160</td>\n",
2231 |        "      <td>1634</td>\n",
2232 |        "      <td>2113</td>\n",
2233 |        "      <td>1728</td>\n",
2234 |        "      <td>1795</td>\n",
2235 |        "      <td>1757</td>\n",
2236 |        "      <td>1738</td>\n",
2237 |        "    </tr>\n",
2238 |        "    <tr>\n",
2239 |        "      <th>23</th>\n",
2240 |        "      <td>1936</td>\n",
2241 |        "      <td>1243</td>\n",
2242 |        "      <td>1951</td>\n",
2243 |        "      <td>1380</td>\n",
2244 |        "      <td>1492</td>\n",
2245 |        "      <td>1296</td>\n",
2246 |        "      <td>1298</td>\n",
2247 |        "    </tr>\n",
2248 |        "  </tbody>\n",
2249 |        "</table>\n",
2250 |        "<p>24 rows × 7 columns</p>\n",
2251 |        "</div>"
2252 |       ],
2253 |       "text/plain": [
2254 |        "DAY_OF_WEEK  Friday  Monday  Saturday  Sunday  Thursday  Tuesday  Wednesday\n",
2255 |        "HOUR                                                                       \n",
2256 |        "0              2161    2000      2612    2400      2039     1897       1997\n",
2257 |        "1              1275    1058      1855    2043      1077     1017        942\n",
2258 |        "2               952     846      1827    1855       774      641        798\n",
2259 |        "3               532     583       957    1119       526      460        412\n",
2260 |        "4               441     386       672     704       436      399        370\n",
2261 |        "...             ...     ...       ...     ...       ...      ...        ...\n",
2262 |        "19             2564    2606      2301    2114      2510     2768       2724\n",
2263 |        "20             2307    2319      2131    2109      2349     2369       2265\n",
2264 |        "21             2089    2003      2077    1902      2070     1925       2043\n",
2265 |        "22             2160    1634      2113    1728      1795     1757       1738\n",
2266 |        "23             1936    1243      1951    1380      1492     1296       1298\n",
2267 |        "\n",
2268 |        "[24 rows x 7 columns]"
2269 |       ]
2270 |      },
2271 |      "execution_count": 40,
2272 |      "metadata": {},
2273 |      "output_type": "execute_result"
2274 |     }
2275 |    ],
2276 |    "source": [
2277 |     "crime.groupby(['HOUR','DAY_OF_WEEK']).count()['INCIDENT_NUMBER'].unstack()"
2278 |    ]
2279 |   },
2280 |   {
2281 |    "cell_type": "code",
2282 |    "execution_count": 41,
2283 |    "id": "a968bcce",
2284 |    "metadata": {},
2285 |    "outputs": [],
2286 |    "source": [
2287 |     "week_and_hour = crime.groupby(['HOUR','DAY_OF_WEEK']).count()['INCIDENT_NUMBER'].unstack()"
2288 |    ]
2289 |   },
2290 |   {
2291 |    "cell_type": "code",
2292 |    "execution_count": 42,
2293 |    "id": "1470a2a8",
2294 |    "metadata": {},
2295 |    "outputs": [],
2296 |    "source": [
2297 |     "week_and_hour = week_and_hour[['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']]"
2298 |    ]
2299 |   },
2300 |   {
2301 |    "cell_type": "code",
2302 |    "execution_count": 43,
2303 |    "id": "7053f34d",
2304 |    "metadata": {
2305 |     "scrolled": false
2306 |    },
2307 |    "outputs": [
2308 |     {
2309 |      "data": {
2310 |       "image/png": 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+yDZhYWEIDQ012Jdx6qixoxEREZGETKpA0el0OH36NF5++eUi26jVaqjVaoN9ORzeISIiBeFKsiVT9BDP1KlTERsbi+TkZBw8eBCvv/467t27hyFDhsgdjYiIiIxI0T0o165dw5tvvol///0XNWvWRLt27RAfHw9PT0+5oxERET0zC3aglEjRBcqmTZvkjkBEREQyUPQQDxEREVVOiu5BISIiMkdcB6VklaJAybqZLncEo8vLzpU7glGJfCF3BKPLz82XO4LRWdtayx3BqNTV7eWOQCZCjlVgIyIisHXrVpw5cwa2trbo0KED5s2bh8aNG+vbDB06tMBaYz4+PoiPj9c/1ul0mDp1Kn744QdkZWWha9euWLp0KWrVqqVvk56ejkmTJmHHjh0AgN69e+Prr79GtWrVSp2XQzxERESVQGxsLMaPH4/4+HhERUUhNzcXAQEByMzMNGjXo0cPpKSk6LfffvvN4HhISAi2bduGTZs2IS4uDhkZGejZsyfy8vL0bQYPHoykpCRERkYiMjISSUlJCA4OLlNexfegXL9+HTNmzMDvv/+OrKwsNGrUCCtXrkTr1q3ljkZERPRM5BjiiYyMNHi8evVquLi4IDExEZ07d9bvV6vVRd6kV6vVYuXKlfj+++/RrVs3AMD69etRu3Zt7N69G4GBgTh9+jQiIyMRHx8PHx8fAMC3336L9u3b4+zZswY9NsVRdA9Keno6OnbsCGtra/z+++84deoUFixYUKYuIiIiInOl0+lw7949g+3p+9EVRavVAgCcnJwM9sfExMDFxQWNGjXCqFGjkJaWpj+WmJiInJwcBAQE6Pd5eHjA29sb+/fvBwAcOHAAGo1GX5wAQLt27aDRaPRtSkPRBcq8efNQu3ZtrF69Gm3btkXdunXRtWtX1K9fX+5oREREz8wCKkm2wu4/FxERUeLrCyEQGhqKTp06wdvbW78/KCgIGzZswJ49e7BgwQIcPnwYXbp00Rc9qampsLGxQfXq1Q3O5+rqitTUVH0bFxeXAq/p4uKib1Maih7i2bFjBwIDA/HGG28gNjYWzz33HMaNG4dRo0bJHY2IiEh2hd1/7unbvRRmwoQJOHbsGOLi4gz2Dxw4UP9vb29vvPTSS/D09MTOnTvRr1+/Is8nhDAYtipsCOvpNiVRdA/KxYsXsWzZMjRs2BB//PEHxo4di0mTJmHdunVyRyMiInpmKpVKkk2tVsPR0dFgK6lAmThxInbs2IHo6GiDK28K4+7uDk9PT/1Net3c3JCdnY30dMOrY9PS0uDq6qpvc/PmzQLnunXrlr5NaSi6QMnPz8eLL76IOXPmoFWrVhgzZgxGjRqFZcuWFfmcQsfjcrIrMDUREZHyCCEwYcIEbN26FXv27IGXl1eJz7l9+zauXr0Kd3d3AEDr1q1hbW2NqKgofZuUlBScOHECHTp0AAC0b98eWq0Whw4d0rc5ePAgtFqtvk1pKLpAcXd3R7NmzQz2NW3aFFeuXCnyOYWNx/33x5+MHZWIiEjRxo8fj/Xr12Pjxo1wcHBAamoqUlNTkZWVBQDIyMjA1KlTceDAAVy6dAkxMTHo1asXnJ2d8dprrwEANBoNRowYgffeew9//vknjhw5grfffhstWrTQX9XTtGlT9OjRA6NGjUJ8fDzi4+MxatQo9OzZs9RX8AAKn4PSsWNHnD171mDfuXPnir1ZYGHjcbdiYowRj4iI6JnIsVDb49EHPz8/g/2rV6/G0KFDYWlpiePHj2PdunW4e/cu3N3d4e/vjx9//BEODg769osWLYKVlRUGDBigX6htzZo1sLS01LfZsGEDJk2apL/ap3fv3liyZEmZ8qqEEIpdovPw4cPo0KEDPvnkEwwYMACHDh3CqFGj8M033+Ctt94q9Xmu/R5ZciMTx5VkTR9XkjV9XEnWPLh07Fxyo3Ka3n26JOeZHzVfkvMokaKHeNq0aYNt27bhhx9+gLe3Nz777DMsXry4TMUJERERmR5FD/EAQM+ePdGzZ0+5YxAREVEFUnyBQkREZG7kmINiahQ9xENERESVE3tQiIiIKpgK7EEpSaUoUK4lpcgdwehyc8z7ChCRb97vDwAsLNmhaeqqu5v/VTwObg4lN6ISyXE3Y1PD34hERESkOIovUOrWrVvo/QfGjx8vdzQiIiIyEsUP8Rw+fBh5eXn6xydOnED37t3xxhtvyJiKiIjo2fEqnpIpvkCpWbOmweO5c+eifv368PX1lSkRERFR+bA+KZnih3ielJ2djfXr12P48OGcYERERGTGFN+D8qTt27fj7t27GDp0aJFtdDoddDqdwb7s3BzYWJn3PUCIiIjMiUn1oKxcuRJBQUHw8PAosk1ERAQ0Go3Bti5mZwWmJCIiKp6FSiXJZs5MpkC5fPkydu/ejZEjRxbbLiwsDFqt1mB7x+/VCkpJREREUjCZIZ7Vq1fDxcUFr75afLGhVquhVqsN9nF4h4iIlIQryZbMJAqU/Px8rF69GkOGDIGVlUlEJiIiKpK5D89IwSSGeHbv3o0rV65g+PDhckchIiKiCmAS3REBAQEQQsgdg4iIiCqISRQoRERE5oQjPCVjgUJERFTBuNhoySpFgXL0+E25Ixjdw5xcuSNQOVWxrhQfR7PWSGf+n8P83Hy5IxhdbbkDEAATmSRLRERElYuiC5Tc3Fx8+OGH8PLygq2tLerVq4dPP/0U+fnmX8ETEZH54kqyJVN0n/K8efOwfPlyrF27Fs2bN0dCQgKGDRsGjUaDyZMnyx2PiIiIjETRBcqBAwfQp08f/eqxdevWxQ8//ICEhASZkxERET07M+/8kISih3g6deqEP//8E+fOnQMAHD16FHFxcXjllVdkTkZERETGpOgelBkzZkCr1aJJkyawtLREXl4ePv/8c7z55ptyRyMiIiIjUnSB8uOPP2L9+vXYuHEjmjdvjqSkJISEhMDDwwNDhgwp9Dk6nQ46nc5gX05eLqwtFf1WiYioEjH3Ca5SUPQQz7Rp0/D+++9j0KBBaNGiBYKDgzFlyhREREQU+ZyIiAhoNBqDLfJUTMWFJiIiKoFKov+ZM0UXKA8ePICFhWFES0vLYi8zDgsLg1arNdh6NPMzclIiIiKSkqLHPXr16oXPP/8cderUQfPmzXHkyBEsXLiw2Lsaq9VqqNVqg30c3iEiIiXhUvclU/Q399dff42PPvoI48aNQ1paGjw8PDBmzBh8/PHHckcjIiIiI1J0geLg4IDFixdj8eLFckchIiKSjAU7UEqk6DkoREREVDkpugeFiIjIHHEOSskqRYHyMMf8b4GufaAruZEJy87LkzuC0dlYWsodwejM/Xdy9X+ryB3B6Kys2PFOFYP/TyMiIiLFUXyBcv/+fYSEhMDT0xO2trbo0KEDDh8+LHcsIiKiZ6ZSqSTZzJniC5SRI0ciKioK33//PY4fP46AgAB069YN169flzsaERHRM7FQSbOZM0UXKFlZWdiyZQvmz5+Pzp07o0GDBggPD4eXlxeWLVsmdzwiIiIyEkUXKLm5ucjLy0OVKoYTz2xtbREXFydTKiIiIjI2RRcoDg4OaN++PT777DPcuHEDeXl5WL9+PQ4ePIiUlBS54xERET0TzkEpmaILFAD4/vvvIYTAc889B7Vaja+++gqDBw+GZRGXZOp0Oty7d89gy80z/8uMiYiIzIniC5T69esjNjYWGRkZuHr1Kg4dOoScnBx4eXkV2j4iIgIajcZgizqzt4JTExERFU2lkmYzZ4ovUB6zs7ODu7s70tPT8ccff6BPnz6FtgsLC4NWqzXYujfpXMFpiYiIimahUkmymTPFryT7xx9/QAiBxo0b48KFC5g2bRoaN26MYcOGFdperVZDrVYb7LOyVPzbJCIioicovgdFq9Vi/PjxaNKkCd555x106tQJu3btgrW1tdzRiIiIyEgU37UwYMAADBgwQO4YREREklHBvIdnpKD4AoWIiMjcmPn0EUkofoiHiIiIKh8WKERERKQ4lWKI5/rde3JHMDpLlXnXmrpc819s74HcASrA3Yfm/S6dHarKHcHoLMz9DnUVxNwvEZaCeX+rERERkUmStUDZu3cvevXqBQ8PD6hUKmzfvt3guBAC4eHh8PDwgK2tLfz8/HDy5El5whIREUlEjnvxREREoE2bNnBwcICLiwv69u2Ls2fPGrQpzfeuTqfDxIkT4ezsDDs7O/Tu3RvXrl0zaJOeno7g4GD9iu7BwcG4e/dumfLKWqBkZmaiZcuWWLJkSaHH58+fj4ULF2LJkiU4fPgw3Nzc0L17d9y/f7+CkxIREZm22NhYjB8/HvHx8YiKikJubi4CAgKQmZmpb1Oa792QkBBs27YNmzZtQlxcHDIyMtCzZ0/k5eXp2wwePBhJSUmIjIxEZGQkkpKSEBwcXKa8KiGEKP/bLj+VSoVt27ahb9++AB5VcR4eHggJCcGMGTMAPKraXF1dMW/ePIwZM6bU557efboxIisK56CQKTD3OShtPGvLHcHoalY3/3k2ry+dbPTX+OateZKcZ/SGGc/83Fu3bsHFxQWxsbHo3Llzqb53tVotatasie+//x4DBw4EANy4cQO1a9fGb7/9hsDAQJw+fRrNmjVDfHw8fHx8AADx8fFo3749zpw5g8aNG5cqn2K/1ZKTk5GamoqAgAD9PrVaDV9fX+zfv1/GZEREROUj1c0CdTod7t27Z7DpdLpSZdBqtQAAJycnAKX73k1MTEROTo5BGw8PD3h7e+vbHDhwABqNRl+cAEC7du2g0WjK9P2t2AIlNTUVAODq6mqw39XVVX+sMIX9sHLz+dc3EREph1RzUCIiIvTzPB5vERERJb6+EAKhoaHo1KkTvL29AZTuezc1NRU2NjaoXr16sW1cXFwKvKaLi0ux399PU2yB8tjTk4CEEMVODCrsh3Uw+aCxYxIREZWahUqaLSwsDFqt1mALCwsr8fUnTJiAY8eO4YcffihwrKzfu4W1Kax9ac7zJMUWKG5ubgBQoNpKS0srUN09qbAflo+XT5HtiYiITJVarYajo6PBplari33OxIkTsWPHDkRHR6NWrVr6/aX53nVzc0N2djbS09OLbXPz5s0Cr3vr1q1iv7+fptgCxcvLC25uboiKitLvy87ORmxsLDp06FDk8wr7YVlZVIr16IiIiIokhMCECROwdetW7NmzB15eXgbHS/O927p1a1hbWxu0SUlJwYkTJ/Rt2rdvD61Wi0OHDunbHDx4EFqtttjv76fJ+s2dkZGBCxcu6B8nJycjKSkJTk5OqFOnDkJCQjBnzhw0bNgQDRs2xJw5c1C1alUMHjxYxtRERETlU9Y1TKQwfvx4bNy4ET///DMcHBz0PSUajQa2trZQqVQlfu9qNBqMGDEC7733HmrUqAEnJydMnToVLVq0QLdu3QAATZs2RY8ePTBq1CisWLECADB69Gj07Nmz1FfwADIXKAkJCfD399c/Dg0NBQAMGTIEa9aswfTp05GVlYVx48YhPT0dPj4+2LVrFxwcHOSKTEREZJKWLVsGAPDz8zPYv3r1agwdOhQASvW9u2jRIlhZWWHAgAHIyspC165dsWbNGlhaWurbbNiwAZMmTdJf7dO7d+8i1zwrimLWQTEmroNi+rgOinngOiimj+ugSGPN0C8kOc/QNdMkOY8SSfqtlpKSggkTJkh5SiIiIqqEyjzEc+rUKURHR8Pa2hoDBgxAtWrV8O+//+Lzzz/H8uXLC0y6ISIiIiqrMhUov/76K/r374+cnBwAj9bs//bbbzFgwAB4e3tj8+bN6Nmzp1GClofayvyv4rn38KHcEYwq+4l7PJgry0pw+/Wq1jZyRzAqi0rwM6wEswIqRGX4/0p5lWmI5/PPP8fYsWNx7949/Oc//8HFixcxduxYbNmyBdHR0YosToiIiJRGjrsZm5oyFSinT5/G+PHjYW9vj0mTJsHCwgKLFy9G586dn+nF9+7di169esHDwwMqlQrbt283OL5161YEBgbC2dkZKpUKSUlJz/Q6REREZFrKVKDcu3cP1apVAwBYWVnB1tYWjRo1euYXz8zMRMuWLYu89CgzMxMdO3bE3Llzn/k1iIiIlEaqmwWas2eaJPt4cRchBM6ePYvMzEyDNs8//3ypzhUUFISgoKAijwcHBwMALl26VNaYREREZMLKXKB07drVYJLU43knKpVKfyOgvEowoZGIiOhZmfv8ESmUqUBJTk42Vg4iIiIivTIVKJ6ensbKIRmdTgedTmewLzc/lzcMJCIixWAHSsnK9K29d+/eQvdrNBo0aNAAdnZ2koQqj4iICHzyyScG+16u3wm+DV+WKRERERGVVZkKlKdvMPQkS0tLvPvuu1iwYAGsra3Lm+uZhYWF6W86+FjEG7NlSkNERETPokwFSnp6eqH77969i0OHDmHatGlwc3PDBx98UKrzZWRk4MKFC/rHycnJSEpKgpOTE+rUqYM7d+7gypUruHHjBgDg7NmzAAA3Nze4ubkVek61Wg21Wm2wj8M7RESkJFxJtmRl+ubWaDRF7vf09ISNjQ0++OCDUhcoCQkJ8Pf31z9+3PMxZMgQrFmzBjt27MCwYcP0xwcNGgQAmDVrFsLDw8sSnYiISDFYn5RM0q6Fli1b4vLly6Vu7+fnV+x9HYYOHYqhQ4dKkIyIiIhMSZlWki3JjRs34OLiIuUpiYiIqBKSrAclLS0NH374Ibp06SLVKYmIiMwSF2orWZkKlFatWhX6H1Wr1eLatWto2rQpNm3aJFk4IiIiqpzKVKD07du30P2Ojo5o0qQJAgICYGlpKUUuSSXf+VfuCEZnZaG8/+5S0j7MLLmRibM0858h8GjRRHOWV8ycOnPRJKem3BHMAjtQSlamAmXWrFmSvvjevXvxxRdfIDExESkpKdi2bZu+CMrJycGHH36I3377DRcvXoRGo0G3bt0wd+5ceHh4SJqDiIioInGIp2TPNAclKysLUVFROHfuHFQqFRo1aoRu3brB1ta2TOfJzMxEy5YtMWzYMPTv39/g2IMHD/D333/jo48+QsuWLZGeno6QkBD07t0bCQkJzxKbiIiITESZC5QdO3Zg5MiR+Pdfw2ETZ2dnrFy5Er169Sr1uYKCghAUFFToMY1Gg6ioKIN9X3/9Ndq2bYsrV66gTp06ZY1OREREJqJMlxnv378fr7/+Ojp37oy//voLd+7cwZ07dxAXF4eXX34Zr7/+Og4cOGCsrNBqtVCpVKhWrZrRXoOIiMjYVCppNnNWph6U2bNnY9iwYVixYoXB/g4dOqBDhw4YM2YMPvvsM/z222+ShgSAhw8f4v3338fgwYPh6Ogo+fmJiIgqCpe6L1mZCpQDBw5g3rx5RR4fP348fH19yx3qaTk5ORg0aBDy8/OxdOnSYtvqdDrodDqDfXn5eZXiCgkiIiJzUaYhnocPHxbbe6HRaAoUB+WVk5ODAQMGIDk5GVFRUSX2nkRERECj0RhsJ28kSZqJiIiIjKtMBUqjRo2wZ8+eIo//+eefaNCgQblDPfa4ODl//jx2796NGjVqlPicsLAwaLVag625xwuSZSIiIiovzkEpWZmGeIYOHYqpU6fC1dUVr7zyisGxnTt3Yvr06Zg5c2apz5eRkYELFy7oHycnJyMpKQlOTk7w8PDA66+/jr///hu//vor8vLykJqaCgBwcnKCjY1NoedUq9VQq9UG+zi8Q0REZFrKVKBMnjwZ+/fvR8+ePdG4cWM0bdoUAHDq1CmcP38effv2xeTJk0t9voSEBPj7++sfh4aGAgCGDBmC8PBw7NixAwDwwgsvGDwvOjoafn5+ZYlORESkGFyorWRlKlAsLCywefNm/Pjjj9i4cSPOnDkDAGjSpAnCw8MxaNCgMr24n58fRDFLQxd3jIiIiMxXmQqUe/fuASh6gbXHx3kZMBEREZVHmQqUatWqFdstJYSASqVCXl5euYMRERGZK47wlKxMBUp0dLT+30IIvPLKK/juu+/w3HPPSR6MiIjIXHEOSsnKVKA8vQibpaUl2rVrh3r16kkaSmqu9hq5Ixiduc/XsbUu/Kotc6KC+f/CsrEy7yvqPCrB8LaDrfl/FisC65OSlWkdFCIiIqKKIGuBsnfvXvTq1QseHh5QqVTYvn27wfHw8HA0adIEdnZ2qF69Orp164aDBw/KE5aIiIgqTLkLlPKMo2VmZqJly5ZYsmRJoccbNWqEJUuW4Pjx44iLi0PdunUREBCAW7duPfNrEhERyU2lUkmymbMyzUHp16+fweOHDx9i7NixsLOzM9i/devWUp2vqMuVHxs8eLDB44ULF2LlypU4duwYunbtWsrUREREZGrKVKBoNIaTTd9++21JwxQnOzsb33zzDTQaDVq2bFlhr0tERCQ1M+/8kESZCpTVq1cbK0eRfv31VwwaNAgPHjyAu7s7oqKi4OzsXOE5iIiIqOIo/ioef39/JCUlYf/+/ejRowcGDBiAtLS0ItvrdDrcu3fPYMvNz63AxERERFReii9Q7Ozs0KBBA7Rr1w4rV66ElZUVVq5cWWT7iIgIaDQag+3QJV75Q0REysFJsiVTfIHyNCEEdDpdkcfDwsKg1WoNtrZ1fSowIRERUfFUKmk2c1amOShSy8jIwIULF/SPk5OTkZSUBCcnJ9SoUQOff/45evfuDXd3d9y+fRtLly7FtWvX8MYbbxR5TrVaDbVabbDPykLWt0lERERlJOs3d0JCAvz9/fWPQ0NDAQBDhgzB8uXLcebMGaxduxb//vsvatSogTZt2mDfvn1o3ry5XJGJiIjKzcLcuz8kIGuB4ufnV+w9ZEq7ngoRERGZF459EBERVTB2oJTM5CbJEhERkfljD4qZyM4z77VeihsKNBdWlub/98LDnBy5IxjVvYdFX2FoLqrZVZE7glkw90uEpWD+vxGJiIjI5MhaoOzduxe9evWCh4cHVCoVtm/fXmTbMWPGQKVSYfHixRWWj4iIiOQha4GSmZmJli1bYsmSJcW22759Ow4ePAgPD48KSkZERGQ8XKitZLLOQQkKCkJQUFCxba5fv44JEybgjz/+wKuvvlpByYiIiIxHZWHm1YUEFD0HJT8/H8HBwZg2bRoXZyMiIiqHkqZVDB06tMC9ftq1a2fQRqfTYeLEiXB2doadnR169+6Na9euGbRJT09HcHCw/n54wcHBuHv3bpnzKrpAmTdvHqysrDBp0iS5oxAREZm00kyr6NGjB1JSUvTbb7/9ZnA8JCQE27Ztw6ZNmxAXF4eMjAz07NkTeXl5+jaDBw9GUlISIiMjERkZiaSkJAQHB5c5r2IvM05MTMSXX36Jv//+u0yXY+l0ugI3E8zNz+X9eIiISDHkmD9SmmkVarUabm5uhR7TarVYuXIlvv/+e3Tr1g0AsH79etSuXRu7d+9GYGAgTp8+jcjISMTHx8PH59GNer/99lu0b98eZ8+eRePGjUudV7E9KPv27UNaWhrq1KkDKysrWFlZ4fLly3jvvfdQt27dIp8XERGh71Z6vB26dLDighMREVUQnU6He/fuGWxP/5FeFjExMXBxcUGjRo0watQopKWl6Y8lJiYiJycHAQEB+n0eHh7w9vbG/v37AQAHDhyARqPRFycA0K5dO2g0Gn2b0lJsgRIcHIxjx44hKSlJv3l4eGDatGn4448/inxeWFgYtFqtwda2rk+R7YmIiCra03M9nnUr7I/yiIiIZ8oUFBSEDRs2YM+ePViwYAEOHz6MLl266Aue1NRU2NjYoHr16gbPc3V1RWpqqr6Ni4tLgXO7uLjo25SWrOMeGRkZuHDhgv5xcnIykpKS4OTkhDp16qBGjRoG7a2treHm5lZsF5FarYZarTbYx+EdIiJSEqmGeMLCwhAaGmqw7+nvwNIaOHCg/t/e3t546aWX4OnpiZ07d6Jfv35FPk8IYTAVo7BpGU+3KQ1Zv7kTEhLg7++vf/z4P/KQIUOwZs0amVIRERGZhsL+KJeKu7s7PD09cf78eQCAm5sbsrOzkZ6ebtCLkpaWhg4dOujb3Lx5s8C5bt26BVdX1zK9vqwFip+fX5nusXLp0iXjhSEiIiK927dv4+rVq3B3dwcAtG7dGtbW1oiKisKAAQMAACkpKThx4gTmz58PAGjfvj20Wi0OHTqEtm3bAgAOHjwIrVarL2JKi2MfREREFUyOmwUWN63CyckJ4eHh6N+/P9zd3XHp0iV88MEHcHZ2xmuvvQYA0Gg0GDFiBN577z3UqFEDTk5OmDp1Klq0aKG/qqdp06bo0aMHRo0ahRUrVgAARo8ejZ49e5bpCh6ABQoREVGFk+My4+KmVSxbtgzHjx/HunXrcPfuXbi7u8Pf3x8//vgjHBwc9M9ZtGgRrKysMGDAAGRlZaFr165Ys2YNLC0t9W02bNiASZMm6a/26d27d4m3tCmMSlSC+9i/13Wa3BGMTpdr3rexz83PlzuC0Vk/8QE3VzlPLOZkjpzt7OWOYHR1amjkjmB0ozfMMPpr7Av/VpLzvBw+SpLzKFGl6EGxrAT3PLAy8y83tZW13BGMLjffvL+8AcDKQrErG0jCvZr5Fyi26krxtUEKwP+nERERVTRzvxWxBGT9c0aKGxcRERGR+ZG1QJHixkVERESmRqqVZM2ZrEM85b1xEREREZknxc9YK+7GRURERGSeFD1JNigoCG+88QY8PT2RnJyMjz76CF26dEFiYmKRS/vqdLoCd3LMzc/l/XiIiEgxzHx0RhKK/tZ+lhsXRURE4JNPPjHY18GrAzrW72jUrERERKWlqgTLX5SX4od4nvT0jYsKExYWBq1Wa7D5ePlUYEoiIqLiqVTSbOZM0T0oT3v6xkWFKezOjhzeISIiMi2yfnOX98ZFREREZJ5kLVCkuHERERGRqTH3NUykIGuB4ufnh+LuVfjHH39UYBoiIiJSCk7OICIiqmDsQCmZSV3FQ0RERJVDpehBOXTtnNwRjC43P1fuCEZlobKUO4LRVbGykTuC0T3IyZI7glFVtTH/n2FNnb3cEaiSqBQFChERkZJwkmzJZB3i2bt3L3r16gUPDw+oVCps3769QJvTp0+jd+/e0Gg0cHBwQLt27XDlypWKD0tERCQRLtRWMlkLlMzMTLRs2RJLliwp9Pg///yDTp06oUmTJoiJicHRo0fx0UcfoUqVKhWclIiIiCqSrEM8QUFBCAoKKvL4zJkz8corr2D+/Pn6ffXq1auIaERERCQjxV7Fk5+fj507d6JRo0YIDAyEi4sLfHx8Ch0GIiIiMiUqlUqSzZwptkBJS0tDRkYG5s6dix49emDXrl147bXX0K9fP8TGxsodj4iI6NlZSLSZMcVexZOfnw8A6NOnD6ZMmQIAeOGFF7B//34sX74cvr6+hT5Pp9NBp9M9da48WFiY/2WqRERE5kKx9ZezszOsrKzQrFkzg/1NmzYt9iqeiIgIaDQag+3K7fPGjktERFRqHOIpmWILFBsbG7Rp0wZnz5412H/u3Dl4enoW+bywsDBotVqDrU6NhsaOS0RERBKSdYgnIyMDFy5c0D9OTk5GUlISnJycUKdOHUybNg0DBw5E586d4e/vj8jISPzyyy+IiYkp8pxqtRpqtdpgH4d3iIiITIusBUpCQgL8/f31j0NDQwEAQ4YMwZo1a/Daa69h+fLliIiIwKRJk9C4cWNs2bIFnTp1kisyERFRuZn56IwkZC1Q/Pz8IIQots3w4cMxfPjwCkpERERkfOY+f0QKip2DQkRERJUXCxQiIiJSHMWug0JERGSuOMJTskpRoNx5kC53BKMz9/FMtZW65EYmzt6mqtwRjO7ew/tyRzCqB9nZckcwOm3WQ7kjUCVRKQoUIiIiRTHzPyqlIOsclL1796JXr17w8PCASqUqcCPAolbO++KLL+QJTEREJAGVhUqSzZzJWqBkZmaiZcuWWLJkSaHHU1JSDLZVq1ZBpVKhf//+FZyUiIiIKpKsQzxBQUEICgoq8ribm5vB459//hn+/v6oV6+esaMRERGRjExmDsrNmzexc+dOrF27Vu4oRERE5cIpKCUzmQJl7dq1cHBwQL9+/Yptp9PpoNPpDPbli3xYqLjkCxERKYO5X3kpBZP51l61ahXeeustVKlSpdh2ERER0Gg0Btst7ZUKSklERERSMIkCZd++fTh79ixGjhxZYtuwsDBotVqDraamTgWkJCIiIqmYxBDPypUr0bp1a7Rs2bLEtmq1Gmq14aJeHN4hIiIl4QhPyWQtUDIyMnDhwgX94+TkZCQlJcHJyQl16jzq9bh37x42b96MBQsWyBWTiIiIKpisBUpCQgL8/f31j0NDQwEAQ4YMwZo1awAAmzZtghACb775phwRiYiIpMculBLJWqD4+flBCFFsm9GjR2P06NEVlIiIiIiUgJMziIiISHFMYpIsERGROTH3++hIoVIUKI5VHOSOYHQZ2Q/kjmBUVhbm/3/VBzlZckcwupp2znJHMCpLC/PvlLaxtJQ7glngFJSSmf9vfSIiIqVhhVIiWcv9vXv3olevXvDw8IBKpcL27dsNjmdkZGDChAmoVasWbG1t0bRpUyxbtkyesERERFRhZC1QMjMz0bJlSyxZsqTQ41OmTEFkZCTWr1+P06dPY8qUKZg4cSJ+/vnnCk5KREREFUnWIZ6goCAEBQUVefzAgQMYMmQI/Pz8ADy65HjFihVISEhAnz59KiglERGRtDjCUzJFz+jq1KkTduzYgevXr0MIgejoaJw7dw6BgYFyRyMiIiIjUvQk2a+++gqjRo1CrVq1YGVlBQsLC3z33Xfo1KmT3NGIiIieGS8zLpmie1C++uorxMfHY8eOHUhMTMSCBQswbtw47N69u8jn6HQ63Lt3z2DLz8+rwNRERETKU9KFKUIIhIeHw8PDA7a2tvDz88PJkycN2uh0OkycOBHOzs6ws7ND7969ce3aNYM26enpCA4OhkajgUajQXBwMO7evVvmvIotULKysvDBBx9g4cKF6NWrF55//nlMmDABAwcOxH/+858inxcREaH/j/J4u3bnnwpMTkREpDwlXZgyf/58LFy4EEuWLMHhw4fh5uaG7t274/79+/o2ISEh2LZtGzZt2oS4uDhkZGSgZ8+eyMv7v46AwYMHIykpCZGRkYiMjERSUhKCg4PLnFexQzw5OTnIycmBxVMLH1laWiI/P7/I54WFhelvOvhYj9YDjZKRiIjoWahkmCVb3IUpQggsXrwYM2fORL9+/QAAa9euhaurKzZu3IgxY8ZAq9Vi5cqV+P7779GtWzcAwPr161G7dm3s3r0bgYGBOH36NCIjIxEfHw8fHx8AwLfffov27dvj7NmzaNy4canzytqDkpGRgaSkJCQlJQEAkpOTkZSUhCtXrsDR0RG+vr6YNm0aYmJikJycjDVr1mDdunV47bXXijynWq2Go6OjwWZhwZUPiYhIQVQSbRJJTk5GamoqAgIC9PvUajV8fX2xf/9+AEBiYiJycnIM2nh4eMDb21vf5sCBA9BoNPriBADatWsHjUajb1NasvagJCQkwN/fX//4cc/HkCFDsGbNGmzatAlhYWF46623cOfOHXh6euLzzz/H2LFj5YpMRESkGDqdDjqdzmCfWq2GWq0u03lSU1MBAK6urgb7XV1dcfnyZX0bGxsbVK9evUCbx89PTU2Fi4tLgfO7uLjo25SWrAWKn58fhBBFHndzc8Pq1asrMBEREZHpiIiIwCeffGKwb9asWQgPD3+m8z099CSEKHE46uk2hbUvzXmepthJskREROZKpVJJsoWFhUGr1RpsYWFhZc7j5uYGAAV6OdLS0vS9Km5ubsjOzkZ6enqxbW7evFng/Ldu3SrQO1MSFihEREQVTKoCpbB5l2Ud3gEALy8vuLm5ISoqSr8vOzsbsbGx6NChAwCgdevWsLa2NmiTkpKCEydO6Nu0b98eWq0Whw4d0rc5ePAgtFqtvk1pKfYqHil5u9STO4LRaR8+kDuCUVWG29g72znIHcHocvPMe00iZzs7uSMYXTW7KnJHoGeUkZGBCxcu6B8/vjDFyckJderUQUhICObMmYOGDRuiYcOGmDNnDqpWrYrBgwcDADQaDUaMGIH33nsPNWrUgJOTE6ZOnYoWLVror+pp2rQpevTogVGjRmHFihUAHt2mpmfPnmW6ggeoJAUKERGRosjwN1dJF6ZMnz4dWVlZGDduHNLT0+Hj44Ndu3bBweH//nhatGgRrKysMGDAAGRlZaFr165Ys2YNLC3/72rZDRs2YNKkSfqrfXr37l3k2ivFUYniZqka2d69e/HFF18gMTERKSkp2LZtG/r27as/fvPmTcyYMQO7du3C3bt30blzZ3z99ddo2LBhmV5n7MuTJE6uPOxBMX3sQTF9rg7m/zOsDD0oE3+cafTXOL9hiyTnafhWf0nOo0Sy/tYvblU7IQT69u2Lixcv4ueff8aRI0fg6emJbt26ITMzU4a0REREVFFkHeIpblW78+fPIz4+HidOnEDz5s0BAEuXLoWLiwt++OEHjBw5siKjEhERSUaOlWRNjWL7zR8vPFOlyv91J1paWsLGxgZxcXFyxSIiIio3qa7iMWeKLVCaNGkCT09PhIWFIT09HdnZ2Zg7dy5SU1ORkpIidzwiIiIyIsUWKNbW1tiyZQvOnTsHJycnVK1aFTExMQgKCjKYLfw0nU6He/fuGWx5+eY9MY+IiMjcKLZAAR4tCpOUlIS7d+8iJSUFkZGRuH37Nry8vIp8TkREBDQajcF25GpCBaYmIiIqgcJuFqhEii5QHtNoNKhZsybOnz+PhIQE9OnTp8i2hS3726r2SxWYloiIqHgqC5UkmzmT9Sqekla127x5M2rWrIk6derg+PHjmDx5Mvr27Wtwq+enFXYXR0uLooeEiIiISHlkLVBKWtUuJSUFoaGhuHnzJtzd3fHOO+/go48+kisuERERVRBZCxQ/Pz8Ut5DtpEmTMGmS+a8CS0RElYyZXyIsBZOYg0JERESVC28WSEREVMHYgVIyFihEREQVzNxXgZVCpShQPBw1ckcwupr29nJHMCpba2u5IxidLjdX7ghGZ2Xmd6V2tFWX3IiISsW8f1sQERGRSZK1QImIiECbNm3g4OAAFxcX9O3bF2fPnjVoI4RAeHg4PDw8YGtrCz8/P5w8eVKmxERERBKwUEmzmTFZC5TY2FiMHz8e8fHxiIqKQm5uLgICApCZmalvM3/+fCxcuBBLlizB4cOH4ebmhu7du+P+/fsyJiciIiJjknUOSmRkpMHj1atXw8XFBYmJiejcuTOEEFi8eDFmzpyJfv36AQDWrl0LV1dXbNy4EWPGjJEjNhERUblwkmzJFDUHRavVAgCcnJwAPFr6PjU11WBpe7VaDV9fX+zfv1+WjERERGR8irmKRwiB0NBQdOrUCd7e3gCA1NRUAICrq6tBW1dXV1y+fLnQ8+h0Ouh0OoN9uXm5sLJUzFslIqLKjh0oJVJMD8qECRNw7Ngx/PDDDwWOPd0VJoQosnssIiICGo3GYNv3z19GyUxERETGoYgCZeLEidixYweio6NRq1Yt/X43NzcA/9eT8lhaWlqBXpXHwsLCoNVqDbaX63c0XngiIqIyUqlUkmzmTNYCRQiBCRMmYOvWrdizZw+8vLwMjnt5ecHNzQ1RUVH6fdnZ2YiNjUWHDh0KPadarYajo6PBxuEdIiJSEpWFSpLNnMn6zT1+/Hhs3LgRP//8MxwcHPQ9JRqNBra2tlCpVAgJCcGcOXPQsGFDNGzYEHPmzEHVqlUxePBgOaMTERGREclaoCxbtgwA4OfnZ7B/9erVGDp0KABg+vTpyMrKwrhx45Ceng4fHx/s2rULDg4OFZyWiIiIKoqsBYoQosQ2KpUK4eHhCA8PN34gIiKiimDm80ekoIhJskRERERP4uxRIiKiCmbuV+BIoVIUKO7V7eWOYHRqa0u5IxhVli5X7ghGl1+KIU9TZ+7vUVUJVt9ytLOROwJVEhziISIiIsWRtUCJiIhAmzZt4ODgABcXF/Tt2xdnz541aLN161YEBgbC2dkZKpUKSUlJ8oQlIiKSikqizYzJWqDExsZi/PjxiI+PR1RUFHJzcxEQEIDMzEx9m8zMTHTs2BFz586VMSkREZF0uFBbyWSdgxIZGWnwePXq1XBxcUFiYiI6d+4MAAgODgYAXLp0qaLjERERkUwUNQdFq9UCAJycnGROQkRERHJSzFU8QgiEhoaiU6dO8Pb2ljsOERGR8fAy4xIppkCZMGECjh07hri4uHKdR6fTQafTGezLycuFNW8YSERECsF1UEqmiCGeiRMnYseOHYiOjkatWrXKda6IiAhoNBqD7feT0RIlJSIioooga4EihMCECROwdetW7NmzB15eXuU+Z1hYGLRarcEW1NxfgrREREQSsVBJs5kxWcc9xo8fj40bN+Lnn3+Gg4MDUlNTAQAajQa2trYAgDt37uDKlSu4ceMGAOjXSXFzc4Obm1uBc6rVaqjVaoN9HN4hIiIyLbL2oCxbtgxarRZ+fn5wd3fXbz/++KO+zY4dO9CqVSu8+uqrAIBBgwahVatWWL58uVyxiYiIyMhk7VoQpbgvx9ChQzF06FDjhyEiIqognCRbMo59EBERVTTWJyVSxFU8RERERE9igUJERESKUymGeJ5ztZc7gtHpdHlyRzAqR3sbuSMYXW5eyXOyTN3VtPtyRzAqK0vz77d3sDP/z2JF4ByUksnagxIREYE2bdrAwcEBLi4u6Nu3r/4yYgDIycnBjBkz0KJFC9jZ2cHDwwPvvPOO/pJjIiIik8R1UEoka4ESGxuL8ePHIz4+HlFRUcjNzUVAQAAyMzMBAA8ePMDff/+Njz76CH///Te2bt2Kc+fOoXfv3nLGJiIiIiOTdYgnMjLS4PHq1avh4uKCxMREdO7cGRqNBlFRUQZtvv76a7Rt2xZXrlxBnTp1KjIuERERVRBFzUHRarUAACcnp2LbqFQqVKtWrYJSERERSYtzUEqmmKt4hBAIDQ1Fp06d4O3tXWibhw8f4v3338fgwYPh6OhYwQmJiIiooiimB2XChAk4duwY4uLiCj2ek5ODQYMGIT8/H0uXLi3yPDqdDjqdzvC5uTmwtrKWNC8REdEzYw9KiRTRgzJx4kTs2LED0dHRqFWrVoHjOTk5GDBgAJKTkxEVFVVs70lERAQ0Go3B9tOhXcaMT0REVCYqlUqSzZzJWqAIITBhwgRs3boVe/bsgZeXV4E2j4uT8+fPY/fu3ahRo0ax5wwLC4NWqzXYBrQNMNZbICIiIiOQtUAZP3481q9fj40bN8LBwQGpqalITU1FVlYWACA3Nxevv/46EhISsGHDBuTl5enbZGdnF3pOtVoNR0dHg43DO0REVNmFh4cX6IFxc3PTHxdCIDw8HB4eHrC1tYWfnx9OnjxpcA6dToeJEyfC2dkZdnZ26N27N65du2aUvLIWKMuWLYNWq4Wfnx/c3d31248//ggAuHbtGnbs2IFr167hhRdeMGizf/9+OaMTERE9O5kWamvevDlSUlL02/Hjx/XH5s+fj4ULF2LJkiU4fPgw3Nzc0L17d9y//38rQIeEhGDbtm3YtGkT4uLikJGRgZ49eyIvT/rVzGWdJCtE8Ut7161bt8Q2REREVDpWVlYGvSaPCSGwePFizJw5E/369QMArF27Fq6urti4cSPGjBkDrVaLlStX4vvvv0e3bt0AAOvXr0ft2rWxe/duBAYGSppVEZNkiYiIKhO5JsmeP38eHh4e8PLywqBBg3Dx4kUAQHJyMlJTUxEQ8H9zNtVqNXx9ffUjFomJicjJyTFo4+HhAW9vb6OMaijmMmMiIiIqm8KW1lCr1VCr1QXa+vj4YN26dWjUqBFu3ryJ2bNno0OHDjh58iRSU1MBAK6urgbPcXV1xeXLlwEAqampsLGxQfXq1Qu0efx8KbEHhYiIqKKpVJJshS2tERERUehLBgUFoX///mjRogW6deuGnTt3Ang0lPN/sQx7ZYQQJfbUlKbNs6gUPShutRzkjmB0eTn5ckegcnqQUfiVaeYk44H5v0dz5+JmJ3cEekJYWBhCQ0MN9hXWe1IYOzs7tGjRAufPn0ffvn0BPOolcXd317dJS0vT96q4ubkhOzsb6enpBr0oaWlp6NChQznfSUHsQSEiIqpgKguVJFthS2uUtkDR6XQ4ffo03N3d4eXlBTc3N4Mb9GZnZyM2NlZffLRu3RrW1tYGbVJSUnDixAnzK1AiIiLQpk0bODg4wMXFBX379sXZs2cN2oSHh6NJkyaws7ND9erV0a1bNxw8eFCmxERERKZp6tSpiI2NRXJyMg4ePIjXX38d9+7dw5AhQ6BSqRASEoI5c+Zg27ZtOHHiBIYOHYqqVati8ODBAACNRoMRI0bgvffew59//okjR47g7bff1g8ZSU3WIZ7Y2FiMHz8ebdq0QW5uLmbOnImAgACcOnUKdnaPuhEbNWqEJUuWoF69esjKysKiRYsQEBCACxcuoGbNmnLGJyIiMhnXrl3Dm2++iX///Rc1a9ZEu3btEB8fD09PTwDA9OnTkZWVhXHjxiE9PR0+Pj7YtWsXHBz+b5rEokWLYGVlhQEDBiArKwtdu3bFmjVrYGlpKXlelVDQQiO3bt2Ci4sLYmNj0blz50Lb3Lt3DxqNBrt370bXrl1Ldd6/F62TMqYicQ6K6asMc1CSr2jljkDl1LhR8bcbMQftPxhu9Nf4N+GAJOdxfqm9JOdRIkVNktVqH/3ycnJyKvR4dnY2vvnmG2g0GrRs2bIioxEREUnG3G/0JwXFFChCCISGhqJTp07w9vY2OPbrr79i0KBBePDgAdzd3REVFQVnZ2eZkhIREZGxKaZAmTBhAo4dO4a4uLgCx/z9/ZGUlIR///0X3377LQYMGICDBw/CxcWlQNvCFq3Jzs2BDW8YSERESsEelBIp4jLjiRMnYseOHYiOjkatWrUKHLezs0ODBg3Qrl07rFy5ElZWVli5cmWh5yps0ZrVu3819lsgIiIiCclaoAghMGHCBGzduhV79uyBl5dXqZ/3dC/JY2FhYdBqtQbbsG49pYxNRERERibrEM/48eOxceNG/Pzzz3BwcNCv5a/RaGBra4vMzEx8/vnn6N27N9zd3XH79m0sXboU165dwxtvvFHoOQu7BwGHd4iISElUFhziKYmsBcqyZcsAAH5+fgb7V69ejaFDh8LS0hJnzpzB2rVr8e+//6JGjRpo06YN9u3bh+bNm8uQmIiISAKcg1IiWQuUkpZgqVKlCrZu3VpBaYiIiEgpFDFJloiIiOhJirnMmIiIqNLgEE/JBEnq4cOHYtasWeLhw4dyRzEavkfTZ+7vTwi+R3Ng7u+Piqeoe/GYg8f3CtJqtXB0dJQ7jlHwPZo+c39/AN+jOTD390fF4xwUIiIiUhwWKERERKQ4LFCIiIhIcVigSEytVmPWrFkFVrM1J3yPps/c3x/A92gOzP39UfE4SZaIiIgUhz0oREREpDgsUIiIiEhxWKAQERGR4rBAISIiIsVhgUIlyszMlDsClVNycrLcEYiIyoQFCpXI1dUVw4cPR1xcnNxR6Bk1aNAA/v7+WL9+PR4+fCh3HCqHmJgYuSMY1Zo1a/DgwQO5Y5ACsECRiDl/qH744QdotVp07doVjRo1wty5c3Hjxg25Y0nKz88P69atQ1ZWltxRjOLo0aNo1aoV3nvvPbi5uWHMmDE4dOiQ3LHoGfTo0QP169fH7NmzcfXqVbnjSC4sLAxubm4YMWIE9u/fL3cckhELFImY84eqV69e2LJlC27cuIF3330XP/zwAzw9PdGzZ09s3boVubm5ckcst9atW2P69Olwc3PDqFGjEB8fL3ckSXl7e2PhwoW4fv06Vq9ejdTUVHTq1AnNmzfHwoULcevWLbkjSsLcC00AuHHjBiZPnoytW7fCy8sLgYGB+Omnn5CdnS13NElcu3YN69evR3p6Ovz9/dGkSRPMmzcPqampckejiibvzZTNR25urvj555/Fa6+9JmxsbETjxo3F3LlzRUpKitzRjOKrr74SarVaqFQqUbNmTfHRRx+JzMxMuWOVS25urti+fbvo06ePsLa2Fk2bNhVffPGFSE1NlTua5B4+fCgWLlyo/xna2NiI4OBgcePGDbmjlUtoaKhwdXUVjo6OYuTIkeLAgQNyRzKqI0eOiIkTJwpnZ2fh5OQkJk6cKJKSkuSOJZmbN2+KBQsWiBYtWghra2vRq1cvsX37dpGXlyd3NKoALFCMwFw/VCkpKWLevHmiSZMmomrVquKtt94Se/bsEevXrxfe3t6ie/fuckeUTFpamvjss89ElSpVhLW1tejTp4/4888/5Y5VbocPHxbvvvuuqF69uqhVq5aYOXOmuHjxooiLixNdunQRbdq0kTtiuVWmQlMIIa5fvy5mzZol1Gq1sLOzE5aWlqJTp07ixIkTckeTRHx8vBg9erRQq9Wibt26olq1aqJu3boiOjpa7mhkZCxQjMScPlRbtmwRPXv2FNbW1qJly5bi66+/Funp6QZtTpw4IaytreUJKLGDBw+KsWPHCo1GI+rUqSM+/vhjMWrUKFG1alXx3nvvyR3vmSxYsEB4e3vri61ffvmlQMF8/vx5YWlpKVNC4zDXQjM7O1ts3rxZBAUFCSsrK9GuXTvx7bffioyMDHHlyhXx5ptviqZNm8od85mlpqaKL774QjRr1kxUqVJFDBo0SERFRQkhhHjw4IEIDQ0VderUkTklGRsLFAmZ64fK0dFRjB49Whw6dKjINg8ePBDh4eEVmEpaN2/eFP/5z39E8+bNhY2Njejfv7/4/fffRX5+vr5NVFSUsLOzkzHls2vQoIGYM2dOsUOOOp1OrFmzpgJTGZc5FppCCDFhwgRRo0YNUaNGDTF58mRx/PjxAm0uX74sVCqVDOnK7/EfQ82bNxeLFi0St2/fLtDm+vXrJvv+qPRYoEjEnD9Upj63pDSsra1FkyZNxPz580VaWlqhbbRarfDz86vgZFQW5l5oCiFEly5dxMaNG4VOpyuyTU5OjoiJianAVNIZPny42L9/f7Ft8vPzxaVLlyooEcmFdzOWyIgRIzBy5Ei0b9++yDZCCFy5cgWenp4VmExaWVlZyMnJMdjn6OgoUxrp7Nu3Dy+//LLcMYzuwYMHuHLlSoErPp5//nmZEknLxsYG9evXx/DhwzF06FDUrFmzQJt79+6hT58+iI6OliEhEZUWCxQqUWZmJmbMmIGffvoJt2/fLnA8Ly9PhlRUFrdu3cLQoUMRGRlZ6HFz+RlWlkITAE6dOlVosdm7d2+ZEkknMzMTsbGxhb6/SZMmyZSKKpqV3AHMibl+qKZPn47o6GgsXboU77zzDv773//i+vXrWLFiBebOnSt3PMn873//w08//VToz+/vv/+WKZU0QkJCcPfuXcTHx8Pf3x/btm3DzZs3MXv2bCxYsEDueJKpDMXJxYsX8dprr+H48eNQqVR4/DemSqUCYPrF5pEjR/DKK6/gwYMHyMzMhJOTE/79919UrVoVLi4uJv27lMpIzvElc/L3338LNzc34ejoKCwtLUXNmjWFSqUSdnZ2wsvLS+545VK7dm391UcODg7i/PnzQggh1q1bJ4KCgmRMJp0vv/xS2Nvbi/HjxwsbGxsxZswY0a1bN6HRaMQHH3wgd7xyc3NzEwcPHhRCPPoZnj17VgghxM8//yw6duwoZzTJbd68WbzxxhvCx8dHtGrVymAzBz179hR9+vQRaWlpwt7eXpw6dUrs27dPtG3bVuzdu1fueOXm6+srRo0aJXJzc4W9vb34559/xJUrV0Tnzp3Fli1b5I5HFYgFikTM+UNlZ2enn5D23HPP6b/oLl68aNKTDZ/UuHFjsXHjRiGE0P/8hBDio48+EuPHj5czmiQcHBxEcnKyEEIIT09PERcXJ4R49DO0tbWVMZm0zL3QFEKIGjVqiKNHjwohHl1hd+bMGSGEEH/++ad44YUX5IwmCY1Go39PGo1GnDp1SgjxaOmGxo0byxmNKhiXupdIUlIS3nvvPVhaWsLS0hI6nQ61a9fG/Pnz8cEHH8gdr1zq1auHS5cuAQCaNWuGn376CQDwyy+/oFq1avIFk9CVK1fQoUMHAICtrS3u378PAAgODsYPP/wgZzRJNG7cGGfPngUAvPDCC1ixYgWuX7+O5cuXw93dXeZ00lm6dCm++eYbLFmyBDY2Npg+fTqioqIwadIkaLVaueNJIi8vD/b29gAAZ2dn/X2xPD099T9jU2Ztba0frnJ1dcWVK1cAABqNRv9vqhxYoEjEnD9Uw4YNw9GjRwE8uufQ0qVLoVarMWXKFEybNk3mdNJwc3PTTwD29PTU34snOTlZP8ZvykJCQpCSkgIAmDVrFiIjI1GnTh189dVXmDNnjszppGPuhSbw6L5Kx44dAwD4+Phg/vz5+Ouvv/Dpp5+iXr16Mqcrv1atWiEhIQEA4O/vj48//hgbNmxASEgIWrRoIXM6qlByd+GYi+7du4sNGzYIIYQYM2aMaNu2rVi/fr0IDAwUbdu2lTmdtC5fviy2bNliVvf8GDFihH6huWXLlglbW1vRrVs3Ua1aNTF8+HCZ00kvMzNTJCYmilu3bskdRVJeXl4iMTFRCCHESy+9JJYvXy6EEOKPP/4Q1atXlzOaZCIjI/XDxv/8849o2rSpUKlUwtnZ2SxWyT18+LDYs2ePEOLRSsBBQUHCwcFBtGrVyqx+51DJeJmxRBISEnD//n34+/vj1q1bGDJkCOLi4tCgQQOsXr0aLVu2lDsiFSM/Px/5+fmwsnp0YdtPP/2k//mNHTsWNjY2Miek0hg5ciRq166NWbNmYfny5QgNDUXHjh2RkJCAfv36YeXKlXJHNIo7d+6gevXq+l5cInPAAoUK9dVXX5W6LS/7U6bQ0NBSt124cKERk1QcFppE5oMFChXKy8vL4PGtW7fw4MED/aTYu3fv6tcluHjxogwJy+/xOH5pmOJKq/7+/gaPExMTkZeXh8aNGwMAzp07B0tLS7Ru3Rp79uyRIyKVUr9+/UrdduvWrUZMYhytWrUqde+Pqa9JRKXHhdrKwZw/VMnJyfp/b9y4EUuXLsXKlSv1X25nz57FqFGjMGbMGLkiltsLL7ygX+iqpJ+jKS5+9eRS7gsXLoSDgwPWrl2L6tWrAwDS09MxbNgwk1/czNwLTeDRZPvHhBDYtm0bNBoNXnrpJQCPis+7d++WqZBRkr59++r//fDhQyxduhTNmjXT3zokPj4eJ0+exLhx42RKSHJgD0o5fPLJJ/p/l/ShioiIkCtmudWvXx//+9//0KpVK4P9iYmJeP311w2KGVNy+fJl/b+PHDmCqVOnYtq0afqf34EDB7BgwQLMnz/f4BeoKXruueewa9cuNG/e3GD/iRMnEBAQoL9U1RRZWFiYdaH5tBkzZuDOnTtYvnw5LC0tATx6X+PGjYOjoyO++OILmROWz8iRI+Hu7o7PPvvMYP+sWbNw9epVrFq1SqZkVOFkmpxrdkaMGCE+/PDDAvs//vhjMWzYMBkSScfW1la/ONuTDh48aDaLfLVp00bs3LmzwP6dO3eKF198UYZE0rK3ty/0Co8///xT2Nvby5BIOpcuXdJv27ZtE/Xr1xfLly8XR48eFUePHhXLly8XDRs2FNu2bZM7qiScnZ31C5k96cyZM8LJyUmGRNJydHQU586dK7D/3LlzwtHRUYZEJBcWKBIx5w9Vz549xfPPPy8OHz6sv2394cOHxQsvvCB69eolczppVKlSRb9i5ZNOnTolqlSpIkMiaQUHB4s6deqIzZs3i6tXr4qrV6+KzZs3i7p164p33nlH7niSMfdCUwghqlWrVmixtW3bNlGtWrWKDyQxV1dXsWrVqgL7V61aJVxcXGRIRHLhHBSJ2NraIi4uDg0bNjTYHxcXhypVqsiUShqrVq3CkCFD0LZtW1hbWwMAcnNzERgYiO+++07mdNJo2rQpZs+ejZUrV+p/XjqdDrNnz0bTpk1lTld+y5cvx9SpU/H2228jJycHAGBlZYURI0aY/JDAk44fP15ggjfwaNL3qVOnZEgkvWHDhmH48OG4cOEC2rVrB+DRcPLcuXMxbNgwmdOVX0hICN59910kJiYavL9Vq1bh448/ljkdVSTOQZHI3LlzER4ejpEjRxb6oXr//fdlTlh+58+fx+nTpyGEQNOmTdGoUSO5I0nm0KFD6NWrF/Lz8/Vr1hw9ehQqlQq//vor2rZtK3NCaWRmZuKff/6BEAINGjSAnZ2d3JEk9eKLL6Jp06YFCs3hw4fj9OnTJjdZvTD5+fn4z3/+gy+//FK/OrC7uzsmT56sv92Gqfvpp5/w5Zdf4vTp0wAe/QExefJkDBgwQOZkVJFYoEiosnyo8vLycPz4cXh6euqvCDEHDx48wPr163HmzBkIIdCsWTMMHjzY7L7EAeDevXvYs2cPmjRpgiZNmsgdRzLmXmjm5uZiw4YNCAwMhJubG+7duwcAcHR0lDkZkfRYoFCJHt8DY8SIEcjLy4Ovry/279+PqlWr4tdff4Wfn5/cEakEAwYMQOfOnTFhwgRkZWWhZcuWuHTpEoQQ2LRpE/r37y93RMmYe6FZtWpVnD59Gp6ennJHMars7GykpaUhPz/fYH+dOnVkSkQVjXNQJGaOH6r//e9/ePvttwE8uoPxxYsXcebMGaxbtw4zZ87EX3/9JXPC8lu7di2cnZ3x6quvAgCmT5+Ob775Bs2aNcMPP/xg8l8Ge/fuxcyZMwEA27ZtgxACd+/exdq1azF79myzKlCqVq2K0aNHyx3DaHx8fHDkyBGT//9kUc6fP4/hw4dj//79BvvF/7+M3BwuFafSYQ+KRMz5Q1WlShVcuHABtWrVwujRo1G1alUsXrwYycnJaNmypb6b2ZQ1btwYy5YtQ5cuXXDgwAF07doVixcvxq+//gorKyuTXJ3zSba2tjh37hxq166Nd955Bx4eHpg7dy6uXLmCZs2aISMjQ+6Iz2zHjh0ICgqCtbU1duzYUWzb3r17V1Aq49m8eTPef/99TJkyBa1bty7QM2Sqi9E91rFjR1hZWeH999+Hu7t7gbVteF+zyoM9KBIZOnQorKys8Ouvvxb6oTJlrq6uOHXqFNzd3REZGYmlS5cCeNSVbg4T8gDg6tWraNCgAQBg+/bteP311zF69Gh07NjRLIawateujQMHDsDJyQmRkZHYtGkTgEeryZr6VWZ9+/ZFamoqXFxcil1Qz9T/UHhs4MCBAAzvgfXkQnWm/h6TkpKQmJhoVnOj6NmwQJGIOX+ohg0bhgEDBugLr+7duwMADh48aDbv197eHrdv30adOnWwa9cuTJkyBcCj3qOsrCyZ05VfSEgI3nrrLdjb28PT01NfdO3duxctWrSQN1w5PTmc+vTQqjky1ZWbS6tZs2b4999/5Y5BCsACRSLm/KEKDw+Ht7c3rl69ijfeeANqtRoAYGlpaRaXTwNA9+7dMXLkSLRq1Qrnzp3Tz0U5efIk6tatK284CYwbNw4+Pj64cuUKunfvDgsLCwBAvXr1MHv2bJnTSSMnJwcBAQFYsWKFWV0C/zRznXvy2Lx58zB9+nTMmTMHLVq00K+99BivWKo8OAdFInv27MGHH35o9h+qhw8fmvyQQGHu3r2LDz/8EFevXsW7776LHj16AHh0/w8bGxv9BFNTlJOTg8aNG+PXX39Fs2bN5I5jVDVr1sT+/fsLLJhoTtatW1fs8XfeeaeCkhjH4+L56WFycxnCotJjgSIRc/5Q5eXlYc6cOVi+fDlu3ryJc+fOoV69evjoo49Qt25djBgxQu6IVILnnnsOu3fvNotVcYvz3nvvwdraGnPnzpU7itE8vfZQTk4OHjx4ABsbG1StWhV37tyRKZk0YmNjiz3u6+tbQUlIbhzikciTt7Y3N59//jnWrl2L+fPnY9SoUfr9LVq0wKJFi8ymQNm3bx9WrFiBixcvYvPmzXjuuefw/fffw8vLC506dZI7XrlMnDgR8+bNw3fffQcrK/P92GdnZ+O7775DVFQUXnrppQJXuCxcuFCmZNJJT08vsO/8+fN49913MW3aNBkSSYsFCD3GHhQqUYMGDbBixQp07doVDg4OOHr0KOrVq4czZ86gffv2hf7CNDVbtmxBcHAw3nrrLXz//fc4deoU6tWrh6VLl+LXX3/Fb7/9JnfEcnnttdfw559/wt7eHi1atCjwxW3ql1FfvHgRdevWRdeuXYtso1KpsGfPngpMVbESEhLw9ttv48yZM3JHKZe9e/cWe7xz584VlITkZr5/Ssng7t27WLlyJU6fPg2VSoVmzZph+PDh0Gg0ckcrl+vXr+svwX1Sfn6+/sZzpm727NlYvnw53nnnHf0luADQoUMHfPrppzImk0a1atXMajG2pzVs2BApKSn6nsyBAwfiq6++gqurq8zJKo6lpSVu3Lghd4xyK+yy/ieHzk15uJzKhgWKRBISEhAYGAhbW1u0bdsWQggsXLgQn3/+OXbt2oUXX3xR7ojPrHnz5ti3b1+Bqwc2b96MVq1ayZRKWmfPni30LzNHR0fcvXu34gNJbPXq1XJHMKqnO4J///13ZGZmypTGuJ5ejE4IgZSUFCxZsgQdO3aUKZV0nu6RzcnJwZEjR/DRRx/h888/lykVyYEFikSmTJmC3r1749tvv9WP8efm5mLkyJEICQkpsdtSyWbNmoXg4GBcv34d+fn52Lp1K86ePYt169bh119/lTueJNzd3XHhwoUClxTHxcWhXr168oSiZ2bOI9dPL0anUqlQs2ZNdOnSBQsWLJAnlIQK63Hu3r071Go1pkyZgsTERBlSkRxYoEgkISHBoDgBACsrK0yfPh0vvfSSjMnKr1evXvjxxx8xZ84cqFQqfPzxx3jxxRfxyy+/6BdtM3VjxozB5MmTsWrVKqhUKty4cQMHDhzA1KlT8fHHH8sdr9y8vLyKXd344sWLFZhGeiqVqsD7M6fVnJ9UGRajK0zNmjVx9uxZuWNQBWKBIhFHR0dcuXKlwMqqV69ehYODg0yppBMYGIjAwEC5YxjN9OnTodVq4e/vj4cPH6Jz585Qq9WYOnUqJkyYIHe8cgsJCTF4/LjbPDIy0iyu/BBCYOjQofpFBB8+fIixY8ea3WRgAPj0008xdepUVK1a1WB/VlYWvvjiC5MvqI8dO2bw+PEQ1ty5c3kfnkqGV/FIZNKkSdi2bRv+85//oEOHDlCpVIiLi8O0adPQv39/LF68WO6IVAoPHjzAqVOnkJ+fj2bNmsHe3l7uSEb13//+FwkJCSY/R2XYsGGlamfq7xN4NBk2JSUFLi4uBvtv374NFxcXk59EamFhob+30JPatWuHVatWmc3tNahkLFAkkp2djWnTpmH58uXIzc2FEAI2NjZ49913MXfuXP1fdqbo8S+Mopj6L8TK7OLFi3jhhRfM4o7UlYWFhQVu3ryJmjVrGuzfs2cPBg4ciFu3bsmUTBqXL182eGxhYYGaNWua5QrWVDwO8UjExsYGX375JSIiIvDPP/9ACIEGDRoU6IY1Rdu2bTN4/Hh4YO3atfjkk09kSiWtzMxMzJ07F3/++SfS0tIKjPOb+hyNovzvf/+Dk5OT3DGoFKpXr66fa9OoUaMCl95mZGRg7NixMiYsn4MHD+LOnTsICgrS71u3bh1mzZqFzMxM9O3bF19//bVJ/7FHZcMCpZyGDx9eqnarVq0ychLj6dOnT4F9r7/+Opo3b44ff/zRLFaSHTlyJGJjYxEcHKy/a7M5adWqlcF7EkIgNTUVt27dwtKlS2VMRqW1ePFiCCEwfPhwfPLJJwZXu9jY2KBu3bpo3769jAnLJzw8HH5+fvoC5fjx4xgxYgSGDh2Kpk2b4osvvoCHhwfCw8PlDUoVhkM85WRhYQFPT0+0atWq2Esbn+6FMAf//PMPnn/+ebNYb6JatWrYuXOnWawjUZine7oed5v7+flxTN/ExMbGokOHDgVuSGrq3N3d8csvv+ivepw5cyZiY2MRFxcH4NG6S7NmzcKpU6fkjEkViD0o5TR27Fhs2rQJFy9exPDhw/H2229Xii7zrKwsfP3116hVq5bcUSRRvXp1s/65zZo1S+4IJJEn71WTlZVVYDVnU71zenp6usHKv7Gxsfq7igNAmzZtcPXqVTmikUws5A5g6pYuXYqUlBTMmDEDv/zyC2rXro0BAwbgjz/+MPnFooYPH4579+7pv7wfb9WrV4eDgwNWrVqFL774Qu6Ykvjss8/w8ccf48GDB3JHMZr8/HycO3cOcXFx2Lt3r8FGpuPBgweYMGECXFxcYG9vj+rVqxtspsrV1RXJyckAHl108PfffxsMWd2/f9/seo2oeBzikdjly5exZs0arFu3Djk5OTh16pTJXqr6+HLG3377zWD+wuPhAR8fH5P+hfikVq1a6Sc3161bt8Avwr///lumZNKIj4/H4MGDcfny5QKFs0ql4pVYJmT8+PGIjo7Gp59+infeeQf//e9/cf36daxYsQJz587FW2+9JXfEZzJmzBgcP34c8+bNw/bt27F27VrcuHEDNjY2AIANGzZg8eLFOHz4sMxJqaJwiEdij2fZCyFMfsXHx19kQ4cOlTdIBXh6+XBzM3bsWLz00kvYuXOnWU4Crkx++eUXrFu3Dn5+fhg+fDhefvllNGjQAJ6entiwYYPJFiizZ89Gv3794OvrC3t7e6xdu1ZfnACPLjQICAiQMSFVNPagSECn02Hr1q1YtWoV4uLi0LNnTwwbNgw9evSAhYXpjqIVtd4CmR47OzscPXq00LtSk2mxt7fHyZMn4enpiVq1amHr1q1o27YtkpOT0aJFC2RkZMgdsVy0Wi3s7e1haWlpsP/OnTuwt7c3KFrIvLEHpZzGjRuHTZs2oU6dOhg2bBg2bdqEGjVqyB1LMk+vt1CYO3fuVFAaelY+Pj64cOECCxQzUK9ePVy6dAmenp5o1qwZfvrpJ7Rt2xa//PILqlWrJne8civsZoEAzHoSOxWOPSjlZGFhgTp16hRYZ+JppngPEAsLCyxevLjIXxiPDRkypIISSevxwlelYYpF2JP3NPnnn3/w4YcfYtq0aWjRokWBOTbPP/98RcejZ7Ro0SJYWlpi0qRJiI6Oxquvvoq8vDzk5uZi4cKFmDx5stwRiSTBAqWchg4dWqovOVO8B4iFhQVSU1ML3PPDXKxdu1b/79u3b2P27NkIDAzUXzlw4MAB/PHHH/joo48wZcoUuWI+s6LuafLY42OcJGvarly5goSEBNSvX5830yOzwgKFilTUTcnMUf/+/eHv71/gzsVLlizB7t27sX37dnmClcPT9zQpjqenpxGTkBS4FDxVNixQqEjm3oPyJHt7eyQlJRWYo3H+/Hm0atXKZCceDh8+HF9++SUcHBzkjkLlFBQUBD8/P8yYMQPAo6XgX3zxRQwdOhTNmjXD/PnzMWbMGC4FT2bDdC8xIaPLz8+vFMUJANSoUaPQ2xFs377dpCc9r127FllZWXLHIAkkJSWha9eu+sebNm2Cj48Pvv32W0yZMgVfffUVfvrpJxkTEkmLV/EQ4dG9akaMGIGYmBj9HJT4+HhERkbiu+++kznds2MHqfngUvBU2bAHhQiPJjvv378f1apVw9atW7FlyxZoNBr89ddfJr9QHRdlMw9cCp4qG/agEP1/Pj4+2LBhg9wxJMe1bMxDjx498P777+uXgq9atSpefvll/fFjx46hfv36MiYkkhYLFKL/759//sHq1atx8eJFLF68GC4uLoiMjETt2rXRvHlzueM9s08++aTEtWxI+bgUPFU2vIqHCI/G84OCgtCxY0fs3bsXp0+fRr169TB//nwcOnQI//vf/+SO+Ewq05VYlQWXgqfKgnNQiAC8//77mD17NqKiogx+wfv7++PAgQMyJisfzj8xPxqNpkBxAjxaCp7FCZkTFihEeLSmxGuvvVZgf82aNXH79m0ZEkmDHaREZKpYoBABqFatGlJSUgrsP3LkCJ577jkZEkmjMq1lQ0TmhQUKEYDBgwdjxowZSE1NhUqlQn5+Pv766y9MnToV77zzjtzxiIgqHU6SpUrtwoULaNCgAXJycjBs2DD88MMPEELAysoKeXl5GDx4MNasWVPomD8RERkPCxSq1CwsLPDcc8/B398f/v7+8PX1xd9//438/Hy0atUKDRs2lDsiEVGlxHVQqFKLjY1FbGwsYmJiMGHCBDx8+BB16tRBly5dkJ2djapVq5r0HBQiIlPFHhSi/y8nJwcHDhxATEwMYmJiEB8fD51OhwYNGuDs2bNyxyMiqlRYoBA9JSsrC3Fxcfjjjz/w7bffIiMjA3l5eXLHIiKqVFigUKX38OFD7N+/H9HR0YiJicHhw4fh5eUFX19fdO7cGb6+vhzmISKqYCxQqFLz9fXF4cOHUb9+fX0x4uvra3BbeyIiqngsUKhSs7a2hru7O/r27Qs/Pz907twZzs7OcsciIqr0WKBQpZaZmYl9+/YhJiYG0dHRSEpKQqNGjeDr6ws/Pz/4+vqiZs2acsckIqp0WKAQPeH+/fuIi4vTz0c5evQoGjZsiBMnTsgdjYioUuFS90RPsLOzg5OTE5ycnFC9enVYWVnh9OnTcsciIqp02INClVp+fj4SEhL0Qzx//fUXMjMzDVaX9ff3h6enp9xRiYgqFRYoVKk5OjoiMzMT7u7u8PPzg5+fH/z9/VG/fn25oxERVWosUKhSW7FiBfz9/dGoUSO5oxAR0RNYoBAREZHicJIsERERKQ4LFCIiIlIcFihERESkOCxQiIiISHFYoBA9o6FDh0KlUkGlUsHa2hqurq7o3r07Vq1ahfz8/ALtAwICYGlpifj4eACATqdD8+bNMXr06AJtp0+fDk9PT9y7d6/EHFlZWZg1axYaN24MtVoNZ2dnvP766zh58qRBu/DwcH3eJ7fdu3cXe/7IyEioVCqkpqYa7Hdzc0Pt2rUN9l27dg0qlQq7du0CAPj5+RX6mmPHjtU/p7DjKpUKmzZtAgDExMRApVLh7t27+ufcuHED3t7e6NSpk8F+IjIfLFCIyqFHjx5ISUnBpUuX8Pvvv8Pf3x+TJ09Gz549kZubq2935coVHDhwABMmTMDKlSsBAGq1GuvWrcOaNWsQGRmpbxsfH49FixZhzZo1cHR0LPb1dTodunXrhlWrVuGzzz7DuXPn8NtvvyEvLw8+Pj76Yuix5s2bIyUlxWDr3Llzsa/RqVMnWFlZISYmRr/v9OnTePjwIe7du4cLFy7o90dHR8Pa2hodO3bU7xs1alSB15w/f77Ba6xevbpAm759+xaa559//kGnTp1Qp04d7Nq1C9WqVSs2PxGZJiu5AxCZMrVaDTc3NwDAc889hxdffBHt2rVD165dsWbNGowcORLAoy/gnj174t1330Xbtm2xePFi2NnZoXXr1pg5cyZGjhyJEydOoEqVKhg2bBjGjx8Pf3//El9/8eLFOHDgAI4cOYKWLVsCADw9PbFlyxb4+PhgxIgROHHiBFQqFQDAyspKn7e07O3t0aZNG8TExGDQoEEAHvVqdOrUCUIIxMTEoEGDBvr9bdu2hZ2dnf75VatWLfE1q1WrVqpcx44dQ2BgIPz8/LBu3TpYW1uX6b0QkelgDwqRxLp06YKWLVti69atAAAhBFavXo23334bTZo0QaNGjfDTTz/p28+cORPu7u6YNGkSPvzwQwBAREREqV5r48aN6N69u744eczCwgJTpkzBqVOncPTo0XK/J39/f0RHR+sfR0dH6+/2/PT+0hRWz2L//v3w9fVFv379sGHDBhYnRGaOBQqRETRp0gSXLl0CAOzevRsPHjxAYGAgAODtt9/WD/MAj3o11q1bh82bN+Prr7/GunXrYGtrW6rXOXfuHJo2bVroscf7z507p993/Phx2Nvb67e2bduW6nX8/Pxw7tw5pKSkAABiY2Ph6+sLX19f/dDP1atXkZycXKBAWbp0qcFr2tvbY+3atQZt3nzzzQJtLl68aNDmtddeQ69evfDf//4XFhb81UVk7jjEQ2QEQgj9sMrKlSsxcOBAWFk9+ri9+eabmDZtGs6ePYvGjRsDeFRM9O/fH3fv3kWbNm0kywBAnwMAGjdujB07dugfq9XqUp2rY8eOsLGxQUxMDFq2bImsrCy8+OKLEELg3r17OH/+PA4cOAC1Wo0OHToYPPett97CzJkzDfa5uLgYPF60aBG6detmsO/pCbh9+vTBtm3bsG/fPrz88sulyk1EposFCpERnD59Gl5eXrhz5w62b9+OnJwcLFu2TH88Ly8Pq1atwrx58/T7rKys9EVMaTVq1AinTp0q9NiZM2cAAA0bNtTvs7Gx0c8XKYuqVauibdu2iI6Oxp07d9CpUydYWloCADp06IDo6GgcOHAA7du3R5UqVQyeq9FoSnxNNze3EtusWLECM2bMQFBQEHbu3AlfX98yvw8iMh3sJyWS2J49e3D8+HH0798fGzZsQK1atXD06FEkJSXpt8WLF2Pt2rUGV/o8i0GDBmH37t0F5pnk5+dj0aJFaNasWYH5Kc/K398fMTExiImJgZ+fn37/42GemJgYo80/AR71BK1YsQLBwcF45ZVXDK4qIiLzwx4UonLQ6XRITU1FXl4ebt68icjISERERKBnz55455130Lp1a7z++uvw9vY2eJ6npydmzJiBnTt3ok+fPs/8+lOmTMHPP/+MXr16YcGCBfDx8cHNmzcxZ84cnD59Grt37zYY4ikPf39/fPbZZ0hJScHUqVP1+319fTF37lzcv3+/0ALlwYMHBdZQUavVqF69uv7x3bt3C7RxcHAwuBoIeFSkLF26FJaWlnj11Vfxyy+/oEuXLlK8PSJSGkFEz2TIkCECgAAgrKysRM2aNUW3bt3EqlWrRF5enkhISBAAxKFDhwp9fq9evUSvXr0MztenT58y58jMzBQffvihaNCggbC2thZOTk6if//+4vjx4wbtZs2aJVq2bFnm8z+WlZUl1Gq1sLe3Fzk5Ofr9Op1OVK1aVdja2gqdTmfwHF9fX/1/oye3wMBAfZvCjgMQERERQgghoqOjBQCRnp5ucO5JkyYJW1tbERUV9czviYiUSyXE/59JR0RERKQQnINCREREisMChUjBmjdvXmB9kMfbhg0bJHudol7D3t4e+/btk+x1iIhKi0M8RAp2+fJl5OTkFHrM1dUVDg4OkrzOk/fTedpzzz1X6oXjiIikwgKFiIiIFIdDPERERKQ4LFCIiIhIcVigEBERkeKwQCEiIiLFYYFCREREisMChYiIiBSHBQoREREpDgsUIiIiUpz/ByVb5DNjqVcEAAAAAElFTkSuQmCC",
2311 |       "text/plain": [
2312 |        "<Figure size 640x480 with 2 Axes>"
2313 |       ]
2314 |      },
2315 |      "metadata": {},
2316 |      "output_type": "display_data"
2317 |     }
2318 |    ],
2319 |    "source": [
2320 |     "sns.heatmap(week_and_hour, cmap=sns.cubehelix_palette(as_cmap=True));"
2321 |    ]
2322 |   },
2323 |   {
2324 |    "cell_type": "code",
2325 |    "execution_count": 44,
2326 |    "id": "bb5d8f47",
2327 |    "metadata": {},
2328 |    "outputs": [],
2329 |    "source": [
2330 |     "# In which months were the number of crimes below average? \n",
2331 |     "# In which months on average did the most crimes occur?"
2332 |    ]
2333 |   },
2334 |   {
2335 |    "cell_type": "code",
2336 |    "execution_count": 45,
2337 |    "id": "e43ec5b7",
2338 |    "metadata": {},
2339 |    "outputs": [
2340 |     {
2341 |      "name": "stdout",
2342 |      "output_type": "stream",
2343 |      "text": [
2344 |       "The average number of crimes is 7976.25\n"
2345 |      ]
2346 |     },
2347 |     {
2348 |      "data": {
2349 |       "text/html": [
2350 |        "<style type=\"text/css\">\n",
2351 |        "#T_f3483_row0_col1, #T_f3483_row0_col3, #T_f3483_row1_col1, #T_f3483_row1_col2, #T_f3483_row1_col3, #T_f3483_row2_col3, #T_f3483_row3_col3, #T_f3483_row5_col0, #T_f3483_row8_col3, #T_f3483_row10_col0, #T_f3483_row10_col1, #T_f3483_row10_col2, #T_f3483_row11_col1, #T_f3483_row11_col2 {\n",
2352 |        "  color: blue;\n",
2353 |        "}\n",
2354 |        "</style>\n",
2355 |        "<table id=\"T_f3483\">\n",
2356 |        "  <thead>\n",
2357 |        "    <tr>\n",
2358 |        "      <th class=\"index_name level0\" >YEAR</th>\n",
2359 |        "      <th id=\"T_f3483_level0_col0\" class=\"col_heading level0 col0\" >2015</th>\n",
2360 |        "      <th id=\"T_f3483_level0_col1\" class=\"col_heading level0 col1\" >2016</th>\n",
2361 |        "      <th id=\"T_f3483_level0_col2\" class=\"col_heading level0 col2\" >2017</th>\n",
2362 |        "      <th id=\"T_f3483_level0_col3\" class=\"col_heading level0 col3\" >2018</th>\n",
2363 |        "    </tr>\n",
2364 |        "    <tr>\n",
2365 |        "      <th class=\"index_name level0\" >MONTH</th>\n",
2366 |        "      <th class=\"blank col0\" >&nbsp;</th>\n",
2367 |        "      <th class=\"blank col1\" >&nbsp;</th>\n",
2368 |        "      <th class=\"blank col2\" >&nbsp;</th>\n",
2369 |        "      <th class=\"blank col3\" >&nbsp;</th>\n",
2370 |        "    </tr>\n",
2371 |        "  </thead>\n",
2372 |        "  <tbody>\n",
2373 |        "    <tr>\n",
2374 |        "      <th id=\"T_f3483_level0_row0\" class=\"row_heading level0 row0\" >1</th>\n",
2375 |        "      <td id=\"T_f3483_row0_col0\" class=\"data row0 col0\" >nan</td>\n",
2376 |        "      <td id=\"T_f3483_row0_col1\" class=\"data row0 col1\" >7835.000000</td>\n",
2377 |        "      <td id=\"T_f3483_row0_col2\" class=\"data row0 col2\" >7991.000000</td>\n",
2378 |        "      <td id=\"T_f3483_row0_col3\" class=\"data row0 col3\" >7782.000000</td>\n",
2379 |        "    </tr>\n",
2380 |        "    <tr>\n",
2381 |        "      <th id=\"T_f3483_level0_row1\" class=\"row_heading level0 row1\" >2</th>\n",
2382 |        "      <td id=\"T_f3483_row1_col0\" class=\"data row1 col0\" >nan</td>\n",
2383 |        "      <td id=\"T_f3483_row1_col1\" class=\"data row1 col1\" >7307.000000</td>\n",
2384 |        "      <td id=\"T_f3483_row1_col2\" class=\"data row1 col2\" >7408.000000</td>\n",
2385 |        "      <td id=\"T_f3483_row1_col3\" class=\"data row1 col3\" >6937.000000</td>\n",
2386 |        "    </tr>\n",
2387 |        "    <tr>\n",
2388 |        "      <th id=\"T_f3483_level0_row2\" class=\"row_heading level0 row2\" >3</th>\n",
2389 |        "      <td id=\"T_f3483_row2_col0\" class=\"data row2 col0\" >nan</td>\n",
2390 |        "      <td id=\"T_f3483_row2_col1\" class=\"data row2 col1\" >8199.000000</td>\n",
2391 |        "      <td id=\"T_f3483_row2_col2\" class=\"data row2 col2\" >8179.000000</td>\n",
2392 |        "      <td id=\"T_f3483_row2_col3\" class=\"data row2 col3\" >7768.000000</td>\n",
2393 |        "    </tr>\n",
2394 |        "    <tr>\n",
2395 |        "      <th id=\"T_f3483_level0_row3\" class=\"row_heading level0 row3\" >4</th>\n",
2396 |        "      <td id=\"T_f3483_row3_col0\" class=\"data row3 col0\" >nan</td>\n",
2397 |        "      <td id=\"T_f3483_row3_col1\" class=\"data row3 col1\" >8101.000000</td>\n",
2398 |        "      <td id=\"T_f3483_row3_col2\" class=\"data row3 col2\" >8069.000000</td>\n",
2399 |        "      <td id=\"T_f3483_row3_col3\" class=\"data row3 col3\" >7916.000000</td>\n",
2400 |        "    </tr>\n",
2401 |        "    <tr>\n",
2402 |        "      <th id=\"T_f3483_level0_row4\" class=\"row_heading level0 row4\" >5</th>\n",
2403 |        "      <td id=\"T_f3483_row4_col0\" class=\"data row4 col0\" >nan</td>\n",
2404 |        "      <td id=\"T_f3483_row4_col1\" class=\"data row4 col1\" >8578.000000</td>\n",
2405 |        "      <td id=\"T_f3483_row4_col2\" class=\"data row4 col2\" >8715.000000</td>\n",
2406 |        "      <td id=\"T_f3483_row4_col3\" class=\"data row4 col3\" >8906.000000</td>\n",
2407 |        "    </tr>\n",
2408 |        "    <tr>\n",
2409 |        "      <th id=\"T_f3483_level0_row5\" class=\"row_heading level0 row5\" >6</th>\n",
2410 |        "      <td id=\"T_f3483_row5_col0\" class=\"data row5 col0\" >4188.000000</td>\n",
2411 |        "      <td id=\"T_f3483_row5_col1\" class=\"data row5 col1\" >8558.000000</td>\n",
2412 |        "      <td id=\"T_f3483_row5_col2\" class=\"data row5 col2\" >8985.000000</td>\n",
2413 |        "      <td id=\"T_f3483_row5_col3\" class=\"data row5 col3\" >8834.000000</td>\n",
2414 |        "    </tr>\n",
2415 |        "    <tr>\n",
2416 |        "      <th id=\"T_f3483_level0_row6\" class=\"row_heading level0 row6\" >7</th>\n",
2417 |        "      <td id=\"T_f3483_row6_col0\" class=\"data row6 col0\" >8322.000000</td>\n",
2418 |        "      <td id=\"T_f3483_row6_col1\" class=\"data row6 col1\" >8618.000000</td>\n",
2419 |        "      <td id=\"T_f3483_row6_col2\" class=\"data row6 col2\" >9075.000000</td>\n",
2420 |        "      <td id=\"T_f3483_row6_col3\" class=\"data row6 col3\" >8538.000000</td>\n",
2421 |        "    </tr>\n",
2422 |        "    <tr>\n",
2423 |        "      <th id=\"T_f3483_level0_row7\" class=\"row_heading level0 row7\" >8</th>\n",
2424 |        "      <td id=\"T_f3483_row7_col0\" class=\"data row7 col0\" >8340.000000</td>\n",
2425 |        "      <td id=\"T_f3483_row7_col1\" class=\"data row7 col1\" >8938.000000</td>\n",
2426 |        "      <td id=\"T_f3483_row7_col2\" class=\"data row7 col2\" >9206.000000</td>\n",
2427 |        "      <td id=\"T_f3483_row7_col3\" class=\"data row7 col3\" >8337.000000</td>\n",
2428 |        "    </tr>\n",
2429 |        "    <tr>\n",
2430 |        "      <th id=\"T_f3483_level0_row8\" class=\"row_heading level0 row8\" >9</th>\n",
2431 |        "      <td id=\"T_f3483_row8_col0\" class=\"data row8 col0\" >8411.000000</td>\n",
2432 |        "      <td id=\"T_f3483_row8_col1\" class=\"data row8 col1\" >8521.000000</td>\n",
2433 |        "      <td id=\"T_f3483_row8_col2\" class=\"data row8 col2\" >8940.000000</td>\n",
2434 |        "      <td id=\"T_f3483_row8_col3\" class=\"data row8 col3\" >667.000000</td>\n",
2435 |        "    </tr>\n",
2436 |        "    <tr>\n",
2437 |        "      <th id=\"T_f3483_level0_row9\" class=\"row_heading level0 row9\" >10</th>\n",
2438 |        "      <td id=\"T_f3483_row9_col0\" class=\"data row9 col0\" >8305.000000</td>\n",
2439 |        "      <td id=\"T_f3483_row9_col1\" class=\"data row9 col1\" >8582.000000</td>\n",
2440 |        "      <td id=\"T_f3483_row9_col2\" class=\"data row9 col2\" >8846.000000</td>\n",
2441 |        "      <td id=\"T_f3483_row9_col3\" class=\"data row9 col3\" >nan</td>\n",
2442 |        "    </tr>\n",
2443 |        "    <tr>\n",
2444 |        "      <th id=\"T_f3483_level0_row10\" class=\"row_heading level0 row10\" >11</th>\n",
2445 |        "      <td id=\"T_f3483_row10_col0\" class=\"data row10 col0\" >7818.000000</td>\n",
2446 |        "      <td id=\"T_f3483_row10_col1\" class=\"data row10 col1\" >7922.000000</td>\n",
2447 |        "      <td id=\"T_f3483_row10_col2\" class=\"data row10 col2\" >7935.000000</td>\n",
2448 |        "      <td id=\"T_f3483_row10_col3\" class=\"data row10 col3\" >nan</td>\n",
2449 |        "    </tr>\n",
2450 |        "    <tr>\n",
2451 |        "      <th id=\"T_f3483_level0_row11\" class=\"row_heading level0 row11\" >12</th>\n",
2452 |        "      <td id=\"T_f3483_row11_col0\" class=\"data row11 col0\" >7987.000000</td>\n",
2453 |        "      <td id=\"T_f3483_row11_col1\" class=\"data row11 col1\" >7951.000000</td>\n",
2454 |        "      <td id=\"T_f3483_row11_col2\" class=\"data row11 col2\" >7535.000000</td>\n",
2455 |        "      <td id=\"T_f3483_row11_col3\" class=\"data row11 col3\" >nan</td>\n",
2456 |        "    </tr>\n",
2457 |        "  </tbody>\n",
2458 |        "</table>\n"
2459 |       ],
2460 |       "text/plain": [
2461 |        "<pandas.io.formats.style.Styler at 0x24d5d868e50>"
2462 |       ]
2463 |      },
2464 |      "execution_count": 45,
2465 |      "metadata": {},
2466 |      "output_type": "execute_result"
2467 |     }
2468 |    ],
2469 |    "source": [
2470 |     "# if the value is less than the average crime per month, highlight the value in blue\n",
2471 |     "\n",
2472 |     "avg_crime = crime.groupby(['YEAR', 'MONTH']).count()['INCIDENT_NUMBER'].mean()\n",
2473 |     "print(\"The average number of crimes is \" + str(avg_crime))\n",
2474 |     "\n",
2475 |     "year_and_month = crime.groupby(['MONTH', 'YEAR']).count()['INCIDENT_NUMBER'].unstack()\n",
2476 |     "\n",
2477 |     "def style_negative(v, props=''):\n",
2478 |     "    return props if v < avg_crime else None\n",
2479 |     "s2 = year_and_month.style.applymap(style_negative, props='color:blue;')\\\n",
2480 |     "              .applymap(lambda v: 'opacity: 20%;' if (v < 0.3) and (v > -0.3) else None)\n",
2481 |     "s2"
2482 |    ]
2483 |   },
2484 |   {
2485 |    "cell_type": "code",
2486 |    "execution_count": 46,
2487 |    "id": "4e9cfcfc",
2488 |    "metadata": {
2489 |     "scrolled": true
2490 |    },
2491 |    "outputs": [
2492 |     {
2493 |      "data": {
2494 |       "text/html": [
2495 |        "<style type=\"text/css\">\n",
2496 |        "#T_f3483_row0_col1, #T_f3483_row0_col3, #T_f3483_row1_col1, #T_f3483_row1_col2, #T_f3483_row1_col3, #T_f3483_row2_col3, #T_f3483_row3_col3, #T_f3483_row5_col0, #T_f3483_row8_col3, #T_f3483_row10_col0, #T_f3483_row10_col1, #T_f3483_row10_col2, #T_f3483_row11_col1, #T_f3483_row11_col2 {\n",
2497 |        "  color: blue;\n",
2498 |        "}\n",
2499 |        "#T_f3483_row4_col3, #T_f3483_row7_col1, #T_f3483_row7_col2, #T_f3483_row8_col0 {\n",
2500 |        "  color: white;\n",
2501 |        "  background-color: darkgreen;\n",
2502 |        "}\n",
2503 |        "</style>\n",
2504 |        "<table id=\"T_f3483\">\n",
2505 |        "  <thead>\n",
2506 |        "    <tr>\n",
2507 |        "      <th class=\"index_name level0\" >YEAR</th>\n",
2508 |        "      <th id=\"T_f3483_level0_col0\" class=\"col_heading level0 col0\" >2015</th>\n",
2509 |        "      <th id=\"T_f3483_level0_col1\" class=\"col_heading level0 col1\" >2016</th>\n",
2510 |        "      <th id=\"T_f3483_level0_col2\" class=\"col_heading level0 col2\" >2017</th>\n",
2511 |        "      <th id=\"T_f3483_level0_col3\" class=\"col_heading level0 col3\" >2018</th>\n",
2512 |        "    </tr>\n",
2513 |        "    <tr>\n",
2514 |        "      <th class=\"index_name level0\" >MONTH</th>\n",
2515 |        "      <th class=\"blank col0\" >&nbsp;</th>\n",
2516 |        "      <th class=\"blank col1\" >&nbsp;</th>\n",
2517 |        "      <th class=\"blank col2\" >&nbsp;</th>\n",
2518 |        "      <th class=\"blank col3\" >&nbsp;</th>\n",
2519 |        "    </tr>\n",
2520 |        "  </thead>\n",
2521 |        "  <tbody>\n",
2522 |        "    <tr>\n",
2523 |        "      <th id=\"T_f3483_level0_row0\" class=\"row_heading level0 row0\" >1</th>\n",
2524 |        "      <td id=\"T_f3483_row0_col0\" class=\"data row0 col0\" >nan</td>\n",
2525 |        "      <td id=\"T_f3483_row0_col1\" class=\"data row0 col1\" >7835.000000</td>\n",
2526 |        "      <td id=\"T_f3483_row0_col2\" class=\"data row0 col2\" >7991.000000</td>\n",
2527 |        "      <td id=\"T_f3483_row0_col3\" class=\"data row0 col3\" >7782.000000</td>\n",
2528 |        "    </tr>\n",
2529 |        "    <tr>\n",
2530 |        "      <th id=\"T_f3483_level0_row1\" class=\"row_heading level0 row1\" >2</th>\n",
2531 |        "      <td id=\"T_f3483_row1_col0\" class=\"data row1 col0\" >nan</td>\n",
2532 |        "      <td id=\"T_f3483_row1_col1\" class=\"data row1 col1\" >7307.000000</td>\n",
2533 |        "      <td id=\"T_f3483_row1_col2\" class=\"data row1 col2\" >7408.000000</td>\n",
2534 |        "      <td id=\"T_f3483_row1_col3\" class=\"data row1 col3\" >6937.000000</td>\n",
2535 |        "    </tr>\n",
2536 |        "    <tr>\n",
2537 |        "      <th id=\"T_f3483_level0_row2\" class=\"row_heading level0 row2\" >3</th>\n",
2538 |        "      <td id=\"T_f3483_row2_col0\" class=\"data row2 col0\" >nan</td>\n",
2539 |        "      <td id=\"T_f3483_row2_col1\" class=\"data row2 col1\" >8199.000000</td>\n",
2540 |        "      <td id=\"T_f3483_row2_col2\" class=\"data row2 col2\" >8179.000000</td>\n",
2541 |        "      <td id=\"T_f3483_row2_col3\" class=\"data row2 col3\" >7768.000000</td>\n",
2542 |        "    </tr>\n",
2543 |        "    <tr>\n",
2544 |        "      <th id=\"T_f3483_level0_row3\" class=\"row_heading level0 row3\" >4</th>\n",
2545 |        "      <td id=\"T_f3483_row3_col0\" class=\"data row3 col0\" >nan</td>\n",
2546 |        "      <td id=\"T_f3483_row3_col1\" class=\"data row3 col1\" >8101.000000</td>\n",
2547 |        "      <td id=\"T_f3483_row3_col2\" class=\"data row3 col2\" >8069.000000</td>\n",
2548 |        "      <td id=\"T_f3483_row3_col3\" class=\"data row3 col3\" >7916.000000</td>\n",
2549 |        "    </tr>\n",
2550 |        "    <tr>\n",
2551 |        "      <th id=\"T_f3483_level0_row4\" class=\"row_heading level0 row4\" >5</th>\n",
2552 |        "      <td id=\"T_f3483_row4_col0\" class=\"data row4 col0\" >nan</td>\n",
2553 |        "      <td id=\"T_f3483_row4_col1\" class=\"data row4 col1\" >8578.000000</td>\n",
2554 |        "      <td id=\"T_f3483_row4_col2\" class=\"data row4 col2\" >8715.000000</td>\n",
2555 |        "      <td id=\"T_f3483_row4_col3\" class=\"data row4 col3\" >8906.000000</td>\n",
2556 |        "    </tr>\n",
2557 |        "    <tr>\n",
2558 |        "      <th id=\"T_f3483_level0_row5\" class=\"row_heading level0 row5\" >6</th>\n",
2559 |        "      <td id=\"T_f3483_row5_col0\" class=\"data row5 col0\" >4188.000000</td>\n",
2560 |        "      <td id=\"T_f3483_row5_col1\" class=\"data row5 col1\" >8558.000000</td>\n",
2561 |        "      <td id=\"T_f3483_row5_col2\" class=\"data row5 col2\" >8985.000000</td>\n",
2562 |        "      <td id=\"T_f3483_row5_col3\" class=\"data row5 col3\" >8834.000000</td>\n",
2563 |        "    </tr>\n",
2564 |        "    <tr>\n",
2565 |        "      <th id=\"T_f3483_level0_row6\" class=\"row_heading level0 row6\" >7</th>\n",
2566 |        "      <td id=\"T_f3483_row6_col0\" class=\"data row6 col0\" >8322.000000</td>\n",
2567 |        "      <td id=\"T_f3483_row6_col1\" class=\"data row6 col1\" >8618.000000</td>\n",
2568 |        "      <td id=\"T_f3483_row6_col2\" class=\"data row6 col2\" >9075.000000</td>\n",
2569 |        "      <td id=\"T_f3483_row6_col3\" class=\"data row6 col3\" >8538.000000</td>\n",
2570 |        "    </tr>\n",
2571 |        "    <tr>\n",
2572 |        "      <th id=\"T_f3483_level0_row7\" class=\"row_heading level0 row7\" >8</th>\n",
2573 |        "      <td id=\"T_f3483_row7_col0\" class=\"data row7 col0\" >8340.000000</td>\n",
2574 |        "      <td id=\"T_f3483_row7_col1\" class=\"data row7 col1\" >8938.000000</td>\n",
2575 |        "      <td id=\"T_f3483_row7_col2\" class=\"data row7 col2\" >9206.000000</td>\n",
2576 |        "      <td id=\"T_f3483_row7_col3\" class=\"data row7 col3\" >8337.000000</td>\n",
2577 |        "    </tr>\n",
2578 |        "    <tr>\n",
2579 |        "      <th id=\"T_f3483_level0_row8\" class=\"row_heading level0 row8\" >9</th>\n",
2580 |        "      <td id=\"T_f3483_row8_col0\" class=\"data row8 col0\" >8411.000000</td>\n",
2581 |        "      <td id=\"T_f3483_row8_col1\" class=\"data row8 col1\" >8521.000000</td>\n",
2582 |        "      <td id=\"T_f3483_row8_col2\" class=\"data row8 col2\" >8940.000000</td>\n",
2583 |        "      <td id=\"T_f3483_row8_col3\" class=\"data row8 col3\" >667.000000</td>\n",
2584 |        "    </tr>\n",
2585 |        "    <tr>\n",
2586 |        "      <th id=\"T_f3483_level0_row9\" class=\"row_heading level0 row9\" >10</th>\n",
2587 |        "      <td id=\"T_f3483_row9_col0\" class=\"data row9 col0\" >8305.000000</td>\n",
2588 |        "      <td id=\"T_f3483_row9_col1\" class=\"data row9 col1\" >8582.000000</td>\n",
2589 |        "      <td id=\"T_f3483_row9_col2\" class=\"data row9 col2\" >8846.000000</td>\n",
2590 |        "      <td id=\"T_f3483_row9_col3\" class=\"data row9 col3\" >nan</td>\n",
2591 |        "    </tr>\n",
2592 |        "    <tr>\n",
2593 |        "      <th id=\"T_f3483_level0_row10\" class=\"row_heading level0 row10\" >11</th>\n",
2594 |        "      <td id=\"T_f3483_row10_col0\" class=\"data row10 col0\" >7818.000000</td>\n",
2595 |        "      <td id=\"T_f3483_row10_col1\" class=\"data row10 col1\" >7922.000000</td>\n",
2596 |        "      <td id=\"T_f3483_row10_col2\" class=\"data row10 col2\" >7935.000000</td>\n",
2597 |        "      <td id=\"T_f3483_row10_col3\" class=\"data row10 col3\" >nan</td>\n",
2598 |        "    </tr>\n",
2599 |        "    <tr>\n",
2600 |        "      <th id=\"T_f3483_level0_row11\" class=\"row_heading level0 row11\" >12</th>\n",
2601 |        "      <td id=\"T_f3483_row11_col0\" class=\"data row11 col0\" >7987.000000</td>\n",
2602 |        "      <td id=\"T_f3483_row11_col1\" class=\"data row11 col1\" >7951.000000</td>\n",
2603 |        "      <td id=\"T_f3483_row11_col2\" class=\"data row11 col2\" >7535.000000</td>\n",
2604 |        "      <td id=\"T_f3483_row11_col3\" class=\"data row11 col3\" >nan</td>\n",
2605 |        "    </tr>\n",
2606 |        "  </tbody>\n",
2607 |        "</table>\n"
2608 |       ],
2609 |       "text/plain": [
2610 |        "<pandas.io.formats.style.Styler at 0x24d5d868e50>"
2611 |       ]
2612 |      },
2613 |      "execution_count": 46,
2614 |      "metadata": {},
2615 |      "output_type": "execute_result"
2616 |     }
2617 |    ],
2618 |    "source": [
2619 |     "# Use apply to highlight the maximum in a column in darkgreen\n",
2620 |     "\n",
2621 |     "def highlight_max(s, props=''):\n",
2622 |     "    return np.where(s == np.nanmax(s.values), props, '')\n",
2623 |     "s2.apply(highlight_max, props='color:white;background-color:darkgreen', axis=0)"
2624 |    ]
2625 |   },
2626 |   {
2627 |    "cell_type": "code",
2628 |    "execution_count": 47,
2629 |    "id": "3d7bb38f",
2630 |    "metadata": {
2631 |     "scrolled": false
2632 |    },
2633 |    "outputs": [],
2634 |    "source": [
2635 |     "# Question 4 - In which districts were the most crimes commmitted on yearly basis? \n",
2636 |     "# Try and use everything you've learned in this video to answer this question.\n",
2637 |     "# Feel free to use functions, tables and other visuals."
2638 |    ]
2639 |   }
2640 |  ],
2641 |  "metadata": {
2642 |   "kernelspec": {
2643 |    "display_name": "Python 3 (ipykernel)",
2644 |    "language": "python",
2645 |    "name": "python3"
2646 |   },
2647 |   "language_info": {
2648 |    "codemirror_mode": {
2649 |     "name": "ipython",
2650 |     "version": 3
2651 |    },
2652 |    "file_extension": ".py",
2653 |    "mimetype": "text/x-python",
2654 |    "name": "python",
2655 |    "nbconvert_exporter": "python",
2656 |    "pygments_lexer": "ipython3",
2657 |    "version": "3.10.6"
2658 |   }
2659 |  },
2660 |  "nbformat": 4,
2661 |  "nbformat_minor": 5
2662 | }
2663 | 


--------------------------------------------------------------------------------