Doubt_Activity_Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Doubt Activity Analysis\n",
    "\n",
    "<font size=\"3\"> **Done by** - Aayush Bajaj"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing Dependencies\n",
    "\n",
    "from IPython.display import display\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "pd.set_option('display.max_rows', 10)\n",
    "pd.set_option('display.max_columns', 500)\n",
    "pd.set_option('display.width', 1000)\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set(style=\"whitegrid\")\n",
    "\n",
    "from tqdm import tqdm\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "RANDOM_SEED = np.random.seed(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"img/DRM_StateDiagram.png\" width=\"1500\" height=\"600\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>user_id</th>\n",
       "      <th>doubt_id</th>\n",
       "      <th>created_at</th>\n",
       "      <th>activity_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2121357</td>\n",
       "      <td>120623</td>\n",
       "      <td>248193</td>\n",
       "      <td>2020-01-01 04:26:08+00:00</td>\n",
       "      <td>assign</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2121358</td>\n",
       "      <td>120623</td>\n",
       "      <td>248193</td>\n",
       "      <td>2020-01-01 04:26:15+00:00</td>\n",
       "      <td>accept</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2121359</td>\n",
       "      <td>165368</td>\n",
       "      <td>248199</td>\n",
       "      <td>2020-01-01 04:29:18+00:00</td>\n",
       "      <td>assign</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2121360</td>\n",
       "      <td>165368</td>\n",
       "      <td>248199</td>\n",
       "      <td>2020-01-01 04:29:34+00:00</td>\n",
       "      <td>reject</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2121399</td>\n",
       "      <td>265305</td>\n",
       "      <td>248191</td>\n",
       "      <td>2020-01-01 04:35:07+00:00</td>\n",
       "      <td>assign</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76203</th>\n",
       "      <td>4322614</td>\n",
       "      <td>229989</td>\n",
       "      <td>252698</td>\n",
       "      <td>2020-05-01 12:26:00+00:00</td>\n",
       "      <td>rate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76204</th>\n",
       "      <td>4322618</td>\n",
       "      <td>229989</td>\n",
       "      <td>249512</td>\n",
       "      <td>2020-05-01 12:26:07+00:00</td>\n",
       "      <td>rate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76205</th>\n",
       "      <td>4699532</td>\n",
       "      <td>47336</td>\n",
       "      <td>249949</td>\n",
       "      <td>2020-05-12 13:53:29+00:00</td>\n",
       "      <td>rate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76206</th>\n",
       "      <td>4905034</td>\n",
       "      <td>175678</td>\n",
       "      <td>252370</td>\n",
       "      <td>2020-05-18 13:24:14+00:00</td>\n",
       "      <td>rate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76207</th>\n",
       "      <td>4905041</td>\n",
       "      <td>175678</td>\n",
       "      <td>252373</td>\n",
       "      <td>2020-05-18 13:24:16+00:00</td>\n",
       "      <td>rate</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>76208 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            id  user_id  doubt_id                created_at activity_type\n",
       "0      2121357   120623    248193 2020-01-01 04:26:08+00:00        assign\n",
       "1      2121358   120623    248193 2020-01-01 04:26:15+00:00        accept\n",
       "2      2121359   165368    248199 2020-01-01 04:29:18+00:00        assign\n",
       "3      2121360   165368    248199 2020-01-01 04:29:34+00:00        reject\n",
       "4      2121399   265305    248191 2020-01-01 04:35:07+00:00        assign\n",
       "...        ...      ...       ...                       ...           ...\n",
       "76203  4322614   229989    252698 2020-05-01 12:26:00+00:00          rate\n",
       "76204  4322618   229989    249512 2020-05-01 12:26:07+00:00          rate\n",
       "76205  4699532    47336    249949 2020-05-12 13:53:29+00:00          rate\n",
       "76206  4905034   175678    252370 2020-05-18 13:24:14+00:00          rate\n",
       "76207  4905041   175678    252373 2020-05-18 13:24:16+00:00          rate\n",
       "\n",
       "[76208 rows x 5 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Activities Set Memory Usage = 2.91 MB\n",
      "----------------------------------------\n"
     ]
    },
    {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>user_id</th>\n",
       "      <th>course_id</th>\n",
       "      <th>created_at</th>\n",
       "      <th>content_type</th>\n",
       "      <th>user_rating</th>\n",
       "      <th>state</th>\n",
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       "      <th>0</th>\n",
       "      <td>248188</td>\n",
       "      <td>280541</td>\n",
       "      <td>10</td>\n",
       "      <td>2020-01-01 00:58:22+00:00</td>\n",
       "      <td>Code Problem</td>\n",
       "      <td>5.0</td>\n",
       "      <td>resolved</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>248189</td>\n",
       "      <td>32227</td>\n",
       "      <td>12</td>\n",
       "      <td>2020-01-01 02:02:21+00:00</td>\n",
       "      <td>Multiple Problem</td>\n",
       "      <td>5.0</td>\n",
       "      <td>resolved</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>248190</td>\n",
       "      <td>357687</td>\n",
       "      <td>1</td>\n",
       "      <td>2020-01-01 02:14:37+00:00</td>\n",
       "      <td>Single_choice Problem</td>\n",
       "      <td>5.0</td>\n",
       "      <td>resolved</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>248191</td>\n",
       "      <td>486150</td>\n",
       "      <td>11</td>\n",
       "      <td>2020-01-01 02:15:15+00:00</td>\n",
       "      <td>No_submission Problem</td>\n",
       "      <td>4.0</td>\n",
       "      <td>resolved</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>248192</td>\n",
       "      <td>367920</td>\n",
       "      <td>13</td>\n",
       "      <td>2020-01-01 03:16:19+00:00</td>\n",
       "      <td>Datascience Problem</td>\n",
       "      <td>5.0</td>\n",
       "      <td>resolved</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <th>4995</th>\n",
       "      <td>253194</td>\n",
       "      <td>332893</td>\n",
       "      <td>1</td>\n",
       "      <td>2020-01-08 14:33:21+00:00</td>\n",
       "      <td>VideoOffering</td>\n",
       "      <td>4.0</td>\n",
       "      <td>resolved</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4996</th>\n",
       "      <td>253195</td>\n",
       "      <td>388697</td>\n",
       "      <td>2</td>\n",
       "      <td>2020-01-08 14:33:27+00:00</td>\n",
       "      <td>Code Problem</td>\n",
       "      <td>5.0</td>\n",
       "      <td>resolved</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4997</th>\n",
       "      <td>253196</td>\n",
       "      <td>282065</td>\n",
       "      <td>1</td>\n",
       "      <td>2020-01-08 14:34:12+00:00</td>\n",
       "      <td>Code Problem</td>\n",
       "      <td>5.0</td>\n",
       "      <td>resolved</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4998</th>\n",
       "      <td>253197</td>\n",
       "      <td>32802</td>\n",
       "      <td>12</td>\n",
       "      <td>2020-01-08 14:34:22+00:00</td>\n",
       "      <td>Frontend Problem</td>\n",
       "      <td>5.0</td>\n",
       "      <td>resolved</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4999</th>\n",
       "      <td>253198</td>\n",
       "      <td>279096</td>\n",
       "      <td>1</td>\n",
       "      <td>2020-01-08 14:34:30+00:00</td>\n",
       "      <td>Single_choice Problem</td>\n",
       "      <td>3.0</td>\n",
       "      <td>resolved</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5000 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          id  user_id  course_id                created_at           content_type  user_rating     state\n",
       "0     248188   280541         10 2020-01-01 00:58:22+00:00           Code Problem          5.0  resolved\n",
       "1     248189    32227         12 2020-01-01 02:02:21+00:00       Multiple Problem          5.0  resolved\n",
       "2     248190   357687          1 2020-01-01 02:14:37+00:00  Single_choice Problem          5.0  resolved\n",
       "3     248191   486150         11 2020-01-01 02:15:15+00:00  No_submission Problem          4.0  resolved\n",
       "4     248192   367920         13 2020-01-01 03:16:19+00:00    Datascience Problem          5.0  resolved\n",
       "...      ...      ...        ...                       ...                    ...          ...       ...\n",
       "4995  253194   332893          1 2020-01-08 14:33:21+00:00          VideoOffering          4.0  resolved\n",
       "4996  253195   388697          2 2020-01-08 14:33:27+00:00           Code Problem          5.0  resolved\n",
       "4997  253196   282065          1 2020-01-08 14:34:12+00:00           Code Problem          5.0  resolved\n",
       "4998  253197    32802         12 2020-01-08 14:34:22+00:00       Frontend Problem          5.0  resolved\n",
       "4999  253198   279096          1 2020-01-08 14:34:30+00:00  Single_choice Problem          3.0  resolved\n",
       "\n",
       "[5000 rows x 7 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "doubts Set Memory Usage = 0.27 MB\n"
     ]
    }
   ],
   "source": [
    "'''\n",
    "Importing Tables and checking memory used\n",
    "'''\n",
    "\n",
    "activities = pd.read_csv('data/activities.csv', parse_dates = ['created_at'])\n",
    "doubts = pd.read_csv('data/doubts.csv', parse_dates = ['created_at'])\n",
    "\n",
    "display(activities)\n",
    "print('Activities Set Memory Usage = {:.2f} MB'.format(activities.memory_usage().sum() / 1024**2))\n",
    "print('----------------------------------------')\n",
    "display(doubts)\n",
    "print('doubts Set Memory Usage = {:.2f} MB'.format(doubts.memory_usage().sum() / 1024**2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "def draw_missing_data_table(df):\n",
    "    '''\n",
    "    Docstring: Returns a datarframe with percent of missing/nan values per feature/column\n",
    "    \n",
    "    Parameters:\n",
    "    ------------\n",
    "    df: dataframe object\n",
    "    \n",
    "    Returns:\n",
    "    ------------\n",
    "    Dataframe containing missing value information\n",
    "    '''\n",
    "    total = df.isnull().sum().sort_values(ascending=False)\n",
    "    percent = (df.isnull().sum()/df.isnull().count()).sort_values(ascending=False)\n",
    "    missing_data = pd.concat([total, percent], axis=1, keys=['Total', 'Percent'])\n",
    "    return missing_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total</th>\n",
       "      <th>Percent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>activity_type</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>created_at</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>doubt_id</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user_id</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <td>0</td>\n",
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      ],
      "text/plain": [
       "               Total  Percent\n",
       "activity_type      0      0.0\n",
       "created_at         0      0.0\n",
       "doubt_id           0      0.0\n",
       "user_id            0      0.0\n",
       "id                 0      0.0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "draw_missing_data_table(activities)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Percent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>user_rating</th>\n",
       "      <td>12</td>\n",
       "      <td>0.0024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>state</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>content_type</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>created_at</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>course_id</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user_id</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Total  Percent\n",
       "user_rating      12   0.0024\n",
       "state             0   0.0000\n",
       "content_type      0   0.0000\n",
       "created_at        0   0.0000\n",
       "course_id         0   0.0000\n",
       "user_id           0   0.0000\n",
       "id                0   0.0000"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "draw_missing_data_table(doubts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size='3'> There are 12 missing values in doubts table for `user_rating` which is plausible for number of reasons\n",
    "- <font size='3'>User didn't gave a review (major one)\n",
    "- <font size='3'>It wasn't recorded\n",
    "    \n",
    "<font size='3'>So to fill in for it I'll just replace the NaN's with `No Rating` string."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "doubts.user_rating.fillna('NR',inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# First Impressions of the Dataset\n",
    "- <font size='3'>**id** in activities table should be `activity_id`\n",
    "- <font size='3'>Since **user_id** in **activity table** contains three different information as metadata. To counter that we'll introduce three ```One-Hot-Features``` to the activities table which will signify if the user_id is student_id or TA_id or system_id.\n",
    "- <font size='3'>`created_at` is formated by \"date HH:MM:SS\" and Time is in `UTC`\n",
    "- <font size='3'>**id** in **doubts table** is `doubt_id` (to be used as a foriegn key, used in joining tables)\n",
    "- <font size='3'>**user_id** in **doubts table** is just student_id (same in user_id in activities since the activity_type is resolved in doubts table)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=\"3\">To make ```One-Hot-Features``` following logic is applied.\n",
    "```python\n",
    "if activity_type ∈ (resolve, rate, review_not_resolved, not_pending_action_done, not_pending_info_submit):\n",
    "    user_id = student_id\n",
    "    \n",
    "elif activity_type ∈ (activate, reject, pending_action_required, pending_information_required, review_resolution,assign, accept,reactivate, escalate):\n",
    "    user_id = TA_id\n",
    "    \n",
    "elif activity_type ∈ (unassign, inactivate, pending_sms, pending_reminder_first, pending_reminder_second, dead):\n",
    "    user_id = system_id\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['assign', 'accept', 'reject', 'review_resolution', 'reactivate',\n",
       "       'resolve', 'rate', 'pending_action_required', 'inactivate',\n",
       "       'unassign', 'escalate', 'review_not_resolved',\n",
       "       'not_pending_action_done', 'pending_information_required',\n",
       "       'not_pending_info_submit', 'activate', 'pending_reminder_first',\n",
       "       'pending_reminder_second'], dtype=object)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# getting the unique activity_types for reference\n",
    "activities.activity_type.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def is_student(x):\n",
    "    if (x['activity_type'] in ['rate', 'review_not_resolved', 'not_pending_action_done', 'not_pending_info_submit','resolve']):\n",
    "        return '1'\n",
    "    else:\n",
    "        return '0'\n",
    "\n",
    "activities['is_student_id'] = activities.apply(lambda x:is_student(x),axis=1)\n",
    "activities.is_student_id = activities.is_student_id.astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def is_TA(x):\n",
    "    if (x['activity_type'] in ['activate','reject','pending_action_required','pending_information_required',\n",
    "                               'review_resolution','assign','accept','reactivate','escalate']):\n",
    "        return '1'\n",
    "    else:\n",
    "        return '0'\n",
    "\n",
    "activities['is_TA_id'] = activities.apply(lambda x:is_TA(x),axis=1)\n",
    "activities.is_TA_id = activities.is_TA_id.astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def is_sys(x):\n",
    "    if (x['activity_type'] in ['unassign', 'inactivate', 'pending_sms', 'pending_reminder_first', 'pending_reminder_second', 'dead']):\n",
    "        return '1'\n",
    "    else:\n",
    "        return '0'\n",
    "\n",
    "activities['is_sys_id'] = activities.apply(lambda x:is_sys(x),axis=1)\n",
    "activities.is_sys_id = activities.is_sys_id.astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
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       "    }\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>user_id</th>\n",
       "      <th>doubt_id</th>\n",
       "      <th>created_at</th>\n",
       "      <th>activity_type</th>\n",
       "      <th>is_student_id</th>\n",
       "      <th>is_TA_id</th>\n",
       "      <th>is_sys_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2121357</td>\n",
       "      <td>120623</td>\n",
       "      <td>248193</td>\n",
       "      <td>2020-01-01 04:26:08+00:00</td>\n",
       "      <td>assign</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2121358</td>\n",
       "      <td>120623</td>\n",
       "      <td>248193</td>\n",
       "      <td>2020-01-01 04:26:15+00:00</td>\n",
       "      <td>accept</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2121359</td>\n",
       "      <td>165368</td>\n",
       "      <td>248199</td>\n",
       "      <td>2020-01-01 04:29:18+00:00</td>\n",
       "      <td>assign</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2121360</td>\n",
       "      <td>165368</td>\n",
       "      <td>248199</td>\n",
       "      <td>2020-01-01 04:29:34+00:00</td>\n",
       "      <td>reject</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2121399</td>\n",
       "      <td>265305</td>\n",
       "      <td>248191</td>\n",
       "      <td>2020-01-01 04:35:07+00:00</td>\n",
       "      <td>assign</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76203</th>\n",
       "      <td>4322614</td>\n",
       "      <td>229989</td>\n",
       "      <td>252698</td>\n",
       "      <td>2020-05-01 12:26:00+00:00</td>\n",
       "      <td>rate</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76204</th>\n",
       "      <td>4322618</td>\n",
       "      <td>229989</td>\n",
       "      <td>249512</td>\n",
       "      <td>2020-05-01 12:26:07+00:00</td>\n",
       "      <td>rate</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76205</th>\n",
       "      <td>4699532</td>\n",
       "      <td>47336</td>\n",
       "      <td>249949</td>\n",
       "      <td>2020-05-12 13:53:29+00:00</td>\n",
       "      <td>rate</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76206</th>\n",
       "      <td>4905034</td>\n",
       "      <td>175678</td>\n",
       "      <td>252370</td>\n",
       "      <td>2020-05-18 13:24:14+00:00</td>\n",
       "      <td>rate</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76207</th>\n",
       "      <td>4905041</td>\n",
       "      <td>175678</td>\n",
       "      <td>252373</td>\n",
       "      <td>2020-05-18 13:24:16+00:00</td>\n",
       "      <td>rate</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>76208 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            id  user_id  doubt_id                created_at activity_type  is_student_id  is_TA_id  is_sys_id\n",
       "0      2121357   120623    248193 2020-01-01 04:26:08+00:00        assign              0         1          0\n",
       "1      2121358   120623    248193 2020-01-01 04:26:15+00:00        accept              0         1          0\n",
       "2      2121359   165368    248199 2020-01-01 04:29:18+00:00        assign              0         1          0\n",
       "3      2121360   165368    248199 2020-01-01 04:29:34+00:00        reject              0         1          0\n",
       "4      2121399   265305    248191 2020-01-01 04:35:07+00:00        assign              0         1          0\n",
       "...        ...      ...       ...                       ...           ...            ...       ...        ...\n",
       "76203  4322614   229989    252698 2020-05-01 12:26:00+00:00          rate              1         0          0\n",
       "76204  4322618   229989    249512 2020-05-01 12:26:07+00:00          rate              1         0          0\n",
       "76205  4699532    47336    249949 2020-05-12 13:53:29+00:00          rate              1         0          0\n",
       "76206  4905034   175678    252370 2020-05-18 13:24:14+00:00          rate              1         0          0\n",
       "76207  4905041   175678    252373 2020-05-18 13:24:16+00:00          rate              1         0          0\n",
       "\n",
       "[76208 rows x 8 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "activities"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Resolution Time"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Resolution time for every doubt resolved in doubts table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>user_id</th>\n",
       "      <th>course_id</th>\n",
       "      <th>created_at</th>\n",
       "      <th>content_type</th>\n",
       "      <th>user_rating</th>\n",
       "      <th>state</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>248188</td>\n",
       "      <td>280541</td>\n",
       "      <td>10</td>\n",
       "      <td>2020-01-01 00:58:22+00:00</td>\n",
       "      <td>Code Problem</td>\n",
       "      <td>5</td>\n",
       "      <td>resolved</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>248189</td>\n",
       "      <td>32227</td>\n",
       "      <td>12</td>\n",
       "      <td>2020-01-01 02:02:21+00:00</td>\n",
       "      <td>Multiple Problem</td>\n",
       "      <td>5</td>\n",
       "      <td>resolved</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>248190</td>\n",
       "      <td>357687</td>\n",
       "      <td>1</td>\n",
       "      <td>2020-01-01 02:14:37+00:00</td>\n",
       "      <td>Single_choice Problem</td>\n",
       "      <td>5</td>\n",
       "      <td>resolved</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>248191</td>\n",
       "      <td>486150</td>\n",
       "      <td>11</td>\n",
       "      <td>2020-01-01 02:15:15+00:00</td>\n",
       "      <td>No_submission Problem</td>\n",
       "      <td>4</td>\n",
       "      <td>resolved</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>248192</td>\n",
       "      <td>367920</td>\n",
       "      <td>13</td>\n",
       "      <td>2020-01-01 03:16:19+00:00</td>\n",
       "      <td>Datascience Problem</td>\n",
       "      <td>5</td>\n",
       "      <td>resolved</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4995</th>\n",
       "      <td>253194</td>\n",
       "      <td>332893</td>\n",
       "      <td>1</td>\n",
       "      <td>2020-01-08 14:33:21+00:00</td>\n",
       "      <td>VideoOffering</td>\n",
       "      <td>4</td>\n",
       "      <td>resolved</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4996</th>\n",
       "      <td>253195</td>\n",
       "      <td>388697</td>\n",
       "      <td>2</td>\n",
       "      <td>2020-01-08 14:33:27+00:00</td>\n",
       "      <td>Code Problem</td>\n",
       "      <td>5</td>\n",
       "      <td>resolved</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4997</th>\n",
       "      <td>253196</td>\n",
       "      <td>282065</td>\n",
       "      <td>1</td>\n",
       "      <td>2020-01-08 14:34:12+00:00</td>\n",
       "      <td>Code Problem</td>\n",
       "      <td>5</td>\n",
       "      <td>resolved</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4998</th>\n",
       "      <td>253197</td>\n",
       "      <td>32802</td>\n",
       "      <td>12</td>\n",
       "      <td>2020-01-08 14:34:22+00:00</td>\n",
       "      <td>Frontend Problem</td>\n",
       "      <td>5</td>\n",
       "      <td>resolved</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4999</th>\n",
       "      <td>253198</td>\n",
       "      <td>279096</td>\n",
       "      <td>1</td>\n",
       "      <td>2020-01-08 14:34:30+00:00</td>\n",
       "      <td>Single_choice Problem</td>\n",
       "      <td>3</td>\n",
       "      <td>resolved</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5000 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          id  user_id  course_id                created_at           content_type user_rating     state\n",
       "0     248188   280541         10 2020-01-01 00:58:22+00:00           Code Problem           5  resolved\n",
       "1     248189    32227         12 2020-01-01 02:02:21+00:00       Multiple Problem           5  resolved\n",
       "2     248190   357687          1 2020-01-01 02:14:37+00:00  Single_choice Problem           5  resolved\n",
       "3     248191   486150         11 2020-01-01 02:15:15+00:00  No_submission Problem           4  resolved\n",
       "4     248192   367920         13 2020-01-01 03:16:19+00:00    Datascience Problem           5  resolved\n",
       "...      ...      ...        ...                       ...                    ...         ...       ...\n",
       "4995  253194   332893          1 2020-01-08 14:33:21+00:00          VideoOffering           4  resolved\n",
       "4996  253195   388697          2 2020-01-08 14:33:27+00:00           Code Problem           5  resolved\n",
       "4997  253196   282065          1 2020-01-08 14:34:12+00:00           Code Problem           5  resolved\n",
       "4998  253197    32802         12 2020-01-08 14:34:22+00:00       Frontend Problem           5  resolved\n",
       "4999  253198   279096          1 2020-01-08 14:34:30+00:00  Single_choice Problem           3  resolved\n",
       "\n",
       "[5000 rows x 7 columns]"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# id in doubts table (doubt_id) is unique (primary key)\n",
    "doubts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "      <th>created_at</th>\n",
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       "      <th>is_sys_id</th>\n",
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      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [id, user_id, doubt_id, created_at, activity_type, is_student_id, is_TA_id, is_sys_id]\n",
       "Index: []"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "activities.loc[activities.activity_type=='available']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=\"3\"> Now according to the document the start state of the doubt resolution process is ```available``` but no such state exists in activities table.<br>\n",
    "<font size=\"3\"> To tackle this I'm assuming the ***assign*** state the starting point of Doubt resolution process (first state)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "Standardizing Time to minutes\n",
    "'''\n",
    "\n",
    "def convert_timedelta(duration):\n",
    "    days, seconds = duration.days, duration.seconds\n",
    "\n",
    "    minutes = (seconds % 3600) / 60\n",
    "    return minutes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>user_id</th>\n",
       "      <th>doubt_id</th>\n",
       "      <th>created_at</th>\n",
       "      <th>activity_type</th>\n",
       "      <th>is_student_id</th>\n",
       "      <th>is_TA_id</th>\n",
       "      <th>is_sys_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>2121762</td>\n",
       "      <td>175223</td>\n",
       "      <td>248205</td>\n",
       "      <td>2020-01-01 06:46:24+00:00</td>\n",
       "      <td>assign</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>2121763</td>\n",
       "      <td>175223</td>\n",
       "      <td>248205</td>\n",
       "      <td>2020-01-01 06:46:34+00:00</td>\n",
       "      <td>accept</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>2121772</td>\n",
       "      <td>175223</td>\n",
       "      <td>248205</td>\n",
       "      <td>2020-01-01 06:49:38+00:00</td>\n",
       "      <td>review_resolution</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>2121777</td>\n",
       "      <td>417024</td>\n",
       "      <td>248205</td>\n",
       "      <td>2020-01-01 06:50:07+00:00</td>\n",
       "      <td>review_not_resolved</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>2121779</td>\n",
       "      <td>175223</td>\n",
       "      <td>248205</td>\n",
       "      <td>2020-01-01 06:50:11+00:00</td>\n",
       "      <td>assign</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>363</th>\n",
       "      <td>2122266</td>\n",
       "      <td>175223</td>\n",
       "      <td>248205</td>\n",
       "      <td>2020-01-01 09:19:49+00:00</td>\n",
       "      <td>pending_action_required</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24310</th>\n",
       "      <td>2152208</td>\n",
       "      <td>71</td>\n",
       "      <td>248205</td>\n",
       "      <td>2020-01-04 10:00:43+00:00</td>\n",
       "      <td>pending_reminder_first</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50302</th>\n",
       "      <td>2179121</td>\n",
       "      <td>71</td>\n",
       "      <td>248205</td>\n",
       "      <td>2020-01-07 11:00:42+00:00</td>\n",
       "      <td>pending_reminder_second</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59960</th>\n",
       "      <td>2188894</td>\n",
       "      <td>417024</td>\n",
       "      <td>248205</td>\n",
       "      <td>2020-01-08 11:00:43+00:00</td>\n",
       "      <td>resolve</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61909</th>\n",
       "      <td>2190892</td>\n",
       "      <td>417024</td>\n",
       "      <td>248205</td>\n",
       "      <td>2020-01-08 13:55:11+00:00</td>\n",
       "      <td>rate</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>23 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            id  user_id  doubt_id                created_at            activity_type  is_student_id  is_TA_id  is_sys_id\n",
       "82     2121762   175223    248205 2020-01-01 06:46:24+00:00                   assign              0         1          0\n",
       "83     2121763   175223    248205 2020-01-01 06:46:34+00:00                   accept              0         1          0\n",
       "89     2121772   175223    248205 2020-01-01 06:49:38+00:00        review_resolution              0         1          0\n",
       "93     2121777   417024    248205 2020-01-01 06:50:07+00:00      review_not_resolved              1         0          0\n",
       "95     2121779   175223    248205 2020-01-01 06:50:11+00:00                   assign              0         1          0\n",
       "...        ...      ...       ...                       ...                      ...            ...       ...        ...\n",
       "363    2122266   175223    248205 2020-01-01 09:19:49+00:00  pending_action_required              0         1          0\n",
       "24310  2152208       71    248205 2020-01-04 10:00:43+00:00   pending_reminder_first              0         0          1\n",
       "50302  2179121       71    248205 2020-01-07 11:00:42+00:00  pending_reminder_second              0         0          1\n",
       "59960  2188894   417024    248205 2020-01-08 11:00:43+00:00                  resolve              1         0          0\n",
       "61909  2190892   417024    248205 2020-01-08 13:55:11+00:00                     rate              1         0          0\n",
       "\n",
       "[23 rows x 8 columns]"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "activities.loc[activities.doubt_id==248205]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=\"3\"> Another major thing to note here is for a particluar doubt **id** in doubts table there are multiple assign states. Which is considerable since a partcular doubt can be raised again for multiple reasons:\n",
    "- <font size=\"3\">TA needed some more information or Information wasn't sufficient\n",
    "- <font size=\"3\">User wasn't satisfied so the doubt was made ```available``` after ```resolved_review_pending```.\n",
    "- <font size=\"3\">Some technical issues\n",
    "    \n",
    "<font size=\"3\"> To tackle this while calculating the DRT I've made few **Assumptions**:\n",
    "- <font size=\"3\">For a user perspective the total time will be first time he raised a doubt to the point of resolution, but for an intermediate TA the time is different. So I introduce two features (which will be calculated):<br>\n",
    "    \n",
    "    <font size=\"3\">**DRT** = Doubt Resolution Time, which wil signify time taken by the last TA to solve the problem (which he'll be credited for)<br>\n",
    "    <font size=\"3\">**Doubt_life_cycle** = Total time, doubt with a particular `doubt_id` remained in the system<br>\n",
    "\n",
    "- <font size=\"3\">Multiple TA's are assigned to and worked on one doubts which is plausible since a doubt can be made available again if the user isnt satisfied or the doubt needed some new information to be solved.\n",
    "To work this I'm assuming the doubt assigned to last TA (according to assigned Timestamp) has solved it and it should be him who should get the credit for solving."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 5000/5000 [01:19<00:00, 63.16it/s]\n"
     ]
    }
   ],
   "source": [
    "# pick one id from doubts table (doubt_id) and extract corresponding created in activities table\n",
    "\n",
    "dlc = []\n",
    "drt = []\n",
    "TA_id = []\n",
    "with tqdm(total=len(doubts)) as pbar:\n",
    "    for index, row in doubts.iterrows():\n",
    "        doubt_id = row['id']\n",
    "        \n",
    "        # finding when was the doubt attained its first \"assign\" state by sorting through all assign states and picking the earliest\n",
    "        try:\n",
    "            # Doubt_life_cycle\n",
    "            assign_time = activities.loc[(activities.doubt_id==doubt_id) & \n",
    "                                         (activities.activity_type=='assign'),\n",
    "                                         ('created_at')].sort_values().iloc[0]\n",
    "\n",
    "            resolution_time = row['created_at']\n",
    "\n",
    "            dlc.append(convert_timedelta(resolution_time-assign_time))\n",
    "            \n",
    "        except Exception as e:\n",
    "            #print(\"Exception Occurred:\",e)\n",
    "            dlc.append(1)\n",
    "            \n",
    "        try:\n",
    "            # DRT\n",
    "            t = activities.loc[(activities.doubt_id==doubt_id) & \n",
    "                                (activities.activity_type=='assign') &\n",
    "                                (activities.is_TA_id==1),\n",
    "                                ('created_at','user_id')].sort_values(by='created_at').iloc[-1]\n",
    "            \n",
    "            assign_time = t['created_at']\n",
    "            resolution_time = row['created_at']\n",
    "            \n",
    "            TA_id.append(t['user_id'])\n",
    "            drt.append(convert_timedelta(resolution_time-assign_time))\n",
    "            \n",
    "        except Exception as e:\n",
    "            #print(\"Exception Occurred:\",e)\n",
    "            drt.append(1)\n",
    "            TA_id.append('not_assigned/resolve')\n",
    "            \n",
    "        pbar.update(1)\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "doubts['Doubt_resolution_time(min)'] = drt\n",
    "doubts['Doubt_life_cycle(min)'] = dlc\n",
    "doubts['TA_id'] = TA_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>user_id</th>\n",
       "      <th>course_id</th>\n",
       "      <th>created_at</th>\n",
       "      <th>content_type</th>\n",
       "      <th>user_rating</th>\n",
       "      <th>state</th>\n",
       "      <th>Doubt_resolution_time(min)</th>\n",
       "      <th>Doubt_life_cycle(min)</th>\n",
       "      <th>TA_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>248188</td>\n",
       "      <td>280541</td>\n",
       "      <td>10</td>\n",
       "      <td>2020-01-01 00:58:22+00:00</td>\n",
       "      <td>Code Problem</td>\n",
       "      <td>5</td>\n",
       "      <td>resolved</td>\n",
       "      <td>41.050000</td>\n",
       "      <td>44.850000</td>\n",
       "      <td>34120</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>248189</td>\n",
       "      <td>32227</td>\n",
       "      <td>12</td>\n",
       "      <td>2020-01-01 02:02:21+00:00</td>\n",
       "      <td>Multiple Problem</td>\n",
       "      <td>5</td>\n",
       "      <td>resolved</td>\n",
       "      <td>23.216667</td>\n",
       "      <td>23.216667</td>\n",
       "      <td>156881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>248190</td>\n",
       "      <td>357687</td>\n",
       "      <td>1</td>\n",
       "      <td>2020-01-01 02:14:37+00:00</td>\n",
       "      <td>Single_choice Problem</td>\n",
       "      <td>5</td>\n",
       "      <td>resolved</td>\n",
       "      <td>29.316667</td>\n",
       "      <td>29.316667</td>\n",
       "      <td>165368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>248191</td>\n",
       "      <td>486150</td>\n",
       "      <td>11</td>\n",
       "      <td>2020-01-01 02:15:15+00:00</td>\n",
       "      <td>No_submission Problem</td>\n",
       "      <td>4</td>\n",
       "      <td>resolved</td>\n",
       "      <td>40.716667</td>\n",
       "      <td>40.133333</td>\n",
       "      <td>208720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>248192</td>\n",
       "      <td>367920</td>\n",
       "      <td>13</td>\n",
       "      <td>2020-01-01 03:16:19+00:00</td>\n",
       "      <td>Datascience Problem</td>\n",
       "      <td>5</td>\n",
       "      <td>resolved</td>\n",
       "      <td>5.633333</td>\n",
       "      <td>33.233333</td>\n",
       "      <td>163412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4995</th>\n",
       "      <td>253194</td>\n",
       "      <td>332893</td>\n",
       "      <td>1</td>\n",
       "      <td>2020-01-08 14:33:21+00:00</td>\n",
       "      <td>VideoOffering</td>\n",
       "      <td>4</td>\n",
       "      <td>resolved</td>\n",
       "      <td>20.950000</td>\n",
       "      <td>52.333333</td>\n",
       "      <td>126618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4996</th>\n",
       "      <td>253195</td>\n",
       "      <td>388697</td>\n",
       "      <td>2</td>\n",
       "      <td>2020-01-08 14:33:27+00:00</td>\n",
       "      <td>Code Problem</td>\n",
       "      <td>5</td>\n",
       "      <td>resolved</td>\n",
       "      <td>57.566667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>112367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4997</th>\n",
       "      <td>253196</td>\n",
       "      <td>282065</td>\n",
       "      <td>1</td>\n",
       "      <td>2020-01-08 14:34:12+00:00</td>\n",
       "      <td>Code Problem</td>\n",
       "      <td>5</td>\n",
       "      <td>resolved</td>\n",
       "      <td>23.133333</td>\n",
       "      <td>42.633333</td>\n",
       "      <td>331381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4998</th>\n",
       "      <td>253197</td>\n",
       "      <td>32802</td>\n",
       "      <td>12</td>\n",
       "      <td>2020-01-08 14:34:22+00:00</td>\n",
       "      <td>Frontend Problem</td>\n",
       "      <td>5</td>\n",
       "      <td>resolved</td>\n",
       "      <td>57.350000</td>\n",
       "      <td>57.450000</td>\n",
       "      <td>38663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4999</th>\n",
       "      <td>253198</td>\n",
       "      <td>279096</td>\n",
       "      <td>1</td>\n",
       "      <td>2020-01-08 14:34:30+00:00</td>\n",
       "      <td>Single_choice Problem</td>\n",
       "      <td>3</td>\n",
       "      <td>resolved</td>\n",
       "      <td>42.283333</td>\n",
       "      <td>52.733333</td>\n",
       "      <td>44368</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5000 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          id  user_id  course_id                created_at           content_type user_rating     state  Doubt_resolution_time(min)  Doubt_life_cycle(min)   TA_id\n",
       "0     248188   280541         10 2020-01-01 00:58:22+00:00           Code Problem           5  resolved                   41.050000              44.850000   34120\n",
       "1     248189    32227         12 2020-01-01 02:02:21+00:00       Multiple Problem           5  resolved                   23.216667              23.216667  156881\n",
       "2     248190   357687          1 2020-01-01 02:14:37+00:00  Single_choice Problem           5  resolved                   29.316667              29.316667  165368\n",
       "3     248191   486150         11 2020-01-01 02:15:15+00:00  No_submission Problem           4  resolved                   40.716667              40.133333  208720\n",
       "4     248192   367920         13 2020-01-01 03:16:19+00:00    Datascience Problem           5  resolved                    5.633333              33.233333  163412\n",
       "...      ...      ...        ...                       ...                    ...         ...       ...                         ...                    ...     ...\n",
       "4995  253194   332893          1 2020-01-08 14:33:21+00:00          VideoOffering           4  resolved                   20.950000              52.333333  126618\n",
       "4996  253195   388697          2 2020-01-08 14:33:27+00:00           Code Problem           5  resolved                   57.566667               0.000000  112367\n",
       "4997  253196   282065          1 2020-01-08 14:34:12+00:00           Code Problem           5  resolved                   23.133333              42.633333  331381\n",
       "4998  253197    32802         12 2020-01-08 14:34:22+00:00       Frontend Problem           5  resolved                   57.350000              57.450000   38663\n",
       "4999  253198   279096          1 2020-01-08 14:34:30+00:00  Single_choice Problem           3  resolved                   42.283333              52.733333   44368\n",
       "\n",
       "[5000 rows x 10 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "doubts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Doubt Resolution Time for every TA (TA analysis)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=\"3\"> In this section we'll see:\n",
    "- <font size=\"3\">`Distribution of DRT` in multiple aspects\n",
    "- <font size=\"3\">Identifying the `Top performers` on the basis of time they took to solve doubts they were assigned."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "q1= 31.769523809523818\n",
      "q3= 40.21441441441441\n",
      "IQR: 8.444890604890588\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Sampling Distribution of mean Doubt Resolution by each TA')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(15, 5))\n",
    "\n",
    "x =doubts.groupby('TA_id', as_index=False)['Doubt_resolution_time(min)'].mean().sort_values(by='Doubt_resolution_time(min)')\n",
    "\n",
    "print('q1=',np.percentile(x['Doubt_resolution_time(min)'], 25))\n",
    "print('q3=',np.percentile(x['Doubt_resolution_time(min)'], 75))\n",
    "print(\"IQR:\",np.subtract(*np.percentile(x['Doubt_resolution_time(min)'].values, [75, 25])))\n",
    "\n",
    "sns.distplot(x['Doubt_resolution_time(min)'].values,ax=ax);\n",
    "ax.set_title('Sampling Distribution of mean Doubt Resolution by each TA', fontsize=15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=\"3\"> In the distribution plot above we can see that higher proportion of the TA's solves the **average doubt assigned to them in [31-40] minutes range**. This Sampling Distribution is close to a Normal Distribution with some degree of `left skewness`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Performance of a TA will also depend on the number of problems solved by them. So looking at that distribution will also be necesaary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = doubts.groupby('TA_id', as_index=False)['Doubt_resolution_time(min)'].mean()\n",
    "df.to_csv('DRT_perTA.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Number of Problems solved by TAs  : 4765\n",
      "Average Number of Problems solved by TAs: 33\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(15, 5))\n",
    "\n",
    "TOTAL_NO_OF_PROBLEMS_SOLVED_BY_EACH_TA = doubts.loc[doubts.TA_id!='not_assigned/resolve'].TA_id.value_counts()\n",
    "\n",
    "print(\"Total Number of Problems solved by TAs  :\",TOTAL_NO_OF_PROBLEMS_SOLVED_BY_EACH_TA.sum())\n",
    "print(\"Average Number of Problems solved by TAs:\",int(TOTAL_NO_OF_PROBLEMS_SOLVED_BY_EACH_TA.mean()))\n",
    "sns.distplot(TOTAL_NO_OF_PROBLEMS_SOLVED_BY_EACH_TA.values,ax=ax);\n",
    "ax.set_title(\"Distribution of Number of Problems solved by TA's\", fontsize=15)\n",
    "ax.set_xlabel('No of Problems Solved', size=15, labelpad=20)\n",
    "\n",
    "TOTAL_NO_OF_PROBLEMS_SOLVED_BY_EACH_TA.to_csv(\"TOTAL_NO_OF_PROBLEMS_SOLVED_BY_EACH_TA.csv\",index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- <font size=\"3\">You can see in the distribution plot above that very less number of TAs (highly competent or experienced ones') solves more than 100 problems.\n",
    "- <font size=\"3\">Most concentration of the no of problems solved by TA's is around 0-50 if we're being flexible"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **Thoughts after Distribution check:**\n",
    "- <font size=\"3\">Both distribution seems totally different. DRT Distribution sees a almost Normal Distribution which is a good sign but on the other hand No of problems distribution is highly skewed to the right which is a bad sign.\n",
    "- <font size=\"3\">This means TA's are solving problems at reasonable times but the frequency is very less to make it to adequate level of solving."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Identifying Top performers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=\"3\">We define Rank Metric for TA's as\n",
    "```python\n",
    "Rank_Metric = No_of_Problems_solved*User_rating/ Average_Doubt_Resolution_Time\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **The above metric constitutes all the cases to judge a TA on its merit:**\n",
    "- <font size=\"3\">A TA should be solving problems more frequently\n",
    "- <font size=\"3\">He should be recieving a good rating from the users\n",
    "- <font size=\"3\">Solving doubts at reasonable time at best\n",
    "\n",
    "#### **But it has it some ambiguties like:**\n",
    "- <font size=\"3\">A low user rating may not be a TA's fault (other paramters like Doubt Life Cycle and technical issue)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "t = doubts.loc[doubts.user_rating!='NR']  #NR won't be considered for the metric\n",
    "t.user_rating = t.user_rating.astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "RM = t.groupby('TA_id').agg({'TA_id':'count', \n",
    "                            'Doubt_resolution_time(min)':'mean',\n",
    "                            'user_rating':'mean'\n",
    "                             })\n",
    "RM.columns = ['No_of_doubts_cleared','DRT','Mean_user_rating']\n",
    "RM.drop(['not_assigned/resolve'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>No_of_doubts_cleared</th>\n",
       "      <th>DRT</th>\n",
       "      <th>Mean_user_rating</th>\n",
       "      <th>Rank_Metric</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TA_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1940</th>\n",
       "      <td>26</td>\n",
       "      <td>34.631410</td>\n",
       "      <td>3.884615</td>\n",
       "      <td>2.916428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6974</th>\n",
       "      <td>1</td>\n",
       "      <td>41.716667</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.095885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8709</th>\n",
       "      <td>26</td>\n",
       "      <td>41.186538</td>\n",
       "      <td>4.500000</td>\n",
       "      <td>2.840734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10064</th>\n",
       "      <td>16</td>\n",
       "      <td>40.598958</td>\n",
       "      <td>4.562500</td>\n",
       "      <td>1.798076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11519</th>\n",
       "      <td>8</td>\n",
       "      <td>40.143750</td>\n",
       "      <td>4.375000</td>\n",
       "      <td>0.871867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>296700</th>\n",
       "      <td>124</td>\n",
       "      <td>34.832930</td>\n",
       "      <td>4.467742</td>\n",
       "      <td>15.904490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>309608</th>\n",
       "      <td>3</td>\n",
       "      <td>34.544444</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.434223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>331381</th>\n",
       "      <td>32</td>\n",
       "      <td>30.975521</td>\n",
       "      <td>4.562500</td>\n",
       "      <td>4.713399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>343098</th>\n",
       "      <td>39</td>\n",
       "      <td>37.417949</td>\n",
       "      <td>4.564103</td>\n",
       "      <td>4.757075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>368274</th>\n",
       "      <td>1</td>\n",
       "      <td>1.166667</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.285714</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>144 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        No_of_doubts_cleared        DRT  Mean_user_rating  Rank_Metric\n",
       "TA_id                                                                 \n",
       "1940                      26  34.631410          3.884615     2.916428\n",
       "6974                       1  41.716667          4.000000     0.095885\n",
       "8709                      26  41.186538          4.500000     2.840734\n",
       "10064                     16  40.598958          4.562500     1.798076\n",
       "11519                      8  40.143750          4.375000     0.871867\n",
       "...                      ...        ...               ...          ...\n",
       "296700                   124  34.832930          4.467742    15.904490\n",
       "309608                     3  34.544444          5.000000     0.434223\n",
       "331381                    32  30.975521          4.562500     4.713399\n",
       "343098                    39  37.417949          4.564103     4.757075\n",
       "368274                     1   1.166667          5.000000     4.285714\n",
       "\n",
       "[144 rows x 4 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "RM['Rank_Metric'] = RM.No_of_doubts_cleared*RM.Mean_user_rating/RM.DRT\n",
    "display(RM)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>No_of_doubts_cleared</th>\n",
       "      <th>DRT</th>\n",
       "      <th>Mean_user_rating</th>\n",
       "      <th>Rank_Metric</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TA_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>13443</th>\n",
       "      <td>170</td>\n",
       "      <td>41.198824</td>\n",
       "      <td>4.605882</td>\n",
       "      <td>19.005397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44538</th>\n",
       "      <td>141</td>\n",
       "      <td>37.118440</td>\n",
       "      <td>4.638298</td>\n",
       "      <td>17.619275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>296700</th>\n",
       "      <td>124</td>\n",
       "      <td>34.832930</td>\n",
       "      <td>4.467742</td>\n",
       "      <td>15.904490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43738</th>\n",
       "      <td>96</td>\n",
       "      <td>34.057465</td>\n",
       "      <td>4.708333</td>\n",
       "      <td>13.271686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50408</th>\n",
       "      <td>96</td>\n",
       "      <td>34.479688</td>\n",
       "      <td>4.552083</td>\n",
       "      <td>12.674129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44368</th>\n",
       "      <td>96</td>\n",
       "      <td>36.593576</td>\n",
       "      <td>4.552083</td>\n",
       "      <td>11.941987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129364</th>\n",
       "      <td>107</td>\n",
       "      <td>41.983333</td>\n",
       "      <td>4.644860</td>\n",
       "      <td>11.838031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200604</th>\n",
       "      <td>91</td>\n",
       "      <td>38.029487</td>\n",
       "      <td>4.692308</td>\n",
       "      <td>11.228129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19839</th>\n",
       "      <td>73</td>\n",
       "      <td>32.497489</td>\n",
       "      <td>4.753425</td>\n",
       "      <td>10.677748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47173</th>\n",
       "      <td>73</td>\n",
       "      <td>34.944292</td>\n",
       "      <td>4.739726</td>\n",
       "      <td>9.901474</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        No_of_doubts_cleared        DRT  Mean_user_rating  Rank_Metric\n",
       "TA_id                                                                 \n",
       "13443                    170  41.198824          4.605882    19.005397\n",
       "44538                    141  37.118440          4.638298    17.619275\n",
       "296700                   124  34.832930          4.467742    15.904490\n",
       "43738                     96  34.057465          4.708333    13.271686\n",
       "50408                     96  34.479688          4.552083    12.674129\n",
       "44368                     96  36.593576          4.552083    11.941987\n",
       "129364                   107  41.983333          4.644860    11.838031\n",
       "200604                    91  38.029487          4.692308    11.228129\n",
       "19839                     73  32.497489          4.753425    10.677748\n",
       "47173                     73  34.944292          4.739726     9.901474"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Top 10 Performers')"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "top_performers = RM.sort_values(by='Rank_Metric',ascending=False).iloc[:10]\n",
    "\n",
    "display(top_performers)\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(15, 8))\n",
    "sns.barplot(x=top_performers.index,y='Rank_Metric', data=top_performers,ax=ax,capsize=.2)\n",
    "ax.set_title('Top 10 Performers', fontsize=15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### From the bar plot above we know who our top performers are (don't forget to give them a raise xD)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Doubt analysis on content type and course id"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=\"3\"> Effect of Doubt life cycle time on different content types and Courses."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax1 = plt.subplots(figsize=(25, 6))\n",
    "sns.barplot(x=\"content_type\",\n",
    "            y='Doubt_life_cycle(min)', \n",
    "            data= doubts.groupby('content_type', as_index=False)['Doubt_life_cycle(min)'].mean(),\n",
    "            ax=ax1,\n",
    "            capsize=.2)\n",
    "ax1.set_title('Average Time taken by doubt from each Content_type (red horizontal line denotes mean of DLC)', fontsize=15)\n",
    "\n",
    "ax2 = ax1.twiny()\n",
    "ax2 = plt.axhline(y=doubts['Doubt_life_cycle(min)'].mean(),color='red')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### In the plot above we can observe that\n",
    "- <font size=\"3\">Code Problem, Frontend problem, Note Offering, Single Problem Offering are the content_types (no particular course) in which the doubts took longer than average case.\n",
    "- <font size=\"3\">Single Problem Offering tops the list almost 55min average problem solving time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax1 = plt.subplots(figsize=(25, 6))\n",
    "\n",
    "sns.barplot(x=\"course_id\",\n",
    "            y='Doubt_life_cycle(min)', \n",
    "            data= doubts.groupby('course_id', as_index=False)['Doubt_life_cycle(min)'].mean(),\n",
    "            ax=ax1,\n",
    "            capsize=.2)\n",
    "ax1.set_title('Average Time taken by doubt from each Course', fontsize=15)\n",
    "\n",
    "ax2 = ax1.twiny()\n",
    "ax2 = plt.axhline(y=doubts['Doubt_life_cycle(min)'].mean(),color='red')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=\"3\">This denotes a similar plot now with different ```Course Ids```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Number of Requests')"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# which content_type recieved the most doubt requests (and were completed)\n",
    "fig, ax = plt.subplots(figsize=(25, 6))\n",
    "sns.barplot(x=doubts.content_type.value_counts().index,y=doubts.content_type.value_counts(),ax=ax,capsize=.2)\n",
    "ax.set_title('Content Type by recieving Number of doubts request', fontsize=15)\n",
    "ax.set_xlabel('Content Type',fontsize=15)\n",
    "ax.set_ylabel('Number of Requests',fontsize=15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Observation from above plot:\n",
    "- <font size=\"3\">Code Problem has exceedingly large doubt request as compared to the other type of content type\n",
    "- <font size=\"3\">This leads to knowing that most of course contents involve a lot of Coding and Logic so this outcome is not surprising"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Combining Course Ids and Content Types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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 th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Doubt_life_cycle(min)</th>\n",
       "      <th>id</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>course_id</th>\n",
       "      <th>content_type</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"9\" valign=\"top\">1</th>\n",
       "      <th>Code Problem</th>\n",
       "      <td>42.118379</td>\n",
       "      <td>1363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Datascience Problem</th>\n",
       "      <td>56.233333</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fill_up Problem</th>\n",
       "      <td>44.772727</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Multiple Problem</th>\n",
       "      <td>43.466667</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>No_submission Problem</th>\n",
       "      <td>41.136364</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NoteOffering</th>\n",
       "      <td>46.672222</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SingleProblemOffering</th>\n",
       "      <td>57.541667</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Single_choice Problem</th>\n",
       "      <td>44.611974</td>\n",
       "      <td>103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VideoOffering</th>\n",
       "      <td>36.330236</td>\n",
       "      <td>113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"7\" valign=\"top\">2</th>\n",
       "      <th>Code Problem</th>\n",
       "      <td>40.179062</td>\n",
       "      <td>519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fill_up Problem</th>\n",
       "      <td>28.001852</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Multiple Problem</th>\n",
       "      <td>37.218519</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>No_submission Problem</th>\n",
       "      <td>29.505556</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NoteOffering</th>\n",
       "      <td>33.366667</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Single_choice Problem</th>\n",
       "      <td>28.371970</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VideoOffering</th>\n",
       "      <td>38.373016</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">4</th>\n",
       "      <th>Code Problem</th>\n",
       "      <td>32.786920</td>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Multiple Problem</th>\n",
       "      <td>36.829762</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Single_choice Problem</th>\n",
       "      <td>33.790000</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VideoOffering</th>\n",
       "      <td>40.654762</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">6</th>\n",
       "      <th>Code Problem</th>\n",
       "      <td>30.690351</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fill_up Problem</th>\n",
       "      <td>42.406250</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Machine_learning Problem</th>\n",
       "      <td>37.384444</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Multiple Problem</th>\n",
       "      <td>47.362500</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NoteOffering</th>\n",
       "      <td>39.666667</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ProjectsOffering</th>\n",
       "      <td>31.633333</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Single_choice Problem</th>\n",
       "      <td>32.440909</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VideoOffering</th>\n",
       "      <td>39.531579</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"7\" valign=\"top\">10</th>\n",
       "      <th>Code Problem</th>\n",
       "      <td>39.006146</td>\n",
       "      <td>762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fill_up Problem</th>\n",
       "      <td>40.516667</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Multiple Problem</th>\n",
       "      <td>41.640556</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NoteOffering</th>\n",
       "      <td>6.816667</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SingleProblemOffering</th>\n",
       "      <td>58.166667</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Single_choice Problem</th>\n",
       "      <td>38.472222</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VideoOffering</th>\n",
       "      <td>36.993725</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"7\" valign=\"top\">11</th>\n",
       "      <th>Code Problem</th>\n",
       "      <td>48.338797</td>\n",
       "      <td>604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fill_up Problem</th>\n",
       "      <td>59.316667</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Multiple Problem</th>\n",
       "      <td>36.620370</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>No_submission Problem</th>\n",
       "      <td>49.492593</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NoteOffering</th>\n",
       "      <td>57.055556</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Single_choice Problem</th>\n",
       "      <td>46.889247</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VideoOffering</th>\n",
       "      <td>50.363043</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">12</th>\n",
       "      <th>Fill_up Problem</th>\n",
       "      <td>40.161111</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Frontend Problem</th>\n",
       "      <td>40.350407</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Multiple Problem</th>\n",
       "      <td>36.523656</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>No_submission Problem</th>\n",
       "      <td>47.133333</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NoteOffering</th>\n",
       "      <td>58.775000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ProjectsOffering</th>\n",
       "      <td>27.466667</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Single_choice Problem</th>\n",
       "      <td>36.341667</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VideoOffering</th>\n",
       "      <td>37.451258</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"9\" valign=\"top\">13</th>\n",
       "      <th>Code Problem</th>\n",
       "      <td>29.185870</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Datascience Problem</th>\n",
       "      <td>34.639795</td>\n",
       "      <td>276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fill_up Problem</th>\n",
       "      <td>35.711333</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Machine_learning Problem</th>\n",
       "      <td>34.458333</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Multiple Problem</th>\n",
       "      <td>33.660417</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>No_submission Problem</th>\n",
       "      <td>12.546667</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ProjectsOffering</th>\n",
       "      <td>40.597531</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Single_choice Problem</th>\n",
       "      <td>30.485185</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VideoOffering</th>\n",
       "      <td>41.725758</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">21</th>\n",
       "      <th>Single_choice Problem</th>\n",
       "      <td>31.550641</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VideoOffering</th>\n",
       "      <td>32.883333</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"7\" valign=\"top\">23</th>\n",
       "      <th>Code Problem</th>\n",
       "      <td>48.610638</td>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fill_up Problem</th>\n",
       "      <td>59.087500</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Frontend Problem</th>\n",
       "      <td>49.421429</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Multiple Problem</th>\n",
       "      <td>53.678333</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>No_submission Problem</th>\n",
       "      <td>47.012500</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Single_choice Problem</th>\n",
       "      <td>41.046970</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VideoOffering</th>\n",
       "      <td>50.652778</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    Doubt_life_cycle(min)    id\n",
       "course_id content_type                                         \n",
       "1         Code Problem                          42.118379  1363\n",
       "          Datascience Problem                   56.233333     1\n",
       "          Fill_up Problem                       44.772727    55\n",
       "          Multiple Problem                      43.466667    63\n",
       "          No_submission Problem                 41.136364    22\n",
       "          NoteOffering                          46.672222     3\n",
       "          SingleProblemOffering                 57.541667     2\n",
       "          Single_choice Problem                 44.611974   103\n",
       "          VideoOffering                         36.330236   113\n",
       "2         Code Problem                          40.179062   519\n",
       "          Fill_up Problem                       28.001852     9\n",
       "          Multiple Problem                      37.218519     9\n",
       "          No_submission Problem                 29.505556     6\n",
       "          NoteOffering                          33.366667     4\n",
       "          Single_choice Problem                 28.371970    22\n",
       "          VideoOffering                         38.373016    42\n",
       "4         Code Problem                          32.786920    79\n",
       "          Multiple Problem                      36.829762    14\n",
       "          Single_choice Problem                 33.790000     5\n",
       "          VideoOffering                         40.654762     7\n",
       "6         Code Problem                          30.690351    19\n",
       "          Fill_up Problem                       42.406250     8\n",
       "          Machine_learning Problem              37.384444    15\n",
       "          Multiple Problem                      47.362500     4\n",
       "          NoteOffering                          39.666667     3\n",
       "          ProjectsOffering                      31.633333     9\n",
       "          Single_choice Problem                 32.440909    11\n",
       "          VideoOffering                         39.531579    19\n",
       "10        Code Problem                          39.006146   762\n",
       "          Fill_up Problem                       40.516667    17\n",
       "          Multiple Problem                      41.640556    30\n",
       "          NoteOffering                           6.816667     1\n",
       "          SingleProblemOffering                 58.166667     4\n",
       "          Single_choice Problem                 38.472222    18\n",
       "          VideoOffering                         36.993725    85\n",
       "11        Code Problem                          48.338797   604\n",
       "          Fill_up Problem                       59.316667     1\n",
       "          Multiple Problem                      36.620370     9\n",
       "          No_submission Problem                 49.492593    18\n",
       "          NoteOffering                          57.055556     6\n",
       "          Single_choice Problem                 46.889247    31\n",
       "          VideoOffering                         50.363043    69\n",
       "12        Fill_up Problem                       40.161111     3\n",
       "          Frontend Problem                      40.350407    41\n",
       "          Multiple Problem                      36.523656    31\n",
       "          No_submission Problem                 47.133333     1\n",
       "          NoteOffering                          58.775000     2\n",
       "          ProjectsOffering                      27.466667     8\n",
       "          Single_choice Problem                 36.341667    38\n",
       "          VideoOffering                         37.451258    53\n",
       "13        Code Problem                          29.185870    46\n",
       "          Datascience Problem                   34.639795   276\n",
       "          Fill_up Problem                       35.711333    25\n",
       "          Machine_learning Problem              34.458333    18\n",
       "          Multiple Problem                      33.660417     8\n",
       "          No_submission Problem                 12.546667     5\n",
       "          ProjectsOffering                      40.597531    27\n",
       "          Single_choice Problem                 30.485185     9\n",
       "          VideoOffering                         41.725758    22\n",
       "21        Single_choice Problem                 31.550641    26\n",
       "          VideoOffering                         32.883333     7\n",
       "23        Code Problem                          48.610638    94\n",
       "          Fill_up Problem                       59.087500     4\n",
       "          Frontend Problem                      49.421429    14\n",
       "          Multiple Problem                      53.678333    10\n",
       "          No_submission Problem                 47.012500     8\n",
       "          Single_choice Problem                 41.046970    22\n",
       "          VideoOffering                         50.652778    18"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.set_option('display.max_rows', 68)\n",
    "display(doubts.pivot_table(index=['course_id','content_type'],\n",
    "                           values=['Doubt_life_cycle(min)','id'],\n",
    "                           aggfunc={'Doubt_life_cycle(min)':'mean', 'id':'size'}))\n",
    "pd.set_option('display.max_rows', 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "pt = pd.DataFrame(doubts.groupby(['course_id','content_type'], as_index=False)\\\n",
    "                 .agg({'Doubt_life_cycle(min)':'mean', 'id':'size'}) \\\n",
    "                 .rename(columns={'Doubt_life_cycle(min)':'DLC','id':'Doubts_solved'}))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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 th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>course_id</th>\n",
       "      <th>content_type</th>\n",
       "      <th>DLC</th>\n",
       "      <th>Doubts_solved</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Code Problem</td>\n",
       "      <td>42.118379</td>\n",
       "      <td>1363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Datascience Problem</td>\n",
       "      <td>56.233333</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>Fill_up Problem</td>\n",
       "      <td>44.772727</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>Multiple Problem</td>\n",
       "      <td>43.466667</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>No_submission Problem</td>\n",
       "      <td>41.136364</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>23</td>\n",
       "      <td>Frontend Problem</td>\n",
       "      <td>49.421429</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>23</td>\n",
       "      <td>Multiple Problem</td>\n",
       "      <td>53.678333</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>23</td>\n",
       "      <td>No_submission Problem</td>\n",
       "      <td>47.012500</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>23</td>\n",
       "      <td>Single_choice Problem</td>\n",
       "      <td>41.046970</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>23</td>\n",
       "      <td>VideoOffering</td>\n",
       "      <td>50.652778</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>68 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    course_id           content_type        DLC  Doubts_solved\n",
       "0           1           Code Problem  42.118379           1363\n",
       "1           1    Datascience Problem  56.233333              1\n",
       "2           1        Fill_up Problem  44.772727             55\n",
       "3           1       Multiple Problem  43.466667             63\n",
       "4           1  No_submission Problem  41.136364             22\n",
       "..        ...                    ...        ...            ...\n",
       "63         23       Frontend Problem  49.421429             14\n",
       "64         23       Multiple Problem  53.678333             10\n",
       "65         23  No_submission Problem  47.012500              8\n",
       "66         23  Single_choice Problem  41.046970             22\n",
       "67         23          VideoOffering  50.652778             18\n",
       "\n",
       "[68 rows x 4 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "content_types = pt.content_type.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x3600 with 24 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(nrows=int(len(content_types)/2),ncols=2,figsize=(20, 50))\n",
    "ax=ax.flatten()\n",
    "dlc_avg = doubts['Doubt_life_cycle(min)'].mean()\n",
    "for i,ctype in enumerate(content_types):\n",
    "    t = pt.loc[pt.content_type==ctype]\n",
    "\n",
    "    g = sns.barplot(x='course_id',y='DLC', data=t, ax=ax[i], capsize=.2)\n",
    "    \n",
    "    #for index, row in t.iterrows():\n",
    "    #    g.text(row.course_id, row.DLC, row.Doubts_solved, color='black', ha=\"center\")\n",
    "    \n",
    "    ax[i].set_title(ctype, fontsize=15)\n",
    "    \n",
    "    ax2 = ax[i].twiny()\n",
    "    ax2 = plt.axhline(y=dlc_avg,color='red')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x3600 with 24 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(nrows=int(len(content_types)/2),ncols=2,figsize=(20, 50))\n",
    "ax=ax.flatten()\n",
    "req_avg = pt['Doubts_solved'].mean()\n",
    "\n",
    "for i,ctype in enumerate(content_types):\n",
    "    t = pt.loc[pt.content_type==ctype]\n",
    "\n",
    "    g = sns.barplot(x='course_id',y='Doubts_solved', data=t, ax=ax[i], capsize=.2)\n",
    "    \n",
    "    #for index, row in t.iterrows():\n",
    "    #    g.text(row.course_id, row.DLC, row.Doubts_solved, color='black', ha=\"center\")\n",
    "    \n",
    "    ax[i].set_title(ctype, fontsize=15)\n",
    "    \n",
    "    ax2 = ax[i].twiny()\n",
    "    ax2 = plt.axhline(y=req_avg,color='red')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note**: Observe that the scales of the y-axis in the plot above are different"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The table above and the subplots above denote the same information with tabular and plot form. Let's breakdown the results and information we get.\n",
    "- <font size=\"3\">```Code Problem```- Courses 1,11 and 13 were there in the system more than the average doubt life cycle and more number of problems are solved and raised which leads us the decision that more TAs should be assigned (or hired) towards increased demand towards code problem solving. Similar issue can be seen with the requests made to this content type\n",
    "- <font size=\"3\">```Fill up problem``` - Courses 1,6,11,23 have more than average solve times.\n",
    "- <font size=\"3\">```Multiple problem``` - Courses 1,6,23 have more than average solve times.\n",
    "- <font size=\"3\">```Single Choice problem``` - Courses 1,11 have more than average solve times.\n",
    "\n",
    "#### **Takeaways:**\n",
    "- <font size=\"3\"> Courses 1,11,23 and 6 are pretty popular once, gathering almost all doubt requests and hogging the doubt cycle times.\n",
    "- <font size=\"3\"> Manepower although already heavy may require stacking up because of popularity"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Realtionship between Doubt Life Cycle and User Ratings"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Too see a relationship or interdependency of Doubt Life Cycle(continuous) and User Rating(categorical) we need to see multiple tests and quantify the result if possible. Methods to try:\n",
    "- <font size=\"3\">`Point biserial Correlation` - Similar to Pearsons correlation but work dichotomous (binarized) variables and assumes normal distribution and linear behaviour.\n",
    "- <font size=\"3\">`Logistic Regression Coefficients` - It lay out the relationship by transforming the hyperpace using logit function and fits a straight line.\n",
    "- <font size=\"3\">`ANOVA test` - It works by analyzing how much variance in the continuous data is explained by the variance in categorical one."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "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 th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Doubt_life_cycle(min)</th>\n",
       "      <th>user_rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>44.850000</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>23.216667</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>29.316667</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40.133333</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>33.233333</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4995</th>\n",
       "      <td>52.333333</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4996</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4997</th>\n",
       "      <td>42.633333</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4998</th>\n",
       "      <td>57.450000</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4999</th>\n",
       "      <td>52.733333</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4988 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Doubt_life_cycle(min)  user_rating\n",
       "0                 44.850000            5\n",
       "1                 23.216667            5\n",
       "2                 29.316667            5\n",
       "3                 40.133333            4\n",
       "4                 33.233333            5\n",
       "...                     ...          ...\n",
       "4995              52.333333            4\n",
       "4996               0.000000            5\n",
       "4997              42.633333            5\n",
       "4998              57.450000            5\n",
       "4999              52.733333            3\n",
       "\n",
       "[4988 rows x 2 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "DRT_UR = doubts[['Doubt_life_cycle(min)','user_rating']]\n",
    "DRT_UR = DRT_UR.loc[DRT_UR.user_rating!='NR']\n",
    "DRT_UR.user_rating = DRT_UR.user_rating.astype(int)\n",
    "display(DRT_UR)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Write about point biserial and kruskal not working"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy import stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PointbiserialrResult(correlation=0.0377768014070839, pvalue=0.0076236253931961885)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.pointbiserialr(DRT_UR.user_rating.values, DRT_UR['Doubt_life_cycle(min)'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=\"3\">```Point Biserial``` reports **0.03** correlation with **p-value of 0.0076** (<0.05) which signifies there isnt enough evidence to rule out null Hypothesis of DLC and User rating of having a relation.\n",
    "<font size=\"3\"> So, relationship does exist but its close to 0 (according to this method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr = LogisticRegression(C=1.0, \n",
    "                        class_weight='balanced', #n_samples / (n_classes * np.bincount(y)) \n",
    "                        dual=False, \n",
    "                        fit_intercept=True,\n",
    "                        intercept_scaling=1, \n",
    "                        l1_ratio=None, \n",
    "                        max_iter=100,\n",
    "                        multi_class='multinomial', \n",
    "                        n_jobs=-1, \n",
    "                        penalty='l2',\n",
    "                        random_state=RANDOM_SEED, \n",
    "                        solver='newton-cg', \n",
    "                        tol=0.0001, \n",
    "                        verbose=0,\n",
    "                        warm_start=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = DRT_UR['Doubt_life_cycle(min)'].values.reshape(-1,1)\n",
    "y = DRT_UR.user_rating.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight='balanced', dual=False,\n",
       "                   fit_intercept=True, intercept_scaling=1, l1_ratio=None,\n",
       "                   max_iter=100, multi_class='multinomial', n_jobs=-1,\n",
       "                   penalty='l2', random_state=None, solver='newton-cg',\n",
       "                   tol=0.0001, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-0.00794608]\n",
      " [ 0.0062662 ]\n",
      " [ 0.00224408]\n",
      " [-0.00179991]\n",
      " [ 0.0012357 ]]\n",
      "[ 0.31805654 -0.26011984 -0.08877791  0.07756717 -0.04672595]\n"
     ]
    }
   ],
   "source": [
    "print(lr.coef_)\n",
    "print(lr.intercept_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=\"3\">According to the Logit method we can see some slopes being negative. We can interpret that **increasing Doubt Times negatively impact the user ratings**. But the quantities are way to less to point to something conclusive."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Box Plot to analyze the DLC for every User Rating')"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(15, 6))\n",
    "sns.boxplot(x=\"user_rating\", y=\"Doubt_life_cycle(min)\", data=DRT_UR,ax=ax)\n",
    "\n",
    "ax.set_title('Box Plot to analyze the DLC for every User Rating', fontsize=15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### This can be the possible reason why we aren't able to quanitify our relationship setting.\n",
    "- <font size=\"3\">User Rating are spread acroess different Doubts times, indirectly hampering the predicting quantification of relationship between the two.\n",
    "- <font size=\"3\">We can interpret that User rating is not completely dependent on Doubt times (atleast not in this dataset)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Activities table analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "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 th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>user_id</th>\n",
       "      <th>doubt_id</th>\n",
       "      <th>created_at</th>\n",
       "      <th>activity_type</th>\n",
       "      <th>is_student_id</th>\n",
       "      <th>is_TA_id</th>\n",
       "      <th>is_sys_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2121357</td>\n",
       "      <td>120623</td>\n",
       "      <td>248193</td>\n",
       "      <td>2020-01-01 04:26:08+00:00</td>\n",
       "      <td>assign</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2121358</td>\n",
       "      <td>120623</td>\n",
       "      <td>248193</td>\n",
       "      <td>2020-01-01 04:26:15+00:00</td>\n",
       "      <td>accept</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2121359</td>\n",
       "      <td>165368</td>\n",
       "      <td>248199</td>\n",
       "      <td>2020-01-01 04:29:18+00:00</td>\n",
       "      <td>assign</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2121360</td>\n",
       "      <td>165368</td>\n",
       "      <td>248199</td>\n",
       "      <td>2020-01-01 04:29:34+00:00</td>\n",
       "      <td>reject</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2121399</td>\n",
       "      <td>265305</td>\n",
       "      <td>248191</td>\n",
       "      <td>2020-01-01 04:35:07+00:00</td>\n",
       "      <td>assign</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76203</th>\n",
       "      <td>4322614</td>\n",
       "      <td>229989</td>\n",
       "      <td>252698</td>\n",
       "      <td>2020-05-01 12:26:00+00:00</td>\n",
       "      <td>rate</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76204</th>\n",
       "      <td>4322618</td>\n",
       "      <td>229989</td>\n",
       "      <td>249512</td>\n",
       "      <td>2020-05-01 12:26:07+00:00</td>\n",
       "      <td>rate</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76205</th>\n",
       "      <td>4699532</td>\n",
       "      <td>47336</td>\n",
       "      <td>249949</td>\n",
       "      <td>2020-05-12 13:53:29+00:00</td>\n",
       "      <td>rate</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76206</th>\n",
       "      <td>4905034</td>\n",
       "      <td>175678</td>\n",
       "      <td>252370</td>\n",
       "      <td>2020-05-18 13:24:14+00:00</td>\n",
       "      <td>rate</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76207</th>\n",
       "      <td>4905041</td>\n",
       "      <td>175678</td>\n",
       "      <td>252373</td>\n",
       "      <td>2020-05-18 13:24:16+00:00</td>\n",
       "      <td>rate</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>76208 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            id  user_id  doubt_id                created_at activity_type  is_student_id  is_TA_id  is_sys_id\n",
       "0      2121357   120623    248193 2020-01-01 04:26:08+00:00        assign              0         1          0\n",
       "1      2121358   120623    248193 2020-01-01 04:26:15+00:00        accept              0         1          0\n",
       "2      2121359   165368    248199 2020-01-01 04:29:18+00:00        assign              0         1          0\n",
       "3      2121360   165368    248199 2020-01-01 04:29:34+00:00        reject              0         1          0\n",
       "4      2121399   265305    248191 2020-01-01 04:35:07+00:00        assign              0         1          0\n",
       "...        ...      ...       ...                       ...           ...            ...       ...        ...\n",
       "76203  4322614   229989    252698 2020-05-01 12:26:00+00:00          rate              1         0          0\n",
       "76204  4322618   229989    249512 2020-05-01 12:26:07+00:00          rate              1         0          0\n",
       "76205  4699532    47336    249949 2020-05-12 13:53:29+00:00          rate              1         0          0\n",
       "76206  4905034   175678    252370 2020-05-18 13:24:14+00:00          rate              1         0          0\n",
       "76207  4905041   175678    252373 2020-05-18 13:24:16+00:00          rate              1         0          0\n",
       "\n",
       "[76208 rows x 8 columns]"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "activities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Count')"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(25, 6))\n",
    "sns.barplot(x=activities.activity_type.value_counts().index,y=activities.activity_type.value_counts(),ax=ax,capsize=.2)\n",
    "\n",
    "ax.set_title('Distribution of different activity states', fontsize=15)\n",
    "ax.set_xlabel('Activity States',fontsize=15)\n",
    "ax.set_xticklabels(ax.get_xticklabels(), rotation=30, ha=\"right\")\n",
    "ax.set_ylabel('Count',fontsize=15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- <font size=\"3\">Lots of doubts have assign state but not make their way through the whole pipeline which is possible since they are intermediate states.\n",
    "- <font size=\"3\">Reject count is quite high, which is not a good thing."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Which of the doubts were assigned but never completed or resolved (in this database) and which course id and content type do they belong to"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "d_id1 = activities.doubt_id.unique()\n",
    "d_id2 = doubts.id.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([], dtype=int64)"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.setdiff1d(d_id1,d_id2,assume_unique=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=\"3\"> Since ever doubt every entered the database is resolved which is a feat in itself, its quite surprising how the doubt isn't gone through the pipline shown in above bar plot. Most probable reason can be data storing or removal of non-essential intermediate states."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "kaggle",
   "language": "python",
   "name": "kaggle"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}