Pairs.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['harry', 'potter', 'stormtrooper', 'wookie']\n"
     ]
    }
   ],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "text = [\"wookie stormtrooper\",\n",
    "        \"wookie wookie wookie stormtrooper stormtrooper stormtrooper\",\n",
    "        \"harry potter\"]\n",
    "# CountVectorizer is a class; so `vectorizer` below represents an instance of that object.\n",
    "vectorizer = CountVectorizer()\n",
    "\n",
    "# call `fit` to build the vocabulary\n",
    "vectorizer.fit(text)\n",
    "\n",
    "# then, use `get_feature_names` to return the tokens\n",
    "print(vectorizer.get_feature_names())\n",
    "\n",
    "# finally, call `transform` to convert text to a bag of words\n",
    "x = vectorizer.transform(text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  (0, 2)\t1\n",
      "  (0, 3)\t1\n",
      "  (1, 2)\t3\n",
      "  (1, 3)\t3\n",
      "  (2, 0)\t1\n",
      "  (2, 1)\t1\n"
     ]
    }
   ],
   "source": [
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "vect = TfidfVectorizer(min_df=1)\n",
    "tfidf = vect.fit_transform(text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<3x3 sparse matrix of type '<class 'numpy.float64'>'\n",
       "\twith 5 stored elements in Compressed Sparse Row format>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(tfidf * tfidf.T)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Generate the numbers 1 to 100.\n",
    "\n",
    "Do the above via list comprehension; then do it by using map and filter (hint: use lambda functions):\n",
    "\n",
    "Square the numbers.\n",
    "Select just the squares that are odd.\n",
    "What does reduce do? Use it to do the following:\n",
    "\n",
    "Add up the numbers 1 to 100.\n",
    "Square the numbers.\n",
    "Select just the numbers that are odd."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from functools import reduce\n",
    "nums = [x for x in range(1,101)]\n",
    "sums = reduce(lambda x, y: x + y, nums)\n",
    "squares = list(map(lambda x: x**2, nums))\n",
    "odd = list(filter(lambda x: x%2==1, squares))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function __main__.reading>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import sys\n",
    "\n",
    "def reading(self):\n",
    "    s = open('/Users/petermin/sea18_ds4/class_lectures/week09-kojak1/01-docker-hadoop-hive/input.txt', 'r').read()\n",
    "    self.whip = eval(s)\n",
    "reading"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. The mapping function will have to find each user_name.  Hint: it's nested.  You might use pretty-print to figure out the data structure.  \n",
    "Try using sys.stdin.readline() to read just one line and deduce its structure.\n",
    "eval(line) will turn the stringified dictionary into an actual dict data structure, from which you can use dictionary logic. \n",
    "2. Use sys.stdin to read the Twitter data in streaming fashion, line-by-line.  It creates an iterator on lines.\n",
    "4. We want fail-safe code.  (More on distributed fault tolerance later.)  Make sure your code doesn't bomb if it doesn't find a user_name in the line.\n",
    "5. You can simply use the print function to generate output to send to the sort step."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "{'contributors': None, 'truncated': False, 'text': 'ATENTI! Seguimos agitando el #sorteo de los 3 discos de #SiempreElCorazonManda !!!\\n\\nAbajo toda la data ;)... http://t.co/0Ap6ydzy6L', 'in_reply_to_status_id': None, 'id': 439025611294912512, 'favorite_count': 0, 'source': '<a href=\"http://www.facebook.com/twitter\" rel=\"nofollow\">Facebook</a>', 'retweeted': False, 'coordinates': None, 'entities': {'symbols': [], 'user_mentions': [], 'hashtags': [{'indices': [29, 36], 'text': 'sorteo'}, {'indices': [56, 78], 'text': 'SiempreElCorazonManda'}], 'urls': [{'url': 'http://t.co/0Ap6ydzy6L', 'indices': [109, 131], 'expanded_url': 'http://fb.me/299AmQSfx', 'display_url': 'fb.me/299AmQSfx'}]}, 'in_reply_to_screen_name': None, 'id_str': '439025611294912512', 'retweet_count': 0, 'in_reply_to_user_id': None, 'favorited': False, 'user': {'follow_request_sent': None, 'profile_use_background_image': True, 'default_profile_image': False, 'id': 99521941, 'profile_background_image_url_https': 'https://pbs.twimg.com/profile_background_images/378800000144369888/0cs1fWW6.png', 'verified': False, 'profile_image_url_https': 'https://pbs.twimg.com/profile_images/378800000806915002/c3c89b4375848595b007e8ed439bd3ae_normal.png', 'profile_sidebar_fill_color': 'DDEEF6', 'profile_text_color': '333333', 'followers_count': 18, 'profile_sidebar_border_color': '000000', 'id_str': '99521941', 'profile_background_color': 'FFFFFF', 'listed_count': 0, 'is_translation_enabled': False, 'utc_offset': -10800, 'statuses_count': 82, 'description': 'Banda PunkSkaHC desde Victoria, para el mundo Emi (Trombon Voz Coros) Leo (Bajo Voz Coros) Fer (Bateria Voz coros) Maku (Guitarra Voz Coros)', 'friends_count': 8, 'location': 'Victoria Buenos Aires Argentin', 'profile_link_color': '12207D', 'profile_image_url': 'http://pbs.twimg.com/profile_images/378800000806915002/c3c89b4375848595b007e8ed439bd3ae_normal.png', 'following': None, 'geo_enabled': False, 'profile_banner_url': 'https://pbs.twimg.com/profile_banners/99521941/1385745859', 'profile_background_image_url': 'http://pbs.twimg.com/profile_background_images/378800000144369888/0cs1fWW6.png', 'name': 'EX-IN ', 'lang': 'es', 'profile_background_tile': False, 'favourites_count': 0, 'screen_name': 'EXINMUSICA', 'notifications': None, 'url': 'http://www.ex-inoficial.com.ar', 'created_at': 'Sat Dec 26 15:22:21 +0000 2009', 'contributors_enabled': False, 'time_zone': 'Buenos Aires', 'protected': False, 'default_profile': False, 'is_translator': False}, 'geo': None, 'in_reply_to_user_id_str': None, 'possibly_sensitive': False, 'lang': 'es', 'created_at': 'Thu Feb 27 13:13:57 +0000 2014', 'filter_level': 'medium', 'in_reply_to_status_id_str': None, 'place': None}\n",
      "{'contributors': None, 'truncated': False, 'text': 'Played with data in #SASVA.. I think the Red Devils HAVE TO get to the next round on the WorldCup (based on stats) http://t.co/eVNYGubzgu', 'in_reply_to_status_id': None, 'id': 439025609306427394, 'favorite_count': 0, 'source': 'web', 'retweeted': False, 'coordinates': None, 'entities': {'symbols': [], 'user_mentions': [], 'hashtags': [{'indices': [20, 26], 'text': 'SASVA'}], 'urls': [], 'media': [{'expanded_url': 'http://twitter.com/RobinToubac/status/439025609306427394/photo/1', 'display_url': 'pic.twitter.com/eVNYGubzgu', 'url': 'http://t.co/eVNYGubzgu', 'media_url_https': 'https://pbs.twimg.com/media/Bhe7dzKCUAEEtRZ.png', 'id_str': '439025609314816001', 'sizes': {'large': {'h': 611, 'resize': 'fit', 'w': 1024}, 'small': {'h': 203, 'resize': 'fit', 'w': 340}, 'medium': {'h': 358, 'resize': 'fit', 'w': 600}, 'thumb': {'h': 150, 'resize': 'crop', 'w': 150}}, 'indices': [115, 137], 'type': 'photo', 'id': 439025609314816001, 'media_url': 'http://pbs.twimg.com/media/Bhe7dzKCUAEEtRZ.png'}]}, 'in_reply_to_screen_name': None, 'id_str': '439025609306427394', 'retweet_count': 0, 'in_reply_to_user_id': None, 'favorited': False, 'user': {'follow_request_sent': None, 'profile_use_background_image': True, 'default_profile_image': False, 'id': 568358023, 'profile_background_image_url_https': 'https://abs.twimg.com/images/themes/theme14/bg.gif', 'verified': False, 'profile_image_url_https': 'https://pbs.twimg.com/profile_images/432820795581034496/xQxhob1j_normal.jpeg', 'profile_sidebar_fill_color': 'EFEFEF', 'profile_text_color': '333333', 'followers_count': 39, 'profile_sidebar_border_color': 'EEEEEE', 'id_str': '568358023', 'profile_background_color': '131516', 'listed_count': 0, 'is_translation_enabled': False, 'utc_offset': 3600, 'statuses_count': 77, 'description': 'Junior Consultant at Sbi-consulting\\r\\n#BI - #CRM - #SocialMedia - #Fitness', 'friends_count': 156, 'location': 'Bertem, Belgium', 'profile_link_color': '009999', 'profile_image_url': 'http://pbs.twimg.com/profile_images/432820795581034496/xQxhob1j_normal.jpeg', 'following': None, 'geo_enabled': False, 'profile_background_image_url': 'http://abs.twimg.com/images/themes/theme14/bg.gif', 'name': 'Robin Toubac', 'lang': 'nl', 'profile_background_tile': True, 'favourites_count': 1, 'screen_name': 'RobinToubac', 'notifications': None, 'url': None, 'created_at': 'Tue May 01 16:47:09 +0000 2012', 'contributors_enabled': False, 'time_zone': 'Amsterdam', 'protected': False, 'default_profile': False, 'is_translator': False}, 'geo': None, 'in_reply_to_user_id_str': None, 'possibly_sensitive': False, 'lang': 'en', 'created_at': 'Thu Feb 27 13:13:56 +0000 2014', 'filter_level': 'medium', 'in_reply_to_status_id_str': None, 'place': None}\n",
      "{'contributors': None, 'truncated': False, 'text': 'Big Data And The Cloud http://t.co/x2TN2swIWp', 'in_reply_to_status_id': None, 'id': 439025611898892288, 'favorite_count': 0, 'source': '<a href=\"http://www.linkedin.com/\" rel=\"nofollow\">LinkedIn</a>', 'retweeted': False, 'coordinates': None, 'entities': {'symbols': [], 'user_mentions': [], 'hashtags': [], 'urls': [{'url': 'http://t.co/x2TN2swIWp', 'indices': [23, 45], 'expanded_url': 'http://lnkd.in/dZXfJ2U', 'display_url': 'lnkd.in/dZXfJ2U'}]}, 'in_reply_to_screen_name': None, 'id_str': '439025611898892288', 'retweet_count': 0, 'in_reply_to_user_id': None, 'favorited': False, 'user': {'follow_request_sent': None, 'profile_use_background_image': True, 'default_profile_image': False, 'id': 1291950810, 'profile_background_image_url_https': 'https://abs.twimg.com/images/themes/theme1/bg.png', 'verified': False, 'profile_image_url_https': 'https://pbs.twimg.com/profile_images/3463484389/0cc59597288a36228cf87eb7380f384e_normal.jpeg', 'profile_sidebar_fill_color': 'DDEEF6', 'profile_text_color': '333333', 'followers_count': 40, 'profile_sidebar_border_color': 'C0DEED', 'id_str': '1291950810', 'profile_background_color': 'C0DEED', 'listed_count': 0, 'is_translation_enabled': False, 'utc_offset': 3600, 'statuses_count': 81, 'description': 'IT Helpdesk Support Manager -Admin Sistemas en Busqueda Activa Con una experiencia consolidada en entornos multinacionales y liderando proyectos.Inglés Bilngüe', 'friends_count': 117, 'location': 'Madrid', 'profile_link_color': '0084B4', 'profile_image_url': 'http://pbs.twimg.com/profile_images/3463484389/0cc59597288a36228cf87eb7380f384e_normal.jpeg', 'following': None, 'geo_enabled': False, 'profile_background_image_url': 'http://abs.twimg.com/images/themes/theme1/bg.png', 'name': 'Nuria Maria Torres', 'lang': 'es', 'profile_background_tile': False, 'favourites_count': 2, 'screen_name': 'NuriaMariaT', 'notifications': None, 'url': None, 'created_at': 'Sat Mar 23 17:03:51 +0000 2013', 'contributors_enabled': False, 'time_zone': 'Madrid', 'protected': False, 'default_profile': True, 'is_translator': False}, 'geo': None, 'in_reply_to_user_id_str': None, 'possibly_sensitive': False, 'lang': 'et', 'created_at': 'Thu Feb 27 13:13:57 +0000 2014', 'filter_level': 'medium', 'in_reply_to_status_id_str': None, 'place': None}\n",
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      "{'contributors': None, 'truncated': False, 'text': \"@Smileforlive_ Già..poi gliel'ha data A quindi non credo ci sia da fidarsi .-. spero che ne esca da questa situazione!\", 'in_reply_to_status_id': 439025069806080000, 'id': 439025628948738048, 'favorite_count': 0, 'source': 'web', 'retweeted': False, 'coordinates': None, 'entities': {'symbols': [], 'user_mentions': [{'id': 390232828, 'indices': [0, 14], 'id_str': '390232828', 'screen_name': 'Smileforlive_', 'name': 'Spencer Hastings'}], 'hashtags': [], 'urls': []}, 'in_reply_to_screen_name': 'Smileforlive_', 'id_str': '439025628948738048', 'retweet_count': 0, 'in_reply_to_user_id': 390232828, 'favorited': False, 'user': {'follow_request_sent': None, 'profile_use_background_image': True, 'default_profile_image': False, 'id': 703619424, 'profile_background_image_url_https': 'https://pbs.twimg.com/profile_background_images/435091064177709058/YSsYRVt1.jpeg', 'verified': False, 'profile_image_url_https': 'https://pbs.twimg.com/profile_images/435089344257216513/pvONhAHg_normal.jpeg', 'profile_sidebar_fill_color': '7AC3EE', 'profile_text_color': '3D1957', 'followers_count': 1655, 'profile_sidebar_border_color': 'FFFFFF', 'id_str': '703619424', 'profile_background_color': '642D8B', 'listed_count': 2, 'is_translation_enabled': False, 'utc_offset': 3600, 'statuses_count': 77785, 'description': 'Tiziano Ferro 3 14-07-2012 3 Glee/HIMYM/PLL/TVD/OUAT/Scrubs/The OC/New Girl/Awkward/My Mad Fat Diary', 'friends_count': 1693, 'location': '', 'profile_link_color': 'FF00AE', 'profile_image_url': 'http://pbs.twimg.com/profile_images/435089344257216513/pvONhAHg_normal.jpeg', 'following': None, 'geo_enabled': False, 'profile_banner_url': 'https://pbs.twimg.com/profile_banners/703619424/1392145538', 'profile_background_image_url': 'http://pbs.twimg.com/profile_background_images/435091064177709058/YSsYRVt1.jpeg', 'name': 'love is love', 'lang': 'it', 'profile_background_tile': True, 'favourites_count': 12715, 'screen_name': '___louder', 'notifications': None, 'url': None, 'created_at': 'Wed Jul 18 19:10:32 +0000 2012', 'contributors_enabled': False, 'time_zone': 'Rome', 'protected': False, 'default_profile': False, 'is_translator': False}, 'geo': None, 'in_reply_to_user_id_str': '390232828', 'lang': 'it', 'created_at': 'Thu Feb 27 13:14:01 +0000 2014', 'filter_level': 'medium', 'in_reply_to_status_id_str': '439025069806080000', 'place': None}\n",
      "{'contributors': None, 'truncated': False, 'text': '@74dyh nah, udah gw kirim ke email lo. hapusin data gw yak..soalnya file polosnya udah ke delete jadinya yg gw kirim pnya gw yg belum slesai', 'in_reply_to_status_id': 439024205707747329, 'id': 439025631494291456, 'favorite_count': 0, 'source': 'web', 'retweeted': False, 'coordinates': None, 'entities': {'symbols': [], 'user_mentions': [{'id': 398665715, 'indices': [0, 6], 'id_str': '398665715', 'screen_name': '74dyh', 'name': 'ADHY M'}], 'hashtags': [], 'urls': []}, 'in_reply_to_screen_name': '74dyh', 'id_str': '439025631494291456', 'retweet_count': 0, 'in_reply_to_user_id': 398665715, 'favorited': False, 'user': {'follow_request_sent': None, 'profile_use_background_image': True, 'default_profile_image': False, 'id': 559079524, 'profile_background_image_url_https': 'https://pbs.twimg.com/profile_background_images/378800000167339277/jLZs3nJH.jpeg', 'verified': False, 'profile_image_url_https': 'https://pbs.twimg.com/profile_images/438904416121663488/ALLBYqSz_normal.jpeg', 'profile_sidebar_fill_color': 'EFEFEF', 'profile_text_color': '333333', 'followers_count': 1296, 'profile_sidebar_border_color': '000000', 'id_str': '559079524', 'profile_background_color': '080804', 'listed_count': 0, 'is_translation_enabled': False, 'utc_offset': 25200, 'statuses_count': 19871, 'description': '~05-05-1995 || † || You can do that || Brawijaya University || 750BFC41\\r\\n\\r\\n~Photograph~', 'friends_count': 452, 'location': 'Malang, Jawa Timur', 'profile_link_color': '0099CC', 'profile_image_url': 'http://pbs.twimg.com/profile_images/438904416121663488/ALLBYqSz_normal.jpeg', 'following': None, 'geo_enabled': True, 'profile_banner_url': 'https://pbs.twimg.com/profile_banners/559079524/1388888769', 'profile_background_image_url': 'http://pbs.twimg.com/profile_background_images/378800000167339277/jLZs3nJH.jpeg', 'name': 'daniel junior ', 'lang': 'id', 'profile_background_tile': True, 'favourites_count': 64, 'screen_name': 'daniel_junior_s', 'notifications': None, 'url': 'https://www.facebook.com/danielcool.sinambela1', 'created_at': 'Sat Apr 21 01:06:18 +0000 2012', 'contributors_enabled': False, 'time_zone': 'Bangkok', 'protected': False, 'default_profile': False, 'is_translator': False}, 'geo': None, 'in_reply_to_user_id_str': '398665715', 'lang': 'in', 'created_at': 'Thu Feb 27 13:14:01 +0000 2014', 'filter_level': 'medium', 'in_reply_to_status_id_str': '439024205707747329', 'place': None}\n",
      "{'contributors': None, 'truncated': False, 'text': 'RT @lovedebe: 【兵庫より：迷子犬探してます】2/23\\u3000兵庫県小野市片山町より行方不明。メス。人懐っこいですがよく吠える。 老犬で歯があまりない。腹部に手術の跡としこり。情報元→http://t.co/UaG9ossAWh … … http://t.co/dejC3…', 'in_reply_to_status_id': None, 'id': 439025632756772864, 'favorite_count': 0, 'source': '<a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\">Twitter for iPhone</a>', 'retweeted': False, 'coordinates': None, 'entities': {'symbols': [], 'user_mentions': [{'id': 1587303494, 'indices': [3, 12], 'id_str': '1587303494', 'screen_name': 'lovedebe', 'name': 'デベママ'}], 'hashtags': [], 'urls': [{'url': 'http://t.co/UaG9ossAWh', 'indices': [95, 117], 'expanded_url': 'http://www.vets.ne.jp/log/maigo/data/6101/files/20140223172705.html', 'display_url': 'vets.ne.jp/log/maigo/data…'}], 'media': [{'source_status_id_str': '437814523458621440', 'expanded_url': 'http://twitter.com/lovedebe/status/437814523458621440/photo/1', 'display_url': 'pic.twitter.com/dejC3EsIzq', 'url': 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      "{'contributors': None, 'truncated': False, 'text': 'RT @FAFSA: Tired of scanning tax documents to answer #FAFSA questions? 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      "{'contributors': None, 'truncated': False, 'text': \"Proper retirement of #mobile devices is critical to meeting their unique data security challenges and protecting your organization's brand.\", 'in_reply_to_status_id': None, 'id': 439025624578293760, 'favorite_count': 0, 'source': '<a href=\"http://www.sprinklr.com\" rel=\"nofollow\">Sprinklr</a>', 'retweeted': False, 'coordinates': None, 'entities': {'symbols': [], 'user_mentions': [], 'hashtags': [{'indices': [21, 28], 'text': 'mobile'}], 'urls': []}, 'in_reply_to_screen_name': None, 'id_str': '439025624578293760', 'retweet_count': 0, 'in_reply_to_user_id': None, 'favorited': False, 'user': {'follow_request_sent': None, 'profile_use_background_image': True, 'default_profile_image': False, 'id': 39497803, 'profile_background_image_url_https': 'https://pbs.twimg.com/profile_background_images/378800000101560273/f81a4a1d879f28b706c09668e2a1a4a4.jpeg', 'verified': False, 'profile_image_url_https': 'https://pbs.twimg.com/profile_images/378800000645698653/84338d0d9fd8491f547fd8dfe138146e_normal.jpeg', 'profile_sidebar_fill_color': 'E6ECC2', 'profile_text_color': '333333', 'followers_count': 929, 'profile_sidebar_border_color': 'FFFFFF', 'id_str': '39497803', 'profile_background_color': 'D2D8DA', 'listed_count': 51, 'is_translation_enabled': False, 'utc_offset': -18000, 'statuses_count': 1357, 'description': 'Arrow Value Recovery provides secure IT asset disposition, specialized reverse, & remarketing solutions that offer data security and brand protection.', 'friends_count': 941, 'location': 'Worldwide', 'profile_link_color': '4E6787', 'profile_image_url': 'http://pbs.twimg.com/profile_images/378800000645698653/84338d0d9fd8491f547fd8dfe138146e_normal.jpeg', 'following': None, 'geo_enabled': False, 'profile_banner_url': 'https://pbs.twimg.com/profile_banners/39497803/1382709327', 'profile_background_image_url': 'http://pbs.twimg.com/profile_background_images/378800000101560273/f81a4a1d879f28b706c09668e2a1a4a4.jpeg', 'name': 'Arrow Value Recovery', 'lang': 'en', 'profile_background_tile': False, 'favourites_count': 0, 'screen_name': 'ValueRecovery', 'notifications': None, 'url': 'http://www.arrowvaluerecovery.com/', 'created_at': 'Tue May 12 13:25:51 +0000 2009', 'contributors_enabled': False, 'time_zone': 'Eastern Time (US & Canada)', 'protected': False, 'default_profile': False, 'is_translator': False}, 'geo': None, 'in_reply_to_user_id_str': None, 'lang': 'en', 'created_at': 'Thu Feb 27 13:14:00 +0000 2014', 'filter_level': 'medium', 'in_reply_to_status_id_str': None, 'place': None}\n",
      "{'contributors': None, 'truncated': False, 'text': 'Google Announces An Online Data Interpretation Class For The General Public http://t.co/Vjd4Zh2NVF', 'in_reply_to_status_id': None, 'id': 439025627543650305, 'favorite_count': 0, 'source': '<a href=\"http://www.flipboard.com\" rel=\"nofollow\">Flipboard</a>', 'retweeted': False, 'coordinates': None, 'entities': {'symbols': [], 'user_mentions': [], 'hashtags': [], 'urls': [{'url': 'http://t.co/Vjd4Zh2NVF', 'indices': [76, 98], 'expanded_url': 'http://flip.it/r4KxI', 'display_url': 'flip.it/r4KxI'}]}, 'in_reply_to_screen_name': None, 'id_str': '439025627543650305', 'retweet_count': 0, 'in_reply_to_user_id': None, 'favorited': False, 'user': {'follow_request_sent': None, 'profile_use_background_image': True, 'default_profile_image': False, 'id': 93570019, 'profile_background_image_url_https': 'https://abs.twimg.com/images/themes/theme14/bg.gif', 'verified': False, 'profile_image_url_https': 'https://pbs.twimg.com/profile_images/1840431258/sidebox_normal.jpg', 'profile_sidebar_fill_color': 'EFEFEF', 'profile_text_color': '333333', 'followers_count': 83, 'profile_sidebar_border_color': 'EEEEEE', 'id_str': '93570019', 'profile_background_color': '131516', 'listed_count': 3, 'is_translation_enabled': False, 'utc_offset': -28800, 'statuses_count': 108, 'description': 'Security researcher', 'friends_count': 237, 'location': '', 'profile_link_color': '009999', 'profile_image_url': 'http://pbs.twimg.com/profile_images/1840431258/sidebox_normal.jpg', 'following': None, 'geo_enabled': True, 'profile_background_image_url': 'http://abs.twimg.com/images/themes/theme14/bg.gif', 'name': 'Anh Le', 'lang': 'en', 'profile_background_tile': True, 'favourites_count': 21, 'screen_name': 'anhldbk', 'notifications': None, 'url': None, 'created_at': 'Mon Nov 30 05:41:37 +0000 2009', 'contributors_enabled': False, 'time_zone': 'Pacific Time (US & Canada)', 'protected': False, 'default_profile': False, 'is_translator': False}, 'geo': None, 'in_reply_to_user_id_str': None, 'possibly_sensitive': False, 'lang': 'en', 'created_at': 'Thu Feb 27 13:14:00 +0000 2014', 'filter_level': 'medium', 'in_reply_to_status_id_str': None, 'place': None}\n",
      "{'contributors': None, 'truncated': False, 'text': \"@Smileforlive_ Già..poi gliel'ha data A quindi non credo ci sia da fidarsi .-. spero che ne esca da questa situazione!\", 'in_reply_to_status_id': 439025069806080000, 'id': 439025628948738048, 'favorite_count': 0, 'source': 'web', 'retweeted': False, 'coordinates': None, 'entities': {'symbols': [], 'user_mentions': [{'id': 390232828, 'indices': [0, 14], 'id_str': '390232828', 'screen_name': 'Smileforlive_', 'name': 'Spencer Hastings'}], 'hashtags': [], 'urls': []}, 'in_reply_to_screen_name': 'Smileforlive_', 'id_str': '439025628948738048', 'retweet_count': 0, 'in_reply_to_user_id': 390232828, 'favorited': False, 'user': {'follow_request_sent': None, 'profile_use_background_image': True, 'default_profile_image': False, 'id': 703619424, 'profile_background_image_url_https': 'https://pbs.twimg.com/profile_background_images/435091064177709058/YSsYRVt1.jpeg', 'verified': False, 'profile_image_url_https': 'https://pbs.twimg.com/profile_images/435089344257216513/pvONhAHg_normal.jpeg', 'profile_sidebar_fill_color': '7AC3EE', 'profile_text_color': '3D1957', 'followers_count': 1655, 'profile_sidebar_border_color': 'FFFFFF', 'id_str': '703619424', 'profile_background_color': '642D8B', 'listed_count': 2, 'is_translation_enabled': False, 'utc_offset': 3600, 'statuses_count': 77785, 'description': 'Tiziano Ferro 3 14-07-2012 3 Glee/HIMYM/PLL/TVD/OUAT/Scrubs/The OC/New Girl/Awkward/My Mad Fat Diary', 'friends_count': 1693, 'location': '', 'profile_link_color': 'FF00AE', 'profile_image_url': 'http://pbs.twimg.com/profile_images/435089344257216513/pvONhAHg_normal.jpeg', 'following': None, 'geo_enabled': False, 'profile_banner_url': 'https://pbs.twimg.com/profile_banners/703619424/1392145538', 'profile_background_image_url': 'http://pbs.twimg.com/profile_background_images/435091064177709058/YSsYRVt1.jpeg', 'name': 'love is love', 'lang': 'it', 'profile_background_tile': True, 'favourites_count': 12715, 'screen_name': '___louder', 'notifications': None, 'url': None, 'created_at': 'Wed Jul 18 19:10:32 +0000 2012', 'contributors_enabled': False, 'time_zone': 'Rome', 'protected': False, 'default_profile': False, 'is_translator': False}, 'geo': None, 'in_reply_to_user_id_str': '390232828', 'lang': 'it', 'created_at': 'Thu Feb 27 13:14:01 +0000 2014', 'filter_level': 'medium', 'in_reply_to_status_id_str': '439025069806080000', 'place': None}\n",
      "{'contributors': None, 'truncated': False, 'text': 'RT @FAFSA: Tired of scanning tax documents to answer #FAFSA questions? 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     ]
    }
   ],
   "source": [
    "import ast\n",
    "import sys\n",
    "filename = '/Users/petermin/sea18_ds4/class_lectures/week09-kojak1/01-docker-hadoop-hive/input.txt'\n",
    "# s = open(filename, 'r').read()\n",
    "# ast.literal_eval(s)\n",
    "file = open(filename,\"r\")\n",
    "users = []\n",
    "for line in file:\n",
    "#    users.append(eval(line)[\"id\"])\n",
    "    print(eval(line))\n",
    "#eval(open(filename, 'r').read())\n",
    "#users"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# amoeba pairs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "import numpy\n",
    "class amoeba(object):\n",
    "    def __init__(self,ps):\n",
    "        self.ProbDivide = ps\n",
    "        self.ProbDies = 1 - ps\n",
    "    def getProbDivide(self):\n",
    "        return self.ProbDivide\n",
    "    def lives(self):\n",
    "        if random.random() <= self.getProbDivide():\n",
    "            return True\n",
    "        else:\n",
    "            return False\n",
    "    def reproduce(self):\n",
    "        return amoeba(self.ProbDivide)\n",
    "class amoebas(object):\n",
    "    def __init__(self, amoebas):\n",
    "        self.amoebas = amoebas\n",
    "    def getAmoebas(self):\n",
    "        return self.amoebas\n",
    "    def getTotalPop(self):\n",
    "        return len(self.amoebas)     \n",
    "    def update(self):\n",
    "        amoebas_copy = self.amoebas[:]\n",
    "        temp = []\n",
    "        for amoeba in self.amoebas:\n",
    "            if amoeba.lives():\n",
    "                temp.append(amoeba.reproduce())\n",
    "            else:\n",
    "                self.amoebas.remove(amoeba)\n",
    "        for NewAmoebas in temp:\n",
    "            self.amoebas.append(NewAmoebas)\n",
    "        return len(self.amoebas)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[15, 0, 11, 9, 11, 0, 6, 12, 6, 5]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def simulate(ps, numPeriods, numTrials):\n",
    "    trials = []\n",
    "    for i in range(numTrials):\n",
    "        a = amoebas([amoeba(ps)])\n",
    "        for period in range(numPeriods):\n",
    "            a.update()\n",
    "        trials.append(a.getTotalPop())\n",
    "    return trials\n",
    "simulate(0.8,5,10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = amoebas([amoeba(0.6)])\n",
    "a.update()\n",
    "a.update()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5571428571428572"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "def check(number,ps=0.5):\n",
    "    remain = 0\n",
    "    for i in range(number):\n",
    "        if (np.random.choice([0, 1], p=[1-ps,ps])) == 1:\n",
    "            remain += 2\n",
    "    return(remain)\n",
    "\n",
    "def generations(ps,itera=20):\n",
    "    remain = 1\n",
    "    count = 0\n",
    "    while remain != 0:\n",
    "        remain = check(remain,ps)\n",
    "        count += 1\n",
    "        if count == itera:\n",
    "            return(1)\n",
    "    return(0)\n",
    "\n",
    "def prob_survive(number,ps,itera):\n",
    "    count = 0\n",
    "    for i in range(number):\n",
    "        count += generations(ps,itera)\n",
    "    return(count/number)\n",
    "\n",
    "prob_survive(70,0.7,15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'jaccard'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-30-dff9c0f66a3f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0m__future__\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mdivision\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0munittest\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mjaccard\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mj\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'jaccard'"
     ]
    }
   ],
   "source": [
    "from __future__ import division\n",
    "import unittest\n",
    "from jaccard import j\n",
    "\n",
    "\n",
    "class TestJ(unittest.TestCase):\n",
    "\n",
    "    def test_fails_on_no_args(self):\n",
    "        with self.assertRaises(TypeError):\n",
    "            j()\n",
    "\n",
    "    def test_fails_on_one_arg(self):\n",
    "        with self.assertRaises(TypeError):\n",
    "            j(1)\n",
    "\n",
    "    def test_fails_on_three_args(self):\n",
    "        with self.assertRaises(TypeError):\n",
    "            j(1, 2, 3)\n",
    "\n",
    "    def test_one_for_same(self):\n",
    "        result = j([1, 2, 3], [1, 2, 3])\n",
    "        self.assertEqual(result, 1)\n",
    "\n",
    "    def test_result_is_float(self):\n",
    "        result = j([1, 2, 3], [1, 2, 3])\n",
    "        self.assertIs(type(result), type(1.0))\n",
    "\n",
    "    def test_order_not_important(self):\n",
    "        result = j([3, 2, 1], [1, 3, 2])\n",
    "        self.assertEqual(result, 1)\n",
    "\n",
    "    def test_zero_for_different(self):\n",
    "        result = j([1, 2, 3], [4, 5, 6])\n",
    "        self.assertEqual(result, 0)\n",
    "\n",
    "    def test_half_for_double(self):\n",
    "        result = j(['cow'], ['dog', 'cow'])\n",
    "        self.assertEqual(result, 0.5)\n",
    "\n",
    "    def test_third_for_half_same(self):\n",
    "        result = j([1, 2], [2, 3])\n",
    "        self.assertEqual(result, 1/3)\n",
    "\n",
    "    def test_half_for_two_thirds_same(self):\n",
    "        result = j([1, 2, 3], [2, 3, 4])\n",
    "        self.assertEqual(result, 0.5)\n",
    "\n",
    "    def test_immune_to_duplicates(self):\n",
    "        result = j([1, 1, 2], [3, 3, 3, 2, 2])\n",
    "        self.assertEqual(result, 1/3)\n",
    "\n",
    "    def test_larger_lists(self):\n",
    "        result = j(range(1, 10), [10, 9, 8, 7, 6])\n",
    "        self.assertEqual(result, 2/5)\n",
    "\n",
    "if __name__ == '__main__':\n",
    "    unittest.main()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 1, 2],\n",
       "       [1, 0, 2],\n",
       "       [2, 1, 0]])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "test = np.array([[0,1,2],[1,0,2],[2,1,0]])\n",
    "test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0., 0., 0.])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.zeros(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1. 0. 2.]\n"
     ]
    }
   ],
   "source": [
    "def shortest_travel(start,end,T,num_stops):\n",
    "    #first check going straight\n",
    "    shortest = T[start,end]\n",
    "    #check 1 stop over\n",
    "    path = np.zeros(num_stops)\n",
    "    \n",
    "    paths = np.zeros(len(T[start]))\n",
    "    for i in range(len(paths)):\n",
    "        #first stop\n",
    "        paths[i] += T[start][i]\n",
    "    #last stop\n",
    "    for j in range(len(paths)):\n",
    "        paths[i] += T[end][i] \n",
    "    print(paths)\n",
    "    #see if shortest\n",
    "#     shortest\n",
    "    return\n",
    "shortest_travel(1,2,test,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "test = [4,7,2,1,0,6]\n",
    "cond = True\n",
    "while cond:\n",
    "    test_copy = test[:]\n",
    "    swap = True\n",
    "    for i, n in enumerate(test_copy[:-1]):\n",
    "        if test[i+1] < n:\n",
    "            test[i+1], test[i] = n, test[i+1]\n",
    "            swap = False\n",
    "    if swap:\n",
    "        cond=False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 1, 2, 4, 6, 7]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test = [4,7,2,1,0,6]\n",
    "def crappy_sort(test):\n",
    "    cond = True\n",
    "    while cond:\n",
    "        new_list = []\n",
    "        swap = True\n",
    "        while test:\n",
    "            if (len(new_list)==0):\n",
    "                new_list.append(test.pop(0))\n",
    "            elif (new_list[-1] > test[0]):\n",
    "                new_list.insert(len(new_list)-1,test.pop(0))\n",
    "                swap = False\n",
    "            else:\n",
    "                new_list.append(test.pop(0))\n",
    "        test = new_list[:]\n",
    "        if swap:\n",
    "            cond=False\n",
    "    return test\n",
    "crappy_sort(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 1, 2, 4, 6, 7]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test = [4,7,2,1,0,6]\n",
    "def mySortpop(inputlist):\n",
    "    for item in inputlist:\n",
    "        for i in range(len(inputlist)-1):\n",
    "            if inputlist[i] > inputlist[i+1]:\n",
    "                inputlist.append(inputlist.pop(i))\n",
    "    return inputlist\n",
    "mySortpop(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 1, 1, 1, 1, 6]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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