{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import re\n",
    "import time\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.feature_extraction.text import CountVectorizer,TfidfTransformer, TfidfVectorizer\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "full = pd.read_json(\"full.json\")\n",
    "full['Start Date'] =  pd.to_datetime(full['Start Date'],unit='ms')\n",
    "full['End Date'] =  pd.to_datetime(full['End Date'],unit='ms')\n",
    "originalRaw = pd.read_csv(\"raw_report.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(15690, 25)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "originalRaw.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15690"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(originalRaw)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(14897, 34)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "full.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(14604, 34)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "full[full['segCode'] == 0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "full = full[full['segCode'] == 0].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(11915, 34)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "full[full['READ']!='Unknown'].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Id', 'Start Date', 'Start Time', 'End Date', 'End Time', 'Duration',\n",
       "       'Entered by', 'Notes', 'Question', 'Answer', 'Notes FULL',\n",
       "       'Library Dept./Branch/Service', 'Where were you?', 'Who answers',\n",
       "       'Who Asked?', 'How many in the group?', 'Question Format',\n",
       "       'Question Type', 'Referred to?', 'READ', 'Time Spent',\n",
       "       'Class/Discipline', 'tags', 'Room reservation', 'Reported to:',\n",
       "       'READ_1_vs_2', 'READ_2_vs_3', 'Transcript', 'TransLength', 'segTrans',\n",
       "       'segCode', 'PatronTextString', 'StaffTextString', 'AllTextString'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "full.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "lengths = []\n",
    "for i in full.loc[full['READ']!='Unknown','PatronTextString'].str.split():\n",
    "    lengths.append(len(i))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "73.77675199328577"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(lengths)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9:00 6476885001398391168263262 I'm looking for a 19th century article about women fashion in France but cant seem to find anything? Do you have any good links? 9:00 me hello 9:01 me Hmm...I have a few ideas! 9:01 me Are you looking for articles that are about 19th centruy french fashion OR articles written in the 19th century about french fashion? (the former will definitely be easier I think) 9:02 6476885001398391168263262 I am looking for scholarly articles about 19th century French fashon 9:02 6476885001398391168263262 Mainly women 9:04 me Ok, let me see what I can find! 9:04 6476885001398391168263262 Thanks you rock! 9:04 me I am going to start with our Search It tool. Also, we have some fashion databases as well http://apps.lib.k-state.edu/databases/category/human-ecology/apparel-textiles/ 9:04 me Have you had a chance to try either of those sources? 9:05 6476885001398391168263262 Yeah I have tried.. I am no sure if im too specific or not specific enough. 9:06 me Gotcha. Also, do they have to be scholarly articles? Would library books work as well? 9:06 6476885001398391168263262 Yes, I believe so. 9:07 me I found one promising book in Search It \"Accessories to modernity : fashion and the feminine in nineteenth-century France\" 9:07 6476885001398391168263262 My assignment details just say two scholarly sources. 9:09 me Ok, I certianly think many of the books in the library qualify as \"scholarly\" Obviously some will not (ex. Batman comics), but I think you should be able to identify if a book is a scholarly source (they will have lots of references, detailed info, neutral tone, etc...) 9:10 me Here is a quick video showing how I found some books.... 9:10 me http://screencast.com/t/s8gDSF1fm5C 9:12 6476885001398391168263262 Okay, Thank you so much! 9:12 me In that video I highlighted the call number for the book 9:12 me Call numbers are ordered by subject, so if you can find that book, you should be able to find many other relevant books right next to it 9:12 me Also, for research articles, I think the \"Berg Fashion Library\" databases may be another good place to search 9:13 6476885001398391168263262 Thank you, I appreciate it. 9:14 me Does that give you a good starting point? 9:16 6476885001398391168263262 Yes, Thanks! 9:17 me Great! Please don't hesitate to come back if you have more questions 9:26 6476885001398391168263262 Awesome thank you!\n"
     ]
    }
   ],
   "source": [
    "for i in full.loc[full['Id'] == 4855433,'Transcript']:\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['9:00', 'patron', \"I'm looking for a 19th century article about women fashion in France but cant seem to find anything? Do you have any good links?\"]\n",
      "['9:00', 'staff', 'hello']\n",
      "['9:01', 'staff', 'Hmm...I have a few ideas!']\n",
      "['9:01', 'staff', 'Are you looking for articles that are about 19th centruy french fashion OR articles written in the 19th century about french fashion? (the former will definitely be easier I think)']\n",
      "['9:02', 'patron', 'I am looking for scholarly articles about 19th century French fashon']\n",
      "['9:02', 'patron', 'Mainly women']\n",
      "['9:04', 'staff', 'Ok, let me see what I can find!']\n",
      "['9:04', 'patron', 'Thanks you rock!']\n",
      "['9:04', 'staff', 'I am going to start with our Search It tool. Also, we have some fashion databases as well http://apps.lib.k-state.edu/databases/category/human-ecology/apparel-textiles/']\n",
      "['9:04', 'staff', 'Have you had a chance to try either of those sources?']\n",
      "['9:05', 'patron', 'Yeah I have tried.. I am no sure if im too specific or not specific enough.']\n",
      "['9:06', 'staff', 'Gotcha. Also, do they have to be scholarly articles? Would library books work as well?']\n",
      "['9:06', 'patron', 'Yes, I believe so.']\n",
      "['9:07', 'staff', 'I found one promising book in Search It \"Accessories to modernity : fashion and the feminine in nineteenth-century France\"']\n",
      "['9:07', 'patron', 'My assignment details just say two scholarly sources.']\n",
      "['9:09', 'staff', 'Ok, I certianly think many of the books in the library qualify as \"scholarly\" Obviously some will not (ex. Batman comics), but I think you should be able to identify if a book is a scholarly source (they will have lots of references, detailed info, neutral tone, etc...)']\n",
      "['9:10', 'staff', 'Here is a quick video showing how I found some books....']\n",
      "['9:10', 'staff', 'http://screencast.com/t/s8gDSF1fm5C']\n",
      "['9:12', 'patron', 'Okay, Thank you so much!']\n",
      "['9:12', 'staff', 'In that video I highlighted the call number for the book']\n",
      "['9:12', 'staff', 'Call numbers are ordered by subject, so if you can find that book, you should be able to find many other relevant books right next to it']\n",
      "['9:12', 'staff', 'Also, for research articles, I think the \"Berg Fashion Library\" databases may be another good place to search']\n",
      "['9:13', 'patron', 'Thank you, I appreciate it.']\n",
      "['9:14', 'staff', 'Does that give you a good starting point?']\n",
      "['9:16', 'patron', 'Yes, Thanks!']\n",
      "['9:17', 'staff', \"Great! Please don't hesitate to come back if you have more questions\"]\n",
      "['9:26', 'patron', 'Awesome thank you!']\n"
     ]
    }
   ],
   "source": [
    "for i in full.loc[full['Id'] == 4855433,'segTrans']:\n",
    "    for k in i:\n",
    "        print(k)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I'm looking for a 19th century article about women fashion in France but cant seem to find anything? Do you have any good links? I am looking for scholarly articles about 19th century French fashon Mainly women Thanks you rock! Yeah I have tried.. I am no sure if im too specific or not specific enough. Yes, I believe so. My assignment details just say two scholarly sources. Okay, Thank you so much! Thank you, I appreciate it. Yes, Thanks! Awesome thank you! \n"
     ]
    }
   ],
   "source": [
    "for i in full.loc[full['Id'] == 4855433,'PatronTextString']:\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2          5430\n",
       "1          3355\n",
       "Unknown    2689\n",
       "3          2532\n",
       "4           492\n",
       "5            89\n",
       "6            17\n",
       "Name: READ, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "full['READ'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reference\n",
      "ResearchConsultation\n",
      "Technical\n",
      "Misc\n",
      "Directional\n",
      "Reserves\n",
      "Circulation\n",
      "Building\n",
      "Unknown\n",
      "KREx\n",
      "NewPrairiePress\n",
      "KAPI\n",
      "Copyright\n"
     ]
    }
   ],
   "source": [
    "for i in full['Question Type'].unique():\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(11915, 34)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fullRead = full[full['READ']!='Unknown']\n",
    "fullRead.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2          5430\n",
       "1          3355\n",
       "Unknown    2689\n",
       "3          2532\n",
       "4           492\n",
       "5            89\n",
       "6            17\n",
       "Name: READ, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "full['READ'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.set(font_scale=1.25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,8))\n",
    "ax = sns.countplot(full['READ'],\n",
    "                   order=['1','2','3','4','5','6','Unknown',],\n",
    "                   palette='colorblind')\n",
    "ax.set_title(\"Raw Count of VRS Interactions by READ Rating\")\n",
    "ax.set(xlabel='READ Rating', ylabel='Transcript Counts')\n",
    "ax.spines['bottom'].set_color('0.25')\n",
    "ax.spines['top'].set_color('0.25')\n",
    "ax.spines['right'].set_color('0.25')\n",
    "ax.spines['left'].set_color('0.25')\n",
    "plt.savefig(\"plots/READCountPlot.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,8))\n",
    "ax = sns.countplot(full['Question Type'],\n",
    "                   palette='colorblind')\n",
    "ax.set_title(\"Raw Count of VRS Interactions by Question Type\")\n",
    "ax.set(xlabel='Question Type', ylabel='Transcript Counts')\n",
    "plt.xticks(rotation=-90)\n",
    "ax.spines['bottom'].set_color('0.25')\n",
    "ax.spines['top'].set_color('0.25')\n",
    "ax.spines['right'].set_color('0.25')\n",
    "ax.spines['left'].set_color('0.25')\n",
    "plt.savefig(\"plots/QuestionTypeCountPlot.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Reference               9770\n",
       "Technical               1318\n",
       "Misc                    1246\n",
       "Building                 959\n",
       "Circulation              659\n",
       "Directional              308\n",
       "Reserves                 155\n",
       "ResearchConsultation      80\n",
       "Unknown                   63\n",
       "KREx                      16\n",
       "Copyright                 14\n",
       "NewPrairiePress            9\n",
       "KAPI                       7\n",
       "Name: Question Type, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "full['Question Type'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"7\" halign=\"left\">Id</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>READ</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>Unknown</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Question Type</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Building</th>\n",
       "      <td>507.0</td>\n",
       "      <td>251.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>180.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Circulation</th>\n",
       "      <td>208.0</td>\n",
       "      <td>286.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>98.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Copyright</th>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Directional</th>\n",
       "      <td>151.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>73.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KAPI</th>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KREx</th>\n",
       "      <td>4.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Misc</th>\n",
       "      <td>606.0</td>\n",
       "      <td>419.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>157.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NewPrairiePress</th>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Reference</th>\n",
       "      <td>1399.0</td>\n",
       "      <td>3753.0</td>\n",
       "      <td>2223.0</td>\n",
       "      <td>441.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1860.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ResearchConsultation</th>\n",
       "      <td>8.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Reserves</th>\n",
       "      <td>36.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Technical</th>\n",
       "      <td>413.0</td>\n",
       "      <td>518.0</td>\n",
       "      <td>122.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>232.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Unknown</th>\n",
       "      <td>20.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Id                                           \n",
       "READ                       1       2       3      4     5     6 Unknown\n",
       "Question Type                                                          \n",
       "Building               507.0   251.0    19.0    2.0   0.0   0.0   180.0\n",
       "Circulation            208.0   286.0    60.0    6.0   1.0   0.0    98.0\n",
       "Copyright                0.0     8.0     1.0    1.0   0.0   0.0     4.0\n",
       "Directional            151.0    74.0    10.0    0.0   0.0   0.0    73.0\n",
       "KAPI                     1.0     3.0     2.0    0.0   0.0   0.0     1.0\n",
       "KREx                     4.0     8.0     2.0    0.0   0.0   0.0     2.0\n",
       "Misc                   606.0   419.0    54.0    8.0   1.0   1.0   157.0\n",
       "NewPrairiePress          2.0     4.0     1.0    1.0   0.0   0.0     1.0\n",
       "Reference             1399.0  3753.0  2223.0  441.0  79.0  15.0  1860.0\n",
       "ResearchConsultation     8.0    18.0    21.0    5.0   2.0   1.0    25.0\n",
       "Reserves                36.0    69.0    14.0    1.0   0.0   0.0    35.0\n",
       "Technical              413.0   518.0   122.0   27.0   6.0   0.0   232.0\n",
       "Unknown                 20.0    19.0     3.0    0.0   0.0   0.0    21.0"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "full.loc[:,['Id','READ','Question Type']].groupby(by=['READ','Question Type']).count().unstack(level=-2).fillna(value=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in full['Question Type'].unique():\n",
    "    plt.figure(figsize=(8,6))\n",
    "    ax = sns.countplot(full.loc[full['Question Type']==i,'READ'],\n",
    "                       order=['1','2','3','4','5','6','Unknown',],\n",
    "                       palette='deep')\n",
    "    ax.set_title(\"\\\"{}\\\" Questions\".format(i))\n",
    "    ax.set(xlabel='READ Difficulty Scale', ylabel='Transcript Counts')\n",
    "    ax.spines['bottom'].set_color('0.25')\n",
    "    ax.spines['top'].set_color('0.25')\n",
    "    ax.spines['right'].set_color('0.25')\n",
    "    ax.spines['left'].set_color('0.25')\n",
    "    plt.savefig(\"plots/READcountplot_{}.png\".format(i))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "corpus = full.loc[:,'PatronTextString']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "patronStringLength = []\n",
    "\n",
    "for i in corpus.str.split():\n",
    "    patronStringLength.append(len(i))\n",
    "    \n",
    "patronStringLength = pd.Series(patronStringLength,index=full.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    14604.000000\n",
       "mean        72.695631\n",
       "std         65.841815\n",
       "min          1.000000\n",
       "25%         32.000000\n",
       "50%         54.000000\n",
       "75%         91.000000\n",
       "max       1061.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "patronStringLength.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,6))\n",
    "ax = sns.distplot(patronStringLength)\n",
    "ax.set_title(\"Distribution of PatronText Length\")\n",
    "ax.set(xlabel='Number of Space-Delimited Tokens', ylabel='Distribution Density')\n",
    "ax.spines['bottom'].set_color('0.25')\n",
    "ax.spines['top'].set_color('0.25')\n",
    "ax.spines['right'].set_color('0.25')\n",
    "ax.spines['left'].set_color('0.25')\n",
    "plt.savefig(\"plots/PatronTextLength.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Short Transcript Example #1\n",
      "['1:35', 'patron']\n",
      "['1:35', 'staff', 'Hi']\n",
      "['1:35', 'patron', 'Hey']\n",
      "['1:35', 'staff', \"I'd be happy to help you with this. One moment.\"]\n",
      "['1:38', 'staff', \"I was also only able to find previews, one at Amazon and one at Google books. We don't have it here at the library, so your best bet will be to interlibrary loan it.\"]\n",
      "['1:38', 'staff', 'How soon do you need it?']\n",
      "['1:39', 'patron', 'Within 2 weeks']\n",
      "['1:39', 'staff', \"That shouldn't be a problem. Have you used interlibrary loan before?\"]\n",
      "\n",
      "Short Transcript Example #2\n",
      "['7:52', 'patron']\n",
      "['7:52', 'staff', 'Hello']\n",
      "['7:53', 'staff', \"Generally speaking, we can't do that unless you designate them as a proxy.\"]\n",
      "['7:54', 'staff', 'They could check out something under their account and lend it to you, but they would be responsible for the book.']\n",
      "['7:54', 'patron', 'okay thank you!']\n",
      "['7:54', 'staff', 'No problem!']\n",
      "['7:54', 'patron', '']\n",
      "\n",
      "Short Transcript Example #3\n",
      "['12:22', 'patron', 'P96.742.T89 2010']\n",
      "['12:22', 'staff', 'Hi!']\n",
      "['12:22', 'staff', 'Are you wanted to know where this is located?']\n",
      "\n",
      "Short Transcript Example #4\n",
      "['2:10', 'patron', 'Hi']\n",
      "['2:10', 'staff', 'hi']\n",
      "['2:10', 'patron', 'Hi']\n",
      "['2:12', 'patron', '']\n",
      "['2:15', 'patron', '']\n",
      "\n",
      "Short Transcript Example #5\n",
      "['6:22', 'patron', 'do you have condoms?']\n",
      "['6:22', 'staff', \"Hi! We sure don't.\"]\n",
      "['6:23', 'patron', '']\n",
      "\n",
      "Short Transcript Example #6\n",
      "['8:05', 'patron', '.']\n",
      "['8:05', 'staff', 'Hi.']\n",
      "['8:05', 'staff', 'How can I help you?']\n",
      "['8:05', 'patron', 'who is this']\n",
      "['8:05', 'staff', 'This is Heather. May I help you with something?']\n",
      "['8:08', 'staff', 'On Chat with a Librarian, you can get help with library or research questions. Can I help you with anything?']\n",
      "\n",
      "Short Transcript Example #7\n",
      "['1:19', 'patron']\n",
      "['1:19', 'staff', 'Hi, there. How can we help you?']\n",
      "['1:19', 'patron', 'thank you']\n",
      "['1:20', 'staff', 'You are welcome, cheers']\n",
      "['1:20', 'patron', '']\n",
      "\n",
      "Short Transcript Example #8\n",
      "['2:13', 'patron', 'eyyyyyyyy']\n",
      "['2:13', 'staff', 'hello']\n",
      "['2:14', 'patron', 'thank ya kindly']\n",
      "['2:15', 'staff', \"you're welcome?\"]\n",
      "\n",
      "Short Transcript Example #9\n",
      "['2:53', 'patron', 'Gracias']\n",
      "['2:53', 'staff', 'hello']\n",
      "['2:54', 'staff', 'do you have a question?']\n",
      "['2:59', 'patron', '']\n",
      "\n",
      "Short Transcript Example #10\n",
      "['1:18', 'patron', 'Hello']\n",
      "['1:18', 'staff', 'Hi, how can we help?']\n",
      "['1:19', 'patron']\n",
      "['1:19', 'patron', ':)good bye']\n",
      "['1:20', 'patron', '']\n",
      "\n",
      "Short Transcript Example #11\n",
      "['6:56', 'patron', '']\n",
      "['6:56', 'patron', '']\n",
      "['6:57', 'patron', '']\n",
      "['6:57', 'staff', \"Hi, we'll be right with you...\"]\n",
      "['6:57', 'staff', 'Hello, let me look for you']\n",
      "['6:57', 'staff', 'I will walk you through it']\n",
      "['6:57', 'patron', 'Thanks']\n",
      "['7:00', 'staff', 'Ok, I went to the libraries home page, and then in the box where it says \"Search It\" click on the purple colored one that says \"Search It\" at the bottom. From there it will pull up our search it catalog and then you will want to click on advanced search']\n",
      "['7:00', 'staff', 'from there you can type in key words or phrases such as \"self esteem\" in the first box, and then social media in the second box.']\n",
      "['7:02', 'staff', 'There are the drop down boxes that say \"Any\" you can chose which one you want them to be in. I chose \"in subject\\' that way it is narrowed down to what you are wanting.']\n",
      "['7:02', 'staff', 'Is this clear, or do you need me to go back a few steps?']\n",
      "\n",
      "Short Transcript Example #12\n",
      "['11:35', 'patron', 'i need help']\n",
      "['11:35', 'staff', 'hello']\n",
      "['11:36', 'patron', '']\n",
      "\n",
      "Short Transcript Example #13\n",
      "[':30', 'patron', 'Hello,']\n",
      "['3:30', 'staff', 'Hi']\n",
      "['3:30', 'staff', 'What can we do for you? 3:30 +17853201932 Hi! I’d like to reserve a projector. Do I call 785.532.4918 to do 3:30 +17853201932 that?']\n",
      "['3:30', 'staff', \"That would be through the iTAC equipment checkout - I'm not sure if that's the correct number, give me just a moment\"]\n",
      "['3:31', 'staff', \"Yes, that's the number to call 3:31 +17853201932 Thanks so much! Have a great day! Go cats!\"]\n",
      "['3:32', 'staff', 'Thanks, you too!']\n",
      "\n"
     ]
    }
   ],
   "source": [
    "counter = 1\n",
    "\n",
    "for i in patronStringLength[patronStringLength<5].index:\n",
    "    print(\"Short Transcript Example #{}\".format(counter))\n",
    "#     print()\n",
    "    counter+=1\n",
    "    for k in full.loc[i,'segTrans']:\n",
    "        print(k)\n",
    "    print()\n",
    "#     print()\n",
    "#     print(full.loc[i,'Transcript'])\n",
    "#     print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "truncatedCorpus = []\n",
    "for i in corpus.str.split(pat=\"\\s\"):\n",
    "    truncatedCorpus.append(\" \".join(i[:20]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        Is \"An Unfinished Life by Robert Diek\" availab...\n",
       "1        Hi I'm confused with Search It I keep getting ...\n",
       "100      I am in the library and consistently having pr...\n",
       "1000     I want to find some chinese database, do we ha...\n",
       "10000    I'm interested in a book that says: In transit...\n",
       "dtype: object"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "truncatedCorpus = pd.Series(truncatedCorpus, index=full.index)\n",
    "truncatedCorpus.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "i          18756\n",
      "the         9960\n",
      "a           9257\n",
      "to          8504\n",
      "for         5332\n",
      "hi          5075\n",
      "you         5022\n",
      "and         4410\n",
      "is          4314\n",
      "am          4164\n",
      "hello       3838\n",
      "in          3747\n",
      "have        3599\n",
      "of          3576\n",
      "it          3313\n",
      "on          3026\n",
      "can         2996\n",
      "do          2595\n",
      "that        2377\n",
      "book        2302\n",
      "m           2255\n",
      "if          2234\n",
      "find        2223\n",
      "library     2179\n",
      "how         2113\n",
      "there       2099\n",
      "my          2087\n",
      "was         1854\n",
      "looking     1838\n",
      "an          1825\n",
      "dtype: int64\n",
      "\n",
      "count    15347.000000\n",
      "mean        19.222780\n",
      "std        249.354261\n",
      "min          1.000000\n",
      "25%          1.000000\n",
      "50%          1.000000\n",
      "75%          3.000000\n",
      "max      18756.000000\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "counter = CountVectorizer(    \n",
    "            token_pattern=r\"(?u)\\b\\w{1,}\\b\",\n",
    ")\n",
    "\n",
    "tfidfer = TfidfTransformer()\n",
    "\n",
    "cdf = counter.fit_transform(truncatedCorpus)\n",
    "cdf = pd.DataFrame(cdf.todense(), index=full.index, columns=counter.get_feature_names())\n",
    "# tdf = tfidfer.fit_transform(cdf)\n",
    "# tdf = pd.DataFrame(tdf.todense(), index=full.index, columns=counter.get_feature_names())\n",
    "\n",
    "countList = cdf.sum().sort_values(ascending=False)\n",
    "print(countList.head(30))\n",
    "print()\n",
    "print(countList.describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the          9960\n",
      "for          5332\n",
      "you          5022\n",
      "and          4410\n",
      "hello        3838\n",
      "have         3599\n",
      "can          2996\n",
      "that         2377\n",
      "book         2302\n",
      "find         2223\n",
      "library      2179\n",
      "how          2113\n",
      "there        2099\n",
      "was          1854\n",
      "looking      1838\n",
      "this         1798\n",
      "help         1762\n",
      "article      1651\n",
      "thank        1516\n",
      "but          1483\n",
      "need         1480\n",
      "trying       1394\n",
      "about        1326\n",
      "are          1238\n",
      "from         1210\n",
      "wondering    1185\n",
      "thanks       1176\n",
      "could        1094\n",
      "with         1074\n",
      "research     1069\n",
      "dtype: int64\n",
      "\n",
      "count    14898.000000\n",
      "mean        13.643375\n",
      "std        142.644386\n",
      "min          1.000000\n",
      "25%          1.000000\n",
      "50%          1.000000\n",
      "75%          3.000000\n",
      "max       9960.000000\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "counter = CountVectorizer(    \n",
    "            token_pattern=r\"(?u)\\b\\w{3,}\\b\",\n",
    "#     ngram_range=(1,1),\n",
    "#     min_df=int(np.floor(len(corpus.index)/1000)),\n",
    ")\n",
    "\n",
    "tfidfer = TfidfTransformer()\n",
    "\n",
    "cdf = counter.fit_transform(truncatedCorpus)\n",
    "cdf = pd.DataFrame(cdf.todense(), index=full.index, columns=counter.get_feature_names())\n",
    "# tdf = tfidfer.fit_transform(cdf)\n",
    "# tdf = pd.DataFrame(tdf.todense(), index=full.index, columns=counter.get_feature_names())\n",
    "\n",
    "countList = cdf.sum().sort_values(ascending=False)\n",
    "print(countList.head(30))\n",
    "print()\n",
    "print(countList.describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "for          5332\n",
      "you          5022\n",
      "and          4410\n",
      "hello        3838\n",
      "have         3599\n",
      "can          2996\n",
      "that         2377\n",
      "book         2302\n",
      "find         2223\n",
      "library      2179\n",
      "how          2113\n",
      "there        2099\n",
      "was          1854\n",
      "looking      1838\n",
      "this         1798\n",
      "help         1762\n",
      "article      1651\n",
      "thank        1516\n",
      "but          1483\n",
      "need         1480\n",
      "trying       1394\n",
      "about        1326\n",
      "are          1238\n",
      "from         1210\n",
      "wondering    1185\n",
      "thanks       1176\n",
      "could        1094\n",
      "with         1074\n",
      "research     1069\n",
      "not          1054\n",
      "dtype: int64\n",
      "\n",
      "count    6208.000000\n",
      "mean       29.698615\n",
      "std       180.042633\n",
      "min         2.000000\n",
      "25%         2.000000\n",
      "50%         4.000000\n",
      "75%        11.000000\n",
      "max      5332.000000\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "counter = CountVectorizer(\n",
    "    token_pattern=r\"(?u)\\b\\w{3,}\\b\",\n",
    "    ngram_range=(1,1),\n",
    "    max_df=0.5,\n",
    "    min_df=2\n",
    ")\n",
    "\n",
    "tfidfer = TfidfTransformer()\n",
    "\n",
    "cdf = counter.fit_transform(truncatedCorpus)\n",
    "cdf = pd.DataFrame(cdf.todense(), index=full.index, columns=counter.get_feature_names())\n",
    "# tdf = tfidfer.fit_transform(cdf)\n",
    "# tdf = pd.DataFrame(tdf.todense(), index=full.index, columns=counter.get_feature_names())\n",
    "\n",
    "countList = cdf.sum().sort_values(ascending=False)\n",
    "print(countList.head(30))\n",
    "print()\n",
    "print(countList.describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "hello           1.000000\n",
      "thanks          1.000000\n",
      "gracias         1.000000\n",
      "condoms         0.943144\n",
      "kindly          0.929958\n",
      "rft             0.919056\n",
      "abortion        0.917412\n",
      "dad             0.916097\n",
      "awakening       0.895737\n",
      "mintel          0.881443\n",
      "artstor         0.880185\n",
      "restroom        0.877197\n",
      "scholarships    0.876986\n",
      "lib             0.873610\n",
      "jason           0.867091\n",
      "tax             0.866714\n",
      "atm             0.863716\n",
      "arc             0.862351\n",
      "bye             0.861964\n",
      "well            0.855044\n",
      "tarac           0.852934\n",
      "gas             0.851586\n",
      "highlighters    0.851204\n",
      "psycinfo        0.848539\n",
      "who             0.844665\n",
      "mam             0.844004\n",
      "dsm             0.842721\n",
      "floors          0.841936\n",
      "narrow          0.834032\n",
      "thank           0.833659\n",
      "dtype: float64\n",
      "\n",
      "count    6208.000000\n",
      "mean        0.502618\n",
      "std         0.108226\n",
      "min         0.029799\n",
      "25%         0.450606\n",
      "50%         0.492034\n",
      "75%         0.550894\n",
      "max         1.000000\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "counter = CountVectorizer(\n",
    "    token_pattern=r\"(?u)\\b\\w{3,}\\b\",\n",
    "    ngram_range=(1,1),\n",
    "    max_df=0.5,\n",
    "    min_df=2\n",
    ")\n",
    "\n",
    "tfidfer = TfidfTransformer()\n",
    "\n",
    "cdf = counter.fit_transform(truncatedCorpus)\n",
    "cdf = pd.DataFrame(cdf.todense(), index=full.index, columns=counter.get_feature_names())\n",
    "tdf = tfidfer.fit_transform(cdf)\n",
    "tdf = pd.DataFrame(tdf.todense(), index=full.index, columns=counter.get_feature_names())\n",
    "\n",
    "countList = tdf.max().sort_values(ascending=False)\n",
    "print(countList.head(30))\n",
    "print()\n",
    "print(countList.describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "i am                3919\n",
      "hi i                2581\n",
      "hello i             2302\n",
      "i m                 2168\n",
      "i have              1630\n",
      "looking for         1608\n",
      "i was               1447\n",
      "trying to           1361\n",
      "thank you           1346\n",
      "i need              1247\n",
      "the library         1234\n",
      "have a              1213\n",
      "for a               1140\n",
      "to find             1123\n",
      "in the              1079\n",
      "a book              1045\n",
      "do you              1018\n",
      "am looking           922\n",
      "was wondering        920\n",
      "i am looking         902\n",
      "wondering if         868\n",
      "and i                858\n",
      "i was wondering      856\n",
      "can i                853\n",
      "am looking for       842\n",
      "i am looking for     826\n",
      "hello i am           821\n",
      "i can                794\n",
      "i have a             779\n",
      "on the               777\n",
      "dtype: int64\n",
      "\n",
      "count    59688.000000\n",
      "mean         7.256115\n",
      "std         39.562901\n",
      "min          2.000000\n",
      "25%          2.000000\n",
      "50%          2.000000\n",
      "75%          4.000000\n",
      "max       3919.000000\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "counter = CountVectorizer(\n",
    "    token_pattern=r\"(?u)\\b\\w{1,}\\b\",\n",
    "    ngram_range=(2,4),\n",
    "#     max_df=0.5,\n",
    "    min_df=2\n",
    ")\n",
    "\n",
    "tfidfer = TfidfTransformer()\n",
    "\n",
    "cdf = counter.fit_transform(truncatedCorpus)\n",
    "cdf = pd.DataFrame(cdf.todense(), index=full.index, columns=counter.get_feature_names())\n",
    "# tdf = tfidfer.fit_transform(cdf)\n",
    "# tdf = pd.DataFrame(tdf.todense(), index=full.index, columns=counter.get_feature_names())\n",
    "\n",
    "countList = cdf.sum().sort_values(ascending=False)\n",
    "print(countList.head(30))\n",
    "print()\n",
    "print(countList.describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "i was wondering if you               302\n",
      "i am trying to find                  264\n",
      "i am looking for a                   253\n",
      "hello i am looking for               207\n",
      "hi i was wondering if                201\n",
      "hi i am looking for                  194\n",
      "i have a question about              182\n",
      "hello i was wondering if             176\n",
      "was wondering if you could           172\n",
      "hello i am trying to                 170\n",
      "i was wondering if you could         160\n",
      "hi i am trying to                    155\n",
      "hello i have a question              149\n",
      "hi i have a question                 141\n",
      "i m looking for a                    133\n",
      "i m trying to find                   132\n",
      "hi i m trying to                     125\n",
      "hi i m looking for                   114\n",
      "hi i was wondering if you            107\n",
      "i am looking for the                 104\n",
      "if you could help me                 103\n",
      "wondering if you could help          100\n",
      "was wondering if you could help       94\n",
      "i have a quick question               89\n",
      "i am looking for this                 88\n",
      "i am looking for a book               88\n",
      "i was wondering if you could help     88\n",
      "wondering if you could help me        88\n",
      "am looking for a book                 88\n",
      "is there a way to                     87\n",
      "dtype: int64\n",
      "\n",
      "count    32167.000000\n",
      "mean         3.130942\n",
      "std          5.923549\n",
      "min          2.000000\n",
      "25%          2.000000\n",
      "50%          2.000000\n",
      "75%          3.000000\n",
      "max        302.000000\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "counter = CountVectorizer(\n",
    "    token_pattern=r\"(?u)\\b\\w{1,}\\b\",\n",
    "    ngram_range=(5,10),\n",
    "#     max_df=0.5,\n",
    "    min_df=2\n",
    ")\n",
    "\n",
    "tfidfer = TfidfTransformer()\n",
    "\n",
    "cdf = counter.fit_transform(truncatedCorpus)\n",
    "cdf = pd.DataFrame(cdf.todense(), index=full.index, columns=counter.get_feature_names())\n",
    "# tdf = tfidfer.fit_transform(cdf)\n",
    "# tdf = pd.DataFrame(tdf.todense(), index=full.index, columns=counter.get_feature_names())\n",
    "\n",
    "countList = cdf.sum().sort_values(ascending=False)\n",
    "print(countList.head(30))\n",
    "print()\n",
    "print(countList.describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "urls = []\n",
    "for i in corpus:\n",
    "    for k in re.findall(string=i, pattern=\"//\\S{10,600}/\"):\n",
    "        urls.append(k)\n",
    "urls = pd.Series(urls)\n",
    "\n",
    "truncURL = []\n",
    "\n",
    "for i in urls:\n",
    "    truncURL.append(i[2:40])\n",
    "    \n",
    "truncURL = pd.Series(truncURL,index=urls.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "chat.downloads.s3-us-west-1.amazonaws.    154\n",
       "chat.downloads.s3.amazonaws.com/          107\n",
       "searchit.lib.ksu.edu/primo_library/lib     62\n",
       "search.proquest.com.er.lib.k-state.edu     45\n",
       "www.lib.k-state.edu/                       36\n",
       "guides.lib.k-state.edu/                    33\n",
       "www.sciencedirect.com/science/article/     31\n",
       "catalog.lib.ksu.edu/vwebv/                 24\n",
       "ksu-primo.hosted.exlibrisgroup.com:170     24\n",
       "k-state-primo.hosted.exlibrisgroup.com     23\n",
       "www.ncbi.nlm.nih.gov/pubmed/               17\n",
       "apps.lib.k-state.edu/databases/categor     14\n",
       "books.google.com/                          14\n",
       "www.sciencedirect.com.er.lib.k-state.e     11\n",
       "login.er.lib.k-state.edu/                  11\n",
       "www.google.com/                            11\n",
       "apps.lib.k-state.edu/databases/            11\n",
       "catalog2.lib.ksu.edu/vwebv/                10\n",
       "searchit.lib.ksu.edu/                      10\n",
       "onlinelibrary.wiley.com.er.lib.k-state      9\n",
       "dtype: int64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "truncURL.value_counts().head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LD2668 .D5      4\n",
       "HM22.F8         4\n",
       "LD2668 .R4      3\n",
       "ML3531 .G46     3\n",
       "HF5439.C6       2\n",
       "LB2341 .K63     2\n",
       "LC221.J33       2\n",
       "QL673 .H25      2\n",
       "T55 .A5         2\n",
       "QL463 .I65      2\n",
       "E184.P7         2\n",
       "F319.O7         2\n",
       "ML410.D99       2\n",
       "GV1796.H8       2\n",
       "HD9397.U54      2\n",
       "HN90.S6         2\n",
       "QL458 .I575     2\n",
       "BR517.H37       2\n",
       "PS3554.E4425    2\n",
       "LD2668.D5       2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calls = []\n",
    "for i in corpus:\n",
    "    for k in re.findall(string=i, pattern=\"[a-z]{1,2}\\d{2,4}\\s{0,1}\\.[a-z]\\d{1,}\", flags=re.IGNORECASE):\n",
    "        calls.append(k)\n",
    "calls = pd.Series(calls)\n",
    "\n",
    "calls.value_counts().head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LD2668 .D5      4\n",
       "HM22.F8         4\n",
       "LD2668 .R4      3\n",
       "ML3531 .G46     3\n",
       "HF5439.C6       2\n",
       "LB2341 .K63     2\n",
       "LC221.J33       2\n",
       "QL673 .H25      2\n",
       "T55 .A5         2\n",
       "QL463 .I65      2\n",
       "E184.P7         2\n",
       "F319.O7         2\n",
       "ML410.D99       2\n",
       "GV1796.H8       2\n",
       "HD9397.U54      2\n",
       "HN90.S6         2\n",
       "QL458 .I575     2\n",
       "BR517.H37       2\n",
       "PS3554.E4425    2\n",
       "LD2668.D5       2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calls.value_counts().head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "n =[]\n",
    "for i in truncatedCorpus.index:\n",
    "    if re.search(string=truncatedCorpus[i],pattern=\"//\\S{10,600}/\"):\n",
    "        n.append(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[10023,\n",
       " 10181,\n",
       " 10206,\n",
       " 10248,\n",
       " 10264,\n",
       " 10367,\n",
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       " 104,\n",
       " 10408,\n",
       " 10417,\n",
       " 10567,\n",
       " 10588,\n",
       " 10617,\n",
       " 10848,\n",
       " 10992,\n",
       " 11005,\n",
       " 11007,\n",
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       " 1247,\n",
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       " 12616,\n",
       " 12633,\n",
       " 12643,\n",
       " 12698,\n",
       " 12755,\n",
       " 12927,\n",
       " 12999,\n",
       " 13050,\n",
       " 13117,\n",
       " 13336,\n",
       " 13343,\n",
       " 13348,\n",
       " 13423,\n",
       " 13529,\n",
       " 13567,\n",
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       " 1804,\n",
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       " 1819,\n",
       " 182,\n",
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       " 1880,\n",
       " 1887,\n",
       " 1939,\n",
       " 2171,\n",
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       " 2953,\n",
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       " 3011,\n",
       " 3040,\n",
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       " 3079,\n",
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       " 3203,\n",
       " 3242,\n",
       " 3403,\n",
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       " 3679,\n",
       " 3682,\n",
       " 3693,\n",
       " 3703,\n",
       " 3704,\n",
       " 3891,\n",
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       " 4012,\n",
       " 4064,\n",
       " 409,\n",
       " 4096,\n",
       " 4131,\n",
       " 4265,\n",
       " 4293,\n",
       " 4407,\n",
       " 4422,\n",
       " 4437,\n",
       " 4495,\n",
       " 4500,\n",
       " 4509,\n",
       " 4511,\n",
       " 4555,\n",
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       " 4573,\n",
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       " 8811,\n",
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       " 8839,\n",
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       " 8887,\n",
       " 8925,\n",
       " 9063,\n",
       " 9073,\n",
       " 9129,\n",
       " 9156,\n",
       " 9196,\n",
       " 9222,\n",
       " 9225,\n",
       " 9257,\n",
       " 9300,\n",
       " 9435,\n",
       " 9554,\n",
       " 9571,\n",
       " 9612,\n",
       " 9631,\n",
       " 964,\n",
       " 9778,\n",
       " 9826,\n",
       " 9860,\n",
       " 988,\n",
       " 998,\n",
       " 9980,\n",
       " 9986]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "urlCorpus = truncatedCorpus.loc[n].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10023    Hello I'm looking for a book download from htt...\n",
       "10181    hello I'm looking for this http://dl.acm.org/c...\n",
       "10206    Good Morning I'm looking fr a book but cant fi...\n",
       "10248    Hello. I have a question about what type of re...\n",
       "10264    I'm seeking this book through interlibrary loa...\n",
       "10367    hi Im trying to get this article http://getit....\n",
       "10377    I'm trying to look at an article on a datebase...\n",
       "104      hi. I'm working on a reference and cannot find...\n",
       "10408    Hello! I'm wanting to check out the Godfather ...\n",
       "10417    Hi I'm wondering how can I complete ISIS gradu...\n",
       "10567    Is ESG research data available through K-State...\n",
       "10588    is http://well.blogs.nytimes.com/ a reputable ...\n",
       "10617    Hi, Is it possible to check out the following ...\n",
       "10848    hey is there a way i can access this IEEE Xplo...\n",
       "10992    Is this a scholarly source? I'm not quite sure...\n",
       "11005    https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3...\n",
       "11007    Is this full text not available? https://searc...\n",
       "11293    This book is indicated as being available onli...\n",
       "11327    You have W-chat？ Yes My no. is 8618718561065 h...\n",
       "11387    i http://www.sciencedirect.com/science/article...\n",
       "11392    would it be possible to access this document?h...\n",
       "11393    a friend just sent me a link to the power poin...\n",
       "11494    Hi! Could you please help me find this article...\n",
       "11526    hi https://search-proquest-com.er.lib.k-state....\n",
       "11602    Hi, good morning im looking for this book http...\n",
       "11643    hi http://searchit.lib.ksu.edu/primo_library/l...\n",
       "11699    ImFine Homebuilding 236 pp. 40-46 May 16, 2013...\n",
       "11708    Good morning! Any chance you can help me find ...\n",
       "11721    http://www.sciencedirect.com.er.lib.k-state.ed...\n",
       "11722    Hello, good morning I am looking for this book...\n",
       "                               ...                        \n",
       "8730     Hello I need help with APA style for a citatio...\n",
       "8801     Hello I need this artical Design of Human-Cent...\n",
       "8811     I need this book, but can't find it if I searc...\n",
       "8813     hi Isn't the following book available in hale ...\n",
       "8839     Hi, please help me I need to cite the document...\n",
       "8847     hi i need to download this paper http://link.s...\n",
       "8887     Hi download from http://chat.downloads.s3.amaz...\n",
       "8925     hi I need to get this article http://www.ncbi....\n",
       "9063     Hi there, I need help in citing this book. My ...\n",
       "9073     I think we have a subscription to Cabell's (ht...\n",
       "9129     hello I want to download one thesis Developmen...\n",
       "9156     hi! http://apps.webofknowledge.com/full_record...\n",
       "9196     Hi! I wanted to inquire if K-State's library h...\n",
       "9222     Hi I was accessing a link in lib.ksu.edu https...\n",
       "9225     hi sorry I lost the connection I was asking ab...\n",
       "9257     Good morning I was hoping you could help me fi...\n",
       "9300     Hello I was needing help finding some census i...\n",
       "9435     Hi, I was wondering if there is any possible w...\n",
       "9554     Hi. I was wondering what years you have of Var...\n",
       "9571     Hello! I was wondering, how much is the pay ra...\n",
       "9612     hello I would like to check out this book http...\n",
       "9631     I would like to know how to cite the following...\n",
       "964      Hi, I am looking for this article. Could you h...\n",
       "9778     Quick Question! If I searched something and it...\n",
       "9826     I want this, how do I get it? did you get the ...\n",
       "9860     hello I had a question about getting studies f...\n",
       "988      Hi. On K-State Get It, there is a bad link or ...\n",
       "998      Is this only available through interlibrary lo...\n",
       "9980     im having trouble viewing an online article ht...\n",
       "9986     hi, good morning i'm here at library right now...\n",
       "Length: 302, dtype: object"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "urlCorpus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "# def whiteSpaceTokens(s):\n",
    "#     return s.split(\"\\s\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "i          18756\n",
      "the         9960\n",
      "a           9257\n",
      "to          8504\n",
      "for         5332\n",
      "hi          5075\n",
      "you         5022\n",
      "and         4410\n",
      "is          4314\n",
      "am          4164\n",
      "hello       3838\n",
      "in          3747\n",
      "have        3599\n",
      "of          3576\n",
      "it          3313\n",
      "on          3026\n",
      "can         2996\n",
      "do          2595\n",
      "that        2377\n",
      "book        2302\n",
      "m           2255\n",
      "if          2234\n",
      "find        2223\n",
      "library     2179\n",
      "how         2113\n",
      "there       2099\n",
      "my          2087\n",
      "was         1854\n",
      "looking     1838\n",
      "an          1825\n",
      "dtype: int64\n",
      "\n",
      "count    15347.000000\n",
      "mean        19.222780\n",
      "std        249.354261\n",
      "min          1.000000\n",
      "25%          1.000000\n",
      "50%          1.000000\n",
      "75%          3.000000\n",
      "max      18756.000000\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "counter = CountVectorizer(\n",
    "    token_pattern=r\"(?u)\\b\\w{1,}\\b\",\n",
    "#     ngram_range=(5,10),\n",
    "#     max_df=0.5,\n",
    "#     min_df=2\n",
    ")\n",
    "\n",
    "tfidfer = TfidfTransformer()\n",
    "\n",
    "cdf = counter.fit_transform(truncatedCorpus)\n",
    "cdf = pd.DataFrame(cdf.todense(), index=truncatedCorpus.index, columns=counter.get_feature_names())\n",
    "# tdf = tfidfer.fit_transform(cdf)\n",
    "# tdf = pd.DataFrame(tdf.todense(), index=full.index, columns=counter.get_feature_names())\n",
    "\n",
    "countList = cdf.sum().sort_values(ascending=False)\n",
    "print(countList.head(30))\n",
    "print()\n",
    "print(countList.describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "37\n",
      "udent. She has borrowed a book from the vet med library and wondered if she can return \n",
      "is one too i did long back how about in vet med library Oh..okay Is it possible to get \n",
      "Can anyone check out books from the vet med library? Hello Perfect! Thanks! Thanks \n",
      " can regular students borrow books from vet med? How long do they have with it? thanks \n",
      "cs for veterinary and animal science in Vet Medical Library Ok Thanks very much \n",
      " for me to lend that book directly from vet med library perfect. yes thats what I wante\n",
      "e at Hale lib, but i want it to ship to vet med lib whether can i do that. yes http://s\n",
      "nd I would like to reserve a room for a vet med study session, either 301 or 401? for t\n",
      "e can I find the KSU and KSU College of Vet Med logs for a poster presentation? I could\n",
      "Perhaps I am the only one interested in vet med occ safety and ag safety? thanks for yo\n",
      "Hi, this is Gayle from the vet med library. Who is the person at Hale who \n",
      " not have that but I do have a staff ID vet med thanks \n",
      "there is a book I'm needing over at the Vet Med library. Are titles there accessible to\n",
      " non-vet med faculty? no problem! How Learning Works\n",
      "oks like there are copies at Salina and Vet Med That's ok... I'm fine hiking over to Ve\n",
      "estrictions on who could check out from Vet Med. great! Thanks for your help! \n",
      "ct adulteration of foods and drugs  The Vet Med Library It takes me to the log in page,\n",
      "an Journal of Pathology is available in vet med library Thats great Thanks much that is\n",
      "Hello! I work at the Vet Med library and need to speak with someone \n",
      "Hi! I was wondering if the vet med library in Trotter Hall had a printer? \n",
      "Hi, I checked out a book from the Vet Med library. Can I return it to Hale? Okay,\n",
      "nd I would like to check them out (2 at vet med) and get 1 of them sent to vetmed Yes! \n",
      " hold for both volumes and selected the Vet Med library as the library of choice Ok, so\n",
      "w and just joint KSU Okay, I will go to Vet Med lib to get this book Thank you \n",
      "edirected to ksu libraries, not sure if vet med has the 54 volume or if I need to reque\n",
      "ployee at K State. I work facilities at Vet Med. Am I allowed to use the library? Check\n",
      "y that there are more? Can I access the vet med library? Okay, we will head over there.\n",
      "great ok do you think it's available in vet med library ok will do thanks that would be\n",
      "look? Oh ok, looks like some are at the vet med library actually! Thank you very much  \n",
      "osis in weaned pigs I need to go to the Vet med library ? yes , I am a vet med student \n",
      " edition book? I am staff at College of Vet Medicine. I need to read this book. OK. Gre\n",
      " I'd like to return, but it is from the Vet Med library. I just called out there and no\n",
      "en? Do I need to return the book to the Vet Med library? Ok, I'll do that then. Accordi\n",
      "Hi. I requested a book be sent to the Vet Med Lib. They sent me an email saying they \n",
      "nk... yes I'm using the KSU internet in Vet Med Yes, I've tried several times, and I'm \n",
      "iginally from Hale but was then sent to vet med library for pick up. I believe I need t\n",
      "go. ok good lol I picked it up from the vet med building if that makes a difference The\n"
     ]
    }
   ],
   "source": [
    "target = \".{,40}vet med.{,40}\"\n",
    "\n",
    "keyword = []\n",
    "for i in corpus:\n",
    "    for k in re.findall(string=i, pattern=target, flags=re.IGNORECASE):\n",
    "        keyword.append(k)\n",
    "keyword = pd.Series(keyword)\n",
    "\n",
    "print(len(keyword))\n",
    "for i in keyword[:50]:\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2383\n",
      "ed in accessing the full-text articles of the Journal of green buil\n",
      "helo  im look for articles published in the Journal of \n",
      "i'm i need to look at journal articles i cant\n",
      "'m having a hard time finding articles that I want! I'm researching\n",
      " I'm looking at the following article Journal of rheology volume54 \n",
      "u I'm looking for 3 different articles, but it seems like I\n",
      "'m looking for a 19th century article about women fashion in France\n",
      "paper. I am trying to find an article that talks about Olympic Stad\n",
      "I finally found and article about grad school that I coul\n",
      "hi im looking for a journal article Climate change and coastal aq\n",
      "I'm looking for a journal article but haven't found it in any o\n",
      "ers. I have a project to find articles about nonnutritive sucking\n",
      "Im looking for a journal article that dicusses classroom asses\n",
      "I'm looking for a newspaper article called Magic of filmi music b\n",
      " I'm looking for a particular article that the catalog says is avai\n",
      " Aflatoxins Review or journal article on it Growing conditions\n",
      "'m looking for access to this article, but can't find it anywhere. \n",
      "ed me off. I'm looking for an article about hotels using wristbands\n",
      "g for a very specific type of article. I'm currently student teachi\n",
      "I'm looking for an article and we were just given the au\n",
      "on for you I'm looking for an article but I only have the name of t\n",
      "commended to find a scholarly article that I\n",
      "im looking for an article for a school project and i'm \n",
      "Hi! I'm looking for an article in the databases. I usually u\n",
      "I'm looking for an article in the International Journal \n",
      "I'm looking for an article in the Journal of Food Scienc\n",
      "Hi. I'm looking for an article that covers \"workplace in the\n",
      "Im looking for an article that covers race, economics, \n",
      "Hi, I'm looking for an article that doesn't appear to be ava\n",
      "Hi, I'm looking for an article that examines a pharmacologic\n",
      "I'm looking for an article that used to be on the Commun\n",
      "how do I search for journal articles on personality disorders and\n",
      "I'm looking for an article that's not available through \n",
      "HI Im looking for an article to gain a practical understan\n",
      "hi there I'm looking for an article, but I'm having trouble pulli\n",
      "i, I'm looking for an journal article about the treatment for Gende\n",
      "I'm looking for articles on how professional developm\n",
      "opment. However, a lot of the articles I\n",
      "Hi! I'm looking for articles on Muriel Rukeyser's poem \"B\n",
      "Who can assist in finding articles? great I'm having the bigges\n",
      "t difficulty finding any article Im looking for case worker\n",
      "ch. I'm looking for scholarly articles having to do with the Tempes\n",
      "Hi! Im looking for journal articles that have used focus groups \n",
      "Hi! I am looking for articles about how dietary Saturated \n",
      "I'm looking for magazine articles on home and garden. Which da\n",
      "I'm looking for peer reviewed articles on feeding GMO (GE) crops to\n",
      " livestock. I've found a few articles but others\n",
      "ientific journals or research articles that refer to or describe im\n",
      "Im looking for the following article: Science. 1988 Jan 29;239(483\n",
      "ing for the full text of this article... Fruit Juice and Change in \n"
     ]
    }
   ],
   "source": [
    "target = \".{,30}article.{,30}\"\n",
    "\n",
    "keyword = []\n",
    "for i in truncatedCorpus:\n",
    "    for k in re.findall(string=i, pattern=target, flags=re.IGNORECASE):\n",
    "        keyword.append(k)\n",
    "keyword = pd.Series(keyword)\n",
    "\n",
    "print(len(keyword))\n",
    "for i in keyword[:50]:\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "493\n",
      "hi i'm looking for the print copy of Marvelous Worlds: The Grimms' F\n",
      "as an alum and I am able to utilize the printing services at the library? yes its not\n",
      "Hi, I'd like to know if you have a printed copy of \"U.S News & World Report\" mag\n",
      "okay-- no problem! I'm looking to get a printable version of\n",
      "Hi, I'm on the first floor trying to print and an error message saying something i\n",
      "was wondering where i get the copies in print? ya, the ones that aren't\n",
      "n anyway in the library or on campus to print of on colored paper? Even if i\n",
      "hello! is it possible to connect to the printer through wireless? great! yes thanks! \n",
      "Hello Is it possible to print in color at Hale? okay thank you! that \n",
      "is it possible to print of a colored report in the library? wit\n",
      "h a color printer? sweet! Thank you! \n",
      "Is it possible to pull the printed materials published at kstate that co\n",
      "Is the black and white printer inkjet or laser? oh, yes great! thank\n",
      "Is the color printer working Last night it was down  Okay \n",
      "alary book was available in the past in print form. IS it now available electronicall\n",
      "biology and I have a question regarding printing is there a\n",
      "is there a color printer in the library available to students?\n",
      "is there a color printer in the library? sweet thanks \n",
      "hi is there a color printer on 3rd floor thanks \n",
      "Is there a color printer somewhere in Hale? If so, what the ho\n",
      "urs for that printer? Thanks! Alright, thanks! Thanks!\n",
      "ampus that does lenticular drawings and printing\n",
      "is there a printer on the first floor? so when I print f\n",
      "is there printing on the bottom floor okay thanks \n",
      "Is there a printer on the third floor sorry i pritned fr\n",
      "Is there a printing station on the third floor oh, ok th\n",
      "Is there a way to do a photo print in the library? thank you thanks  \n",
      "is there a way to print if you do not have your student ID with\n",
      "Is there a way to print pictures on picture type paper at the l\n",
      "hi Is there a way to send print to hale library printer directly from y\n",
      "Is there any color printing in the library? for students if we u\n",
      "hi, is there any printer at fourth floor? okay, thank you \n",
      "any way to connect my own laptop to the printer in the library? Not really. Thank you\n",
      " anyway to get mircofilm transcribed or printed? Awesome, is there a charge for that?\n",
      "e anywhere in the library to do colored printing? Okay, do you happen to know what ro\n",
      "Is there colored printing within the library? Yes! Where is th\n",
      "Is there free printing in the library? \n",
      "Hi there, is there printers on all floors of Hale? Okay thank yo\n",
      "Is there something wrong with the printing network? I keep trying to print a re\n",
      "How do I print on the libraries desktop mac I printed \n",
      "et someone know that the stapler at the print station is out west printer on\n",
      " graduate study room. what if i want to print something> should i\n",
      "m trying to set up laptop to be able to print at Hale but the option I am\n",
      "u know both staplers at the first floor printer are out of staples Thanks so much \n",
      "oking for a journal paper- Do we have a print copy of it in the library?\n",
      "lp me locate this article so that I can print it? Thank you. Title: The effect of cha\n",
      "oint arithmetic of Muller Do you have a printed version? Thank you\n",
      "Can a personal laptop connect to the printers in the library? Well Im at home, can\n",
      "cle online and I want to pull it up and print it using\n",
      "ce/book/9780124809024 im ok with either printed book or pdf but i don't\n"
     ]
    }
   ],
   "source": [
    "target = \".{,40}print.{,40}\"\n",
    "\n",
    "keyword = []\n",
    "for i in truncatedCorpus:\n",
    "    for k in re.findall(string=i, pattern=target, flags=re.IGNORECASE):\n",
    "        keyword.append(k)\n",
    "keyword = pd.Series(keyword)\n",
    "\n",
    "print(len(keyword))\n",
    "for i in keyword[:50]:\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " am  trying  to               226\n",
      " am  looking  for             217\n",
      " i  am  trying                177\n",
      " i  am  trying  to            175\n",
      " i  am  looking               175\n",
      " trying  to  find             172\n",
      " i  am  looking  for          163\n",
      " for  an  article             128\n",
      " you  help  me                108\n",
      " find  an  article            104\n",
      " am  trying  to  find         101\n",
      " i  need  to                   97\n",
      " looking  for  an              96\n",
      " help  me  find                95\n",
      " i  trying  to                 91\n",
      " looking  for  an  article     90\n",
      " a  journal  article           87\n",
      " can  you  help                86\n",
      " an  article  that             83\n",
      " i  am  trying  to  find       82\n",
      " to  find  a                   80\n",
      " i  was  wondering             79\n",
      " to  find  an                  78\n",
      " i  have  a                    77\n",
      " was  wondering  if            76\n",
      " trying  to  access            74\n",
      " to  find  an  article         69\n",
      " an  article  from             67\n",
      " have  a  question             64\n",
      " i  looking  for               64\n",
      "dtype: int64\n",
      "\n",
      "count    9013.000000\n",
      "mean        4.077444\n",
      "std         8.199709\n",
      "min         2.000000\n",
      "25%         2.000000\n",
      "50%         2.000000\n",
      "75%         3.000000\n",
      "max       226.000000\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "n =[]\n",
    "for i in truncatedCorpus.index:\n",
    "    if re.search(string=truncatedCorpus[i],pattern=\"article\"):\n",
    "        n.append(i)\n",
    "        \n",
    "articleCorpus = truncatedCorpus.loc[n].copy()\n",
    "\n",
    "counter = CountVectorizer(\n",
    "    token_pattern=r\"(?u)\\s\\w{1,}\",\n",
    "    ngram_range=(3,10),\n",
    "#     max_df=0.5,\n",
    "    min_df=2\n",
    ")\n",
    "\n",
    "tfidfer = TfidfTransformer()\n",
    "\n",
    "cdf = counter.fit_transform(articleCorpus)\n",
    "cdf = pd.DataFrame(cdf.todense(), index=articleCorpus.index, columns=counter.get_feature_names())\n",
    "# tdf = tfidfer.fit_transform(cdf)\n",
    "# tdf = pd.DataFrame(tdf.todense(), index=full.index, columns=counter.get_feature_names())\n",
    "\n",
    "countList = cdf.sum().sort_values(ascending=False)\n",
    "print(countList.head(30))\n",
    "print()\n",
    "print(countList.describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " the  third  floor            93\n",
      " the  3rd  floor              79\n",
      " on  the  third               67\n",
      " on  the  third  floor        65\n",
      " the  4th  floor              55\n",
      " on  the  3rd                 55\n",
      " on  the  3rd  floor          54\n",
      " there  is  a                 39\n",
      " on  the  4th                 39\n",
      " on  the  4th  floor          39\n",
      " the  first  floor            36\n",
      " send  someone  up            32\n",
      " on  third  floor             30\n",
      " the  fourth  floor           30\n",
      " please  send  someone        28\n",
      " someone  up  to              26\n",
      " am  on  the                  26\n",
      " i  am  on                    25\n",
      " please  send  someone  up    24\n",
      " i  am  on  the               24\n",
      " send  someone  up  to        23\n",
      " on  the  fourth              21\n",
      " on  the  fourth  floor       21\n",
      " on  the  first               21\n",
      " on  the  first  floor        21\n",
      " to  third  floor             18\n",
      " the  second  floor           18\n",
      " on  3rd  floor               17\n",
      " floor  of  hale              17\n",
      " of  the  library             16\n",
      "dtype: int64\n",
      "\n",
      "count    2547.000000\n",
      "mean        3.133098\n",
      "std         4.456029\n",
      "min         2.000000\n",
      "25%         2.000000\n",
      "50%         2.000000\n",
      "75%         3.000000\n",
      "max        93.000000\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "n =[]\n",
    "for i in truncatedCorpus.index:\n",
    "    if re.search(string=truncatedCorpus[i],pattern=\"floor\"):\n",
    "        n.append(i)\n",
    "        \n",
    "articleCorpus = truncatedCorpus.loc[n].copy()\n",
    "\n",
    "counter = CountVectorizer(\n",
    "    token_pattern=r\"(?u)\\s\\w{1,}\",\n",
    "    ngram_range=(3,10),\n",
    "#     max_df=0.5,\n",
    "    min_df=2\n",
    ")\n",
    "\n",
    "tfidfer = TfidfTransformer()\n",
    "\n",
    "cdf = counter.fit_transform(articleCorpus)\n",
    "cdf = pd.DataFrame(cdf.todense(), index=articleCorpus.index, columns=counter.get_feature_names())\n",
    "# tdf = tfidfer.fit_transform(cdf)\n",
    "# tdf = pd.DataFrame(tdf.todense(), index=full.index, columns=counter.get_feature_names())\n",
    "\n",
    "countList = cdf.sum().sort_values(ascending=False)\n",
    "print(countList.head(30))\n",
    "print()\n",
    "print(countList.describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " am  looking  for             253\n",
      " looking  for  a              240\n",
      " for  a  book                 227\n",
      " i  am  looking               224\n",
      " i  am  looking  for          208\n",
      " was  wondering  if           195\n",
      " i  was  wondering            181\n",
      " looking  for  a  book        171\n",
      " i  was  wondering  if        155\n",
      " i  have  a                   121\n",
      " am  looking  for  a          121\n",
      " i  looking  for              119\n",
      " wondering  if  you           117\n",
      " i  am  looking  for  a       103\n",
      " am  trying  to               103\n",
      " was  wondering  if  you      100\n",
      " do  you  have                100\n",
      " a  book  i                    97\n",
      " find  a  book                 96\n",
      " a  book  that                 95\n",
      " to  check  out                93\n",
      " i  want  to                   92\n",
      " if  you  have                 90\n",
      " am  looking  for  a  book     88\n",
      " in  the  library              86\n",
      " out  a  book                  84\n",
      " i  am  trying  to             82\n",
      " i  am  trying                 82\n",
      " i  need  to                   80\n",
      " to  find  a                   79\n",
      "dtype: int64\n",
      "\n",
      "count    11629.000000\n",
      "mean         4.015737\n",
      "std          8.581276\n",
      "min          2.000000\n",
      "25%          2.000000\n",
      "50%          2.000000\n",
      "75%          3.000000\n",
      "max        253.000000\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "n =[]\n",
    "for i in truncatedCorpus.index:\n",
    "    if re.search(string=truncatedCorpus[i],pattern=\"book\"):\n",
    "        n.append(i)\n",
    "        \n",
    "articleCorpus = truncatedCorpus.loc[n].copy()\n",
    "\n",
    "counter = CountVectorizer(\n",
    "    token_pattern=r\"(?u)\\s\\w{1,}\",\n",
    "    ngram_range=(3,10),\n",
    "#     max_df=0.5,\n",
    "    min_df=2\n",
    ")\n",
    "\n",
    "tfidfer = TfidfTransformer()\n",
    "\n",
    "cdf = counter.fit_transform(articleCorpus)\n",
    "cdf = pd.DataFrame(cdf.todense(), index=articleCorpus.index, columns=counter.get_feature_names())\n",
    "# tdf = tfidfer.fit_transform(cdf)\n",
    "# tdf = pd.DataFrame(tdf.todense(), index=full.index, columns=counter.get_feature_names())\n",
    "\n",
    "countList = cdf.sum().sort_values(ascending=False)\n",
    "print(countList.head(30))\n",
    "print()\n",
    "print(countList.describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        Is \"An Unfinished Life by Robert Diek\" availab...\n",
       "1        Hi I'm confused with Search It I keep getting ...\n",
       "100      I am in the library and consistently having pr...\n",
       "1000     I want to find some chinese database, do we ha...\n",
       "10000    I'm interested in a book that says: In transit...\n",
       "10001    I'm interested in accessing the full-text arti...\n",
       "10002    hi there so I'm interested in getting a copy o...\n",
       "10003    Hi - I'm interested in recommending that a stu...\n",
       "10004    Hello there! I'm the library director at the x...\n",
       "10005    hi how are you im just bored, you wanna chat, ...\n",
       "10006    I'm just letting you all know there is a group...\n",
       "10007    Hi there, I'm just typing this to let you know...\n",
       "10008    helo  im look for articles published in the Jo...\n",
       "10009    hi there i'm looking at a specific research to...\n",
       "1001     Hi! I'm working on a research project and I'm ...\n",
       "10010    Im looking at applying for a part time job at ...\n",
       "10011    Hello I'm looking at the following article Jou...\n",
       "10012                                  Hey Within 2 weeks \n",
       "10013    Hi, I would like some help. I'm looking for \"t...\n",
       "10014    I'm looking for \"The Turing Omnibus\"    Sure Y...\n",
       "10015    Happy Monday! I'm looking for \"Work Release in...\n",
       "10016    Hi I'm looking for @MidnitLibrarian Is that a ...\n",
       "10017    I'm looking for 2 books. One is Go Mobile, whi...\n",
       "10018    I'm looking for 2 papers but the full texts ar...\n",
       "10019    Good morning I have a quick question for you I...\n",
       "1002     hello I'm looking for some research for my top...\n",
       "10020    I'm looking for a 19th century article about w...\n",
       "10021    I'm looking for a biography. Do ksu libraries ...\n",
       "10022    Hello Im looking for a book Ball Redbook: Gree...\n",
       "10023    Hello I'm looking for a book download from htt...\n",
       "                               ...                        \n",
       "997      I'm looking for research articles about how re...\n",
       "9970     Hi I'm having trouble finding imformation abou...\n",
       "9971     Hi I'm having trouble finding imformation abou...\n",
       "9972     I'm having trouble finding information on Prod...\n",
       "9973     I'm having trouble finding the full text of an...\n",
       "9974     Im having trouble finding the writing center p...\n",
       "9975     Hello I'm having trouble getting a dissertatio...\n",
       "9976     Could I speak to someone about reworks? I'm ha...\n",
       "9977     Hello I'm having trouble listening to music on...\n",
       "9978       thanks! hi! I'm having trouble locating scho...\n",
       "9979     I'm having trouble using the databases. I need...\n",
       "998      Is this only available through interlibrary lo...\n",
       "9980     im having trouble viewing an online article ht...\n",
       "9981     Hello! I'm having trouble viewing the article,...\n",
       "9983     I'm having troubler finding a document that sa...\n",
       "9984     I'm having troubles locating articles pertaini...\n",
       "9985     hi i'm sorry for interruption i'm a ksu studen...\n",
       "9986     hi, good morning i'm here at library right now...\n",
       "9987     Hello. I'm hoping you can help me figure out h...\n",
       "9988     I'm in Dr. Vicki Sherbert's EDCI 716 course an...\n",
       "9989     Hello I'm in ENGL 100, and I need to find sour...\n",
       "999       my name is nameredacted  I am working on find...\n",
       "9990     Im in expos 100 and im wirting about racial/et...\n",
       "9992     I'm in HN320 Prevention and care of athletic i...\n",
       "9993     Im in Mrs. Reekie Engl 415 class, and we have ...\n",
       "9994     Hello I'm in need of a list of resources for i...\n",
       "9995     Hi! I'm in Room 301 and the drawer for the key...\n",
       "9997     I'm in the library now by the maps. I want to ...\n",
       "9998     I'm in the stacks looking for a book called \"S...\n",
       "9999     I'm interested accessing the industry report:T...\n",
       "Length: 14604, dtype: object"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "truncatedCorpus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "count = CountVectorizer(\n",
    "    token_pattern=r\"(?u)\\b\\w{1,}\\b\",\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "countDF = pd.DataFrame(\n",
    "    count.fit_transform(truncatedCorpus).todense(),\n",
    "    index=truncatedCorpus.index,\n",
    "    columns=count.get_feature_names()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "countList = countDF.sum().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "i        18756\n",
       "the       9960\n",
       "a         9257\n",
       "to        8504\n",
       "for       5332\n",
       "hi        5075\n",
       "you       5022\n",
       "and       4410\n",
       "is        4314\n",
       "am        4164\n",
       "hello     3838\n",
       "in        3747\n",
       "have      3599\n",
       "of        3576\n",
       "it        3313\n",
       "on        3026\n",
       "can       2996\n",
       "do        2595\n",
       "that      2377\n",
       "book      2302\n",
       "dtype: int64"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countList[:20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "count = TfidfVectorizer(\n",
    "    token_pattern=r\"(?u)\\b\\w{1,}\\b\",\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "countDF = pd.DataFrame(\n",
    "    count.fit_transform(truncatedCorpus).todense(),\n",
    "    index=truncatedCorpus.index,\n",
    "    columns=count.get_feature_names()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "countList = countDF.sum().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "i        1105.334316\n",
       "the       778.476680\n",
       "a         763.928223\n",
       "to        720.517038\n",
       "you       542.965171\n",
       "for       530.922554\n",
       "hi        499.797325\n",
       "is        484.763353\n",
       "am        475.763902\n",
       "and       460.751314\n",
       "have      432.722247\n",
       "hello     431.196813\n",
       "in        425.448631\n",
       "it        419.292949\n",
       "of        400.472010\n",
       "can       383.206342\n",
       "on        375.737729\n",
       "do        352.029154\n",
       "that      331.178122\n",
       "if        327.336392\n",
       "dtype: float64"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countList[:20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "count = CountVectorizer(\n",
    "    token_pattern=r\"(?u)\\b\\w{3,}\\b\",\n",
    "    min_df=2,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "countDF = pd.DataFrame(\n",
    "    count.fit_transform(truncatedCorpus).todense(),\n",
    "    index=truncatedCorpus.index,\n",
    "    columns=count.get_feature_names()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "countList = countDF.sum().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "the        9960\n",
       "for        5332\n",
       "you        5022\n",
       "and        4410\n",
       "hello      3838\n",
       "have       3599\n",
       "can        2996\n",
       "that       2377\n",
       "book       2302\n",
       "find       2223\n",
       "library    2179\n",
       "how        2113\n",
       "there      2099\n",
       "was        1854\n",
       "looking    1838\n",
       "this       1798\n",
       "help       1762\n",
       "article    1651\n",
       "thank      1516\n",
       "but        1483\n",
       "dtype: int64"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countList[:20]"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
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 },
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