my-captain-data-analytics-with-python-task-3 (1).ipynb

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    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5"
   },
   "source": [
    "<h2> My Captain Data Analytics with Python task 3: Note down the marks you have scored in all the cycle tests in your previous year of college and visualize the data in a bar plot.</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Importing the required modules and libraries of Python\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
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       "      <th></th>\n",
       "      <th>Paper Code</th>\n",
       "      <th>Paper Name</th>\n",
       "      <th>Sem</th>\n",
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       "      <th>Maximum Marks</th>\n",
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       "      <td>Engineering Mathematics - 1</td>\n",
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       "      <td>59</td>\n",
       "      <td>100</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>58651 TW</td>\n",
       "      <td>Engineering Mathematics - 1 Term Work</td>\n",
       "      <td>1</td>\n",
       "      <td>22</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>58652</td>\n",
       "      <td>Engineering Physics -1</td>\n",
       "      <td>1</td>\n",
       "      <td>41</td>\n",
       "      <td>75</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>FEL101</td>\n",
       "      <td>Engineering Physics -1 (Practical)</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>58655</td>\n",
       "      <td>Engineering Chemistry - 1</td>\n",
       "      <td>1</td>\n",
       "      <td>58</td>\n",
       "      <td>75</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>FEL102</td>\n",
       "      <td>Engineering Chemistry - 1 (Practical)</td>\n",
       "      <td>1</td>\n",
       "      <td>23</td>\n",
       "      <td>25</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>58653</td>\n",
       "      <td>Engineering Mechanics</td>\n",
       "      <td>1</td>\n",
       "      <td>62</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>FEL103</td>\n",
       "      <td>Engineering Mechanics (Practical)</td>\n",
       "      <td>1</td>\n",
       "      <td>41</td>\n",
       "      <td>50</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>58654</td>\n",
       "      <td>Basic Electrical Engineering</td>\n",
       "      <td>1</td>\n",
       "      <td>61</td>\n",
       "      <td>100</td>\n",
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       "      <th>9</th>\n",
       "      <td>FEL104</td>\n",
       "      <td>Basic Electrical Engineering (Practical)</td>\n",
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       "      <td>38</td>\n",
       "      <td>50</td>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>FEL105</td>\n",
       "      <td>Basic Workshop practice - 1 (Practical)</td>\n",
       "      <td>1</td>\n",
       "      <td>42</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>FEC201</td>\n",
       "      <td>Engineering Mathematics - 2</td>\n",
       "      <td>2</td>\n",
       "      <td>63</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>FEC201 TW</td>\n",
       "      <td>Engineering Mathematics - 2 Term Work</td>\n",
       "      <td>2</td>\n",
       "      <td>23</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>FEC202</td>\n",
       "      <td>Engineering Physics -2</td>\n",
       "      <td>2</td>\n",
       "      <td>61</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>FEL201</td>\n",
       "      <td>Engineering Physics -2 (TW)</td>\n",
       "      <td>2</td>\n",
       "      <td>22</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>FEC203</td>\n",
       "      <td>Engineering Chemistry - 2</td>\n",
       "      <td>2</td>\n",
       "      <td>56</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>FEL202</td>\n",
       "      <td>Engineering Chemistry - 2 (TW)</td>\n",
       "      <td>2</td>\n",
       "      <td>22</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>FEC204</td>\n",
       "      <td>Engineering Graphics</td>\n",
       "      <td>2</td>\n",
       "      <td>56</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>FEL203</td>\n",
       "      <td>Engineering Graphics (TW)</td>\n",
       "      <td>2</td>\n",
       "      <td>66</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>FEC205</td>\n",
       "      <td>C - Programming</td>\n",
       "      <td>2</td>\n",
       "      <td>63</td>\n",
       "      <td>75</td>\n",
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       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>FEL204</td>\n",
       "      <td>C - Programming (TW)</td>\n",
       "      <td>2</td>\n",
       "      <td>47</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>FEC206</td>\n",
       "      <td>Professional Communication &amp; Ethics -1</td>\n",
       "      <td>2</td>\n",
       "      <td>40</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>FEL205</td>\n",
       "      <td>Professional Communication &amp; Ethics -1 (TW)</td>\n",
       "      <td>2</td>\n",
       "      <td>22</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>FEL206</td>\n",
       "      <td>Basic Workshop practice - 2 (Practical)</td>\n",
       "      <td>2</td>\n",
       "      <td>45</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "   Paper Code                                   Paper Name  Sem  \\\n",
       "0       58651                  Engineering Mathematics - 1    1   \n",
       "1    58651 TW        Engineering Mathematics - 1 Term Work    1   \n",
       "2       58652                      Engineering Physics -1     1   \n",
       "3      FEL101           Engineering Physics -1 (Practical)    1   \n",
       "4       58655                    Engineering Chemistry - 1    1   \n",
       "5      FEL102        Engineering Chemistry - 1 (Practical)    1   \n",
       "6       58653                        Engineering Mechanics    1   \n",
       "7      FEL103           Engineering Mechanics (Practical)     1   \n",
       "8       58654                 Basic Electrical Engineering    1   \n",
       "9      FEL104     Basic Electrical Engineering (Practical)    1   \n",
       "10     FEL105      Basic Workshop practice - 1 (Practical)    1   \n",
       "11     FEC201                 Engineering Mathematics - 2     2   \n",
       "12  FEC201 TW        Engineering Mathematics - 2 Term Work    2   \n",
       "13     FEC202                       Engineering Physics -2    2   \n",
       "14     FEL201                  Engineering Physics -2 (TW)    2   \n",
       "15     FEC203                    Engineering Chemistry - 2    2   \n",
       "16     FEL202               Engineering Chemistry - 2 (TW)    2   \n",
       "17     FEC204                         Engineering Graphics    2   \n",
       "18     FEL203                    Engineering Graphics (TW)    2   \n",
       "19     FEC205                              C - Programming    2   \n",
       "20     FEL204                         C - Programming (TW)    2   \n",
       "21     FEC206       Professional Communication & Ethics -1    2   \n",
       "22     FEL205  Professional Communication & Ethics -1 (TW)    2   \n",
       "23     FEL206      Basic Workshop practice - 2 (Practical)    2   \n",
       "\n",
       "    Marks Received   Maximum Marks  \n",
       "0                59            100  \n",
       "1                22             25  \n",
       "2                41             75  \n",
       "3                20             25  \n",
       "4                58             75  \n",
       "5                23             25  \n",
       "6                62            100  \n",
       "7                41             50  \n",
       "8                61            100  \n",
       "9                38             50  \n",
       "10               42             50  \n",
       "11               63            100  \n",
       "12               23             25  \n",
       "13               61             75  \n",
       "14               22             25  \n",
       "15               56             75  \n",
       "16               22             25  \n",
       "17               56             75  \n",
       "18               66             75  \n",
       "19               63             75  \n",
       "20               47             50  \n",
       "21               40             50  \n",
       "22               22             25  \n",
       "23               45             50  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Reading the Dataset i.e. the 1_Year_Results.csv file\n",
    "df = pd.read_csv('1_Year_Results.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
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       "      <td>Engineering Mathematics - 1</td>\n",
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       "    <tr>\n",
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       "      <td>58651 TW</td>\n",
       "      <td>Engineering Mathematics - 1 Term Work</td>\n",
       "      <td>1</td>\n",
       "      <td>22</td>\n",
       "      <td>25</td>\n",
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       "      <th>2</th>\n",
       "      <td>58652</td>\n",
       "      <td>Engineering Physics -1</td>\n",
       "      <td>1</td>\n",
       "      <td>41</td>\n",
       "      <td>75</td>\n",
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       "      <th>3</th>\n",
       "      <td>FEL101</td>\n",
       "      <td>Engineering Physics -1 (Practical)</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>25</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>58655</td>\n",
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       "      <td>1</td>\n",
       "      <td>58</td>\n",
       "      <td>75</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>FEL102</td>\n",
       "      <td>Engineering Chemistry - 1 (Practical)</td>\n",
       "      <td>1</td>\n",
       "      <td>23</td>\n",
       "      <td>25</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>58653</td>\n",
       "      <td>Engineering Mechanics</td>\n",
       "      <td>1</td>\n",
       "      <td>62</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>FEL103</td>\n",
       "      <td>Engineering Mechanics (Practical)</td>\n",
       "      <td>1</td>\n",
       "      <td>41</td>\n",
       "      <td>50</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>58654</td>\n",
       "      <td>Basic Electrical Engineering</td>\n",
       "      <td>1</td>\n",
       "      <td>61</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>FEL104</td>\n",
       "      <td>Basic Electrical Engineering (Practical)</td>\n",
       "      <td>1</td>\n",
       "      <td>38</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>FEL105</td>\n",
       "      <td>Basic Workshop practice - 1 (Practical)</td>\n",
       "      <td>1</td>\n",
       "      <td>42</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "   Paper Code                                Paper Name  Sem  Marks Received   \\\n",
       "0       58651               Engineering Mathematics - 1    1               59   \n",
       "1    58651 TW     Engineering Mathematics - 1 Term Work    1               22   \n",
       "2       58652                   Engineering Physics -1     1               41   \n",
       "3      FEL101        Engineering Physics -1 (Practical)    1               20   \n",
       "4       58655                 Engineering Chemistry - 1    1               58   \n",
       "5      FEL102     Engineering Chemistry - 1 (Practical)    1               23   \n",
       "6       58653                     Engineering Mechanics    1               62   \n",
       "7      FEL103        Engineering Mechanics (Practical)     1               41   \n",
       "8       58654              Basic Electrical Engineering    1               61   \n",
       "9      FEL104  Basic Electrical Engineering (Practical)    1               38   \n",
       "10     FEL105   Basic Workshop practice - 1 (Practical)    1               42   \n",
       "\n",
       "    Maximum Marks  \n",
       "0             100  \n",
       "1              25  \n",
       "2              75  \n",
       "3              25  \n",
       "4              75  \n",
       "5              25  \n",
       "6             100  \n",
       "7              50  \n",
       "8             100  \n",
       "9              50  \n",
       "10             50  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
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    }
   ],
   "source": [
    "#Creating 2 different datasets of Sem 1 and Sem 2 respectively\n",
    "Sem_1 = df[df['Sem'] == 1]\n",
    "Sem_2 = df[df['Sem'] == 2]\n",
    "Sem_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
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       "      <th></th>\n",
       "      <th>Paper Code</th>\n",
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       "      <th>Maximum Marks</th>\n",
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       "      <th>11</th>\n",
       "      <td>FEC201</td>\n",
       "      <td>Engineering Mathematics - 2</td>\n",
       "      <td>2</td>\n",
       "      <td>63</td>\n",
       "      <td>100</td>\n",
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       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>FEC201 TW</td>\n",
       "      <td>Engineering Mathematics - 2 Term Work</td>\n",
       "      <td>2</td>\n",
       "      <td>23</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>FEC202</td>\n",
       "      <td>Engineering Physics -2</td>\n",
       "      <td>2</td>\n",
       "      <td>61</td>\n",
       "      <td>75</td>\n",
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       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>FEL201</td>\n",
       "      <td>Engineering Physics -2 (TW)</td>\n",
       "      <td>2</td>\n",
       "      <td>22</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>FEC203</td>\n",
       "      <td>Engineering Chemistry - 2</td>\n",
       "      <td>2</td>\n",
       "      <td>56</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>FEL202</td>\n",
       "      <td>Engineering Chemistry - 2 (TW)</td>\n",
       "      <td>2</td>\n",
       "      <td>22</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>FEC204</td>\n",
       "      <td>Engineering Graphics</td>\n",
       "      <td>2</td>\n",
       "      <td>56</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>FEL203</td>\n",
       "      <td>Engineering Graphics (TW)</td>\n",
       "      <td>2</td>\n",
       "      <td>66</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>FEC205</td>\n",
       "      <td>C - Programming</td>\n",
       "      <td>2</td>\n",
       "      <td>63</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>FEL204</td>\n",
       "      <td>C - Programming (TW)</td>\n",
       "      <td>2</td>\n",
       "      <td>47</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>FEC206</td>\n",
       "      <td>Professional Communication &amp; Ethics -1</td>\n",
       "      <td>2</td>\n",
       "      <td>40</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>FEL205</td>\n",
       "      <td>Professional Communication &amp; Ethics -1 (TW)</td>\n",
       "      <td>2</td>\n",
       "      <td>22</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>FEL206</td>\n",
       "      <td>Basic Workshop practice - 2 (Practical)</td>\n",
       "      <td>2</td>\n",
       "      <td>45</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Paper Code                                   Paper Name  Sem  \\\n",
       "11     FEC201                 Engineering Mathematics - 2     2   \n",
       "12  FEC201 TW        Engineering Mathematics - 2 Term Work    2   \n",
       "13     FEC202                       Engineering Physics -2    2   \n",
       "14     FEL201                  Engineering Physics -2 (TW)    2   \n",
       "15     FEC203                    Engineering Chemistry - 2    2   \n",
       "16     FEL202               Engineering Chemistry - 2 (TW)    2   \n",
       "17     FEC204                         Engineering Graphics    2   \n",
       "18     FEL203                    Engineering Graphics (TW)    2   \n",
       "19     FEC205                              C - Programming    2   \n",
       "20     FEL204                         C - Programming (TW)    2   \n",
       "21     FEC206       Professional Communication & Ethics -1    2   \n",
       "22     FEL205  Professional Communication & Ethics -1 (TW)    2   \n",
       "23     FEL206      Basic Workshop practice - 2 (Practical)    2   \n",
       "\n",
       "    Marks Received   Maximum Marks  \n",
       "11               63            100  \n",
       "12               23             25  \n",
       "13               61             75  \n",
       "14               22             25  \n",
       "15               56             75  \n",
       "16               22             25  \n",
       "17               56             75  \n",
       "18               66             75  \n",
       "19               63             75  \n",
       "20               47             50  \n",
       "21               40             50  \n",
       "22               22             25  \n",
       "23               45             50  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Sem_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>59.000000</td>\n",
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       "      <th>1</th>\n",
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       "      <th>2</th>\n",
       "      <td>54.666667</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>80.000000</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>77.333333</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>92.000000</td>\n",
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       "      <th>6</th>\n",
       "      <td>62.000000</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>82.000000</td>\n",
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       "      <td>61.000000</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>76.000000</td>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>84.000000</td>\n",
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       "            0\n",
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       "7   82.000000\n",
       "8   61.000000\n",
       "9   76.000000\n",
       "10  84.000000"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Calculating Percentage of marks scored for each subject in Sem_1 and storing them as list in Sem_1_percent\n",
    "Sem_1_percent = []\n",
    "for x in range(0,1):\n",
    "    Sem_1_percent.append((Sem_1['Marks Received '] / Sem_1['Maximum Marks'])*100)\n",
    "Sem_1_percent_data = pd.DataFrame(Sem_1_percent)\n",
    "Sem_1_percent_data.transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>Paper Code</th>\n",
       "      <th>Paper Name</th>\n",
       "      <th>Sem</th>\n",
       "      <th>Marks Received</th>\n",
       "      <th>Maximum Marks</th>\n",
       "      <th>Percentage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>58651</td>\n",
       "      <td>Engineering Mathematics - 1</td>\n",
       "      <td>1</td>\n",
       "      <td>59</td>\n",
       "      <td>100</td>\n",
       "      <td>59.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>58651 TW</td>\n",
       "      <td>Engineering Mathematics - 1 Term Work</td>\n",
       "      <td>1</td>\n",
       "      <td>22</td>\n",
       "      <td>25</td>\n",
       "      <td>88.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>58652</td>\n",
       "      <td>Engineering Physics -1</td>\n",
       "      <td>1</td>\n",
       "      <td>41</td>\n",
       "      <td>75</td>\n",
       "      <td>54.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>FEL101</td>\n",
       "      <td>Engineering Physics -1 (Practical)</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>25</td>\n",
       "      <td>80.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>58655</td>\n",
       "      <td>Engineering Chemistry - 1</td>\n",
       "      <td>1</td>\n",
       "      <td>58</td>\n",
       "      <td>75</td>\n",
       "      <td>77.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>FEL102</td>\n",
       "      <td>Engineering Chemistry - 1 (Practical)</td>\n",
       "      <td>1</td>\n",
       "      <td>23</td>\n",
       "      <td>25</td>\n",
       "      <td>92.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>58653</td>\n",
       "      <td>Engineering Mechanics</td>\n",
       "      <td>1</td>\n",
       "      <td>62</td>\n",
       "      <td>100</td>\n",
       "      <td>62.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>FEL103</td>\n",
       "      <td>Engineering Mechanics (Practical)</td>\n",
       "      <td>1</td>\n",
       "      <td>41</td>\n",
       "      <td>50</td>\n",
       "      <td>82.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>58654</td>\n",
       "      <td>Basic Electrical Engineering</td>\n",
       "      <td>1</td>\n",
       "      <td>61</td>\n",
       "      <td>100</td>\n",
       "      <td>61.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>FEL104</td>\n",
       "      <td>Basic Electrical Engineering (Practical)</td>\n",
       "      <td>1</td>\n",
       "      <td>38</td>\n",
       "      <td>50</td>\n",
       "      <td>76.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>FEL105</td>\n",
       "      <td>Basic Workshop practice - 1 (Practical)</td>\n",
       "      <td>1</td>\n",
       "      <td>42</td>\n",
       "      <td>50</td>\n",
       "      <td>84.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Paper Code                                Paper Name  Sem  Marks Received   \\\n",
       "0       58651               Engineering Mathematics - 1    1               59   \n",
       "1    58651 TW     Engineering Mathematics - 1 Term Work    1               22   \n",
       "2       58652                   Engineering Physics -1     1               41   \n",
       "3      FEL101        Engineering Physics -1 (Practical)    1               20   \n",
       "4       58655                 Engineering Chemistry - 1    1               58   \n",
       "5      FEL102     Engineering Chemistry - 1 (Practical)    1               23   \n",
       "6       58653                     Engineering Mechanics    1               62   \n",
       "7      FEL103        Engineering Mechanics (Practical)     1               41   \n",
       "8       58654              Basic Electrical Engineering    1               61   \n",
       "9      FEL104  Basic Electrical Engineering (Practical)    1               38   \n",
       "10     FEL105   Basic Workshop practice - 1 (Practical)    1               42   \n",
       "\n",
       "    Maximum Marks  Percentage  \n",
       "0             100   59.000000  \n",
       "1              25   88.000000  \n",
       "2              75   54.666667  \n",
       "3              25   80.000000  \n",
       "4              75   77.333333  \n",
       "5              25   92.000000  \n",
       "6             100   62.000000  \n",
       "7              50   82.000000  \n",
       "8             100   61.000000  \n",
       "9              50   76.000000  \n",
       "10             50   84.000000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Sem_1['Percentage'] = Sem_1_percent_data.transpose()\n",
    "Sem_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>11</th>\n",
       "      <td>63.000000</td>\n",
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       "      <th>12</th>\n",
       "      <td>92.000000</td>\n",
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       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>81.333333</td>\n",
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       "      <th>14</th>\n",
       "      <td>88.000000</td>\n",
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       "      <th>15</th>\n",
       "      <td>74.666667</td>\n",
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       "      <th>16</th>\n",
       "      <td>88.000000</td>\n",
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       "      <th>17</th>\n",
       "      <td>74.666667</td>\n",
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       "      <th>18</th>\n",
       "      <td>88.000000</td>\n",
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       "      <th>19</th>\n",
       "      <td>84.000000</td>\n",
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       "      <th>20</th>\n",
       "      <td>94.000000</td>\n",
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       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>80.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>88.000000</td>\n",
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       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>90.000000</td>\n",
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      ],
      "text/plain": [
       "            0\n",
       "11  63.000000\n",
       "12  92.000000\n",
       "13  81.333333\n",
       "14  88.000000\n",
       "15  74.666667\n",
       "16  88.000000\n",
       "17  74.666667\n",
       "18  88.000000\n",
       "19  84.000000\n",
       "20  94.000000\n",
       "21  80.000000\n",
       "22  88.000000\n",
       "23  90.000000"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Similarly add percentage column to Sem_2 Dataset\n",
    "Sem_2_percent = []\n",
    "for x in range(0,1):\n",
    "    Sem_2_percent.append((Sem_2['Marks Received '] / Sem_2['Maximum Marks'])*100)\n",
    "Sem_2_percent_data = pd.DataFrame(Sem_2_percent)\n",
    "Sem_2_percent_data.transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Paper Code</th>\n",
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       "      <th>Sem</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>FEC201</td>\n",
       "      <td>Engineering Mathematics - 2</td>\n",
       "      <td>2</td>\n",
       "      <td>63</td>\n",
       "      <td>100</td>\n",
       "      <td>63.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>FEC201 TW</td>\n",
       "      <td>Engineering Mathematics - 2 Term Work</td>\n",
       "      <td>2</td>\n",
       "      <td>23</td>\n",
       "      <td>25</td>\n",
       "      <td>92.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>FEC202</td>\n",
       "      <td>Engineering Physics -2</td>\n",
       "      <td>2</td>\n",
       "      <td>61</td>\n",
       "      <td>75</td>\n",
       "      <td>81.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>FEL201</td>\n",
       "      <td>Engineering Physics -2 (TW)</td>\n",
       "      <td>2</td>\n",
       "      <td>22</td>\n",
       "      <td>25</td>\n",
       "      <td>88.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>FEC203</td>\n",
       "      <td>Engineering Chemistry - 2</td>\n",
       "      <td>2</td>\n",
       "      <td>56</td>\n",
       "      <td>75</td>\n",
       "      <td>74.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>FEL202</td>\n",
       "      <td>Engineering Chemistry - 2 (TW)</td>\n",
       "      <td>2</td>\n",
       "      <td>22</td>\n",
       "      <td>25</td>\n",
       "      <td>88.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>FEC204</td>\n",
       "      <td>Engineering Graphics</td>\n",
       "      <td>2</td>\n",
       "      <td>56</td>\n",
       "      <td>75</td>\n",
       "      <td>74.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>FEL203</td>\n",
       "      <td>Engineering Graphics (TW)</td>\n",
       "      <td>2</td>\n",
       "      <td>66</td>\n",
       "      <td>75</td>\n",
       "      <td>88.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>FEC205</td>\n",
       "      <td>C - Programming</td>\n",
       "      <td>2</td>\n",
       "      <td>63</td>\n",
       "      <td>75</td>\n",
       "      <td>84.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>FEL204</td>\n",
       "      <td>C - Programming (TW)</td>\n",
       "      <td>2</td>\n",
       "      <td>47</td>\n",
       "      <td>50</td>\n",
       "      <td>94.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>FEC206</td>\n",
       "      <td>Professional Communication &amp; Ethics -1</td>\n",
       "      <td>2</td>\n",
       "      <td>40</td>\n",
       "      <td>50</td>\n",
       "      <td>80.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>FEL205</td>\n",
       "      <td>Professional Communication &amp; Ethics -1 (TW)</td>\n",
       "      <td>2</td>\n",
       "      <td>22</td>\n",
       "      <td>25</td>\n",
       "      <td>88.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>FEL206</td>\n",
       "      <td>Basic Workshop practice - 2 (Practical)</td>\n",
       "      <td>2</td>\n",
       "      <td>45</td>\n",
       "      <td>50</td>\n",
       "      <td>90.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Paper Code                                   Paper Name  Sem  \\\n",
       "11     FEC201                 Engineering Mathematics - 2     2   \n",
       "12  FEC201 TW        Engineering Mathematics - 2 Term Work    2   \n",
       "13     FEC202                       Engineering Physics -2    2   \n",
       "14     FEL201                  Engineering Physics -2 (TW)    2   \n",
       "15     FEC203                    Engineering Chemistry - 2    2   \n",
       "16     FEL202               Engineering Chemistry - 2 (TW)    2   \n",
       "17     FEC204                         Engineering Graphics    2   \n",
       "18     FEL203                    Engineering Graphics (TW)    2   \n",
       "19     FEC205                              C - Programming    2   \n",
       "20     FEL204                         C - Programming (TW)    2   \n",
       "21     FEC206       Professional Communication & Ethics -1    2   \n",
       "22     FEL205  Professional Communication & Ethics -1 (TW)    2   \n",
       "23     FEL206      Basic Workshop practice - 2 (Practical)    2   \n",
       "\n",
       "    Marks Received   Maximum Marks  Percentage  \n",
       "11               63            100   63.000000  \n",
       "12               23             25   92.000000  \n",
       "13               61             75   81.333333  \n",
       "14               22             25   88.000000  \n",
       "15               56             75   74.666667  \n",
       "16               22             25   88.000000  \n",
       "17               56             75   74.666667  \n",
       "18               66             75   88.000000  \n",
       "19               63             75   84.000000  \n",
       "20               47             50   94.000000  \n",
       "21               40             50   80.000000  \n",
       "22               22             25   88.000000  \n",
       "23               45             50   90.000000  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Sem_2['Percentage'] = Sem_2_percent_data.transpose()\n",
    "Sem_2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-29T18:18:53.706687Z",
     "iopub.status.busy": "2021-12-29T18:18:53.706406Z",
     "iopub.status.idle": "2021-12-29T18:18:53.712963Z",
     "shell.execute_reply": "2021-12-29T18:18:53.711642Z",
     "shell.execute_reply.started": "2021-12-29T18:18:53.706656Z"
    }
   },
   "source": [
    "<h3> Analysis of Sem 1 result using matplotlib </h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Paper Name'>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Bar Graph plotting of Sem_1 dataset\n",
    "Sem_1.plot(x='Paper Name', y = ['Marks Received ','Maximum Marks'], kind = 'barh', figsize = (10,5), grid = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Paper Name'>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Bar Graph plotting of Sem_1 percentage\n",
    "Sem_1.plot(x='Paper Name', y = 'Percentage', kind = 'bar', figsize = (10,5), grid = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Pie Graph plotting of Sem_1 percentage\n",
    "fig = plt.figure()\n",
    "ax = fig.add_axes([0,0,1,1])\n",
    "ax.axis('equal')\n",
    "ax.pie(Sem_1['Percentage'], labels = Sem_1['Paper Name'],autopct='%1.2f%%')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Analysis of Sem 2 result using matplotlib</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Paper Name'>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Bar Graph plotting of Sem_2 dataset\n",
    "Sem_2.plot(x='Paper Name', y = ['Marks Received ','Maximum Marks'], kind = 'barh', figsize = (10,5), grid = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Paper Name'>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Bar Graph plotting of Sem_2 percentage\n",
    "Sem_2.plot(x='Paper Name', y = 'Percentage', kind = 'bar', figsize = (10,5), grid = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Pie Graph plotting of Sem_2 percentage\n",
    "fig = plt.figure()\n",
    "ax = fig.add_axes([0,0,1,1])\n",
    "ax.axis('equal')\n",
    "ax.pie(Sem_2['Percentage'], labels = Sem_2['Paper Name'],autopct='%1.2f%%')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2> Comparision of Marks scored in Sem 1 and Sem 2 </h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(675, 467, 69.18518518518518)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Total_marks_in_sem_1 = Sem_1['Maximum Marks'].sum()\n",
    "Total_marks_scored_in_sem_1 = Sem_1['Marks Received '].sum()\n",
    "percentage_in_sem_1 = ((Total_marks_scored_in_sem_1/Total_marks_in_sem_1) * 100 )\n",
    "Total_marks_in_sem_1, Total_marks_scored_in_sem_1, percentage_in_sem_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(725, 586, 80.82758620689656)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Total_marks_in_sem_2 = Sem_2['Maximum Marks'].sum()\n",
    "Total_marks_scored_in_sem_2 = Sem_2['Marks Received '].sum()\n",
    "percentage_in_sem_2 = ((Total_marks_scored_in_sem_2/Total_marks_in_sem_2) * 100 )\n",
    "Total_marks_in_sem_2, Total_marks_scored_in_sem_2, percentage_in_sem_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>Sem 1</th>\n",
       "      <th>Sem 2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>675.000000</td>\n",
       "      <td>725.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>467.000000</td>\n",
       "      <td>586.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>69.185185</td>\n",
       "      <td>80.827586</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Sem 1       Sem 2\n",
       "0  675.000000  725.000000\n",
       "1  467.000000  586.000000\n",
       "2   69.185185   80.827586"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Marks = { 'Sem 1' : [Total_marks_in_sem_1, Total_marks_scored_in_sem_1, percentage_in_sem_1],      \n",
    "          'Sem 2' : [Total_marks_in_sem_2, Total_marks_scored_in_sem_2, percentage_in_sem_2]\n",
    "}\n",
    "Sem_1_2_marks = pd.DataFrame(Marks)\n",
    "Sem_1_2_marks\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(Sem_1_2_marks, annot=True, linecolor='yellow',linewidths=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}