├── README.md
├── Ecommerce Purchases Exercise .ipynb
├── SF Salaries Exercise.ipynb
├── Choropleth Maps Exercise .ipynb
└── NLP Project .ipynb


/README.md:
--------------------------------------------------------------------------------
 1 | # Python-for-Data-Analysis-and-Machine-Learning
 2 | 
 3 | This repo contains the exercises made for the course of Jose Portilla on Udemy.
 4 | 
 5 | The course teaches how to use NumPy, Pandas, Seaborn , Matplotlib , Plotly , Scikit-Learn , Machine Learning models, Tensorflow, Deep Neural Networks and more.
 6 | 
 7 | The exercises offer the possibility to reproduce the real analysis process using real data (like datasets on Kaggle) or fake data (created for the scope of the course).
 8 | 
 9 | ### Details of each project: ###
10 | 
11 | 1. *911 Calls Data Capstone Project .ipynb* : Analysis of the [Kaggle 911 Dataset](https://www.kaggle.com/mchirico/montcoalert) with visualizations in Seaborn and Matplotlib.
12 | 
13 | 2. *Choropleth Maps Exercise .ipynb*: choropleth maps with Plotly.
14 | 
15 | 3. *Decision Trees and Random Forest Project .ipynb*: Analysis of a dataset from [LendingClub.com](https://www.lendingclub.com/) with visualizations in Seaborn and Matplotlib and Machine Learning models with Scikit-Learn.
16 | 
17 | 4. *Ecommerce Purchases Exercise .ipynb*: analysis of a dataset with fake Amazon data with Pandas.
18 | 
19 | 5. *K Nearest Neighbors Project .ipynb*: using Numpy, Maplotlib, Pandas, Seaborn to analyze a dataset and the applying the KNN model to the data using Scikit-Learn.
20 | 
21 | 6. *Linear Regression - Project Exercise .ipynb*: using Numpy, Maplotlib, Pandas, Seaborn to analyze a dataset about Ecommerce customers and the applying the Linear Regression model to the data using Scikit-Learn.
22 | 
23 | 7. *Logistic Regression Project .ipynb*: using Numpy, Maplotlib, Pandas, Seaborn to work with a fake advertising data set, indicating whether or not a particular internet user clicked on an Advertisement. Cresating a Logistic Regression model with Scikit-Learn to predict whether or not the users will click on an ad based off the features of the users.
24 | 
25 | 8. *NLP Project .ipynb*: Classify Yelp Reviews into 1 star or 5 star categories based off the text content in the reviews.
26 | The analysis has been done using the [Yelp Review Data Set from Kaggle](https://www.kaggle.com/c/yelp-recsys-2013).
27 | 
28 | 9. *SF Salaries Exercise .ipynb*: simple Pandas analysis using the [SF Salaries datased from Kaggle](https://github.com/marcogdepinto/Python-for-Data-Analysis-and-Machine-Learning/blob/master/SF%20Salaries%20Exercise.ipynb).
29 | 
30 | 10. *Support Vector Machines Project .ipynb*: SVM application with Scikit-Learn on the Iris dataset.
31 | 
32 | 11. *Tensorflow Project Exercise .ipynb*: Tensorflow application on the [UCI dataset](https://archive.ics.uci.edu/ml/datasets/banknote+authentication) - created a Deep Neural Network using the contrib.learn module.
33 | 


--------------------------------------------------------------------------------
/Ecommerce Purchases Exercise .ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "markdown",
  5 |    "metadata": {},
  6 |    "source": [
  7 |     "___\n",
  8 |     "\n",
  9 |     "<a href='http://www.pieriandata.com'> <img src='../../Pierian_Data_Logo.png' /></a>\n",
 10 |     "___\n",
 11 |     "# Ecommerce Purchases Exercise\n",
 12 |     "\n",
 13 |     "In this Exercise you will be given some Fake Data about some purchases done through Amazon! Just go ahead and follow the directions and try your best to answer the questions and complete the tasks. Feel free to reference the solutions. Most of the tasks can be solved in different ways. For the most part, the questions get progressively harder.\n",
 14 |     "\n",
 15 |     "Please excuse anything that doesn't make \"Real-World\" sense in the dataframe, all the data is fake and made-up.\n",
 16 |     "\n",
 17 |     "Also note that all of these questions can be answered with one line of code.\n",
 18 |     "____\n",
 19 |     "** Import pandas and read in the Ecommerce Purchases csv file and set it to a DataFrame called ecom. **"
 20 |    ]
 21 |   },
 22 |   {
 23 |    "cell_type": "code",
 24 |    "execution_count": 3,
 25 |    "metadata": {},
 26 |    "outputs": [],
 27 |    "source": [
 28 |     "import pandas as pd"
 29 |    ]
 30 |   },
 31 |   {
 32 |    "cell_type": "code",
 33 |    "execution_count": 4,
 34 |    "metadata": {},
 35 |    "outputs": [],
 36 |    "source": [
 37 |     "ecom = pd.read_csv('Ecommerce Purchases')"
 38 |    ]
 39 |   },
 40 |   {
 41 |    "cell_type": "markdown",
 42 |    "metadata": {},
 43 |    "source": [
 44 |     "**Check the head of the DataFrame.**"
 45 |    ]
 46 |   },
 47 |   {
 48 |    "cell_type": "code",
 49 |    "execution_count": 4,
 50 |    "metadata": {},
 51 |    "outputs": [
 52 |     {
 53 |      "data": {
 54 |       "text/html": [
 55 |        "<div>\n",
 56 |        "<style scoped>\n",
 57 |        "    .dataframe tbody tr th:only-of-type {\n",
 58 |        "        vertical-align: middle;\n",
 59 |        "    }\n",
 60 |        "\n",
 61 |        "    .dataframe tbody tr th {\n",
 62 |        "        vertical-align: top;\n",
 63 |        "    }\n",
 64 |        "\n",
 65 |        "    .dataframe thead th {\n",
 66 |        "        text-align: right;\n",
 67 |        "    }\n",
 68 |        "</style>\n",
 69 |        "<table border=\"1\" class=\"dataframe\">\n",
 70 |        "  <thead>\n",
 71 |        "    <tr style=\"text-align: right;\">\n",
 72 |        "      <th></th>\n",
 73 |        "      <th>Address</th>\n",
 74 |        "      <th>Lot</th>\n",
 75 |        "      <th>AM or PM</th>\n",
 76 |        "      <th>Browser Info</th>\n",
 77 |        "      <th>Company</th>\n",
 78 |        "      <th>Credit Card</th>\n",
 79 |        "      <th>CC Exp Date</th>\n",
 80 |        "      <th>CC Security Code</th>\n",
 81 |        "      <th>CC Provider</th>\n",
 82 |        "      <th>Email</th>\n",
 83 |        "      <th>Job</th>\n",
 84 |        "      <th>IP Address</th>\n",
 85 |        "      <th>Language</th>\n",
 86 |        "      <th>Purchase Price</th>\n",
 87 |        "    </tr>\n",
 88 |        "  </thead>\n",
 89 |        "  <tbody>\n",
 90 |        "    <tr>\n",
 91 |        "      <th>0</th>\n",
 92 |        "      <td>16629 Pace Camp Apt. 448\\nAlexisborough, NE 77...</td>\n",
 93 |        "      <td>46 in</td>\n",
 94 |        "      <td>PM</td>\n",
 95 |        "      <td>Opera/9.56.(X11; Linux x86_64; sl-SI) Presto/2...</td>\n",
 96 |        "      <td>Martinez-Herman</td>\n",
 97 |        "      <td>6011929061123406</td>\n",
 98 |        "      <td>02/20</td>\n",
 99 |        "      <td>900</td>\n",
100 |        "      <td>JCB 16 digit</td>\n",
101 |        "      <td>pdunlap@yahoo.com</td>\n",
102 |        "      <td>Scientist, product/process development</td>\n",
103 |        "      <td>149.146.147.205</td>\n",
104 |        "      <td>el</td>\n",
105 |        "      <td>98.14</td>\n",
106 |        "    </tr>\n",
107 |        "    <tr>\n",
108 |        "      <th>1</th>\n",
109 |        "      <td>9374 Jasmine Spurs Suite 508\\nSouth John, TN 8...</td>\n",
110 |        "      <td>28 rn</td>\n",
111 |        "      <td>PM</td>\n",
112 |        "      <td>Opera/8.93.(Windows 98; Win 9x 4.90; en-US) Pr...</td>\n",
113 |        "      <td>Fletcher, Richards and Whitaker</td>\n",
114 |        "      <td>3337758169645356</td>\n",
115 |        "      <td>11/18</td>\n",
116 |        "      <td>561</td>\n",
117 |        "      <td>Mastercard</td>\n",
118 |        "      <td>anthony41@reed.com</td>\n",
119 |        "      <td>Drilling engineer</td>\n",
120 |        "      <td>15.160.41.51</td>\n",
121 |        "      <td>fr</td>\n",
122 |        "      <td>70.73</td>\n",
123 |        "    </tr>\n",
124 |        "    <tr>\n",
125 |        "      <th>2</th>\n",
126 |        "      <td>Unit 0065 Box 5052\\nDPO AP 27450</td>\n",
127 |        "      <td>94 vE</td>\n",
128 |        "      <td>PM</td>\n",
129 |        "      <td>Mozilla/5.0 (compatible; MSIE 9.0; Windows NT ...</td>\n",
130 |        "      <td>Simpson, Williams and Pham</td>\n",
131 |        "      <td>675957666125</td>\n",
132 |        "      <td>08/19</td>\n",
133 |        "      <td>699</td>\n",
134 |        "      <td>JCB 16 digit</td>\n",
135 |        "      <td>amymiller@morales-harrison.com</td>\n",
136 |        "      <td>Customer service manager</td>\n",
137 |        "      <td>132.207.160.22</td>\n",
138 |        "      <td>de</td>\n",
139 |        "      <td>0.95</td>\n",
140 |        "    </tr>\n",
141 |        "    <tr>\n",
142 |        "      <th>3</th>\n",
143 |        "      <td>7780 Julia Fords\\nNew Stacy, WA 45798</td>\n",
144 |        "      <td>36 vm</td>\n",
145 |        "      <td>PM</td>\n",
146 |        "      <td>Mozilla/5.0 (Macintosh; Intel Mac OS X 10_8_0 ...</td>\n",
147 |        "      <td>Williams, Marshall and Buchanan</td>\n",
148 |        "      <td>6011578504430710</td>\n",
149 |        "      <td>02/24</td>\n",
150 |        "      <td>384</td>\n",
151 |        "      <td>Discover</td>\n",
152 |        "      <td>brent16@olson-robinson.info</td>\n",
153 |        "      <td>Drilling engineer</td>\n",
154 |        "      <td>30.250.74.19</td>\n",
155 |        "      <td>es</td>\n",
156 |        "      <td>78.04</td>\n",
157 |        "    </tr>\n",
158 |        "    <tr>\n",
159 |        "      <th>4</th>\n",
160 |        "      <td>23012 Munoz Drive Suite 337\\nNew Cynthia, TX 5...</td>\n",
161 |        "      <td>20 IE</td>\n",
162 |        "      <td>AM</td>\n",
163 |        "      <td>Opera/9.58.(X11; Linux x86_64; it-IT) Presto/2...</td>\n",
164 |        "      <td>Brown, Watson and Andrews</td>\n",
165 |        "      <td>6011456623207998</td>\n",
166 |        "      <td>10/25</td>\n",
167 |        "      <td>678</td>\n",
168 |        "      <td>Diners Club / Carte Blanche</td>\n",
169 |        "      <td>christopherwright@gmail.com</td>\n",
170 |        "      <td>Fine artist</td>\n",
171 |        "      <td>24.140.33.94</td>\n",
172 |        "      <td>es</td>\n",
173 |        "      <td>77.82</td>\n",
174 |        "    </tr>\n",
175 |        "  </tbody>\n",
176 |        "</table>\n",
177 |        "</div>"
178 |       ],
179 |       "text/plain": [
180 |        "                                             Address    Lot AM or PM  \\\n",
181 |        "0  16629 Pace Camp Apt. 448\\nAlexisborough, NE 77...  46 in       PM   \n",
182 |        "1  9374 Jasmine Spurs Suite 508\\nSouth John, TN 8...  28 rn       PM   \n",
183 |        "2                   Unit 0065 Box 5052\\nDPO AP 27450  94 vE       PM   \n",
184 |        "3              7780 Julia Fords\\nNew Stacy, WA 45798  36 vm       PM   \n",
185 |        "4  23012 Munoz Drive Suite 337\\nNew Cynthia, TX 5...  20 IE       AM   \n",
186 |        "\n",
187 |        "                                        Browser Info  \\\n",
188 |        "0  Opera/9.56.(X11; Linux x86_64; sl-SI) Presto/2...   \n",
189 |        "1  Opera/8.93.(Windows 98; Win 9x 4.90; en-US) Pr...   \n",
190 |        "2  Mozilla/5.0 (compatible; MSIE 9.0; Windows NT ...   \n",
191 |        "3  Mozilla/5.0 (Macintosh; Intel Mac OS X 10_8_0 ...   \n",
192 |        "4  Opera/9.58.(X11; Linux x86_64; it-IT) Presto/2...   \n",
193 |        "\n",
194 |        "                           Company       Credit Card CC Exp Date  \\\n",
195 |        "0                  Martinez-Herman  6011929061123406       02/20   \n",
196 |        "1  Fletcher, Richards and Whitaker  3337758169645356       11/18   \n",
197 |        "2       Simpson, Williams and Pham      675957666125       08/19   \n",
198 |        "3  Williams, Marshall and Buchanan  6011578504430710       02/24   \n",
199 |        "4        Brown, Watson and Andrews  6011456623207998       10/25   \n",
200 |        "\n",
201 |        "   CC Security Code                  CC Provider  \\\n",
202 |        "0               900                 JCB 16 digit   \n",
203 |        "1               561                   Mastercard   \n",
204 |        "2               699                 JCB 16 digit   \n",
205 |        "3               384                     Discover   \n",
206 |        "4               678  Diners Club / Carte Blanche   \n",
207 |        "\n",
208 |        "                            Email                                     Job  \\\n",
209 |        "0               pdunlap@yahoo.com  Scientist, product/process development   \n",
210 |        "1              anthony41@reed.com                       Drilling engineer   \n",
211 |        "2  amymiller@morales-harrison.com                Customer service manager   \n",
212 |        "3     brent16@olson-robinson.info                       Drilling engineer   \n",
213 |        "4     christopherwright@gmail.com                             Fine artist   \n",
214 |        "\n",
215 |        "        IP Address Language  Purchase Price  \n",
216 |        "0  149.146.147.205       el           98.14  \n",
217 |        "1     15.160.41.51       fr           70.73  \n",
218 |        "2   132.207.160.22       de            0.95  \n",
219 |        "3     30.250.74.19       es           78.04  \n",
220 |        "4     24.140.33.94       es           77.82  "
221 |       ]
222 |      },
223 |      "execution_count": 4,
224 |      "metadata": {},
225 |      "output_type": "execute_result"
226 |     }
227 |    ],
228 |    "source": [
229 |     "ecom.head()"
230 |    ]
231 |   },
232 |   {
233 |    "cell_type": "markdown",
234 |    "metadata": {},
235 |    "source": [
236 |     "** How many rows and columns are there? **"
237 |    ]
238 |   },
239 |   {
240 |    "cell_type": "code",
241 |    "execution_count": 5,
242 |    "metadata": {},
243 |    "outputs": [
244 |     {
245 |      "name": "stdout",
246 |      "output_type": "stream",
247 |      "text": [
248 |       "<class 'pandas.core.frame.DataFrame'>\n",
249 |       "RangeIndex: 10000 entries, 0 to 9999\n",
250 |       "Data columns (total 14 columns):\n",
251 |       "Address             10000 non-null object\n",
252 |       "Lot                 10000 non-null object\n",
253 |       "AM or PM            10000 non-null object\n",
254 |       "Browser Info        10000 non-null object\n",
255 |       "Company             10000 non-null object\n",
256 |       "Credit Card         10000 non-null int64\n",
257 |       "CC Exp Date         10000 non-null object\n",
258 |       "CC Security Code    10000 non-null int64\n",
259 |       "CC Provider         10000 non-null object\n",
260 |       "Email               10000 non-null object\n",
261 |       "Job                 10000 non-null object\n",
262 |       "IP Address          10000 non-null object\n",
263 |       "Language            10000 non-null object\n",
264 |       "Purchase Price      10000 non-null float64\n",
265 |       "dtypes: float64(1), int64(2), object(11)\n",
266 |       "memory usage: 1.1+ MB\n"
267 |      ]
268 |     }
269 |    ],
270 |    "source": [
271 |     "ecom.info()"
272 |    ]
273 |   },
274 |   {
275 |    "cell_type": "markdown",
276 |    "metadata": {},
277 |    "source": [
278 |     "** What is the average Purchase Price? **"
279 |    ]
280 |   },
281 |   {
282 |    "cell_type": "code",
283 |    "execution_count": 6,
284 |    "metadata": {},
285 |    "outputs": [
286 |     {
287 |      "data": {
288 |       "text/plain": [
289 |        "50.34730200000025"
290 |       ]
291 |      },
292 |      "execution_count": 6,
293 |      "metadata": {},
294 |      "output_type": "execute_result"
295 |     }
296 |    ],
297 |    "source": [
298 |     "ecom['Purchase Price'].mean()"
299 |    ]
300 |   },
301 |   {
302 |    "cell_type": "markdown",
303 |    "metadata": {},
304 |    "source": [
305 |     "** What were the highest and lowest purchase prices? **"
306 |    ]
307 |   },
308 |   {
309 |    "cell_type": "code",
310 |    "execution_count": 9,
311 |    "metadata": {},
312 |    "outputs": [
313 |     {
314 |      "data": {
315 |       "text/plain": [
316 |        "99.99"
317 |       ]
318 |      },
319 |      "execution_count": 9,
320 |      "metadata": {},
321 |      "output_type": "execute_result"
322 |     }
323 |    ],
324 |    "source": [
325 |     "ecom['Purchase Price'].max()"
326 |    ]
327 |   },
328 |   {
329 |    "cell_type": "code",
330 |    "execution_count": 10,
331 |    "metadata": {},
332 |    "outputs": [
333 |     {
334 |      "data": {
335 |       "text/plain": [
336 |        "0.0"
337 |       ]
338 |      },
339 |      "execution_count": 10,
340 |      "metadata": {},
341 |      "output_type": "execute_result"
342 |     }
343 |    ],
344 |    "source": [
345 |     "ecom['Purchase Price'].min()"
346 |    ]
347 |   },
348 |   {
349 |    "cell_type": "markdown",
350 |    "metadata": {},
351 |    "source": [
352 |     "** How many people have English 'en' as their Language of choice on the website? **"
353 |    ]
354 |   },
355 |   {
356 |    "cell_type": "code",
357 |    "execution_count": 11,
358 |    "metadata": {},
359 |    "outputs": [
360 |     {
361 |      "data": {
362 |       "text/plain": [
363 |        "Address             1098\n",
364 |        "Lot                 1098\n",
365 |        "AM or PM            1098\n",
366 |        "Browser Info        1098\n",
367 |        "Company             1098\n",
368 |        "Credit Card         1098\n",
369 |        "CC Exp Date         1098\n",
370 |        "CC Security Code    1098\n",
371 |        "CC Provider         1098\n",
372 |        "Email               1098\n",
373 |        "Job                 1098\n",
374 |        "IP Address          1098\n",
375 |        "Language            1098\n",
376 |        "Purchase Price      1098\n",
377 |        "dtype: int64"
378 |       ]
379 |      },
380 |      "execution_count": 11,
381 |      "metadata": {},
382 |      "output_type": "execute_result"
383 |     }
384 |    ],
385 |    "source": [
386 |     "ecom[ecom['Language'] == 'en'].count()"
387 |    ]
388 |   },
389 |   {
390 |    "cell_type": "markdown",
391 |    "metadata": {},
392 |    "source": [
393 |     "** How many people have the job title of \"Lawyer\" ? **\n"
394 |    ]
395 |   },
396 |   {
397 |    "cell_type": "code",
398 |    "execution_count": 16,
399 |    "metadata": {},
400 |    "outputs": [
401 |     {
402 |      "name": "stdout",
403 |      "output_type": "stream",
404 |      "text": [
405 |       "<class 'pandas.core.frame.DataFrame'>\n",
406 |       "Int64Index: 30 entries, 470 to 9979\n",
407 |       "Data columns (total 14 columns):\n",
408 |       "Address             30 non-null object\n",
409 |       "Lot                 30 non-null object\n",
410 |       "AM or PM            30 non-null object\n",
411 |       "Browser Info        30 non-null object\n",
412 |       "Company             30 non-null object\n",
413 |       "Credit Card         30 non-null int64\n",
414 |       "CC Exp Date         30 non-null object\n",
415 |       "CC Security Code    30 non-null int64\n",
416 |       "CC Provider         30 non-null object\n",
417 |       "Email               30 non-null object\n",
418 |       "Job                 30 non-null object\n",
419 |       "IP Address          30 non-null object\n",
420 |       "Language            30 non-null object\n",
421 |       "Purchase Price      30 non-null float64\n",
422 |       "dtypes: float64(1), int64(2), object(11)\n",
423 |       "memory usage: 3.5+ KB\n"
424 |      ]
425 |     }
426 |    ],
427 |    "source": [
428 |     "ecom[ecom['Job'] == 'Lawyer'].info()"
429 |    ]
430 |   },
431 |   {
432 |    "cell_type": "markdown",
433 |    "metadata": {},
434 |    "source": [
435 |     "** How many people made the purchase during the AM and how many people made the purchase during PM ? **\n",
436 |     "\n",
437 |     "**(Hint: Check out [value_counts()](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.value_counts.html) ) **"
438 |    ]
439 |   },
440 |   {
441 |    "cell_type": "code",
442 |    "execution_count": 17,
443 |    "metadata": {},
444 |    "outputs": [
445 |     {
446 |      "data": {
447 |       "text/plain": [
448 |        "PM    5068\n",
449 |        "AM    4932\n",
450 |        "Name: AM or PM, dtype: int64"
451 |       ]
452 |      },
453 |      "execution_count": 17,
454 |      "metadata": {},
455 |      "output_type": "execute_result"
456 |     }
457 |    ],
458 |    "source": [
459 |     "ecom['AM or PM'].value_counts()"
460 |    ]
461 |   },
462 |   {
463 |    "cell_type": "markdown",
464 |    "metadata": {},
465 |    "source": [
466 |     "** What are the 5 most common Job Titles? **"
467 |    ]
468 |   },
469 |   {
470 |    "cell_type": "code",
471 |    "execution_count": 19,
472 |    "metadata": {},
473 |    "outputs": [
474 |     {
475 |      "data": {
476 |       "text/plain": [
477 |        "Interior and spatial designer    31\n",
478 |        "Lawyer                           30\n",
479 |        "Social researcher                28\n",
480 |        "Designer, jewellery              27\n",
481 |        "Purchasing manager               27\n",
482 |        "Name: Job, dtype: int64"
483 |       ]
484 |      },
485 |      "execution_count": 19,
486 |      "metadata": {},
487 |      "output_type": "execute_result"
488 |     }
489 |    ],
490 |    "source": [
491 |     "ecom['Job'].value_counts().head(5)"
492 |    ]
493 |   },
494 |   {
495 |    "cell_type": "markdown",
496 |    "metadata": {},
497 |    "source": [
498 |     "** Someone made a purchase that came from Lot: \"90 WT\" , what was the Purchase Price for this transaction? **"
499 |    ]
500 |   },
501 |   {
502 |    "cell_type": "code",
503 |    "execution_count": 22,
504 |    "metadata": {},
505 |    "outputs": [
506 |     {
507 |      "data": {
508 |       "text/plain": [
509 |        "513    75.1\n",
510 |        "Name: Purchase Price, dtype: float64"
511 |       ]
512 |      },
513 |      "execution_count": 22,
514 |      "metadata": {},
515 |      "output_type": "execute_result"
516 |     }
517 |    ],
518 |    "source": [
519 |     "ecom[ecom['Lot'] == '90 WT']['Purchase Price']"
520 |    ]
521 |   },
522 |   {
523 |    "cell_type": "markdown",
524 |    "metadata": {},
525 |    "source": [
526 |     "** What is the email of the person with the following Credit Card Number: 4926535242672853 **"
527 |    ]
528 |   },
529 |   {
530 |    "cell_type": "code",
531 |    "execution_count": 30,
532 |    "metadata": {},
533 |    "outputs": [
534 |     {
535 |      "data": {
536 |       "text/plain": [
537 |        "1234    bondellen@williams-garza.com\n",
538 |        "Name: Email, dtype: object"
539 |       ]
540 |      },
541 |      "execution_count": 30,
542 |      "metadata": {},
543 |      "output_type": "execute_result"
544 |     }
545 |    ],
546 |    "source": [
547 |     "ecom[ecom['Credit Card'] == 4926535242672853]['Email']"
548 |    ]
549 |   },
550 |   {
551 |    "cell_type": "markdown",
552 |    "metadata": {},
553 |    "source": [
554 |     "** How many people have American Express as their Credit Card Provider *and* made a purchase above $95 ?**"
555 |    ]
556 |   },
557 |   {
558 |    "cell_type": "code",
559 |    "execution_count": 38,
560 |    "metadata": {},
561 |    "outputs": [
562 |     {
563 |      "data": {
564 |       "text/plain": [
565 |        "39"
566 |       ]
567 |      },
568 |      "execution_count": 38,
569 |      "metadata": {},
570 |      "output_type": "execute_result"
571 |     }
572 |    ],
573 |    "source": [
574 |     "sum(ecom[ecom['CC Provider'] == 'American Express']['Purchase Price'] > 95)"
575 |    ]
576 |   },
577 |   {
578 |    "cell_type": "markdown",
579 |    "metadata": {},
580 |    "source": [
581 |     "** Hard: How many people have a credit card that expires in 2025? **"
582 |    ]
583 |   },
584 |   {
585 |    "cell_type": "code",
586 |    "execution_count": 50,
587 |    "metadata": {},
588 |    "outputs": [
589 |     {
590 |      "data": {
591 |       "text/plain": [
592 |        "1033"
593 |       ]
594 |      },
595 |      "execution_count": 50,
596 |      "metadata": {},
597 |      "output_type": "execute_result"
598 |     }
599 |    ],
600 |    "source": [
601 |     "sum(ecom['CC Exp Date'].apply(lambda x: x[3:]) == '25')"
602 |    ]
603 |   },
604 |   {
605 |    "cell_type": "markdown",
606 |    "metadata": {},
607 |    "source": [
608 |     "** Hard: What are the top 5 most popular email providers/hosts (e.g. gmail.com, yahoo.com, etc...) **"
609 |    ]
610 |   },
611 |   {
612 |    "cell_type": "code",
613 |    "execution_count": 56,
614 |    "metadata": {},
615 |    "outputs": [
616 |     {
617 |      "data": {
618 |       "text/plain": [
619 |        "hotmail.com     1638\n",
620 |        "yahoo.com       1616\n",
621 |        "gmail.com       1605\n",
622 |        "smith.com         42\n",
623 |        "williams.com      37\n",
624 |        "Name: Email, dtype: int64"
625 |       ]
626 |      },
627 |      "execution_count": 56,
628 |      "metadata": {},
629 |      "output_type": "execute_result"
630 |     }
631 |    ],
632 |    "source": [
633 |     "ecom['Email'].apply(lambda x: x.split('@')[1]).value_counts().head(5)"
634 |    ]
635 |   },
636 |   {
637 |    "cell_type": "markdown",
638 |    "metadata": {},
639 |    "source": [
640 |     "# Great Job!"
641 |    ]
642 |   }
643 |  ],
644 |  "metadata": {
645 |   "kernelspec": {
646 |    "display_name": "Python 3",
647 |    "language": "python",
648 |    "name": "python3"
649 |   },
650 |   "language_info": {
651 |    "codemirror_mode": {
652 |     "name": "ipython",
653 |     "version": 3
654 |    },
655 |    "file_extension": ".py",
656 |    "mimetype": "text/x-python",
657 |    "name": "python",
658 |    "nbconvert_exporter": "python",
659 |    "pygments_lexer": "ipython3",
660 |    "version": "3.6.4"
661 |   }
662 |  },
663 |  "nbformat": 4,
664 |  "nbformat_minor": 1
665 | }
666 | 


--------------------------------------------------------------------------------
/SF Salaries Exercise.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "markdown",
  5 |    "metadata": {},
  6 |    "source": [
  7 |     "___\n",
  8 |     "\n",
  9 |     "<a href='http://www.pieriandata.com'> <img src='../../Pierian_Data_Logo.png' /></a>\n",
 10 |     "___"
 11 |    ]
 12 |   },
 13 |   {
 14 |    "cell_type": "markdown",
 15 |    "metadata": {},
 16 |    "source": [
 17 |     "# SF Salaries Exercise \n",
 18 |     "\n",
 19 |     "Welcome to a quick exercise for you to practice your pandas skills! We will be using the [SF Salaries Dataset](https://www.kaggle.com/kaggle/sf-salaries) from Kaggle! Just follow along and complete the tasks outlined in bold below. The tasks will get harder and harder as you go along."
 20 |    ]
 21 |   },
 22 |   {
 23 |    "cell_type": "markdown",
 24 |    "metadata": {},
 25 |    "source": [
 26 |     "** Import pandas as pd.**"
 27 |    ]
 28 |   },
 29 |   {
 30 |    "cell_type": "code",
 31 |    "execution_count": 2,
 32 |    "metadata": {},
 33 |    "outputs": [],
 34 |    "source": [
 35 |     "import pandas as pd"
 36 |    ]
 37 |   },
 38 |   {
 39 |    "cell_type": "markdown",
 40 |    "metadata": {},
 41 |    "source": [
 42 |     "** Read Salaries.csv as a dataframe called sal.**"
 43 |    ]
 44 |   },
 45 |   {
 46 |    "cell_type": "code",
 47 |    "execution_count": 3,
 48 |    "metadata": {},
 49 |    "outputs": [],
 50 |    "source": [
 51 |     "sal = pd.read_csv('Salaries.csv')"
 52 |    ]
 53 |   },
 54 |   {
 55 |    "cell_type": "markdown",
 56 |    "metadata": {},
 57 |    "source": [
 58 |     "** Check the head of the DataFrame. **"
 59 |    ]
 60 |   },
 61 |   {
 62 |    "cell_type": "code",
 63 |    "execution_count": 5,
 64 |    "metadata": {},
 65 |    "outputs": [
 66 |     {
 67 |      "data": {
 68 |       "text/html": [
 69 |        "<div>\n",
 70 |        "<style scoped>\n",
 71 |        "    .dataframe tbody tr th:only-of-type {\n",
 72 |        "        vertical-align: middle;\n",
 73 |        "    }\n",
 74 |        "\n",
 75 |        "    .dataframe tbody tr th {\n",
 76 |        "        vertical-align: top;\n",
 77 |        "    }\n",
 78 |        "\n",
 79 |        "    .dataframe thead th {\n",
 80 |        "        text-align: right;\n",
 81 |        "    }\n",
 82 |        "</style>\n",
 83 |        "<table border=\"1\" class=\"dataframe\">\n",
 84 |        "  <thead>\n",
 85 |        "    <tr style=\"text-align: right;\">\n",
 86 |        "      <th></th>\n",
 87 |        "      <th>Id</th>\n",
 88 |        "      <th>EmployeeName</th>\n",
 89 |        "      <th>JobTitle</th>\n",
 90 |        "      <th>BasePay</th>\n",
 91 |        "      <th>OvertimePay</th>\n",
 92 |        "      <th>OtherPay</th>\n",
 93 |        "      <th>Benefits</th>\n",
 94 |        "      <th>TotalPay</th>\n",
 95 |        "      <th>TotalPayBenefits</th>\n",
 96 |        "      <th>Year</th>\n",
 97 |        "      <th>Notes</th>\n",
 98 |        "      <th>Agency</th>\n",
 99 |        "      <th>Status</th>\n",
100 |        "    </tr>\n",
101 |        "  </thead>\n",
102 |        "  <tbody>\n",
103 |        "    <tr>\n",
104 |        "      <th>0</th>\n",
105 |        "      <td>1</td>\n",
106 |        "      <td>NATHANIEL FORD</td>\n",
107 |        "      <td>GENERAL MANAGER-METROPOLITAN TRANSIT AUTHORITY</td>\n",
108 |        "      <td>167411.18</td>\n",
109 |        "      <td>0.00</td>\n",
110 |        "      <td>400184.25</td>\n",
111 |        "      <td>NaN</td>\n",
112 |        "      <td>567595.43</td>\n",
113 |        "      <td>567595.43</td>\n",
114 |        "      <td>2011</td>\n",
115 |        "      <td>NaN</td>\n",
116 |        "      <td>San Francisco</td>\n",
117 |        "      <td>NaN</td>\n",
118 |        "    </tr>\n",
119 |        "    <tr>\n",
120 |        "      <th>1</th>\n",
121 |        "      <td>2</td>\n",
122 |        "      <td>GARY JIMENEZ</td>\n",
123 |        "      <td>CAPTAIN III (POLICE DEPARTMENT)</td>\n",
124 |        "      <td>155966.02</td>\n",
125 |        "      <td>245131.88</td>\n",
126 |        "      <td>137811.38</td>\n",
127 |        "      <td>NaN</td>\n",
128 |        "      <td>538909.28</td>\n",
129 |        "      <td>538909.28</td>\n",
130 |        "      <td>2011</td>\n",
131 |        "      <td>NaN</td>\n",
132 |        "      <td>San Francisco</td>\n",
133 |        "      <td>NaN</td>\n",
134 |        "    </tr>\n",
135 |        "    <tr>\n",
136 |        "      <th>2</th>\n",
137 |        "      <td>3</td>\n",
138 |        "      <td>ALBERT PARDINI</td>\n",
139 |        "      <td>CAPTAIN III (POLICE DEPARTMENT)</td>\n",
140 |        "      <td>212739.13</td>\n",
141 |        "      <td>106088.18</td>\n",
142 |        "      <td>16452.60</td>\n",
143 |        "      <td>NaN</td>\n",
144 |        "      <td>335279.91</td>\n",
145 |        "      <td>335279.91</td>\n",
146 |        "      <td>2011</td>\n",
147 |        "      <td>NaN</td>\n",
148 |        "      <td>San Francisco</td>\n",
149 |        "      <td>NaN</td>\n",
150 |        "    </tr>\n",
151 |        "    <tr>\n",
152 |        "      <th>3</th>\n",
153 |        "      <td>4</td>\n",
154 |        "      <td>CHRISTOPHER CHONG</td>\n",
155 |        "      <td>WIRE ROPE CABLE MAINTENANCE MECHANIC</td>\n",
156 |        "      <td>77916.00</td>\n",
157 |        "      <td>56120.71</td>\n",
158 |        "      <td>198306.90</td>\n",
159 |        "      <td>NaN</td>\n",
160 |        "      <td>332343.61</td>\n",
161 |        "      <td>332343.61</td>\n",
162 |        "      <td>2011</td>\n",
163 |        "      <td>NaN</td>\n",
164 |        "      <td>San Francisco</td>\n",
165 |        "      <td>NaN</td>\n",
166 |        "    </tr>\n",
167 |        "    <tr>\n",
168 |        "      <th>4</th>\n",
169 |        "      <td>5</td>\n",
170 |        "      <td>PATRICK GARDNER</td>\n",
171 |        "      <td>DEPUTY CHIEF OF DEPARTMENT,(FIRE DEPARTMENT)</td>\n",
172 |        "      <td>134401.60</td>\n",
173 |        "      <td>9737.00</td>\n",
174 |        "      <td>182234.59</td>\n",
175 |        "      <td>NaN</td>\n",
176 |        "      <td>326373.19</td>\n",
177 |        "      <td>326373.19</td>\n",
178 |        "      <td>2011</td>\n",
179 |        "      <td>NaN</td>\n",
180 |        "      <td>San Francisco</td>\n",
181 |        "      <td>NaN</td>\n",
182 |        "    </tr>\n",
183 |        "  </tbody>\n",
184 |        "</table>\n",
185 |        "</div>"
186 |       ],
187 |       "text/plain": [
188 |        "   Id       EmployeeName                                        JobTitle  \\\n",
189 |        "0   1     NATHANIEL FORD  GENERAL MANAGER-METROPOLITAN TRANSIT AUTHORITY   \n",
190 |        "1   2       GARY JIMENEZ                 CAPTAIN III (POLICE DEPARTMENT)   \n",
191 |        "2   3     ALBERT PARDINI                 CAPTAIN III (POLICE DEPARTMENT)   \n",
192 |        "3   4  CHRISTOPHER CHONG            WIRE ROPE CABLE MAINTENANCE MECHANIC   \n",
193 |        "4   5    PATRICK GARDNER    DEPUTY CHIEF OF DEPARTMENT,(FIRE DEPARTMENT)   \n",
194 |        "\n",
195 |        "     BasePay  OvertimePay   OtherPay  Benefits   TotalPay  TotalPayBenefits  \\\n",
196 |        "0  167411.18         0.00  400184.25       NaN  567595.43         567595.43   \n",
197 |        "1  155966.02    245131.88  137811.38       NaN  538909.28         538909.28   \n",
198 |        "2  212739.13    106088.18   16452.60       NaN  335279.91         335279.91   \n",
199 |        "3   77916.00     56120.71  198306.90       NaN  332343.61         332343.61   \n",
200 |        "4  134401.60      9737.00  182234.59       NaN  326373.19         326373.19   \n",
201 |        "\n",
202 |        "   Year  Notes         Agency  Status  \n",
203 |        "0  2011    NaN  San Francisco     NaN  \n",
204 |        "1  2011    NaN  San Francisco     NaN  \n",
205 |        "2  2011    NaN  San Francisco     NaN  \n",
206 |        "3  2011    NaN  San Francisco     NaN  \n",
207 |        "4  2011    NaN  San Francisco     NaN  "
208 |       ]
209 |      },
210 |      "execution_count": 5,
211 |      "metadata": {},
212 |      "output_type": "execute_result"
213 |     }
214 |    ],
215 |    "source": [
216 |     "sal.head()"
217 |    ]
218 |   },
219 |   {
220 |    "cell_type": "markdown",
221 |    "metadata": {},
222 |    "source": [
223 |     "** Use the .info() method to find out how many entries there are.**"
224 |    ]
225 |   },
226 |   {
227 |    "cell_type": "code",
228 |    "execution_count": 8,
229 |    "metadata": {},
230 |    "outputs": [
231 |     {
232 |      "name": "stdout",
233 |      "output_type": "stream",
234 |      "text": [
235 |       "<class 'pandas.core.frame.DataFrame'>\n",
236 |       "RangeIndex: 148654 entries, 0 to 148653\n",
237 |       "Data columns (total 13 columns):\n",
238 |       "Id                  148654 non-null int64\n",
239 |       "EmployeeName        148654 non-null object\n",
240 |       "JobTitle            148654 non-null object\n",
241 |       "BasePay             148045 non-null float64\n",
242 |       "OvertimePay         148650 non-null float64\n",
243 |       "OtherPay            148650 non-null float64\n",
244 |       "Benefits            112491 non-null float64\n",
245 |       "TotalPay            148654 non-null float64\n",
246 |       "TotalPayBenefits    148654 non-null float64\n",
247 |       "Year                148654 non-null int64\n",
248 |       "Notes               0 non-null float64\n",
249 |       "Agency              148654 non-null object\n",
250 |       "Status              0 non-null float64\n",
251 |       "dtypes: float64(8), int64(2), object(3)\n",
252 |       "memory usage: 14.7+ MB\n"
253 |      ]
254 |     }
255 |    ],
256 |    "source": [
257 |     "sal.info()"
258 |    ]
259 |   },
260 |   {
261 |    "cell_type": "markdown",
262 |    "metadata": {},
263 |    "source": [
264 |     "**What is the average BasePay ?**"
265 |    ]
266 |   },
267 |   {
268 |    "cell_type": "code",
269 |    "execution_count": 9,
270 |    "metadata": {},
271 |    "outputs": [
272 |     {
273 |      "data": {
274 |       "text/plain": [
275 |        "66325.44884050643"
276 |       ]
277 |      },
278 |      "execution_count": 9,
279 |      "metadata": {},
280 |      "output_type": "execute_result"
281 |     }
282 |    ],
283 |    "source": [
284 |     "sal['BasePay'].mean()"
285 |    ]
286 |   },
287 |   {
288 |    "cell_type": "markdown",
289 |    "metadata": {},
290 |    "source": [
291 |     "** What is the highest amount of OvertimePay in the dataset ? **"
292 |    ]
293 |   },
294 |   {
295 |    "cell_type": "code",
296 |    "execution_count": 10,
297 |    "metadata": {},
298 |    "outputs": [
299 |     {
300 |      "data": {
301 |       "text/plain": [
302 |        "245131.88"
303 |       ]
304 |      },
305 |      "execution_count": 10,
306 |      "metadata": {},
307 |      "output_type": "execute_result"
308 |     }
309 |    ],
310 |    "source": [
311 |     "sal['OvertimePay'].max()"
312 |    ]
313 |   },
314 |   {
315 |    "cell_type": "markdown",
316 |    "metadata": {},
317 |    "source": [
318 |     "** What is the job title of  JOSEPH DRISCOLL ? Note: Use all caps, otherwise you may get an answer that doesn't match up (there is also a lowercase Joseph Driscoll). **"
319 |    ]
320 |   },
321 |   {
322 |    "cell_type": "code",
323 |    "execution_count": 15,
324 |    "metadata": {},
325 |    "outputs": [
326 |     {
327 |      "data": {
328 |       "text/plain": [
329 |        "24    CAPTAIN, FIRE SUPPRESSION\n",
330 |        "Name: JobTitle, dtype: object"
331 |       ]
332 |      },
333 |      "execution_count": 15,
334 |      "metadata": {},
335 |      "output_type": "execute_result"
336 |     }
337 |    ],
338 |    "source": [
339 |     "sal[sal['EmployeeName']=='JOSEPH DRISCOLL']['JobTitle']"
340 |    ]
341 |   },
342 |   {
343 |    "cell_type": "markdown",
344 |    "metadata": {},
345 |    "source": [
346 |     "** How much does JOSEPH DRISCOLL make (including benefits)? **"
347 |    ]
348 |   },
349 |   {
350 |    "cell_type": "code",
351 |    "execution_count": 16,
352 |    "metadata": {},
353 |    "outputs": [
354 |     {
355 |      "data": {
356 |       "text/plain": [
357 |        "24    270324.91\n",
358 |        "Name: TotalPayBenefits, dtype: float64"
359 |       ]
360 |      },
361 |      "execution_count": 16,
362 |      "metadata": {},
363 |      "output_type": "execute_result"
364 |     }
365 |    ],
366 |    "source": [
367 |     "sal[sal['EmployeeName']=='JOSEPH DRISCOLL']['TotalPayBenefits']"
368 |    ]
369 |   },
370 |   {
371 |    "cell_type": "markdown",
372 |    "metadata": {},
373 |    "source": [
374 |     "** What is the name of highest paid person (including benefits)?**"
375 |    ]
376 |   },
377 |   {
378 |    "cell_type": "code",
379 |    "execution_count": 18,
380 |    "metadata": {},
381 |    "outputs": [
382 |     {
383 |      "data": {
384 |       "text/html": [
385 |        "<div>\n",
386 |        "<style scoped>\n",
387 |        "    .dataframe tbody tr th:only-of-type {\n",
388 |        "        vertical-align: middle;\n",
389 |        "    }\n",
390 |        "\n",
391 |        "    .dataframe tbody tr th {\n",
392 |        "        vertical-align: top;\n",
393 |        "    }\n",
394 |        "\n",
395 |        "    .dataframe thead th {\n",
396 |        "        text-align: right;\n",
397 |        "    }\n",
398 |        "</style>\n",
399 |        "<table border=\"1\" class=\"dataframe\">\n",
400 |        "  <thead>\n",
401 |        "    <tr style=\"text-align: right;\">\n",
402 |        "      <th></th>\n",
403 |        "      <th>Id</th>\n",
404 |        "      <th>EmployeeName</th>\n",
405 |        "      <th>JobTitle</th>\n",
406 |        "      <th>BasePay</th>\n",
407 |        "      <th>OvertimePay</th>\n",
408 |        "      <th>OtherPay</th>\n",
409 |        "      <th>Benefits</th>\n",
410 |        "      <th>TotalPay</th>\n",
411 |        "      <th>TotalPayBenefits</th>\n",
412 |        "      <th>Year</th>\n",
413 |        "      <th>Notes</th>\n",
414 |        "      <th>Agency</th>\n",
415 |        "      <th>Status</th>\n",
416 |        "    </tr>\n",
417 |        "  </thead>\n",
418 |        "  <tbody>\n",
419 |        "    <tr>\n",
420 |        "      <th>0</th>\n",
421 |        "      <td>1</td>\n",
422 |        "      <td>NATHANIEL FORD</td>\n",
423 |        "      <td>GENERAL MANAGER-METROPOLITAN TRANSIT AUTHORITY</td>\n",
424 |        "      <td>167411.18</td>\n",
425 |        "      <td>0.0</td>\n",
426 |        "      <td>400184.25</td>\n",
427 |        "      <td>NaN</td>\n",
428 |        "      <td>567595.43</td>\n",
429 |        "      <td>567595.43</td>\n",
430 |        "      <td>2011</td>\n",
431 |        "      <td>NaN</td>\n",
432 |        "      <td>San Francisco</td>\n",
433 |        "      <td>NaN</td>\n",
434 |        "    </tr>\n",
435 |        "  </tbody>\n",
436 |        "</table>\n",
437 |        "</div>"
438 |       ],
439 |       "text/plain": [
440 |        "   Id    EmployeeName                                        JobTitle  \\\n",
441 |        "0   1  NATHANIEL FORD  GENERAL MANAGER-METROPOLITAN TRANSIT AUTHORITY   \n",
442 |        "\n",
443 |        "     BasePay  OvertimePay   OtherPay  Benefits   TotalPay  TotalPayBenefits  \\\n",
444 |        "0  167411.18          0.0  400184.25       NaN  567595.43         567595.43   \n",
445 |        "\n",
446 |        "   Year  Notes         Agency  Status  \n",
447 |        "0  2011    NaN  San Francisco     NaN  "
448 |       ]
449 |      },
450 |      "execution_count": 18,
451 |      "metadata": {},
452 |      "output_type": "execute_result"
453 |     }
454 |    ],
455 |    "source": [
456 |     "sal[sal['TotalPayBenefits']== sal['TotalPayBenefits'].max()]"
457 |    ]
458 |   },
459 |   {
460 |    "cell_type": "markdown",
461 |    "metadata": {},
462 |    "source": [
463 |     "** What is the name of lowest paid person (including benefits)? Do you notice something strange about how much he or she is paid?**"
464 |    ]
465 |   },
466 |   {
467 |    "cell_type": "code",
468 |    "execution_count": 19,
469 |    "metadata": {},
470 |    "outputs": [
471 |     {
472 |      "data": {
473 |       "text/html": [
474 |        "<div>\n",
475 |        "<style scoped>\n",
476 |        "    .dataframe tbody tr th:only-of-type {\n",
477 |        "        vertical-align: middle;\n",
478 |        "    }\n",
479 |        "\n",
480 |        "    .dataframe tbody tr th {\n",
481 |        "        vertical-align: top;\n",
482 |        "    }\n",
483 |        "\n",
484 |        "    .dataframe thead th {\n",
485 |        "        text-align: right;\n",
486 |        "    }\n",
487 |        "</style>\n",
488 |        "<table border=\"1\" class=\"dataframe\">\n",
489 |        "  <thead>\n",
490 |        "    <tr style=\"text-align: right;\">\n",
491 |        "      <th></th>\n",
492 |        "      <th>Id</th>\n",
493 |        "      <th>EmployeeName</th>\n",
494 |        "      <th>JobTitle</th>\n",
495 |        "      <th>BasePay</th>\n",
496 |        "      <th>OvertimePay</th>\n",
497 |        "      <th>OtherPay</th>\n",
498 |        "      <th>Benefits</th>\n",
499 |        "      <th>TotalPay</th>\n",
500 |        "      <th>TotalPayBenefits</th>\n",
501 |        "      <th>Year</th>\n",
502 |        "      <th>Notes</th>\n",
503 |        "      <th>Agency</th>\n",
504 |        "      <th>Status</th>\n",
505 |        "    </tr>\n",
506 |        "  </thead>\n",
507 |        "  <tbody>\n",
508 |        "    <tr>\n",
509 |        "      <th>148653</th>\n",
510 |        "      <td>148654</td>\n",
511 |        "      <td>Joe Lopez</td>\n",
512 |        "      <td>Counselor, Log Cabin Ranch</td>\n",
513 |        "      <td>0.0</td>\n",
514 |        "      <td>0.0</td>\n",
515 |        "      <td>-618.13</td>\n",
516 |        "      <td>0.0</td>\n",
517 |        "      <td>-618.13</td>\n",
518 |        "      <td>-618.13</td>\n",
519 |        "      <td>2014</td>\n",
520 |        "      <td>NaN</td>\n",
521 |        "      <td>San Francisco</td>\n",
522 |        "      <td>NaN</td>\n",
523 |        "    </tr>\n",
524 |        "  </tbody>\n",
525 |        "</table>\n",
526 |        "</div>"
527 |       ],
528 |       "text/plain": [
529 |        "            Id EmployeeName                    JobTitle  BasePay  OvertimePay  \\\n",
530 |        "148653  148654    Joe Lopez  Counselor, Log Cabin Ranch      0.0          0.0   \n",
531 |        "\n",
532 |        "        OtherPay  Benefits  TotalPay  TotalPayBenefits  Year  Notes  \\\n",
533 |        "148653   -618.13       0.0   -618.13           -618.13  2014    NaN   \n",
534 |        "\n",
535 |        "               Agency  Status  \n",
536 |        "148653  San Francisco     NaN  "
537 |       ]
538 |      },
539 |      "execution_count": 19,
540 |      "metadata": {},
541 |      "output_type": "execute_result"
542 |     }
543 |    ],
544 |    "source": [
545 |     "sal[sal['TotalPayBenefits']== sal['TotalPayBenefits'].min()]\n",
546 |     "\n",
547 |     "#Negative Value"
548 |    ]
549 |   },
550 |   {
551 |    "cell_type": "markdown",
552 |    "metadata": {},
553 |    "source": [
554 |     "** What was the average (mean) BasePay of all employees per year? (2011-2014) ? **"
555 |    ]
556 |   },
557 |   {
558 |    "cell_type": "code",
559 |    "execution_count": 20,
560 |    "metadata": {},
561 |    "outputs": [
562 |     {
563 |      "data": {
564 |       "text/plain": [
565 |        "Year\n",
566 |        "2011    63595.956517\n",
567 |        "2012    65436.406857\n",
568 |        "2013    69630.030216\n",
569 |        "2014    66564.421924\n",
570 |        "Name: BasePay, dtype: float64"
571 |       ]
572 |      },
573 |      "execution_count": 20,
574 |      "metadata": {},
575 |      "output_type": "execute_result"
576 |     }
577 |    ],
578 |    "source": [
579 |     "sal.groupby('Year').mean()['BasePay']"
580 |    ]
581 |   },
582 |   {
583 |    "cell_type": "markdown",
584 |    "metadata": {},
585 |    "source": [
586 |     "** How many unique job titles are there? **"
587 |    ]
588 |   },
589 |   {
590 |    "cell_type": "code",
591 |    "execution_count": 4,
592 |    "metadata": {},
593 |    "outputs": [
594 |     {
595 |      "data": {
596 |       "text/plain": [
597 |        "2159"
598 |       ]
599 |      },
600 |      "execution_count": 4,
601 |      "metadata": {},
602 |      "output_type": "execute_result"
603 |     }
604 |    ],
605 |    "source": [
606 |     "sal['JobTitle'].nunique()"
607 |    ]
608 |   },
609 |   {
610 |    "cell_type": "markdown",
611 |    "metadata": {},
612 |    "source": [
613 |     "** What are the top 5 most common jobs? **"
614 |    ]
615 |   },
616 |   {
617 |    "cell_type": "code",
618 |    "execution_count": 8,
619 |    "metadata": {},
620 |    "outputs": [
621 |     {
622 |      "data": {
623 |       "text/plain": [
624 |        "Transit Operator                7036\n",
625 |        "Special Nurse                   4389\n",
626 |        "Registered Nurse                3736\n",
627 |        "Public Svc Aide-Public Works    2518\n",
628 |        "Police Officer 3                2421\n",
629 |        "Name: JobTitle, dtype: int64"
630 |       ]
631 |      },
632 |      "execution_count": 8,
633 |      "metadata": {},
634 |      "output_type": "execute_result"
635 |     }
636 |    ],
637 |    "source": [
638 |     "sal['JobTitle'].value_counts().head(5)"
639 |    ]
640 |   },
641 |   {
642 |    "cell_type": "markdown",
643 |    "metadata": {},
644 |    "source": [
645 |     "** How many Job Titles were represented by only one person in 2013? (e.g. Job Titles with only one occurence in 2013?) **"
646 |    ]
647 |   },
648 |   {
649 |    "cell_type": "code",
650 |    "execution_count": 9,
651 |    "metadata": {},
652 |    "outputs": [
653 |     {
654 |      "data": {
655 |       "text/plain": [
656 |        "202"
657 |       ]
658 |      },
659 |      "execution_count": 9,
660 |      "metadata": {},
661 |      "output_type": "execute_result"
662 |     }
663 |    ],
664 |    "source": [
665 |     "sum(sal[sal['Year']==2013]['JobTitle'].value_counts() == 1) "
666 |    ]
667 |   },
668 |   {
669 |    "cell_type": "markdown",
670 |    "metadata": {},
671 |    "source": [
672 |     "** How many people have the word Chief in their job title? (This is pretty tricky) **"
673 |    ]
674 |   },
675 |   {
676 |    "cell_type": "code",
677 |    "execution_count": 16,
678 |    "metadata": {},
679 |    "outputs": [],
680 |    "source": [
681 |     "def chief_string(title):\n",
682 |     "    if 'chief' in title.lower():\n",
683 |     "        return True\n",
684 |     "    else:\n",
685 |     "        return False"
686 |    ]
687 |   },
688 |   {
689 |    "cell_type": "code",
690 |    "execution_count": 17,
691 |    "metadata": {},
692 |    "outputs": [
693 |     {
694 |      "data": {
695 |       "text/plain": [
696 |        "627"
697 |       ]
698 |      },
699 |      "execution_count": 17,
700 |      "metadata": {},
701 |      "output_type": "execute_result"
702 |     }
703 |    ],
704 |    "source": [
705 |     "sum(sal['JobTitle'].apply(lambda x: chief_string(x)))"
706 |    ]
707 |   },
708 |   {
709 |    "cell_type": "markdown",
710 |    "metadata": {},
711 |    "source": [
712 |     "** Bonus: Is there a correlation between length of the Job Title string and Salary? **"
713 |    ]
714 |   },
715 |   {
716 |    "cell_type": "code",
717 |    "execution_count": 18,
718 |    "metadata": {},
719 |    "outputs": [],
720 |    "source": [
721 |     "sal['title_len'] = sal['JobTitle'].apply(len)"
722 |    ]
723 |   },
724 |   {
725 |    "cell_type": "code",
726 |    "execution_count": 19,
727 |    "metadata": {},
728 |    "outputs": [
729 |     {
730 |      "data": {
731 |       "text/html": [
732 |        "<div>\n",
733 |        "<style scoped>\n",
734 |        "    .dataframe tbody tr th:only-of-type {\n",
735 |        "        vertical-align: middle;\n",
736 |        "    }\n",
737 |        "\n",
738 |        "    .dataframe tbody tr th {\n",
739 |        "        vertical-align: top;\n",
740 |        "    }\n",
741 |        "\n",
742 |        "    .dataframe thead th {\n",
743 |        "        text-align: right;\n",
744 |        "    }\n",
745 |        "</style>\n",
746 |        "<table border=\"1\" class=\"dataframe\">\n",
747 |        "  <thead>\n",
748 |        "    <tr style=\"text-align: right;\">\n",
749 |        "      <th></th>\n",
750 |        "      <th>title_len</th>\n",
751 |        "      <th>TotalPayBenefits</th>\n",
752 |        "    </tr>\n",
753 |        "  </thead>\n",
754 |        "  <tbody>\n",
755 |        "    <tr>\n",
756 |        "      <th>title_len</th>\n",
757 |        "      <td>1.000000</td>\n",
758 |        "      <td>-0.036878</td>\n",
759 |        "    </tr>\n",
760 |        "    <tr>\n",
761 |        "      <th>TotalPayBenefits</th>\n",
762 |        "      <td>-0.036878</td>\n",
763 |        "      <td>1.000000</td>\n",
764 |        "    </tr>\n",
765 |        "  </tbody>\n",
766 |        "</table>\n",
767 |        "</div>"
768 |       ],
769 |       "text/plain": [
770 |        "                  title_len  TotalPayBenefits\n",
771 |        "title_len          1.000000         -0.036878\n",
772 |        "TotalPayBenefits  -0.036878          1.000000"
773 |       ]
774 |      },
775 |      "execution_count": 19,
776 |      "metadata": {},
777 |      "output_type": "execute_result"
778 |     }
779 |    ],
780 |    "source": [
781 |     "sal[['title_len','TotalPayBenefits']].corr() # No correlation."
782 |    ]
783 |   },
784 |   {
785 |    "cell_type": "markdown",
786 |    "metadata": {},
787 |    "source": [
788 |     "# Great Job!"
789 |    ]
790 |   }
791 |  ],
792 |  "metadata": {
793 |   "kernelspec": {
794 |    "display_name": "Python 3",
795 |    "language": "python",
796 |    "name": "python3"
797 |   },
798 |   "language_info": {
799 |    "codemirror_mode": {
800 |     "name": "ipython",
801 |     "version": 3
802 |    },
803 |    "file_extension": ".py",
804 |    "mimetype": "text/x-python",
805 |    "name": "python",
806 |    "nbconvert_exporter": "python",
807 |    "pygments_lexer": "ipython3",
808 |    "version": "3.6.4"
809 |   }
810 |  },
811 |  "nbformat": 4,
812 |  "nbformat_minor": 1
813 | }
814 | 


--------------------------------------------------------------------------------
/Choropleth Maps Exercise .ipynb:
--------------------------------------------------------------------------------
   1 | {
   2 |  "cells": [
   3 |   {
   4 |    "cell_type": "markdown",
   5 |    "metadata": {},
   6 |    "source": [
   7 |     "___\n",
   8 |     "\n",
   9 |     "<a href='http://www.pieriandata.com'> <img src='../Pierian_Data_Logo.png' /></a>\n",
  10 |     "___"
  11 |    ]
  12 |   },
  13 |   {
  14 |    "cell_type": "markdown",
  15 |    "metadata": {},
  16 |    "source": [
  17 |     "# Choropleth Maps Exercise \n",
  18 |     "\n",
  19 |     "Welcome to the Choropleth Maps Exercise! In this exercise we will give you some simple datasets and ask you to create Choropleth Maps from them. Due to the Nature of Plotly we can't show you examples\n",
  20 |     "\n",
  21 |     "[Full Documentation Reference](https://plot.ly/python/reference/#choropleth)\n",
  22 |     "\n",
  23 |     "## Plotly Imports"
  24 |    ]
  25 |   },
  26 |   {
  27 |    "cell_type": "code",
  28 |    "execution_count": 1,
  29 |    "metadata": {},
  30 |    "outputs": [
  31 |     {
  32 |      "data": {
  33 |       "text/html": [
  34 |        "<script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window.Plotly) {{require(['plotly'],function(plotly) {window.Plotly=plotly;});}}</script>"
  35 |       ],
  36 |       "text/vnd.plotly.v1+html": [
  37 |        "<script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window.Plotly) {{require(['plotly'],function(plotly) {window.Plotly=plotly;});}}</script>"
  38 |       ]
  39 |      },
  40 |      "metadata": {},
  41 |      "output_type": "display_data"
  42 |     }
  43 |    ],
  44 |    "source": [
  45 |     "import plotly.graph_objs as go \n",
  46 |     "from plotly.offline import init_notebook_mode,iplot\n",
  47 |     "init_notebook_mode(connected=True) "
  48 |    ]
  49 |   },
  50 |   {
  51 |    "cell_type": "markdown",
  52 |    "metadata": {},
  53 |    "source": [
  54 |     "** Import pandas and read the csv file: 2014_World_Power_Consumption**"
  55 |    ]
  56 |   },
  57 |   {
  58 |    "cell_type": "code",
  59 |    "execution_count": 2,
  60 |    "metadata": {},
  61 |    "outputs": [],
  62 |    "source": [
  63 |     "import pandas as pd"
  64 |    ]
  65 |   },
  66 |   {
  67 |    "cell_type": "code",
  68 |    "execution_count": 3,
  69 |    "metadata": {},
  70 |    "outputs": [],
  71 |    "source": [
  72 |     "df = pd.read_csv('2014_World_Power_Consumption')"
  73 |    ]
  74 |   },
  75 |   {
  76 |    "cell_type": "markdown",
  77 |    "metadata": {},
  78 |    "source": [
  79 |     "** Check the head of the DataFrame. **"
  80 |    ]
  81 |   },
  82 |   {
  83 |    "cell_type": "code",
  84 |    "execution_count": 4,
  85 |    "metadata": {},
  86 |    "outputs": [
  87 |     {
  88 |      "data": {
  89 |       "text/html": [
  90 |        "<div>\n",
  91 |        "<style scoped>\n",
  92 |        "    .dataframe tbody tr th:only-of-type {\n",
  93 |        "        vertical-align: middle;\n",
  94 |        "    }\n",
  95 |        "\n",
  96 |        "    .dataframe tbody tr th {\n",
  97 |        "        vertical-align: top;\n",
  98 |        "    }\n",
  99 |        "\n",
 100 |        "    .dataframe thead th {\n",
 101 |        "        text-align: right;\n",
 102 |        "    }\n",
 103 |        "</style>\n",
 104 |        "<table border=\"1\" class=\"dataframe\">\n",
 105 |        "  <thead>\n",
 106 |        "    <tr style=\"text-align: right;\">\n",
 107 |        "      <th></th>\n",
 108 |        "      <th>Country</th>\n",
 109 |        "      <th>Power Consumption KWH</th>\n",
 110 |        "      <th>Text</th>\n",
 111 |        "    </tr>\n",
 112 |        "  </thead>\n",
 113 |        "  <tbody>\n",
 114 |        "    <tr>\n",
 115 |        "      <th>0</th>\n",
 116 |        "      <td>China</td>\n",
 117 |        "      <td>5.523000e+12</td>\n",
 118 |        "      <td>China 5,523,000,000,000</td>\n",
 119 |        "    </tr>\n",
 120 |        "    <tr>\n",
 121 |        "      <th>1</th>\n",
 122 |        "      <td>United States</td>\n",
 123 |        "      <td>3.832000e+12</td>\n",
 124 |        "      <td>United 3,832,000,000,000</td>\n",
 125 |        "    </tr>\n",
 126 |        "    <tr>\n",
 127 |        "      <th>2</th>\n",
 128 |        "      <td>European</td>\n",
 129 |        "      <td>2.771000e+12</td>\n",
 130 |        "      <td>European 2,771,000,000,000</td>\n",
 131 |        "    </tr>\n",
 132 |        "    <tr>\n",
 133 |        "      <th>3</th>\n",
 134 |        "      <td>Russia</td>\n",
 135 |        "      <td>1.065000e+12</td>\n",
 136 |        "      <td>Russia 1,065,000,000,000</td>\n",
 137 |        "    </tr>\n",
 138 |        "    <tr>\n",
 139 |        "      <th>4</th>\n",
 140 |        "      <td>Japan</td>\n",
 141 |        "      <td>9.210000e+11</td>\n",
 142 |        "      <td>Japan 921,000,000,000</td>\n",
 143 |        "    </tr>\n",
 144 |        "  </tbody>\n",
 145 |        "</table>\n",
 146 |        "</div>"
 147 |       ],
 148 |       "text/plain": [
 149 |        "         Country  Power Consumption KWH                        Text\n",
 150 |        "0          China           5.523000e+12     China 5,523,000,000,000\n",
 151 |        "1  United States           3.832000e+12    United 3,832,000,000,000\n",
 152 |        "2       European           2.771000e+12  European 2,771,000,000,000\n",
 153 |        "3         Russia           1.065000e+12    Russia 1,065,000,000,000\n",
 154 |        "4          Japan           9.210000e+11       Japan 921,000,000,000"
 155 |       ]
 156 |      },
 157 |      "execution_count": 4,
 158 |      "metadata": {},
 159 |      "output_type": "execute_result"
 160 |     }
 161 |    ],
 162 |    "source": [
 163 |     "df.head()"
 164 |    ]
 165 |   },
 166 |   {
 167 |    "cell_type": "markdown",
 168 |    "metadata": {},
 169 |    "source": [
 170 |     "** Referencing the lecture notes, create a Choropleth Plot of the Power Consumption for Countries using the data and layout dictionary. **"
 171 |    ]
 172 |   },
 173 |   {
 174 |    "cell_type": "code",
 175 |    "execution_count": 16,
 176 |    "metadata": {},
 177 |    "outputs": [],
 178 |    "source": [
 179 |     "data = dict(type = 'choropleth',\n",
 180 |     "            locations = df['Country'],\n",
 181 |     "            locationmode = 'country names',\n",
 182 |     "            colorscale = 'Viridis',\n",
 183 |     "            reversescale = True,\n",
 184 |     "            text= df['Country'],\n",
 185 |     "            z = df['Power Consumption KWH'],\n",
 186 |     "            colorbar = {'title':'Power Consumption'})\n",
 187 |     "\n",
 188 |     "layout = dict(title = '2014 Power Consumption KWH',\n",
 189 |     "                geo = dict(showframe = False,projection = {'type':'Mercator'})\n",
 190 |     "             )"
 191 |    ]
 192 |   },
 193 |   {
 194 |    "cell_type": "code",
 195 |    "execution_count": 17,
 196 |    "metadata": {},
 197 |    "outputs": [
 198 |     {
 199 |      "data": {
 200 |       "application/vnd.plotly.v1+json": {
 201 |        "data": [
 202 |         {
 203 |          "colorbar": {
 204 |           "title": "Power Consumption"
 205 |          },
 206 |          "colorscale": "Viridis",
 207 |          "locationmode": "country names",
 208 |          "locations": [
 209 |           "China",
 210 |           "United States",
 211 |           "European",
 212 |           "Russia",
 213 |           "Japan",
 214 |           "India",
 215 |           "Germany",
 216 |           "Canada",
 217 |           "Brazil",
 218 |           "Korea,",
 219 |           "France",
 220 |           "United Kingdom",
 221 |           "Italy",
 222 |           "Taiwan",
 223 |           "Spain",
 224 |           "Mexico",
 225 |           "Saudi",
 226 |           "Australia",
 227 |           "South",
 228 |           "Turkey",
 229 |           "Iran",
 230 |           "Indonesia",
 231 |           "Ukraine",
 232 |           "Thailand",
 233 |           "Poland",
 234 |           "Egypt",
 235 |           "Sweden",
 236 |           "Norway",
 237 |           "Malaysia",
 238 |           "Argentina",
 239 |           "Netherlands",
 240 |           "Vietnam",
 241 |           "Venezuela",
 242 |           "United Arab Emirates",
 243 |           "Finland",
 244 |           "Belgium",
 245 |           "Kazakhstan",
 246 |           "Pakistan",
 247 |           "Philippines",
 248 |           "Austria",
 249 |           "Chile",
 250 |           "Czechia",
 251 |           "Israel",
 252 |           "Switzerland",
 253 |           "Greece",
 254 |           "Iraq",
 255 |           "Romania",
 256 |           "Kuwait",
 257 |           "Colombia",
 258 |           "Singapore",
 259 |           "Portugal",
 260 |           "Uzbekistan",
 261 |           "Hong",
 262 |           "Algeria",
 263 |           "Bangladesh",
 264 |           "New",
 265 |           "Bulgaria",
 266 |           "Belarus",
 267 |           "Peru",
 268 |           "Denmark",
 269 |           "Qatar",
 270 |           "Slovakia",
 271 |           "Libya",
 272 |           "Serbia",
 273 |           "Morocco",
 274 |           "Syria",
 275 |           "Nigeria",
 276 |           "Ireland",
 277 |           "Hungary",
 278 |           "Oman",
 279 |           "Ecuador",
 280 |           "Puerto",
 281 |           "Azerbaijan",
 282 |           "Croatia",
 283 |           "Iceland",
 284 |           "Cuba",
 285 |           "Korea,",
 286 |           "Dominican",
 287 |           "Jordan",
 288 |           "Tajikistan",
 289 |           "Tunisia",
 290 |           "Slovenia",
 291 |           "Lebanon",
 292 |           "Bosnia",
 293 |           "Turkmenistan",
 294 |           "Bahrain",
 295 |           "Mozambique",
 296 |           "Ghana",
 297 |           "Sri",
 298 |           "Kyrgyzstan",
 299 |           "Lithuania",
 300 |           "Uruguay",
 301 |           "Costa",
 302 |           "Guatemala",
 303 |           "Georgia",
 304 |           "Trinidad",
 305 |           "Zambia",
 306 |           "Paraguay",
 307 |           "Albania",
 308 |           "Burma",
 309 |           "Estonia",
 310 |           "Congo,",
 311 |           "Panama",
 312 |           "Latvia",
 313 |           "Macedonia",
 314 |           "Zimbabwe",
 315 |           "Kenya",
 316 |           "Bolivia",
 317 |           "Luxembourg",
 318 |           "Sudan",
 319 |           "El",
 320 |           "Cameroon",
 321 |           "West",
 322 |           "Ethiopia",
 323 |           "Armenia",
 324 |           "Honduras",
 325 |           "Angola",
 326 |           "Cote",
 327 |           "Tanzania",
 328 |           "Nicaragua",
 329 |           "Moldova",
 330 |           "Cyprus",
 331 |           "Macau",
 332 |           "Namibia",
 333 |           "Mongolia",
 334 |           "Afghanistan",
 335 |           "Yemen",
 336 |           "Brunei",
 337 |           "Cambodia",
 338 |           "Montenegro",
 339 |           "Nepal",
 340 |           "Botswana",
 341 |           "Papua",
 342 |           "Jamaica",
 343 |           "Kosovo",
 344 |           "Laos",
 345 |           "Uganda",
 346 |           "New",
 347 |           "Mauritius",
 348 |           "Senegal",
 349 |           "Bhutan",
 350 |           "Malawi",
 351 |           "Madagascar",
 352 |           "Bahamas,",
 353 |           "Gabon",
 354 |           "Suriname",
 355 |           "Guam",
 356 |           "Liechtenstein",
 357 |           "Swaziland",
 358 |           "Burkina",
 359 |           "Togo",
 360 |           "Curacao",
 361 |           "Mauritania",
 362 |           "Barbados",
 363 |           "Niger",
 364 |           "Aruba",
 365 |           "Benin",
 366 |           "Guinea",
 367 |           "Mali",
 368 |           "Fiji",
 369 |           "Congo,",
 370 |           "Virgin",
 371 |           "Lesotho",
 372 |           "South",
 373 |           "Bermuda",
 374 |           "French",
 375 |           "Jersey",
 376 |           "Belize",
 377 |           "Andorra",
 378 |           "Guyana",
 379 |           "Cayman",
 380 |           "Haiti",
 381 |           "Rwanda",
 382 |           "Saint",
 383 |           "Djibouti",
 384 |           "Seychelles",
 385 |           "Somalia",
 386 |           "Antigua",
 387 |           "Greenland",
 388 |           "Cabo",
 389 |           "Eritrea",
 390 |           "Burundi",
 391 |           "Liberia",
 392 |           "Maldives",
 393 |           "Faroe",
 394 |           "Gambia,",
 395 |           "Chad",
 396 |           "Micronesia,",
 397 |           "Grenada",
 398 |           "Central",
 399 |           "Turks",
 400 |           "Gibraltar",
 401 |           "American",
 402 |           "Sierra",
 403 |           "Saint",
 404 |           "Saint",
 405 |           "Timor-Leste",
 406 |           "Equatorial",
 407 |           "Samoa",
 408 |           "Dominica",
 409 |           "Western",
 410 |           "Solomon",
 411 |           "Sao",
 412 |           "British",
 413 |           "Vanuatu",
 414 |           "Guinea-Bissau",
 415 |           "Tonga",
 416 |           "Saint",
 417 |           "Comoros",
 418 |           "Cook",
 419 |           "Kiribati",
 420 |           "Montserrat",
 421 |           "Nauru",
 422 |           "Falkland",
 423 |           "Saint",
 424 |           "Niue",
 425 |           "Gaza",
 426 |           "Malta",
 427 |           "Northern"
 428 |          ],
 429 |          "reversescale": true,
 430 |          "text": [
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 432 |           "United States",
 433 |           "European",
 434 |           "Russia",
 435 |           "Japan",
 436 |           "India",
 437 |           "Germany",
 438 |           "Canada",
 439 |           "Brazil",
 440 |           "Korea,",
 441 |           "France",
 442 |           "United Kingdom",
 443 |           "Italy",
 444 |           "Taiwan",
 445 |           "Spain",
 446 |           "Mexico",
 447 |           "Saudi",
 448 |           "Australia",
 449 |           "South",
 450 |           "Turkey",
 451 |           "Iran",
 452 |           "Indonesia",
 453 |           "Ukraine",
 454 |           "Thailand",
 455 |           "Poland",
 456 |           "Egypt",
 457 |           "Sweden",
 458 |           "Norway",
 459 |           "Malaysia",
 460 |           "Argentina",
 461 |           "Netherlands",
 462 |           "Vietnam",
 463 |           "Venezuela",
 464 |           "United Arab Emirates",
 465 |           "Finland",
 466 |           "Belgium",
 467 |           "Kazakhstan",
 468 |           "Pakistan",
 469 |           "Philippines",
 470 |           "Austria",
 471 |           "Chile",
 472 |           "Czechia",
 473 |           "Israel",
 474 |           "Switzerland",
 475 |           "Greece",
 476 |           "Iraq",
 477 |           "Romania",
 478 |           "Kuwait",
 479 |           "Colombia",
 480 |           "Singapore",
 481 |           "Portugal",
 482 |           "Uzbekistan",
 483 |           "Hong",
 484 |           "Algeria",
 485 |           "Bangladesh",
 486 |           "New",
 487 |           "Bulgaria",
 488 |           "Belarus",
 489 |           "Peru",
 490 |           "Denmark",
 491 |           "Qatar",
 492 |           "Slovakia",
 493 |           "Libya",
 494 |           "Serbia",
 495 |           "Morocco",
 496 |           "Syria",
 497 |           "Nigeria",
 498 |           "Ireland",
 499 |           "Hungary",
 500 |           "Oman",
 501 |           "Ecuador",
 502 |           "Puerto",
 503 |           "Azerbaijan",
 504 |           "Croatia",
 505 |           "Iceland",
 506 |           "Cuba",
 507 |           "Korea,",
 508 |           "Dominican",
 509 |           "Jordan",
 510 |           "Tajikistan",
 511 |           "Tunisia",
 512 |           "Slovenia",
 513 |           "Lebanon",
 514 |           "Bosnia",
 515 |           "Turkmenistan",
 516 |           "Bahrain",
 517 |           "Mozambique",
 518 |           "Ghana",
 519 |           "Sri",
 520 |           "Kyrgyzstan",
 521 |           "Lithuania",
 522 |           "Uruguay",
 523 |           "Costa",
 524 |           "Guatemala",
 525 |           "Georgia",
 526 |           "Trinidad",
 527 |           "Zambia",
 528 |           "Paraguay",
 529 |           "Albania",
 530 |           "Burma",
 531 |           "Estonia",
 532 |           "Congo,",
 533 |           "Panama",
 534 |           "Latvia",
 535 |           "Macedonia",
 536 |           "Zimbabwe",
 537 |           "Kenya",
 538 |           "Bolivia",
 539 |           "Luxembourg",
 540 |           "Sudan",
 541 |           "El",
 542 |           "Cameroon",
 543 |           "West",
 544 |           "Ethiopia",
 545 |           "Armenia",
 546 |           "Honduras",
 547 |           "Angola",
 548 |           "Cote",
 549 |           "Tanzania",
 550 |           "Nicaragua",
 551 |           "Moldova",
 552 |           "Cyprus",
 553 |           "Macau",
 554 |           "Namibia",
 555 |           "Mongolia",
 556 |           "Afghanistan",
 557 |           "Yemen",
 558 |           "Brunei",
 559 |           "Cambodia",
 560 |           "Montenegro",
 561 |           "Nepal",
 562 |           "Botswana",
 563 |           "Papua",
 564 |           "Jamaica",
 565 |           "Kosovo",
 566 |           "Laos",
 567 |           "Uganda",
 568 |           "New",
 569 |           "Mauritius",
 570 |           "Senegal",
 571 |           "Bhutan",
 572 |           "Malawi",
 573 |           "Madagascar",
 574 |           "Bahamas,",
 575 |           "Gabon",
 576 |           "Suriname",
 577 |           "Guam",
 578 |           "Liechtenstein",
 579 |           "Swaziland",
 580 |           "Burkina",
 581 |           "Togo",
 582 |           "Curacao",
 583 |           "Mauritania",
 584 |           "Barbados",
 585 |           "Niger",
 586 |           "Aruba",
 587 |           "Benin",
 588 |           "Guinea",
 589 |           "Mali",
 590 |           "Fiji",
 591 |           "Congo,",
 592 |           "Virgin",
 593 |           "Lesotho",
 594 |           "South",
 595 |           "Bermuda",
 596 |           "French",
 597 |           "Jersey",
 598 |           "Belize",
 599 |           "Andorra",
 600 |           "Guyana",
 601 |           "Cayman",
 602 |           "Haiti",
 603 |           "Rwanda",
 604 |           "Saint",
 605 |           "Djibouti",
 606 |           "Seychelles",
 607 |           "Somalia",
 608 |           "Antigua",
 609 |           "Greenland",
 610 |           "Cabo",
 611 |           "Eritrea",
 612 |           "Burundi",
 613 |           "Liberia",
 614 |           "Maldives",
 615 |           "Faroe",
 616 |           "Gambia,",
 617 |           "Chad",
 618 |           "Micronesia,",
 619 |           "Grenada",
 620 |           "Central",
 621 |           "Turks",
 622 |           "Gibraltar",
 623 |           "American",
 624 |           "Sierra",
 625 |           "Saint",
 626 |           "Saint",
 627 |           "Timor-Leste",
 628 |           "Equatorial",
 629 |           "Samoa",
 630 |           "Dominica",
 631 |           "Western",
 632 |           "Solomon",
 633 |           "Sao",
 634 |           "British",
 635 |           "Vanuatu",
 636 |           "Guinea-Bissau",
 637 |           "Tonga",
 638 |           "Saint",
 639 |           "Comoros",
 640 |           "Cook",
 641 |           "Kiribati",
 642 |           "Montserrat",
 643 |           "Nauru",
 644 |           "Falkland",
 645 |           "Saint",
 646 |           "Niue",
 647 |           "Gaza",
 648 |           "Malta",
 649 |           "Northern"
 650 |          ],
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 887 |       ],
 888 |       "text/vnd.plotly.v1+html": [
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 890 |       ]
 891 |      },
 892 |      "metadata": {},
 893 |      "output_type": "display_data"
 894 |     }
 895 |    ],
 896 |    "source": [
 897 |     "choromap = go.Figure(data = [data],layout = layout)\n",
 898 |     "iplot(choromap,validate=False)"
 899 |    ]
 900 |   },
 901 |   {
 902 |    "cell_type": "markdown",
 903 |    "metadata": {},
 904 |    "source": [
 905 |     "## USA Choropleth\n",
 906 |     "\n",
 907 |     "** Import the 2012_Election_Data csv file using pandas. **"
 908 |    ]
 909 |   },
 910 |   {
 911 |    "cell_type": "code",
 912 |    "execution_count": 8,
 913 |    "metadata": {},
 914 |    "outputs": [],
 915 |    "source": [
 916 |     "df2 = pd.read_csv('2012_Election_Data')"
 917 |    ]
 918 |   },
 919 |   {
 920 |    "cell_type": "markdown",
 921 |    "metadata": {},
 922 |    "source": [
 923 |     "** Check the head of the DataFrame. **"
 924 |    ]
 925 |   },
 926 |   {
 927 |    "cell_type": "code",
 928 |    "execution_count": 19,
 929 |    "metadata": {},
 930 |    "outputs": [
 931 |     {
 932 |      "data": {
 933 |       "text/html": [
 934 |        "<div>\n",
 935 |        "<style scoped>\n",
 936 |        "    .dataframe tbody tr th:only-of-type {\n",
 937 |        "        vertical-align: middle;\n",
 938 |        "    }\n",
 939 |        "\n",
 940 |        "    .dataframe tbody tr th {\n",
 941 |        "        vertical-align: top;\n",
 942 |        "    }\n",
 943 |        "\n",
 944 |        "    .dataframe thead th {\n",
 945 |        "        text-align: right;\n",
 946 |        "    }\n",
 947 |        "</style>\n",
 948 |        "<table border=\"1\" class=\"dataframe\">\n",
 949 |        "  <thead>\n",
 950 |        "    <tr style=\"text-align: right;\">\n",
 951 |        "      <th></th>\n",
 952 |        "      <th>Year</th>\n",
 953 |        "      <th>ICPSR State Code</th>\n",
 954 |        "      <th>Alphanumeric State Code</th>\n",
 955 |        "      <th>State</th>\n",
 956 |        "      <th>VEP Total Ballots Counted</th>\n",
 957 |        "      <th>VEP Highest Office</th>\n",
 958 |        "      <th>VAP Highest Office</th>\n",
 959 |        "      <th>Total Ballots Counted</th>\n",
 960 |        "      <th>Highest Office</th>\n",
 961 |        "      <th>Voting-Eligible Population (VEP)</th>\n",
 962 |        "      <th>Voting-Age Population (VAP)</th>\n",
 963 |        "      <th>% Non-citizen</th>\n",
 964 |        "      <th>Prison</th>\n",
 965 |        "      <th>Probation</th>\n",
 966 |        "      <th>Parole</th>\n",
 967 |        "      <th>Total Ineligible Felon</th>\n",
 968 |        "      <th>State Abv</th>\n",
 969 |        "    </tr>\n",
 970 |        "  </thead>\n",
 971 |        "  <tbody>\n",
 972 |        "    <tr>\n",
 973 |        "      <th>0</th>\n",
 974 |        "      <td>2012</td>\n",
 975 |        "      <td>41</td>\n",
 976 |        "      <td>1</td>\n",
 977 |        "      <td>Alabama</td>\n",
 978 |        "      <td>NaN</td>\n",
 979 |        "      <td>58.6%</td>\n",
 980 |        "      <td>56.0%</td>\n",
 981 |        "      <td>NaN</td>\n",
 982 |        "      <td>2,074,338</td>\n",
 983 |        "      <td>3,539,217</td>\n",
 984 |        "      <td>3707440.0</td>\n",
 985 |        "      <td>2.6%</td>\n",
 986 |        "      <td>32,232</td>\n",
 987 |        "      <td>57,993</td>\n",
 988 |        "      <td>8,616</td>\n",
 989 |        "      <td>71,584</td>\n",
 990 |        "      <td>AL</td>\n",
 991 |        "    </tr>\n",
 992 |        "    <tr>\n",
 993 |        "      <th>1</th>\n",
 994 |        "      <td>2012</td>\n",
 995 |        "      <td>81</td>\n",
 996 |        "      <td>2</td>\n",
 997 |        "      <td>Alaska</td>\n",
 998 |        "      <td>58.9%</td>\n",
 999 |        "      <td>58.7%</td>\n",
1000 |        "      <td>55.3%</td>\n",
1001 |        "      <td>301,694</td>\n",
1002 |        "      <td>300,495</td>\n",
1003 |        "      <td>511,792</td>\n",
1004 |        "      <td>543763.0</td>\n",
1005 |        "      <td>3.8%</td>\n",
1006 |        "      <td>5,633</td>\n",
1007 |        "      <td>7,173</td>\n",
1008 |        "      <td>1,882</td>\n",
1009 |        "      <td>11,317</td>\n",
1010 |        "      <td>AK</td>\n",
1011 |        "    </tr>\n",
1012 |        "    <tr>\n",
1013 |        "      <th>2</th>\n",
1014 |        "      <td>2012</td>\n",
1015 |        "      <td>61</td>\n",
1016 |        "      <td>3</td>\n",
1017 |        "      <td>Arizona</td>\n",
1018 |        "      <td>53.0%</td>\n",
1019 |        "      <td>52.6%</td>\n",
1020 |        "      <td>46.5%</td>\n",
1021 |        "      <td>2,323,579</td>\n",
1022 |        "      <td>2,306,559</td>\n",
1023 |        "      <td>4,387,900</td>\n",
1024 |        "      <td>4959270.0</td>\n",
1025 |        "      <td>9.9%</td>\n",
1026 |        "      <td>35,188</td>\n",
1027 |        "      <td>72,452</td>\n",
1028 |        "      <td>7,460</td>\n",
1029 |        "      <td>81,048</td>\n",
1030 |        "      <td>AZ</td>\n",
1031 |        "    </tr>\n",
1032 |        "    <tr>\n",
1033 |        "      <th>3</th>\n",
1034 |        "      <td>2012</td>\n",
1035 |        "      <td>42</td>\n",
1036 |        "      <td>4</td>\n",
1037 |        "      <td>Arkansas</td>\n",
1038 |        "      <td>51.1%</td>\n",
1039 |        "      <td>50.7%</td>\n",
1040 |        "      <td>47.7%</td>\n",
1041 |        "      <td>1,078,548</td>\n",
1042 |        "      <td>1,069,468</td>\n",
1043 |        "      <td>2,109,847</td>\n",
1044 |        "      <td>2242740.0</td>\n",
1045 |        "      <td>3.5%</td>\n",
1046 |        "      <td>14,471</td>\n",
1047 |        "      <td>30,122</td>\n",
1048 |        "      <td>23,372</td>\n",
1049 |        "      <td>53,808</td>\n",
1050 |        "      <td>AR</td>\n",
1051 |        "    </tr>\n",
1052 |        "    <tr>\n",
1053 |        "      <th>4</th>\n",
1054 |        "      <td>2012</td>\n",
1055 |        "      <td>71</td>\n",
1056 |        "      <td>5</td>\n",
1057 |        "      <td>California</td>\n",
1058 |        "      <td>55.7%</td>\n",
1059 |        "      <td>55.1%</td>\n",
1060 |        "      <td>45.1%</td>\n",
1061 |        "      <td>13,202,158</td>\n",
1062 |        "      <td>13,038,547</td>\n",
1063 |        "      <td>23,681,837</td>\n",
1064 |        "      <td>28913129.0</td>\n",
1065 |        "      <td>17.4%</td>\n",
1066 |        "      <td>119,455</td>\n",
1067 |        "      <td>0</td>\n",
1068 |        "      <td>89,287</td>\n",
1069 |        "      <td>208,742</td>\n",
1070 |        "      <td>CA</td>\n",
1071 |        "    </tr>\n",
1072 |        "  </tbody>\n",
1073 |        "</table>\n",
1074 |        "</div>"
1075 |       ],
1076 |       "text/plain": [
1077 |        "   Year  ICPSR State Code  Alphanumeric State Code       State  \\\n",
1078 |        "0  2012                41                        1     Alabama   \n",
1079 |        "1  2012                81                        2      Alaska   \n",
1080 |        "2  2012                61                        3     Arizona   \n",
1081 |        "3  2012                42                        4    Arkansas   \n",
1082 |        "4  2012                71                        5  California   \n",
1083 |        "\n",
1084 |        "  VEP Total Ballots Counted VEP Highest Office VAP Highest Office  \\\n",
1085 |        "0                       NaN              58.6%              56.0%   \n",
1086 |        "1                     58.9%              58.7%              55.3%   \n",
1087 |        "2                     53.0%              52.6%              46.5%   \n",
1088 |        "3                     51.1%              50.7%              47.7%   \n",
1089 |        "4                     55.7%              55.1%              45.1%   \n",
1090 |        "\n",
1091 |        "  Total Ballots Counted Highest Office Voting-Eligible Population (VEP)  \\\n",
1092 |        "0                   NaN      2,074,338                        3,539,217   \n",
1093 |        "1               301,694        300,495                          511,792   \n",
1094 |        "2             2,323,579      2,306,559                        4,387,900   \n",
1095 |        "3             1,078,548      1,069,468                        2,109,847   \n",
1096 |        "4            13,202,158     13,038,547                       23,681,837   \n",
1097 |        "\n",
1098 |        "   Voting-Age Population (VAP) % Non-citizen   Prison Probation  Parole  \\\n",
1099 |        "0                    3707440.0          2.6%   32,232    57,993   8,616   \n",
1100 |        "1                     543763.0          3.8%    5,633     7,173   1,882   \n",
1101 |        "2                    4959270.0          9.9%   35,188    72,452   7,460   \n",
1102 |        "3                    2242740.0          3.5%   14,471    30,122  23,372   \n",
1103 |        "4                   28913129.0         17.4%  119,455         0  89,287   \n",
1104 |        "\n",
1105 |        "  Total Ineligible Felon State Abv  \n",
1106 |        "0                 71,584        AL  \n",
1107 |        "1                 11,317        AK  \n",
1108 |        "2                 81,048        AZ  \n",
1109 |        "3                 53,808        AR  \n",
1110 |        "4                208,742        CA  "
1111 |       ]
1112 |      },
1113 |      "execution_count": 19,
1114 |      "metadata": {},
1115 |      "output_type": "execute_result"
1116 |     }
1117 |    ],
1118 |    "source": [
1119 |     "df2.head()"
1120 |    ]
1121 |   },
1122 |   {
1123 |    "cell_type": "markdown",
1124 |    "metadata": {},
1125 |    "source": [
1126 |     "** Now create a plot that displays the Voting-Age Population (VAP) per state. If you later want to play around with other columns, make sure you consider their data type. VAP has already been transformed to a float for you. **"
1127 |    ]
1128 |   },
1129 |   {
1130 |    "cell_type": "code",
1131 |    "execution_count": 34,
1132 |    "metadata": {},
1133 |    "outputs": [],
1134 |    "source": [
1135 |     "data = dict(type='choropleth',\n",
1136 |     "            colorscale = 'Viridis',\n",
1137 |     "            reversescale = True,\n",
1138 |     "            locations = df2['State Abv'],\n",
1139 |     "            z = df2['Voting-Age Population (VAP)'],\n",
1140 |     "            locationmode = 'USA-states',\n",
1141 |     "            text = df2['State'],\n",
1142 |     "            marker = dict(line = dict(color = 'rgb(255,255,255)',width = 1)),\n",
1143 |     "            colorbar = {'title':\"Voting-Age Population (VAP)\"}\n",
1144 |     "            ) \n",
1145 |     "\n",
1146 |     "layout = dict(title = '2012 General Election Voting Data',\n",
1147 |     "              geo = dict(scope='usa',\n",
1148 |     "                         showlakes = True,\n",
1149 |     "                         lakecolor = 'rgb(85,173,240)')\n",
1150 |     "             )"
1151 |    ]
1152 |   },
1153 |   {
1154 |    "cell_type": "code",
1155 |    "execution_count": 35,
1156 |    "metadata": {},
1157 |    "outputs": [
1158 |     {
1159 |      "data": {
1160 |       "application/vnd.plotly.v1+json": {
1161 |        "data": [
1162 |         {
1163 |          "colorbar": {
1164 |           "title": "Voting-Age Population (VAP)"
1165 |          },
1166 |          "colorscale": "Viridis",
1167 |          "locationmode": "USA-states",
1168 |          "locations": [
1169 |           "AL",
1170 |           "AK",
1171 |           "AZ",
1172 |           "AR",
1173 |           "CA",
1174 |           "CO",
1175 |           "CT",
1176 |           "DE",
1177 |           "District of Columbia",
1178 |           "FL",
1179 |           "GA",
1180 |           "HI",
1181 |           "ID",
1182 |           "IL",
1183 |           "IN",
1184 |           "IA",
1185 |           "KS",
1186 |           "KY",
1187 |           "LA",
1188 |           "ME",
1189 |           "MD",
1190 |           "MA",
1191 |           "MI",
1192 |           "MN",
1193 |           "MS",
1194 |           "MO",
1195 |           "MT",
1196 |           "NE",
1197 |           "NV",
1198 |           "NH",
1199 |           "NJ",
1200 |           "NM",
1201 |           "NY",
1202 |           "NC",
1203 |           "ND",
1204 |           "OH",
1205 |           "OK",
1206 |           "OR",
1207 |           "PA",
1208 |           "RI",
1209 |           "SC",
1210 |           "SD",
1211 |           "TN",
1212 |           "TX",
1213 |           "UT",
1214 |           "VT",
1215 |           "VA",
1216 |           "WA",
1217 |           "WV",
1218 |           "WI",
1219 |           "WY"
1220 |          ],
1221 |          "marker": {
1222 |           "line": {
1223 |            "color": "rgb(255,255,255)",
1224 |            "width": 1
1225 |           }
1226 |          },
1227 |          "reversescale": true,
1228 |          "text": [
1229 |           "Alabama",
1230 |           "Alaska",
1231 |           "Arizona",
1232 |           "Arkansas",
1233 |           "California",
1234 |           "Colorado",
1235 |           "Connecticut",
1236 |           "Delaware",
1237 |           "District of Columbia",
1238 |           "Florida",
1239 |           "Georgia",
1240 |           "Hawaii",
1241 |           "Idaho",
1242 |           "Illinois",
1243 |           "Indiana",
1244 |           "Iowa",
1245 |           "Kansas",
1246 |           "Kentucky",
1247 |           "Louisiana",
1248 |           "Maine",
1249 |           "Maryland",
1250 |           "Massachusetts",
1251 |           "Michigan",
1252 |           "Minnesota",
1253 |           "Mississippi",
1254 |           "Missouri",
1255 |           "Montana",
1256 |           "Nebraska",
1257 |           "Nevada",
1258 |           "New Hampshire",
1259 |           "New Jersey",
1260 |           "New Mexico",
1261 |           "New York",
1262 |           "North Carolina",
1263 |           "North Dakota",
1264 |           "Ohio",
1265 |           "Oklahoma",
1266 |           "Oregon",
1267 |           "Pennsylvania",
1268 |           "Rhode Island",
1269 |           "South Carolina",
1270 |           "South Dakota",
1271 |           "Tennessee",
1272 |           "Texas",
1273 |           "Utah",
1274 |           "Vermont",
1275 |           "Virginia",
1276 |           "Washington",
1277 |           "West Virginia",
1278 |           "Wisconsin",
1279 |           "Wyoming"
1280 |          ],
1281 |          "type": "choropleth",
1282 |          "z": [
1283 |           3707440,
1284 |           543763,
1285 |           4959270,
1286 |           2242740,
1287 |           28913129,
1288 |           3981208,
1289 |           2801375,
1290 |           715708,
1291 |           528848,
1292 |           15380947,
1293 |           7452696,
1294 |           1088335,
1295 |           1173727,
1296 |           9827043,
1297 |           4960376,
1298 |           2356209,
1299 |           2162442,
1300 |           3368684,
1301 |           3495847,
1302 |           1064779,
1303 |           4553853,
1304 |           5263550,
1305 |           7625576,
1306 |           4114820,
1307 |           2246931,
1308 |           4628500,
1309 |           785454,
1310 |           1396507,
1311 |           2105976,
1312 |           1047978,
1313 |           6847503,
1314 |           1573400,
1315 |           15344671,
1316 |           7496980,
1317 |           549955,
1318 |           8896930,
1319 |           2885093,
1320 |           3050747,
1321 |           10037099,
1322 |           834983,
1323 |           3662322,
1324 |           631472,
1325 |           4976284,
1326 |           19185395,
1327 |           1978956,
1328 |           502242,
1329 |           6348827,
1330 |           5329782,
1331 |           1472642,
1332 |           4417273,
1333 |           441726
1334 |          ]
1335 |         }
1336 |        ],
1337 |        "layout": {
1338 |         "geo": {
1339 |          "lakecolor": "rgb(85,173,240)",
1340 |          "scope": "usa",
1341 |          "showlakes": true
1342 |         },
1343 |         "title": "2012 General Election Voting Data"
1344 |        }
1345 |       },
1346 |       "text/html": [
1347 |        "<div id=\"3ee75520-ad69-4dc7-9c29-257920ce1f59\" style=\"height: 525px; width: 100%;\" class=\"plotly-graph-div\"></div><script type=\"text/javascript\">require([\"plotly\"], function(Plotly) { window.PLOTLYENV=window.PLOTLYENV || {};window.PLOTLYENV.BASE_URL=\"https://plot.ly\";Plotly.newPlot(\"3ee75520-ad69-4dc7-9c29-257920ce1f59\", [{\"type\": \"choropleth\", \"colorscale\": \"Viridis\", \"reversescale\": true, \"locations\": [\"AL\", \"AK\", \"AZ\", \"AR\", \"CA\", \"CO\", \"CT\", \"DE\", \"District of Columbia\", \"FL\", \"GA\", \"HI\", \"ID\", \"IL\", \"IN\", \"IA\", \"KS\", \"KY\", \"LA\", \"ME\", \"MD\", \"MA\", \"MI\", \"MN\", \"MS\", \"MO\", \"MT\", \"NE\", \"NV\", \"NH\", \"NJ\", \"NM\", \"NY\", \"NC\", \"ND\", \"OH\", \"OK\", \"OR\", \"PA\", \"RI\", \"SC\", \"SD\", \"TN\", \"TX\", \"UT\", \"VT\", \"VA\", \"WA\", \"WV\", \"WI\", \"WY\"], \"z\": [3707440.0, 543763.0, 4959270.0, 2242740.0, 28913129.0, 3981208.0, 2801375.0, 715708.0, 528848.0, 15380947.0, 7452696.0, 1088335.0, 1173727.0, 9827043.0, 4960376.0, 2356209.0, 2162442.0, 3368684.0, 3495847.0, 1064779.0, 4553853.0, 5263550.0, 7625576.0, 4114820.0, 2246931.0, 4628500.0, 785454.0, 1396507.0, 2105976.0, 1047978.0, 6847503.0, 1573400.0, 15344671.0, 7496980.0, 549955.0, 8896930.0, 2885093.0, 3050747.0, 10037099.0, 834983.0, 3662322.0, 631472.0, 4976284.0, 19185395.0, 1978956.0, 502242.0, 6348827.0, 5329782.0, 1472642.0, 4417273.0, 441726.0], \"locationmode\": \"USA-states\", \"text\": [\"Alabama\", \"Alaska\", \"Arizona\", \"Arkansas\", \"California\", \"Colorado\", \"Connecticut\", \"Delaware\", \"District of Columbia\", \"Florida\", \"Georgia\", \"Hawaii\", \"Idaho\", \"Illinois\", \"Indiana\", \"Iowa\", \"Kansas\", \"Kentucky\", \"Louisiana\", \"Maine\", \"Maryland\", \"Massachusetts\", \"Michigan\", \"Minnesota\", \"Mississippi\", \"Missouri\", \"Montana\", \"Nebraska\", \"Nevada\", \"New Hampshire\", \"New Jersey\", \"New Mexico\", \"New York\", \"North Carolina\", \"North Dakota\", \"Ohio\", \"Oklahoma\", \"Oregon\", \"Pennsylvania\", \"Rhode Island\", \"South Carolina\", \"South Dakota\", \"Tennessee\", \"Texas\", \"Utah\", \"Vermont\", \"Virginia\", \"Washington\", \"West Virginia\", \"Wisconsin\", \"Wyoming\"], \"marker\": {\"line\": {\"color\": \"rgb(255,255,255)\", \"width\": 1}}, \"colorbar\": {\"title\": \"Voting-Age Population (VAP)\"}}], {\"title\": \"2012 General Election Voting Data\", \"geo\": {\"scope\": \"usa\", \"showlakes\": true, \"lakecolor\": \"rgb(85,173,240)\"}}, {\"showLink\": true, \"linkText\": \"Export to plot.ly\"})});</script>"
1348 |       ],
1349 |       "text/vnd.plotly.v1+html": [
1350 |        "<div id=\"3ee75520-ad69-4dc7-9c29-257920ce1f59\" style=\"height: 525px; width: 100%;\" class=\"plotly-graph-div\"></div><script type=\"text/javascript\">require([\"plotly\"], function(Plotly) { window.PLOTLYENV=window.PLOTLYENV || {};window.PLOTLYENV.BASE_URL=\"https://plot.ly\";Plotly.newPlot(\"3ee75520-ad69-4dc7-9c29-257920ce1f59\", [{\"type\": \"choropleth\", \"colorscale\": \"Viridis\", \"reversescale\": true, \"locations\": [\"AL\", \"AK\", \"AZ\", \"AR\", \"CA\", \"CO\", \"CT\", \"DE\", \"District of Columbia\", \"FL\", \"GA\", \"HI\", \"ID\", \"IL\", \"IN\", \"IA\", \"KS\", \"KY\", \"LA\", \"ME\", \"MD\", \"MA\", \"MI\", \"MN\", \"MS\", \"MO\", \"MT\", \"NE\", \"NV\", \"NH\", \"NJ\", \"NM\", \"NY\", \"NC\", \"ND\", \"OH\", \"OK\", \"OR\", \"PA\", \"RI\", \"SC\", \"SD\", \"TN\", \"TX\", \"UT\", \"VT\", \"VA\", \"WA\", \"WV\", \"WI\", \"WY\"], \"z\": [3707440.0, 543763.0, 4959270.0, 2242740.0, 28913129.0, 3981208.0, 2801375.0, 715708.0, 528848.0, 15380947.0, 7452696.0, 1088335.0, 1173727.0, 9827043.0, 4960376.0, 2356209.0, 2162442.0, 3368684.0, 3495847.0, 1064779.0, 4553853.0, 5263550.0, 7625576.0, 4114820.0, 2246931.0, 4628500.0, 785454.0, 1396507.0, 2105976.0, 1047978.0, 6847503.0, 1573400.0, 15344671.0, 7496980.0, 549955.0, 8896930.0, 2885093.0, 3050747.0, 10037099.0, 834983.0, 3662322.0, 631472.0, 4976284.0, 19185395.0, 1978956.0, 502242.0, 6348827.0, 5329782.0, 1472642.0, 4417273.0, 441726.0], \"locationmode\": \"USA-states\", \"text\": [\"Alabama\", \"Alaska\", \"Arizona\", \"Arkansas\", \"California\", \"Colorado\", \"Connecticut\", \"Delaware\", \"District of Columbia\", \"Florida\", \"Georgia\", \"Hawaii\", \"Idaho\", \"Illinois\", \"Indiana\", \"Iowa\", \"Kansas\", \"Kentucky\", \"Louisiana\", \"Maine\", \"Maryland\", \"Massachusetts\", \"Michigan\", \"Minnesota\", \"Mississippi\", \"Missouri\", \"Montana\", \"Nebraska\", \"Nevada\", \"New Hampshire\", \"New Jersey\", \"New Mexico\", \"New York\", \"North Carolina\", \"North Dakota\", \"Ohio\", \"Oklahoma\", \"Oregon\", \"Pennsylvania\", \"Rhode Island\", \"South Carolina\", \"South Dakota\", \"Tennessee\", \"Texas\", \"Utah\", \"Vermont\", \"Virginia\", \"Washington\", \"West Virginia\", \"Wisconsin\", \"Wyoming\"], \"marker\": {\"line\": {\"color\": \"rgb(255,255,255)\", \"width\": 1}}, \"colorbar\": {\"title\": \"Voting-Age Population (VAP)\"}}], {\"title\": \"2012 General Election Voting Data\", \"geo\": {\"scope\": \"usa\", \"showlakes\": true, \"lakecolor\": \"rgb(85,173,240)\"}}, {\"showLink\": true, \"linkText\": \"Export to plot.ly\"})});</script>"
1351 |       ]
1352 |      },
1353 |      "metadata": {},
1354 |      "output_type": "display_data"
1355 |     }
1356 |    ],
1357 |    "source": [
1358 |     "choromap = go.Figure(data = [data],layout = layout)\n",
1359 |     "iplot(choromap,validate=False)"
1360 |    ]
1361 |   },
1362 |   {
1363 |    "cell_type": "markdown",
1364 |    "metadata": {},
1365 |    "source": [
1366 |     "# Great Job!"
1367 |    ]
1368 |   }
1369 |  ],
1370 |  "metadata": {
1371 |   "kernelspec": {
1372 |    "display_name": "Python 3",
1373 |    "language": "python",
1374 |    "name": "python3"
1375 |   },
1376 |   "language_info": {
1377 |    "codemirror_mode": {
1378 |     "name": "ipython",
1379 |     "version": 3
1380 |    },
1381 |    "file_extension": ".py",
1382 |    "mimetype": "text/x-python",
1383 |    "name": "python",
1384 |    "nbconvert_exporter": "python",
1385 |    "pygments_lexer": "ipython3",
1386 |    "version": "3.6.4"
1387 |   }
1388 |  },
1389 |  "nbformat": 4,
1390 |  "nbformat_minor": 1
1391 | }
1392 | 


--------------------------------------------------------------------------------
/NLP Project .ipynb:
--------------------------------------------------------------------------------
   1 | {
   2 |  "cells": [
   3 |   {
   4 |    "cell_type": "markdown",
   5 |    "metadata": {},
   6 |    "source": [
   7 |     "___\n",
   8 |     "\n",
   9 |     "<a href='http://www.pieriandata.com'> <img src='../Pierian_Data_Logo.png' /></a>\n",
  10 |     "___"
  11 |    ]
  12 |   },
  13 |   {
  14 |    "cell_type": "markdown",
  15 |    "metadata": {},
  16 |    "source": [
  17 |     "# Natural Language Processing Project\n",
  18 |     "\n",
  19 |     "Welcome to the NLP Project for this section of the course. In this NLP project you will be attempting to classify Yelp Reviews into 1 star or 5 star categories based off the text content in the reviews. This will be a simpler procedure than the lecture, since we will utilize the pipeline methods for more complex tasks.\n",
  20 |     "\n",
  21 |     "We will use the [Yelp Review Data Set from Kaggle](https://www.kaggle.com/c/yelp-recsys-2013).\n",
  22 |     "\n",
  23 |     "Each observation in this dataset is a review of a particular business by a particular user.\n",
  24 |     "\n",
  25 |     "The \"stars\" column is the number of stars (1 through 5) assigned by the reviewer to the business. (Higher stars is better.) In other words, it is the rating of the business by the person who wrote the review.\n",
  26 |     "\n",
  27 |     "The \"cool\" column is the number of \"cool\" votes this review received from other Yelp users. \n",
  28 |     "\n",
  29 |     "All reviews start with 0 \"cool\" votes, and there is no limit to how many \"cool\" votes a review can receive. In other words, it is a rating of the review itself, not a rating of the business.\n",
  30 |     "\n",
  31 |     "The \"useful\" and \"funny\" columns are similar to the \"cool\" column.\n",
  32 |     "\n",
  33 |     "Let's get started! Just follow the directions below!"
  34 |    ]
  35 |   },
  36 |   {
  37 |    "cell_type": "markdown",
  38 |    "metadata": {},
  39 |    "source": [
  40 |     "## Imports\n",
  41 |     " **Import the usual suspects. :) **"
  42 |    ]
  43 |   },
  44 |   {
  45 |    "cell_type": "code",
  46 |    "execution_count": 25,
  47 |    "metadata": {},
  48 |    "outputs": [],
  49 |    "source": [
  50 |     "import numpy as np\n",
  51 |     "import pandas as pd\n",
  52 |     "import matplotlib.pyplot as plt\n",
  53 |     "import seaborn as sns \n",
  54 |     "\n",
  55 |     "%matplotlib inline"
  56 |    ]
  57 |   },
  58 |   {
  59 |    "cell_type": "markdown",
  60 |    "metadata": {},
  61 |    "source": [
  62 |     "## The Data\n",
  63 |     "\n",
  64 |     "**Read the yelp.csv file and set it as a dataframe called yelp.**"
  65 |    ]
  66 |   },
  67 |   {
  68 |    "cell_type": "code",
  69 |    "execution_count": 37,
  70 |    "metadata": {},
  71 |    "outputs": [],
  72 |    "source": [
  73 |     "yelp = pd.read_csv('yelp.csv')"
  74 |    ]
  75 |   },
  76 |   {
  77 |    "cell_type": "markdown",
  78 |    "metadata": {},
  79 |    "source": [
  80 |     "** Check the head, info , and describe methods on yelp.**"
  81 |    ]
  82 |   },
  83 |   {
  84 |    "cell_type": "code",
  85 |    "execution_count": 38,
  86 |    "metadata": {},
  87 |    "outputs": [
  88 |     {
  89 |      "data": {
  90 |       "text/html": [
  91 |        "<div>\n",
  92 |        "<style scoped>\n",
  93 |        "    .dataframe tbody tr th:only-of-type {\n",
  94 |        "        vertical-align: middle;\n",
  95 |        "    }\n",
  96 |        "\n",
  97 |        "    .dataframe tbody tr th {\n",
  98 |        "        vertical-align: top;\n",
  99 |        "    }\n",
 100 |        "\n",
 101 |        "    .dataframe thead th {\n",
 102 |        "        text-align: right;\n",
 103 |        "    }\n",
 104 |        "</style>\n",
 105 |        "<table border=\"1\" class=\"dataframe\">\n",
 106 |        "  <thead>\n",
 107 |        "    <tr style=\"text-align: right;\">\n",
 108 |        "      <th></th>\n",
 109 |        "      <th>business_id</th>\n",
 110 |        "      <th>date</th>\n",
 111 |        "      <th>review_id</th>\n",
 112 |        "      <th>stars</th>\n",
 113 |        "      <th>text</th>\n",
 114 |        "      <th>type</th>\n",
 115 |        "      <th>user_id</th>\n",
 116 |        "      <th>cool</th>\n",
 117 |        "      <th>useful</th>\n",
 118 |        "      <th>funny</th>\n",
 119 |        "    </tr>\n",
 120 |        "  </thead>\n",
 121 |        "  <tbody>\n",
 122 |        "    <tr>\n",
 123 |        "      <th>0</th>\n",
 124 |        "      <td>9yKzy9PApeiPPOUJEtnvkg</td>\n",
 125 |        "      <td>2011-01-26</td>\n",
 126 |        "      <td>fWKvX83p0-ka4JS3dc6E5A</td>\n",
 127 |        "      <td>5</td>\n",
 128 |        "      <td>My wife took me here on my birthday for breakf...</td>\n",
 129 |        "      <td>review</td>\n",
 130 |        "      <td>rLtl8ZkDX5vH5nAx9C3q5Q</td>\n",
 131 |        "      <td>2</td>\n",
 132 |        "      <td>5</td>\n",
 133 |        "      <td>0</td>\n",
 134 |        "    </tr>\n",
 135 |        "    <tr>\n",
 136 |        "      <th>1</th>\n",
 137 |        "      <td>ZRJwVLyzEJq1VAihDhYiow</td>\n",
 138 |        "      <td>2011-07-27</td>\n",
 139 |        "      <td>IjZ33sJrzXqU-0X6U8NwyA</td>\n",
 140 |        "      <td>5</td>\n",
 141 |        "      <td>I have no idea why some people give bad review...</td>\n",
 142 |        "      <td>review</td>\n",
 143 |        "      <td>0a2KyEL0d3Yb1V6aivbIuQ</td>\n",
 144 |        "      <td>0</td>\n",
 145 |        "      <td>0</td>\n",
 146 |        "      <td>0</td>\n",
 147 |        "    </tr>\n",
 148 |        "    <tr>\n",
 149 |        "      <th>2</th>\n",
 150 |        "      <td>6oRAC4uyJCsJl1X0WZpVSA</td>\n",
 151 |        "      <td>2012-06-14</td>\n",
 152 |        "      <td>IESLBzqUCLdSzSqm0eCSxQ</td>\n",
 153 |        "      <td>4</td>\n",
 154 |        "      <td>love the gyro plate. Rice is so good and I als...</td>\n",
 155 |        "      <td>review</td>\n",
 156 |        "      <td>0hT2KtfLiobPvh6cDC8JQg</td>\n",
 157 |        "      <td>0</td>\n",
 158 |        "      <td>1</td>\n",
 159 |        "      <td>0</td>\n",
 160 |        "    </tr>\n",
 161 |        "    <tr>\n",
 162 |        "      <th>3</th>\n",
 163 |        "      <td>_1QQZuf4zZOyFCvXc0o6Vg</td>\n",
 164 |        "      <td>2010-05-27</td>\n",
 165 |        "      <td>G-WvGaISbqqaMHlNnByodA</td>\n",
 166 |        "      <td>5</td>\n",
 167 |        "      <td>Rosie, Dakota, and I LOVE Chaparral Dog Park!!...</td>\n",
 168 |        "      <td>review</td>\n",
 169 |        "      <td>uZetl9T0NcROGOyFfughhg</td>\n",
 170 |        "      <td>1</td>\n",
 171 |        "      <td>2</td>\n",
 172 |        "      <td>0</td>\n",
 173 |        "    </tr>\n",
 174 |        "    <tr>\n",
 175 |        "      <th>4</th>\n",
 176 |        "      <td>6ozycU1RpktNG2-1BroVtw</td>\n",
 177 |        "      <td>2012-01-05</td>\n",
 178 |        "      <td>1uJFq2r5QfJG_6ExMRCaGw</td>\n",
 179 |        "      <td>5</td>\n",
 180 |        "      <td>General Manager Scott Petello is a good egg!!!...</td>\n",
 181 |        "      <td>review</td>\n",
 182 |        "      <td>vYmM4KTsC8ZfQBg-j5MWkw</td>\n",
 183 |        "      <td>0</td>\n",
 184 |        "      <td>0</td>\n",
 185 |        "      <td>0</td>\n",
 186 |        "    </tr>\n",
 187 |        "  </tbody>\n",
 188 |        "</table>\n",
 189 |        "</div>"
 190 |       ],
 191 |       "text/plain": [
 192 |        "              business_id        date               review_id  stars  \\\n",
 193 |        "0  9yKzy9PApeiPPOUJEtnvkg  2011-01-26  fWKvX83p0-ka4JS3dc6E5A      5   \n",
 194 |        "1  ZRJwVLyzEJq1VAihDhYiow  2011-07-27  IjZ33sJrzXqU-0X6U8NwyA      5   \n",
 195 |        "2  6oRAC4uyJCsJl1X0WZpVSA  2012-06-14  IESLBzqUCLdSzSqm0eCSxQ      4   \n",
 196 |        "3  _1QQZuf4zZOyFCvXc0o6Vg  2010-05-27  G-WvGaISbqqaMHlNnByodA      5   \n",
 197 |        "4  6ozycU1RpktNG2-1BroVtw  2012-01-05  1uJFq2r5QfJG_6ExMRCaGw      5   \n",
 198 |        "\n",
 199 |        "                                                text    type  \\\n",
 200 |        "0  My wife took me here on my birthday for breakf...  review   \n",
 201 |        "1  I have no idea why some people give bad review...  review   \n",
 202 |        "2  love the gyro plate. Rice is so good and I als...  review   \n",
 203 |        "3  Rosie, Dakota, and I LOVE Chaparral Dog Park!!...  review   \n",
 204 |        "4  General Manager Scott Petello is a good egg!!!...  review   \n",
 205 |        "\n",
 206 |        "                  user_id  cool  useful  funny  \n",
 207 |        "0  rLtl8ZkDX5vH5nAx9C3q5Q     2       5      0  \n",
 208 |        "1  0a2KyEL0d3Yb1V6aivbIuQ     0       0      0  \n",
 209 |        "2  0hT2KtfLiobPvh6cDC8JQg     0       1      0  \n",
 210 |        "3  uZetl9T0NcROGOyFfughhg     1       2      0  \n",
 211 |        "4  vYmM4KTsC8ZfQBg-j5MWkw     0       0      0  "
 212 |       ]
 213 |      },
 214 |      "execution_count": 38,
 215 |      "metadata": {},
 216 |      "output_type": "execute_result"
 217 |     }
 218 |    ],
 219 |    "source": [
 220 |     "yelp.head()"
 221 |    ]
 222 |   },
 223 |   {
 224 |    "cell_type": "code",
 225 |    "execution_count": 39,
 226 |    "metadata": {},
 227 |    "outputs": [
 228 |     {
 229 |      "name": "stdout",
 230 |      "output_type": "stream",
 231 |      "text": [
 232 |       "<class 'pandas.core.frame.DataFrame'>\n",
 233 |       "RangeIndex: 10000 entries, 0 to 9999\n",
 234 |       "Data columns (total 10 columns):\n",
 235 |       "business_id    10000 non-null object\n",
 236 |       "date           10000 non-null object\n",
 237 |       "review_id      10000 non-null object\n",
 238 |       "stars          10000 non-null int64\n",
 239 |       "text           10000 non-null object\n",
 240 |       "type           10000 non-null object\n",
 241 |       "user_id        10000 non-null object\n",
 242 |       "cool           10000 non-null int64\n",
 243 |       "useful         10000 non-null int64\n",
 244 |       "funny          10000 non-null int64\n",
 245 |       "dtypes: int64(4), object(6)\n",
 246 |       "memory usage: 781.3+ KB\n"
 247 |      ]
 248 |     }
 249 |    ],
 250 |    "source": [
 251 |     "yelp.info()"
 252 |    ]
 253 |   },
 254 |   {
 255 |    "cell_type": "code",
 256 |    "execution_count": 40,
 257 |    "metadata": {},
 258 |    "outputs": [
 259 |     {
 260 |      "data": {
 261 |       "text/html": [
 262 |        "<div>\n",
 263 |        "<style scoped>\n",
 264 |        "    .dataframe tbody tr th:only-of-type {\n",
 265 |        "        vertical-align: middle;\n",
 266 |        "    }\n",
 267 |        "\n",
 268 |        "    .dataframe tbody tr th {\n",
 269 |        "        vertical-align: top;\n",
 270 |        "    }\n",
 271 |        "\n",
 272 |        "    .dataframe thead th {\n",
 273 |        "        text-align: right;\n",
 274 |        "    }\n",
 275 |        "</style>\n",
 276 |        "<table border=\"1\" class=\"dataframe\">\n",
 277 |        "  <thead>\n",
 278 |        "    <tr style=\"text-align: right;\">\n",
 279 |        "      <th></th>\n",
 280 |        "      <th>stars</th>\n",
 281 |        "      <th>cool</th>\n",
 282 |        "      <th>useful</th>\n",
 283 |        "      <th>funny</th>\n",
 284 |        "    </tr>\n",
 285 |        "  </thead>\n",
 286 |        "  <tbody>\n",
 287 |        "    <tr>\n",
 288 |        "      <th>count</th>\n",
 289 |        "      <td>10000.000000</td>\n",
 290 |        "      <td>10000.000000</td>\n",
 291 |        "      <td>10000.000000</td>\n",
 292 |        "      <td>10000.000000</td>\n",
 293 |        "    </tr>\n",
 294 |        "    <tr>\n",
 295 |        "      <th>mean</th>\n",
 296 |        "      <td>3.777500</td>\n",
 297 |        "      <td>0.876800</td>\n",
 298 |        "      <td>1.409300</td>\n",
 299 |        "      <td>0.701300</td>\n",
 300 |        "    </tr>\n",
 301 |        "    <tr>\n",
 302 |        "      <th>std</th>\n",
 303 |        "      <td>1.214636</td>\n",
 304 |        "      <td>2.067861</td>\n",
 305 |        "      <td>2.336647</td>\n",
 306 |        "      <td>1.907942</td>\n",
 307 |        "    </tr>\n",
 308 |        "    <tr>\n",
 309 |        "      <th>min</th>\n",
 310 |        "      <td>1.000000</td>\n",
 311 |        "      <td>0.000000</td>\n",
 312 |        "      <td>0.000000</td>\n",
 313 |        "      <td>0.000000</td>\n",
 314 |        "    </tr>\n",
 315 |        "    <tr>\n",
 316 |        "      <th>25%</th>\n",
 317 |        "      <td>3.000000</td>\n",
 318 |        "      <td>0.000000</td>\n",
 319 |        "      <td>0.000000</td>\n",
 320 |        "      <td>0.000000</td>\n",
 321 |        "    </tr>\n",
 322 |        "    <tr>\n",
 323 |        "      <th>50%</th>\n",
 324 |        "      <td>4.000000</td>\n",
 325 |        "      <td>0.000000</td>\n",
 326 |        "      <td>1.000000</td>\n",
 327 |        "      <td>0.000000</td>\n",
 328 |        "    </tr>\n",
 329 |        "    <tr>\n",
 330 |        "      <th>75%</th>\n",
 331 |        "      <td>5.000000</td>\n",
 332 |        "      <td>1.000000</td>\n",
 333 |        "      <td>2.000000</td>\n",
 334 |        "      <td>1.000000</td>\n",
 335 |        "    </tr>\n",
 336 |        "    <tr>\n",
 337 |        "      <th>max</th>\n",
 338 |        "      <td>5.000000</td>\n",
 339 |        "      <td>77.000000</td>\n",
 340 |        "      <td>76.000000</td>\n",
 341 |        "      <td>57.000000</td>\n",
 342 |        "    </tr>\n",
 343 |        "  </tbody>\n",
 344 |        "</table>\n",
 345 |        "</div>"
 346 |       ],
 347 |       "text/plain": [
 348 |        "              stars          cool        useful         funny\n",
 349 |        "count  10000.000000  10000.000000  10000.000000  10000.000000\n",
 350 |        "mean       3.777500      0.876800      1.409300      0.701300\n",
 351 |        "std        1.214636      2.067861      2.336647      1.907942\n",
 352 |        "min        1.000000      0.000000      0.000000      0.000000\n",
 353 |        "25%        3.000000      0.000000      0.000000      0.000000\n",
 354 |        "50%        4.000000      0.000000      1.000000      0.000000\n",
 355 |        "75%        5.000000      1.000000      2.000000      1.000000\n",
 356 |        "max        5.000000     77.000000     76.000000     57.000000"
 357 |       ]
 358 |      },
 359 |      "execution_count": 40,
 360 |      "metadata": {},
 361 |      "output_type": "execute_result"
 362 |     }
 363 |    ],
 364 |    "source": [
 365 |     "yelp.describe()"
 366 |    ]
 367 |   },
 368 |   {
 369 |    "cell_type": "markdown",
 370 |    "metadata": {},
 371 |    "source": [
 372 |     "**Create a new column called \"text length\" which is the number of words in the text column.**"
 373 |    ]
 374 |   },
 375 |   {
 376 |    "cell_type": "code",
 377 |    "execution_count": 41,
 378 |    "metadata": {},
 379 |    "outputs": [],
 380 |    "source": [
 381 |     "yelp['text length'] = yelp['text'].apply(len)"
 382 |    ]
 383 |   },
 384 |   {
 385 |    "cell_type": "code",
 386 |    "execution_count": 42,
 387 |    "metadata": {},
 388 |    "outputs": [
 389 |     {
 390 |      "data": {
 391 |       "text/html": [
 392 |        "<div>\n",
 393 |        "<style scoped>\n",
 394 |        "    .dataframe tbody tr th:only-of-type {\n",
 395 |        "        vertical-align: middle;\n",
 396 |        "    }\n",
 397 |        "\n",
 398 |        "    .dataframe tbody tr th {\n",
 399 |        "        vertical-align: top;\n",
 400 |        "    }\n",
 401 |        "\n",
 402 |        "    .dataframe thead th {\n",
 403 |        "        text-align: right;\n",
 404 |        "    }\n",
 405 |        "</style>\n",
 406 |        "<table border=\"1\" class=\"dataframe\">\n",
 407 |        "  <thead>\n",
 408 |        "    <tr style=\"text-align: right;\">\n",
 409 |        "      <th></th>\n",
 410 |        "      <th>business_id</th>\n",
 411 |        "      <th>date</th>\n",
 412 |        "      <th>review_id</th>\n",
 413 |        "      <th>stars</th>\n",
 414 |        "      <th>text</th>\n",
 415 |        "      <th>type</th>\n",
 416 |        "      <th>user_id</th>\n",
 417 |        "      <th>cool</th>\n",
 418 |        "      <th>useful</th>\n",
 419 |        "      <th>funny</th>\n",
 420 |        "      <th>text length</th>\n",
 421 |        "    </tr>\n",
 422 |        "  </thead>\n",
 423 |        "  <tbody>\n",
 424 |        "    <tr>\n",
 425 |        "      <th>0</th>\n",
 426 |        "      <td>9yKzy9PApeiPPOUJEtnvkg</td>\n",
 427 |        "      <td>2011-01-26</td>\n",
 428 |        "      <td>fWKvX83p0-ka4JS3dc6E5A</td>\n",
 429 |        "      <td>5</td>\n",
 430 |        "      <td>My wife took me here on my birthday for breakf...</td>\n",
 431 |        "      <td>review</td>\n",
 432 |        "      <td>rLtl8ZkDX5vH5nAx9C3q5Q</td>\n",
 433 |        "      <td>2</td>\n",
 434 |        "      <td>5</td>\n",
 435 |        "      <td>0</td>\n",
 436 |        "      <td>889</td>\n",
 437 |        "    </tr>\n",
 438 |        "    <tr>\n",
 439 |        "      <th>1</th>\n",
 440 |        "      <td>ZRJwVLyzEJq1VAihDhYiow</td>\n",
 441 |        "      <td>2011-07-27</td>\n",
 442 |        "      <td>IjZ33sJrzXqU-0X6U8NwyA</td>\n",
 443 |        "      <td>5</td>\n",
 444 |        "      <td>I have no idea why some people give bad review...</td>\n",
 445 |        "      <td>review</td>\n",
 446 |        "      <td>0a2KyEL0d3Yb1V6aivbIuQ</td>\n",
 447 |        "      <td>0</td>\n",
 448 |        "      <td>0</td>\n",
 449 |        "      <td>0</td>\n",
 450 |        "      <td>1345</td>\n",
 451 |        "    </tr>\n",
 452 |        "    <tr>\n",
 453 |        "      <th>2</th>\n",
 454 |        "      <td>6oRAC4uyJCsJl1X0WZpVSA</td>\n",
 455 |        "      <td>2012-06-14</td>\n",
 456 |        "      <td>IESLBzqUCLdSzSqm0eCSxQ</td>\n",
 457 |        "      <td>4</td>\n",
 458 |        "      <td>love the gyro plate. Rice is so good and I als...</td>\n",
 459 |        "      <td>review</td>\n",
 460 |        "      <td>0hT2KtfLiobPvh6cDC8JQg</td>\n",
 461 |        "      <td>0</td>\n",
 462 |        "      <td>1</td>\n",
 463 |        "      <td>0</td>\n",
 464 |        "      <td>76</td>\n",
 465 |        "    </tr>\n",
 466 |        "    <tr>\n",
 467 |        "      <th>3</th>\n",
 468 |        "      <td>_1QQZuf4zZOyFCvXc0o6Vg</td>\n",
 469 |        "      <td>2010-05-27</td>\n",
 470 |        "      <td>G-WvGaISbqqaMHlNnByodA</td>\n",
 471 |        "      <td>5</td>\n",
 472 |        "      <td>Rosie, Dakota, and I LOVE Chaparral Dog Park!!...</td>\n",
 473 |        "      <td>review</td>\n",
 474 |        "      <td>uZetl9T0NcROGOyFfughhg</td>\n",
 475 |        "      <td>1</td>\n",
 476 |        "      <td>2</td>\n",
 477 |        "      <td>0</td>\n",
 478 |        "      <td>419</td>\n",
 479 |        "    </tr>\n",
 480 |        "    <tr>\n",
 481 |        "      <th>4</th>\n",
 482 |        "      <td>6ozycU1RpktNG2-1BroVtw</td>\n",
 483 |        "      <td>2012-01-05</td>\n",
 484 |        "      <td>1uJFq2r5QfJG_6ExMRCaGw</td>\n",
 485 |        "      <td>5</td>\n",
 486 |        "      <td>General Manager Scott Petello is a good egg!!!...</td>\n",
 487 |        "      <td>review</td>\n",
 488 |        "      <td>vYmM4KTsC8ZfQBg-j5MWkw</td>\n",
 489 |        "      <td>0</td>\n",
 490 |        "      <td>0</td>\n",
 491 |        "      <td>0</td>\n",
 492 |        "      <td>469</td>\n",
 493 |        "    </tr>\n",
 494 |        "  </tbody>\n",
 495 |        "</table>\n",
 496 |        "</div>"
 497 |       ],
 498 |       "text/plain": [
 499 |        "              business_id        date               review_id  stars  \\\n",
 500 |        "0  9yKzy9PApeiPPOUJEtnvkg  2011-01-26  fWKvX83p0-ka4JS3dc6E5A      5   \n",
 501 |        "1  ZRJwVLyzEJq1VAihDhYiow  2011-07-27  IjZ33sJrzXqU-0X6U8NwyA      5   \n",
 502 |        "2  6oRAC4uyJCsJl1X0WZpVSA  2012-06-14  IESLBzqUCLdSzSqm0eCSxQ      4   \n",
 503 |        "3  _1QQZuf4zZOyFCvXc0o6Vg  2010-05-27  G-WvGaISbqqaMHlNnByodA      5   \n",
 504 |        "4  6ozycU1RpktNG2-1BroVtw  2012-01-05  1uJFq2r5QfJG_6ExMRCaGw      5   \n",
 505 |        "\n",
 506 |        "                                                text    type  \\\n",
 507 |        "0  My wife took me here on my birthday for breakf...  review   \n",
 508 |        "1  I have no idea why some people give bad review...  review   \n",
 509 |        "2  love the gyro plate. Rice is so good and I als...  review   \n",
 510 |        "3  Rosie, Dakota, and I LOVE Chaparral Dog Park!!...  review   \n",
 511 |        "4  General Manager Scott Petello is a good egg!!!...  review   \n",
 512 |        "\n",
 513 |        "                  user_id  cool  useful  funny  text length  \n",
 514 |        "0  rLtl8ZkDX5vH5nAx9C3q5Q     2       5      0          889  \n",
 515 |        "1  0a2KyEL0d3Yb1V6aivbIuQ     0       0      0         1345  \n",
 516 |        "2  0hT2KtfLiobPvh6cDC8JQg     0       1      0           76  \n",
 517 |        "3  uZetl9T0NcROGOyFfughhg     1       2      0          419  \n",
 518 |        "4  vYmM4KTsC8ZfQBg-j5MWkw     0       0      0          469  "
 519 |       ]
 520 |      },
 521 |      "execution_count": 42,
 522 |      "metadata": {},
 523 |      "output_type": "execute_result"
 524 |     }
 525 |    ],
 526 |    "source": [
 527 |     "yelp.head(5)"
 528 |    ]
 529 |   },
 530 |   {
 531 |    "cell_type": "markdown",
 532 |    "metadata": {},
 533 |    "source": [
 534 |     "# EDA\n",
 535 |     "\n",
 536 |     "Let's explore the data\n",
 537 |     "\n",
 538 |     "## Imports\n",
 539 |     "\n",
 540 |     "**Import the data visualization libraries if you haven't done so already.**"
 541 |    ]
 542 |   },
 543 |   {
 544 |    "cell_type": "code",
 545 |    "execution_count": 32,
 546 |    "metadata": {},
 547 |    "outputs": [],
 548 |    "source": [
 549 |     "#Already done"
 550 |    ]
 551 |   },
 552 |   {
 553 |    "cell_type": "markdown",
 554 |    "metadata": {},
 555 |    "source": [
 556 |     "**Use FacetGrid from the seaborn library to create a grid of 5 histograms of text length based off of the star ratings. Reference the seaborn documentation for hints on this**"
 557 |    ]
 558 |   },
 559 |   {
 560 |    "cell_type": "code",
 561 |    "execution_count": 44,
 562 |    "metadata": {},
 563 |    "outputs": [
 564 |     {
 565 |      "data": {
 566 |       "text/plain": [
 567 |        "<seaborn.axisgrid.FacetGrid at 0x7f73bba950b8>"
 568 |       ]
 569 |      },
 570 |      "execution_count": 44,
 571 |      "metadata": {},
 572 |      "output_type": "execute_result"
 573 |     },
 574 |     {
 575 |      "data": {
 576 |       "image/png": 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\n",
 577 |       "text/plain": [
 578 |        "<Figure size 1080x216 with 5 Axes>"
 579 |       ]
 580 |      },
 581 |      "metadata": {},
 582 |      "output_type": "display_data"
 583 |     }
 584 |    ],
 585 |    "source": [
 586 |     "g = sns.FacetGrid(yelp,col='stars')\n",
 587 |     "g.map(plt.hist,'text length')"
 588 |    ]
 589 |   },
 590 |   {
 591 |    "cell_type": "markdown",
 592 |    "metadata": {},
 593 |    "source": [
 594 |     "**Create a boxplot of text length for each star category.**"
 595 |    ]
 596 |   },
 597 |   {
 598 |    "cell_type": "code",
 599 |    "execution_count": 45,
 600 |    "metadata": {},
 601 |    "outputs": [
 602 |     {
 603 |      "data": {
 604 |       "text/plain": [
 605 |        "<matplotlib.axes._subplots.AxesSubplot at 0x7f73bb85a160>"
 606 |       ]
 607 |      },
 608 |      "execution_count": 45,
 609 |      "metadata": {},
 610 |      "output_type": "execute_result"
 611 |     },
 612 |     {
 613 |      "data": {
 614 |       "image/png": 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\n",
 615 |       "text/plain": [
 616 |        "<Figure size 432x288 with 1 Axes>"
 617 |       ]
 618 |      },
 619 |      "metadata": {},
 620 |      "output_type": "display_data"
 621 |     }
 622 |    ],
 623 |    "source": [
 624 |     "sns.boxplot(x='stars',y='text length',data=yelp,palette='rainbow')"
 625 |    ]
 626 |   },
 627 |   {
 628 |    "cell_type": "markdown",
 629 |    "metadata": {},
 630 |    "source": [
 631 |     "**Create a countplot of the number of occurrences for each type of star rating.**"
 632 |    ]
 633 |   },
 634 |   {
 635 |    "cell_type": "code",
 636 |    "execution_count": 48,
 637 |    "metadata": {},
 638 |    "outputs": [
 639 |     {
 640 |      "data": {
 641 |       "text/plain": [
 642 |        "<matplotlib.axes._subplots.AxesSubplot at 0x7f73bb88c7b8>"
 643 |       ]
 644 |      },
 645 |      "execution_count": 48,
 646 |      "metadata": {},
 647 |      "output_type": "execute_result"
 648 |     },
 649 |     {
 650 |      "data": {
 651 |       "image/png": 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77x26hV58/txzh25B+rE2Vy6OS5LmCYNDktSJwSFJ6sTgkCR1YnBIkjoxOCRJnRgckqRODA5JUicGhySpE4NDktRJb7ccmasu+u1Hhm6hF5/7Vz81dAuSFgiPOCRJnRgckqRODA5JUicGhySpE4NDktSJwSFJ6sTgkCR1YnBIkjoxOCRJnRgckqROBgmOJI8meSDJtiRbWu3UJJuTPNyeT2n1JPlgkskk25OcPUTPkqSRIY84XlNVq6tqor2+Fri7qlYBd7fXABcDq9pjHXDzrHcqSfr/5tKpqkuBW9vyrcBlY/XbauReYHGSpUM0KEkaLjgK+FySrUnWtdrpVbUHoD2/tNWXAY+Nbbur1X5EknVJtiTZsm/fvh5bl6SFbajbqp9XVbuTvBTYnOTr04zNFLV6VqFqPbAeYGJi4lnrJUnHxyBHHFW1uz3vBT4FnAM8fvAUVHve24bvAlaMbb4c2D173UqSxs16cCR5UZKTDy4DFwEPAhuBtW3YWuDTbXkj8NY2u+pc4ImDp7QkSbNviFNVpwOfSnLw/T9WVZ9Ncj9wZ5IrgW8Bb2jjNwGXAJPAU8AVs9+yJOmgWQ+OqnoE+Nkp6n8OXDhFvYCrZqE1SdIMzKXpuJKkecDgkCR1YnBIkjoxOCRJnRgckqRODA5JUicGhySpE4NDktSJwSFJ6sTgkCR1YnBIkjoxOCRJnRgckqRODA5JUicGhySpE4NDktSJwSFJ6sTgkCR1YnBIkjoxOCRJncyb4EiyJsk3kkwmuXbofiRpoZoXwZFkEXATcDFwJvCmJGcO25UkLUzzIjiAc4DJqnqkqv4PcAdw6cA9SdKCNF+CYxnw2NjrXa0mSZplJwzdwAxlilr9yIBkHbCuvXwyyTd67+roTgO+MxtvlH89G+9yTGbvs5iNNzk2s/ZZXDMbb3JsZu2zgN+Ynbd57mbvs7ju9iOt+Wsz2Xy+BMcuYMXY6+XA7vEBVbUeWD+bTR1Nki1VNTF0H3OBn8UhfhaH+FkcMp8+i/lyqup+YFWSM5I8H7gc2DhwT5K0IM2LI46qOpDkauAuYBGwoap2DNyWJC1I8yI4AKpqE7Bp6D46mlOnzgbmZ3GIn8UhfhaHzJvPIlV19FGSJDXz5RqHJGmOMDh6kGRDkr1JHhy6lyElWZHkniQ7k+xI8vahexpKkpOS3Jfka+2zeOfQPQ0tyaIkX03ymaF7GVKSR5M8kGRbki1D9zMTnqrqQZJ/CDwJ3FZVPzN0P0NJshRYWlVfSXIysBW4rKoeGri1WZckwIuq6skkJwJfAt5eVfcO3NpgkvxzYAJ4SVX9/ND9DCXJo8BEVc3S91mOnUccPaiqLwL7h+5jaFW1p6q+0pZ/AOxkgX7jv0aebC9PbI8F+7+2JMuB1wN/OHQv6s7g0KxIshI4C/jysJ0Mp52a2QbsBTZX1YL9LIB/D/wL4IdDNzIHFPC5JFvbHTDmPINDvUvyYuATwDVV9f2h+xlKVT1TVasZ3fngnCQL8jRmkp8H9lbV1qF7mSPOq6qzGd39+6p2qntOMzjUq3Y+/xPAR6vqk0P3MxdU1feALwBrBm5lKOcBv9DO7d8BXJDkI8O2NJyq2t2e9wKfYnQ38DnN4FBv2gXhW4CdVfX+ofsZUpIlSRa35RcCrwW+PmxXw6iq66pqeVWtZHT7oM9X1ZsHbmsQSV7UJo6Q5EXARcCcn41pcPQgye3A/wD+ZpJdSa4cuqeBnAe8hdH/KLe1xyVDNzWQpcA9SbYzuvfa5qpa0NNQBcDpwJeSfA24D/iTqvrswD0dldNxJUmdeMQhSerE4JAkdWJwSJI6MTgkSZ0YHJKkTgwOqQdJrknyV4buQ+qD03GlHjyXO54mWVRVz/TXlXR8zJufjpXmqvaN3zsZ3YNqEfAfgZcx+sLfd6rqNUluBn4OeCHw8aq6vm37KLCB0TeG/0OSlwK/ChwAHqqqy2f775GOxuCQjt0aYHdVvR4gyU8AVwCvGTvi+JdVtT/JIuDuJK+qqu1t3V9W1d9v2+4Gzqiqpw/eokSaa7zGIR27B4DXJvmdJP+gqp6YYswbk3wF+CrwSuDMsXV/PLa8HfhokjczOuqQ5hyDQzpGVfU/gb/DKED+XZJ/M74+yRnArwMXVtWrgD8BThob8hdjy68Hbmr725rEswKacwwO6RgleRnwVFV9BHgvcDbwA+DkNuQljMLhiSSnM/rdhan28zxgRVXdw+hHjhYDL+65fakz/zcjHbu/Dfxukh8C/xf4p8DfBf40yZ52cfyrwA7gEeC/HWE/i4CPtGskAW5sv90hzSlOx5UkdeKpKklSJwaHJKkTg0OS1InBIUnqxOCQJHVicEiSOjE4JEmdGBySpE7+H94+y9vNtvrQAAAAAElFTkSuQmCC\n",
 652 |       "text/plain": [
 653 |        "<Figure size 432x288 with 1 Axes>"
 654 |       ]
 655 |      },
 656 |      "metadata": {},
 657 |      "output_type": "display_data"
 658 |     }
 659 |    ],
 660 |    "source": [
 661 |     "sns.countplot(data=yelp, x='stars', palette='rainbow')"
 662 |    ]
 663 |   },
 664 |   {
 665 |    "cell_type": "markdown",
 666 |    "metadata": {},
 667 |    "source": [
 668 |     "** Use groupby to get the mean values of the numerical columns, you should be able to create this dataframe with the operation:**"
 669 |    ]
 670 |   },
 671 |   {
 672 |    "cell_type": "code",
 673 |    "execution_count": 50,
 674 |    "metadata": {},
 675 |    "outputs": [
 676 |     {
 677 |      "data": {
 678 |       "text/html": [
 679 |        "<div>\n",
 680 |        "<style scoped>\n",
 681 |        "    .dataframe tbody tr th:only-of-type {\n",
 682 |        "        vertical-align: middle;\n",
 683 |        "    }\n",
 684 |        "\n",
 685 |        "    .dataframe tbody tr th {\n",
 686 |        "        vertical-align: top;\n",
 687 |        "    }\n",
 688 |        "\n",
 689 |        "    .dataframe thead th {\n",
 690 |        "        text-align: right;\n",
 691 |        "    }\n",
 692 |        "</style>\n",
 693 |        "<table border=\"1\" class=\"dataframe\">\n",
 694 |        "  <thead>\n",
 695 |        "    <tr style=\"text-align: right;\">\n",
 696 |        "      <th></th>\n",
 697 |        "      <th>cool</th>\n",
 698 |        "      <th>useful</th>\n",
 699 |        "      <th>funny</th>\n",
 700 |        "      <th>text length</th>\n",
 701 |        "    </tr>\n",
 702 |        "    <tr>\n",
 703 |        "      <th>stars</th>\n",
 704 |        "      <th></th>\n",
 705 |        "      <th></th>\n",
 706 |        "      <th></th>\n",
 707 |        "      <th></th>\n",
 708 |        "    </tr>\n",
 709 |        "  </thead>\n",
 710 |        "  <tbody>\n",
 711 |        "    <tr>\n",
 712 |        "      <th>1</th>\n",
 713 |        "      <td>0.576769</td>\n",
 714 |        "      <td>1.604806</td>\n",
 715 |        "      <td>1.056075</td>\n",
 716 |        "      <td>826.515354</td>\n",
 717 |        "    </tr>\n",
 718 |        "    <tr>\n",
 719 |        "      <th>2</th>\n",
 720 |        "      <td>0.719525</td>\n",
 721 |        "      <td>1.563107</td>\n",
 722 |        "      <td>0.875944</td>\n",
 723 |        "      <td>842.256742</td>\n",
 724 |        "    </tr>\n",
 725 |        "    <tr>\n",
 726 |        "      <th>3</th>\n",
 727 |        "      <td>0.788501</td>\n",
 728 |        "      <td>1.306639</td>\n",
 729 |        "      <td>0.694730</td>\n",
 730 |        "      <td>758.498289</td>\n",
 731 |        "    </tr>\n",
 732 |        "    <tr>\n",
 733 |        "      <th>4</th>\n",
 734 |        "      <td>0.954623</td>\n",
 735 |        "      <td>1.395916</td>\n",
 736 |        "      <td>0.670448</td>\n",
 737 |        "      <td>712.923142</td>\n",
 738 |        "    </tr>\n",
 739 |        "    <tr>\n",
 740 |        "      <th>5</th>\n",
 741 |        "      <td>0.944261</td>\n",
 742 |        "      <td>1.381780</td>\n",
 743 |        "      <td>0.608631</td>\n",
 744 |        "      <td>624.999101</td>\n",
 745 |        "    </tr>\n",
 746 |        "  </tbody>\n",
 747 |        "</table>\n",
 748 |        "</div>"
 749 |       ],
 750 |       "text/plain": [
 751 |        "           cool    useful     funny  text length\n",
 752 |        "stars                                           \n",
 753 |        "1      0.576769  1.604806  1.056075   826.515354\n",
 754 |        "2      0.719525  1.563107  0.875944   842.256742\n",
 755 |        "3      0.788501  1.306639  0.694730   758.498289\n",
 756 |        "4      0.954623  1.395916  0.670448   712.923142\n",
 757 |        "5      0.944261  1.381780  0.608631   624.999101"
 758 |       ]
 759 |      },
 760 |      "execution_count": 50,
 761 |      "metadata": {},
 762 |      "output_type": "execute_result"
 763 |     }
 764 |    ],
 765 |    "source": [
 766 |     "stars = yelp.groupby('stars').mean()\n",
 767 |     "stars"
 768 |    ]
 769 |   },
 770 |   {
 771 |    "cell_type": "markdown",
 772 |    "metadata": {},
 773 |    "source": [
 774 |     "**Use the corr() method on that groupby dataframe to produce this dataframe:**"
 775 |    ]
 776 |   },
 777 |   {
 778 |    "cell_type": "code",
 779 |    "execution_count": 51,
 780 |    "metadata": {},
 781 |    "outputs": [
 782 |     {
 783 |      "data": {
 784 |       "text/html": [
 785 |        "<div>\n",
 786 |        "<style scoped>\n",
 787 |        "    .dataframe tbody tr th:only-of-type {\n",
 788 |        "        vertical-align: middle;\n",
 789 |        "    }\n",
 790 |        "\n",
 791 |        "    .dataframe tbody tr th {\n",
 792 |        "        vertical-align: top;\n",
 793 |        "    }\n",
 794 |        "\n",
 795 |        "    .dataframe thead th {\n",
 796 |        "        text-align: right;\n",
 797 |        "    }\n",
 798 |        "</style>\n",
 799 |        "<table border=\"1\" class=\"dataframe\">\n",
 800 |        "  <thead>\n",
 801 |        "    <tr style=\"text-align: right;\">\n",
 802 |        "      <th></th>\n",
 803 |        "      <th>cool</th>\n",
 804 |        "      <th>useful</th>\n",
 805 |        "      <th>funny</th>\n",
 806 |        "      <th>text length</th>\n",
 807 |        "    </tr>\n",
 808 |        "  </thead>\n",
 809 |        "  <tbody>\n",
 810 |        "    <tr>\n",
 811 |        "      <th>cool</th>\n",
 812 |        "      <td>1.000000</td>\n",
 813 |        "      <td>-0.743329</td>\n",
 814 |        "      <td>-0.944939</td>\n",
 815 |        "      <td>-0.857664</td>\n",
 816 |        "    </tr>\n",
 817 |        "    <tr>\n",
 818 |        "      <th>useful</th>\n",
 819 |        "      <td>-0.743329</td>\n",
 820 |        "      <td>1.000000</td>\n",
 821 |        "      <td>0.894506</td>\n",
 822 |        "      <td>0.699881</td>\n",
 823 |        "    </tr>\n",
 824 |        "    <tr>\n",
 825 |        "      <th>funny</th>\n",
 826 |        "      <td>-0.944939</td>\n",
 827 |        "      <td>0.894506</td>\n",
 828 |        "      <td>1.000000</td>\n",
 829 |        "      <td>0.843461</td>\n",
 830 |        "    </tr>\n",
 831 |        "    <tr>\n",
 832 |        "      <th>text length</th>\n",
 833 |        "      <td>-0.857664</td>\n",
 834 |        "      <td>0.699881</td>\n",
 835 |        "      <td>0.843461</td>\n",
 836 |        "      <td>1.000000</td>\n",
 837 |        "    </tr>\n",
 838 |        "  </tbody>\n",
 839 |        "</table>\n",
 840 |        "</div>"
 841 |       ],
 842 |       "text/plain": [
 843 |        "                 cool    useful     funny  text length\n",
 844 |        "cool         1.000000 -0.743329 -0.944939    -0.857664\n",
 845 |        "useful      -0.743329  1.000000  0.894506     0.699881\n",
 846 |        "funny       -0.944939  0.894506  1.000000     0.843461\n",
 847 |        "text length -0.857664  0.699881  0.843461     1.000000"
 848 |       ]
 849 |      },
 850 |      "execution_count": 51,
 851 |      "metadata": {},
 852 |      "output_type": "execute_result"
 853 |     }
 854 |    ],
 855 |    "source": [
 856 |     "stars.corr()"
 857 |    ]
 858 |   },
 859 |   {
 860 |    "cell_type": "markdown",
 861 |    "metadata": {},
 862 |    "source": [
 863 |     "**Then use seaborn to create a heatmap based off that .corr() dataframe:**"
 864 |    ]
 865 |   },
 866 |   {
 867 |    "cell_type": "code",
 868 |    "execution_count": 54,
 869 |    "metadata": {},
 870 |    "outputs": [
 871 |     {
 872 |      "data": {
 873 |       "text/plain": [
 874 |        "<matplotlib.axes._subplots.AxesSubplot at 0x7f73bb367e48>"
 875 |       ]
 876 |      },
 877 |      "execution_count": 54,
 878 |      "metadata": {},
 879 |      "output_type": "execute_result"
 880 |     },
 881 |     {
 882 |      "data": {
 883 |       "image/png": 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5BpBjZl+Y2XFm9s7399g3s2wzW9XkT6wOmelh8osilesFxRFatay9j3TIkc35aOHu1sih7ZqRlR7m8yUNcuNOiQGJOa0p3bSpcr00N5fE1q2r1Vn7xBNkn3IK/WbOpMcDD7Bq4v0A7Fi2jFbHnwDhMEnt2pPaowdJbdo0afyNJZSeSaQov3I9UpRPOL1VjXXDrbJJyMxh1zeLaiyPaQ34eyRBiOWurRuALs65XWaW4W27GXjbOTfG2/aJmb0FnA7Mcs71BbAYzdp1qasXYnCfFCa9WAhEJ3ecNyKdx/9R2ESRSdOo4T27x3sia/jJ5L0yiw3PP09a7x/T7Y47WPib35A7cyYpnbvw46lPs2vjBrZ+uRAXiex9vHi0H/+XU44aQsmCj+OzSy8OP7OqiuVEshB4zsz+BfzL2zYcON3MxnnryUAnoKSG/feLdyvmsQADT/kzh/U9z+8hqzlpUCo/GdAcgBVry8jKCMPqaFlmepiirTX/x+/UNoFQ2CoH45MTjY5tErhlbHQgPj0tzHWjs5g4Lf+gHHD/oSjNzSWxSisiMSeH0s3Vx81yTj+dJVdfBcC2RV8SSkwiISOD8sJCVj+4u0e31xNPsnPNGg4GFUX5hDOyKtfDGVlEimv+EpVy1BCK/zGlqUJrUJq15V851bvYkr1/TwOOJ9rauNXMehH92vZL59zSqgcws851HDOZeqh6a+ZRN65r8K80b360nTc/2g5A3+5JDB+cxocLSjisYzNKdjqKtlbUuN/gI5vz4YIdlesluxyX3rWxcv3mi7N5/tViJZE4t23x1yR37EhSu/aU5uWSddJwlt96a7U6pRs3kj5gAHmvvEJy586EEhMpLywklJQEZlTs3En6wIG4SKTaIH08K/3uWxJatyWc2ZpIcQHNjxpCwbSH96qXkNOOUEoqpau+CSDKBhCjXVb1FQuJZBPR8Y0sYBswguiYR0fn3H/M7H2id69MI3ojsivN7ErnnDOzfs65z2s45iqgP/AJcFZTPIn98cXSXfTtnsxfxrWhtCw6/fd7d1/ZutpsrUF9Urh3an5Nhzno9X1mIlknDCQxuxXDVr7LsgmPsOapl4IOq3FEIqy6/z56PPxwdPrvzJmUrFzBIWPHsn3xYgrnzGH1ww/R9cabaPubc8E5vr1zAgDNMjPp8dDDUFFBaV4ey++4PeAn04AqKij65xSyL7sJQiG2f/QO5RvX0uJnv6JszQp2LvoUgJSjjqHk87kBB3vgXJwnEnMx0J9oZlcR/f3glURvfbwOOAFIJ9oKedY592czSwEeBIZ421c550Z4LZJZzrne3vF6AC8STUxvA+c55zrXd/pvY7RI4tW59w8LOoSYkHVUetAhxIyOg7oEHUJM6fDQ330PcGz7eGa9P3PS/mtkzA2oxEKLBOfcw8De7dW965UAl9SwfRXRH2v5fn0J0KdKlVu87e8A7/gKVkSkgcV7iyQmEomIyA9aA/0eSVCUSEREAharFxrWlxKJiEjQ1LUlIiJ+uJouSI0jSiQiIgHTYLuIiPijRCIiIn5UaNaWiIj4ollbIiLih8ZIRETEF83aEhERX9QiERERfzRGIiIiflSYZm2JiIgP6toSERFfNNguIiK+qEUiIiK+6DbyIiLiiwbbRUTEF42RiIiILxojERERX+K9RRLfaVBE5CDgLFTvZV/M7BQzW2pmy83shhrKk8zs7175x2bW2W/8apHUYPWi5UGHEDOyjkoPOoSYkf9ZcdAhxISUVhuCDiGmdGiAYzRUi8TMwsBjwEnAWmCemc1wzn1dpdqFQKFz7jAzOwe4B/i1n/OqRSJSD0oi0pgqCNV72YeBwHLn3ArnXCnwAnDGHnXOAJ72Hr8EnGjmb/6xEomISMAcoXov+9ABWFNlfS17N5oq6zjnyoFiIMtP/EokIiIBc1i9FzMba2bzqyxjqxyqppaF22O9PnX2i8ZIREQCtj9jJM65ycDkWorXAh2rrB8CrK+lzlozSwDSgYJ6B1ADtUhERAK2Py2SfZgHHG5mXcwsETgHmLFHnRnA77zHZwFvO+fUIhERiWcNNWvLOVduZlcArwNhYIpz7iszmwDMd87NAP4PeMbMlhNtiZzj97xKJCIiAatwDdc55Jx7FXh1j223VXm8E/hVg50QJRIRkcDF+5XtSiQiIgFTIhEREV+cUyIREREfKtQiERERPxpysD0ISiQiIgHTGImIiPiiMRIREfFFLRIREfFFLRIREfGlIugAfFIiEREJmGZtiYiIL+raEhERXzTYLiIivlT4+jWQ4CmRiIgETC0SOSBXj+3G4P5Z7NwV4e6HlvLNt9v2qjPs2NaMPrsT4bAxd14Bf526olr50CHZ3HVjLy689lOWLt97/3iQPmgQnf/7OiwUInfGy6yfNq1aeWKbNnS7/XYS0lpAKMSaSY9RNHculpBAlxtvJK1HT5xzrP7LRLZ89llAz6Lx9XnibnJOHUppbj7v9RsZdDiNrkX/gbS/7CosFKLgtVfIffG5auXNWufQadxNhFPTIBxmw5TH2Trvo2rl3SdPY9OzU8n75wtNHf5+i/cxkpiaKmBmV5nZYjN7bt+149eg/pl0bN+ccy75hPse+4Zxlx2+V52WLRL4/ZiuXHPLQn77+/lkZjSjf5+MyvKUlDBnjezAV0u2NGXoDSsUosv461lyzdUsOOfXZA0/mZQuXapV6TBmDAVvzebL0b9l+a230GX89QDknHkmAAtHncviK6+g09VXg8X3f8a6rH16Op+MuCjoMJpGKESH31/LylvGs3TsaDKGnkhSp0OrVWnzm9EUvfcfvrniIlb/6Q4OueLaauXtL7mSrfM/bsqofYk4q/cSi2IqkQCXA6c650YFHUhjOm5QFq+9vRGAr5ZuJS01gaxWidXqtG+bwpp1JRRtKQNg/oJChh6TXVl+8ajOPD99DaVl8TsDPe1Hvdi5di271q/HlZeT/+YbtDr++OqVnCOcmgpAODWN0s2bAUjp0oUt8+YBUF5YSGTrNlJ79mzS+JtSwfvzKSsoDjqMJtG8e09KN6yjdOMGXHk5Re/OJn3wsXvVCzXf/b4oy8+v3N5y8LGUblzPztWrmipk35yzei+xKGYSiZn9L9AVmGFmxWY2rkrZIjPr7C2LzewJM/vKzN4wsxSvzjtmdo+ZfWJm35jZcd72OWbWt8qxPjCzPk39/KrKzkoid/OuyvXc/F1kZ1VPJOvWl9DpkOa0zUkiHILjBmWTk50MwOFd08hpncTceQVNGndDS8xpTemmTZXrpbm5JLZuXa3O2ieeIPuUU+g3cyY9HniAVRPvB2DHsmW0Ov4ECIdJatee1B49SGrTpknjl8bRLCub0rzcyvWyzXk0y6r+vtj47FO0Gjacns+8RJcJ97Ju0oMAhJKSyTn7XDY9O7UpQ/bNufovsShmEolz7lJgPfAT4IE6qh4OPOac6wUUAb+sUpbgnBsIXAPc7m17EjgfwMyOAJKccwv3PKiZjTWz+WY2f+PqmX6fTp1q/E6xxxtk6/ZyJk5axoTrf8Rj9/Rj46adRCIOM7jqom48+n/fNmqMTaOGV2KP1yFr+MnkvTKLz0eOZMm119LtjjvAjNyZMynNzeXHU5/m0P++lq1fLsRFIk0StTSymroo9/gEzRh6IoVv/pvFvz2LlbddT6fxt4AZbX47hrzp/6BiZ0kTBdswHFbvJRbF42D7SufcF97jT4HOVcqm17D9H8CtZjYeGANMremgzrnJwGSAY0e+2+B5/xentmfkye0AWLxsKznZSZVlOVlJbC4o3WufD+bl88G8aJP99JPbEamA5ilhuhyayiN3RxtZma0SueeW3vzhrkVxN+BemptLYpVWRGJODqWb86rVyTn9dJZcfRUA2xZ9SSgxiYSMDMoLC1n94O7vG72eeJKda9Y0TeDSqMo255HYOqdyvVl2a8oKNlerk3Xyaay4eTwAOxZ/hSUmktAyneY9epJx3Am0v+hSwqlpOOeoKC0lf+Z0Ypmm/zaOcqq3lpKrPN5V5XEESKmhLIL33JxzO8zsTeAM4Gzg6AaPth6mv7qe6a+uB2Dw0Zn8ckQH3novj17dW7BtRzn5hXsnkoz0ZhQVl9EiNYGfn9qe2+75mu07IowYNbeyziN3H8mjU76NuyQCsG3x1yR37EhSu/aU5uWSddJwlt96a7U6pRs3kj5gAHmvvEJy586EEhMpLywklJQEZlTs3En6wIG4SISSlSsDeibSkHYsXUJi+0NIbNOOsvw8Mk44kdX3TKhWpzR3E2n9jqLwzddI6nho9H1RXMS3466srNPmvAuoKCmJ+SQCUFERmy2N+orVRLIKGAFgZkcBXeqsvW9PAjOBOc65wAcWPpxfwOCjM/n75IGV03+/99RD/bng6k8BuObiw+jWJTqgOPWF1axZH1/N9X2KRFh1/330ePjh6PTfmTMpWbmCQ8aOZfvixRTOmcPqhx+i64030fY354JzfHtn9AOlWWYmPR56GCoqKM3LY/kdt+/jZPGt7zMTyTphIInZrRi28l2WTXiENU+9FHRYjaMiwrpJD9L1j/dDKETBG6+ya/Uq2vx2DCXLlrLlow9Y/8RjdLz6elr//Gxwju8m/inoqH2J95/aNRdDozdmtopoi2E78DKQA8wDjgV+5lWb5Zzr7dUfB6Q55+4ws3eAcc65+WaWDcx3znWucuwlwDXOudf2FUdjdG3Fq/tzxwcdQkzI/+yHMWOqPg45UZMaqjrytfd8Z4GZn5bX+zNnZP+EmMs6MdUiqfrBDwyvpVrvKvXvr/J4aJXHm6kydmJm7Yl2lb3RMJGKiDScWJ3WW18xM2ursZjZaOBj4GbnXPxedCEiB60KV/8lFsVUi6QxOOemAdP2WVFEJCAxNMJwQA76RCIiEuti9dYn9aVEIiISMLVIRETEFyUSERHxpUJdWyIi4odaJCIi4kskzi9MUCIREQlYvF+QqEQiIhIwdW2JiIgvsXrFen0d9LdIERGJdU31C4lmlmlmb5rZMu/fVnXUbWlm68zs0X0dV4lERCRgTfhTuzcAs51zhwOzvfXa3Am8W5+DKpGIiAQsUlH/xaczgKe9x08DZ9ZUycz6A22o5x3TlUhERAJWUVH/xac2zrkNAN6/OXtWMLMQMBGo948RabBdRCRg+9NlZWZjgbFVNk12zk2uUv4W0LaGXW+u5ykuB151zq0xq9+0ZCUSEZGA7U8i8ZLG5DrKf1pbmZltMrN2zrkNZtYOyK2h2mDgODO7HEgDEs1sm3Ou1vEUJRIRkYA14fTfGcDvgD97/768ZwXn3KjvH5vZ+cDRdSUR0BiJiEjgnHP1Xnz6M3CSmS0DTvLWMbOjzezJAz2oWiQiIgGLRJrmPM65fODEGrbPBy6qYftUYOq+jqtEIiISMN0i5SDUIqvWiz1/cDp27RJ0CDGh4yDYvHRD0GHEhLWzNwUdQkw5sgGOEe+3SFEiEakHJRFpTGqRiIiIL26/miSxd8t5JRIRkYDph61ERMSXijgfJFEiEREJmMZIRETEFyUSERHxpSLOM4kSiYhIwJwG20VExI9IRC0SERHxoQFuxhgoJRIRkYDF+exfJRIRkaDt35XtsUeJREQkYHHes6VEIiISNF3ZLiIivlRo1paIiPihCxJFRMQXTf8VERFfNEYiIiK+xHmDRIlERCRokTj/ZSslEhGRgOmCRBER8UWJRA7IpaPaM6BPC3aVVjDxybV8u7pkrzon/FcGvx7RGoD8onLue/w7tmyLAHD6T7MYeWI2kQrHJwu2MOXFjU0af2NI6nEk6b84HwuF2P7R22x76+Vq5ek/H03iYb0ACCUmEkpLZ8ONY4IItVG06D+Q9pddhYVCFLz2CrkvPletvFnrHDqNu4lwahqEw2yY8jhb531Urbz75GlsenYqef98oanDbzJ9nribnFOHUpqbz3v9RgYdToOI8zxSdyIxswzgXOfcpAM5uJl1BoY4556vpWyWc673gRy7jnMOBUqdc3O99aneeV5qyPP4MaBPC9q3SeTCPyylR7fmXDG6A9feubxanVAommwuuWkpW7ZFGHN2W0b+NJvn/rWJPj1SGdSvJZff+g1l5Y70FuGAnkkDMiPjV2PYPOmPRIryybnuT+yksxPsAAAOJElEQVT8cj7lm9ZVVin+f9MqH6cedwrNDukcQKCNJBSiw++vZcVN/03Z5jwOf3gyxR+9z67vVldWafOb0RS99x/yX3mZpE6H0vXOe1n8u19Xlre/5Eq2zv84iOib1Nqnp7Nq0rP0nXJP0KE0mHhvkYT2UZ4BXO7j+J2Bc33sfyCGAkOa+Jz7ZVC/lsz+oAiAJd/uIK15mFbp1XO6GRiQnBT9EzVPCVNQWAbAacOyePGVPMrKo2++4q2Rpgu+kSQeehjleZuI5OdCJMKOz+aS/OMBtdZP6T+Eks8+aMIIG1fz7j0p3bCO0o0bcOXlFL07m/TBx+5VL9Q8FYBwahpl+fmV21sOPpbSjevZuXpVU4UcmIL351NWUBx0GA3KOVfvJRbtK5H8GehmZl+Y2X0AZjbezOaZ2UIz+x9v2wBvPdnMUs3sKzPr7e1/nLf/tbWdxMzCZnZfleNe4m0fambvmNlLZrbEzJ4zM/PKTvW2vW9mD5vZLK+VcylwrXfO47xTHG9mc81shZmd5eP1ahBZrZqxuaC0cn1zYSnZrZpVqxOJwKPT1vHXu47guQd70ql9Eq+/VwBAh7ZJ9D4ilQduPYx7b+jKEV1SmjT+xhBKzyRStPuDMVKUTzi9VY11w62yScjMYdc3i5oqvEbXLCub0rzcyvWyzXk0y2pdrc7GZ5+i1bDh9HzmJbpMuJd1kx4EIJSUTM7Z57Lp2alNGbI0oEikot5LLNrXGMkNQG/nXF8AMxsOHA4MJPqFeYaZHe+ce8/MZgB3ASnAs865RWZ2AzDOOTdiH+e5ECh2zg0wsyTgAzN7wyvrB/QC1gMfAMeY2XzgceB459xKM/sbgHNulZn9L7DNOXe/F/OFQDvgWKAHMAMItJsrmgqr2/OLRjgMpw3L5IrblrEhr5TLzmvP2SNyeGFmLuGQkZYa5to7l3NElxRuvPxQLhi/pGmCbyw1vSi1SDlqCCULPo7/yfdV1eNNkTH0RArf/Dd50/9O85696DT+FpZe+jva/HYMedP/QcXOvcfZJD7Ee9fW/g62D/eWz731NKKJ5T1gAjAP2AlcdQDH7VOltZDuHbcU+MQ5txbAzL4g2l22DVjhnFvp1f8bMLaO4//LOVcBfG1mbWqqYGZjvz9Gr8G30vGIhm24jDgxi1NOyATgm5U7yM5MBHYAkN0qkfyismr1u3WKtjI25EVbLnM+Kebs06LfUDcXlvHBp8XesUpwLjpOEs9dXBVF+YQzsirXwxlZRIoLa6ybctQQiv8xpalCaxJlm/NIbJ1Tud4suzVlBZur1ck6+TRW3DwegB2Lv8ISE0lomU7zHj3JOO4E2l90KeHUNJxzVJSWkj9zepM+BzlwP7REYsCfnHOP11CWSTSxNAOSge37edwrnXOvV9sYHTjfVWVThGjM9f/6GlX1GDXu65ybDEwG+Nn5Cxv8rzprdj6zZke7bgYc2YKRJ2bx7sdF9OjWnO0lEQqLy6vV31xYRqf2yZUJol/vNL7bEH0aH35WTN+eaXy5ZDsd2iSSELa4TiIApd99S0LrtoQzWxMpLqD5UUMomPbwXvUSctoRSkmldNU3AUTZeHYsXUJi+0NIbNOOsvw8Mk44kdX3TKhWpzR3E2n9jqLwzddI6ngoocREyouL+HbclZV12px3ARUlJUoiceZgv2njVqBFlfXXgTvN7Dnn3DYz6wCUOedyiX4I3wp0Ae4Brqhh/9q8DlxmZm8758rM7AhgXR31lwBdzayzc24V8OsqZVuBlvU4Z2DmLdjKgD4tmHJvd3buquCB/1tbWfbohMO54rZlFBSV89zLm7j3xm5EIo7c/DImPrEGgDfeK+TaCw/hr3cdQXm5Y+KTa4J6Kg2nooKif04h+7KbIBRi+0fvUL5xLS1+9ivK1qxg56JPAUg56hhKPp8bcLCNoCLCukkP0vWP90MoRMEbr7Jr9Sra/HYMJcuWsuWjD1j/xGN0vPp6Wv/8bHCO7yb+KeioA9H3mYlknTCQxOxWDFv5LssmPMKap2JmUuYBifcWie1rFoCZPQ/0Af7tnBtvZlcDF3nF24DzgGOAM51zvzCzMDAXuBGYA7wGZANTnXMPVDluZ7zpv2YWIjq+MpJoiyEPOJPo+EjlGIuZPQrMd85NNbORwH3AZuAToI1zbpSXhF4CKoAriY6/VE7/NbNtzrm0up5zY7RI4tWT6X8MOoSYsHnphqBDiBlrZ28KOoSYclrZ0v3tIdnL6Fs31PszZ9qd7Xyfr6HtM5HEKjNL81pFBjwGLKuaqPxQItlNiSRKiWQ3JZLqGiKRjLpxXb0/c577U4eYSyT7mv4byy72Bt+/Ijo4X9O4jYhIzIv360ji9hYpXuujQVogIiJBchWxeX1IfcVzi0RE5KBQUeHqvfhhZplm9qaZLfP+rfGqXzO717uwfLF3wXed3WlKJCIiAWvCrq0bgNnOucOB2d56NWY2hOgEqj5Ab2AAcEJdB43bri0RkYNFRXmTdW2dQfR+hABPA+8Af9ijjiN6LWAi0Vm0zYA6Z1gokYiIBKzCNVkiaeOc2wDgnNtgZjl7VnDOfWhm/wE2EE0kjzrnFtd1UCUSEZGA7c8FiVVv5+SZ7N2Z4/vyt4C2Nex6cz2PfxjQEzjE2/Tm9/dUrG0fJRIRkYDtTyKpejunWsp/WluZmW0ys3Zea6QdkFtDtZ8DHznntnn7/BsYRPSeijXSYLuISMCacLB9BvA77/HvgJdrqPMdcIKZJZhZM6ID7XV2bSmRiIgErKKiot6LT38GTjKzZcBJ3jpmdrSZPenVeQn4FvgSWAAscM7NrOug6toSEQlYRaRp7t7tnMsHTqxh+3y8eyg65yLAJftzXCUSEZGAxfvdf5VIREQCpkQiIiK+NOF1JI1CiUREJGBqkYiIiC/xfvdfJRIRkYA11aytxqJEIiISML+3hw+aEomISMDUtSUiIr5osF1ERHxxmv4rcvDL7t6OzUs3BB2GHKQqyuN7sN0a4G6S0kjMbGzV3xn4IdNrEaXXYTe9FrFDd/+NbWP3XeUHQ69FlF6H3fRaxAglEhER8UWJREREfFEiiW3q/91Nr0WUXofd9FrECA22i4iIL2qRiIiIL0okccrM7jCzcUHHEQQz+5uZLTSza+uoM9TMZjVlXPvLzK4ys8Vm9lzQsTQUM8sws8t97N/ZzM6to2zRgUdX6zmHmtmQKutTzeyshj7PwUyJROKKmbUFhjjn+jjnHgg6Hp8uB051zo0KOpAGlEH0eR2ozkCNiaQRDQWG7KuS1E6JJCBmNtr7Vr3AzJ4xs0PNbLa3bbaZdfLq1bg9Hu35jdLMxnktq6vM7GvvOb7glaWa2RQzm2dmn5vZGd5ubwA5ZvaFmR1nZu+Y2dHePtlmtqrJn9gBMLP/BboCM8ysuGrr0swWea9VZ6/F8oSZfWVmb5hZilfnHTO7x8w+MbNvzOw4b/scM+tb5VgfmFmfJnxqfwa6eX+f+7wYxnt/x4Vm9j/etgHeerL3t/7KzHp7+x/n7V9XizNsZvdVOe4l3vah3mvzkpktMbPnzMy8slO9be+b2cNmNsvMOgOXAtd+/57yTnG8mc01sxVqneybEkkAzKwXcDMwzDl3JHA18CgwzTnXB3gOeNirXtv2g8kNQD/vOV7qbbsZeNs5NwD4CXCfmaUCpwPfOuf6OufmBBOuf865S4H1RJ9bXS2rw4HHnHO9gCLgl1XKEpxzA4FrgNu9bU8C5wOY2RFAknNuYcNGX6cb2P33GW9mw4k+h4FAX6C/mR3vnJsHzADuAu4FnnXOLfL2n+PtX9frciFQ7L0/BgAXm1kXr6wf0dfkR0ST9TFmlgw8DvzMOXcs0BrAObcK+F/ggT3eU+2AY4ERRJOb1EGJJBjDgJecc5sBnHMFwGDgea/8GaJvYurYfjBZCDxnZucB5d624cANZvYF8A6QDMRta8yHlc65L7zHnxLt+vne9Bq2/wMYYWbNgDHA1MYPsU7DveVz4DOgB9HEAjABOAk4mmgy2d/jjvbeHx8DWVWO+4lzbq2L3gnxC6KvTQ9ghXNupVfnb/s4/r+ccxXOua+BNvsZ2w+ObtoYDAP2Ne+6tvJ4nq9dTvUvL8nev6cBxxNtbdzqtdgM+KVzbmnVA3hdEbUdM5n4VNvrArCryuMIkFJDWQTv/7JzboeZvQmcAZxN9EM6SAb8yTn3eA1lmUAa0Izoc96+n8e90jn3erWNZkPZ+zVL8Orvj6rH2N99f3DUIgnGbOBsM8sCMLNMYC5wjlc+Cnjfe1zb9ni0iej4RpaZJRHtNggBHZ1z/wGuJzpYmwa8DlxZpX+7Xy3HXAX09x7Ha1/2KuAoADM7CuhSZ+19e5JoF+g8r7XblLYCLaqsvw6MMbM0ADPrYGY5Xtlk4FaiXbb31LJ/bV4HLvNaXpjZEV7XZ22WAF2rfBH5dR0xy35SiyQAzrmvzOyPwLtmFiHa7L8KmGJm44E84AKvem3b445zrszMJhDtilhJ9D93GHjWzNKJfvN7wDlXZGZ3Ag8CC71ksopo4tnT/cCLZvZb4O0meBqN4Z/s7qaZB3zj52DOuU/NbAvwVEMEt5/nzvcG+BcB//bGSXoCH3rfCbYB55nZKUC5c+55MwsDc81sGDAHKDezBcDUOsZJniTaZfWZ9/7IA86sI64Si05Lfs3MNgOfVCmeCbzkTei40sfT/8HSle0iBxkza090XKmHi/dfTGpAZpbmnNvmJZ7HgGUHwRTymKCuLZGDiJmNJtriu1lJZC8Xe62+r4B0orO4pAGoRSIiIr6oRSIiIr4okYiIiC9KJCIi4osSiYiI+KJEIiIiviiRiIiIL/8fS2yiJvUKKo0AAAAASUVORK5CYII=\n",
 884 |       "text/plain": [
 885 |        "<Figure size 432x288 with 2 Axes>"
 886 |       ]
 887 |      },
 888 |      "metadata": {},
 889 |      "output_type": "display_data"
 890 |     }
 891 |    ],
 892 |    "source": [
 893 |     "sns.heatmap(stars.corr(), cmap='coolwarm', annot=True)"
 894 |    ]
 895 |   },
 896 |   {
 897 |    "cell_type": "markdown",
 898 |    "metadata": {},
 899 |    "source": [
 900 |     "## NLP Classification Task\n",
 901 |     "\n",
 902 |     "Let's move on to the actual task. To make things a little easier, go ahead and only grab reviews that were either 1 star or 5 stars.\n",
 903 |     "\n",
 904 |     "**Create a dataframe called yelp_class that contains the columns of yelp dataframe but for only the 1 or 5 star reviews.**"
 905 |    ]
 906 |   },
 907 |   {
 908 |    "cell_type": "code",
 909 |    "execution_count": 55,
 910 |    "metadata": {},
 911 |    "outputs": [],
 912 |    "source": [
 913 |     "yelp_class = yelp[(yelp.stars==1) | (yelp.stars==5)]"
 914 |    ]
 915 |   },
 916 |   {
 917 |    "cell_type": "markdown",
 918 |    "metadata": {},
 919 |    "source": [
 920 |     "** Create two objects X and y. X will be the 'text' column of yelp_class and y will be the 'stars' column of yelp_class. (Your features and target/labels)**"
 921 |    ]
 922 |   },
 923 |   {
 924 |    "cell_type": "code",
 925 |    "execution_count": 56,
 926 |    "metadata": {},
 927 |    "outputs": [],
 928 |    "source": [
 929 |     "X = yelp_class['text']\n",
 930 |     "y = yelp_class['stars']"
 931 |    ]
 932 |   },
 933 |   {
 934 |    "cell_type": "markdown",
 935 |    "metadata": {},
 936 |    "source": [
 937 |     "**Import CountVectorizer and create a CountVectorizer object.**"
 938 |    ]
 939 |   },
 940 |   {
 941 |    "cell_type": "code",
 942 |    "execution_count": 57,
 943 |    "metadata": {},
 944 |    "outputs": [],
 945 |    "source": [
 946 |     "from sklearn.feature_extraction.text import CountVectorizer\n",
 947 |     "cv = CountVectorizer()"
 948 |    ]
 949 |   },
 950 |   {
 951 |    "cell_type": "markdown",
 952 |    "metadata": {},
 953 |    "source": [
 954 |     "** Use the fit_transform method on the CountVectorizer object and pass in X (the 'text' column). Save this result by overwriting X.**"
 955 |    ]
 956 |   },
 957 |   {
 958 |    "cell_type": "code",
 959 |    "execution_count": 58,
 960 |    "metadata": {},
 961 |    "outputs": [],
 962 |    "source": [
 963 |     "X = cv.fit_transform(X)"
 964 |    ]
 965 |   },
 966 |   {
 967 |    "cell_type": "markdown",
 968 |    "metadata": {},
 969 |    "source": [
 970 |     "## Train Test Split\n",
 971 |     "\n",
 972 |     "Let's split our data into training and testing data.\n",
 973 |     "\n",
 974 |     "** Use train_test_split to split up the data into X_train, X_test, y_train, y_test. Use test_size=0.3 and random_state=101 **"
 975 |    ]
 976 |   },
 977 |   {
 978 |    "cell_type": "code",
 979 |    "execution_count": 59,
 980 |    "metadata": {},
 981 |    "outputs": [],
 982 |    "source": [
 983 |     "from sklearn.model_selection import train_test_split"
 984 |    ]
 985 |   },
 986 |   {
 987 |    "cell_type": "code",
 988 |    "execution_count": 60,
 989 |    "metadata": {},
 990 |    "outputs": [],
 991 |    "source": [
 992 |     "X_train, X_test, y_train, y_test = train_test_split(X, y,test_size=0.3,random_state=101)"
 993 |    ]
 994 |   },
 995 |   {
 996 |    "cell_type": "markdown",
 997 |    "metadata": {},
 998 |    "source": [
 999 |     "## Training a Model\n",
1000 |     "\n",
1001 |     "Time to train a model!\n",
1002 |     "\n",
1003 |     "** Import MultinomialNB and create an instance of the estimator and call is nb **"
1004 |    ]
1005 |   },
1006 |   {
1007 |    "cell_type": "code",
1008 |    "execution_count": 61,
1009 |    "metadata": {},
1010 |    "outputs": [],
1011 |    "source": [
1012 |     "from sklearn.naive_bayes import MultinomialNB\n",
1013 |     "nb = MultinomialNB()"
1014 |    ]
1015 |   },
1016 |   {
1017 |    "cell_type": "markdown",
1018 |    "metadata": {},
1019 |    "source": [
1020 |     "**Now fit nb using the training data.**"
1021 |    ]
1022 |   },
1023 |   {
1024 |    "cell_type": "code",
1025 |    "execution_count": 63,
1026 |    "metadata": {},
1027 |    "outputs": [
1028 |     {
1029 |      "data": {
1030 |       "text/plain": [
1031 |        "MultinomialNB(alpha=1.0, class_prior=None, fit_prior=True)"
1032 |       ]
1033 |      },
1034 |      "execution_count": 63,
1035 |      "metadata": {},
1036 |      "output_type": "execute_result"
1037 |     }
1038 |    ],
1039 |    "source": [
1040 |     "nb.fit(X_train, y_train)"
1041 |    ]
1042 |   },
1043 |   {
1044 |    "cell_type": "markdown",
1045 |    "metadata": {},
1046 |    "source": [
1047 |     "## Predictions and Evaluations\n",
1048 |     "\n",
1049 |     "Time to see how our model did!\n",
1050 |     "\n",
1051 |     "**Use the predict method off of nb to predict labels from X_test.**"
1052 |    ]
1053 |   },
1054 |   {
1055 |    "cell_type": "code",
1056 |    "execution_count": 65,
1057 |    "metadata": {},
1058 |    "outputs": [],
1059 |    "source": [
1060 |     "predictions = nb.predict(X_test)"
1061 |    ]
1062 |   },
1063 |   {
1064 |    "cell_type": "markdown",
1065 |    "metadata": {},
1066 |    "source": [
1067 |     "** Create a confusion matrix and classification report using these predictions and y_test **"
1068 |    ]
1069 |   },
1070 |   {
1071 |    "cell_type": "code",
1072 |    "execution_count": 66,
1073 |    "metadata": {},
1074 |    "outputs": [],
1075 |    "source": [
1076 |     "from sklearn.metrics import confusion_matrix,classification_report"
1077 |    ]
1078 |   },
1079 |   {
1080 |    "cell_type": "code",
1081 |    "execution_count": 67,
1082 |    "metadata": {},
1083 |    "outputs": [
1084 |     {
1085 |      "name": "stdout",
1086 |      "output_type": "stream",
1087 |      "text": [
1088 |       "[[159  69]\n",
1089 |       " [ 22 976]]\n",
1090 |       "\n",
1091 |       "\n",
1092 |       "             precision    recall  f1-score   support\n",
1093 |       "\n",
1094 |       "          1       0.88      0.70      0.78       228\n",
1095 |       "          5       0.93      0.98      0.96       998\n",
1096 |       "\n",
1097 |       "avg / total       0.92      0.93      0.92      1226\n",
1098 |       "\n"
1099 |      ]
1100 |     }
1101 |    ],
1102 |    "source": [
1103 |     "print(confusion_matrix(y_test,predictions))\n",
1104 |     "print('\\n')\n",
1105 |     "print(classification_report(y_test,predictions))"
1106 |    ]
1107 |   },
1108 |   {
1109 |    "cell_type": "markdown",
1110 |    "metadata": {},
1111 |    "source": [
1112 |     "**Great! Let's see what happens if we try to include TF-IDF to this process using a pipeline.**"
1113 |    ]
1114 |   },
1115 |   {
1116 |    "cell_type": "markdown",
1117 |    "metadata": {},
1118 |    "source": [
1119 |     "# Using Text Processing\n",
1120 |     "\n",
1121 |     "** Import TfidfTransformer from sklearn. **"
1122 |    ]
1123 |   },
1124 |   {
1125 |    "cell_type": "code",
1126 |    "execution_count": 68,
1127 |    "metadata": {},
1128 |    "outputs": [],
1129 |    "source": [
1130 |     "from sklearn.feature_extraction.text import  TfidfTransformer"
1131 |    ]
1132 |   },
1133 |   {
1134 |    "cell_type": "markdown",
1135 |    "metadata": {},
1136 |    "source": [
1137 |     "** Import Pipeline from sklearn. **"
1138 |    ]
1139 |   },
1140 |   {
1141 |    "cell_type": "code",
1142 |    "execution_count": 69,
1143 |    "metadata": {},
1144 |    "outputs": [],
1145 |    "source": [
1146 |     "from sklearn.pipeline import Pipeline"
1147 |    ]
1148 |   },
1149 |   {
1150 |    "cell_type": "markdown",
1151 |    "metadata": {},
1152 |    "source": [
1153 |     "** Now create a pipeline with the following steps:CountVectorizer(), TfidfTransformer(),MultinomialNB()**"
1154 |    ]
1155 |   },
1156 |   {
1157 |    "cell_type": "code",
1158 |    "execution_count": 70,
1159 |    "metadata": {},
1160 |    "outputs": [],
1161 |    "source": [
1162 |     "pipeline = Pipeline([\n",
1163 |     "    ('bow', CountVectorizer()),  # strings to token integer counts\n",
1164 |     "    ('tfidf', TfidfTransformer()),  # integer counts to weighted TF-IDF scores\n",
1165 |     "    ('classifier', MultinomialNB()),  # train on TF-IDF vectors w/ Naive Bayes classifier\n",
1166 |     "])"
1167 |    ]
1168 |   },
1169 |   {
1170 |    "cell_type": "markdown",
1171 |    "metadata": {},
1172 |    "source": [
1173 |     "## Using the Pipeline\n",
1174 |     "\n",
1175 |     "**Time to use the pipeline! Remember this pipeline has all your pre-process steps in it already, meaning we'll need to re-split the original data (Remember that we overwrote X as the CountVectorized version. What we need is just the text**"
1176 |    ]
1177 |   },
1178 |   {
1179 |    "cell_type": "markdown",
1180 |    "metadata": {},
1181 |    "source": [
1182 |     "### Train Test Split\n",
1183 |     "\n",
1184 |     "**Redo the train test split on the yelp_class object.**"
1185 |    ]
1186 |   },
1187 |   {
1188 |    "cell_type": "code",
1189 |    "execution_count": 71,
1190 |    "metadata": {},
1191 |    "outputs": [],
1192 |    "source": [
1193 |     "X = yelp_class['text']\n",
1194 |     "y = yelp_class['stars']\n",
1195 |     "X_train, X_test, y_train, y_test = train_test_split(X, y,test_size=0.3,random_state=101)"
1196 |    ]
1197 |   },
1198 |   {
1199 |    "cell_type": "markdown",
1200 |    "metadata": {},
1201 |    "source": [
1202 |     "**Now fit the pipeline to the training data. Remember you can't use the same training data as last time because that data has already been vectorized. We need to pass in just the text and labels**"
1203 |    ]
1204 |   },
1205 |   {
1206 |    "cell_type": "code",
1207 |    "execution_count": 72,
1208 |    "metadata": {},
1209 |    "outputs": [
1210 |     {
1211 |      "data": {
1212 |       "text/plain": [
1213 |        "Pipeline(memory=None,\n",
1214 |        "     steps=[('bow', CountVectorizer(analyzer='word', binary=False, decode_error='strict',\n",
1215 |        "        dtype=<class 'numpy.int64'>, encoding='utf-8', input='content',\n",
1216 |        "        lowercase=True, max_df=1.0, max_features=None, min_df=1,\n",
1217 |        "        ngram_range=(1, 1), preprocessor=None, stop_words=None,\n",
1218 |        "        strip_...f=False, use_idf=True)), ('classifier', MultinomialNB(alpha=1.0, class_prior=None, fit_prior=True))])"
1219 |       ]
1220 |      },
1221 |      "execution_count": 72,
1222 |      "metadata": {},
1223 |      "output_type": "execute_result"
1224 |     }
1225 |    ],
1226 |    "source": [
1227 |     "# May take some time\n",
1228 |     "pipeline.fit(X_train,y_train)"
1229 |    ]
1230 |   },
1231 |   {
1232 |    "cell_type": "markdown",
1233 |    "metadata": {},
1234 |    "source": [
1235 |     "### Predictions and Evaluation\n",
1236 |     "\n",
1237 |     "** Now use the pipeline to predict from the X_test and create a classification report and confusion matrix. You should notice strange results.**"
1238 |    ]
1239 |   },
1240 |   {
1241 |    "cell_type": "code",
1242 |    "execution_count": 74,
1243 |    "metadata": {},
1244 |    "outputs": [],
1245 |    "source": [
1246 |     "predictions = pipeline.predict(X_test)"
1247 |    ]
1248 |   },
1249 |   {
1250 |    "cell_type": "code",
1251 |    "execution_count": 75,
1252 |    "metadata": {},
1253 |    "outputs": [
1254 |     {
1255 |      "name": "stdout",
1256 |      "output_type": "stream",
1257 |      "text": [
1258 |       "[[  0 228]\n",
1259 |       " [  0 998]]\n",
1260 |       "\n",
1261 |       "\n",
1262 |       "             precision    recall  f1-score   support\n",
1263 |       "\n",
1264 |       "          1       0.00      0.00      0.00       228\n",
1265 |       "          5       0.81      1.00      0.90       998\n",
1266 |       "\n",
1267 |       "avg / total       0.66      0.81      0.73      1226\n",
1268 |       "\n"
1269 |      ]
1270 |     },
1271 |     {
1272 |      "name": "stderr",
1273 |      "output_type": "stream",
1274 |      "text": [
1275 |       "/home/marco/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
1276 |       "  'precision', 'predicted', average, warn_for)\n"
1277 |      ]
1278 |     }
1279 |    ],
1280 |    "source": [
1281 |     "print(confusion_matrix(y_test,predictions))\n",
1282 |     "print('\\n')\n",
1283 |     "print(classification_report(y_test,predictions))"
1284 |    ]
1285 |   },
1286 |   {
1287 |    "cell_type": "markdown",
1288 |    "metadata": {},
1289 |    "source": [
1290 |     "Looks like Tf-Idf actually made things worse! That is it for this project. But there is still a lot more you can play with:\n",
1291 |     "\n",
1292 |     "**Some other things to try....**\n",
1293 |     "Try going back and playing around with the pipeline steps and seeing if creating a custom analyzer like we did in the lecture helps (note: it probably won't). Or recreate the pipeline with just the CountVectorizer() and NaiveBayes. Does changing the ML model at the end to another classifier help at all?"
1294 |    ]
1295 |   },
1296 |   {
1297 |    "cell_type": "markdown",
1298 |    "metadata": {},
1299 |    "source": [
1300 |     "# Great Job!"
1301 |    ]
1302 |   }
1303 |  ],
1304 |  "metadata": {
1305 |   "kernelspec": {
1306 |    "display_name": "Python 3",
1307 |    "language": "python",
1308 |    "name": "python3"
1309 |   },
1310 |   "language_info": {
1311 |    "codemirror_mode": {
1312 |     "name": "ipython",
1313 |     "version": 3
1314 |    },
1315 |    "file_extension": ".py",
1316 |    "mimetype": "text/x-python",
1317 |    "name": "python",
1318 |    "nbconvert_exporter": "python",
1319 |    "pygments_lexer": "ipython3",
1320 |    "version": "3.6.5"
1321 |   }
1322 |  },
1323 |  "nbformat": 4,
1324 |  "nbformat_minor": 1
1325 | }
1326 | 


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