Decoder2704/python-chatbot-jay
0
1{2 "cells": [3 {4 "cell_type": "code",5 "execution_count": 1,6 "id": "d6a3c639",7 "metadata": {},8 "outputs": [],9 "source": [10 "import pandas as pd\n",11 "import matplotlib.pyplot as plt\n",12 "from bs4 import BeautifulSoup"13 ]14 },15 {16 "cell_type": "code",17 "execution_count": 2,18 "id": "9ad19adb",19 "metadata": {},20 "outputs": [],21 "source": [22 "questions = pd.read_csv('Dataset/Questions.csv',encoding='latin-1')"23 ]24 },25 {26 "cell_type": "code",27 "execution_count": 3,28 "id": "36ae43e2",29 "metadata": {},30 "outputs": [31 {32 "name": "stdout",33 "output_type": "stream",34 "text": [35 "Index(['Id', 'OwnerUserId', 'CreationDate', 'Score', 'Title', 'Body'], dtype='object')\n"36 ]37 }38 ],39 "source": [40 "print(questions.columns)"41 ]42 },43 {44 "cell_type": "code",45 "execution_count": 4,46 "id": "4549b751",47 "metadata": {},48 "outputs": [49 {50 "name": "stdout",51 "output_type": "stream",52 "text": [53 "Number of Questions in the Dataset: 607282\n"54 ]55 }56 ],57 "source": [58 "print(\"Number of Questions in the Dataset: \",len(questions))"59 ]60 },61 {62 "cell_type": "code",63 "execution_count": 5,64 "id": "594578cc",65 "metadata": {},66 "outputs": [67 {68 "data": {69 "text/html": [70 "<div>\n",71 "<style scoped>\n",72 " .dataframe tbody tr th:only-of-type {\n",73 " vertical-align: middle;\n",74 " }\n",75 "\n",76 " .dataframe tbody tr th {\n",77 " vertical-align: top;\n",78 " }\n",79 "\n",80 " .dataframe thead th {\n",81 " text-align: right;\n",82 " }\n",83 "</style>\n",84 "<table border=\"1\" class=\"dataframe\">\n",85 " <thead>\n",86 " <tr style=\"text-align: right;\">\n",87 " <th></th>\n",88 " <th>Id</th>\n",89 " <th>OwnerUserId</th>\n",90 " <th>CreationDate</th>\n",91 " <th>Score</th>\n",92 " <th>Title</th>\n",93 " <th>Body</th>\n",94 " </tr>\n",95 " </thead>\n",96 " <tbody>\n",97 " <tr>\n",98 " <th>0</th>\n",99 " <td>469</td>\n",100 " <td>147.0</td>\n",101 " <td>2008-08-02T15:11:16Z</td>\n",102 " <td>21</td>\n",103 " <td>How can I find the full path to a font from it...</td>\n",104 " <td><p>I am using the Photoshop's javascript API t...</td>\n",105 " </tr>\n",106 " <tr>\n",107 " <th>1</th>\n",108 " <td>502</td>\n",109 " <td>147.0</td>\n",110 " <td>2008-08-02T17:01:58Z</td>\n",111 " <td>27</td>\n",112 " <td>Get a preview JPEG of a PDF on Windows?</td>\n",113 " <td><p>I have a cross-platform (Python) applicatio...</td>\n",114 " </tr>\n",115 " <tr>\n",116 " <th>2</th>\n",117 " <td>535</td>\n",118 " <td>154.0</td>\n",119 " <td>2008-08-02T18:43:54Z</td>\n",120 " <td>40</td>\n",121 " <td>Continuous Integration System for a Python Cod...</td>\n",122 " <td><p>I'm starting work on a hobby project with a...</td>\n",123 " </tr>\n",124 " <tr>\n",125 " <th>3</th>\n",126 " <td>594</td>\n",127 " <td>116.0</td>\n",128 " <td>2008-08-03T01:15:08Z</td>\n",129 " <td>25</td>\n",130 " <td>cx_Oracle: How do I iterate over a result set?</td>\n",131 " <td><p>There are several ways to iterate over a re...</td>\n",132 " </tr>\n",133 " <tr>\n",134 " <th>4</th>\n",135 " <td>683</td>\n",136 " <td>199.0</td>\n",137 " <td>2008-08-03T13:19:16Z</td>\n",138 " <td>28</td>\n",139 " <td>Using 'in' to match an attribute of Python obj...</td>\n",140 " <td><p>I don't remember whether I was dreaming or ...</td>\n",141 " </tr>\n",142 " </tbody>\n",143 "</table>\n",144 "</div>"145 ],146 "text/plain": [147 " Id OwnerUserId CreationDate Score \\\n",148 "0 469 147.0 2008-08-02T15:11:16Z 21 \n",149 "1 502 147.0 2008-08-02T17:01:58Z 27 \n",150 "2 535 154.0 2008-08-02T18:43:54Z 40 \n",151 "3 594 116.0 2008-08-03T01:15:08Z 25 \n",152 "4 683 199.0 2008-08-03T13:19:16Z 28 \n",153 "\n",154 " Title \\\n",155 "0 How can I find the full path to a font from it... \n",156 "1 Get a preview JPEG of a PDF on Windows? \n",157 "2 Continuous Integration System for a Python Cod... \n",158 "3 cx_Oracle: How do I iterate over a result set? \n",159 "4 Using 'in' to match an attribute of Python obj... \n",160 "\n",161 " Body \n",162 "0 <p>I am using the Photoshop's javascript API t... \n",163 "1 <p>I have a cross-platform (Python) applicatio... \n",164 "2 <p>I'm starting work on a hobby project with a... \n",165 "3 <p>There are several ways to iterate over a re... \n",166 "4 <p>I don't remember whether I was dreaming or ... "167 ]168 },169 "execution_count": 5,170 "metadata": {},171 "output_type": "execute_result"172 }173 ],174 "source": [175 "questions.head()"176 ]177 },178 {179 "cell_type": "code",180 "execution_count": 6,181 "id": "4b0f79fb",182 "metadata": {},183 "outputs": [],184 "source": [185 "answers = pd.read_csv('Dataset/Answers.csv',encoding = 'latin-1')"186 ]187 },188 {189 "cell_type": "code",190 "execution_count": 7,191 "id": "2c143273",192 "metadata": {},193 "outputs": [194 {195 "name": "stdout",196 "output_type": "stream",197 "text": [198 "Index(['Id', 'OwnerUserId', 'CreationDate', 'ParentId', 'Score', 'Body'], dtype='object')\n"199 ]200 }201 ],202 "source": [203 "print(answers.columns)"204 ]205 },206 {207 "cell_type": "code",208 "execution_count": 8,209 "id": "4625de97",210 "metadata": {},211 "outputs": [212 {213 "name": "stdout",214 "output_type": "stream",215 "text": [216 "Number of Answers: 987122\n"217 ]218 }219 ],220 "source": [221 "print(\"Number of Answers: \",len(answers))"222 ]223 },224 {225 "cell_type": "code",226 "execution_count": 9,227 "id": "d8cb1ca9",228 "metadata": {},229 "outputs": [230 {231 "data": {232 "text/html": [233 "<div>\n",234 "<style scoped>\n",235 " .dataframe tbody tr th:only-of-type {\n",236 " vertical-align: middle;\n",237 " }\n",238 "\n",239 " .dataframe tbody tr th {\n",240 " vertical-align: top;\n",241 " }\n",242 "\n",243 " .dataframe thead th {\n",244 " text-align: right;\n",245 " }\n",246 "</style>\n",247 "<table border=\"1\" class=\"dataframe\">\n",248 " <thead>\n",249 " <tr style=\"text-align: right;\">\n",250 " <th></th>\n",251 " <th>Id</th>\n",252 " <th>OwnerUserId</th>\n",253 " <th>CreationDate</th>\n",254 " <th>ParentId</th>\n",255 " <th>Score</th>\n",256 " <th>Body</th>\n",257 " </tr>\n",258 " </thead>\n",259 " <tbody>\n",260 " <tr>\n",261 " <th>0</th>\n",262 " <td>497</td>\n",263 " <td>50.0</td>\n",264 " <td>2008-08-02T16:56:53Z</td>\n",265 " <td>469</td>\n",266 " <td>4</td>\n",267 " <td><p>open up a terminal (Applications-&gt;Utilit...</td>\n",268 " </tr>\n",269 " <tr>\n",270 " <th>1</th>\n",271 " <td>518</td>\n",272 " <td>153.0</td>\n",273 " <td>2008-08-02T17:42:28Z</td>\n",274 " <td>469</td>\n",275 " <td>2</td>\n",276 " <td><p>I haven't been able to find anything that d...</td>\n",277 " </tr>\n",278 " <tr>\n",279 " <th>2</th>\n",280 " <td>536</td>\n",281 " <td>161.0</td>\n",282 " <td>2008-08-02T18:49:07Z</td>\n",283 " <td>502</td>\n",284 " <td>9</td>\n",285 " <td><p>You can use ImageMagick's convert utility f...</td>\n",286 " </tr>\n",287 " <tr>\n",288 " <th>3</th>\n",289 " <td>538</td>\n",290 " <td>156.0</td>\n",291 " <td>2008-08-02T18:56:56Z</td>\n",292 " <td>535</td>\n",293 " <td>23</td>\n",294 " <td><p>One possibility is Hudson. It's written in...</td>\n",295 " </tr>\n",296 " <tr>\n",297 " <th>4</th>\n",298 " <td>541</td>\n",299 " <td>157.0</td>\n",300 " <td>2008-08-02T19:06:40Z</td>\n",301 " <td>535</td>\n",302 " <td>20</td>\n",303 " <td><p>We run <a href=\"http://buildbot.net/trac\">B...</td>\n",304 " </tr>\n",305 " </tbody>\n",306 "</table>\n",307 "</div>"308 ],309 "text/plain": [310 " Id OwnerUserId CreationDate ParentId Score \\\n",311 "0 497 50.0 2008-08-02T16:56:53Z 469 4 \n",312 "1 518 153.0 2008-08-02T17:42:28Z 469 2 \n",313 "2 536 161.0 2008-08-02T18:49:07Z 502 9 \n",314 "3 538 156.0 2008-08-02T18:56:56Z 535 23 \n",315 "4 541 157.0 2008-08-02T19:06:40Z 535 20 \n",316 "\n",317 " Body \n",318 "0 <p>open up a terminal (Applications->Utilit... \n",319 "1 <p>I haven't been able to find anything that d... \n",320 "2 <p>You can use ImageMagick's convert utility f... \n",321 "3 <p>One possibility is Hudson. It's written in... \n",322 "4 <p>We run <a href=\"http://buildbot.net/trac\">B... "323 ]324 },325 "execution_count": 9,326 "metadata": {},327 "output_type": "execute_result"328 }329 ],330 "source": [331 "answers.head()"332 ]333 },334 {335 "cell_type": "code",336 "execution_count": 10,337 "id": "8125fd0d",338 "metadata": {},339 "outputs": [],340 "source": [341 "tags = pd.read_csv('Dataset/Tags.csv',encoding = 'latin-1')"342 ]343 },344 {345 "cell_type": "code",346 "execution_count": 11,347 "id": "4c51b9c1",348 "metadata": {},349 "outputs": [350 {351 "name": "stdout",352 "output_type": "stream",353 "text": [354 "Index(['Id', 'Tag'], dtype='object')\n"355 ]356 }357 ],358 "source": [359 "print(tags.columns)"360 ]361 },362 {363 "cell_type": "code",364 "execution_count": 12,365 "id": "4bc802ab",366 "metadata": {},367 "outputs": [368 {369 "name": "stdout",370 "output_type": "stream",371 "text": [372 "Number of tags : 1885078\n"373 ]374 }375 ],376 "source": [377 "print(\"Number of tags : \",len(tags))"378 ]379 },380 {381 "cell_type": "code",382 "execution_count": 13,383 "id": "dfb780bd",384 "metadata": {},385 "outputs": [386 {387 "data": {388 "text/html": [389 "<div>\n",390 "<style scoped>\n",391 " .dataframe tbody tr th:only-of-type {\n",392 " vertical-align: middle;\n",393 " }\n",394 "\n",395 " .dataframe tbody tr th {\n",396 " vertical-align: top;\n",397 " }\n",398 "\n",399 " .dataframe thead th {\n",400 " text-align: right;\n",401 " }\n",402 "</style>\n",403 "<table border=\"1\" class=\"dataframe\">\n",404 " <thead>\n",405 " <tr style=\"text-align: right;\">\n",406 " <th></th>\n",407 " <th>Id</th>\n",408 " <th>Tag</th>\n",409 " </tr>\n",410 " </thead>\n",411 " <tbody>\n",412 " <tr>\n",413 " <th>0</th>\n",414 " <td>469</td>\n",415 " <td>python</td>\n",416 " </tr>\n",417 " <tr>\n",418 " <th>1</th>\n",419 " <td>469</td>\n",420 " <td>osx</td>\n",421 " </tr>\n",422 " <tr>\n",423 " <th>2</th>\n",424 " <td>469</td>\n",425 " <td>fonts</td>\n",426 " </tr>\n",427 " <tr>\n",428 " <th>3</th>\n",429 " <td>469</td>\n",430 " <td>photoshop</td>\n",431 " </tr>\n",432 " <tr>\n",433 " <th>4</th>\n",434 " <td>502</td>\n",435 " <td>python</td>\n",436 " </tr>\n",437 " </tbody>\n",438 "</table>\n",439 "</div>"440 ],441 "text/plain": [442 " Id Tag\n",443 "0 469 python\n",444 "1 469 osx\n",445 "2 469 fonts\n",446 "3 469 photoshop\n",447 "4 502 python"448 ]449 },450 "execution_count": 13,451 "metadata": {},452 "output_type": "execute_result"453 }454 ],455 "source": [456 "tags.head()"457 ]458 },459 {460 "cell_type": "code",461 "execution_count": 14,462 "id": "b995a431",463 "metadata": {},464 "outputs": [465 {466 "name": "stdout",467 "output_type": "stream",468 "text": [469 "Number of unique Tags : 16896\n"470 ]471 }472 ],473 "source": [474 "print(\"Number of unique Tags : \",len(tags['Tag'].unique()))"475 ]476 },477 {478 "cell_type": "code",479 "execution_count": 15,480 "id": "092f8010",481 "metadata": {},482 "outputs": [483 {484 "name": "stdout",485 "output_type": "stream",486 "text": [487 "Number of Tags which have appeared more than 1000 times: 207\n"488 ]489 }490 ],491 "source": [492 "countOfTag=tags['Tag'].value_counts()\n",493 "countTags=tags[tags.Tag.isin(countOfTag.index[countOfTag.gt(1000)])]\n",494 "print(\"Number of Tags which have appeared more than 1000 times: \",len(countTags.Tag.unique()))"495 ]496 },497 {498 "cell_type": "code",499 "execution_count": 16,500 "id": "d2eeedb7",501 "metadata": {},502 "outputs": [503 {504 "name": "stdout",505 "output_type": "stream",506 "text": [507 "Tage which have appeared more than 1000 times: \n",508 "['python' 'osx' 'windows' 'image' 'pdf' 'sql' 'database' 'arrays'\n",509 " 'iteration' 'django' 'oop' 'mysql' 'postgresql' 'methods' 'syntax' 'xml'\n",510 " 'list' 'tuples' 'php' 'user-interface' 'tkinter' 'linux' 'ruby' 'apache'\n",511 " 'c' 'matlab' 'unicode' 'urllib' 'xpath' 'c++' 'unit-testing' 'lxml'\n",512 " 'loops' 'string' 'regex' 'audio' 'ctypes' 'object' 'matrix' 'orm'\n",513 " 'django-models' 'java' 'datetime' 'subprocess' 'git' 'io' 'gtk' 'pygtk'\n",514 " 'pygame' 'wxpython' 'perl' 'multithreading' 'file-io' 'ssh' 'performance'\n",515 " 'http' 'html' 'shell' 'google-app-engine' 'sorting' 'mod-wsgi'\n",516 " 'python-3.x' 'parsing' 'inheritance' 'variables' 'dictionary' 'file'\n",517 " 'javascript' 'authentication' 'python-2.x' 'date' 'url' 'cgi' 'wsgi'\n",518 " 'math' 'debugging' 'list-comprehension' 'generator' 'sqlalchemy'\n",519 " 'networking' 'for-loop' 'encryption' 'numpy' 'mongodb' 'time' 'path'\n",520 " 'optimization' 'jython' 'ajax' 'class' 'exception' 'sockets' 'set'\n",521 " 'ubuntu' 'function' 'python-import' 'post' 'python-2.7' 'input' 'import'\n",522 " 'email' 'bash' 'c#' 'twisted' 'qt' 'pyqt' 'split' 'api' 'ssl' 'templates'\n",523 " 'django-templates' 'android' 'algorithm' 'encoding' 'excel'\n",524 " 'django-views' 'recursion' 'testing' 'django-forms' 'py2exe' 'logging'\n",525 " 'matplotlib' 'text' 'csv' 'urllib2' 'eclipse' 'jquery' 'beautifulsoup'\n",526 " 'facebook' 'decorator' 'lambda' 'mechanize' 'module' 'pydev' 'pyramid'\n",527 " 'pickle' 'nginx' 'django-admin' 'unix' 'setuptools' 'search'\n",528 " 'if-statement' 'image-processing' 'utf-8' 'json' 'memory' 'elementtree'\n",529 " 'rest' 'nltk' 'nlp' 'sqlite' 'forms' 'printing' 'amazon-web-services'\n",530 " 'python-imaging-library' 'virtualenv' 'replace' 'twitter' 'mocking'\n",531 " 'cython' 'ipython' 'random' 'opencv' 'graph' 'plot' 'multiprocessing'\n",532 " 'web-crawler' 'while-loop' 'pip' 'mysql-python' 'jinja2'\n",533 " 'django-queryset' 'scipy' 'selenium' 'indexing' 'pyqt4' 'web-scraping'\n",534 " 'multidimensional-array' 'boto' 'sqlite3' 'machine-learning' 'scrapy'\n",535 " 'tornado' 'networkx' 'pyside' 'argparse' 'celery' 'python-requests'\n",536 " 'openerp' 'pymongo' 'flask' 'pycharm' 'dataframe' 'heroku' 'scikit-learn'\n",537 " 'selenium-webdriver' 'pandas' 'kivy' 'flask-sqlalchemy' 'web'\n",538 " 'django-rest-framework' 'raspberry-pi' 'anaconda' 'python-3.4'\n",539 " 'apache-spark' 'pyspark' 'tensorflow']\n"540 ]541 }542 ],543 "source": [544 "print(\"Tage which have appeared more than 1000 times: \")\n",545 "print(countTags.Tag.unique())"546 ]547 },548 {549 "cell_type": "code",550 "execution_count": 17,551 "id": "23293d51",552 "metadata": {},553 "outputs": [554 {555 "name": "stdout",556 "output_type": "stream",557 "text": [558 "Top 50 Tagse and their Frequency: \n",559 "python 607283\n",560 "django 62818\n",561 "python-2.7 34616\n",562 "pandas 26854\n",563 "python-3.x 26814\n",564 "numpy 25848\n",565 "list 18951\n",566 "matplotlib 16521\n",567 "regex 14047\n",568 "dictionary 13413\n",569 "tkinter 10766\n",570 "string 10616\n",571 "flask 10488\n",572 "google-app-engine 10286\n",573 "csv 9323\n",574 "arrays 9170\n",575 "json 8023\n",576 "mysql 7529\n",577 "linux 7121\n",578 "html 7118\n",579 "scipy 6908\n",580 "multithreading 6804\n",581 "sqlalchemy 6661\n",582 "windows 6267\n",583 "beautifulsoup 6054\n",584 "django-models 5797\n",585 "javascript 5788\n",586 "selenium 5654\n",587 "xml 5332\n",588 "pyqt 5217\n",589 "class 5083\n",590 "osx 5056\n",591 "pygame 5050\n",592 "opencv 4995\n",593 "dataframe 4958\n",594 "function 4867\n",595 "c++ 4850\n",596 "file 4665\n",597 "scrapy 4638\n",598 "sockets 4585\n",599 "subprocess 4418\n",600 "loops 4355\n",601 "scikit-learn 4289\n",602 "pip 4178\n",603 "algorithm 4168\n",604 "unicode 4135\n",605 "parsing 4062\n",606 "datetime 4040\n",607 "web-scraping 3940\n",608 "multiprocessing 3876\n",609 "Name: Tag, dtype: int64\n"610 ]611 }612 ],613 "source": [614 "frequentTags=countOfTag[:50]\n",615 "print(\"Top 50 Tagse and their Frequency: \")\n",616 "print(frequentTags)"617 ]618 },619 {620 "cell_type": "code",621 "execution_count": 18,622 "id": "d37d6c1f",623 "metadata": {},624 "outputs": [625 {626 "data": {627 "text/plain": [628 "<AxesSubplot:>"629 ]630 },631 "execution_count": 18,632 "metadata": {},633 "output_type": "execute_result"634 },635 {636 "data": {637 "image/png": 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\n",638 "text/plain": [639 "<Figure size 1440x360 with 1 Axes>"640 ]641 },642 "metadata": {643 "needs_background": "light"644 },645 "output_type": "display_data"646 }647 ],648 "source": [649 "frequentTags[1:].plot.bar(figsize=(20,5))"650 ]651 },652 {653 "cell_type": "code",654 "execution_count": 19,655 "id": "a7d63148",656 "metadata": {},657 "outputs": [658 {659 "name": "stdout",660 "output_type": "stream",661 "text": [662 "Raw data from Questions Dataset: \n",663 "\n",664 "<p>I have a cross-platform (Python) application which needs to generate a JPEG preview of the first page of a PDF.</p>\n",665 "\n",666 "<p>On the Mac I am spawning <a href=\"http://developer.apple.com/documentation/Darwin/Reference/ManPages/man1/sips.1.html\">sips</a>. Is there something similarly simple I can do on Windows?</p>\n",667 "\n"668 ]669 }670 ],671 "source": [672 "print(\"Raw data from Questions Dataset: \",end=\"\\n\\n\")\n",673 "print(questions.Body[1])"674 ]675 },676 {677 "cell_type": "code",678 "execution_count": 21,679 "id": "0977e5d7",680 "metadata": {},681 "outputs": [682 {683 "name": "stdout",684 "output_type": "stream",685 "text": [686 "I have a cross-platform (Python) application which needs to generate a JPEG preview of the first page of a PDF.\n",687 "On the Mac I am spawning sips. Is there something similarly simple I can do on Windows?\n",688 "\n"689 ]690 }691 ],692 "source": [693 "print(BeautifulSoup(questions.Body[1]).get_text())"694 ]695 },696 {697 "cell_type": "code",698 "execution_count": null,699 "id": "5e0a3528",700 "metadata": {},701 "outputs": [],702 "source": []703 }704 ],705 "metadata": {706 "kernelspec": {707 "display_name": "Python 3",708 "language": "python",709 "name": "python3"710 },711 "language_info": {712 "codemirror_mode": {713 "name": "ipython",714 "version": 3715 },716 "file_extension": ".py",717 "mimetype": "text/x-python",718 "name": "python",719 "nbconvert_exporter": "python",720 "pygments_lexer": "ipython3",721 "version": "3.8.8"722 }723 },724 "nbformat": 4,725 "nbformat_minor": 5726}727