IS362Week7.ipynb

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    "<h1>IS362 Week 7 </h1>\n",
    "\n",
    "Analysis and Normalization of surveys for popular movies with a direct survey. \n",
    "6 movies were selected to survey for rating with people I know based on relationship.\n",
    "The ratings were collected for each movie. Then put it into a MySQL database and exported into a CSV file.\n",
    "The CSV file is analyzed.\n",
    "\n",
    "<h2> First "
   ]
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movie</th>\n",
       "      <th>survey</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Hundreds of Beavers</td>\n",
       "      <td>mom</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>The Crime is Mine</td>\n",
       "      <td>mom</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Dune Part 2</td>\n",
       "      <td>mom</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Monolith</td>\n",
       "      <td>mom</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Drive Away Dolls</td>\n",
       "      <td>mom</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "                 movie survey  rating\n",
       "0  Hundreds of Beavers    mom       1\n",
       "1    The Crime is Mine    mom       2\n",
       "2          Dune Part 2    mom       2\n",
       "3             Monolith    mom       1\n",
       "4     Drive Away Dolls    mom       2"
      ]
     },
     "execution_count": 44,
     "metadata": {},
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   ],
   "source": [
    "import pandas as pd\n",
    "movies= r'C:\\Users\\turin\\Downloads\\IS362Week7movies.csv'\n",
    "df=pd.read_csv(movies)\n",
    "df.head()"
   ]
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  {
   "cell_type": "markdown",
   "id": "92b0079a-defb-411b-9430-2457d89ac064",
   "metadata": {},
   "source": [
    "<h1>1. Load the ratings by user information that you collected into a pandas dataframe.</h1>  "
   ]
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>rating</th>\n",
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       "      <th rowspan=\"8\" valign=\"top\">Drive Away Dolls</th>\n",
       "      <th>brother in law</th>\n",
       "      <td>3</td>\n",
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       "      <th>cousin</th>\n",
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       "      <th>coworker</th>\n",
       "      <td>5</td>\n",
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       "      <th>friend</th>\n",
       "      <td>4</td>\n",
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       "    <tr>\n",
       "      <th>imaginary friend</th>\n",
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       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">Dune Part 2</th>\n",
       "      <th>brother in law</th>\n",
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       "      <th>peer</th>\n",
       "      <td>5</td>\n",
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       "      <th>sibling</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">Hundreds of Beavers</th>\n",
       "      <th>brother in law</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cousin</th>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>coworker</th>\n",
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       "      <th>friend</th>\n",
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       "      <th>mom</th>\n",
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       "    <tr>\n",
       "      <th>sibling</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">Monolith</th>\n",
       "      <th>brother in law</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cousin</th>\n",
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       "      <th>imaginary friend</th>\n",
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       "      <td>5</td>\n",
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       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">The Crime is Mine</th>\n",
       "      <th>brother in law</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cousin</th>\n",
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       "      <th>imaginary friend</th>\n",
       "      <td>1</td>\n",
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       "      <th>mom</th>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>peer</th>\n",
       "      <td>5</td>\n",
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       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">The Greatest Night in Pop</th>\n",
       "      <th>brother in law</th>\n",
       "      <td>4</td>\n",
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       "      <th>imaginary friend</th>\n",
       "      <td>3</td>\n",
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       "      <th>mom</th>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>peer</th>\n",
       "      <td>1</td>\n",
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      "text/plain": [
       "                                            rating\n",
       "movie                     survey                  \n",
       "Drive Away Dolls          brother in law         3\n",
       "                          cousin                 1\n",
       "                          coworker               5\n",
       "                          friend                 4\n",
       "                          imaginary friend       3\n",
       "                          mom                    2\n",
       "                          peer                   3\n",
       "                          sibling                5\n",
       "Dune Part 2               brother in law         1\n",
       "                          cousin                 1\n",
       "                          coworker               2\n",
       "                          friend                 2\n",
       "                          imaginary friend       2\n",
       "                          mom                    2\n",
       "                          peer                   5\n",
       "                          sibling                5\n",
       "Hundreds of Beavers       brother in law         1\n",
       "                          cousin                 3\n",
       "                          coworker               4\n",
       "                          friend                 2\n",
       "                          imaginary friend       1\n",
       "                          mom                    1\n",
       "                          peer                   4\n",
       "                          sibling                5\n",
       "Monolith                  brother in law         2\n",
       "                          cousin                 2\n",
       "                          coworker               3\n",
       "                          friend                 3\n",
       "                          imaginary friend       3\n",
       "                          mom                    1\n",
       "                          peer                   4\n",
       "                          sibling                5\n",
       "The Crime is Mine         brother in law         1\n",
       "                          cousin                 2\n",
       "                          coworker               1\n",
       "                          friend                 1\n",
       "                          imaginary friend       1\n",
       "                          mom                    2\n",
       "                          peer                   5\n",
       "                          sibling                5\n",
       "The Greatest Night in Pop brother in law         4\n",
       "                          cousin                 1\n",
       "                          coworker               3\n",
       "                          friend                 5\n",
       "                          imaginary friend       3\n",
       "                          mom                    3\n",
       "                          peer                   1\n",
       "                          sibling                4"
      ]
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     "execution_count": 59,
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   "source": [
    "# Show the count of ratings for each survey. There was one rating per survey for each movie.\n",
    "groupbydf = df.groupby(['movie', 'survey'])\n",
    "groupbydf.sum()\n",
    "#Since there is only one rating per survey per movie, the sum() function is just showing the corresponding rating."
   ]
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   "cell_type": "markdown",
   "id": "09d80439-21e8-4468-b844-e8b817a15110",
   "metadata": {},
   "source": [
    "<h1>2. Show the average ratings for each user and each movie. </h1>"
   ]
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       "      <th>rating</th>\n",
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       "      <th>Drive Away Dolls</th>\n",
       "      <td>3.250</td>\n",
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       "      <th>Dune Part 2</th>\n",
       "      <td>2.500</td>\n",
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       "      <th>Hundreds of Beavers</th>\n",
       "      <td>2.625</td>\n",
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       "      <th>Monolith</th>\n",
       "      <td>2.875</td>\n",
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       "      <th>The Crime is Mine</th>\n",
       "      <td>2.250</td>\n",
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      "text/plain": [
       "                           rating\n",
       "movie                            \n",
       "Drive Away Dolls            3.250\n",
       "Dune Part 2                 2.500\n",
       "Hundreds of Beavers         2.625\n",
       "Monolith                    2.875\n",
       "The Crime is Mine           2.250\n",
       "The Greatest Night in Pop   3.000"
      ]
     },
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   "source": [
    "moviesratings= df[['movie','rating']]\n",
    "moviesratings.groupby('movie').mean()"
   ]
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       "      <td>2.166667</td>\n",
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       "      <th>mom</th>\n",
       "      <td>1.833333</td>\n",
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       "      <th>peer</th>\n",
       "      <td>3.666667</td>\n",
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      "text/plain": [
       "                    rating\n",
       "survey                    \n",
       "brother in law    2.000000\n",
       "cousin            1.666667\n",
       "coworker          3.000000\n",
       "friend            2.833333\n",
       "imaginary friend  2.166667\n",
       "mom               1.833333\n",
       "peer              3.666667\n",
       "sibling           4.833333"
      ]
     },
     "execution_count": 96,
     "metadata": {},
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   ],
   "source": [
    "surveyratings= df[['survey','rating']]\n",
    "surveyratings.groupby('survey').mean()"
   ]
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   "id": "d38997b2-9d5e-4860-ae87-2809ae0b167f",
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    "<h1> 3. Create a new pandas dataframe, with normalized ratings for each user. Again, show the average \n",
    "ratings for each user and each movie.</h1>\n",
    "\n",
    "This took a very long time to figure out but it did succeed. I used the normalization function:\n",
    "\n",
    "normalized value= ((value to be normalized) - Minimum value)/ (Max value - min value))*100\n",
    "\n",
    "This will convert the values into a percentage. But any form of normalization can be used. For this case, since every rating was from 1-5 (No 0 was selected.) Any normalization formula can be used by defining the function as below and passing this data frame through with .apply(). "
   ]
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       "      <th rowspan=\"8\" valign=\"top\">Drive Away Dolls</th>\n",
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       "      <td>60.0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>friend</th>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>imaginary friend</th>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mom</th>\n",
       "      <td>40.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>peer</th>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sibling</th>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">Dune Part 2</th>\n",
       "      <th>brother in law</th>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cousin</th>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>coworker</th>\n",
       "      <td>40.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>friend</th>\n",
       "      <td>40.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>imaginary friend</th>\n",
       "      <td>40.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mom</th>\n",
       "      <td>40.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>peer</th>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sibling</th>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">Hundreds of Beavers</th>\n",
       "      <th>brother in law</th>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cousin</th>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>coworker</th>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>friend</th>\n",
       "      <td>40.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>imaginary friend</th>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mom</th>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>peer</th>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sibling</th>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">Monolith</th>\n",
       "      <th>brother in law</th>\n",
       "      <td>40.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cousin</th>\n",
       "      <td>40.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>coworker</th>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>friend</th>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>imaginary friend</th>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mom</th>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>peer</th>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sibling</th>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">The Crime is Mine</th>\n",
       "      <th>brother in law</th>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cousin</th>\n",
       "      <td>40.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>coworker</th>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>friend</th>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>imaginary friend</th>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mom</th>\n",
       "      <td>40.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>peer</th>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sibling</th>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">The Greatest Night in Pop</th>\n",
       "      <th>brother in law</th>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cousin</th>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>coworker</th>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>friend</th>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>imaginary friend</th>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mom</th>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>peer</th>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sibling</th>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            rating\n",
       "movie                     survey                  \n",
       "Drive Away Dolls          brother in law      60.0\n",
       "                          cousin              20.0\n",
       "                          coworker           100.0\n",
       "                          friend              80.0\n",
       "                          imaginary friend    60.0\n",
       "                          mom                 40.0\n",
       "                          peer                60.0\n",
       "                          sibling            100.0\n",
       "Dune Part 2               brother in law      20.0\n",
       "                          cousin              20.0\n",
       "                          coworker            40.0\n",
       "                          friend              40.0\n",
       "                          imaginary friend    40.0\n",
       "                          mom                 40.0\n",
       "                          peer               100.0\n",
       "                          sibling            100.0\n",
       "Hundreds of Beavers       brother in law      20.0\n",
       "                          cousin              60.0\n",
       "                          coworker            80.0\n",
       "                          friend              40.0\n",
       "                          imaginary friend    20.0\n",
       "                          mom                 20.0\n",
       "                          peer                80.0\n",
       "                          sibling            100.0\n",
       "Monolith                  brother in law      40.0\n",
       "                          cousin              40.0\n",
       "                          coworker            60.0\n",
       "                          friend              60.0\n",
       "                          imaginary friend    60.0\n",
       "                          mom                 20.0\n",
       "                          peer                80.0\n",
       "                          sibling            100.0\n",
       "The Crime is Mine         brother in law      20.0\n",
       "                          cousin              40.0\n",
       "                          coworker            20.0\n",
       "                          friend              20.0\n",
       "                          imaginary friend    20.0\n",
       "                          mom                 40.0\n",
       "                          peer               100.0\n",
       "                          sibling            100.0\n",
       "The Greatest Night in Pop brother in law      80.0\n",
       "                          cousin              20.0\n",
       "                          coworker            60.0\n",
       "                          friend             100.0\n",
       "                          imaginary friend    60.0\n",
       "                          mom                 60.0\n",
       "                          peer                20.0\n",
       "                          sibling             80.0"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "groupbedf = df.groupby(['movie', 'survey']).sum()\n",
    "def normalize(x):\n",
    "       return ((x-0)/5)*100\n",
    "normaldf= groupbedf.apply(normalize)\n",
    "normaldf"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da98c7f8-a128-464d-92df-04f23d5311c3",
   "metadata": {},
   "source": [
    "<h1>4. Provide a text-based conclusion: explain what might be advantages and disadvantages of using \n",
    "normalized ratings instead of the actual ratings.</h1>\n",
    "\n",
    "Using the describe() on normalized data and plotting it shows that Drive Away Dolls and The Greatest Night in Pop were clear winners. The advantage of normalized data is the ability to compare data on the same scale for different statistical purposes. If I wanted to see which movie deviated the most from the standard deviation this plot shows the Crime is Mine was clearly the largest and least popular, while Drive Away Dolls was least and rather popular.\n",
    "\n",
    "Using the regular rating data makes the bar chart much less interesting. See below charts comparing visualization of Describe() of normalized Df and unaltered Df. The normalized chart makes it much easier to study statistical comparisons in reference to a normalized range versus a numerical range, which can skew interpretation easily."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "id": "7f98078a-9bb8-40d9-b1cc-1eb2eff03659",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                 rating                                                   \\\n",
      "                  count       mean        std   min    25%    50%    75%   \n",
      "survey                                                                     \n",
      "brother in law      6.0  40.000000  25.298221  20.0   20.0   30.0   55.0   \n",
      "cousin              6.0  33.333333  16.329932  20.0   20.0   30.0   40.0   \n",
      "coworker            6.0  60.000000  28.284271  20.0   45.0   60.0   75.0   \n",
      "friend              6.0  56.666667  29.439203  20.0   40.0   50.0   75.0   \n",
      "imaginary friend    6.0  43.333333  19.663842  20.0   25.0   50.0   60.0   \n",
      "mom                 6.0  36.666667  15.055453  20.0   25.0   40.0   40.0   \n",
      "peer                6.0  73.333333  30.110906  20.0   65.0   80.0   95.0   \n",
      "sibling             6.0  96.666667   8.164966  80.0  100.0  100.0  100.0   \n",
      "\n",
      "                         \n",
      "                    max  \n",
      "survey                   \n",
      "brother in law     80.0  \n",
      "cousin             60.0  \n",
      "coworker          100.0  \n",
      "friend            100.0  \n",
      "imaginary friend   60.0  \n",
      "mom                60.0  \n",
      "peer              100.0  \n",
      "sibling           100.0   Axes(0.125,0.11;0.775x0.77)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.close(\"all\")\n",
    "\n",
    "normalized=normaldf.groupby(['movie']).describe()\n",
    "print(\n",
    "    normalized,\n",
    "normalized.plot.bar())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "id": "0d1dc292-d6b7-4d00-a540-dd6d08e90755",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='movie'>"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "regular=df.groupby(['movie']).describe()\n",
    "regular.plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "24dbf718-ecdf-4cb6-94ab-ee4bec5fec0d",
   "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": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}