{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['survname', 'year', 'period', 'crs_num', 'crs_sect', 'crs_name',\n",
       "       'crs_dept', 'crs_cohort', 'username', 'person', 'expected', 'received',\n",
       "       'pct', 'mean1', 'resp1', 'mean2', 'resp2', 'mean3', 'resp3', 'mean4',\n",
       "       'resp4', 'mean5', 'resp5', 'mean6', 'resp6', 'mean7', 'resp7', 'mean8',\n",
       "       'resp8', 'mean9', 'resp9', 'mean10', 'resp10', 'mean11', 'resp11',\n",
       "       'mean12', 'resp12', 'mean13', 'resp13', 'mean14', 'resp14', 'mean15',\n",
       "       'resp15', 'mean16', 'resp16', 'overall'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(r'G:\\My Drive\\full_professor_promotion_portfolio\\supporting_materials\\1_teaching\\student_evaluations_data\\raw_data.csv')\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        text-align: right;\n",
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       "      <th></th>\n",
       "      <th>survname</th>\n",
       "      <th>year</th>\n",
       "      <th>period</th>\n",
       "      <th>crs_num</th>\n",
       "      <th>crs_sect</th>\n",
       "      <th>crs_name</th>\n",
       "      <th>crs_dept</th>\n",
       "      <th>crs_cohort</th>\n",
       "      <th>username</th>\n",
       "      <th>person</th>\n",
       "      <th>...</th>\n",
       "      <th>resp12</th>\n",
       "      <th>mean13</th>\n",
       "      <th>resp13</th>\n",
       "      <th>mean14</th>\n",
       "      <th>resp14</th>\n",
       "      <th>mean15</th>\n",
       "      <th>resp15</th>\n",
       "      <th>mean16</th>\n",
       "      <th>resp16</th>\n",
       "      <th>overall</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Fall 2021 Undergrad Semester End</td>\n",
       "      <td>2021</td>\n",
       "      <td>202210</td>\n",
       "      <td>ENGL200</td>\n",
       "      <td>D</td>\n",
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       "      <td>English</td>\n",
       "      <td>1</td>\n",
       "      <td>KSpicer</td>\n",
       "      <td>Spicer, Kevin</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>4.6667</td>\n",
       "      <td>6</td>\n",
       "      <td>5.0000</td>\n",
       "      <td>6</td>\n",
       "      <td>5.0000</td>\n",
       "      <td>6</td>\n",
       "      <td>5.0000</td>\n",
       "      <td>6</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Fall 2021 Undergrad Semester End</td>\n",
       "      <td>2021</td>\n",
       "      <td>202210</td>\n",
       "      <td>ENGL200</td>\n",
       "      <td>E</td>\n",
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       "      <td>1</td>\n",
       "      <td>KSpicer</td>\n",
       "      <td>Spicer, Kevin</td>\n",
       "      <td>...</td>\n",
       "      <td>11</td>\n",
       "      <td>4.6364</td>\n",
       "      <td>11</td>\n",
       "      <td>4.9091</td>\n",
       "      <td>11</td>\n",
       "      <td>4.8182</td>\n",
       "      <td>11</td>\n",
       "      <td>4.9091</td>\n",
       "      <td>11</td>\n",
       "      <td>4.5</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Fall 2021 Undergrad Semester End</td>\n",
       "      <td>2021</td>\n",
       "      <td>202210</td>\n",
       "      <td>ENGL321</td>\n",
       "      <td>A</td>\n",
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       "      <td>English</td>\n",
       "      <td>1</td>\n",
       "      <td>KSpicer</td>\n",
       "      <td>Spicer, Kevin</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>4.8750</td>\n",
       "      <td>8</td>\n",
       "      <td>4.5000</td>\n",
       "      <td>8</td>\n",
       "      <td>4.7500</td>\n",
       "      <td>8</td>\n",
       "      <td>5.0000</td>\n",
       "      <td>8</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Fall 2022 - December Undergraduate end Dec11</td>\n",
       "      <td>2022</td>\n",
       "      <td>202310</td>\n",
       "      <td>ENGL200</td>\n",
       "      <td>D</td>\n",
       "      <td>Intro to Lit: Weird Fiction</td>\n",
       "      <td>English</td>\n",
       "      <td>1</td>\n",
       "      <td>KSpicer</td>\n",
       "      <td>Spicer, Kevin</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>3.5000</td>\n",
       "      <td>6</td>\n",
       "      <td>4.1667</td>\n",
       "      <td>6</td>\n",
       "      <td>4.3333</td>\n",
       "      <td>6</td>\n",
       "      <td>4.3333</td>\n",
       "      <td>6</td>\n",
       "      <td>4.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Fall 2022 - December Undergraduate end Dec11</td>\n",
       "      <td>2022</td>\n",
       "      <td>202310</td>\n",
       "      <td>ENGL200</td>\n",
       "      <td>E</td>\n",
       "      <td>Intro to Lit: Weird Fiction</td>\n",
       "      <td>English</td>\n",
       "      <td>1</td>\n",
       "      <td>KSpicer</td>\n",
       "      <td>Spicer, Kevin</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>3.3333</td>\n",
       "      <td>3</td>\n",
       "      <td>5.0000</td>\n",
       "      <td>3</td>\n",
       "      <td>5.0000</td>\n",
       "      <td>3</td>\n",
       "      <td>5.0000</td>\n",
       "      <td>3</td>\n",
       "      <td>4.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 46 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       survname  year  period  crs_num   \n",
       "0              Fall 2021 Undergrad Semester End  2021  202210  ENGL200  \\\n",
       "1              Fall 2021 Undergrad Semester End  2021  202210  ENGL200   \n",
       "2              Fall 2021 Undergrad Semester End  2021  202210  ENGL321   \n",
       "3  Fall 2022 - December Undergraduate end Dec11  2022  202310  ENGL200   \n",
       "4  Fall 2022 - December Undergraduate end Dec11  2022  202310  ENGL200   \n",
       "\n",
       "  crs_sect                       crs_name crs_dept  crs_cohort username   \n",
       "0        D    Intro to Lit: Weird Fiction  English           1  KSpicer  \\\n",
       "1        E    Intro to Lit: Weird Fiction  English           1  KSpicer   \n",
       "2        A  Phil, Rhetoric, Grammar, Lang  English           1  KSpicer   \n",
       "3        D    Intro to Lit: Weird Fiction  English           1  KSpicer   \n",
       "4        E    Intro to Lit: Weird Fiction  English           1  KSpicer   \n",
       "\n",
       "          person  ...  resp12  mean13  resp13  mean14  resp14  mean15  resp15   \n",
       "0  Spicer, Kevin  ...       6  4.6667       6  5.0000       6  5.0000       6  \\\n",
       "1  Spicer, Kevin  ...      11  4.6364      11  4.9091      11  4.8182      11   \n",
       "2  Spicer, Kevin  ...       8  4.8750       8  4.5000       8  4.7500       8   \n",
       "3  Spicer, Kevin  ...       6  3.5000       6  4.1667       6  4.3333       6   \n",
       "4  Spicer, Kevin  ...       3  3.3333       3  5.0000       3  5.0000       3   \n",
       "\n",
       "   mean16  resp16  overall  \n",
       "0  5.0000       6      4.5  \n",
       "1  4.9091      11      4.5  \n",
       "2  5.0000       8      4.5  \n",
       "3  4.3333       6      4.1  \n",
       "4  5.0000       3      4.6  \n",
       "\n",
       "[5 rows x 46 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>9</th>\n",
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       "      <th>10</th>\n",
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       "      <td>KSpicer</td>\n",
       "      <td>Spicer, Kevin</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>4.0000</td>\n",
       "      <td>5</td>\n",
       "      <td>4.2000</td>\n",
       "      <td>5</td>\n",
       "      <td>4.2000</td>\n",
       "      <td>5</td>\n",
       "      <td>4.8000</td>\n",
       "      <td>5</td>\n",
       "      <td>4.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>spring</td>\n",
       "      <td>2022</td>\n",
       "      <td>202220</td>\n",
       "      <td>ENGL200</td>\n",
       "      <td>F</td>\n",
       "      <td>Intro to Lit: Weird Fiction</td>\n",
       "      <td>English</td>\n",
       "      <td>1</td>\n",
       "      <td>KSpicer</td>\n",
       "      <td>Spicer, Kevin</td>\n",
       "      <td>...</td>\n",
       "      <td>11</td>\n",
       "      <td>3.8182</td>\n",
       "      <td>11</td>\n",
       "      <td>4.6364</td>\n",
       "      <td>11</td>\n",
       "      <td>4.6364</td>\n",
       "      <td>11</td>\n",
       "      <td>4.7273</td>\n",
       "      <td>11</td>\n",
       "      <td>4.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>spring</td>\n",
       "      <td>2022</td>\n",
       "      <td>202220</td>\n",
       "      <td>ENGL291</td>\n",
       "      <td>A</td>\n",
       "      <td>Adolescent Literature</td>\n",
       "      <td>English</td>\n",
       "      <td>1</td>\n",
       "      <td>KSpicer</td>\n",
       "      <td>Spicer, Kevin</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>4.8000</td>\n",
       "      <td>5</td>\n",
       "      <td>4.8000</td>\n",
       "      <td>5</td>\n",
       "      <td>4.8000</td>\n",
       "      <td>5</td>\n",
       "      <td>4.8000</td>\n",
       "      <td>5</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>spring</td>\n",
       "      <td>2022</td>\n",
       "      <td>202220</td>\n",
       "      <td>ENGL440</td>\n",
       "      <td>A</td>\n",
       "      <td>Genre: YA Lit</td>\n",
       "      <td>English</td>\n",
       "      <td>1</td>\n",
       "      <td>KSpicer</td>\n",
       "      <td>Spicer, Kevin</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>4.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>spring</td>\n",
       "      <td>2023</td>\n",
       "      <td>202320</td>\n",
       "      <td>ENGE515</td>\n",
       "      <td>B</td>\n",
       "      <td>Digital Rhetoric</td>\n",
       "      <td>English</td>\n",
       "      <td>1</td>\n",
       "      <td>KSpicer</td>\n",
       "      <td>Spicer, Kevin</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>4.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>spring</td>\n",
       "      <td>2023</td>\n",
       "      <td>202320</td>\n",
       "      <td>ENGL200</td>\n",
       "      <td>D</td>\n",
       "      <td>Intro to Lit: Weird Fiction</td>\n",
       "      <td>English</td>\n",
       "      <td>1</td>\n",
       "      <td>KSpicer</td>\n",
       "      <td>Spicer, Kevin</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>4.3333</td>\n",
       "      <td>6</td>\n",
       "      <td>4.6667</td>\n",
       "      <td>6</td>\n",
       "      <td>4.3333</td>\n",
       "      <td>6</td>\n",
       "      <td>4.5000</td>\n",
       "      <td>6</td>\n",
       "      <td>4.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>spring</td>\n",
       "      <td>2023</td>\n",
       "      <td>202320</td>\n",
       "      <td>ENGL200</td>\n",
       "      <td>E</td>\n",
       "      <td>Intro to Lit: Weird Fiction</td>\n",
       "      <td>English</td>\n",
       "      <td>1</td>\n",
       "      <td>KSpicer</td>\n",
       "      <td>Spicer, Kevin</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>4.2857</td>\n",
       "      <td>7</td>\n",
       "      <td>4.5714</td>\n",
       "      <td>7</td>\n",
       "      <td>4.4286</td>\n",
       "      <td>7</td>\n",
       "      <td>4.2857</td>\n",
       "      <td>7</td>\n",
       "      <td>4.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>spring</td>\n",
       "      <td>2023</td>\n",
       "      <td>202320</td>\n",
       "      <td>ENGL200</td>\n",
       "      <td>F</td>\n",
       "      <td>Intro to Lit: Weird Fiction</td>\n",
       "      <td>English</td>\n",
       "      <td>1</td>\n",
       "      <td>KSpicer</td>\n",
       "      <td>Spicer, Kevin</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>4.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>spring</td>\n",
       "      <td>2023</td>\n",
       "      <td>202320</td>\n",
       "      <td>ENGL335</td>\n",
       "      <td>A</td>\n",
       "      <td>Ancient Literature</td>\n",
       "      <td>English</td>\n",
       "      <td>1</td>\n",
       "      <td>KSpicer</td>\n",
       "      <td>Spicer, Kevin</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>5.0000</td>\n",
       "      <td>4</td>\n",
       "      <td>4.2500</td>\n",
       "      <td>4</td>\n",
       "      <td>4.7500</td>\n",
       "      <td>4</td>\n",
       "      <td>4.5000</td>\n",
       "      <td>4</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>summer</td>\n",
       "      <td>2021</td>\n",
       "      <td>202130</td>\n",
       "      <td>ENGL200</td>\n",
       "      <td>A</td>\n",
       "      <td>Intro to Lit:�  Weird Fiction</td>\n",
       "      <td>English</td>\n",
       "      <td>1</td>\n",
       "      <td>KSpicer</td>\n",
       "      <td>Spicer, Kevin</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>5.0000</td>\n",
       "      <td>5</td>\n",
       "      <td>5.0000</td>\n",
       "      <td>5</td>\n",
       "      <td>5.0000</td>\n",
       "      <td>5</td>\n",
       "      <td>5.0000</td>\n",
       "      <td>5</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>summer</td>\n",
       "      <td>2022</td>\n",
       "      <td>202230</td>\n",
       "      <td>ENGE510</td>\n",
       "      <td>B</td>\n",
       "      <td>Rhetorical Theory I, Classical</td>\n",
       "      <td>English</td>\n",
       "      <td>1</td>\n",
       "      <td>KSpicer</td>\n",
       "      <td>Spicer, Kevin</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>4.0000</td>\n",
       "      <td>4</td>\n",
       "      <td>4.7500</td>\n",
       "      <td>4</td>\n",
       "      <td>4.5000</td>\n",
       "      <td>4</td>\n",
       "      <td>5.0000</td>\n",
       "      <td>4</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>21 rows × 46 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   survname  year  period  crs_num crs_sect                        crs_name   \n",
       "0      fall  2021  202210  ENGL200        D     Intro to Lit: Weird Fiction  \\\n",
       "1      fall  2021  202210  ENGL200        E     Intro to Lit: Weird Fiction   \n",
       "2      fall  2021  202210  ENGL321        A   Phil, Rhetoric, Grammar, Lang   \n",
       "3      fall  2022  202310  ENGL200        D     Intro to Lit: Weird Fiction   \n",
       "4      fall  2022  202310  ENGL200        E     Intro to Lit: Weird Fiction   \n",
       "5      fall  2022  202310  ENGL200        F     Intro to Lit: Weird Fiction   \n",
       "6      fall  2022  202310  ENGL372        A                     Shakespeare   \n",
       "7    spring  2021  202120  ENGL200   Master     Intro to Lit: Weird Fiction   \n",
       "8    spring  2021  202120  ENGL354        A  British Lit: Beowulf to Milton   \n",
       "9    spring  2021  202120  ENGM510        A  Rhetorical Theory I, Classical   \n",
       "10   spring  2022  202220  ENGL200        E     Intro to Lit: Weird Fiction   \n",
       "11   spring  2022  202220  ENGL200        F     Intro to Lit: Weird Fiction   \n",
       "12   spring  2022  202220  ENGL291        A           Adolescent Literature   \n",
       "13   spring  2022  202220  ENGL440        A                   Genre: YA Lit   \n",
       "14   spring  2023  202320  ENGE515        B                Digital Rhetoric   \n",
       "15   spring  2023  202320  ENGL200        D     Intro to Lit: Weird Fiction   \n",
       "16   spring  2023  202320  ENGL200        E     Intro to Lit: Weird Fiction   \n",
       "17   spring  2023  202320  ENGL200        F     Intro to Lit: Weird Fiction   \n",
       "18   spring  2023  202320  ENGL335        A              Ancient Literature   \n",
       "19   summer  2021  202130  ENGL200        A   Intro to Lit:�  Weird Fiction   \n",
       "20   summer  2022  202230  ENGE510        B  Rhetorical Theory I, Classical   \n",
       "\n",
       "   crs_dept  crs_cohort username         person  ...  resp12  mean13  resp13   \n",
       "0   English           1  KSpicer  Spicer, Kevin  ...       6  4.6667       6  \\\n",
       "1   English           1  KSpicer  Spicer, Kevin  ...      11  4.6364      11   \n",
       "2   English           1  KSpicer  Spicer, Kevin  ...       8  4.8750       8   \n",
       "3   English           1  KSpicer  Spicer, Kevin  ...       6  3.5000       6   \n",
       "4   English           1  KSpicer  Spicer, Kevin  ...       3  3.3333       3   \n",
       "5   English           1  KSpicer  Spicer, Kevin  ...       8  3.7500       8   \n",
       "6   English           1  KSpicer  Spicer, Kevin  ...       3  5.0000       3   \n",
       "7   English           1  KSpicer  Spicer, Kevin  ...      20  4.4000      20   \n",
       "8   English           1  KSpicer  Spicer, Kevin  ...       6  4.5000       6   \n",
       "9   Default           1  KSpicer  Spicer, Kevin  ...       1  5.0000       1   \n",
       "10  English           1  KSpicer  Spicer, Kevin  ...       5  4.0000       5   \n",
       "11  English           1  KSpicer  Spicer, Kevin  ...      11  3.8182      11   \n",
       "12  English           1  KSpicer  Spicer, Kevin  ...       5  4.8000       5   \n",
       "13  English           1  KSpicer  Spicer, Kevin  ...       1  5.0000       1   \n",
       "14  English           1  KSpicer  Spicer, Kevin  ...       1  5.0000       1   \n",
       "15  English           1  KSpicer  Spicer, Kevin  ...       6  4.3333       6   \n",
       "16  English           1  KSpicer  Spicer, Kevin  ...       7  4.2857       7   \n",
       "17  English           1  KSpicer  Spicer, Kevin  ...       1  5.0000       1   \n",
       "18  English           1  KSpicer  Spicer, Kevin  ...       4  5.0000       4   \n",
       "19  English           1  KSpicer  Spicer, Kevin  ...       5  5.0000       5   \n",
       "20  English           1  KSpicer  Spicer, Kevin  ...       4  4.0000       4   \n",
       "\n",
       "    mean14  resp14  mean15  resp15  mean16  resp16  overall  \n",
       "0   5.0000       6  5.0000       6  5.0000       6      4.5  \n",
       "1   4.9091      11  4.8182      11  4.9091      11      4.5  \n",
       "2   4.5000       8  4.7500       8  5.0000       8      4.5  \n",
       "3   4.1667       6  4.3333       6  4.3333       6      4.1  \n",
       "4   5.0000       3  5.0000       3  5.0000       3      4.6  \n",
       "5   4.7500       8  4.7500       8  4.7500       8      4.5  \n",
       "6   4.6667       3  4.6667       3  5.0000       3      4.6  \n",
       "7   4.8500      20  4.8000      20  4.8000      20      4.5  \n",
       "8   4.3333       6  4.8333       6  4.8333       6      4.5  \n",
       "9   5.0000       1  5.0000       1  5.0000       1      5.0  \n",
       "10  4.2000       5  4.2000       5  4.8000       5      4.2  \n",
       "11  4.6364      11  4.6364      11  4.7273      11      4.4  \n",
       "12  4.8000       5  4.8000       5  4.8000       5      4.5  \n",
       "13  5.0000       1  5.0000       1  5.0000       1      4.7  \n",
       "14  5.0000       1  5.0000       1  5.0000       1      4.6  \n",
       "15  4.6667       6  4.3333       6  4.5000       6      4.3  \n",
       "16  4.5714       7  4.4286       7  4.2857       7      4.1  \n",
       "17  5.0000       1  5.0000       1  5.0000       1      4.8  \n",
       "18  4.2500       4  4.7500       4  4.5000       4      4.5  \n",
       "19  5.0000       5  5.0000       5  5.0000       5      4.9  \n",
       "20  4.7500       4  4.5000       4  5.0000       4      4.5  \n",
       "\n",
       "[21 rows x 46 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for index, row in df.iterrows():\n",
    "    df.at[index, 'survname'] = row['survname'].split(' ')[0].lower()\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Group the DataFrame by 'survname' and 'crs_num'\n",
    "grouped_data = df.groupby(['survname', 'crs_num'])['overall'].mean().reset_index()\n",
    "\n",
    "# Plot the 'overall' column for each group as a bar graph\n",
    "x_ticks_order = ['ENGL200', 'ENGL291', 'ENGL321', 'ENGL335', 'ENGL354', 'ENGL372', 'ENGL440', 'ENGM510', 'ENGE510', 'ENGE515']\n",
    "bar_plot = sns.barplot(x='crs_num', y='overall', data=grouped_data, palette='viridis', order=x_ticks_order, errorbar=None)\n",
    "\n",
    "for x in bar_plot.patches:\n",
    "    bar_plot.annotate(format(x.get_height(), '.1f'),\n",
    "    (x.get_x() + x.get_width() / 2., x.get_height()),\n",
    "    ha = 'center', va='center',\n",
    "    xytext = (0, 9),\n",
    "    textcoords = 'offset points')\n",
    "\n",
    "ax = plt.gca()\n",
    "ax.margins(0, 0.1)\n",
    "\n",
    "plt.xlabel('Course Number')\n",
    "plt.ylabel('Overall Score')\n",
    "plt.title('Overall Student Evaluation Scores by Course')\n",
    "plt.xticks(rotation=90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Group the DataFrame by 'survname' and 'crs_num'\n",
    "grouped_data = df.groupby(['survname', 'crs_num'])['pct'].mean().reset_index()\n",
    "\n",
    "# Plot the 'overall' column for each group as a bar graph\n",
    "x_ticks_order = ['ENGL200', 'ENGL291', 'ENGL321', 'ENGL335', 'ENGL354', 'ENGL372', 'ENGL440', 'ENGM510', 'ENGE510', 'ENGE515']\n",
    "# Plot the 'expected' column for each group as a bar graph with explicit xtick label ordering\n",
    "sns.barplot(x='crs_num', y='pct', hue='survname', data=grouped_data, order=x_ticks_order, alpha=0.5)\n",
    "\n",
    "# Plot the 'received' column for each group as a bar graph with explicit xtick label ordering\n",
    "sns.barplot(x='crs_num', y='pct', hue='survname', data=grouped_data, order=x_ticks_order, alpha=0.5)\n",
    "\n",
    "plt.xlabel('Course Number')\n",
    "plt.ylabel('Count')\n",
    "plt.title('Evaluation Counts by Survey and Course')\n",
    "plt.legend(title='Semester')\n",
    "plt.legend(bbox_to_anchor=(1.04, 1))\n",
    "plt.xticks(rotation=90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>semester</th>\n",
       "      <th>crs_num</th>\n",
       "      <th>year</th>\n",
       "      <th>expected</th>\n",
       "      <th>received</th>\n",
       "      <th>pct</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>fall</td>\n",
       "      <td>ENGL200</td>\n",
       "      <td>2021</td>\n",
       "      <td>19</td>\n",
       "      <td>6</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>fall</td>\n",
       "      <td>ENGL200</td>\n",
       "      <td>2021</td>\n",
       "      <td>21</td>\n",
       "      <td>11</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>fall</td>\n",
       "      <td>ENGL321</td>\n",
       "      <td>2021</td>\n",
       "      <td>15</td>\n",
       "      <td>8</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>fall</td>\n",
       "      <td>ENGL200</td>\n",
       "      <td>2022</td>\n",
       "      <td>20</td>\n",
       "      <td>6</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>fall</td>\n",
       "      <td>ENGL200</td>\n",
       "      <td>2022</td>\n",
       "      <td>19</td>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>fall</td>\n",
       "      <td>ENGL200</td>\n",
       "      <td>2022</td>\n",
       "      <td>18</td>\n",
       "      <td>8</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>fall</td>\n",
       "      <td>ENGL372</td>\n",
       "      <td>2022</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>summer</td>\n",
       "      <td>ENGL200</td>\n",
       "      <td>2021</td>\n",
       "      <td>21</td>\n",
       "      <td>5</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>summer</td>\n",
       "      <td>ENGE510</td>\n",
       "      <td>2022</td>\n",
       "      <td>16</td>\n",
       "      <td>4</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>spring</td>\n",
       "      <td>ENGL200</td>\n",
       "      <td>2021</td>\n",
       "      <td>48</td>\n",
       "      <td>20</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>spring</td>\n",
       "      <td>ENGL354</td>\n",
       "      <td>2021</td>\n",
       "      <td>13</td>\n",
       "      <td>6</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>spring</td>\n",
       "      <td>ENGM510</td>\n",
       "      <td>2021</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>spring</td>\n",
       "      <td>ENGL200</td>\n",
       "      <td>2022</td>\n",
       "      <td>17</td>\n",
       "      <td>5</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>spring</td>\n",
       "      <td>ENGL200</td>\n",
       "      <td>2022</td>\n",
       "      <td>20</td>\n",
       "      <td>11</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>spring</td>\n",
       "      <td>ENGL291</td>\n",
       "      <td>2022</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>spring</td>\n",
       "      <td>ENGL440</td>\n",
       "      <td>2022</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>spring</td>\n",
       "      <td>ENGE515</td>\n",
       "      <td>2023</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>spring</td>\n",
       "      <td>ENGL200</td>\n",
       "      <td>2023</td>\n",
       "      <td>20</td>\n",
       "      <td>6</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>spring</td>\n",
       "      <td>ENGL200</td>\n",
       "      <td>2023</td>\n",
       "      <td>16</td>\n",
       "      <td>7</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>spring</td>\n",
       "      <td>ENGL200</td>\n",
       "      <td>2023</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>spring</td>\n",
       "      <td>ENGL335</td>\n",
       "      <td>2023</td>\n",
       "      <td>14</td>\n",
       "      <td>4</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   semester  crs_num  year  expected  received  pct\n",
       "0      fall  ENGL200  2021        19         6   32\n",
       "1      fall  ENGL200  2021        21        11   52\n",
       "2      fall  ENGL321  2021        15         8   53\n",
       "3      fall  ENGL200  2022        20         6   30\n",
       "4      fall  ENGL200  2022        19         3   16\n",
       "5      fall  ENGL200  2022        18         8   44\n",
       "6      fall  ENGL372  2022        12         3   25\n",
       "19   summer  ENGL200  2021        21         5   24\n",
       "20   summer  ENGE510  2022        16         4   25\n",
       "7    spring  ENGL200  2021        48        20   42\n",
       "8    spring  ENGL354  2021        13         6   46\n",
       "9    spring  ENGM510  2021         1         1  100\n",
       "10   spring  ENGL200  2022        17         5   29\n",
       "11   spring  ENGL200  2022        20        11   55\n",
       "12   spring  ENGL291  2022         8         5   63\n",
       "13   spring  ENGL440  2022         2         1   50\n",
       "14   spring  ENGE515  2023        13         1    8\n",
       "15   spring  ENGL200  2023        20         6   30\n",
       "16   spring  ENGL200  2023        16         7   44\n",
       "17   spring  ENGL200  2023         4         1   25\n",
       "18   spring  ENGL335  2023        14         4   29"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "expected_received = df[['survname', 'crs_num', 'year', 'expected', 'received', 'pct']]\n",
    "expected_received = expected_received.rename(columns={'survname': 'semester'})\n",
    "\n",
    "semester_order = ['fall', 'summer', 'spring']\n",
    "expected_received_sorted = expected_received.sort_values(by=['semester', 'year'], key=lambda x: x.map({v: i for i, v in enumerate(semester_order)}))\n",
    "#expected_received.sort_values(by=['semester', 'year'], inplace=True)\n",
    "expected_received_sorted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "expected_received.to_html(r'G:\\My Drive\\full_professor_promotion_portfolio\\supporting_materials\\1_teaching\\student_evaluations_data\\survey_response_table_percentages.html', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "labels = expected_received['crs_num']\n",
    "\n",
    "ax = expected_received[['pct']].plot(kind='bar')\n",
    "\n",
    "# Set the x-axis tick labels to the values in the 'crs_num' column\n",
    "ax.set_xticklabels(labels)\n",
    "\n",
    "plt.xlabel('Course Number')\n",
    "plt.ylabel('Percentage')\n",
    "plt.title('Received Percentages by Course')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "labels = expected_received['crs_num']\n",
    "\n",
    "ax = expected_received[['expected', 'received']].plot(kind='bar')\n",
    "\n",
    "# Set the x-axis tick labels to the values in the 'crs_num' column\n",
    "ax.set_xticklabels(labels)\n",
    "\n",
    "plt.xlabel('Course Number')\n",
    "plt.ylabel('Percentage')\n",
    "plt.title('Received Percentages by Course')\n",
    "plt.show()\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "python311",
   "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": "3.11.0"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}