Rock Artist Biography Project.ipynb

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    "This analysis examines the rock artists selected by students in a History of Rock Music course to write for an end of term rock artist biography assignment at a community college located in the Pacific Northwest. 500 students wrote biographies between 2018 - 2023, 273 were female, 198 male, and 29 students of unknown gender. The most popular artists selected were identified, with Elvis Presley being at the top of the list, approximately one out of every ten female students (36 students choose to write about Elvis, 29 female, 7 male). Elvis was chosen more than twice as often as the second place artist, Freddie Mercury, at 17. Students were over three times as likely to write about an individual than a band (392 to 108), even if the individual came to fame in a band (in other words, students overwhelmingly chose Jim Morrison instead of The Doors, Kurt Cobain instead of Nirvana, etc.). Similarly, students were over three times as likely to write about a North American artist than a British artist (373 to 114). This held true across both genders. \n",
    "\n",
    "In the analysis of rock genres, hard rock emerged as the predominant subgenre among students. A notable observation is the consistency in genre preferences between male and female students, with nine out of the top 10 genres being the same for both groups. For genre classification, I utilized the Spotify API, which provided a comprehensive genre dataset. However, it's worth noting that we excluded certain genres from our analysis. Genres like mellow gold, album rock, classic rock, rock, and pop were omitted. These were either too obscure or too general to yield meaningful insights for this study's objectives.\n",
    "\n",
    "\n"
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       "      <td>David Bowie</td>\n",
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       "      <td>Creedence Clearwater Revival</td>\n",
       "      <td>?</td>\n",
       "      <td>Band</td>\n",
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       "<p>500 rows × 115 columns</p>\n",
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      "text/plain": [
       "                           Artist Gender Of Student Band/Individual  \\\n",
       "0                     James Brown                 F      Individual   \n",
       "1                     David Bowie                 M      Individual   \n",
       "2                           AC/DC                 F            Band   \n",
       "3    Creedence Clearwater Revival                 ?            Band   \n",
       "4                  The Beach Boys                 F            Band   \n",
       "..                            ...               ...             ...   \n",
       "495                  Robert Fripp                 M      Individual   \n",
       "496                  Adam Gontier                 F      Individual   \n",
       "497                   The Strokes                 M            Band   \n",
       "498                Carlos Santana                 F      Individual   \n",
       "499                      Gorillaz                 M            Band   \n",
       "\n",
       "    North American/British/Other  Year album rock art rock alternative rock  \\\n",
       "0                 North American  2018                                        \n",
       "1                        British  2018                 1.0              1.0   \n",
       "2                          Other  2018                                        \n",
       "3                 North American  2018                                        \n",
       "4                 North American  2018                                        \n",
       "..                           ...   ...        ...      ...              ...   \n",
       "495                      British  2023                 1.0                    \n",
       "496               North American  2023                                        \n",
       "497               North American  2023                                  1.0   \n",
       "498               North American  2023                                        \n",
       "499                      British  2023                 1.0              1.0   \n",
       "\n",
       "    arena rock blues  ... motown quiet storm britpop industrial metal  \\\n",
       "0                1.0  ...                                               \n",
       "1                     ...                                               \n",
       "2          1.0        ...                                               \n",
       "3                     ...                                               \n",
       "4                     ...                                               \n",
       "..         ...   ...  ...    ...         ...     ...              ...   \n",
       "495                   ...                                               \n",
       "496                   ...                                               \n",
       "497                   ...                                               \n",
       "498                   ...                                               \n",
       "499                   ...                                               \n",
       "\n",
       "    industrial rock slayer post-punk synth punk synthpop neo soul  \n",
       "0                                                                  \n",
       "1                                                                  \n",
       "2                                                                  \n",
       "3                                                                  \n",
       "4                                                                  \n",
       "..              ...    ...       ...        ...      ...      ...  \n",
       "495                                                                \n",
       "496                                                                \n",
       "497                                                                \n",
       "498                                                                \n",
       "499                                                                \n",
       "\n",
       "[500 rows x 115 columns]"
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   "source": [
    "import pandas as pd\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Replace the following path with the path to your CSV file\n",
    "file_path = '/Users/lancevallis/Desktop/Rock Artist Biography Project/Master Rock Artist Spreadsheet Dec. 29.csv'\n",
    "\n",
    "# Load the CSV file into a DataFrame\n",
    "df = pd.read_csv(file_path)\n",
    "\n",
    "# Replace NaN values with blank spaces\n",
    "df.fillna('', inplace=True)\n",
    "\n",
    "df"
   ]
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       "      <td>AC/DC</td>\n",
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       "      <th>3</th>\n",
       "      <td>Creedence Clearwater Revival</td>\n",
       "      <td>?</td>\n",
       "      <td>Band</td>\n",
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       "      <td>...</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>496</th>\n",
       "      <td>Adam Gontier</td>\n",
       "      <td>F</td>\n",
       "      <td>Individual</td>\n",
       "      <td>North American</td>\n",
       "      <td>2023</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>...</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>497</th>\n",
       "      <td>The Strokes</td>\n",
       "      <td>M</td>\n",
       "      <td>Band</td>\n",
       "      <td>North American</td>\n",
       "      <td>2023</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>...</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>498</th>\n",
       "      <td>Carlos Santana</td>\n",
       "      <td>F</td>\n",
       "      <td>Individual</td>\n",
       "      <td>North American</td>\n",
       "      <td>2023</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>...</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>499</th>\n",
       "      <td>Gorillaz</td>\n",
       "      <td>M</td>\n",
       "      <td>Band</td>\n",
       "      <td>British</td>\n",
       "      <td>2023</td>\n",
       "      <td></td>\n",
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       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>500 rows × 115 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                           Artist Gender Of Student Band/Individual  \\\n",
       "0                     James Brown                 F      Individual   \n",
       "1                     David Bowie                 M      Individual   \n",
       "2                           AC/DC                 F            Band   \n",
       "3    Creedence Clearwater Revival                 ?            Band   \n",
       "4                  The Beach Boys                 F            Band   \n",
       "..                            ...               ...             ...   \n",
       "495                  Robert Fripp                 M      Individual   \n",
       "496                  Adam Gontier                 F      Individual   \n",
       "497                   The Strokes                 M            Band   \n",
       "498                Carlos Santana                 F      Individual   \n",
       "499                      Gorillaz                 M            Band   \n",
       "\n",
       "    North American/British/Other  Year album rock art rock alternative rock  \\\n",
       "0                 North American  2018                                        \n",
       "1                        British  2018                   1                1   \n",
       "2                          Other  2018                                        \n",
       "3                 North American  2018                                        \n",
       "4                 North American  2018                                        \n",
       "..                           ...   ...        ...      ...              ...   \n",
       "495                      British  2023                   1                    \n",
       "496               North American  2023                                        \n",
       "497               North American  2023                                    1   \n",
       "498               North American  2023                                        \n",
       "499                      British  2023                   1                1   \n",
       "\n",
       "    arena rock blues  ... motown quiet storm britpop industrial metal  \\\n",
       "0                  1  ...                                               \n",
       "1                     ...                                               \n",
       "2            1        ...                                               \n",
       "3                     ...                                               \n",
       "4                     ...                                               \n",
       "..         ...   ...  ...    ...         ...     ...              ...   \n",
       "495                   ...                                               \n",
       "496                   ...                                               \n",
       "497                   ...                                               \n",
       "498                   ...                                               \n",
       "499                   ...                                               \n",
       "\n",
       "    industrial rock slayer post-punk synth punk synthpop neo soul  \n",
       "0                                                                  \n",
       "1                                                                  \n",
       "2                                                                  \n",
       "3                                                                  \n",
       "4                                                                  \n",
       "..              ...    ...       ...        ...      ...      ...  \n",
       "495                                                                \n",
       "496                                                                \n",
       "497                                                                \n",
       "498                                                                \n",
       "499                                                                \n",
       "\n",
       "[500 rows x 115 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert data types to integer for further processing\n",
    "# List of columns to exclude from conversion to integer\n",
    "exclude_columns = ['Artist', 'Gender Of Student', 'Band/Individual', 'North American/British/Other']\n",
    "\n",
    "# Iterate over columns and convert to integer where applicable\n",
    "for col in df.columns:\n",
    "    if col not in exclude_columns:\n",
    "        # Convert column to numeric, then to integer if possible\n",
    "        df[col] = pd.to_numeric(df[col], errors='coerce').fillna(0).astype(int)\n",
    "\n",
    "# Replace zeros with blank spaces in the converted columns\n",
    "for col in df.columns:\n",
    "    if col not in exclude_columns:\n",
    "        df[col] = df[col].replace(0, '')\n",
    "        \n",
    "# Display the DataFrame to confirm the change (optional)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6f2ea01c",
   "metadata": {},
   "outputs": [
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       "      <th>2</th>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Creedence Clearwater Revival</td>\n",
       "      <td>?</td>\n",
       "      <td>Band</td>\n",
       "      <td>North American</td>\n",
       "      <td>2018</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
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       "      <th>4</th>\n",
       "      <td>The Beach Boys</td>\n",
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       "      <td>Band</td>\n",
       "      <td>North American</td>\n",
       "      <td>2018</td>\n",
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       "<p>5 rows × 115 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Artist Gender Of Student Band/Individual  \\\n",
       "0                   James Brown                 F      Individual   \n",
       "1                   David Bowie                 M      Individual   \n",
       "2                         AC/DC                 F            Band   \n",
       "3  Creedence Clearwater Revival                 ?            Band   \n",
       "4                The Beach Boys                 F            Band   \n",
       "\n",
       "  North American/British/Other  Year album rock art rock alternative rock  \\\n",
       "0               North American  2018                                        \n",
       "1                      British  2018                   1                1   \n",
       "2                        Other  2018                                        \n",
       "3               North American  2018                                        \n",
       "4               North American  2018                                        \n",
       "\n",
       "  arena rock blues  ... motown quiet storm britpop industrial metal  \\\n",
       "0                1  ...                                               \n",
       "1                   ...                                               \n",
       "2          1        ...                                               \n",
       "3                   ...                                               \n",
       "4                   ...                                               \n",
       "\n",
       "  industrial rock slayer post-punk synth punk synthpop neo soul  \n",
       "0                                                                \n",
       "1                                                                \n",
       "2                                                                \n",
       "3                                                                \n",
       "4                                                                \n",
       "\n",
       "[5 rows x 115 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display the DataFrame to confirm the change \n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f124b732",
   "metadata": {},
   "outputs": [
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       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>...</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>497</th>\n",
       "      <td>The Strokes</td>\n",
       "      <td>M</td>\n",
       "      <td>Band</td>\n",
       "      <td>North American</td>\n",
       "      <td>2023</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>...</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>498</th>\n",
       "      <td>Carlos Santana</td>\n",
       "      <td>F</td>\n",
       "      <td>Individual</td>\n",
       "      <td>North American</td>\n",
       "      <td>2023</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>...</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>499</th>\n",
       "      <td>Gorillaz</td>\n",
       "      <td>M</td>\n",
       "      <td>Band</td>\n",
       "      <td>British</td>\n",
       "      <td>2023</td>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>...</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>500 rows × 115 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                           Artist Gender Of Student Band/Individual  \\\n",
       "0                     James Brown                 F      Individual   \n",
       "1                     David Bowie                 M      Individual   \n",
       "2                           AC/DC                 F            Band   \n",
       "3    Creedence Clearwater Revival                 ?            Band   \n",
       "4                  The Beach Boys                 F            Band   \n",
       "..                            ...               ...             ...   \n",
       "495                  Robert Fripp                 M      Individual   \n",
       "496                  Adam Gontier                 F      Individual   \n",
       "497                   The Strokes                 M            Band   \n",
       "498                Carlos Santana                 F      Individual   \n",
       "499                      Gorillaz                 M            Band   \n",
       "\n",
       "    North American/British/Other  Year album rock art rock alternative rock  \\\n",
       "0                 North American  2018                                        \n",
       "1                        British  2018                   1                1   \n",
       "2                          Other  2018                                        \n",
       "3                 North American  2018                                        \n",
       "4                 North American  2018                                        \n",
       "..                           ...   ...        ...      ...              ...   \n",
       "495                      British  2023                   1                    \n",
       "496               North American  2023                                        \n",
       "497               North American  2023                                    1   \n",
       "498               North American  2023                                        \n",
       "499                      British  2023                   1                1   \n",
       "\n",
       "    arena rock blues  ... motown quiet storm britpop industrial metal  \\\n",
       "0                  1  ...                                               \n",
       "1                     ...                                               \n",
       "2            1        ...                                               \n",
       "3                     ...                                               \n",
       "4                     ...                                               \n",
       "..         ...   ...  ...    ...         ...     ...              ...   \n",
       "495                   ...                                               \n",
       "496                   ...                                               \n",
       "497                   ...                                               \n",
       "498                   ...                                               \n",
       "499                   ...                                               \n",
       "\n",
       "    industrial rock slayer post-punk synth punk synthpop neo soul  \n",
       "0                                                                  \n",
       "1                                                                  \n",
       "2                                                                  \n",
       "3                                                                  \n",
       "4                                                                  \n",
       "..              ...    ...       ...        ...      ...      ...  \n",
       "495                                                                \n",
       "496                                                                \n",
       "497                                                                \n",
       "498                                                                \n",
       "499                                                                \n",
       "\n",
       "[500 rows x 115 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# List of columns to exclude from conversion\n",
    "exclude_columns = ['Artist', 'Gender Of Student', 'Band/Individual', 'North American/British/Other', 'Year']\n",
    "\n",
    "# Iterate over columns and convert to integer where applicable\n",
    "for col in df.columns:\n",
    "    if col not in exclude_columns:\n",
    "        # Convert column to numeric, then to integer\n",
    "        df[col] = pd.to_numeric(df[col], errors='coerce').fillna(0).astype(int)\n",
    "\n",
    "# Replace 0 values with a blank space in the converted columns\n",
    "for col in df.columns:\n",
    "    if col not in exclude_columns:\n",
    "        df[col] = df[col].replace(0, '')\n",
    "\n",
    "# Display the DataFrame to confirm the change\n",
    "df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d1ea11d3",
   "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>Artist</th>\n",
       "      <th>Gender Of Student</th>\n",
       "      <th>Band/Individual</th>\n",
       "      <th>North American/British/Other</th>\n",
       "      <th>Year</th>\n",
       "      <th>album rock</th>\n",
       "      <th>art rock</th>\n",
       "      <th>alternative rock</th>\n",
       "      <th>arena rock</th>\n",
       "      <th>blues</th>\n",
       "      <th>...</th>\n",
       "      <th>motown</th>\n",
       "      <th>quiet storm</th>\n",
       "      <th>britpop</th>\n",
       "      <th>industrial metal</th>\n",
       "      <th>industrial rock</th>\n",
       "      <th>slayer</th>\n",
       "      <th>post-punk</th>\n",
       "      <th>synth punk</th>\n",
       "      <th>synthpop</th>\n",
       "      <th>neo soul</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>James Brown</td>\n",
       "      <td>F</td>\n",
       "      <td>Individual</td>\n",
       "      <td>North American</td>\n",
       "      <td>2018</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>David Bowie</td>\n",
       "      <td>M</td>\n",
       "      <td>Individual</td>\n",
       "      <td>British</td>\n",
       "      <td>2018</td>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>...</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AC/DC</td>\n",
       "      <td>F</td>\n",
       "      <td>Band</td>\n",
       "      <td>Other</td>\n",
       "      <td>2018</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td></td>\n",
       "      <td>...</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Creedence Clearwater Revival</td>\n",
       "      <td>?</td>\n",
       "      <td>Band</td>\n",
       "      <td>North American</td>\n",
       "      <td>2018</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>...</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>The Beach Boys</td>\n",
       "      <td>F</td>\n",
       "      <td>Band</td>\n",
       "      <td>North American</td>\n",
       "      <td>2018</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>...</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 115 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Artist Gender Of Student Band/Individual  \\\n",
       "0                   James Brown                 F      Individual   \n",
       "1                   David Bowie                 M      Individual   \n",
       "2                         AC/DC                 F            Band   \n",
       "3  Creedence Clearwater Revival                 ?            Band   \n",
       "4                The Beach Boys                 F            Band   \n",
       "\n",
       "  North American/British/Other  Year album rock art rock alternative rock  \\\n",
       "0               North American  2018                                        \n",
       "1                      British  2018                   1                1   \n",
       "2                        Other  2018                                        \n",
       "3               North American  2018                                        \n",
       "4               North American  2018                                        \n",
       "\n",
       "  arena rock blues  ... motown quiet storm britpop industrial metal  \\\n",
       "0                1  ...                                               \n",
       "1                   ...                                               \n",
       "2          1        ...                                               \n",
       "3                   ...                                               \n",
       "4                   ...                                               \n",
       "\n",
       "  industrial rock slayer post-punk synth punk synthpop neo soul  \n",
       "0                                                                \n",
       "1                                                                \n",
       "2                                                                \n",
       "3                                                                \n",
       "4                                                                \n",
       "\n",
       "[5 rows x 115 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# List of columns to exclude from conversion\n",
    "exclude_columns = ['Artist', 'Gender Of Student', 'Band/Individual', 'North American/British/Other']\n",
    "\n",
    "# Iterate over columns and convert to integer where applicable\n",
    "for col in df.columns:\n",
    "    if col not in exclude_columns:\n",
    "        # Convert column to numeric, then to integer if possible\n",
    "        df[col] = pd.to_numeric(df[col], errors='coerce').fillna(0).astype(int)\n",
    "        # Replace 0 with a blank space\n",
    "        df[col] = df[col].replace(0, '')\n",
    "\n",
    "# Display the DataFrame to confirm the change\n",
    "df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c8401ad0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of entries: 500\n"
     ]
    }
   ],
   "source": [
    "total_entries = len(df)\n",
    "print(\"Total number of entries:\", total_entries)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "57429d08",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Entries Per Year:\n",
      "2018    85\n",
      "2019    86\n",
      "2020    91\n",
      "2021    88\n",
      "2022    76\n",
      "2023    74\n",
      "Name: Year, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Count the number of entries per year\n",
    "entries_per_year = df['Year'].value_counts()\n",
    "\n",
    "# Sort the years for better readability\n",
    "sorted_entries_per_year = entries_per_year.sort_index()\n",
    "\n",
    "# Display the result\n",
    "print(\"Number of Entries Per Year:\")\n",
    "print(sorted_entries_per_year)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8b122eb5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Artist                          object\n",
      "Gender Of Student               object\n",
      "Band/Individual                 object\n",
      "North American/British/Other    object\n",
      "Year                             int64\n",
      "album rock                      object\n",
      "art rock                        object\n",
      "alternative rock                object\n",
      "arena rock                      object\n",
      "blues                           object\n",
      "blues rock                      object\n",
      "classic rock                    object\n",
      "country                         object\n",
      "folk                            object\n",
      "glam                            object\n",
      "gospel                          object\n",
      "grunge                          object\n",
      "hard rock                       object\n",
      "heartland rock                  object\n",
      "metal                           object\n",
      "mellow gold                     object\n",
      "pop                             object\n",
      "progressive rock                object\n",
      "psychedelic rock                object\n",
      "punk                            object\n",
      "r&b                             object\n",
      "rap rock                        object\n",
      "rock-and-roll                   object\n",
      "rockabilly                      object\n",
      "symphonic rock                  object\n",
      "piano rock                      object\n",
      "rock                            object\n",
      "singer-songwriter               object\n",
      "soft rock                       object\n",
      "old school thrash               object\n",
      "thrash metal                    object\n",
      "glam punk                       object\n",
      "funk                            object\n",
      "funk rock                       object\n",
      "minneapolis sound               object\n",
      "synth funk                      object\n",
      "alternative metal               object\n",
      "groove metal                    object\n",
      "melodic metalcore               object\n",
      "nu metal                        object\n",
      "virginia metal                  object\n",
      "new romantic                    object\n",
      "new wave pop                    object\n",
      "proto-metal                     object\n",
      "acid rock                       object\n",
      "experimental                    object\n",
      "instrumental rock               object\n",
      "jazz fusion                     object\n",
      "jazz rock                       object\n",
      "zolo                            object\n",
      "yacht rock                      object\n",
      "post-grunge                     object\n",
      "rap metal                       object\n",
      "sleaze rock                     object\n",
      "glam metal                      object\n",
      "permanent wave                  object\n",
      "glam rock                       object\n",
      "emo                             object\n",
      "modern rock                     object\n",
      "pop punk                        object\n",
      "pov: indie                      object\n",
      "brill building pop              object\n",
      "folk rock                       object\n",
      "hel                             object\n",
      "dance pop                       object\n",
      "candy pop                       object\n",
      "pixie                           object\n",
      "pop emo                         object\n",
      "art pop                         object\n",
      "art punk                        object\n",
      "solo wave                       object\n",
      "comedy rock                     object\n",
      "comic                           object\n",
      "soul                            object\n",
      "country rock                    object\n",
      "roots rock                      object\n",
      "protopunk                       object\n",
      "ambient guitar                  object\n",
      "experimental ambient            object\n",
      "experimental guitar             object\n",
      "experimental rock               object\n",
      "garage rock                     object\n",
      "alternative hip hop             object\n",
      "southern rock                   object\n",
      "swamp rock                      object\n",
      "baroque pop                     object\n",
      "sunshine pop                    object\n",
      "honky tonk                      object\n",
      "traditional country             object\n",
      "yodeling                        object\n",
      "alternative country             object\n",
      "new americana                   object\n",
      "electric blues                  object\n",
      "new wave                        object\n",
      "dance rock                      object\n",
      "neo mellow                      object\n",
      "adult standards                 object\n",
      "power metal                     object\n",
      "speed metal                     object\n",
      "disco                           object\n",
      "motown                          object\n",
      "quiet storm                     object\n",
      "britpop                         object\n",
      "industrial metal                object\n",
      "industrial rock                 object\n",
      "slayer                          object\n",
      "post-punk                       object\n",
      "synth punk                      object\n",
      "synthpop                        object\n",
      "neo soul                        object\n"
     ]
    }
   ],
   "source": [
    "# Display the data types of all columns without truncation\n",
    "print(df.dtypes.to_string())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b180de78",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Elvis Presley      36\n",
       "Freddie Mercury    17\n",
       "Chuck Berry        15\n",
       "Ozzy Osbourne      14\n",
       "Jimi Hendrix       14\n",
       "                   ..\n",
       "Steve Miller        2\n",
       "Rob Zombie          2\n",
       "John Bonham         2\n",
       "Jack Black          2\n",
       "Stevie Wonder       2\n",
       "Name: Artist, Length: 86, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Count the occurrences of each artist\n",
    "artist_counts = df['Artist'].value_counts()\n",
    "\n",
    "# Filter to keep only artists that appear two or more times\n",
    "frequent_artists = artist_counts[artist_counts >= 2]\n",
    "\n",
    "# Display the artists and their counts\n",
    "frequent_artists"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d7714166",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of artists that appeared once: 119\n",
      "Number of artists that appeared twice: 38\n",
      "Number of artists that appeared three times: 19\n",
      "Number of artists that appeared four times: 9\n"
     ]
    }
   ],
   "source": [
    "# Count the number of artists that appear 1, 2, 3, 4 times\n",
    "artists_appearing_once = (artist_counts == 1).sum()\n",
    "artists_appearing_twice = (artist_counts == 2).sum()\n",
    "artists_appearing_thrice = (artist_counts == 3).sum()\n",
    "artists_appearing_four_times = (artist_counts == 4).sum()\n",
    "\n",
    "# Display the results\n",
    "print(f\"Number of artists that appeared once: {artists_appearing_once}\")\n",
    "print(f\"Number of artists that appeared twice: {artists_appearing_twice}\")\n",
    "print(f\"Number of artists that appeared three times: {artists_appearing_thrice}\")\n",
    "print(f\"Number of artists that appeared four times: {artists_appearing_four_times}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "cf48418b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Filter for artists appearing five or more times\n",
    "artists_five_or_more = artist_counts[artist_counts >= 5]\n",
    "\n",
    "# Plotting the bar chart\n",
    "plt.figure(figsize=(12, 6))  # Adjust the figure size as needed\n",
    "bars = plt.bar(artists_five_or_more.index, artists_five_or_more.values)\n",
    "\n",
    "plt.xlabel('Artist')\n",
    "plt.ylabel('Number of Appearances')\n",
    "plt.title('Number of Appearances for Artists Appearing Five Times or More From 2018 - 2023')\n",
    "plt.xticks(rotation=45)  # Rotates the artist names for better readability\n",
    "\n",
    "\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "135cfdd1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Elvis Presley      36\n",
      "Freddie Mercury    17\n",
      "Chuck Berry        15\n",
      "Ozzy Osbourne      14\n",
      "Jimi Hendrix       14\n",
      "David Bowie        14\n",
      "The Beatles        12\n",
      "Stevie Nicks       11\n",
      "Bob Dylan           9\n",
      "AC/DC               9\n",
      "Kurt Cobain         8\n",
      "Elton John          8\n",
      "Jim Morrison        7\n",
      "Janis Joplin        7\n",
      "Prince              6\n",
      "Buddy Holly         5\n",
      "Linkin Park         5\n",
      "Marilyn Manson      5\n",
      "Dave Grohl          5\n",
      "Michael Jackson     5\n",
      "Name: Artist, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Printout of artists appearing five or more times\n",
    "artist_counts = df['Artist'].value_counts()\n",
    "\n",
    "# Now apply the filtering and printing as shown above\n",
    "artists_five_or_more = artist_counts[artist_counts >= 5]\n",
    "print(artists_five_or_more)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "82edcabe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top 5 Artists Chosen by Male Students:\n",
      "Elvis Presley    7\n",
      "Jimi Hendrix     7\n",
      "Ozzy Osbourne    6\n",
      "Chuck Berry      6\n",
      "Kurt Cobain      6\n",
      "Name: Artist, dtype: int64\n",
      "\n",
      "Top 5 Artists Chosen by Female Students:\n",
      "Elvis Presley      29\n",
      "The Beatles        10\n",
      "Freddie Mercury    10\n",
      "Stevie Nicks        9\n",
      "Ozzy Osbourne       8\n",
      "Name: Artist, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Filter for male and female students\n",
    "df_male_students = df[df['Gender Of Student'] == 'M']\n",
    "df_female_students = df[df['Gender Of Student'] == 'F']\n",
    "\n",
    "# Count the occurrences of each artist for male students\n",
    "top_artists_male = df_male_students['Artist'].value_counts().head(5)\n",
    "\n",
    "# Count the occurrences of each artist for female students\n",
    "top_artists_female = df_female_students['Artist'].value_counts().head(5)\n",
    "\n",
    "# Display the results\n",
    "print(\"Top 5 Artists Chosen by Male Students:\")\n",
    "print(top_artists_male)\n",
    "print(\"\\nTop 5 Artists Chosen by Female Students:\")\n",
    "print(top_artists_female)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "a5d9edc9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top 5 Artists Chosen by Male Students (%):\n",
      "Elvis Presley    4%\n",
      "Jimi Hendrix     4%\n",
      "Ozzy Osbourne    3%\n",
      "Chuck Berry      3%\n",
      "Kurt Cobain      3%\n",
      "Name: Artist, dtype: object\n",
      "\n",
      "Top 5 Artists Chosen by Female Students (%):\n",
      "Elvis Presley      11%\n",
      "The Beatles         4%\n",
      "Freddie Mercury     4%\n",
      "Stevie Nicks        3%\n",
      "Ozzy Osbourne       3%\n",
      "Name: Artist, dtype: object\n"
     ]
    }
   ],
   "source": [
    "# Get the total number of male and female students\n",
    "total_male_students = df_male_students.shape[0]\n",
    "total_female_students = df_female_students.shape[0]\n",
    "\n",
    "# Count the occurrences of each artist for male students and convert to percentage\n",
    "top_artists_male = (df_male_students['Artist'].value_counts() / total_male_students * 100).head(5).round().astype(int)\n",
    "\n",
    "# Count the occurrences of each artist for female students and convert to percentage\n",
    "top_artists_female = (df_female_students['Artist'].value_counts() / total_female_students * 100).head(5).round().astype(int)\n",
    "\n",
    "# Display the results\n",
    "print(\"Top 5 Artists Chosen by Male Students (%):\")\n",
    "print(top_artists_male.astype(str) + '%')\n",
    "print(\"\\nTop 5 Artists Chosen by Female Students (%):\")\n",
    "print(top_artists_female.astype(str) + '%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "9848968e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DataFrame columns: Index(['Artist', 'Gender Of Student', 'Band/Individual',\n",
      "       'North American/British/Other', 'Year', 'album rock', 'art rock',\n",
      "       'alternative rock', 'arena rock', 'blues',\n",
      "       ...\n",
      "       'motown', 'quiet storm', 'britpop', 'industrial metal',\n",
      "       'industrial rock', 'slayer', 'post-punk', 'synth punk', 'synthpop',\n",
      "       'neo soul'],\n",
      "      dtype='object', length=115)\n",
      "Unique values in 'Gender Of Student': ['F' 'M' '?' '']\n",
      "Number of artists chosen by male students: 117\n",
      "Number of artists chosen by female students: 128\n"
     ]
    }
   ],
   "source": [
    "# Check DataFrame column names\n",
    "print(\"DataFrame columns:\", df.columns)\n",
    "\n",
    "# Check unique values in the 'Gender Of Student' column\n",
    "print(\"Unique values in 'Gender Of Student':\", df['Gender Of Student'].unique())\n",
    "\n",
    "# Check if there are any artists associated with male and female students\n",
    "print(\"Number of artists chosen by male students:\", df[df['Gender Of Student'] == 'M']['Artist'].nunique())\n",
    "print(\"Number of artists chosen by female students:\", df[df['Gender Of Student'] == 'F']['Artist'].nunique())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "053a2afa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "North American    373\n",
       "British           114\n",
       "Other              13\n",
       "Name: North American/British/Other, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the total count for each category in 'North American/British/Other'\n",
    "category_counts = df['North American/British/Other'].value_counts()\n",
    "\n",
    "# Display the counts\n",
    "category_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "131544a5",
   "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>Gender Of Student</th>\n",
       "      <th></th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "      <th>Unknown</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>North American/British/Other</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>British</th>\n",
       "      <td>0</td>\n",
       "      <td>64</td>\n",
       "      <td>44</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>North American</th>\n",
       "      <td>2</td>\n",
       "      <td>203</td>\n",
       "      <td>148</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Other</th>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Gender Of Student                  F    M  Unknown\n",
       "North American/British/Other                      \n",
       "British                       0   64   44        6\n",
       "North American                2  203  148       20\n",
       "Other                         0    6    6        1"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Normalize the 'Gender Of Student' column: convert to uppercase and replace '?' with 'Unknown'\n",
    "df['Gender Of Student'] = df['Gender Of Student'].str.upper().replace('?', 'Unknown')\n",
    "\n",
    "# Create a pivot table for the breakdown by nationality and gender\n",
    "breakdown = pd.crosstab(df['North American/British/Other'], df['Gender Of Student'])\n",
    "\n",
    "# Display the pivot table\n",
    "breakdown\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "7ba7f522",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Individual    392\n",
       "Band          108\n",
       "Name: Band/Individual, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Count the occurrences of 'Band' and 'Individual' in the 'Band/Individual' column\n",
    "band_individual_distribution = df['Band/Individual'].value_counts()\n",
    "\n",
    "band_individual_distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "cfafd4d9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Band/Individual distribution for Male Students:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Individual    158\n",
       "Band           40\n",
       "Name: Band/Individual, dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Band/Individual distribution for Female Students:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Individual    211\n",
       "Band           62\n",
       "Name: Band/Individual, dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Assuming df is your DataFrame and it contains 'Gender Of Student' and 'Band/Individual' columns\n",
    "\n",
    "# Filter the DataFrame for male students\n",
    "df_male = df[df['Gender Of Student'] == 'M']\n",
    "\n",
    "# Filter the DataFrame for female students\n",
    "df_female = df[df['Gender Of Student'] == 'F']\n",
    "\n",
    "# Count the occurrences of 'Band' and 'Individual' for male students\n",
    "band_individual_male = df_male['Band/Individual'].value_counts()\n",
    "\n",
    "# Count the occurrences of 'Band' and 'Individual' for female students\n",
    "band_individual_female = df_female['Band/Individual'].value_counts()\n",
    "\n",
    "# Display the results\n",
    "print(\"Band/Individual distribution for Male Students:\")\n",
    "display(band_individual_male)\n",
    "print(\"\\nBand/Individual distribution for Female Students:\")\n",
    "display(band_individual_female)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "542a712f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Band/Individual percentage distribution for Male Students:\n",
      "Individual    80%\n",
      "Band          20%\n",
      "Name: Band/Individual, dtype: object\n",
      "\n",
      "Band/Individual percentage distribution for Female Students:\n",
      "Individual    77%\n",
      "Band          23%\n",
      "Name: Band/Individual, dtype: object\n"
     ]
    }
   ],
   "source": [
    "# Get the total counts for male and female students\n",
    "total_male = df_male.shape[0]\n",
    "total_female = df_female.shape[0]\n",
    "\n",
    "# Count and calculate percentages for male students\n",
    "band_individual_male = ((df_male['Band/Individual'].value_counts() / total_male) * 100).round().astype(int).astype(str) + '%'\n",
    "\n",
    "# Count and calculate percentages for female students\n",
    "band_individual_female = ((df_female['Band/Individual'].value_counts() / total_female) * 100).round().astype(int).astype(str) + '%'\n",
    "\n",
    "# Display the results\n",
    "print(\"Band/Individual percentage distribution for Male Students:\")\n",
    "print(band_individual_male)\n",
    "print(\"\\nBand/Individual percentage distribution for Female Students:\")\n",
    "print(band_individual_female)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "d7c882dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "# Plotting Band vs Individual distribution\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.barplot(x=band_individual_distribution.index, y=band_individual_distribution.values)\n",
    "plt.title(\"Band vs. Individual Distribution\")\n",
    "plt.xlabel(\"Type\")\n",
    "plt.ylabel(\"Count\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "bc78df07",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gender Nationality Counts:\n"
     ]
    },
    {
     "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>Gender Of Student</th>\n",
       "      <th></th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "      <th>UNKNOWN</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>North American/British/Other</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>British</th>\n",
       "      <td>0</td>\n",
       "      <td>64</td>\n",
       "      <td>44</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>North American</th>\n",
       "      <td>2</td>\n",
       "      <td>203</td>\n",
       "      <td>148</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Other</th>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Gender Of Student                  F    M  UNKNOWN\n",
       "North American/British/Other                      \n",
       "British                       0   64   44        6\n",
       "North American                2  203  148       20\n",
       "Other                         0    6    6        1"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gender Nationality Count Percentages:\n"
     ]
    },
    {
     "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>Gender Of Student</th>\n",
       "      <th></th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "      <th>UNKNOWN</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>North American/British/Other</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>British</th>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "      <td>22</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>North American</th>\n",
       "      <td>100</td>\n",
       "      <td>74</td>\n",
       "      <td>75</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Other</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Gender Of Student                   F   M  UNKNOWN\n",
       "North American/British/Other                      \n",
       "British                         0  23  22       22\n",
       "North American                100  74  75       74\n",
       "Other                           0   2   3        4"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Normalize the 'Gender' column: trim whitespace and convert to uppercase\n",
    "df['Gender Of Student'] = df['Gender Of Student'].str.strip().str.upper()\n",
    "\n",
    "# Replace '?' with 'Unknown' for clarity\n",
    "df['Gender Of Student'] = df['Gender Of Student'].replace('?', 'Unknown')\n",
    "\n",
    "# Group by 'North American/British/Other' and 'Gender', then count occurrences\n",
    "gender_nationality_counts = df.groupby(['North American/British/Other', 'Gender Of Student']).size().unstack(fill_value=0)\n",
    "\n",
    "# Calculate the percentages for each nationality category within each gender\n",
    "gender_nationality_percentages = gender_nationality_counts.div(gender_nationality_counts.sum(axis=0), axis=1) * 100\n",
    "\n",
    "# Round the percentages to the nearest whole number\n",
    "gender_nationality_percentages = gender_nationality_percentages.round(0).astype(int)\n",
    "\n",
    "# Display the counts and percentages side by side\n",
    "print(\"Gender Nationality Counts:\")\n",
    "display(gender_nationality_counts)\n",
    "\n",
    "print(\"Gender Nationality Count Percentages:\")\n",
    "display(gender_nationality_percentages)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "09df40d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "F    273\n",
       "M    198\n",
       "?     29\n",
       "Name: Gender Of Student, dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert all entries in 'Gender Of Student' to uppercase \n",
    "df['Gender Of Student'] = df['Gender Of Student'].str.upper()\n",
    "\n",
    "# Replace blank/whitespace-only strings and 'UNKNOWN' with '?'\n",
    "df['Gender Of Student'] = df['Gender Of Student'].replace(['^\\s*$', 'UNKNOWN'], '?', regex=True)\n",
    "\n",
    "# Count the occurrences of each gender\n",
    "gender_counts = df['Gender Of Student'].value_counts()\n",
    "\n",
    "# Display the counts\n",
    "gender_counts\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "241517ce",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Indices of rows with blank 'Gender Of Student':\n",
      "[]\n"
     ]
    }
   ],
   "source": [
    "# Identifying rows where 'Gender Of Student' is an empty string\n",
    "blank_gender_rows = df[df['Gender Of Student'] == '']\n",
    "\n",
    "# Display the indices of these rows\n",
    "print(\"Indices of rows with blank 'Gender Of Student':\")\n",
    "print(blank_gender_rows.index.tolist())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "a8ba7f97",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove rows where 'North American/British/Other' is blank, whitespace-only, or 'Unknown'\n",
    "df = df[~df['North American/British/Other'].isin(['', ' ', 'Unknown'])]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "67341b38",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the counts\n",
    "gender_nationality_counts.plot(kind='bar', figsize=(10, 6))\n",
    "plt.title('Gender and Nationality Counts')\n",
    "plt.xlabel('Nationality')\n",
    "plt.ylabel('Counts')\n",
    "plt.xticks(rotation=45)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "4fd3a254",
   "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>North American/British/Other</th>\n",
       "      <th>British</th>\n",
       "      <th>North American</th>\n",
       "      <th>Other</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Time Bracket</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-2020</th>\n",
       "      <td>61</td>\n",
       "      <td>193</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-2023</th>\n",
       "      <td>53</td>\n",
       "      <td>180</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "North American/British/Other  British  North American  Other\n",
       "Time Bracket                                                \n",
       "2018-2020                          61             193      8\n",
       "2021-2023                          53             180      5"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "# If df is a slice of another DataFrame, create a copy:\n",
    "df = df.copy()\n",
    "\n",
    "# Ensure 'Year' is of type int\n",
    "df['Year'] = pd.to_numeric(df['Year'], errors='coerce').fillna(0).astype(int)\n",
    "\n",
    "# Define a function to categorize years into brackets\n",
    "def categorize_year(year):\n",
    "    if 2018 <= year <= 2020:\n",
    "        return '2018-2020'\n",
    "    elif 2021 <= year <= 2023:\n",
    "        return '2021-2023'\n",
    "    else:\n",
    "        return 'Other'\n",
    "\n",
    "# Apply the function to create a new column for the time bracket\n",
    "df['Time Bracket'] = df['Year'].apply(categorize_year)\n",
    "\n",
    "# Group by the new 'Time Bracket' column and 'North American/British/Other', then count occurrences\n",
    "breakdown_counts = df.groupby(['Time Bracket', 'North American/British/Other']).size().unstack(fill_value=0)\n",
    "\n",
    "\n",
    "breakdown_counts\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "4fb0a974",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# Plotting a bar chart\n",
    "breakdown_counts.plot(kind='bar', figsize=(10, 6))\n",
    "plt.title('Artist Origin Breakdown by Time Bracket')\n",
    "plt.xlabel('Time Bracket')\n",
    "plt.ylabel('Number of Artists')\n",
    "plt.xticks(rotation=45)\n",
    "plt.legend(title='Origin')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "0aa355be",
   "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>North American/British/Other</th>\n",
       "      <th>British</th>\n",
       "      <th>North American</th>\n",
       "      <th>Other</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Time Bracket</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-2020</th>\n",
       "      <td>23%</td>\n",
       "      <td>74%</td>\n",
       "      <td>3%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-2023</th>\n",
       "      <td>22%</td>\n",
       "      <td>76%</td>\n",
       "      <td>2%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "North American/British/Other British North American Other\n",
       "Time Bracket                                             \n",
       "2018-2020                        23%            74%    3%\n",
       "2021-2023                        22%            76%    2%"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the percentages\n",
    "breakdown_percentages = breakdown_counts.div(breakdown_counts.sum(axis=1), axis=0) * 100\n",
    "\n",
    "# Round the percentages to the nearest whole number and convert to integer for cleaner display\n",
    "breakdown_percentages = breakdown_percentages.round(0).astype(int)\n",
    "\n",
    "# Format the percentages with a % sign\n",
    "breakdown_percentages_formatted = breakdown_percentages.applymap(lambda x: f\"{x}%\")\n",
    "\n",
    "# Display the percentages \n",
    "breakdown_percentages_formatted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "11761984",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rock                   357.0\n",
      "pop                    159.0\n",
      "hard rock              129.0\n",
      "blues                   98.0\n",
      "blues rock              98.0\n",
      "classic rock            90.0\n",
      "alternative rock        66.0\n",
      "singer-songwriter       65.0\n",
      "metal                   64.0\n",
      "psychedelic rock        60.0\n",
      "soul                    59.0\n",
      "country                 58.0\n",
      "gospel                  49.0\n",
      "soft rock               45.0\n",
      "rockabilly              44.0\n",
      "glam rock               42.0\n",
      "folk                    42.0\n",
      "folk rock               41.0\n",
      "funk                    40.0\n",
      "art rock                35.0\n",
      "experimental            33.0\n",
      "experimental rock       33.0\n",
      "art pop                 28.0\n",
      "alternative metal       27.0\n",
      "glam metal              27.0\n",
      "country rock            27.0\n",
      "piano rock              25.0\n",
      "progressive rock        23.0\n",
      "arena rock              20.0\n",
      "acid rock               20.0\n",
      "baroque pop             20.0\n",
      "southern rock           16.0\n",
      "punk                    16.0\n",
      "sunshine pop            15.0\n",
      "new wave                15.0\n",
      "nu metal                15.0\n",
      "symphonic rock          15.0\n",
      "r&b                     13.0\n",
      "disco                   12.0\n",
      "funk rock               11.0\n",
      "rap metal                9.0\n",
      "rap rock                 9.0\n",
      "motown                   9.0\n",
      "roots rock               8.0\n",
      "traditional country      7.0\n",
      "electric blues           6.0\n",
      "minneapolis sound        6.0\n",
      "garage rock              6.0\n",
      "synth funk               6.0\n",
      "jazz fusion              5.0\n",
      "speed metal              5.0\n",
      "album rock               5.0\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# If df is a slice from another DataFrame, create a copy:\n",
    "df = df.copy()\n",
    "\n",
    "# List of genre column names \n",
    "genre_columns = [\n",
    "    'album rock', 'art rock', 'alternative rock', 'arena rock', 'blues', 'blues rock', \n",
    "    'classic rock', 'country', 'folk', 'glam', 'gospel', 'grunge', 'hard rock', \n",
    "    'heartland rock', 'metal', 'mellow gold', 'pop', 'progressive rock', \n",
    "    'psychedelic rock', 'punk', 'r&b', 'rap rock', 'rock-and-roll', 'rockabilly', \n",
    "    'symphonic rock', 'piano rock', 'rock', 'singer-songwriter', 'soft rock', \n",
    "    'old school thrash', 'thrash metal', 'glam punk', 'funk', 'funk rock', \n",
    "    'minneapolis sound', 'synth funk', 'alternative metal', 'groove metal', \n",
    "    'melodic metalcore', 'nu metal', 'virginia metal', 'new romantic', 'new wave pop', \n",
    "    'proto-metal', 'acid rock', 'experimental', 'instrumental rock', 'jazz fusion', \n",
    "    'jazz rock', 'zolo', 'yacht rock', 'post-grunge', 'rap metal', 'sleaze rock', \n",
    "    'glam metal', 'permanent wave', 'glam rock', 'emo', 'modern rock', 'pop punk', \n",
    "    'pov: indie', 'brill building pop', 'folk rock', 'hel', 'dance pop', 'candy pop', \n",
    "    'pixie', 'pop emo', 'art pop', 'art punk', 'solo wave', 'comedy rock', 'comic', \n",
    "    'soul', 'country rock', 'roots rock', 'protopunk', 'ambient guitar', \n",
    "    'experimental ambient', 'experimental guitar', 'experimental rock', 'garage rock', \n",
    "    'alternative hip hop', 'southern rock', 'swamp rock', 'baroque pop', 'sunshine pop', \n",
    "    'honky tonk', 'traditional country', 'yodeling', 'alternative country', 'new americana', \n",
    "    'electric blues', 'new wave', 'dance rock', 'neo mellow', 'adult standards', \n",
    "    'power metal', 'speed metal', 'disco', 'motown', 'quiet storm', 'britpop', \n",
    "    'industrial metal', 'industrial rock', 'slayer', 'post-punk', 'synth punk', \n",
    "    'synthpop', 'neo soul'\n",
    "]\n",
    "\n",
    "\n",
    "# Convert genre columns to numeric type, replacing non-numeric values with 0\n",
    "for col in genre_columns:\n",
    "    df[col] = pd.to_numeric(df[col], errors='coerce').fillna(0)\n",
    "\n",
    "# Sum the occurrences of each genre\n",
    "genre_counts = df[genre_columns].sum()\n",
    "\n",
    "# Filter the genre counts to display only those genres that appear five times or more\n",
    "genres_five_or_more = genre_counts[genre_counts >= 5]\n",
    "\n",
    "# Sort the filtered genres in descending order and print them\n",
    "genres_five_or_more_sorted = genres_five_or_more.sort_values(ascending=False)\n",
    "print(genres_five_or_more_sorted)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "0e24d815",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top 10 Genres by Unique Artists (Excluding Specific Genres)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Genres to exclude\n",
    "exclude_genres = ['mellow gold', 'album rock', 'classic rock', 'rock', 'pop']\n",
    "\n",
    "# Filter out the genres to be excluded\n",
    "filtered_genres = [genre for genre in genre_columns if genre not in exclude_genres]\n",
    "\n",
    "# Dictionary to hold genre and unique artist count\n",
    "unique_artist_counts = {}\n",
    "\n",
    "# Iterate through each genre and count unique artists\n",
    "for genre in filtered_genres:\n",
    "    unique_artists = df[df[genre] == 1]['Artist'].nunique()\n",
    "    unique_artist_counts[genre] = unique_artists\n",
    "\n",
    "# Sort the dictionary by count in descending order and take top 10\n",
    "sorted_unique_artist_counts = dict(sorted(unique_artist_counts.items(), key=lambda item: item[1], reverse=True)[:10])\n",
    "\n",
    "# Print the top 10 genres with unique artist counts, excluding the specified genres\n",
    "print(\"Top 10 Genres by Unique Artists (Excluding Specific Genres)\")\n",
    "sorted_unique_artist_counts\n",
    "\n",
    "# Converting the sorted dictionary to a DataFrame for plotting\n",
    "unique_artist_df = pd.DataFrame(list(sorted_unique_artist_counts.items()), columns=['Genre', 'Unique Artists'])\n",
    "\n",
    "# Plotting the bar chart\n",
    "plt.figure(figsize=(12, 6))\n",
    "sns.barplot(x='Unique Artists', y='Genre', data=unique_artist_df, palette='viridis')\n",
    "plt.title('Top 10 Genres by Unique Artists (Excluding Specific Genres)')\n",
    "plt.xlabel('Number of Unique Artists')\n",
    "plt.ylabel('Genre')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "76bf65f1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top 10 Genres for Males (Excluding Specific Genres)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "hard rock            45.0\n",
       "blues rock           44.0\n",
       "blues                36.0\n",
       "metal                30.0\n",
       "alternative rock     28.0\n",
       "psychedelic rock     24.0\n",
       "singer-songwriter    23.0\n",
       "soul                 22.0\n",
       "country              19.0\n",
       "soft rock            19.0\n",
       "dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top 10 Genres for Females (Excluding Specific Genres)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "hard rock            80.0\n",
       "blues                59.0\n",
       "blues rock           50.0\n",
       "singer-songwriter    39.0\n",
       "country              38.0\n",
       "gospel               34.0\n",
       "metal                33.0\n",
       "soul                 33.0\n",
       "psychedelic rock     33.0\n",
       "alternative rock     32.0\n",
       "dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# Filter for male and female\n",
    "df_male = df[df['Gender Of Student'] == 'M']\n",
    "df_female = df[df['Gender Of Student'] == 'F']\n",
    "\n",
    "# Sum genre counts for male and female, excluding specific genres\n",
    "genre_counts_male = df_male[filtered_genres].sum().sort_values(ascending=False).head(10)\n",
    "genre_counts_female = df_female[filtered_genres].sum().sort_values(ascending=False).head(10)\n",
    "\n",
    "# Print top 10 genres for male and female, excluding specific genres\n",
    "print(\"Top 10 Genres for Males (Excluding Specific Genres)\")\n",
    "display(genre_counts_male)\n",
    "\n",
    "print(\"Top 10 Genres for Females (Excluding Specific Genres)\")\n",
    "display(genre_counts_female)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "1c706051",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top 10 Genre Percentages for Males (Excluding Specific Genres)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "hard rock            7%\n",
       "blues rock           7%\n",
       "blues                6%\n",
       "metal                5%\n",
       "alternative rock     4%\n",
       "psychedelic rock     4%\n",
       "singer-songwriter    3%\n",
       "soul                 3%\n",
       "country              3%\n",
       "soft rock            3%\n",
       "dtype: object"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top 10 Genre Percentages for Females (Excluding Specific Genres)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "hard rock            8%\n",
       "blues                6%\n",
       "blues rock           5%\n",
       "singer-songwriter    4%\n",
       "country              4%\n",
       "gospel               3%\n",
       "metal                3%\n",
       "soul                 3%\n",
       "psychedelic rock     3%\n",
       "alternative rock     3%\n",
       "dtype: object"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Filter for male and female\n",
    "df_male = df[df['Gender Of Student'] == 'M']\n",
    "df_female = df[df['Gender Of Student'] == 'F']\n",
    "\n",
    "# Sum genre counts for male and female, excluding specific genres for top 10\n",
    "top_genres_male = df_male[filtered_genres].sum().sort_values(ascending=False).head(10)\n",
    "top_genres_female = df_female[filtered_genres].sum().sort_values(ascending=False).head(10)\n",
    "\n",
    "# Calculate total genre counts for male and female, excluding specific genres\n",
    "total_genres_male_excluded = df_male[filtered_genres].sum().sum()\n",
    "total_genres_female_excluded = df_female[filtered_genres].sum().sum()\n",
    "\n",
    "\n",
    "# Calculate percentages and convert to strings with a percent sign\n",
    "percentages_male = ((top_genres_male / total_genres_male_excluded) * 100).astype(int).astype(str) + '%'\n",
    "percentages_female = ((top_genres_female / total_genres_female_excluded) * 100).astype(int).astype(str) + '%'\n",
    "\n",
    "\n",
    "\n",
    "# Display results\n",
    "print(\"Top 10 Genre Percentages for Males (Excluding Specific Genres)\")\n",
    "display(percentages_male)\n",
    "\n",
    "print(\"Top 10 Genre Percentages for Females (Excluding Specific Genres)\")\n",
    "display(percentages_female)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "1a9d41af",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1400x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "import seaborn as sns\n",
    "\n",
    "# Assuming the DataFrame 'df', and filtered_genres are already defined\n",
    "# Also assuming df_male and df_female have been created as per your previous code\n",
    "\n",
    "# Calculate the total counts for all genres\n",
    "total_genres_count_male = df_male[filtered_genres].sum().sum()\n",
    "total_genres_count_female = df_female[filtered_genres].sum().sum()\n",
    "\n",
    "# Get the top 10 genres and their counts for male and female\n",
    "top_genres_male = df_male[filtered_genres].sum().sort_values(ascending=False).head(10)\n",
    "top_genres_female = df_female[filtered_genres].sum().sort_values(ascending=False).head(10)\n",
    "\n",
    "# Creating DataFrames for plotting\n",
    "top_genres_male_df = pd.DataFrame({'Genre': top_genres_male.index, 'Percentage': (top_genres_male / total_genres_count_male) * 100})\n",
    "top_genres_female_df = pd.DataFrame({'Genre': top_genres_female.index, 'Percentage': (top_genres_female / total_genres_count_female) * 100})\n",
    "\n",
    "# Plotting the bar charts\n",
    "plt.figure(figsize=(14, 8))\n",
    "\n",
    "# Male Genre Percentages\n",
    "plt.subplot(1, 2, 1)\n",
    "sns.barplot(x='Percentage', y='Genre', data=top_genres_male_df, palette='Blues_d')\n",
    "plt.title('Top 10 Genre Percentages for Males (Excluding Specific Genres)')\n",
    "plt.xlabel('Percentage')\n",
    "plt.ylabel('Genre')\n",
    "\n",
    "# Female Genre Percentages\n",
    "plt.subplot(1, 2, 2)\n",
    "sns.barplot(x='Percentage', y='Genre', data=top_genres_female_df, palette='Reds_d')\n",
    "plt.title('Top 10 Genre Percentages for Females (Excluding Specific Genres)')\n",
    "plt.xlabel('Percentage')\n",
    "plt.ylabel('Genre')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "6fd85288",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Getting the top 5 genres excluding the specified ones\n",
    "top_5_genres = df[filtered_genres].sum().sort_values(ascending=False).head(5).index.tolist()\n",
    "\n",
    "# Filter the DataFrame for the years 2018 to 2023\n",
    "df_filtered_years = df[df['Year'].between(2018, 2023)]\n",
    "\n",
    "# Grouping by year and summing the occurrences of each genre\n",
    "genre_by_year = df_filtered_years.groupby('Year')[top_5_genres].sum()\n",
    "\n",
    "# Calculate the total number of artists per year\n",
    "total_artists_per_year = df_filtered_years.groupby('Year')['Artist'].count()\n",
    "\n",
    "# Calculate the percentage of each genre per year\n",
    "genre_percentage_by_year = (genre_by_year.div(total_artists_per_year, axis=0)) * 100\n",
    "\n",
    "# Plotting the percentage trend over time for the top 5 genres\n",
    "plt.figure(figsize=(12, 6))\n",
    "for genre in top_5_genres:\n",
    "    plt.plot(genre_percentage_by_year.index, genre_percentage_by_year[genre], label=genre, marker='o')\n",
    "plt.title(\"Percentage Popularity of Top 5 Genres Over Time (2018-2023, Excluding Pop, Rock, Classic Rock, Album Rock, Mellow Gold)\")\n",
    "plt.xlabel(\"Year\")\n",
    "plt.ylabel(\"Percentage of Artists\")\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "df2ae9bc",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "PythonData",
   "language": "python",
   "name": "pythondata"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}