01-Data-Science-From-Scratch/01-들어가기(Introduction)/0.1 Datum inc by me.ipynb

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      "Counter({0: 2, 5: 1})\n",
      "\n",
      "######################\n",
      "#\n",
      "# FINDING KEY CONNECTORS\n",
      "#\n",
      "######################\n",
      "\n",
      "total connections 24\n",
      "number of users 11\n",
      "average connections 2.1818181818181817\n",
      "\n",
      "users sorted by number of friends:\n",
      "[(1, 3), (2, 3), (3, 3), (5, 3), (8, 3), (0, 2), (4, 2), (6, 2), (7, 2), (9, 1), (10, 0)]\n",
      "\n",
      "######################\n",
      "#\n",
      "# DATA SCIENTISTS YOU MAY KNOW\n",
      "#\n",
      "######################\n",
      "\n",
      "friends of friends bad for user 0: [0, 2, 3, 0, 1, 3]\n",
      "friends of friends for user 3: Counter({0: 2, 5: 1})\n",
      "\n",
      "######################\n",
      "#\n",
      "# SALARIES AND TENURES\n",
      "#\n",
      "######################\n",
      "\n",
      "average salary by tenure {0.7: 48000.0, 1.9: 48000.0, 2.5: 60000.0, 4.2: 63000.0, 6.5: 69000.0, 6: 76000.0, 7.5: 76000.0, 8.1: 88000.0, 10: 83000.0, 8.7: 83000.0}\n",
      "average salary by tenure bucket {'less than two': 48000.0, 'more than five': 79166.66666666667, 'between two and five': 61500.0}\n",
      "\n",
      "######################\n",
      "#\n",
      "# MOST COMMON WORDS\n",
      "#\n",
      "######################\n",
      "\n",
      "data 3\n",
      "big 3\n",
      "learning 3\n",
      "java 3\n",
      "python 3\n",
      "hadoop 2\n",
      "statistics 2\n",
      "hbase 2\n",
      "machine 2\n",
      "probability 2\n",
      "networks 2\n",
      "cassandra 2\n",
      "neural 2\n",
      "regression 2\n",
      "scikit-learn 2\n",
      "r 2\n"
     ]
    }
   ],
   "source": [
    "# at this stage in the book we haven't actually installed matplotlib,\n",
    "# comment this out if you need to\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "##########################\n",
    "#                        #\n",
    "# FINDING KEY CONNECTORS #\n",
    "#                        #\n",
    "##########################\n",
    "\n",
    "users = [\n",
    "    { \"id\": 0, \"name\": \"Hero\" },\n",
    "    { \"id\": 1, \"name\": \"Dunn\" },\n",
    "    { \"id\": 2, \"name\": \"Sue\" },\n",
    "    { \"id\": 3, \"name\": \"Chi\" },\n",
    "    { \"id\": 4, \"name\": \"Thor\" },\n",
    "    { \"id\": 5, \"name\": \"Clive\" },\n",
    "    { \"id\": 6, \"name\": \"Hicks\" },\n",
    "    { \"id\": 7, \"name\": \"Devin\" },\n",
    "    { \"id\": 8, \"name\": \"Kate\" },\n",
    "    { \"id\": 9, \"name\": \"Klein\" },\n",
    "    { \"id\": 10, \"name\": \"Jen\" }\n",
    "]\n",
    "\n",
    "friendships = [(0, 1), (0, 2), (1, 2), (1, 3), (2, 3), (3, 4),\n",
    "               (4, 5), (5, 6), (5, 7), (6, 8), (7, 8), (8, 9)]\n",
    "\n",
    "\n",
    "# first give each user an empty list\n",
    "for user in users:\n",
    "    user[\"friends\"] = []\n",
    "\n",
    "# and then populate the lists with friendships\n",
    "for i, j in friendships:\n",
    "    # this works because users[i] is the user whose id is i\n",
    "    users[i][\"friends\"].append(users[j]) # add i as a friend of j\n",
    "    users[j][\"friends\"].append(users[i]) # add j as a friend of i\n",
    "\n",
    "def number_of_friends(user):\n",
    "    \"\"\"how many friends does _user_ have?\"\"\"\n",
    "    return len(user[\"friends\"]) # length of friend_ids list\n",
    "\n",
    "total_connections = sum(number_of_friends(user)\n",
    "                        for user in users) # 24\n",
    "\n",
    "num_users = len(users)\n",
    "avg_connections = total_connections / num_users # 2.4\n",
    "\n",
    "################################\n",
    "#                              #\n",
    "# DATA SCIENTISTS YOU MAY KNOW #\n",
    "#                              #\n",
    "################################\n",
    "\n",
    "def friends_of_friend_ids_bad(user):\n",
    "    # \"foaf\" is short for \"friend of a friend\"\n",
    "    return [foaf[\"id\"]\n",
    "            for friend in user[\"friends\"] # for each of user's friends\n",
    "            for foaf in friend[\"friends\"]] # get each of _their_ friends\n",
    "\n",
    "from collections import Counter # not loaded by default\n",
    "\n",
    "def not_the_same(user, other_user):\n",
    "    \"\"\"two users are not the same if they have different ids\"\"\"\n",
    "    return user[\"id\"] != other_user[\"id\"]\n",
    "\n",
    "def not_friends(user, other_user):\n",
    "    \"\"\"other_user is not a friend if he's not in user[\"friends\"];\n",
    "    that is, if he's not_the_same as all the people in user[\"friends\"]\"\"\"\n",
    "    return all(not_the_same(friend, other_user)\n",
    "               for friend in user[\"friends\"])\n",
    "\n",
    "def friends_of_friend_ids(user):\n",
    "    return Counter(foaf[\"id\"]\n",
    "                   for friend in user[\"friends\"]  # for each of my friends\n",
    "                   for foaf in friend[\"friends\"]  # count *their* friends\n",
    "                   if not_the_same(user, foaf)    # who aren't me\n",
    "                   and not_friends(user, foaf))   # and aren't my friends\n",
    "\n",
    "print(friends_of_friend_ids(users[3])) # Counter({0: 2, 5: 1})\n",
    "\n",
    "interests = [\n",
    "    (0, \"Hadoop\"), (0, \"Big Data\"), (0, \"HBase\"), (0, \"Java\"),\n",
    "    (0, \"Spark\"), (0, \"Storm\"), (0, \"Cassandra\"),\n",
    "    (1, \"NoSQL\"), (1, \"MongoDB\"), (1, \"Cassandra\"), (1, \"HBase\"),\n",
    "    (1, \"Postgres\"), (2, \"Python\"), (2, \"scikit-learn\"), (2, \"scipy\"),\n",
    "    (2, \"numpy\"), (2, \"statsmodels\"), (2, \"pandas\"), (3, \"R\"), (3, \"Python\"),\n",
    "    (3, \"statistics\"), (3, \"regression\"), (3, \"probability\"),\n",
    "    (4, \"machine learning\"), (4, \"regression\"), (4, \"decision trees\"),\n",
    "    (4, \"libsvm\"), (5, \"Python\"), (5, \"R\"), (5, \"Java\"), (5, \"C++\"),\n",
    "    (5, \"Haskell\"), (5, \"programming languages\"), (6, \"statistics\"),\n",
    "    (6, \"probability\"), (6, \"mathematics\"), (6, \"theory\"),\n",
    "    (7, \"machine learning\"), (7, \"scikit-learn\"), (7, \"Mahout\"),\n",
    "    (7, \"neural networks\"), (8, \"neural networks\"), (8, \"deep learning\"),\n",
    "    (8, \"Big Data\"), (8, \"artificial intelligence\"), (9, \"Hadoop\"),\n",
    "    (9, \"Java\"), (9, \"MapReduce\"), (9, \"Big Data\")\n",
    "]\n",
    "\n",
    "def data_scientists_who_like(target_interest):\n",
    "    return [user_id\n",
    "            for user_id, user_interest in interests\n",
    "            if user_interest == target_interest]\n",
    "\n",
    "from collections import defaultdict\n",
    "\n",
    "# keys are interests, values are lists of user_ids with that interest\n",
    "user_ids_by_interest = defaultdict(list)\n",
    "\n",
    "for user_id, interest in interests:\n",
    "    user_ids_by_interest[interest].append(user_id)\n",
    "\n",
    "# keys are user_ids, values are lists of interests for that user_id\n",
    "interests_by_user_id = defaultdict(list)\n",
    "\n",
    "for user_id, interest in interests:\n",
    "    interests_by_user_id[user_id].append(interest)\n",
    "\n",
    "def most_common_interests_with(user_id):\n",
    "    return Counter(interested_user_id\n",
    "        for interest in interests_by_user_id[\"user_id\"]\n",
    "        for interested_user_id in user_ids_by_interest[interest]\n",
    "        if interested_user_id != user_id)\n",
    "\n",
    "###########################\n",
    "#                         #\n",
    "# SALARIES AND EXPERIENCE #\n",
    "#                         #\n",
    "###########################\n",
    "\n",
    "salaries_and_tenures = [(83000, 8.7), (88000, 8.1),\n",
    "                        (48000, 0.7), (76000, 6),\n",
    "                        (69000, 6.5), (76000, 7.5),\n",
    "                        (60000, 2.5), (83000, 10),\n",
    "                        (48000, 1.9), (63000, 4.2)]\n",
    "\n",
    "def make_chart_salaries_by_tenure():\n",
    "    tenures = [tenure for salary, tenure in salaries_and_tenures]\n",
    "    salaries = [salary for salary, tenure in salaries_and_tenures]\n",
    "    plt.scatter(tenures, salaries)\n",
    "    plt.xlabel(\"Years Experience\")\n",
    "    plt.ylabel(\"Salary\")\n",
    "    plt.show()\n",
    "\n",
    "# keys are years\n",
    "# values are the salaries for each tenure\n",
    "salary_by_tenure = defaultdict(list)\n",
    "\n",
    "for salary, tenure in salaries_and_tenures:\n",
    "    salary_by_tenure[tenure].append(salary)\n",
    "\n",
    "average_salary_by_tenure = {\n",
    "    tenure : sum(salaries) / len(salaries)\n",
    "    for tenure, salaries in salary_by_tenure.items()\n",
    "}\n",
    "\n",
    "def tenure_bucket(tenure):\n",
    "    if tenure < 2: return \"less than two\"\n",
    "    elif tenure < 5: return \"between two and five\"\n",
    "    else: return \"more than five\"\n",
    "\n",
    "salary_by_tenure_bucket = defaultdict(list)\n",
    "\n",
    "for salary, tenure in salaries_and_tenures:\n",
    "    bucket = tenure_bucket(tenure)\n",
    "    salary_by_tenure_bucket[bucket].append(salary)\n",
    "\n",
    "average_salary_by_bucket = {\n",
    "  tenure_bucket : sum(salaries) / len(salaries)\n",
    "  for tenure_bucket, salaries in salary_by_tenure_bucket.items()\n",
    "}\n",
    "\n",
    "\n",
    "#################\n",
    "#               #\n",
    "# PAID_ACCOUNTS #\n",
    "#               #\n",
    "#################\n",
    "\n",
    "def predict_paid_or_unpaid(years_experience):\n",
    "  if years_experience < 3.0: return \"paid\"\n",
    "  elif years_experience < 8.5: return \"unpaid\"\n",
    "  else: return \"paid\"\n",
    "\n",
    "######################\n",
    "#                    #\n",
    "# TOPICS OF INTEREST #\n",
    "#                    #\n",
    "######################\n",
    "\n",
    "words_and_counts = Counter(word\n",
    "                           for user, interest in interests\n",
    "                           for word in interest.lower().split())\n",
    "\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "\n",
    "    print()\n",
    "    print(\"######################\")\n",
    "    print(\"#\")\n",
    "    print(\"# FINDING KEY CONNECTORS\")\n",
    "    print(\"#\")\n",
    "    print(\"######################\")\n",
    "    print()\n",
    "\n",
    "\n",
    "    print(\"total connections\", total_connections)\n",
    "    print(\"number of users\", num_users)\n",
    "    print(\"average connections\", total_connections / num_users)\n",
    "    print()\n",
    "\n",
    "    # create a list (user_id, number_of_friends)\n",
    "    num_friends_by_id = [(user[\"id\"], number_of_friends(user))\n",
    "                         for user in users]\n",
    "\n",
    "    print(\"users sorted by number of friends:\")\n",
    "    print(sorted(num_friends_by_id,\n",
    "                 key=lambda pair: pair[1],                       # by number of friends\n",
    "                 reverse=True))                                  # largest to smallest\n",
    "\n",
    "    print()\n",
    "    print(\"######################\")\n",
    "    print(\"#\")\n",
    "    print(\"# DATA SCIENTISTS YOU MAY KNOW\")\n",
    "    print(\"#\")\n",
    "    print(\"######################\")\n",
    "    print()\n",
    "\n",
    "\n",
    "    print(\"friends of friends bad for user 0:\", friends_of_friend_ids_bad(users[0]))\n",
    "    print(\"friends of friends for user 3:\", friends_of_friend_ids(users[3]))\n",
    "\n",
    "    print()\n",
    "    print(\"######################\")\n",
    "    print(\"#\")\n",
    "    print(\"# SALARIES AND TENURES\")\n",
    "    print(\"#\")\n",
    "    print(\"######################\")\n",
    "    print()\n",
    "\n",
    "    print(\"average salary by tenure\", average_salary_by_tenure)\n",
    "    print(\"average salary by tenure bucket\", average_salary_by_bucket)\n",
    "\n",
    "    print()\n",
    "    print(\"######################\")\n",
    "    print(\"#\")\n",
    "    print(\"# MOST COMMON WORDS\")\n",
    "    print(\"#\")\n",
    "    print(\"######################\")\n",
    "    print()\n",
    "\n",
    "    for word, count in words_and_counts.most_common():\n",
    "        if count > 1:\n",
    "            print(word, count)"
   ]
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VQMC6detYuHDhmKy/U05vXQvMa1s2j3oycynlDqrAccJQYz1x+VjgunrRWuCR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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2aee0a586d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "make_chart_salaries_by_tenure()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Zen of Python, by Tim Peters\n",
      "\n",
      "Beautiful is better than ugly.\n",
      "Explicit is better than implicit.\n",
      "Simple is better than complex.\n",
      "Complex is better than complicated.\n",
      "Flat is better than nested.\n",
      "Sparse is better than dense.\n",
      "Readability counts.\n",
      "Special cases aren't special enough to break the rules.\n",
      "Although practicality beats purity.\n",
      "Errors should never pass silently.\n",
      "Unless explicitly silenced.\n",
      "In the face of ambiguity, refuse the temptation to guess.\n",
      "There should be one-- and preferably only one --obvious way to do it.\n",
      "Although that way may not be obvious at first unless you're Dutch.\n",
      "Now is better than never.\n",
      "Although never is often better than *right* now.\n",
      "If the implementation is hard to explain, it's a bad idea.\n",
      "If the implementation is easy to explain, it may be a good idea.\n",
      "Namespaces are one honking great idea -- let's do more of those!\n"
     ]
    }
   ],
   "source": [
    "import this"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [conda root]",
   "language": "python",
   "name": "conda-root-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}