_notebooks/2020-05-09-Developer-101-Data-Science-Uncovered.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# \"Data Analysis: Developer 101 | Data Science Uncovered\"\n",
    "> \"A Crisp analysis over available data from students registrations and attendees information from GoToMeeting\"\n",
    "\n",
    "- toc: true \n",
    "- badges: true\n",
    "- comments: true\n",
    "- categories: [EDA]\n",
    "- comments: true\n",
    "- author: Teja Kummarikuntla"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Developer 101 | Data Science Uncovered\n",
    "The following notebook is an analysis of an online webinar organised by [Sathyabama Coding Club](https://sites.google.com/view/sathyabamacodingclub)\n",
    "- The data used here is private"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "gotom_data = pd.read_excel(\"Developer 101_ Data Science Uncovered Attendees.xls\")\n",
    "reg_data = pd.read_excel(\"Developer 101 _ Data Science Uncovered (Responses).xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "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>Developer 101: Data Science Uncovered Attendees</th>\n",
       "      <th>Unnamed: 1</th>\n",
       "      <th>Unnamed: 2</th>\n",
       "      <th>Unnamed: 3</th>\n",
       "      <th>Unnamed: 4</th>\n",
       "      <th>Unnamed: 5</th>\n",
       "      <th>GoToMeeting</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Summary</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Meeting Date</td>\n",
       "      <td>Meeting Duration</td>\n",
       "      <td>Number of Attendees</td>\n",
       "      <td>Meeting ID</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>May 9, 2020 7:06 AM PDT</td>\n",
       "      <td>90 minutes</td>\n",
       "      <td>54</td>\n",
       "      <td>905-245-013</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Details</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Developer 101: Data Science Uncovered Attendees        Unnamed: 1  \\\n",
       "0                                         Summary               NaN   \n",
       "1                                    Meeting Date  Meeting Duration   \n",
       "2                         May 9, 2020 7:06 AM PDT        90 minutes   \n",
       "3                                             NaN               NaN   \n",
       "4                                         Details               NaN   \n",
       "\n",
       "            Unnamed: 2   Unnamed: 3 Unnamed: 4  Unnamed: 5  GoToMeeting  \n",
       "0                  NaN          NaN        NaN         NaN          NaN  \n",
       "1  Number of Attendees   Meeting ID        NaN         NaN          NaN  \n",
       "2                   54  905-245-013        NaN         NaN          NaN  \n",
       "3                  NaN          NaN        NaN         NaN          NaN  \n",
       "4                  NaN          NaN        NaN         NaN          NaN  "
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gotom_data.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "attn_data = gotom_data.iloc[6:,:5]\n",
    "attn_data.columns = gotom_data.iloc[5:,:5].iloc[[0]].values.reshape(5,)\n",
    "attn_data.reset_index(drop=True, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "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>Name</th>\n",
       "      <th>Email Address</th>\n",
       "      <th>Join Time</th>\n",
       "      <th>Leave Time</th>\n",
       "      <th>Time in Session (minutes)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>#ReligiousCorona</td>\n",
       "      <td>lol@gmail.com</td>\n",
       "      <td>7:51 AM</td>\n",
       "      <td>8:07 AM</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>#ReligiousCorona</td>\n",
       "      <td>lol@gmail.com</td>\n",
       "      <td>8:07 AM</td>\n",
       "      <td>8:33 AM</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AJ</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7:30 AM</td>\n",
       "      <td>7:30 AM</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AJ</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7:30 AM</td>\n",
       "      <td>8:33 AM</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ANKITKUMAR SINGH</td>\n",
       "      <td>ankitk.as51@gmail.com</td>\n",
       "      <td>7:30 AM</td>\n",
       "      <td>7:31 AM</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Name          Email Address Join Time Leave Time  \\\n",
       "0  #ReligiousCorona          lol@gmail.com   7:51 AM    8:07 AM   \n",
       "1  #ReligiousCorona          lol@gmail.com   8:07 AM    8:33 AM   \n",
       "2                AJ                    NaN   7:30 AM    7:30 AM   \n",
       "3                AJ                    NaN   7:30 AM    8:33 AM   \n",
       "4  ANKITKUMAR SINGH  ankitk.as51@gmail.com   7:30 AM    7:31 AM   \n",
       "\n",
       "  Time in Session (minutes)  \n",
       "0                        15  \n",
       "1                        25  \n",
       "2                         0  \n",
       "3                        62  \n",
       "4                         1  "
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "attn_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Session Duration is Minutes:  90\n"
     ]
    }
   ],
   "source": [
    "SESSION_DURATION = int(attn_data['Time in Session (minutes)'].max())\n",
    "print(\"Session Duration is Minutes: \", SESSION_DURATION)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analysis on Registration Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Univariate Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No of Registrations :  186\n"
     ]
    }
   ],
   "source": [
    "no_of_regs = len(reg_data)\n",
    "REG_COUNT = no_of_regs\n",
    "print(\"No of Registrations : \", REG_COUNT)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Observation:\n",
    "No of registrations: **73**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2021            76\n",
       "Professional    69\n",
       "2022            36\n",
       "2023             5\n",
       "Name: Batch, dtype: int64"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg_data.Batch.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Barplot on Academic year participation')"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x=\"Batch\", data=reg_data)\n",
    "plt.title(\"Barplot on Academic year participation\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Observation\n",
    "Students from 2022 are higher than 2021 and 2023\n",
    "    - 2021 > Professional >> 2022 >> 2023 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No     115\n",
      "Yes     71\n",
      "Name: Have you ever worked with Data Science before?, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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ZmZXiRGJmZqU4kZiZWSlOJGZmVooTiZmZleJEYmZmpTiRmJlZKU4kZmZWihOJmZmV4kRiZmalOJGYmVkpTiRmZlaKE4mZmZXiRGJmZqU4kZiZWSlOJGZmVooTiZmZldJjiUTSeZKekTSj0G+opBskPZr/Dsn9JelnkmZKul/S5j0Vl5mZda+evCK5ANixqt+RwE0RsT5wU/4OsBOwfv6MB37Zg3GZmVk36rFEEhG3Ac9X9d4dmJi7JwJjCv0nRXIXMFjSGj0Vm5mZdZ/evkeyekQ8CZD/rpb7jwDmFMabm/stRtJ4SVMlTW1vb+/RYM3MrGN95Wa7avSLWiNGxISIGB0Ro9va2no4LDMz60hvJ5KnK01W+e8zuf9cYM3CeCOBeb0cm5mZdUFvJ5JrgLG5eyxwdaH/l/LTWx8BXqw0gZmZWd82oKdmLOkSYBtgmKS5wNHAicAUSfsDs4E98+i/A3YGZgL/AvbtqbjMzKx79VgiiYjP1Rm0XY1xA/h6T8ViZmY9p6/cbDczsyWUE4mZmZXiRGJmZqU4kZiZWSlOJGZmVooTiZmZleJEYmZmpTiRmJlZKU4kZmZWihOJmZmV4kRiZmalOJGYmVkpTiRmZlaKE4mZmZXiRGJmZqU4kZiZWSlOJGZmVooTiZmZleJEYmZmpTiRmJlZKU4kZmZWihOJmZmV4kRiZmalOJGYmVkpTiRmZlaKE4mZmZXiRGJmZqW0JJFI+pakByXNkHSJpOUlrSPpbkmPSposadlWxGZmZp3T64lE0gjgIGB0RGwMLA3sDfwYODUi1gfmA/v3dmxmZtZ5rWraGgCsIGkAsCLwJLAtcFkePhEY06LYzMysE3o9kUTEE8DJwGxSAnkRmAa8EBEL8mhzgRG1ppc0XtJUSVPb29t7I2QzM2ugFU1bQ4DdgXWA4cBAYKcao0at6SNiQkSMjojRbW1tPReomZk1pRVNW9sDj0dEe0S8AVwBfBQYnJu6AEYC81oQm5mZdVIrEsls4COSVpQkYDvgIeCPwB55nLHA1S2IzczMOqkV90juJt1Uvxd4IMcwATgC+LakmcCqwLm9HZuZmXXegI5H6X4RcTRwdFXvx4AtWhCOmZmV4F+2m5lZKU4kZmZWihOJmZmV4kRiZmalOJGYmVkpTiRmZlaKE4mZmZXiRGJmZqU4kZiZWSlOJGZmVkpLXpHSl3zwsEmtDsH6oGknfanVIZgtMXxFYmZmpTiRmJlZKU4kZmZWihOJmZmV4kRiZmalOJGYmVkpTiRmZlZKU4lE0k3N9DMzs/6n4Q8SJS0PrAgMkzQEUB60MjC8h2MzM7MlQEe/bP8q8E1S0pjGwkTyEvDzHozLzMyWEA0TSUScDpwu6RsRcUYvxWRmZkuQpt61FRFnSPooMKo4TUT4RVVmZv1cU4lE0oXAusB04M3cOwAnEjOzfq7Zt/+OBjaMiOjJYMzMbMnT7O9IZgDv6slAzMxsydTsFckw4CFJ9wCvVXpGxG49EpWZmS0xmk0kx3RnoZIGA+cAG5PutewHPAJMJt3QnwXsFRHzu7NcMzPrfs0+tXVrN5d7OvCHiNhD0rKkHz0eBdwUESdKOhI4Ejiim8s1M7Nu1uwrUl6W9FL+/EfSm5Je6kqBklYGtgbOBYiI1yPiBWB3YGIebSIwpivzNzOz3tXsFcmg4ndJY4Atuljmu4F24HxJ7yf9Yv5gYPWIeDKX96Sk1WpNLGk8MB5grbXW6mIIZmbWXbr09t+IuArYtotlDgA2B34ZER8AXiU1YzVb9oSIGB0Ro9va2roYgpmZdZdmf5D46cLXpUi/K+nqb0rmAnMj4u78/TJSInla0hr5amQN4Jkuzt/MzHpRs09t7VroXkB6qmr3rhQYEU9JmiPpvRHxCLAd8FD+jAVOzH+v7sr8zcysdzV7j2Tfbi73G8BF+Ymtx4B9SVc6UyTtD8wG9uzmMs3MrAc027Q1EjgD2JLUpHUHcHBEzO1KoRExndQ8Vm27rszPzMxap9mb7ecD15D+L8kI4Nrcz8zM+rlmE0lbRJwfEQvy5wLAj0yZmVnTieRZSftIWjp/9gGe68nAzMxsydBsItkP2At4CngS2IN0g9zMzPq5Zh///SEwtvISRUlDgZNJCcbMzPqxZq9INi2+iTcingc+0DMhmZnZkqTZRLKUpCGVL/mKpNmrGTMzewdrNhn8FLhT0mWk35HsBRzXY1GZmdkSo9lftk+SNJX0okYBn46Ih3o0MjMzWyI03TyVE4eTh5mZLaJLr5E3MzOrcCIxM7NSnEjMzKwUJxIzMyvFicTMzEpxIjEzs1KcSMzMrBQnEjMzK8WJxMzMSnEiMTOzUpxIzMysFCcSMzMrxYnEzMxKcSIxM7NSnEjMzKwUJxIzMyvFicTMzEppWSKRtLSk+yT9Jn9fR9Ldkh6VNFnSsq2KzczMmtfKK5KDgYcL338MnBoR6wPzgf1bEpWZmXVKSxKJpJHALsA5+buAbYHL8igTgTGtiM3MzDqnVVckpwGHA2/l76sCL0TEgvx9LjCi1oSSxkuaKmlqe3t7z0dqZmYN9XoikfQp4JmImFbsXWPUqDV9REyIiNERMbqtra1HYjQzs+YNaEGZWwK7SdoZWB5YmXSFMljSgHxVMhKY14LYzMysk3r9iiQivhMRIyNiFLA3cHNEfAH4I7BHHm0scHVvx2ZmZp3Xl35HcgTwbUkzSfdMzm1xPGZm1oRWNG29LSJuAW7J3Y8BW7QyHjMz67y+dEViZmZLICcSMzMrxYnEzMxKcSIxM7NSnEjMzKwUJxIzMyvFicTMzEpxIjEzs1KcSMzMrBQnEjMzK8WJxMzMSnEiMTOzUpxIzMysFCcSMzMrxYnEzMxKcSIxM7NSnEjMzKwUJxIzMyvFicTMzEpxIjEzs1KcSMzMrBQnEjMzK8WJxMzMSnEiMTOzUpxIzMysFCcSMzMrxYnEzMxK6fVEImlNSX+U9LCkByUdnPsPlXSDpEfz3yG9HZuZmXVeK65IFgCHRMT7gI8AX5e0IXAkcFNErA/clL+bmVkf1+uJJCKejIh7c/fLwMPACGB3YGIebSIwprdjMzOzzmvpPRJJo4APAHcDq0fEk5CSDbBanWnGS5oqaWp7e3tvhWpmZnW0LJFIWgm4HPhmRLzU7HQRMSEiRkfE6La2tp4L0MzMmtKSRCJpGVISuSgirsi9n5a0Rh6+BvBMK2IzM7POacVTWwLOBR6OiFMKg64BxubuscDVvR2bmZl13oAWlLkl8EXgAUnTc7+jgBOBKZL2B2YDe7YgNjMz66ReTyQRcQegOoO3681YzMysPP+y3czMSnEiMTOzUpxIzMysFCcSMzMrxYnEzMxKcSIxM7NSnEjMzKwUJxIzMyvFicTMzEpxIjEzs1KcSMzMrBQnEjMzK8WJxMzMSnEiMTOzUpxIzMysFCcSMzMrxYnEzMxKcSIxM7NSnEjMzKwUJxIzMyvFicTMzEpxIjEzs1KcSMzMrBQnEjMzK8WJxMzMSnEiMTOzUpxIzMyslD6XSCTtKOkRSTMlHdnqeMzMrLE+lUgkLQ38HNgJ2BD4nKQNWxuVmZk10qcSCbAFMDMiHouI14FfA7u3OCYzM2tgQKsDqDICmFP4Phf4cHEESeOB8fnrK5Ie6aXY+oNhwLOtDqIv0MljWx2CLcp1s+Jodcdc1u6OmVT0tURSaw3FIl8iJgATeiec/kXS1IgY3eo4zKq5bvZtfa1pay6wZuH7SGBei2IxM7Mm9LVE8hdgfUnrSFoW2Bu4psUxmZlZA32qaSsiFkg6ELgOWBo4LyIebHFY/YmbDK2vct3swxQRHY9lZmZWR19r2jIzsyWME4mZmZXiRNJPKLlD0k6FfntJ+kMr4zKrJikk/bTw/VBJx7QwJOuAE0k/Eelm2AHAKZKWlzQQOA74emsjM1vMa8CnJQ1rdSDWHCeSfiQiZgDXAkcARwOTIuIfksZKukfSdEm/kLSUpAGSLpT0gKQZkg5qbfTWjywgPaX1reoBktaWdJOk+/PftXo/PKvWpx7/tV5xLHAv8DowWtLGwP8AH82PX08g/X7nH8CwiNgEQNLgVgVs/dLPgfsl/aSq/5mkE6CJkvYDfgaM6fXobBFOJP1MRLwqaTLwSkS8Jml74EPAVEkAK5Ded3Yd8F5JpwO/A65vVczW/0TES5ImAQcB/y4M+i/g07n7QqA60VgLOJH0T2/lD6T3m50XEd+rHknSpqRX+h8EfIaFL8s06w2nka6ez28wjn8I1wf4HondCOxVubEpaVVJa0lqI/1g9VLS/ZTNWxmk9T8R8TwwBdi/0PtOUtMrwBeAO3o7Llucr0j6uYh4QNKxwI2SlgLeID3d9SZwrlJ7V5Bu0Jv1tp8CBxa+HwScJ+kwoB3YtyVR2SL8ihQzMyvFTVtmZlaKE4mZmZXiRGJmZqU4kZiZWSlOJGZmVooTSQtIeqXq+zhJZ7Yqnv6g7DqWNKsrLxGUdI6kDXP3UYX+oyTNaGL6YyQ9kd+D9qikKyrz62C6cZKGdzLW90q6JZf1cH5dTqPxz2kmlu6WYxzdyWkuye/nWuz9XV0o/4D8/rm/+63EiX9HYn2KpKUj4s1unmfL6nlEfLnw9Sjg+C7M5tSIOBlA0meBmyVtEhHtDaYZB8wA5nWinJ/lsq7OZW3SaOSqZeuzJL2L9C65tTsxzYCIWFBn8EzSD3QF/E3SuRExpxtCXWL5iqSPkbSrpLsl3SfpRkmr57fxziq+OFHSzDysTdLlkv6SP1vWmOftkjYrfP+TpE0lDZV0VT5Tuyu/EqVyFnxoYfwZkkbVmO8Okv4s6V5Jl0paSdJOkqYUxtlG0rX1xs/9Z0n6vqQ7gD0L0y4t6TElgyW9JWnrwjKt18EyTJB0PTCpKu5dchzD6q2//Av/6/N2+D/SQaN6+feSdEruPljSY7l73bwsb589SzoRWCGf7V+UZ7G0pLMlPZjLWqFGlVhEREwmvffs83n+389xz8jLK0l7AKOBi3J5K9Qar8bs1wDmFsp6oLAdTlZ6E/T9kr5RXLYmtu2xuf8DkjbI/VeSdH5hnp9pNJ8a9pF0Z16eLfK0AyWdl5fzPkm753GvB1bL6+JjkjbLdeV+SVdKGlJYnuMl3QocXK9uRMSNEfE6qU4sQ3rtff8WEf708of0q/Hphc9s4Mw8bAgLfyj6ZeCnuft0YN/c/WHgxtx9MbBV7l4LeLhGeWOB03L3e4CpufsM4OjcvS0wPXcfAxxamH4GMKpqnsOA24CB+fsRwPdJV7mzC/1/CexTb/zcPQs4vM66+gOwEfAp4C/Ad4HlgMebWIZpwAr5+zjSm2P/B7gdGNJo/ZHOzivx7UL6df+wqtjeBfwld1+W4xuR1/cJuf8twOjc/Uph2lGk16Vvlr9PAfapsfyLbIvc75vAL3P30EL/C4Fdq8ttNF7VfPcFXgR+T3qF++Dc//8BlwMDivOqlNHEtv1G7v4acE7u/jG5Thbqfd35VMV5C3B27t4amJG7j6+sQ2Aw8HdgYF7XMwrT3w98PHf/gIX7xi3ALwrjNdy3SCcoJ7X6eNIXPm7aao1/R0TxCmEcaYcEGAlMlrQGsCzweO4/mXSgPp/0rqHJuf/2wIaFE8yVJQ2KiJcL5V0KfE/ptRL7ARfk/luRXsZIRNycz8JXaXIZPgJsCPwpl70s8OdIr6L/A7CrpMtIB+HDgY/XGr8wv8nUdjvpYLEOcALwFeBW0kG7o2W4JiKKb479BGk97xARL+V+NddfLvPTeb6/lTS/OrCIeCqfWQ8C1iQdeLYGPgZcUWd5ih6PiOm5exrpgNeM4tXEJyQdDqwIDAUeJP3PmWodjhcR50u6DtgR2B34qqT3k9bRWZGbeiK9A6uoZl0oDK+si2ksfHPv9ix8ZxYRMV/SpzqYT9ElebrbJK2sdLW+A7CbFl5NL09KAG/XgVw3BkfErbnXRNL+UVGsh3X3LUm7ka7gxtWJr19xIul7zgBOiYhrJG1DOiOFtEOtp/QyxTHAj3L/pYD/qjpgLiIi/iXpBtLBYS8WJq1azRtBOlMuNnsuX2M8ATdExOdqDJtM+s+Lz5PO2F/OTSn1xgd4tU7/20nv/hpOSqSHAduQzlwbLUOteT4GvO1+GbUAAALISURBVJt8VZb71Vx/+eDRzPuD/kw6k38kx7of6VXnhzQxbbFJ5E3SK/yb8QHSa/+XB35BuvKYo3Tjd7Ft1ex4ABExDziP9D6rGcDGpHXcaF10tG0ry/kmC485tebZ0XwWCbXGdwGfiYhHFplpjWbZBop1ptG+tSlwfUS8VWNYv+N7JH3PKsATuXtspWeka+krgVNIl9jP5UHXU3ipnQr3QqqcQ2qu+UvhjPI20htUyUnr2XymPov8tl9Jm5OuBqrdBWwpab083oqS3pOH3ZKn/woLz/Aajd/I3cBHgbci4j+kpsCvkg7ajZahln+SzognSdoo96u3/orz3YnU9FLLbcCh+e99pKue1yLixRrjviFpmUYL25F8L2EH0hl5JRk8m+8l7FEY9WVgUO5uNF5x3jtW4lO6Qb0qqS5eDxyg/NCCpKFVk3Zl21av9yGdnM9n8zhbAS/m9X0d8I180oKkD1RPlMebL+ljudcXSVe4zcRY3LeuAq7pYBn7DSeSvucY4FJJtwPPVg2bTLrfULz8Poj0nw7vl/QQ6ex9MRExDXiJRf+3wzGVaYETWZi4LgeGSppOah//e435tZMu6y/J098FbJCHvQn8hvS/TH7T0fiNRMRrpH+0dVfudTvpAPlAB8tQb36PkBLEpZLWpf76OxbYWtK9pAP37DqzvJ3UrHVbXu451H+1+QTSf/27qM7wer6VbxQ/Str+20ZEe0S8AJxNWhdXsbC5D1Lz5Vl5G77WYLyiHYAZkv5KOigfFhFPkU5CZufY/0q+0V/RxW37I2BIvln+V+ATnZzPfEl3Amex8DXzPyTd/L4/X039sM60Y4GTchmbke6T1NJo39qKdK/S8Nt/+w2l3xTcAmzgy3Ez606+IukHJH2J1ET0XScRM+tuviIxM7NSfEViZmalOJGYmVkpTiRmZlaKE4mZmZXiRGJmZqX8f0Q0Ba8tyn79AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x=\"Have you ever worked with Data Science before?\", data=reg_data)\n",
    "plt.title(\"Barplot over the background of participants in Data Science\")\n",
    "\n",
    "print(reg_data['Have you ever worked with Data Science before?'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The percentage of people joined the webinar who has worked with Data Science bofore : 38.17204301075269\n",
      "The percentage of people joined the webinar who has never worked with Data Scinece before : 61.82795698924731\n"
     ]
    }
   ],
   "source": [
    "web_mob_yes = reg_data[\"Have you ever worked with Data Science before?\"].value_counts()[1]\n",
    "web_mob_no = reg_data[\"Have you ever worked with Data Science before?\"].value_counts()[0]\n",
    "print(\"The percentage of people joined the webinar who has worked with Data Science bofore :\", \n",
    "      (web_mob_yes/no_of_regs)*100)\n",
    "print(\"The percentage of people joined the webinar who has never worked with Data Scinece before :\", \n",
    "      (web_mob_no/no_of_regs)*100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a0f9995108>"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x=\"Knowledge of Python Programming language \", \n",
    "              data=reg_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a0fb994ac8>"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x=\"Do you think Math is Required for Machine Learning?\", \n",
    "              data=reg_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a0fd177408>"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "reg_data[\"Where do you wish to use Data Science skills?\"].value_counts().plot(kind='barh', figsize=(10,10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Observation:**\n",
    "- Majority of the participants Wished for Learning Data Science for:\n",
    "    - Business Analytics\n",
    "    - Predictions/ Forecast\n",
    "    - Natural Language Processing \n",
    "    - Computer Vision"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analysis on Attendes data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "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>Name</th>\n",
       "      <th>Email Address</th>\n",
       "      <th>Join Time</th>\n",
       "      <th>Leave Time</th>\n",
       "      <th>Time in Session (minutes)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>#ReligiousCorona</td>\n",
       "      <td>lol@gmail.com</td>\n",
       "      <td>7:51 AM</td>\n",
       "      <td>8:07 AM</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>#ReligiousCorona</td>\n",
       "      <td>lol@gmail.com</td>\n",
       "      <td>8:07 AM</td>\n",
       "      <td>8:33 AM</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AJ</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7:30 AM</td>\n",
       "      <td>7:30 AM</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AJ</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7:30 AM</td>\n",
       "      <td>8:33 AM</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ANKITKUMAR SINGH</td>\n",
       "      <td>ankitk.as51@gmail.com</td>\n",
       "      <td>7:30 AM</td>\n",
       "      <td>7:31 AM</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Name          Email Address Join Time Leave Time  \\\n",
       "0  #ReligiousCorona          lol@gmail.com   7:51 AM    8:07 AM   \n",
       "1  #ReligiousCorona          lol@gmail.com   8:07 AM    8:33 AM   \n",
       "2                AJ                    NaN   7:30 AM    7:30 AM   \n",
       "3                AJ                    NaN   7:30 AM    8:33 AM   \n",
       "4  ANKITKUMAR SINGH  ankitk.as51@gmail.com   7:30 AM    7:31 AM   \n",
       "\n",
       "  Time in Session (minutes)  \n",
       "0                        15  \n",
       "1                        25  \n",
       "2                         0  \n",
       "3                        62  \n",
       "4                         1  "
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "attn_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "54"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ATTENDEES_COUNT = len(attn_data['Name'].value_counts())\n",
    "ATTENDEES_COUNT"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Observation\n",
    "Number of attendees without duplicates: **54**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "65"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(attn_data.groupby(by=attn_data.Name, axis=1).sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Name</th>\n",
       "      <th>Time in Session (minutes)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">#ReligiousCorona</th>\n",
       "      <th>15</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">AJ</th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ANKITKUMAR SINGH</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Abhiram</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Abhishek's Mac Book Pro</th>\n",
       "      <th>15</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aditya</th>\n",
       "      <th>18</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aditya Gowrish Menti</th>\n",
       "      <th>68</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Akash M</th>\n",
       "      <th>60</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alok Kumar</th>\n",
       "      <th>61</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Amit</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Anand</th>\n",
       "      <th>50</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Anonymous</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BVN PRANEETH</th>\n",
       "      <th>53</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bhavesh</th>\n",
       "      <th>89</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chetan</th>\n",
       "      <th>9</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Deepansh</th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: []\n",
       "Index: [(#ReligiousCorona, 15), (#ReligiousCorona, 25), (AJ, 0), (AJ, 62), (ANKITKUMAR SINGH, 1), (Abhiram, 1), (Abhiram, 64), (Abhishek's Mac Book Pro, 15), (Aditya, 18), (Aditya Gowrish Menti, 68), (Akash M, 60), (Alok Kumar, 61), (Amit, 1), (Anand, 50), (Anonymous, 4), (Anonymous, 5), (BVN PRANEETH, 53), (Bhavesh, 89), (Chetan, 9), (Deepansh, 0)]"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "attn_data.groupby(['Name', 'Time in Session (minutes)']).sum().iloc[:,:0].head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Converting the 'Time in Session (minutes)' column values to int\n",
    "attn_data['Time in Session (minutes)'] = pd.to_numeric(attn_data['Time in Session (minutes)'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.int64"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(attn_data['Time in Session (minutes)'].iloc[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(attn_data['Time in Session (minutes)'] == attn_data['Time in Session (minutes)'].iloc[0]).all()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "def time_agg(group_series):\n",
    "    if (group_series==group_series.iloc[0]).all():\n",
    "        return group_series.iloc[0]\n",
    "    else:\n",
    "        return group_series.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "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>Name</th>\n",
       "      <th>Join Time</th>\n",
       "      <th>Leave Time</th>\n",
       "      <th>Time in Session (minutes)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>#ReligiousCorona</td>\n",
       "      <td>7:51 AM8:07 AM</td>\n",
       "      <td>8:07 AM8:33 AM</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AJ</td>\n",
       "      <td>7:30 AM</td>\n",
       "      <td>7:30 AM8:33 AM</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ANKITKUMAR SINGH</td>\n",
       "      <td>7:30 AM</td>\n",
       "      <td>7:31 AM</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Abhiram</td>\n",
       "      <td>7:31 AM7:30 AM</td>\n",
       "      <td>8:36 AM7:31 AM</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Abhishek's Mac Book Pro</td>\n",
       "      <td>8:01 AM</td>\n",
       "      <td>8:17 AM</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Aditya</td>\n",
       "      <td>7:30 AM</td>\n",
       "      <td>7:49 AM</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Aditya Gowrish Menti</td>\n",
       "      <td>7:29 AM</td>\n",
       "      <td>8:37 AM</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Akash M</td>\n",
       "      <td>7:22 AM</td>\n",
       "      <td>8:23 AM</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Alok Kumar</td>\n",
       "      <td>7:36 AM</td>\n",
       "      <td>8:37 AM</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Amit</td>\n",
       "      <td>7:49 AM</td>\n",
       "      <td>7:51 AM</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Anand</td>\n",
       "      <td>7:47 AM</td>\n",
       "      <td>8:37 AM</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Anonymous</td>\n",
       "      <td>7:07 AM7:23 AM</td>\n",
       "      <td>7:13 AM7:28 AM</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>BVN PRANEETH</td>\n",
       "      <td>7:44 AM</td>\n",
       "      <td>8:37 AM</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Bhavesh</td>\n",
       "      <td>7:07 AM</td>\n",
       "      <td>8:37 AM</td>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Chetan</td>\n",
       "      <td>7:38 AM</td>\n",
       "      <td>7:47 AM</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Deepansh</td>\n",
       "      <td>7:35 AM</td>\n",
       "      <td>8:37 AM7:35 AM</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Devyash Bordia</td>\n",
       "      <td>7:27 AM</td>\n",
       "      <td>8:11 AM</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Dikshita Basu</td>\n",
       "      <td>7:19 AM</td>\n",
       "      <td>8:37 AM</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Dinesh L</td>\n",
       "      <td>7:30 AM</td>\n",
       "      <td>8:32 AM</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Dr Zakir Naik</td>\n",
       "      <td>7:43 AM</td>\n",
       "      <td>7:51 AM</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Fireflies.ai Notetaker</td>\n",
       "      <td>7:28 AM</td>\n",
       "      <td>8:30 AM</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Gaurav</td>\n",
       "      <td>7:27 AM</td>\n",
       "      <td>8:29 AM</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Gupta, Anuj</td>\n",
       "      <td>7:32 AM</td>\n",
       "      <td>7:42 AM</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>HK</td>\n",
       "      <td>7:45 AM</td>\n",
       "      <td>8:14 AM</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Hardik Gupta</td>\n",
       "      <td>7:40 AM7:38 AM</td>\n",
       "      <td>8:37 AM7:39 AM</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Himanshu Tamboli</td>\n",
       "      <td>7:10 AM7:28 AM</td>\n",
       "      <td>7:16 AM7:50 AM</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Kajjal</td>\n",
       "      <td>7:19 AM</td>\n",
       "      <td>8:37 AM</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Kamal Sharma</td>\n",
       "      <td>8:00 AM</td>\n",
       "      <td>8:37 AM</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Kav</td>\n",
       "      <td>7:47 AM</td>\n",
       "      <td>8:03 AM</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Mohammed Faraz</td>\n",
       "      <td>7:52 AM</td>\n",
       "      <td>8:24 AM</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Mostlyinsane</td>\n",
       "      <td>7:27 AM</td>\n",
       "      <td>8:20 AM</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Mugunthan</td>\n",
       "      <td>7:33 AM</td>\n",
       "      <td>8:33 AM</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>NIKHIL</td>\n",
       "      <td>7:39 AM7:45 AM</td>\n",
       "      <td>7:44 AM7:53 AM</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Neeraj Jayaram</td>\n",
       "      <td>7:33 AM</td>\n",
       "      <td>8:34 AM</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Pruthvi Shetty</td>\n",
       "      <td>8:04 AM</td>\n",
       "      <td>8:37 AM</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Ranjith</td>\n",
       "      <td>7:26 AM</td>\n",
       "      <td>7:34 AM</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Rehan Razak</td>\n",
       "      <td>7:15 AM7:18 AM7:31 AM</td>\n",
       "      <td>7:17 AM7:18 AM8:37 AM</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Revanth</td>\n",
       "      <td>7:10 AM</td>\n",
       "      <td>7:15 AM</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>Roshan Pandey</td>\n",
       "      <td>7:21 AM</td>\n",
       "      <td>7:56 AM</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>Sagar Parida</td>\n",
       "      <td>7:26 AM</td>\n",
       "      <td>8:09 AM</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>Sanjana Birari</td>\n",
       "      <td>7:39 AM</td>\n",
       "      <td>7:41 AM</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>Santhosh</td>\n",
       "      <td>7:29 AM</td>\n",
       "      <td>7:46 AM</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>Santosh Kumar</td>\n",
       "      <td>7:36 AM</td>\n",
       "      <td>8:37 AM</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>Sourav Kumar</td>\n",
       "      <td>7:26 AM7:31 AM</td>\n",
       "      <td>7:31 AM8:37 AM</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>Sri Harish</td>\n",
       "      <td>7:06 AM</td>\n",
       "      <td>8:37 AM</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>Suryanshu Singh</td>\n",
       "      <td>7:16 AM</td>\n",
       "      <td>8:22 AM</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>Teja Kummarikuntla</td>\n",
       "      <td>7:06 AM</td>\n",
       "      <td>8:37 AM</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>keshav</td>\n",
       "      <td>7:40 AM</td>\n",
       "      <td>8:37 AM</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>reconnecting....</td>\n",
       "      <td>7:30 AM</td>\n",
       "      <td>8:37 AM</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>sahib pratap singh</td>\n",
       "      <td>7:29 AM</td>\n",
       "      <td>8:37 AM</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>sateesh sabbineni</td>\n",
       "      <td>8:18 AM</td>\n",
       "      <td>8:32 AM</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>sneha gupta</td>\n",
       "      <td>7:23 AM</td>\n",
       "      <td>8:37 AM</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>sourabh kumar_KOLKATA_ID_3420</td>\n",
       "      <td>7:32 AM</td>\n",
       "      <td>7:43 AM</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>user</td>\n",
       "      <td>7:36 AM</td>\n",
       "      <td>8:18 AM</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             Name              Join Time  \\\n",
       "0                #ReligiousCorona         7:51 AM8:07 AM   \n",
       "1                              AJ                7:30 AM   \n",
       "2                ANKITKUMAR SINGH                7:30 AM   \n",
       "3                         Abhiram         7:31 AM7:30 AM   \n",
       "4         Abhishek's Mac Book Pro                8:01 AM   \n",
       "5                          Aditya                7:30 AM   \n",
       "6            Aditya Gowrish Menti                7:29 AM   \n",
       "7                         Akash M                7:22 AM   \n",
       "8                      Alok Kumar                7:36 AM   \n",
       "9                            Amit                7:49 AM   \n",
       "10                          Anand                7:47 AM   \n",
       "11                      Anonymous         7:07 AM7:23 AM   \n",
       "12                   BVN PRANEETH                7:44 AM   \n",
       "13                        Bhavesh                7:07 AM   \n",
       "14                         Chetan                7:38 AM   \n",
       "15                       Deepansh                7:35 AM   \n",
       "16                 Devyash Bordia                7:27 AM   \n",
       "17                  Dikshita Basu                7:19 AM   \n",
       "18                       Dinesh L                7:30 AM   \n",
       "19                  Dr Zakir Naik                7:43 AM   \n",
       "20         Fireflies.ai Notetaker                7:28 AM   \n",
       "21                         Gaurav                7:27 AM   \n",
       "22                    Gupta, Anuj                7:32 AM   \n",
       "23                             HK                7:45 AM   \n",
       "24                   Hardik Gupta         7:40 AM7:38 AM   \n",
       "25               Himanshu Tamboli         7:10 AM7:28 AM   \n",
       "26                         Kajjal                7:19 AM   \n",
       "27                   Kamal Sharma                8:00 AM   \n",
       "28                            Kav                7:47 AM   \n",
       "29                 Mohammed Faraz                7:52 AM   \n",
       "30                   Mostlyinsane                7:27 AM   \n",
       "31                      Mugunthan                7:33 AM   \n",
       "32                         NIKHIL         7:39 AM7:45 AM   \n",
       "33                 Neeraj Jayaram                7:33 AM   \n",
       "34                 Pruthvi Shetty                8:04 AM   \n",
       "35                        Ranjith                7:26 AM   \n",
       "36                    Rehan Razak  7:15 AM7:18 AM7:31 AM   \n",
       "37                        Revanth                7:10 AM   \n",
       "38                  Roshan Pandey                7:21 AM   \n",
       "39                   Sagar Parida                7:26 AM   \n",
       "40                 Sanjana Birari                7:39 AM   \n",
       "41                       Santhosh                7:29 AM   \n",
       "42                  Santosh Kumar                7:36 AM   \n",
       "43                   Sourav Kumar         7:26 AM7:31 AM   \n",
       "44                     Sri Harish                7:06 AM   \n",
       "45                Suryanshu Singh                7:16 AM   \n",
       "46             Teja Kummarikuntla                7:06 AM   \n",
       "47                         keshav                7:40 AM   \n",
       "48               reconnecting....                7:30 AM   \n",
       "49             sahib pratap singh                7:29 AM   \n",
       "50              sateesh sabbineni                8:18 AM   \n",
       "51                    sneha gupta                7:23 AM   \n",
       "52  sourabh kumar_KOLKATA_ID_3420                7:32 AM   \n",
       "53                           user                7:36 AM   \n",
       "\n",
       "               Leave Time  Time in Session (minutes)  \n",
       "0          8:07 AM8:33 AM                         40  \n",
       "1          7:30 AM8:33 AM                         62  \n",
       "2                 7:31 AM                          1  \n",
       "3          8:36 AM7:31 AM                         65  \n",
       "4                 8:17 AM                         15  \n",
       "5                 7:49 AM                         18  \n",
       "6                 8:37 AM                         68  \n",
       "7                 8:23 AM                         60  \n",
       "8                 8:37 AM                         61  \n",
       "9                 7:51 AM                          1  \n",
       "10                8:37 AM                         50  \n",
       "11         7:13 AM7:28 AM                          9  \n",
       "12                8:37 AM                         53  \n",
       "13                8:37 AM                         89  \n",
       "14                7:47 AM                          9  \n",
       "15         8:37 AM7:35 AM                         62  \n",
       "16                8:11 AM                         43  \n",
       "17                8:37 AM                         78  \n",
       "18                8:32 AM                         61  \n",
       "19                7:51 AM                          7  \n",
       "20                8:30 AM                         61  \n",
       "21                8:29 AM                         61  \n",
       "22                7:42 AM                          9  \n",
       "23                8:14 AM                         29  \n",
       "24         8:37 AM7:39 AM                         58  \n",
       "25         7:16 AM7:50 AM                         26  \n",
       "26                8:37 AM                         78  \n",
       "27                8:37 AM                         37  \n",
       "28                8:03 AM                         16  \n",
       "29                8:24 AM                         32  \n",
       "30                8:20 AM                         53  \n",
       "31                8:33 AM                         60  \n",
       "32         7:44 AM7:53 AM                         12  \n",
       "33                8:34 AM                         61  \n",
       "34                8:37 AM                         32  \n",
       "35                7:34 AM                          8  \n",
       "36  7:17 AM7:18 AM8:37 AM                         68  \n",
       "37                7:15 AM                          5  \n",
       "38                7:56 AM                         34  \n",
       "39                8:09 AM                         42  \n",
       "40                7:41 AM                          1  \n",
       "41                7:46 AM                         16  \n",
       "42                8:37 AM                         61  \n",
       "43         7:31 AM8:37 AM                         70  \n",
       "44                8:37 AM                         90  \n",
       "45                8:22 AM                         65  \n",
       "46                8:37 AM                         90  \n",
       "47                8:37 AM                         57  \n",
       "48                8:37 AM                         67  \n",
       "49                8:37 AM                         68  \n",
       "50                8:32 AM                         13  \n",
       "51                8:37 AM                         74  \n",
       "52                7:43 AM                         10  \n",
       "53                8:18 AM                         41  "
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "attn_data.groupby('Name', as_index=False).agg(time_agg)[['Name', 'Join Time', 'Leave Time', 'Time in Session (minutes)']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [],
   "source": [
    "atten_group_df = attn_data[['Name', 'Time in Session (minutes)', 'Email Address']].groupby('Name', as_index=False).agg(time_agg)\n",
    "atten_group_df.sort_values(by=['Time in Session (minutes)'],ascending=False, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 2160x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.factorplot(x=\"Name\", y=\"Time in Session (minutes)\", \n",
    "               data=atten_group_df, kind=\"bar\", \n",
    "               size = 15, aspect=2,\n",
    "               palette = \"muted\")\n",
    "\n",
    "# for value in plot:\n",
    "#     height = value.get_height()\n",
    "#     plt.text(value.get_x() + value.get_width()/2.,\n",
    "#              1.002*height,'%d' % int(height), ha='center', va='bottom')\n",
    "\n",
    "plt.xticks(rotation=45);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Individual time spent analysis of attendes**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.factorplot(x=\"Name\", y=\"Time in Session (minutes)\", \n",
    "               data=atten_group_df[atten_group_df[\"Time in Session (minutes)\"] >= SESSION_DURATION//2], \n",
    "               kind=\"bar\", \n",
    "               size = 8, aspect=2,\n",
    "               palette = \"muted\")\n",
    "\n",
    "plt.xticks(rotation=45);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "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>Time in Session (minutes)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Name</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Sri Harish</th>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Teja Kummarikuntla</th>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bhavesh</th>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dikshita Basu</th>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kajjal</th>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sneha gupta</th>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sourav Kumar</th>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rehan Razak</th>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sahib pratap singh</th>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aditya Gowrish Menti</th>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>reconnecting....</th>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Suryanshu Singh</th>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Abhiram</th>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AJ</th>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Deepansh</th>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Neeraj Jayaram</th>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dinesh L</th>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alok Kumar</th>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fireflies.ai Notetaker</th>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gaurav</th>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Santosh Kumar</th>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mugunthan</th>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Akash M</th>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hardik Gupta</th>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>keshav</th>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mostlyinsane</th>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BVN PRANEETH</th>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Anand</th>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        Time in Session (minutes)\n",
       "Name                                             \n",
       "Sri Harish                                     90\n",
       "Teja Kummarikuntla                             90\n",
       "Bhavesh                                        89\n",
       "Dikshita Basu                                  78\n",
       "Kajjal                                         78\n",
       "sneha gupta                                    74\n",
       "Sourav Kumar                                   70\n",
       "Rehan Razak                                    68\n",
       "sahib pratap singh                             68\n",
       "Aditya Gowrish Menti                           68\n",
       "reconnecting....                               67\n",
       "Suryanshu Singh                                65\n",
       "Abhiram                                        65\n",
       "AJ                                             62\n",
       "Deepansh                                       62\n",
       "Neeraj Jayaram                                 61\n",
       "Dinesh L                                       61\n",
       "Alok Kumar                                     61\n",
       "Fireflies.ai Notetaker                         61\n",
       "Gaurav                                         61\n",
       "Santosh Kumar                                  61\n",
       "Mugunthan                                      60\n",
       "Akash M                                        60\n",
       "Hardik Gupta                                   58\n",
       "keshav                                         57\n",
       "Mostlyinsane                                   53\n",
       "BVN PRANEETH                                   53\n",
       "Anand                                          50"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "atten_group_df[atten_group_df[\"Time in Session (minutes)\"] >= SESSION_DURATION//2][['Name', 'Time in Session (minutes)']].set_index('Name')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "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>Name</th>\n",
       "      <th>Time in Session (minutes)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>Sri Harish</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>Teja Kummarikuntla</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Bhavesh</td>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Dikshita Basu</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Kajjal</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>sneha gupta</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>Sourav Kumar</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Rehan Razak</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>sahib pratap singh</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Aditya Gowrish Menti</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>reconnecting....</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>Suryanshu Singh</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Abhiram</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AJ</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Deepansh</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Neeraj Jayaram</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Dinesh L</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Alok Kumar</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Fireflies.ai Notetaker</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Gaurav</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>Santosh Kumar</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Mugunthan</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Akash M</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Hardik Gupta</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>keshav</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Mostlyinsane</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>BVN PRANEETH</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Anand</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Devyash Bordia</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>Sagar Parida</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>user</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>#ReligiousCorona</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Kamal Sharma</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>Roshan Pandey</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Mohammed Faraz</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Pruthvi Shetty</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>HK</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Himanshu Tamboli</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Aditya</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Kav</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>Santhosh</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Abhishek's Mac Book Pro</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>sateesh sabbineni</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>NIKHIL</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>sourabh kumar_KOLKATA_ID_3420</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Chetan</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Anonymous</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Gupta, Anuj</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Ranjith</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Dr Zakir Naik</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Revanth</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Amit</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>Sanjana Birari</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ANKITKUMAR SINGH</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             Name  Time in Session (minutes)\n",
       "44                     Sri Harish                         90\n",
       "46             Teja Kummarikuntla                         90\n",
       "13                        Bhavesh                         89\n",
       "17                  Dikshita Basu                         78\n",
       "26                         Kajjal                         78\n",
       "51                    sneha gupta                         74\n",
       "43                   Sourav Kumar                         70\n",
       "36                    Rehan Razak                         68\n",
       "49             sahib pratap singh                         68\n",
       "6            Aditya Gowrish Menti                         68\n",
       "48               reconnecting....                         67\n",
       "45                Suryanshu Singh                         65\n",
       "3                         Abhiram                         65\n",
       "1                              AJ                         62\n",
       "15                       Deepansh                         62\n",
       "33                 Neeraj Jayaram                         61\n",
       "18                       Dinesh L                         61\n",
       "8                      Alok Kumar                         61\n",
       "20         Fireflies.ai Notetaker                         61\n",
       "21                         Gaurav                         61\n",
       "42                  Santosh Kumar                         61\n",
       "31                      Mugunthan                         60\n",
       "7                         Akash M                         60\n",
       "24                   Hardik Gupta                         58\n",
       "47                         keshav                         57\n",
       "30                   Mostlyinsane                         53\n",
       "12                   BVN PRANEETH                         53\n",
       "10                          Anand                         50\n",
       "16                 Devyash Bordia                         43\n",
       "39                   Sagar Parida                         42\n",
       "53                           user                         41\n",
       "0                #ReligiousCorona                         40\n",
       "27                   Kamal Sharma                         37\n",
       "38                  Roshan Pandey                         34\n",
       "29                 Mohammed Faraz                         32\n",
       "34                 Pruthvi Shetty                         32\n",
       "23                             HK                         29\n",
       "25               Himanshu Tamboli                         26\n",
       "5                          Aditya                         18\n",
       "28                            Kav                         16\n",
       "41                       Santhosh                         16\n",
       "4         Abhishek's Mac Book Pro                         15\n",
       "50              sateesh sabbineni                         13\n",
       "32                         NIKHIL                         12\n",
       "52  sourabh kumar_KOLKATA_ID_3420                         10\n",
       "14                         Chetan                          9\n",
       "11                      Anonymous                          9\n",
       "22                    Gupta, Anuj                          9\n",
       "35                        Ranjith                          8\n",
       "19                  Dr Zakir Naik                          7\n",
       "37                        Revanth                          5\n",
       "9                            Amit                          1\n",
       "40                 Sanjana Birari                          1\n",
       "2                ANKITKUMAR SINGH                          1"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "atten_group_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "28"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(atten_group_df[atten_group_df[\"Time in Session (minutes)\"] >= SESSION_DURATION//2].set_index('Name')['Time in Session (minutes)'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percentage of Students registered and attended the session 29.03225806451613\n"
     ]
    }
   ],
   "source": [
    "registerd_attendes_ratio = (ATTENDEES_COUNT/REG_COUNT) * 100\n",
    "print(\"Percentage of Students registered and attended the session {}\".format(registerd_attendes_ratio))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summary [Report]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Registration Data Analysis**\n",
    "- Name of the Event: **Developer 101 | Data Science Uncovered**\n",
    "- No of registrations: **186**\n",
    "- Registration Count with Batch filter\n",
    "    - 2021          :  76\n",
    "    - Professional  :  69\n",
    "    - 2022          :  36\n",
    "    - 2023          :   5\n",
    "\n",
    "- No of registrations **With out** prior knowledge of Data Scinece : 50 [61.82795698924731%]\n",
    "- No of registrations **With** prior knowledge on Data Science : 71 [38.17204301075269%]\n",
    "- No of registrations who are Beginners in Python Programming language : 76\n",
    "- No of registrations who are Intermediate in Python Programming language : 98\n",
    "- No of registrations who are Advanced in Python Programming language: 12\n",
    "- Registrations wish to use Data Science in \n",
    "    - Business Analytics, Prediction / Forecast, Natural Language Processing, Computer Vision                                                       39\n",
    "    - Business Analytics, Prediction / Forecast                                                                                                     24\n",
    "    - Business Analytics, Prediction / Forecast, Natural Language Processing                                                                        16\n",
    "    - Prediction / Forecast, Natural Language Processing, Computer Vision                                                                           15\n",
    "    - Business Analytics                                                                                                                            14\n",
    "    - Prediction / Forecast, Natural Language Processing                                                                                            11\n",
    "    - Computer Vision                                                                                                                               11\n",
    "    - Natural Language Processing, Computer Vision                                                                                                  10\n",
    "    - Prediction / Forecast                                                                                                                         10\n",
    "    - Business Analytics, Computer Vision                                                                                                            7\n",
    "    - Business Analytics, Natural Language Processing                                                                                                6\n",
    "    - Natural Language Processing                                                                                                                    4\n",
    "    - Prediction / Forecast, Computer Vision                                                                                                         4\n",
    "    - Business Analytics, Natural Language Processing, Computer Vision                                                                               3\n",
    "    - Business Analytics, Prediction / Forecast, Computer Vision                                                                                     2\n",
    "    - Natural Language Processing, Computer Vision, Deep Learning                                                                                    1\n",
    "    - Business Analytics, Prediction / Forecast, Information Security                                                                                1\n",
    "     - Business Analytics, Prediction / Forecast, Natural Language Processing, - Computer Vision, AI, Implementation in Web Apps                        1\n",
    "    - Computer Vision, GAN                                                                                                                           1\n",
    "    - Business Analytics, Prediction / Forecast, Natural Language Processing, Computer Vision, Almost every area                                     1\n",
    "    - Business Analytics, Prediction / Forecast, Use in data analysis for payments , health science and machine learning                             1\n",
    "    - Business Analytics, Prediction / Forecast, Natural Language Processing, Computer Vision, BFSI,Medical health care, image processing etc..      1\n",
    "    - AI Music Composer                                                                                                                              1\n",
    "    - Business Analytics, Prediction / Forecast, Natural Language Processing, Computer Vision, Specific Industry                                     1\n",
    "    - Business Analytics, Prediction / Forecast, Natural Language Processing, Computer Vision, Recommendation                                        1\n",
    "\n",
    "**Webinar Attendes Data Analysis**\n",
    "- No of Attendees: 54\n",
    "- No of students spent more than half in the session: 28\n",
    "- ***Percentage of Students registered and attended the session 29.03225806451613%***\n",
    "- Students spent more than half in the session \n",
    "        Sri Harish\n",
    "        Teja Kummarikuntla\n",
    "        Dikshita Basu\n",
    "        Kajjal\n",
    "        sneha gupta\n",
    "        Sourav Kumar\n",
    "        Rehan Razak\n",
    "        sahib pratap singh\n",
    "        Aditya Gowrish Menti\n",
    "        reconnecting....\n",
    "        Suryanshu Singh\n",
    "        Abhiram\t\n",
    "        AJ\t\n",
    "        Deepansh\n",
    "        Neeraj Jayaram\t\n",
    "        Dinesh L\t\n",
    "        Alok Kumar\t\n",
    "        Fireflies.ai Notetaker\n",
    "        Gaurav\t\n",
    "        Santosh Kumar\n",
    "        Mugunthan\t\n",
    "        Akash M\t\n",
    "        Hardik Gupta\n",
    "        keshav\t\n",
    "        Mostlyinsane\n",
    "        BVN PRANEETH\n",
    "        Anand"
   ]
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