└── FinalProject_chitra66.ipynb


/FinalProject_chitra66.ipynb:
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  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "markdown",
  5 |    "metadata": {},
  6 |    "source": [
  7 |     "# COGS 108 - Final Project "
  8 |    ]
  9 |   },
 10 |   {
 11 |    "cell_type": "markdown",
 12 |    "metadata": {},
 13 |    "source": [
 14 |     "# Overview"
 15 |    ]
 16 |   },
 17 |   {
 18 |    "cell_type": "markdown",
 19 |    "metadata": {},
 20 |    "source": [
 21 |     "One important of interacting with people, especially in a society where interacting with people over food is important for personal and professional relationships, takes many forms, including eating at restaurants. When we go out with our friends, families, and colleagues it is crucial that we go to restaurants that are safe and healthy. One way to ensure that our restaurants are inspected are through random health inspections that are unique to each county; in these random health inspections, many things- such as temperature, hygiene, meat/food processing- are checked to make sure that they follow CDC and FDA guidelines. Customer ratings and health inspections are two of the ways that restaurants are kept accountable; this is why I wanted to know if publicizing the health inspection scores on a platform, like Yelp, would improve health in restaurants. After conducting an analysis on this topic, the data does not suggest that publicizing ratings makes restaurants healthier in the future. "
 22 |    ]
 23 |   },
 24 |   {
 25 |    "cell_type": "markdown",
 26 |    "metadata": {},
 27 |    "source": [
 28 |     "# Name & GitHub\n",
 29 |     "\n",
 30 |     "- Name: Chitra Kulkarni\n",
 31 |     "- GitHub Username: chitra66"
 32 |    ]
 33 |   },
 34 |   {
 35 |    "cell_type": "markdown",
 36 |    "metadata": {},
 37 |    "source": [
 38 |     "# Research Question"
 39 |    ]
 40 |   },
 41 |   {
 42 |    "cell_type": "markdown",
 43 |    "metadata": {},
 44 |    "source": [
 45 |     "Does publicizing the health inspection score on platforms, like Yelp, improve the restaurant's overall health (as seen in future inspections) in popular cities such as Raleigh, North Carolina?"
 46 |    ]
 47 |   },
 48 |   {
 49 |    "cell_type": "markdown",
 50 |    "metadata": {},
 51 |    "source": [
 52 |     "## Background and Prior Work"
 53 |    ]
 54 |   },
 55 |   {
 56 |    "cell_type": "markdown",
 57 |    "metadata": {},
 58 |    "source": [
 59 |     "To begin this process, the first thing to research is to look at the restaurant health inspection processes in Raleigh, North Carolina, located in Wake County. In Wake County, restaurants are inspected one to four times a year, depending on what they serve and their food processing [1]. In NC, the grading of food establishments is based on a system of scoring, in which a score of 90 or higher will receive a grade of A, between 80-90 will receive a B, between 70-80 will receive a C; if an establishment receives a score of less than 70, their permit is immediately revoked [2]. In NC, there are 4 risk categories which determine the frequency of the inspections: risk category 1 is for establishments with no potentially hazardous foods, risk category 2 is for establishments with no more than 2 potentially hazardous foods, risk category 3 is for establishments with no more than 3 potentially hazardous foods, and risk category 4 is for establishments with unlimited number of potentially hazardous foods [3]. Risk factors, according to the definitions put forth by the CDC and FDA, are \"food preparation practices and employee behaviors most commonly reported to the CDC as contributing factors in foodborne illness outbreaks. [3]\"\n",
 60 |     "\n",
 61 |     "In an attempt to provide more information to help their customers, Yelp started adding health inspection scores onto their website and apps. This move was based on prior research done on restaurants in LA where restaurants would have to post grade cards in their window. This new rule resulted in a decrease in foodborne illnesses for over two years in the LA area [4]. Since Raleigh, NC is a busy city because of tourism, Yelp started adding restaurant health sanitation scores from March of 2015 [5].\n",
 62 |     "\n",
 63 |     "One example of someone who has tried to use scores to test a value of an item is a research project done on if there is a correlation between the price of wine and its corresponding WineEnthusiast score? This project relates to my research question because it is comparing if an item is better or worse based on its score; similarly, I am interested in seeing if the Yelp ratings and health inspection scores posted on Yelp make a restaurant more or less health-conscious [6].\n",
 64 |     "\n",
 65 |     "References (include links):\n",
 66 |     "\n",
 67 |     "1)http://www.wakegov.com/food/healthinspections/facilities/Pages/restaurants.aspx\n",
 68 |     "\n",
 69 |     "2)https://public.cdpehs.com/NCENVPBL/ESTABLISHMENT/ShowRestaurantRules.aspx\n",
 70 |     "\n",
 71 |     "3)https://ehs.ncpublichealth.com/faf/docs/foodprot/NC-MarkingInstructionsFinal-110419.pdf\n",
 72 |     "\n",
 73 |     "4)https://www.washingtonpost.com/news/voraciously/wp/2018/07/24/yelp-adds-health-inspection-scores-for-restaurants-and-restaurateurs-are-not-happy/\n",
 74 |     "\n",
 75 |     "5)https://gcn.com/articles/2015/03/02/yelp-city-restaurant-inspections.aspx\n",
 76 |     "\n",
 77 |     "6)FinalProject_group085.ipynb"
 78 |    ]
 79 |   },
 80 |   {
 81 |    "cell_type": "markdown",
 82 |    "metadata": {},
 83 |    "source": [
 84 |     "# Hypothesis\n"
 85 |    ]
 86 |   },
 87 |   {
 88 |    "cell_type": "markdown",
 89 |    "metadata": {},
 90 |    "source": [
 91 |     "Since restaurants depend on customers' ratings and reviews for their businesses, I believe that publicizing health inspection scores on Yelp will improve overall restaurant health. Especially when customers write reviews regarding any problems that could reflect a restaurant’s health, other customers will be discouraged to go there; additionally, to improve their ratings, restaurants would be more willing to improve their health standards to pass the next inspection.\n",
 92 |     "\n"
 93 |    ]
 94 |   },
 95 |   {
 96 |    "cell_type": "markdown",
 97 |    "metadata": {},
 98 |    "source": [
 99 |     "# Dataset(s)"
100 |    ]
101 |   },
102 |   {
103 |    "cell_type": "markdown",
104 |    "metadata": {},
105 |    "source": [
106 |     "I used three datasets for this analysis. The first dataset was inspections.csv, which captures all sanitation/health inspections from September 2012 to present. For my analysis, I specifically used the hsisid (state code identifying the restaurant), the facility type (to narrow my analysis to only restaurants), the city (to keep my analysis to just Raleigh, NC), and the zipcode for the restaurants. This dataset had 18466 rows x 36 columns. The second dataset I used was the violations.csv, which has 189802 rows x 18 columns, and describes all violations (and reports which code was violated) at active restaurants in Wake County, from September 2012 to present. In this dataset, I specifically used the hsisid (which is how I merged inspections.csv with violations.csv), violation type, and health inspection scores. There are three violation types described in this dataset: CDI, which is corrected during inspection; VR, which indicates that verification is required within 10 days; and lastly, R, which indicates a repeat offense [1]. The third dataset I used was the yelp.csv (which had 3688 rows x 31 columns), which had information about the restaurants's yelp ratings. From this database, I used the zipcode and ratings information. I used the zipcodes to merge this dataset with violations.csv. \n",
107 |     "\n",
108 |     "1) https://ehs.ncpublichealth.com/faf/docs/foodprot/NC-MarkingInstructionsFinal-110419.pdf"
109 |    ]
110 |   },
111 |   {
112 |    "cell_type": "markdown",
113 |    "metadata": {},
114 |    "source": [
115 |     "# Setup"
116 |    ]
117 |   },
118 |   {
119 |    "cell_type": "code",
120 |    "execution_count": 10,
121 |    "metadata": {},
122 |    "outputs": [],
123 |    "source": [
124 |     "#imports\n",
125 |     "import numpy as np\n",
126 |     "import pandas as pd\n",
127 |     "import matplotlib.pyplot as plt\n",
128 |     "   \n",
129 |     "import seaborn as sns\n",
130 |     "sns.set()\n",
131 |     "sns.set_context('talk')"
132 |    ]
133 |   },
134 |   {
135 |    "cell_type": "code",
136 |    "execution_count": 11,
137 |    "metadata": {},
138 |    "outputs": [],
139 |    "source": [
140 |     "#read data\n",
141 |     "df_insp = pd.read_csv(\"/home/cnkulkar/Project/Data/inspections.csv\")\n",
142 |     "df_viol = pd.read_csv(\"/home/cnkulkar/Project/Data/violations.csv\")\n",
143 |     "df_yelp = pd.read_csv(\"/home/cnkulkar/Project/Data/yelp.csv\")"
144 |    ]
145 |   },
146 |   {
147 |    "cell_type": "markdown",
148 |    "metadata": {},
149 |    "source": [
150 |     "# Data Cleaning"
151 |    ]
152 |   },
153 |   {
154 |    "cell_type": "code",
155 |    "execution_count": 12,
156 |    "metadata": {},
157 |    "outputs": [],
158 |    "source": [
159 |     "#clean df_insp\n",
160 |     "df_insp = df_insp.drop(columns = ['date', 'phonenumber', 'top_match', 'second_match', 'days_from_open_date', 'x', 'y', 'geocodestatus', 'type', 'inspectedby', 'inspector_id', 'previous_inspection_by_same_inspector'])\n",
161 |     "\n",
162 |     "#clean df_viol\n",
163 |     "df_viol = df_viol.drop(columns = ['inspectedby'])\n",
164 |     "\n",
165 |     "#clean df_yelp\n",
166 |     "df_yelp = df_yelp.drop(columns = ['latitude', 'longitude', 'phone', 'hotdogs', 'sandwiches', 'pizza', 'tradamerican', 'burgers', 'mexican', 'grocery', 'breakfast_brunch', 'coffee', 'chinese', 'italian', 'newamerican', 'chicken_wings', 'delis', 'bars', 'salad', 'seafood', 'bbq', 'bakeries', 'sushi'])\n",
167 |     "df_yelp = df_yelp.rename(columns={'zip_code': 'zip'})"
168 |    ]
169 |   },
170 |   {
171 |    "cell_type": "code",
172 |    "execution_count": 13,
173 |    "metadata": {},
174 |    "outputs": [
175 |     {
176 |      "data": {
177 |       "text/html": [
178 |        "<div>\n",
179 |        "<style scoped>\n",
180 |        "    .dataframe tbody tr th:only-of-type {\n",
181 |        "        vertical-align: middle;\n",
182 |        "    }\n",
183 |        "\n",
184 |        "    .dataframe tbody tr th {\n",
185 |        "        vertical-align: top;\n",
186 |        "    }\n",
187 |        "\n",
188 |        "    .dataframe thead th {\n",
189 |        "        text-align: right;\n",
190 |        "    }\n",
191 |        "</style>\n",
192 |        "<table border=\"1\" class=\"dataframe\">\n",
193 |        "  <thead>\n",
194 |        "    <tr style=\"text-align: right;\">\n",
195 |        "      <th></th>\n",
196 |        "      <th>hsisid</th>\n",
197 |        "      <th>name</th>\n",
198 |        "      <th>address1</th>\n",
199 |        "      <th>address2</th>\n",
200 |        "      <th>city</th>\n",
201 |        "      <th>state</th>\n",
202 |        "      <th>postalcode</th>\n",
203 |        "      <th>restaurantopendate</th>\n",
204 |        "      <th>facilitytype</th>\n",
205 |        "      <th>zip</th>\n",
206 |        "      <th>...</th>\n",
207 |        "      <th>violationcode</th>\n",
208 |        "      <th>severity</th>\n",
209 |        "      <th>shortdesc</th>\n",
210 |        "      <th>comments</th>\n",
211 |        "      <th>pointvalue</th>\n",
212 |        "      <th>observationtype</th>\n",
213 |        "      <th>violationtype</th>\n",
214 |        "      <th>count</th>\n",
215 |        "      <th>cdcriskfactor</th>\n",
216 |        "      <th>cdcdataitem</th>\n",
217 |        "    </tr>\n",
218 |        "  </thead>\n",
219 |        "  <tbody>\n",
220 |        "    <tr>\n",
221 |        "      <th>0</th>\n",
222 |        "      <td>4092013748</td>\n",
223 |        "      <td>Cafe 3000 At Wake Med</td>\n",
224 |        "      <td>3000 New Bern Ave</td>\n",
225 |        "      <td>NaN</td>\n",
226 |        "      <td>raleigh</td>\n",
227 |        "      <td>NC</td>\n",
228 |        "      <td>27610</td>\n",
229 |        "      <td>2002-12-21T00:00:00Z</td>\n",
230 |        "      <td>Restaurant</td>\n",
231 |        "      <td>27610</td>\n",
232 |        "      <td>...</td>\n",
233 |        "      <td>3-603.11</td>\n",
234 |        "      <td>Priority Foundation</td>\n",
235 |        "      <td>Consumer advisory provided for raw or undercoo...</td>\n",
236 |        "      <td>3-603.11; Priority Foundation; Establishment f...</td>\n",
237 |        "      <td>0</td>\n",
238 |        "      <td>Out</td>\n",
239 |        "      <td>CDI</td>\n",
240 |        "      <td>NaN</td>\n",
241 |        "      <td>NaN</td>\n",
242 |        "      <td>NaN</td>\n",
243 |        "    </tr>\n",
244 |        "    <tr>\n",
245 |        "      <th>1</th>\n",
246 |        "      <td>4092013748</td>\n",
247 |        "      <td>Cafe 3000 At Wake Med</td>\n",
248 |        "      <td>3000 New Bern Ave</td>\n",
249 |        "      <td>NaN</td>\n",
250 |        "      <td>raleigh</td>\n",
251 |        "      <td>NC</td>\n",
252 |        "      <td>27610</td>\n",
253 |        "      <td>2002-12-21T00:00:00Z</td>\n",
254 |        "      <td>Restaurant</td>\n",
255 |        "      <td>27610</td>\n",
256 |        "      <td>...</td>\n",
257 |        "      <td>3-502.11</td>\n",
258 |        "      <td>Priority Foundation</td>\n",
259 |        "      <td>Compliance with variance, specialized process,...</td>\n",
260 |        "      <td>Pf - 3-502.11 Variance Requirement - Establish...</td>\n",
261 |        "      <td>0</td>\n",
262 |        "      <td>Out</td>\n",
263 |        "      <td>CDI</td>\n",
264 |        "      <td>NaN</td>\n",
265 |        "      <td>NaN</td>\n",
266 |        "      <td>NaN</td>\n",
267 |        "    </tr>\n",
268 |        "    <tr>\n",
269 |        "      <th>2</th>\n",
270 |        "      <td>4092013748</td>\n",
271 |        "      <td>Cafe 3000 At Wake Med</td>\n",
272 |        "      <td>3000 New Bern Ave</td>\n",
273 |        "      <td>NaN</td>\n",
274 |        "      <td>raleigh</td>\n",
275 |        "      <td>NC</td>\n",
276 |        "      <td>27610</td>\n",
277 |        "      <td>2002-12-21T00:00:00Z</td>\n",
278 |        "      <td>Restaurant</td>\n",
279 |        "      <td>27610</td>\n",
280 |        "      <td>...</td>\n",
281 |        "      <td>3-603.11</td>\n",
282 |        "      <td>Priority Foundation</td>\n",
283 |        "      <td>Consumer advisory provided for raw or undercoo...</td>\n",
284 |        "      <td>Pf - 3-603.11 Consumption of Animal Foods that...</td>\n",
285 |        "      <td>0</td>\n",
286 |        "      <td>Out</td>\n",
287 |        "      <td>R</td>\n",
288 |        "      <td>NaN</td>\n",
289 |        "      <td>NaN</td>\n",
290 |        "      <td>NaN</td>\n",
291 |        "    </tr>\n",
292 |        "    <tr>\n",
293 |        "      <th>3</th>\n",
294 |        "      <td>4092013748</td>\n",
295 |        "      <td>Cafe 3000 At Wake Med</td>\n",
296 |        "      <td>3000 New Bern Ave</td>\n",
297 |        "      <td>NaN</td>\n",
298 |        "      <td>raleigh</td>\n",
299 |        "      <td>NC</td>\n",
300 |        "      <td>27610</td>\n",
301 |        "      <td>2002-12-21T00:00:00Z</td>\n",
302 |        "      <td>Restaurant</td>\n",
303 |        "      <td>27610</td>\n",
304 |        "      <td>...</td>\n",
305 |        "      <td>3-603.11</td>\n",
306 |        "      <td>Priority Foundation</td>\n",
307 |        "      <td>Consumer advisory provided for raw or undercoo...</td>\n",
308 |        "      <td>Pf - 3-603.11 Consumption of Animal Foods that...</td>\n",
309 |        "      <td>0</td>\n",
310 |        "      <td>Out</td>\n",
311 |        "      <td>VR</td>\n",
312 |        "      <td>NaN</td>\n",
313 |        "      <td>NaN</td>\n",
314 |        "      <td>NaN</td>\n",
315 |        "    </tr>\n",
316 |        "    <tr>\n",
317 |        "      <th>4</th>\n",
318 |        "      <td>4092013748</td>\n",
319 |        "      <td>Cafe 3000 At Wake Med</td>\n",
320 |        "      <td>3000 New Bern Ave</td>\n",
321 |        "      <td>NaN</td>\n",
322 |        "      <td>raleigh</td>\n",
323 |        "      <td>NC</td>\n",
324 |        "      <td>27610</td>\n",
325 |        "      <td>2002-12-21T00:00:00Z</td>\n",
326 |        "      <td>Restaurant</td>\n",
327 |        "      <td>27610</td>\n",
328 |        "      <td>...</td>\n",
329 |        "      <td>8-201.13</td>\n",
330 |        "      <td>Core</td>\n",
331 |        "      <td>Compliance with variance, specialized process,...</td>\n",
332 |        "      <td>Raw fish is used for sushi (sushi chef not pre...</td>\n",
333 |        "      <td>0</td>\n",
334 |        "      <td>Out</td>\n",
335 |        "      <td>NaN</td>\n",
336 |        "      <td>NaN</td>\n",
337 |        "      <td>NaN</td>\n",
338 |        "      <td>NaN</td>\n",
339 |        "    </tr>\n",
340 |        "  </tbody>\n",
341 |        "</table>\n",
342 |        "<p>5 rows × 40 columns</p>\n",
343 |        "</div>"
344 |       ],
345 |       "text/plain": [
346 |        "       hsisid                   name           address1 address2     city  \\\n",
347 |        "0  4092013748  Cafe 3000 At Wake Med  3000 New Bern Ave      NaN  raleigh   \n",
348 |        "1  4092013748  Cafe 3000 At Wake Med  3000 New Bern Ave      NaN  raleigh   \n",
349 |        "2  4092013748  Cafe 3000 At Wake Med  3000 New Bern Ave      NaN  raleigh   \n",
350 |        "3  4092013748  Cafe 3000 At Wake Med  3000 New Bern Ave      NaN  raleigh   \n",
351 |        "4  4092013748  Cafe 3000 At Wake Med  3000 New Bern Ave      NaN  raleigh   \n",
352 |        "\n",
353 |        "  state postalcode    restaurantopendate facilitytype    zip  ...  \\\n",
354 |        "0    NC      27610  2002-12-21T00:00:00Z   Restaurant  27610  ...   \n",
355 |        "1    NC      27610  2002-12-21T00:00:00Z   Restaurant  27610  ...   \n",
356 |        "2    NC      27610  2002-12-21T00:00:00Z   Restaurant  27610  ...   \n",
357 |        "3    NC      27610  2002-12-21T00:00:00Z   Restaurant  27610  ...   \n",
358 |        "4    NC      27610  2002-12-21T00:00:00Z   Restaurant  27610  ...   \n",
359 |        "\n",
360 |        "  violationcode             severity  \\\n",
361 |        "0      3-603.11  Priority Foundation   \n",
362 |        "1      3-502.11  Priority Foundation   \n",
363 |        "2      3-603.11  Priority Foundation   \n",
364 |        "3      3-603.11  Priority Foundation   \n",
365 |        "4      8-201.13                 Core   \n",
366 |        "\n",
367 |        "                                           shortdesc  \\\n",
368 |        "0  Consumer advisory provided for raw or undercoo...   \n",
369 |        "1  Compliance with variance, specialized process,...   \n",
370 |        "2  Consumer advisory provided for raw or undercoo...   \n",
371 |        "3  Consumer advisory provided for raw or undercoo...   \n",
372 |        "4  Compliance with variance, specialized process,...   \n",
373 |        "\n",
374 |        "                                            comments  pointvalue  \\\n",
375 |        "0  3-603.11; Priority Foundation; Establishment f...           0   \n",
376 |        "1  Pf - 3-502.11 Variance Requirement - Establish...           0   \n",
377 |        "2  Pf - 3-603.11 Consumption of Animal Foods that...           0   \n",
378 |        "3  Pf - 3-603.11 Consumption of Animal Foods that...           0   \n",
379 |        "4  Raw fish is used for sushi (sushi chef not pre...           0   \n",
380 |        "\n",
381 |        "   observationtype  violationtype  count  cdcriskfactor  cdcdataitem  \n",
382 |        "0              Out            CDI    NaN            NaN          NaN  \n",
383 |        "1              Out            CDI    NaN            NaN          NaN  \n",
384 |        "2              Out              R    NaN            NaN          NaN  \n",
385 |        "3              Out             VR    NaN            NaN          NaN  \n",
386 |        "4              Out            NaN    NaN            NaN          NaN  \n",
387 |        "\n",
388 |        "[5 rows x 40 columns]"
389 |       ]
390 |      },
391 |      "execution_count": 13,
392 |      "metadata": {},
393 |      "output_type": "execute_result"
394 |     }
395 |    ],
396 |    "source": [
397 |     "#merge the inspections.csv with violations.csv, using the hsisid\n",
398 |     "df = pd.merge(df_insp, df_viol, on = 'hsisid')\n",
399 |     "df.head()"
400 |    ]
401 |   },
402 |   {
403 |    "cell_type": "code",
404 |    "execution_count": 15,
405 |    "metadata": {},
406 |    "outputs": [
407 |     {
408 |      "data": {
409 |       "text/html": [
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411 |        "<style scoped>\n",
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414 |        "    }\n",
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423 |        "</style>\n",
424 |        "<table border=\"1\" class=\"dataframe\">\n",
425 |        "  <thead>\n",
426 |        "    <tr style=\"text-align: right;\">\n",
427 |        "      <th></th>\n",
428 |        "      <th>hsisid</th>\n",
429 |        "      <th>name_x</th>\n",
430 |        "      <th>address1_x</th>\n",
431 |        "      <th>address2</th>\n",
432 |        "      <th>city</th>\n",
433 |        "      <th>state</th>\n",
434 |        "      <th>postalcode</th>\n",
435 |        "      <th>restaurantopendate</th>\n",
436 |        "      <th>facilitytype</th>\n",
437 |        "      <th>zip</th>\n",
438 |        "      <th>...</th>\n",
439 |        "      <th>avg_neighbor_num_critical</th>\n",
440 |        "      <th>avg_neighbor_num_non_critical</th>\n",
441 |        "      <th>critical</th>\n",
442 |        "      <th>id</th>\n",
443 |        "      <th>name_y</th>\n",
444 |        "      <th>is_closed</th>\n",
445 |        "      <th>rating</th>\n",
446 |        "      <th>review_count</th>\n",
447 |        "      <th>address1_y</th>\n",
448 |        "      <th>price</th>\n",
449 |        "    </tr>\n",
450 |        "  </thead>\n",
451 |        "  <tbody>\n",
452 |        "    <tr>\n",
453 |        "      <th>0</th>\n",
454 |        "      <td>4092013748</td>\n",
455 |        "      <td>Cafe 3000 At Wake Med</td>\n",
456 |        "      <td>3000 New Bern Ave</td>\n",
457 |        "      <td>NaN</td>\n",
458 |        "      <td>raleigh</td>\n",
459 |        "      <td>NC</td>\n",
460 |        "      <td>27610</td>\n",
461 |        "      <td>2002-12-21T00:00:00Z</td>\n",
462 |        "      <td>Restaurant</td>\n",
463 |        "      <td>27610</td>\n",
464 |        "      <td>...</td>\n",
465 |        "      <td>NaN</td>\n",
466 |        "      <td>NaN</td>\n",
467 |        "      <td>1</td>\n",
468 |        "      <td>best-western-raleigh-inn-and-suites-raleigh</td>\n",
469 |        "      <td>holiday inn express &amp; suites raleigh ne - medi...</td>\n",
470 |        "      <td>False</td>\n",
471 |        "      <td>3.0</td>\n",
472 |        "      <td>7</td>\n",
473 |        "      <td>3618 New Bern Ave</td>\n",
474 |        "      <td>$$</td>\n",
475 |        "    </tr>\n",
476 |        "    <tr>\n",
477 |        "      <th>1</th>\n",
478 |        "      <td>4092013748</td>\n",
479 |        "      <td>Cafe 3000 At Wake Med</td>\n",
480 |        "      <td>3000 New Bern Ave</td>\n",
481 |        "      <td>NaN</td>\n",
482 |        "      <td>raleigh</td>\n",
483 |        "      <td>NC</td>\n",
484 |        "      <td>27610</td>\n",
485 |        "      <td>2002-12-21T00:00:00Z</td>\n",
486 |        "      <td>Restaurant</td>\n",
487 |        "      <td>27610</td>\n",
488 |        "      <td>...</td>\n",
489 |        "      <td>NaN</td>\n",
490 |        "      <td>NaN</td>\n",
491 |        "      <td>1</td>\n",
492 |        "      <td>walnut-creek-ampitheatre-raleigh</td>\n",
493 |        "      <td>walnut creek ampitheatre</td>\n",
494 |        "      <td>False</td>\n",
495 |        "      <td>3.0</td>\n",
496 |        "      <td>44</td>\n",
497 |        "      <td>3801 Rock Quarry Rd</td>\n",
498 |        "      <td>$$</td>\n",
499 |        "    </tr>\n",
500 |        "    <tr>\n",
501 |        "      <th>2</th>\n",
502 |        "      <td>4092013748</td>\n",
503 |        "      <td>Cafe 3000 At Wake Med</td>\n",
504 |        "      <td>3000 New Bern Ave</td>\n",
505 |        "      <td>NaN</td>\n",
506 |        "      <td>raleigh</td>\n",
507 |        "      <td>NC</td>\n",
508 |        "      <td>27610</td>\n",
509 |        "      <td>2002-12-21T00:00:00Z</td>\n",
510 |        "      <td>Restaurant</td>\n",
511 |        "      <td>27610</td>\n",
512 |        "      <td>...</td>\n",
513 |        "      <td>NaN</td>\n",
514 |        "      <td>NaN</td>\n",
515 |        "      <td>1</td>\n",
516 |        "      <td>kfc-raleigh-7</td>\n",
517 |        "      <td>kfc</td>\n",
518 |        "      <td>False</td>\n",
519 |        "      <td>2.0</td>\n",
520 |        "      <td>5</td>\n",
521 |        "      <td>3408 Poole Road</td>\n",
522 |        "      <td>$</td>\n",
523 |        "    </tr>\n",
524 |        "    <tr>\n",
525 |        "      <th>3</th>\n",
526 |        "      <td>4092013748</td>\n",
527 |        "      <td>Cafe 3000 At Wake Med</td>\n",
528 |        "      <td>3000 New Bern Ave</td>\n",
529 |        "      <td>NaN</td>\n",
530 |        "      <td>raleigh</td>\n",
531 |        "      <td>NC</td>\n",
532 |        "      <td>27610</td>\n",
533 |        "      <td>2002-12-21T00:00:00Z</td>\n",
534 |        "      <td>Restaurant</td>\n",
535 |        "      <td>27610</td>\n",
536 |        "      <td>...</td>\n",
537 |        "      <td>NaN</td>\n",
538 |        "      <td>NaN</td>\n",
539 |        "      <td>1</td>\n",
540 |        "      <td>sheetz-raleigh</td>\n",
541 |        "      <td>sheetz</td>\n",
542 |        "      <td>False</td>\n",
543 |        "      <td>3.5</td>\n",
544 |        "      <td>6</td>\n",
545 |        "      <td>5200 New Bern Ave</td>\n",
546 |        "      <td>$</td>\n",
547 |        "    </tr>\n",
548 |        "    <tr>\n",
549 |        "      <th>4</th>\n",
550 |        "      <td>4092013748</td>\n",
551 |        "      <td>Cafe 3000 At Wake Med</td>\n",
552 |        "      <td>3000 New Bern Ave</td>\n",
553 |        "      <td>NaN</td>\n",
554 |        "      <td>raleigh</td>\n",
555 |        "      <td>NC</td>\n",
556 |        "      <td>27610</td>\n",
557 |        "      <td>2002-12-21T00:00:00Z</td>\n",
558 |        "      <td>Restaurant</td>\n",
559 |        "      <td>27610</td>\n",
560 |        "      <td>...</td>\n",
561 |        "      <td>NaN</td>\n",
562 |        "      <td>NaN</td>\n",
563 |        "      <td>1</td>\n",
564 |        "      <td>walmart-raleigh-3</td>\n",
565 |        "      <td>walmart</td>\n",
566 |        "      <td>False</td>\n",
567 |        "      <td>2.0</td>\n",
568 |        "      <td>12</td>\n",
569 |        "      <td>4431 New Bern Ave</td>\n",
570 |        "      <td>$</td>\n",
571 |        "    </tr>\n",
572 |        "  </tbody>\n",
573 |        "</table>\n",
574 |        "<p>5 rows × 31 columns</p>\n",
575 |        "</div>"
576 |       ],
577 |       "text/plain": [
578 |        "       hsisid                 name_x         address1_x address2     city  \\\n",
579 |        "0  4092013748  Cafe 3000 At Wake Med  3000 New Bern Ave      NaN  raleigh   \n",
580 |        "1  4092013748  Cafe 3000 At Wake Med  3000 New Bern Ave      NaN  raleigh   \n",
581 |        "2  4092013748  Cafe 3000 At Wake Med  3000 New Bern Ave      NaN  raleigh   \n",
582 |        "3  4092013748  Cafe 3000 At Wake Med  3000 New Bern Ave      NaN  raleigh   \n",
583 |        "4  4092013748  Cafe 3000 At Wake Med  3000 New Bern Ave      NaN  raleigh   \n",
584 |        "\n",
585 |        "  state postalcode    restaurantopendate facilitytype    zip  ...  \\\n",
586 |        "0    NC      27610  2002-12-21T00:00:00Z   Restaurant  27610  ...   \n",
587 |        "1    NC      27610  2002-12-21T00:00:00Z   Restaurant  27610  ...   \n",
588 |        "2    NC      27610  2002-12-21T00:00:00Z   Restaurant  27610  ...   \n",
589 |        "3    NC      27610  2002-12-21T00:00:00Z   Restaurant  27610  ...   \n",
590 |        "4    NC      27610  2002-12-21T00:00:00Z   Restaurant  27610  ...   \n",
591 |        "\n",
592 |        "  avg_neighbor_num_critical  avg_neighbor_num_non_critical critical  \\\n",
593 |        "0                       NaN                            NaN        1   \n",
594 |        "1                       NaN                            NaN        1   \n",
595 |        "2                       NaN                            NaN        1   \n",
596 |        "3                       NaN                            NaN        1   \n",
597 |        "4                       NaN                            NaN        1   \n",
598 |        "\n",
599 |        "                                            id  \\\n",
600 |        "0  best-western-raleigh-inn-and-suites-raleigh   \n",
601 |        "1             walnut-creek-ampitheatre-raleigh   \n",
602 |        "2                                kfc-raleigh-7   \n",
603 |        "3                               sheetz-raleigh   \n",
604 |        "4                            walmart-raleigh-3   \n",
605 |        "\n",
606 |        "                                              name_y  is_closed  rating  \\\n",
607 |        "0  holiday inn express & suites raleigh ne - medi...      False     3.0   \n",
608 |        "1                           walnut creek ampitheatre      False     3.0   \n",
609 |        "2                                                kfc      False     2.0   \n",
610 |        "3                                             sheetz      False     3.5   \n",
611 |        "4                                            walmart      False     2.0   \n",
612 |        "\n",
613 |        "   review_count           address1_y  price  \n",
614 |        "0             7    3618 New Bern Ave     $$  \n",
615 |        "1            44  3801 Rock Quarry Rd     $$  \n",
616 |        "2             5      3408 Poole Road      $  \n",
617 |        "3             6    5200 New Bern Ave      $  \n",
618 |        "4            12    4431 New Bern Ave      $  \n",
619 |        "\n",
620 |        "[5 rows x 31 columns]"
621 |       ]
622 |      },
623 |      "execution_count": 15,
624 |      "metadata": {},
625 |      "output_type": "execute_result"
626 |     }
627 |    ],
628 |    "source": [
629 |     "#merge the inspections.csv and yelp.csv dataframes with the zipcodes\n",
630 |     "df2 = pd.merge(df_insp, df_yelp, on = 'zip')\n",
631 |     "\n",
632 |     "#make sure that the data includes only restaurants that are in the Raleigh\n",
633 |     "df2 = df2.drop(df2[df2['facilitytype'] != 'Restaurant'].index)\n",
634 |     "df2 = df2.drop(df2[df2['city'] != 'raleigh'].index)\n",
635 |     "df2.head()"
636 |    ]
637 |   },
638 |   {
639 |    "cell_type": "code",
640 |    "execution_count": 16,
641 |    "metadata": {},
642 |    "outputs": [
643 |     {
644 |      "data": {
645 |       "text/plain": [
646 |        "<matplotlib.axes._subplots.AxesSubplot at 0x7f27aa056908>"
647 |       ]
648 |      },
649 |      "execution_count": 16,
650 |      "metadata": {},
651 |      "output_type": "execute_result"
652 |     },
653 |     {
654 |      "data": {
655 |       "image/png": 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U1T6LyxT8z0N6azE1LoZ3QptT0pB5ylh7kBWLsfyrHMT3Nmx3YBtdDGvTfN0W/AmRwL9ONb4+GUEQzRNSxLABexUxAPByStbUKc5fK4WLTGy2Tms/OaZ9dogv4w5xvHm3gr/nm0XDUKPUoOB+FdxkYtTU2dxRt09OEaOiSmlR7klfezDt6DUMjAgyW/+X3AKM7N8OxWUKo/sdRXNUHNCH7BM2ZJ8hJOPUiNijPVheqeAd1faFQwDAtIaXmMHRs0W4VlCGmBc6wM1FYrLOL7kFJuNSddsdP7wLWAAqNQu1RguJWGQ6piVi4CWXobxaabNgbl27zNX38nDBSx+kO23LOz0UhA3ZJ2zIaQkIe2ZaxWUKTFuRBU83LVa9p1sejO+tRZzeEhz3f3m1CiKGQdL3/0Pfbq0x4Nk2EDMMCkuqMO+Ln+HiJsL2hbr4kVKlwVvLM1Cl0M22srIO4s+P2gSA20V34dfCD64uYpNjUyjVmLpiP1a/FwU/Lxd+nLYcqa1vl6X6ft4uiJ2bZteR3PZADwVhQ/YJm6bitCim5WA4uaOtH8Yg49g1qNRa7MgGdmSnGdXljh7JOX8XOefvYsOuMwbXq5RapB+9hpH92+HlBRmPSnXbyf8cF4PyaiW85DKkHb2GmP7tcOF6KS7fLMPWf58zO77ss4UY2b+dTbJMI/u34+NSttafEAnsyDa+nyAIwhFQnpYD0Zc7Eoscc2y1qTpc2S+nC/g+GYaBlmVx445tfdozNnvqD4waim8WDaMt7wRBOAVyWg5EX+5Io3XMsdWm6nBlW1PP8dvRWZaFiGEQGmC9T4VKY9fY7JVxyi8oN9oCTxAE4QjIaTkQfbmjN5el2SypZK3OrA1pRmXvrNKVrfxBd5TJil0Z8PeR47nw1hbbey68NSqqVHYdqV1fGSdaGiQIwtEIZiPGr7/+ii+++AJnzpyBSCRCr169MHv2bHTp0sWg3uDBg3H79m2j+9966y3Mnj27Xn3X9xDI7QuHIL+gHBt/zEHy/GiI6mzE4BJ/K6qVuFJQhpbebrhTUo2VX5+ABrpdeRGdW/L1N6ZkoH933c68d1cdQPLCF+Eqk0CtYcEwAKtlIZaIwEA3O/rfxXuQScUovF+Fr/aexdwJfXGjsBxisQgj+7dDbt59i7sBuUMdbTnckqsfO1fnTLl4nSMdFwW6hQ3ZJ2yaykYMQTit3NxcjB8/Hj179kRiYiK0Wi2Sk5Nx/fp17Nq1C23btuXrDh48GEFBQUYOKiAgAIGBgfXq354t73WPpR/wKLeJk1ji/udyncYN6gyJWGQy98lcThS3tV2/DbmL2Gz9CzceoEOQN6oeKvHemiMYHtkW08b2QGW1wng7fh1ZpuLiKpN2mas/Zs5eAMDwyLb8/Y6CHgrChuwTNk3FaQli92BSUhJ8fHywbds2uLq6AgAiIyMxZMgQJCUlYc2aNQb1vby8EBER0eDj1Jdx6t4VmBs/HPm3ytAxyAv5BeX8/9xs5bPp/VFYXGU0exkQEWT12BBu+/ln0/tD7iK2WL9rqA+2/Pt3DO4dgg8m9sH9BzVQaTRIWHIAL0bCSBHjp2zdTKluTCthyQFMiITB9v2UlDTsqFMfIBkngiCcgyBiWqdPn0ZkZCTvsACdY+rVqxeysrKgcXA+UH3Rj/0UFABKpRZJO3XxnW3pOfyR9Sq1Fq4SwN9HbuRovOW6WZS1Y0MG9K5tQ2lD/dH/1wGf/j0Hndr4IDI8EBVVKkglIvyUDcTOTcOYOXsRO1fnsCzFtHbUqb9Dr/7mFN3SYN37CYIgHIUgZloqlQoymbEyuUwmQ01NDW7evIl27drx5dnZ2XjmmWegUqnQvn17xMfHIz4+vt5bsBmmdmpsCbGYQUWVEgsT+6Jn55a4/6AGX304EndLq7F+pi5J+IdPasVmAWDXpyPBsix+v3IPPp5yBPm7I/2oLr/LWw4kL3wRLlIJFCo1piz/CWXVuhyoGXExmDJKA1eZBJU1Kni6abH6/SHwMzHOG3cq0LGNN7q080X2uUIMj2yLGqUaCxP7mo1RyWQiyB/9zvXtMltfwiB++DBknso0ut9RSCS6xGlb3gshQvYJG7LPEGdlvAgiphUbGwuVSoW0tDTe8ahUKgwfPhy3b9/Gd999h2eeeQYAsHTpUoSHhyMkJAQPHjzAv//9bxw4cAATJ07EggUL6tU/y7JQqazP5iQSkUHsp258qUahRm7ePQMNv4jO/nBzkdgc05JKGKgeHbBo6p4juQXoEtoCvl6uRtfvP6jGoV9v8UeIlFcroVRpDY9Q6R4ImVQEuUxi1i5zMa2343qguPyh0f2OgvujUaubxsza0ZB9wobsM0QqFTslV1MQTmvXrl1YuHAhxo8fjylTpkCr1SIpKQmpqanQaDTYuXMnevbsafb+WbNmISMjA5mZmQgODra7f3t2D94pqTEZp9LfZcfFn/46tZ/RycX6dd1kYszbeNSgPPmDIbheZH4XX/tAL1wtNH/dz8sVIQEeAMAv3/n6yvnZn6klPU4wd2vqOQyKBN7Xi2l9kZKGw9nO2S1YFwp0CxuyT9g0lY0YgohpvfTSS5g1axZSUlIwYMAA/OlPf8LVq1fxxhtvAABatWpl8f6xY8dCq9XizJkzFus9LiUl1ZCIRWbjS5/+vTauJYbWZEyLq7v8qxy09JHDVW/CImW00GrNx69W7siBWmM5viV3laK4TGHgXEpKqlFcXGXW4ejHtA7XiWkdNhEDIwiCcBaCcFoAMGXKFJw4cQJ79+7FoUOH8N1336GsrAzBwcFWt7JrtboHuEjkXHN9feUWNfq6tvPjr8+d+JxNen6zJ/Tly5a+/X8W70kc1d2mNv28XeDrK7fLLpmEwcLEvkYJxvoxLXvaJAiCqA+C2IjBIZPJEBYWBgC4desWMjIy8M4771i9LzU1FSKRCN27d3fq+Kxp9Pl6ufDXA3zlyD5XaLG9G3cq0LNzS/5nP2833Ci6brZ+YEt3/HL6ltU27V1nZhgG3+y7gIQRXbFpXpTFPC2CIAhnIgindeHCBWRmZiI8PBwymQx//PEHkpOT0aNHD0ycOJGvl5aWhqysLAwcOBCtW7dGWVkZ/v3vfyMzMxOTJ09GUFCQU8dpTaOvpFyBPt0CAAB3Sqpt0vMr0ltyKy6rsXhP4f0qm9pUqbWQiG13XJy+YX5BOa6czUFcXLRenlYGOnTvS1qDBEE0CILYiJGfn4+PP/4YeXl5qK6uRkhICGJjY5GYmGiwFT43Nxeff/45Ll++jAcPHkAqlaJLly545ZVXMHbs2Hr3b+tGDABgxWKz5065SEX429woTFuRBTG02DhvqNUzqqavOIiHj85SlEuB9XPN3yN3EWH97CirbQKw+6wrS3Y56/ysulCgW9iQfcKmqWzEEITTamya++5BW+zidg+ag3YPPj5kn7Ah+wwhp9WI2Ks9OLh3CPxbyA3znx7laf2Wd0+nR6hhkX22ED0f5WkZxIm6B0ImZvg6deNHai2gZVnj/Coxg19yCxD2KE+rbpuXbz7A/y7e5WNPtuoCcnYdOHHDbB1naA3WhR4KwobsEzZNxWkJIqYlFLiY1vyNR7FnZQz6dAtAz84tUVZZDT9vFzCMK9q0cufjQSP7t+Nftw2Uo2fnlnCRinG9oBwiMYOOQV58nYcKNaZ88hPKHk1kPOUibF04Aq4yMSqqVdCyWnjKZYh5oT0/Hu710TPX8fanB6HUGGsE2mOXJUhrkCCIhoCclgPh8pm+Tj+PvySl4cOJQ/GXzw/zy3SuEtgUx1q5Q3c0yaZ5USguU2DG6ixUKwzrV1RrMX7RPj5G9f++yMLa93WvLbXPneFlT/xJ3y5L7VKeFkEQzka8ePHixY09iKYOywI1NUqb6spkYoR3aolxf3oaYhEQ3qkljp4pgFbLQq0Fnu8RiOd7BPFlHFzMSanSYN+Jm9BqWQT4yvFUe194e7ni1Pk7Rn1ptSzGv9gVYFl0adcSLTxdIBUzBn3Wbd/f2xViEQMfb1fU1KgA6GJWcrkMbm5SvsycXaGyAix5byTih3XBq0PDIKu4jDHDde1qNabzwxyFq6sUAKBQqJ3aT2MhBPts+ayYQwj2PQ5knyFyucwpMk4003IwKoUaHYO84OUu4/X69HOb8m+VYUBEEDbNjzKKV917UI35ehsvbtypgFQsRpCfu9n+pGIxqlldn0o1C1YEdAzyMs6nehTzAsDnVEldJFCqWaQdvWakYaiq88Hk7AppFWVQf2BUlMn6RPPCns8KQTgTclpOQKVQg3WX8WdQBbYEvpxbq9f39so0rHh3GB/zKiqpNtjazhEa4AmVRoMCC5sbVBoNVGoN/vv7Hfx0/Bw2zo4xipkBurjUlE/TcLe0Nq5l7cwu/YeR1EViV32i+UDvPdGUoN2DNmBPnpY+lnKbvD1EWPu+bTlVpmJadesAulhWoL8Wi96wHjfj6tuad0V5Ws6nqdrnqPe+qdrnKMg+Q55owVwh4usrh9SCXt9f4vvapOcHAHMTzNSZ1Bfucgmkj+otTOyLwnsi6+3KRPDzdrGqUchpCVrTVKxbn2g+0HtPNDVoedAJSF0kYBgG31rS65OJ8E3GH4gf1sWsnp9EIsI3+86jjb8H/jZ3ME78XoSbdyoQEuCJ555ujeNnCvBMF39s//fviB/WhY9l/ZJboIubmYlrecmtH86or1FoTVOxbn2i+UDvPdHUIKflYLj1/4jOLXm9vnX/ysH6WUPg7ipF1UMVZqzJxNcfRyO0tRcS/3oQnq7AxnnD4OXuAo2WReapG9i2+wx2fhqD0NZe2Jp6DjsyziNmQEd0CPJGYUkV3v4sE2otMGpAB4N2tn04jM/PYlnW4PV769IwvK9OucIa+nlXlKf15ELvPdHUIKflYLjzsuJ7azEwaigfvJ6w5IBBvc0pGXgpKgpfp59HxUPj61KJCMVlCoP8qD0/5xvVKdGrU/FQiz9/aNzOpnlRmP3FQVQ+rM3T4q7ZkndFeVpPLvTeE00Nimk5EP31/7i4GP51gC+wZ2UMUleNwp6VMQjwBU7naeHn7YJdn47EnpUxaO1X2w4fe5LaEJ+SMHwdrg+unwlDO2Pz/Ch4uknwUFlb38/bxeZ4GmcXnaf15GLPZ4UgnA3tHrQBe7UHD5y4gdRVo/g8LaWaxfFzhUb5LUdyC4zKfskt4GNaXh4uBm2YO8dq/PAuAGCxn36P4mjf/nSB1wjkcm9Mtau/hZmzy9o4SHvw8WjK9tn6WbFEU7bPEZB9hpD2oADQX//XP4PKXH7LwIggJCw5Z1A24NlgfPvTfuzPBnZ+Es3neo2LBBLiYvTOsUpDXFyMThFDaznnamBEEN5YnomFiX0xuHcIH39QKdRgAAMNxJKSaqjq7F7m7EpYcgATIoG4OuPYkV0/TUNCONj6WSEIZ0PLgw6EW/+XSkRISUnj41t1YwEqtRbLv8qBUs0iwLdOmVKL3/J0Sy8VVSq+vR+zgZlfpOH+g4d4eUEGdmQDX6SkQanU2tTPc710r1t6u/ExLf1xFxdXmY1L6Nu1IxuInZuGMXP2InauzmFRXOPJwdpnhSCcDTktB8Ot/+vHtEzB5bdsnh9jVLZpXkxtvEom4uMJ124bxhfej4uBn7eLTf3MHhfDv/bzdrE7/kRxDYIgmgK0POhgOI2++ua3cGWcluDxcwX4v561OVdcDGvTvCj+Xnv6qW9ODWeXuZwykvEhCKIhIKflBPS1By1hKr+FK9OPUW1NPVeby+XhArAs5m9Ow2dTdbM0e/p5nJwaimsQBNHY0PKgk9CPA5mCiwNN/SzNqKy4TGEUo6p4CExYcgAvf5CO4jIFZDJdTIvL5bLWz+of0wzaf5yYBMU1CIJoLMhpOQlbc5vulNQpk4lsilEtTYzB4WwY5GlZ6ufEr4a6g5RTRRCEEKHlQSfBMAy+Mac9+EgD8JfcAgyPbGsQG/KSy2yOUb01JpyPcVmKN/2SW4Av50VBKq7VEiQIghAi5LSchH6e1ubUHHw5O5qPA039LA0MA2yaV5vvNG1FGka9oNMFtDVGxcWWyquVyL9VhojOLQ20BlP2X8DAfu0xtE8IXvv4JzAi3WyrYxtveLpJG+LXQBAE4VBIEcMGHHGeVmggbM+oSsAAACAASURBVDrnamfWQfw5ynq9I1kHcQ3AxKihAKyfjbUz6yD2Zzfc2VfOgBQHhA3ZJ2yaiiIGOS0bqI/T4mJG3ExK/3VKSgZSz2iRvPBFuEglUKo0UKg08JDLcPriXXRu4438gnLsOJCDtdNrZ2j/b2MGJgzTnRTr6S4zaPPDbWk4d1HXd2u/2lkcAL7/2Lm6TR9vjdHN6Oq7kcLDwwVisQgajRaVlQrrNzgIeigIG7JP2JDTEhD2Oi1Op82cDuDAiCDrOoESxmwdwLzOoLVrCUsOYHhk23rpBEpkEqg0xm1LxQzUSufnadFDQdiQfcKGnJaAsMdpcedp1d2yzu3k6xjkZfF6hyAvMIDVNt5Ynml0bfvCIVbvA4BfcgvsnmlJZBJcKbQw7kAvpzsueigIG7JP2DQVpyWYLe+//vorJkyYgIiICDz77LN46623cPHiRZN19+7di9GjR6N79+4YMGAAVq9eDYWiYZaxLOkAfvF9jlWdQJWatUlLMDjA8Jqft20ahEo1Wy+dQJXGyrg19N2HIAjnIwinlZubi4kTJ0Kj0WD16tX47LPPUFpaivHjx+P69esGdVNTUzF79mw8++yz2LJlC6ZOnYp//OMfmD9/vtPHqX+elik2zRtuUw6Wj6eL1Tqfvx9jUL5pnm1ah37eLkZ5Wr6+cvj5uZvN3fLwsE3f0MPDxeR1giAIRyGILe9JSUnw8fHBtm3b4OrqCgCIjIzEkCFDkJSUhDVr1gAANBoNVq1ahcGDB2Px4sV8PalUio8++giTJk1Cz549nTZOa3qDLlKJTTlYErHlKbUp/cD6aB1ysbe0o9eM4l/6WoJiscimtsViQXwHIghCwAjiKXP69GlERkbyDgsAvLy80KtXL2RlZUHzaPt2bm4u7t27h7FjxxrcP2rUKEilUuzfv9+p49Q/T8sUCpXaphwstZWlNlP6gdb61r+PZVldbldBOaatyMLW1HM4cOIGtqaew7QVWcgvKIfUpfb7jEajtaltjcb0TIwgCMJR2O20KisrsWHDBsTHx2PYsGE4ffo0AKCkpAQbNmxAfn6+wwepUqkgk8mMymUyGWpqanDz5k0AQF5eHgCgc+fOBvXc3NwQEhLCX3cW1vQGp63Yb5NO4IMKhdU6M79IMyiftiLNpraLyxQoLlNAqdRajX9xVFbapm/YkNvfCYJ4MrFrebCkpATx8fG4desWQkNDcfPmTTx8+BAA4Ovriz179qCiogIffPCBQwfZqVMn/Pbbb2BZll/eUqlUOHv2LACgtLQU7dq1w4MHDwAA3t7eRm14e3vz1+2FYWp3zlhCLGZQUaXEwsS+JnfZvf9KX14n0NwuPKmEAQNYrCOTMLh9RwSg9lpxmchq2zIJA4gYeLpJkXb0mtUY1cj+7aB5NOt7qFJbbNvVRQRXqfEXC0cikYgB2PZeCBGyT9iQfYY4Sy3OLqf1+eef4/79+9i5cycCAwPx/PPPG1yPiorC8ePHHTpAABg/fjwWLlyIZcuWYcqUKdBqtUhKSkJRUREAQCQynAGY09Zztuaegd7g/ChknzWtAzggIsji9cjugRa1BAGYvB+A7j4zbUOk0zYE7DmDS+e0XKUSdA7xNm67eyBcZSK4SgURHiUIQuDY9aQ5fPgwXnvtNTz99NMoLS01uh4SEoLdu3c7bHAcL730EkpKSvDll1/i22+/BQA888wzeOONN7Blyxa0atUKAODj4wMAePDgAVq0aGHQRllZGdq0aVOv/lnWttwEX185Qlt7IWHJAexZGWNw7hSgc2oj+7dDSkoaUs8AyQtehItMAoVSjbNXitHaV46+3QLg5+WC2LlpaBcMfP5+rT7hzC/ScO12raKFfvu2KGLov7Y1/lXXbgbA0D4hBooYCrUGCqhs/XXWG8qDETZkn7CpT56WM+YJdjmt0tJShIaGmr3OMIzT8qGmTJmCSZMm4dq1a3B3d0dwcDA++ugjBAcHIzBQN8vo1KkTAF1sq3379vy9XNxr0KBBThkbBxfT+jr9PFJS0jCwjobg832AycOG4l+nRFCptXjlw58M7ud0AYvLdDGta7e1vPSSfp3I8ECkpKThX6d09QHgYn7tcmFRca1kE9dmXbhxmtMrtJTLRbErgiAaC7s2Yvj7+/ObHkzxxx9/8A7EGchkMoSFhSE4OBi3bt1CRkYGXn/9df56REQE/P39kZqaanBfWloaVCoVhg0b5rSx8WN8FFf61ymR0TlXx07CprOvbKnzr1P21Tf1z9o9BEEQTQ27ZJwWLVqEgwcPYs+ePZBKpejXrx+++uor9OvXD7/99htef/11TJw4EXPmzHHoIC9cuIDMzEyEh4dDJpPhjz/+QHJyMrp164YtW7ZAIqmdMO7evRvz58/H66+/juHDhyM/Px+rV6/GCy+8gKSkpHr1X1/twexzhRjwSGdQPzY1ICIISg1rFBuSiWvzo/TbMBn3evSaO08LgHGbejEwfWzpQz9Pq6lAyy/ChuwTNk1Fxskup3Xv3j3ExcVBq9Vi8ODB2LVrF0aPHg2VSoUDBw6gVatWSElJ4WNLjiI/Px8ff/wx8vLyUF1djZCQEMTGxiIxMdHkVvjU1FRs3boVV69eRYsWLTBq1Ci89957Bnle9lDfo0l8feUmVd71/wd0cSZzS3Hm2igpqTZ5zRy29lFf5feGgB4KwobsEzaCdFoAUFhYiKVLl+LIkSPQanXxEIZhMHDgQCxevBitW7d2+CAbG0ecp1UXZ5xrRX80wobsEzZknyHOclp271MODAzEl19+icrKSly5cgUAEBoa6vDZldDx9ZXzuVATIoG4uBi987TSsCO7NhfKUbMbsZgBwzDw83Pny9RqbbP9IyII4snDZqdVVVWFZcuWYcCAAXjxxRfh4eGBHj16OHNsgobTAvxm0TAjfb+BUUMxdLguHuWI3DFLGoJKNQvZI0mmphinIgiCsAebnZa7uzsyMjLw7LPPOnM8zQaWZZEwoqvJ862+Tj+PhYl9MX54FyMNQXsxd34X1wd3flfHIC9IXSTkuAiCEDR2bXnv2LEjbt++7ayxNCtKSqptOt/qcZcGbelj057as7QIgiCEjF1O680338S//vUvXL161VnjaTZYO1uL0/czd4bV4/QxIRLYszIGuz4dCT9vF2yaE82fpdVcddEIgngysGsjxpUrVxAYGIhRo0Zh0KBBaNu2rdE2coZhMH36dIcOUojYc74VKxbXKzfKVB+mYmj9wgMxICIIDMNAbOWsLoIgiKaMXU5rw4YN/OuDBw+arENOS4c951tNW5HFx5/scVx1+/hm0TDL8a023nB3kdpvDEEQRBPBLqeVlZXlrHE0O/R1CC3p+6WkpEGlBpZ/laPL26pnH/G9tVbjW5vmRUGpVNjVB0EQRFPCLqcVHBzsrHE0O3x95RbP1uL0/eLiYrAjO83gDCt7NmfIZLq2Ijq3tDkvDECTVr4gCIIwR70PQSotLcWtW7cAAG3atDE6CuRJx+BsLTPnYn27/yLeGVeb61Z7hpXteMll6NjGu0HzwgiCIBoLu53WhQsXsGzZMvz6668G5b1798bChQvRtWtXhw1OyHDxpoQlB8zMfHTnYunnaXExLnv78XSTNlheGEEQRGNil/bgpUuX8Morr0CpVOJPf/oTOnfuDAC4fPkyDh8+DFdXV3z33Xd8eXPBmdqDR7IOYkf242kRsmIx/9pafwAcqnfYkJC2m7Ah+4SNILUHk5KSIJVK8d1336FLly4G1y5duoTx48cjKSkJ69evd+gghQp3ZpWlmBbnsLifPb3ldseb/LxdAICPaZmivjEzgiCIpoRdTuvkyZN47bXXjBwWAISFhSE+Ph7fffedwwYndFQKNToGeVmMab01JtxsjMtWKmpU8JLLbM4LIwiCECp2Oa2amhr4+/ubvd6qVSvU1DTPqXF94HQBr5zNQVxctF5MKwNxcdF4Z1wPizEum/u4VYaIzi1tzgsjCIIQKnbJOIWEhODw4cNmrx8+fBghISGPPajmApc3BdRdstPi5QUZuP/gITanpAHQyS7FvNAeDMPYJe3E9ZGSkoZ+4YGQSky/pVxeWHGZop7WEARBND52zbTGjBmDtWvXYtasWZg2bRo6dOgAQHey8ObNm3H06FHMmjXLKQMVGtx5WtsXDjG7Bf2X3AK89uJwKJVa42NFbDhORP/MrqHDhwGA1Riap7vupGeKaxEEIUTs2j2o0Wgwa9Ys7Nu3DwzDQCTSfavXarVgWRYvvvgi1qxZw5c3F+qze9DPzx3llQqTW9A5J8IdG2LpOmDecfn5uWPjrt8MtrpzTtIghtY9EDIxAxaAt4du00ZxcZWdv4XGh3ZnCRuyT9gIcvegWCzG559/jqNHjyIzMxO3bt3S5SOFhmLIkCF4/vnnHT5AocKyrEVZpStncxDSKsqq7BIAs7JLXC6Yfj+m8sKKyxQ4uD8TA6OiKKZFEISgqZciRv/+/dG/f39Hj6XZwR0bYiq5OC4u2uYt6oDp5bySkmrE9G9n1M6ObGBHdhr/81tjwhEXF430o9doyztBEILGLqf14MEDFBUVmVW9uHDhAgIDA+Ht7e2QwQkZa7JKFTUqh2xRt+cIFO54EoIgCKFil9NatWoVzp8/j927d5u8vmDBAoSHh2Pp0qUOGZyQsUVWKWFEVxw4ccNsG7ZsUbfnCJT8gnJ0bENfKAiCEC527Zg4ceIEBg0aZPb64MGDcfz48cceVHOgpKTa6lEhSjWLCZHWt6hbWs7jjiexttU9JSVN16fS9HIkQRCEELDLad29exeBgYFmrwcEBODu3buPPajmgK+vnI9pmYKLWcXFRRs5HP0t6jKJ9eU87ngSS+3syK7t0548MIIgiKaEXcuDbm5uKCgoMHu9oKAAMpnssQfVHLAn1rRpfhSyzxrLPAGW87Q4vOQynVyUmXYSlhww6pMgCEKI2OW0evbsiT179mDy5Mnw8PAwuFZZWYnU1FT06GG/dl5zxJ5YE6PWYGT/drwzYVnWrh1+LMviSG4BYvq349vRl4cy1SdBEIQQsWt58I033kBRURHi4+Oxb98+XL9+HTdu3MC+ffsQHx+PoqIiTJ482VljFRS2xpo451RSUo3i4ioUF1fZvSWd6yslJQPFZQq89EE6YucaO6y6fRIEQQgNuxQxAOC7777D8uXLoVbXLluxLAupVIoFCxYgPj7e4YMEgPPnz2PDhg04c+YMKisrERQUhNjYWEyaNIlfkkxISEBOTo7RvdHR0Vi3bl29+67veVqcYK4lxQtblv+s4esrR0WNCvm3yqyrbLTxhqpG9dh9NgakOCBsyD5h01QUMex2WgBw584d/PTTT7h+/TpYlkX79u0xYsQIBAQEOHyAgE7bcOzYsWjfvj2mTp2KFi1aIDs7G8nJyRg1ahRWrlwJQOe07t69ixUrVhjc36JFC7Rt27be/dfXaQE6x2Ukq/Qo1qRUs5BJmMd2XH5+7gCA8koF36a5Pr08XAQp4QTQQ0HokH3Cpqk4rXopYgQEBGDSpEkAAJVKhUOHDuHIkSMYNGiQxaNL6ktGRgYUCgXWr1+P0NBQAEC/fv1QUFCA9PR0LF++HFKpFADg6uqKiIgIh4+hvqgUashcJCZjTY6acXHfO47kFkB55xyvvlG3T5eAcF5hgyAIQojY5bRWrlyJEydO4McffwSge1i+8cYbOHXqFFiWhY+PD3bu3Mk7FocNUqIbZt3NH56enpBIJBDrHTffFFGqWby8IMOixuDjfB8pKakGKxajX3ggpqWfN5Bw4pBKRNg0j+JZBEEIG7uc1n/+8x8DUdxDhw7h5MmTePPNN/HUU0/hr3/9K5KTk7Fs2TKHDnLMmDH4+9//jsWLF2POnDn88uDu3buRmJhooCp/9epV9OnTB1VVVWjTpg1iY2Px1ltv8TOx+sAwtVNjexGLGZs1BjUa45VasZjhZ0umrnNUK9WAluWPJhncW4u39fQOc/PuQyYTQS7glASJRPflpL7vRVOH7BM2ZJ8hzsqssctpFRUVGcSGDh8+jDZt2mD27NkAgLy8POzdu9exIwQQFBSE77//HtOnT8eQIUP48mnTpmHmzJn8z7169UJ0dDQ6dOiA6upqZGZmIikpCb///js2btzo8HHZgj35WkCtU6pWqqFUanH8XKHhOVsyEeQy02+bUs2iY5CXyTO8+oWbTwonCIIQCnY5LZVKZbAUd+LECYOZV0hICO7du+e40T3i9u3bmDZtGvz9/bFx40Z4enri5MmT2Lx5MxiG4R2XvgMDgEGDBqFly5bYtGkTTp06hd69e9erf5atf3DV11duc74W14e5XYecZmHdGJh+/e0Lh9h1r5CgQLewIfuETX02YjhjtmVXnlbr1q2Rm5sLQDerunnzJvr06cNfLy4uhlzueImgNWvWoKqqClu2bMGQIUPw3HPP4d1338XUqVOxefNm3Lp1y+y9sbGxAMCPu6GxN18LgE2ahfpw9Qf31tp9L0EQhJCwy2mNHDkSe/bswdSpUzF16lR4eHhg4MCB/PU//vjD4ZswAF2OVqdOneDq6mpQHh4eDq1WiytXrpi9V6vVPbwb8zRlmYSxqg3IYatmIacfqF//7bgYu+4lCIIQGnYtD06dOhWFhYXIysqCh4cHVqxYAS8v3dJXRUUFDh06xG+FdyStWrVCXl4eampq4OZWGwQ8ffo0AFjMD0tNTQWgk6BqLFQKtU4bcF6Uydwp/eU6+2JghvXtvZcgCEJo2OW0ZDIZPvnkE5PX3N3d8d///tdoNuQIJkyYgOnTp2Py5MmYOHEiPD09ceLECWzbtg3PP/88unTpglOnTiE5ORnDhg1DcHAwqqurkZWVhZSUFIwYMQK9evVy+LjsQaVQgwEMcqdKSqqh0hjWs0ezsG59e+8lCIIQGvVSxGgMjh07huTkZFy6dAnV1dUIDg5GdHQ0EhMTIZfLcf36dSxfvhwXLlxAaWkpRCIR2rdvj9jYWCQkJDxWLtfjKGLUB1YsxrQVWSaX+XT5VlFgNBqj+oN7a/HnqKF23SskKNAtbMg+YdNUFDEE47Qak4Z2WvZoFvr6ylFRpbS6e9DReoeNAT0UhA3ZJ2zIaQmIhnZagGXNQn2n4+fnjo27fkPCiK58/QERQcb3dg+ETPz4OoeNCT0UhA3ZJ2yaitOql/Yg4XzsjYElLDmA7l2Bv75Rq4Khfy8AknAiCELwkNNq4lhzNFwe2Nfp53H2ghaxcw11B4UexyIIgtCHnFYj4usrN5hF1RcuD2z5VzmI763lVd55zUEJYzRDIwiCECLktBoBLl5VVxuwvmdrcXlgpDlIEERzh5xWA2OvrqA9NFfNQYIgCI7G0zZ6QnGWNiBpDhIE8SRATqsBsVdXsLHbJQiCaGqQ02pAnKUNSJqDBEE8KZDTakCcpQ1ImoMEQTwpkNNqQOpztlZjtksQBNHUIKfVwNhztlZTaJcgCKIpQdqDNuBo7UFbdQXtbtdNCqVKi+yzjm23qUDabsKG7BM2pD34BGOrrqC9yGUSyGWOb5cgCKKpQE6rEXFWjIliVwRBNFfIaTVx7NUnFIsZ+Pm5P7aeIUEQRFOEnFYTxV59QqmLBA8qlTh+rtAheoYEQRBNEXJaTRB79QmdqWdIEATRlKAt700Qe3UESXeQIIgnBXJaDYSvrxx+fu5W9f/s1REk3UGCIJ4kaHnQydgbm7JXR5B0BwmCeJIgp+VE6hNrsldHkHQHCYJ4kqDlQSdSn1iTvTqCpDtIEMSTBDktJ/E4sSZ7dQRJd5AgiCcFWh50Eo8Ta1Ip1OgY5IVN86Js0ifk68+Para6gwRBEAA5LafxuLEme/UJVQo1fLzdSHeQIIhmjWCWB8+fP4933nkHL7zwAiIiIhAdHY3k5GQolUqDekePHsWf//xn9OjRA/369cPHH3+M8nLLMx5n4KhYU0lJNYqLq2yOSWk0rF31CYIghIQgnFZ+fj5effVV3L59GwsWLMCXX36JoUOHYt26dfjwww/5eidOnMCUKVPQunVrbNq0CfPmzcOhQ4cwZcoUaLWmY0vOhGJNBEEQjkUQy4MZGRlQKBRYv349QkNDAQD9+vVDQUEB0tPTsXz5ckilUqxatQqdO3fG559/DpFI5yj8/f3xxhtvYN++fYiOjm7QcdsbmyIIgiAsIwinJZHohunh4WFQ7unpCYlEArFYjDt37uDs2bOYP38+77AAoH///ggICMD+/fsb3GkBzjs7iyAI4klEEMuDY8aMgY+PDxYvXoybN2+isrISmZmZ2L17NxITEyESiXDp0iUAQOfOnY3uDwsLQ15eXkMP2wB7Y1MEQRCEMYKYaQUFBeH777/H9OnTMWTIEL582rRpmDlzJgDgwYMHAABvb2+j+729vXH+/Pl6988wtUdNN2UkEjEAYYy1PpB9wobsEzb22ucs5ThBOK3bt29j2rRp8Pf3x8aNG+Hp6YmTJ09i8+bNYBiGd1wAzGrskfYeQRCE8BGE01qzZg2qqqqwZ88euLq6AgCee+45AMDGjRvx0ksvwcfHB0DtjEufsrIykzMwW2FZoKyspt73NxTcNyAhjLU+kH3ChuwTNvba5+fn4ZTZliBiWufPn0enTp14h8URHh4OrVaLK1eu8LEsU7GrS5cumYx1EQRBEMJCEE6rVatWyMvLQ02NoYc/ffo0ACAgIACtW7dGeHg49u7da5CTdfz4cdy5cwfDhg1r0DE3FmIxY9O5XQRBEEJEvHjx4sWNPQhreHp64ocffsDJkyfh4eGBu3fvYteuXdi2bRsiIyMxefJkAEBoaCi2b9+Oy5cvw9vbG7/++iuWLFmCzp07G22FtweWBWpqlNYrNiJSFwmqHmpw6NdbyMy5gTulNWjT2gsymRhaTcMnVjsDV1cpAEDRTPPbyD5hQ/YZIpfLnLKXgGEFctDSsWPHkJycjEuXLqG6uhrBwcGIjo5GYmIi5PLaWcUvv/yC9evX48KFC3B3d8eQIUMwZ86cx4ppabUsiosrHWGGUzB3bhenvGHq3C4hQjEDYUP2CZv6xLREoifYaTUmTd1psWIxpq3IMnkMilQiwqZ5UWA0ws9mpoeCsCH7hE1TcVqC2D0oVHx95QYqGM7qI+3oNajUWkyIBOLiYvg+U1LSsCNbd27XyP7tKLGZIAjBQ07LCUhdJFCqWaQdvYYbReUIbe2Ffk7SG+TO7fpm0TCjPgdGDcXQ4Qy+3X+R8tQIgmgW0PKgDdizPNjQ8SVfXzkqqpRW+/R0lwl+pkXLL8KG7BM2TWV5UBBb3oWEUs0aOQ8AUKm1WP5VDpRqx35HKCmptqlPoTssgiAIgJyWQ/H1leP4uUKTGyIAnRPJPlfo0ByqxuiTIAiisSCn5UC4+JIlbtypcGh8qTH6JAiCaCzIaTkQlmUR2trLYp3QAE84MozYGH0SBEE0FuS0HEhJSTX6hQdCKjH9a5VKRIgMD3RofKkx+iQIgmgsyGk5GJmEwcLEvkZOhNvJJ5M4fpmuMfokCIJoDGjLuw3Yq4jB5WllnyvEjTsVCA3wRKST8rQM+tSwyD7bcH02NLSlWNiQfcKmqWx5p+RiJ6BSqMEAGNm/nYEihsqJSkoqhRo+3m4N2idBEERDQ07LiTRGHEmjYVFWRvErgiCaJxTTIgiCIAQDOS2CIAhCMJDTIgiCIAQDOS2CIAhCMJDTIgiCIAQDOS2CIAhCMJDTIgiCIAQDOS2CIAhCMJDTIgiCIAQDOS2CIAhCMJDTIgiCIAQDOS2CIAhCMJDTIgiCIAQDOS2CIAhCMJDTIgiCIASDIM7Tmj9/Pnbv3m32+n//+1/4+/sjISEBOTk5Rtejo6Oxbt06Zw7Rafj6yg0OdSQIgniSEYTTeuedd/Dqq68alKnVakyePBldunSBv78/X96uXTusWLHCoG6LFi0aZJyOROoigVLNIu3oNdwoKkdoay/0Cw+ETMJApVA39vAIgiAaBUE4rdDQUISGhhqUHThwAA8fPsRLL71kUO7q6oqIiIiGHJ7DkbpIkF9QjuVf5UCl1vLlX6efx8LEvugY5EWOiyCIJxLBxrR+/PFHuLm5ITo6urGH4nCUatbIYQGASq3F8q9yoFSzjTQygiCIxkUQM6263L17F//5z38watQoeHh4GFy7evUq+vTpg6qqKrRp0waxsbF46623IJVK690fwwDe3m6PO2ybEIsZpB29ZuSwOFRqLbLPFWJk/3bQaAydl0QiBtBwY21oyD5hQ/YJG3vtYxgnjcM5zTqXPXv2QKPRGC0N9urVC9HR0ejQoQOqq6uRmZmJpKQk/P7779i4cWMjjdY+GIbBjaJyi3Vu3KkAwzAAaMZFEMSThSCdVkpKCtq2bYs+ffoYlM+cOdPg50GDBqFly5bYtGkTTp06hd69e9erP5YFyspq6j1ee/D1lSO0tZfFOqEBnmBZ1mhM3DeghhprQ0P2CRuyT9jYa5+fn4dTZluCi2mdOnUKV69eRVxcnE31Y2NjAQC5ubnOHJbDKCmpRr/wQEglpt8aqUSEyPBA2v5OEMQTieCc1o8//gixWIyxY8faVF+r1cWGRCLhmCqTMFiY2NfIcUklIixM7AuZxEmLxQRBEE0cQS0PVldXY9++fXjhhRcQEBBg0z2pqakAgJ49ezpzaA5FpVCjY5AXNs2LQva5Qty4U4HQAE9EUp4WQRBPOIJyWhkZGaiursa4ceOMrp06dQrJyckYNmwYgoODUV1djaysLKSkpGDEiBHo1atXI4y4/qgUajAARvZvZ6CIodI09sgIgiAaD0E5rZSUFLRo0QKDBw82usapYiQlJaG0tBQikQjt27fH/PnzkZCQ0NBDdRgUuyIIgqiFYVmW9k1bQatlUVxc2djDsArtXhI2ZJ+wIfsM8fPzgEjk+Pi7cHYnEARBEE88NNOyAZZlIYTfEpcTIYSx1geyT9iQfcLGXvsYBo9EEBw8DnJaBEEQwiKO+AAADjtJREFUhFCg5UGCIAhCMJDTIgiCIAQDOS2CIAhCMJDTIgiCIAQDOS2CIAhCMJDTIgiCIAQDOS2CIAhCMJDTIgiCIAQDOS2CIAhCMJDTIgiCIAQDOS2CIAhCMJDTIgiCIASDoA6BFDrHjx9HamoqTp8+jaKiInh7e6NHjx6YMWMGunTpAgC4desWoqKizLbxyiuvYOnSpQZlJ06cwObNm3HmzBmoVCoEBwdj4sSJeOWVVwzq7d27F1u2bMHVq1fRokULjB49GjNmzICLi4tBvfv372PVqlX4+eefoVAo0K1bN8yePRvPPvtsg9t3/vx5bNiwAWfOnEFlZSWCgoIQGxuLSZMmQSaTGdx79OhRfPHFF7hw4QLc3d0xdOhQzJ49G15eXgb1qqqqsG7dOuzbtw/l5eXo1KkTpk+fbnFcttoHAEqlEl9//TX27NmD27dvw8PDA+Hh4Zg+fTq6d+9e77EIzT5b2xOqfXVZv349NmzYgK5duyI1NbXZ2NdUni88LNFgzJgxg01ISGD/+c9/sidOnGDT09PZsWPHsuHh4ezp06dZlmVZhULBnj592ujfxx9/zIaFhbFHjhwxaDMlJYV96qmn2I8//pg9cuQIe+zYMfbbb79lv/nmG4N6e/bsYcPCwthFixaxx48fZ7/99ls2IiKCnTlzpkG9hw8fsiNHjmQHDRrE7t27l/3Pf/7Dvvnmm2z37t3Z33//vUHtu3z5Mtu9e3d29OjRbHp6Onvs2DF27dq1bNeuXdk5c+YY9J2dnc1269aNnTFjBnv06FF29+7dbP/+/dlXXnmF1Wg0BnUnTZrE9u3bl925cyd77Ngxds6cOWzXrl3Zn3/++bHtY1mWnT9/Ptu1a1d27dq17LFjx9j09HR29OjRbHh4OJuXl1evsQjRPlvbE6p9+ly6dInt3r07+/zzz7OjR482ui5U+5rS84WDnFYDcv/+faOysrIytnfv3uy7775r8d5Ro0axAwYMMPiAFxQUsD169GCTk5Mt3qtWq9n+/fuz06ZNMyj//vvv2bCwMDY3N5cv+/bbb9mwsDD23LlzfJlCoWAHDx7MTp48uUHtS0pKYsPCwtjr168b1J09ezbbrVs3VqlU8mXjxo1jx4wZY3D/f//7XzYsLIxNT0/ny37++Wc2LCyMPXDgAF+m1WrZV199lR0xYsRj26dUKtmnn36anT17tkG969evs2FhYWxSUlK9xiJE++z5PAjRPg6NRsO+/PLL7NKlS9nx48ebdFpCtK+pPV84KKbVgPj5+RmVeXl5oW3btigqKjJ739mzZ3Hx4kWMHTsWIlHtW7Zr1y4AQEJCgsV+c3Nzce/ePYwdO9agfNSoUZBKpdi/fz9flpmZibCwMDz99NN8mUwmQ0xMDI4dO4bKysoGs08i0a1ee3h4GNT39PSERCKBWCwGANy5cwdnz57FmDFjDO7v378/AgICDOw7ePAgPD09DZZaGIbB2LFjceXKFVy+fPmx7BOJRGAYBp6engb1OBv0lzRtHYtQ7bP18yBU+zj+/ve/o6ioCH/5y19M9itU+5ra84WDnFYjU1JSgry8PHTu3NlsnR9//BEMw2DcuHEG5SdPnkTHjh1x4MABDB8+HE899RQGDBiA1atXQ6lU8vXy8vIAwKgPNzc3hISE8Ne5umFhYUZj6NKlCzQaDa5cudJg9o0ZMwY+Pj5YvHgxbt68icrKSmRmZmL37t1ITEzkHwCXLl0yaR8AhIWFGdnXqVMng4cHZ59+W/W1TywWY/z48di9ezcyMzNRWVmJmzdvYvHixWjZsiViY2PtHotQ7bOlPaHbd/PmTSQlJeHjjz82+nIldPua6vOFNmI0IizL4qOPPoJWq8XkyZNN1lEoFEhPT0ffvn0REhJicO3u3bu4e/culi1bhvfffx+dOnVCdnY2kpOTUVhYiDVr1gAAHjx4AADw9vY2at/b25u/ztU1Vw8ASktLG8y+oKAgfP/995g+fTqGDBnCl0+bNg0zZ840GLMl+86fP29Qt127dmbt0/9d1Ne+efPmwcPDAzNmzIBWqwUAhIaGYseOHQgICLB7LEK1z9b2hGofy7L48MMP8cILLxh8PusiVPua6vOFnFYjsnLlSmRmZuLTTz9Fx44dTdY5cOAAysvLjWYhgO5DWVVVhbVr12LkyJEAgOeeew4PHz7E9u3b8d5776Ft27Z8fYZhTPZRt9xcPWvX6vK49t2+fRvTpk2Dv78/Nm7cCE9PT5w8eRKbN28GwzAGjsvS2Bravs2bN2Pr1q34y1/+goiICJSWlmLbtm1488038c0336BNmzb1GosQ7bOlPWtja6r27dy5E+fOnUNGRoZN/QnNvqb6fKHlwUZi3bp12L59OxYuXIi4uDiz9X788Ud4enpi+PDhRtd8fHwAAC+88IJB+YABAwAAv//+u0E9U9/SysrKDL75+Pj4mK2n35Y1HGHfmjVrUFVVhS1btmDIkCF47rnn8O6772Lq1KnYvHkzbt265XD7TH0LtMe+/Px8rFu3DjNnzsSUKVPQt29fDB8+HNu2bUNFRQU2btxo91iEap8t7QnVvpKSEqxatQpTp06Fm5sbysvLUV5eDrVaDa1Wi/LycigUCsHapz/upvZ8IafVCHzxxRfYtGkT5syZgwkTJpitd/v2bWRnZ2PkyJFwdXU1um5qbVgfbl28U6dOAGCwtgwANTU1uHnzpsFadKdOnUyum1+8eBFisRgdOnSw2CfgOPvOnz+PTp06GV0LDw+HVqvl17+58de1D9DFAOral5+fzy+L6NcDrP9Ordl34cIFsCyL8PBwg3JPT0+0bdsW+fn5do9FqPbZ0p5Q7btz5w4qKiqwZs0a9OnTh//3v//9D5cuXUKfPn2wfv16wdpnS1+N9Xwhp9XAbNiwAX/729/w/vvv480337RYNyUlBSzLmlw6A4ChQ4cCAI4cOWJQfuTIETAMwycKRkREwN/f3yjhMS0tDSqVCsOGDTNo89KlS/jjjz/4MqVSifT0dPTr189ssNkZ9rVq1Qp5eXmoqakxKD99+jQA8OvvrVu3Rnh4OPbu3Wvwx378+HHcuXPHyL7y8nIcOnTIoM09e/agffv2/B9gfe1r1aoVAN2OSH0ePHiAa9euGcQMbB2LUO2zpT2h2sfFgOr+69q1K3+NS74Von3cWICm9XwBAPHixYsXW61FOITt27dj7dq1GDRoEMaOHYuioiL+X0lJCfz9/fm6LMvigw8+QGBgoNmttG3btsWZM2fw/fffw8XFBdXV1fjhhx/w1Vdf4ZVXXsHo0aMB6L4RtWjRAsnJySgtLYWrqyt++eUXrFy5EoMHD0ZiYiLfZpcuXXDgwAHs3bsX/v7+uHv3Lj777DNcvHgRq1ev5j/0DWGfp6cnfvjhB5w8eRIeHh64e/cudu3ahW3btiEyMtIguBwaGort27fj8uXL8Pb2xq+//oolS5agc+fOmD9/Pv+tsG3btjh58iR27tyJFi1aoLy8HBs2bMDhw4fxySefoH379o9lX+vWrfHzzz/jp59+glarhVqtxtmzZ7Fo0SIUFRVh0aJFCA4OtnssQrTPns+D0OyTyWRo06aN0b+MjAxotVrMnj3bYFlMaPZxY2lKzxcOhmVZ1motwiEkJCQgJyfH5LXg4GCDb1fHjx/HpEmT8MEHH2DSpElm26yursb69euRlpaG0tJSBAYG4uWXX8abb75ptG02NTUVW7du5WVWRo0ahffee89o+e3evXtYuXIljhw5wsuszJo1C717925w+44dO4bk5GRcunQJ1dXVCA4ORnR0NBITEyGXyw3q/vLLL1i/fj0vkzNkyBDMmTPHKA5QWVmJtWvXYv/+/QYyOZZ2gNljX3l5OZKTk5GZmYnCwkJ4enriqaeewjvvvINnnnmm3mMRmn32fB6EaJ+5PsrLy03KOAnRvqb0fOEgp0UQBEEIBoppEQRBEIKBnBZBEAQhGMhpEQRBEIKBnBZBEAQhGMhpEQRBEIKBnBZBEAQhGMhpEYQAGTx4sNVzjgiiOUJOiyAIghAMlFz8/9u7Y5b0ojCO478GlcIpCYK4DoGTS76ABhtqinB1iCZtElq0IWgKu04tCk0FklMODU5N9RZSB2vSwKDB5YIgGE0Gov9y+Hu5R76fzefcA8/2495zPAcw0OgSvmk36QKLjNACABiDSyABjxldrT5NIpHQ5eWldnZ2tLGxoXK5/DM2qp2ensq2bb28vMjn8ykejyubzSoUCrnRPjBXhBbgMYVCYaL29PSkWq32Z/B8fHzo6OhIu7u72tvbU7PZVLVaVb1e1/39vZaXl+fVNuAKQgvwmIODg7Hf9Xpd5+fn2traUiaT+XVuu92eODk/Eokon8+rXC4rlUrNo2XANeweBDys2+3q+PhYq6urKpVKCgQCvz4fDAaVTCbHaslkUsFgUI+Pj/NsFXAFb1qARzmOo3Q6rX6/r5ubm5nWpCzLmthR6Pf7ZVmWOp3OvFoFXENoAR40HA51cnKit7c3XV9fKxKJzDRvaWlpap1NwlgUfB4EPOji4kLPz886OzvT9vb2zPPa7fbPf7hGBoOB3t/fZVnW/24TcB2hBXjM7e2t7u7udHh4OLE+9RfHcVSpVMZqlUpFjuP8eV07YAI+DwIe0mq1ZNu21tbWFI1G9fDwMDYeDocVi8X+OT8cDqtYLOr19VXRaFSNRkPValWbm5ucVYiFQGgBHtLr9fT19aXPz0/lcrmJ8UQi8Wtora+v6+rqSrZtq1aryefzaX9/X7lcTisrK/NsHXAFxzgBC2LaKRnAomFNCwBgDEILAGAMQgsAYAzWtAAAxuBNCwBgDEILAGAMQgsAYAxCCwBgDEILAGAMQgsAYIxvbI/jzwD38ikAAAAASUVORK5CYII=\n",
656 |       "text/plain": [
657 |        "<Figure size 432x288 with 1 Axes>"
658 |       ]
659 |      },
660 |      "metadata": {},
661 |      "output_type": "display_data"
662 |     }
663 |    ],
664 |    "source": [
665 |     "#graph zip and score, grouping by the zip code \n",
666 |     "z = df.groupby(df['zip'])\n",
667 |     "sns.scatterplot(x = df['zip'], y = df2['score'])"
668 |    ]
669 |   },
670 |   {
671 |    "cell_type": "code",
672 |    "execution_count": 17,
673 |    "metadata": {},
674 |    "outputs": [
675 |     {
676 |      "data": {
677 |       "image/png": 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\n",
678 |       "text/plain": [
679 |        "<Figure size 432x288 with 1 Axes>"
680 |       ]
681 |      },
682 |      "metadata": {},
683 |      "output_type": "display_data"
684 |     }
685 |    ],
686 |    "source": [
687 |     "#check the spread of zipcodes, grouping by the zip codes\n",
688 |     "df.groupby(df['zip'])\n",
689 |     "zip_plot = df['zip'].plot.hist(bins = 20)"
690 |    ]
691 |   },
692 |   {
693 |    "cell_type": "markdown",
694 |    "metadata": {},
695 |    "source": [
696 |     "# Data Analysis & Results"
697 |    ]
698 |   },
699 |   {
700 |    "cell_type": "code",
701 |    "execution_count": 19,
702 |    "metadata": {},
703 |    "outputs": [
704 |     {
705 |      "data": {
706 |       "image/png": 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\n",
707 |       "text/plain": [
708 |        "<Figure size 432x288 with 1 Axes>"
709 |       ]
710 |      },
711 |      "metadata": {},
712 |      "output_type": "display_data"
713 |     }
714 |    ],
715 |    "source": [
716 |     "#plot the frequency of the health inspection scores, using yelp and inspections data\n",
717 |     "score_plot = df2['score'].plot.hist(bins = 20)"
718 |    ]
719 |   },
720 |   {
721 |    "cell_type": "code",
722 |    "execution_count": 20,
723 |    "metadata": {},
724 |    "outputs": [
725 |     {
726 |      "data": {
727 |       "image/png": 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\n",
728 |       "text/plain": [
729 |        "<Figure size 432x288 with 1 Axes>"
730 |       ]
731 |      },
732 |      "metadata": {},
733 |      "output_type": "display_data"
734 |     }
735 |    ],
736 |    "source": [
737 |     "#plot the frequency of the yelp rating, using yelp and inspections data\n",
738 |     "rating_plot = df2['rating'].plot.hist(bins = 20)"
739 |    ]
740 |   },
741 |   {
742 |    "cell_type": "code",
743 |    "execution_count": 21,
744 |    "metadata": {},
745 |    "outputs": [],
746 |    "source": [
747 |     "#transform to be able to see the absolute change instead of the relative change\n",
748 |     "df2['score'] = np.log10(df2['score'])\n",
749 |     "df2['rating'] = np.log10(df2['rating'])"
750 |    ]
751 |   },
752 |   {
753 |    "cell_type": "code",
754 |    "execution_count": 26,
755 |    "metadata": {},
756 |    "outputs": [
757 |     {
758 |      "data": {
759 |       "text/plain": [
760 |        "<matplotlib.axes._subplots.AxesSubplot at 0x7f27a7868320>"
761 |       ]
762 |      },
763 |      "execution_count": 26,
764 |      "metadata": {},
765 |      "output_type": "execute_result"
766 |     },
767 |     {
768 |      "data": {
769 |       "image/png": 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\n",
770 |       "text/plain": [
771 |        "<Figure size 432x288 with 1 Axes>"
772 |       ]
773 |      },
774 |      "metadata": {},
775 |      "output_type": "display_data"
776 |     }
777 |    ],
778 |    "source": [
779 |     "#graph the log_transformation of health inspection scores\n",
780 |     "df2['score'].plot(kind = 'hist', bins = 25)"
781 |    ]
782 |   },
783 |   {
784 |    "cell_type": "code",
785 |    "execution_count": 25,
786 |    "metadata": {},
787 |    "outputs": [
788 |     {
789 |      "data": {
790 |       "text/plain": [
791 |        "<matplotlib.axes._subplots.AxesSubplot at 0x7f278b98cb38>"
792 |       ]
793 |      },
794 |      "execution_count": 25,
795 |      "metadata": {},
796 |      "output_type": "execute_result"
797 |     },
798 |     {
799 |      "data": {
800 |       "image/png": 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\n",
801 |       "text/plain": [
802 |        "<Figure size 432x288 with 1 Axes>"
803 |       ]
804 |      },
805 |      "metadata": {},
806 |      "output_type": "display_data"
807 |     }
808 |    ],
809 |    "source": [
810 |     "#graph the log_transformations of yelp rating\n",
811 |     "df2['rating'].plot(kind = 'hist', bins = 25)"
812 |    ]
813 |   },
814 |   {
815 |    "cell_type": "code",
816 |    "execution_count": 28,
817 |    "metadata": {},
818 |    "outputs": [
819 |     {
820 |      "data": {
821 |       "text/plain": [
822 |        "<matplotlib.axes._subplots.AxesSubplot at 0x7f27987fbb00>"
823 |       ]
824 |      },
825 |      "execution_count": 28,
826 |      "metadata": {},
827 |      "output_type": "execute_result"
828 |     },
829 |     {
830 |      "data": {
831 |       "image/png": 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\n",
832 |       "text/plain": [
833 |        "<Figure size 432x288 with 1 Axes>"
834 |       ]
835 |      },
836 |      "metadata": {},
837 |      "output_type": "display_data"
838 |     }
839 |    ],
840 |    "source": [
841 |     "#graph the rating as the x-axis and score as the y-axis\n",
842 |     "sns.scatterplot(x = df2['rating'], y = df2['score'], data = df2)"
843 |    ]
844 |   },
845 |   {
846 |    "cell_type": "markdown",
847 |    "metadata": {},
848 |    "source": [
849 |     "# Ethics & Privacy"
850 |    ]
851 |   },
852 |   {
853 |    "cell_type": "markdown",
854 |    "metadata": {},
855 |    "source": [
856 |     "Since health inspections and their reports are public record, it was crucial to remove the names of health inspectors, their ID's, and anything that would identify them for privacy reasons. Since privacy concerns are more related to individual people, rather than establishments, I did not remove any identifying information for the restaurants, including zipcodes, addresses, hsisid, and phone numbers. \n",
857 |     "\n",
858 |     "On the other hand, there are more considerations when it comes to ethics than privacy in this analysis. Firstly, I only conducted my analysis in one city in US; there is a danger that the results of this analysis could be used to describe restaurants all over the country, which would be wrong. One ethics concern from the start of this analysis, is the bias that comes from choosing only restaurants in only one area. One way to reduce this bias is using all restaurants in Raleigh, and not choosing certain restaurants, which is what I did. In addition, it is crucial in the discussion section to explain how the results are only applicable to Raleigh's restaurants and their connection to Yelp reviews. Lastly, the health inspection informtion is already public but connecting yelp reviews to those inspections could be used to argue against the importance of health inspections, which is not the goal of the analysis. "
859 |    ]
860 |   },
861 |   {
862 |    "cell_type": "markdown",
863 |    "metadata": {},
864 |    "source": [
865 |     "# Conclusion & Discussion"
866 |    ]
867 |   },
868 |   {
869 |    "cell_type": "markdown",
870 |    "metadata": {},
871 |    "source": [
872 |     "Based on this analysis, I can conclude that in Raleigh, NC, the ratings on yelp do not influence future health inspection scores; my hypothesis that the ratings would make restaurants healthier is wrong.\n",
873 |     "\n",
874 |     "One issue I encountered in my analysis was the fact that there were more retaurants from a specific zip code than other zip codes, which is illustrated in the first few graphs which show a skew when graphing the zip code with the scores. However, one I explored this data a bit more, I realized that this skew was not because of health inspection scores, but actually a result of the dataset having more restaurants in the 27600 zip. This is when I decided it would be better to answer the question I asked if I just directly compared the scores to the ratings.\n",
875 |     "\n",
876 |     "Another limitation of the data related to the health inspection process and reports. First, the reports contain mostly non-numerical items, which cannot be used in a numerical analysis. Second, even though this data went back to 2012, the fields that were considered in the analysis would not reflect changes in the process, if any."
877 |    ]
878 |   }
879 |  ],
880 |  "metadata": {
881 |   "kernelspec": {
882 |    "display_name": "Python 3",
883 |    "language": "python",
884 |    "name": "python3"
885 |   },
886 |   "language_info": {
887 |    "codemirror_mode": {
888 |     "name": "ipython",
889 |     "version": 3
890 |    },
891 |    "file_extension": ".py",
892 |    "mimetype": "text/x-python",
893 |    "name": "python",
894 |    "nbconvert_exporter": "python",
895 |    "pygments_lexer": "ipython3",
896 |    "version": "3.6.7"
897 |   }
898 |  },
899 |  "nbformat": 4,
900 |  "nbformat_minor": 2
901 | }
902 | 


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