microproject-bechdel-test.ipynb

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    "<h1 style=\"text-align: center\">\n",
    "<div style=\"color: #DD3403; font-size: 60%\">Data Science DISCOVERY MicroProject</div>\n",
    "<span style=\"\">MicroProject: Bechdel Test</span>\n",
    "<div style=\"font-size: 60%;\"><a href=\"https://discovery.cs.illinois.edu/microproject/bechdel-test/\">https://discovery.cs.illinois.edu/microproject/bechdel-test/</a></div>\n",
    "</h1>\n",
    "\n",
    "<hr style=\"color: #DD3403;\">"
   ]
  },
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   "source": [
    "## Data Source: Bechdel Test\n",
    "\n",
    "The Bechdel Test, or Bechdel-Wallace Test, is a simple way of measuring the representation of women in a film or other work of fiction.  To pass the The Bechdel Test, a work must pass all three criteria:\n",
    "\n",
    "1. The work must have at least two women in it,\n",
    "\n",
    "2. who talk to each other,\n",
    "\n",
    "3. about something other than a man.\n",
    "\n",
    "The test was popularized by Alison Bechdel's comic, in a 1985 strip called \"The Rule\".  The website [BechdelTest.com](BechdelTest.com) provides a searchable database of films and their Bechdel Test results, allowing users to explore and analyze patterns in gender representation in cinema.\n"
   ]
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    "### Importing the Bechdel Test Dataset\n",
    "\n",
    "The [BechdelTest.com](BechdelTest.com) public API provides a easy-to-access data source that contains the Bechdel Test scores for thousands of movies.  Through reading the technical API documentation at [https://bechdeltest.com/api/v1/doc](https://bechdeltest.com/api/v1/doc), you can find that:\n",
    "\n",
    "- The Bechdel Test API uses the JSON format, which can be read into a DataFrame using `pd.read_json(...)`,\n",
    "\n",
    "- The URL endpoint for the list of all movies is `https://bechdeltest.com/api/v1/getAllMovies`.\n",
    "\n",
    "Using `pd.read_json`, create a DataFrame `df_bechdel` to store all the movies and their Bechdel Test scores:"
   ]
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>imdbid</th>\n",
       "      <th>title</th>\n",
       "      <th>rating</th>\n",
       "      <th>id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1874</td>\n",
       "      <td>3155794</td>\n",
       "      <td>Passage de Venus</td>\n",
       "      <td>0</td>\n",
       "      <td>9602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1877</td>\n",
       "      <td>14495706</td>\n",
       "      <td>La Rosace Magique</td>\n",
       "      <td>0</td>\n",
       "      <td>9804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1878</td>\n",
       "      <td>2221420</td>\n",
       "      <td>Sallie Gardner at a Gallop</td>\n",
       "      <td>0</td>\n",
       "      <td>9603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1878</td>\n",
       "      <td>12592084</td>\n",
       "      <td>Le singe musicien</td>\n",
       "      <td>0</td>\n",
       "      <td>9806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1881</td>\n",
       "      <td>7816420</td>\n",
       "      <td>Athlete Swinging a Pick</td>\n",
       "      <td>0</td>\n",
       "      <td>9816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10131</th>\n",
       "      <td>2023</td>\n",
       "      <td>19623240</td>\n",
       "      <td>Winnie-the-Pooh: Blood and Honey</td>\n",
       "      <td>3</td>\n",
       "      <td>10995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10132</th>\n",
       "      <td>2023</td>\n",
       "      <td>1695843</td>\n",
       "      <td>Haunted Mansion</td>\n",
       "      <td>3</td>\n",
       "      <td>10996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10133</th>\n",
       "      <td>2023</td>\n",
       "      <td>11820950</td>\n",
       "      <td>Portable Door, The</td>\n",
       "      <td>3</td>\n",
       "      <td>11009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10134</th>\n",
       "      <td>2023</td>\n",
       "      <td>17009710</td>\n",
       "      <td>Anatomie d&amp;#39;une chute</td>\n",
       "      <td>3</td>\n",
       "      <td>11010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10135</th>\n",
       "      <td>2023</td>\n",
       "      <td>15422224</td>\n",
       "      <td>Slotherhouse</td>\n",
       "      <td>3</td>\n",
       "      <td>11015</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10136 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       year    imdbid                             title  rating     id\n",
       "0      1874   3155794                  Passage de Venus       0   9602\n",
       "1      1877  14495706                 La Rosace Magique       0   9804\n",
       "2      1878   2221420        Sallie Gardner at a Gallop       0   9603\n",
       "3      1878  12592084                 Le singe musicien       0   9806\n",
       "4      1881   7816420           Athlete Swinging a Pick       0   9816\n",
       "...     ...       ...                               ...     ...    ...\n",
       "10131  2023  19623240  Winnie-the-Pooh: Blood and Honey       3  10995\n",
       "10132  2023   1695843                   Haunted Mansion       3  10996\n",
       "10133  2023  11820950                Portable Door, The       3  11009\n",
       "10134  2023  17009710          Anatomie d&#39;une chute       3  11010\n",
       "10135  2023  15422224                      Slotherhouse       3  11015\n",
       "\n",
       "[10136 rows x 5 columns]"
      ]
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   "source": [
    "import pandas as pd\n",
    "df_bechdel = pd.read_json(\"https://bechdeltest.com/api/v1/getAllMovies\")\n",
    "df_bechdel"
   ]
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    "## Part 1: Creating a Pivot Table for Analysis\n",
    "\n",
    "With over 9,000 movies that were released over 100 years, creating a summary of our data may be helpful for analysis!\n",
    "\n",
    "When summarizing data, `groupby` is commonly used to aggregate all data that has an identical value in one or more columns.  For example, to count how many movies have a Bechdel Test score in our dataset each year, we could use:\n",
    "\n",
    "```\n",
    "df_bechdel.groupby(\"year\").agg(\"count\")\n",
    "```\n",
    "\n",
    "Try that below:"
   ]
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       "      <th></th>\n",
       "      <th>imdbid</th>\n",
       "      <th>title</th>\n",
       "      <th>rating</th>\n",
       "      <th>id</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "    </tr>\n",
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       "      <th>1874</th>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>1877</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>1878</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1881</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1883</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>276</td>\n",
       "      <td>276</td>\n",
       "      <td>276</td>\n",
       "      <td>276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>164</td>\n",
       "      <td>164</td>\n",
       "      <td>164</td>\n",
       "      <td>164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021</th>\n",
       "      <td>178</td>\n",
       "      <td>178</td>\n",
       "      <td>178</td>\n",
       "      <td>178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022</th>\n",
       "      <td>174</td>\n",
       "      <td>174</td>\n",
       "      <td>174</td>\n",
       "      <td>174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023</th>\n",
       "      <td>59</td>\n",
       "      <td>59</td>\n",
       "      <td>59</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>142 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      imdbid  title  rating   id\n",
       "year                            \n",
       "1874       1      1       1    1\n",
       "1877       1      1       1    1\n",
       "1878       2      2       2    2\n",
       "1881       1      1       1    1\n",
       "1883       1      1       1    1\n",
       "...      ...    ...     ...  ...\n",
       "2019     276    276     276  276\n",
       "2020     164    164     164  164\n",
       "2021     178    178     178  178\n",
       "2022     174    174     174  174\n",
       "2023      59     59      59   59\n",
       "\n",
       "[142 rows x 4 columns]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
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   ],
   "source": [
    "df_bechdel.groupby(\"year\").agg(\"count\")"
   ]
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    "## Step 1.1: Specify the `index` and `aggfunc`:\n",
    "\n",
    "When we want to summarize data by **two or more** categories, the `pivot_table` function of a DataFrame can be useful.  The `pivot_table` function requires us to specify:\n",
    "\n",
    "- The `index` requires us to specify the data that we values of our **rows** to be?  For example, if we want our rows to be each year in our dataset we would specify `index=\"year\"`.\n",
    "\n",
    "- The `aggfunc` requires us to specify how the data should be combined in of the rows -- just like `.agg()`.  To count the number of rows, our `aggfunc` is just `\"count\"`.\n",
    "\n",
    "\n",
    "Try creating a pivot table where we only specify the `index` value by trying the following different lines:\n",
    "\n",
    "1. First try creating a DataFrame where each row is a \"rating\": `df_bechdel.pivot_table(index=\"rating\", aggfunc=\"count\")`\n",
    "\n",
    "2. Next try creating a DataFrame where each row is a \"year\": `df_bechdel.pivot_table(index=\"year\", aggfunc=\"count\")`"
   ]
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       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>imdbid</th>\n",
       "      <th>title</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rating</th>\n",
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       "      <th>0</th>\n",
       "      <td>1119</td>\n",
       "      <td>1119</td>\n",
       "      <td>1119</td>\n",
       "      <td>1119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2189</td>\n",
       "      <td>2189</td>\n",
       "      <td>2189</td>\n",
       "      <td>2189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1047</td>\n",
       "      <td>1047</td>\n",
       "      <td>1047</td>\n",
       "      <td>1047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5781</td>\n",
       "      <td>5781</td>\n",
       "      <td>5781</td>\n",
       "      <td>5781</td>\n",
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      "text/plain": [
       "          id  imdbid  title  year\n",
       "rating                           \n",
       "0       1119    1119   1119  1119\n",
       "1       2189    2189   2189  2189\n",
       "2       1047    1047   1047  1047\n",
       "3       5781    5781   5781  5781"
      ]
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   "source": [
    "a = df_bechdel.pivot_table(index=\"year\", aggfunc=\"count\")\n",
    "a\n",
    "b = df_bechdel.pivot_table(index=\"rating\", aggfunc=\"count\")\n",
    "b "
   ]
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    "## Step 1.2: Specify the `columns`\n",
    "\n",
    "The third value we will specify is `columns`.  This specifies what column from the original DataFrame should be presented in each column.  This `columns` value is combined together with the rows value (`index`) we specified in Part 1.\n",
    "\n",
    "To complete this step, create a pivot table where:\n",
    "\n",
    "- Each row (`index` parameter) in our pivot table is one year from the original DataFrame,\n",
    "- Each column (`columns` parameter) in our pivot table is one rating from the original DataFrame, **and**\n",
    "- The values are aggregated together by the `\"count\"` function (`aggfunc` parameter)\n",
    "\n",
    "Call this pivot table `df` and we'll check to make sure it looks good:"
   ]
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       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th>1874</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
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       "      <th>1877</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>1878</th>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1881</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1883</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\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>2019</th>\n",
       "      <td>17.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>191.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>191.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>191.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>12.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>120.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021</th>\n",
       "      <td>13.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>118.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>118.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>118.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022</th>\n",
       "      <td>8.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>121.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023</th>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>43.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>142 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          id                    imdbid                    title              \\\n",
       "rating     0     1     2      3      0     1     2      3     0     1     2   \n",
       "year                                                                          \n",
       "1874     1.0   NaN   NaN    NaN    1.0   NaN   NaN    NaN   1.0   NaN   NaN   \n",
       "1877     1.0   NaN   NaN    NaN    1.0   NaN   NaN    NaN   1.0   NaN   NaN   \n",
       "1878     2.0   NaN   NaN    NaN    2.0   NaN   NaN    NaN   2.0   NaN   NaN   \n",
       "1881     1.0   NaN   NaN    NaN    1.0   NaN   NaN    NaN   1.0   NaN   NaN   \n",
       "1883     1.0   NaN   NaN    NaN    1.0   NaN   NaN    NaN   1.0   NaN   NaN   \n",
       "...      ...   ...   ...    ...    ...   ...   ...    ...   ...   ...   ...   \n",
       "2019    17.0  49.0  19.0  191.0   17.0  49.0  19.0  191.0  17.0  49.0  19.0   \n",
       "2020    12.0  20.0  12.0  120.0   12.0  20.0  12.0  120.0  12.0  20.0  12.0   \n",
       "2021    13.0  31.0  16.0  118.0   13.0  31.0  16.0  118.0  13.0  31.0  16.0   \n",
       "2022     8.0  28.0  17.0  121.0    8.0  28.0  17.0  121.0   8.0  28.0  17.0   \n",
       "2023     4.0   7.0   5.0   43.0    4.0   7.0   5.0   43.0   4.0   7.0   5.0   \n",
       "\n",
       "               \n",
       "rating      3  \n",
       "year           \n",
       "1874      NaN  \n",
       "1877      NaN  \n",
       "1878      NaN  \n",
       "1881      NaN  \n",
       "1883      NaN  \n",
       "...       ...  \n",
       "2019    191.0  \n",
       "2020    120.0  \n",
       "2021    118.0  \n",
       "2022    121.0  \n",
       "2023     43.0  \n",
       "\n",
       "[142 rows x 12 columns]"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df_bechdel.pivot_table(index=\"year\",columns=\"rating\", aggfunc=\"count\")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "🎉 All tests passed! 🎉\n"
     ]
    }
   ],
   "source": [
    "### TEST CASE for Step 1.2: Specify the `columns`\n",
    "tada = \"\\N{PARTY POPPER}\"\n",
    "\n",
    "assert( len(df) == len(df_bechdel[\"year\"].unique()) ), \\\n",
    "  \"You should have one row for each year, but your `df` does not. Make sure your `index` parameter is correct.\"\n",
    "\n",
    "assert( len(df.columns) == (len(df_bechdel.columns) - 2) * 4 ), \\\n",
    "  \"You should have a 0,1,2,3 column for each variable. Make sure your `columns` parameter is correct. \"\n",
    "\n",
    "assert( df[\"id\", 0][2021] == len(df_bechdel[ (df_bechdel.year == 2021) & (df_bechdel.rating == 0) ]) ), \\\n",
    "  \"You have the incorrect number of movies with a 0 rating in 2021. Make sure your `aggfunc` parameter is correct.\"\n",
    "\n",
    "print(f\"{tada} All tests passed! {tada}\")\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 1.3: Specify the `values`\n",
    "\n",
    "In the result above, you have a summary for the number of movies rated `0`, `1`, `2`, and `3` for every year of data in the dataset, but it is repeated for every `id` record, every `imdbid`, and every `title`.\n",
    "\n",
    "The `values` parameter allows us to specify the values we want to keep.  Since the `\"count\"` of all three columns are the same (every row has a title, id, and imdbid), the `values=\"title\"` field allows us to choose to only look at data about `titles` and cleans up our pivot table.\n",
    "\n",
    "Copy your code above and extend it to only include data about the `values=\"title\"`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "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>rating</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1874</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1877</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1878</th>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1881</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1883</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>17.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>191.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>12.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>120.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021</th>\n",
       "      <td>13.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>118.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022</th>\n",
       "      <td>8.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>121.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023</th>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>43.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>142 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "rating     0     1     2      3\n",
       "year                           \n",
       "1874     1.0   NaN   NaN    NaN\n",
       "1877     1.0   NaN   NaN    NaN\n",
       "1878     2.0   NaN   NaN    NaN\n",
       "1881     1.0   NaN   NaN    NaN\n",
       "1883     1.0   NaN   NaN    NaN\n",
       "...      ...   ...   ...    ...\n",
       "2019    17.0  49.0  19.0  191.0\n",
       "2020    12.0  20.0  12.0  120.0\n",
       "2021    13.0  31.0  16.0  118.0\n",
       "2022     8.0  28.0  17.0  121.0\n",
       "2023     4.0   7.0   5.0   43.0\n",
       "\n",
       "[142 rows x 4 columns]"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df_bechdel.pivot_table(index=\"year\",columns=\"rating\",values=\"title\", aggfunc=\"count\")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "🎉 All tests passed! 🎉\n"
     ]
    }
   ],
   "source": [
    "### TEST CASE for Step 1.3: Specify the `values`\n",
    "tada = \"\\N{PARTY POPPER}\"\n",
    "\n",
    "assert( len(df) == len(df_bechdel[\"year\"].unique()) ), \\\n",
    "  \"You should have one row for each year, but your `df` does not. Make sure your `index` parameter is correct.\"\n",
    "\n",
    "assert( len(df.columns) == 4 ), \\\n",
    "  \"You should have a single 0,1,2,3 column. Make sure your `columns` parameter is correct. \"\n",
    "\n",
    "assert( df[3][2021] == len(df_bechdel[ (df_bechdel.year == 2021) & (df_bechdel.rating == 3) ]) ), \\\n",
    "  \"You have the incorrect number of movies with a 3 rating in 2021. Make sure your `aggfunc` parameter is correct.\"\n",
    "\n",
    "print(f\"{tada} All tests passed! {tada}\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 1.4: Specify the `fill_value`\n",
    "\n",
    "Finally, you may notice that there are no movies with a **3** rating in **1874**.  Since there is no data for that row/column combination, pandas leaves the value blank and reports a `NaN` or \"Not a Number\".\n",
    "\n",
    "The `fill_values` parameter allows us to give a default value when there is no data.  Since we know no data indicates that there were zero movies that that rating in our dataset, setting `fill_values=0` fills the missing data with zeros.\n",
    "\n",
    "Copy your code above and extend it to fill the values no data with `0`s:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "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>rating</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1874</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1877</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1878</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1881</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1883</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>17</td>\n",
       "      <td>49</td>\n",
       "      <td>19</td>\n",
       "      <td>191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>12</td>\n",
       "      <td>20</td>\n",
       "      <td>12</td>\n",
       "      <td>120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021</th>\n",
       "      <td>13</td>\n",
       "      <td>31</td>\n",
       "      <td>16</td>\n",
       "      <td>118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022</th>\n",
       "      <td>8</td>\n",
       "      <td>28</td>\n",
       "      <td>17</td>\n",
       "      <td>121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023</th>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>142 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "rating   0   1   2    3\n",
       "year                   \n",
       "1874     1   0   0    0\n",
       "1877     1   0   0    0\n",
       "1878     2   0   0    0\n",
       "1881     1   0   0    0\n",
       "1883     1   0   0    0\n",
       "...     ..  ..  ..  ...\n",
       "2019    17  49  19  191\n",
       "2020    12  20  12  120\n",
       "2021    13  31  16  118\n",
       "2022     8  28  17  121\n",
       "2023     4   7   5   43\n",
       "\n",
       "[142 rows x 4 columns]"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df_bechdel.pivot_table(index=\"year\",columns=\"rating\",values=\"title\",fill_value=0, aggfunc=\"count\")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "🎉 All tests passed! 🎉\n"
     ]
    }
   ],
   "source": [
    "### TEST CASE for Step 1.4: Specify the `fill_value`\n",
    "tada = \"\\N{PARTY POPPER}\"\n",
    "\n",
    "assert( len(df) == len(df_bechdel[\"year\"].unique()) ), \\\n",
    "  \"You should have one row for each year, but your `df` does not. Make sure your `index` parameter is correct.\"\n",
    "\n",
    "assert( len(df.columns) == 4 ), \\\n",
    "  \"You should have a single 0,1,2,3 column. Make sure your `columns` parameter is correct. \"\n",
    "\n",
    "assert( df[3][2021] == len(df_bechdel[ (df_bechdel.year == 2021) & (df_bechdel.rating == 3) ]) ), \\\n",
    "  \"You have the incorrect number of movies with a 3 rating in 2021. Make sure your `aggfunc` parameter is correct.\"\n",
    "\n",
    "assert( len(df.dropna()) == len(df) ), \\\n",
    "  \"You have some NaN values remaining. Make sure your `fill_value` parameter is correct.\"\n",
    "\n",
    "print(f\"{tada} All tests passed! {tada}\")\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr style=\"color: #DD3403;\">"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 2: Visual Analysis of the Data\n",
    "\n",
    "First, let's look at the overall analysis of summary you created!  Use `df.plot.line()` to create a line chart of your pivot table:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='year'>"
      ]
     },
     "execution_count": 74,
     "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": [
    "df.plot.line()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Part 2.1: Create a Percentage Graph\n",
    "\n",
    "In the graph above, you can see that the **total number of movies** in the dataset has increased dramatically from the 1880s until today.  However, it is hard to determine if the percentage of movies with a higher rating has increased.\n",
    "\n",
    "To create a graph of the proportion of movies at reach ranking, add at least four additional columns to your DataFrame `df`:\n",
    "\n",
    "- `\"%0\"`, that contains the percentage of movies with a `0` ranking in a given year,\n",
    "- `\"%1\"`, that contains the percentage of movies with a `1` ranking in a given year,\n",
    "- `\"%2\"`, that contains the percentage of movies with a `2` ranking in a given year,\n",
    "- `\"%3\"`, that contains the percentage of movies with a `3` ranking in a given year,\n",
    "- You may find adding a `\"Total\"` column helpful."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "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>rating</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>Total</th>\n",
       "      <th>%0</th>\n",
       "      <th>%1</th>\n",
       "      <th>%2</th>\n",
       "      <th>%3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1874</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1877</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1878</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1881</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1883</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\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>2019</th>\n",
       "      <td>17</td>\n",
       "      <td>49</td>\n",
       "      <td>19</td>\n",
       "      <td>191</td>\n",
       "      <td>276</td>\n",
       "      <td>0.061594</td>\n",
       "      <td>0.177536</td>\n",
       "      <td>0.068841</td>\n",
       "      <td>0.692029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>12</td>\n",
       "      <td>20</td>\n",
       "      <td>12</td>\n",
       "      <td>120</td>\n",
       "      <td>164</td>\n",
       "      <td>0.073171</td>\n",
       "      <td>0.121951</td>\n",
       "      <td>0.073171</td>\n",
       "      <td>0.731707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021</th>\n",
       "      <td>13</td>\n",
       "      <td>31</td>\n",
       "      <td>16</td>\n",
       "      <td>118</td>\n",
       "      <td>178</td>\n",
       "      <td>0.073034</td>\n",
       "      <td>0.174157</td>\n",
       "      <td>0.089888</td>\n",
       "      <td>0.662921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022</th>\n",
       "      <td>8</td>\n",
       "      <td>28</td>\n",
       "      <td>17</td>\n",
       "      <td>121</td>\n",
       "      <td>174</td>\n",
       "      <td>0.045977</td>\n",
       "      <td>0.160920</td>\n",
       "      <td>0.097701</td>\n",
       "      <td>0.695402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023</th>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>43</td>\n",
       "      <td>59</td>\n",
       "      <td>0.067797</td>\n",
       "      <td>0.118644</td>\n",
       "      <td>0.084746</td>\n",
       "      <td>0.728814</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>142 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "rating   0   1   2    3  Total        %0        %1        %2        %3\n",
       "year                                                                  \n",
       "1874     1   0   0    0      1  1.000000  0.000000  0.000000  0.000000\n",
       "1877     1   0   0    0      1  1.000000  0.000000  0.000000  0.000000\n",
       "1878     2   0   0    0      2  1.000000  0.000000  0.000000  0.000000\n",
       "1881     1   0   0    0      1  1.000000  0.000000  0.000000  0.000000\n",
       "1883     1   0   0    0      1  1.000000  0.000000  0.000000  0.000000\n",
       "...     ..  ..  ..  ...    ...       ...       ...       ...       ...\n",
       "2019    17  49  19  191    276  0.061594  0.177536  0.068841  0.692029\n",
       "2020    12  20  12  120    164  0.073171  0.121951  0.073171  0.731707\n",
       "2021    13  31  16  118    178  0.073034  0.174157  0.089888  0.662921\n",
       "2022     8  28  17  121    174  0.045977  0.160920  0.097701  0.695402\n",
       "2023     4   7   5   43     59  0.067797  0.118644  0.084746  0.728814\n",
       "\n",
       "[142 rows x 9 columns]"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Total\"] = df[0]+df[1]+df[2]+df[3]\n",
    "df[\"%0\"] = df[0]/df[\"Total\"]\n",
    "df[\"%1\"] = df[1]/df[\"Total\"]\n",
    "df[\"%2\"] = df[2]/df[\"Total\"]\n",
    "df[\"%3\"] = df[3]/df[\"Total\"]\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "🎉 All tests passed! 🎉\n"
     ]
    }
   ],
   "source": [
    "### TEST CASE for Part 2.1: Create a Percentage Graph\n",
    "tada = \"\\N{PARTY POPPER}\"\n",
    "\n",
    "assert( \"%0\" in df.columns ), \"Make sure you have a \\\"%0\\\" column.\"\n",
    "assert( \"%1\" in df.columns ), \"Make sure you have a \\\"%1\\\" column.\"\n",
    "assert( \"%2\" in df.columns ), \"Make sure you have a \\\"%2\\\" column.\"\n",
    "assert( \"%3\" in df.columns ), \"Make sure you have a \\\"%3\\\" column.\"\n",
    "\n",
    "import math\n",
    "assert( math.isclose(\n",
    "        df.loc[2020][\"%0\"],\n",
    "        len(df_bechdel[ (df_bechdel.year == 2020) & (df_bechdel.rating == 0) ]) / len(df_bechdel[ (df_bechdel.year == 2020) ])\n",
    "      )), \"Your calculation of %0 is incorrect.\"\n",
    "\n",
    "assert( math.isclose(\n",
    "        df.loc[2019][\"%1\"],\n",
    "        len(df_bechdel[ (df_bechdel.year == 2019) & (df_bechdel.rating == 1) ]) / len(df_bechdel[ (df_bechdel.year == 2019) ])\n",
    "      )), \"Your calculation of %1 is incorrect.\"\n",
    "\n",
    "assert( math.isclose(\n",
    "        df.loc[2018][\"%2\"],\n",
    "        len(df_bechdel[ (df_bechdel.year == 2018) & (df_bechdel.rating == 2) ]) / len(df_bechdel[ (df_bechdel.year == 2018) ])\n",
    "      )), \"Your calculation of %2 is incorrect.\"\n",
    "\n",
    "assert( math.isclose(\n",
    "        df.loc[2017][\"%3\"],\n",
    "        len(df_bechdel[ (df_bechdel.year == 2017) & (df_bechdel.rating == 3) ]) / len(df_bechdel[ (df_bechdel.year == 2017) ])\n",
    "      )), \"Your calculation of %3 is incorrect.\"\n",
    "\n",
    "print(f\"{tada} All tests passed! {tada}\")\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Part 2.2: Finding the Percentage of Movies /Year with Each Rating:"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To visualize only the percentage columns, create a new `df_pct` that contains only the `%0`, `%1`, `%2`, and `%3` columns:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "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>rating</th>\n",
       "      <th>%0</th>\n",
       "      <th>%1</th>\n",
       "      <th>%2</th>\n",
       "      <th>%3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1874</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1877</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1878</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1881</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1883</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>0.061594</td>\n",
       "      <td>0.177536</td>\n",
       "      <td>0.068841</td>\n",
       "      <td>0.692029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>0.073171</td>\n",
       "      <td>0.121951</td>\n",
       "      <td>0.073171</td>\n",
       "      <td>0.731707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021</th>\n",
       "      <td>0.073034</td>\n",
       "      <td>0.174157</td>\n",
       "      <td>0.089888</td>\n",
       "      <td>0.662921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022</th>\n",
       "      <td>0.045977</td>\n",
       "      <td>0.160920</td>\n",
       "      <td>0.097701</td>\n",
       "      <td>0.695402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023</th>\n",
       "      <td>0.067797</td>\n",
       "      <td>0.118644</td>\n",
       "      <td>0.084746</td>\n",
       "      <td>0.728814</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>142 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "rating        %0        %1        %2        %3\n",
       "year                                          \n",
       "1874    1.000000  0.000000  0.000000  0.000000\n",
       "1877    1.000000  0.000000  0.000000  0.000000\n",
       "1878    1.000000  0.000000  0.000000  0.000000\n",
       "1881    1.000000  0.000000  0.000000  0.000000\n",
       "1883    1.000000  0.000000  0.000000  0.000000\n",
       "...          ...       ...       ...       ...\n",
       "2019    0.061594  0.177536  0.068841  0.692029\n",
       "2020    0.073171  0.121951  0.073171  0.731707\n",
       "2021    0.073034  0.174157  0.089888  0.662921\n",
       "2022    0.045977  0.160920  0.097701  0.695402\n",
       "2023    0.067797  0.118644  0.084746  0.728814\n",
       "\n",
       "[142 rows x 4 columns]"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_pct = df[['%0','%1','%2','%3']]\n",
    "df_pct"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "🎉 All tests passed! 🎉\n"
     ]
    }
   ],
   "source": [
    "### TEST CASE for Part 2.2: Finding the Percentage of Movies /Year with Each Rating\n",
    "tada = \"\\N{PARTY POPPER}\"\n",
    "\n",
    "assert( \"df_pct\" in vars() ), \"Make sure you have defined the DataFrame `df_pct`.\"\n",
    "assert( \"%0\" in df_pct.columns ), \"Make sure you have a \\\"%0\\\" column.\"\n",
    "assert( \"%1\" in df_pct.columns ), \"Make sure you have a \\\"%1\\\" column.\"\n",
    "assert( \"%2\" in df_pct.columns ), \"Make sure you have a \\\"%2\\\" column.\"\n",
    "assert( \"%3\" in df_pct.columns ), \"Make sure you have a \\\"%3\\\" column.\"\n",
    "assert( len(df_pct.columns) == 4 ), \"Make sure you only have the percentage columns in `df_pct`.\"\n",
    "\n",
    "print(f\"{tada} All tests passed! {tada}\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Part 2.3: Visualizing the Percentage of Movies /Year with Each Rating:"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since `df_pct` has rows that always add up to `1`, this data is **PERFECT** for a stacked area graph.  Using `df.plot.area()`, create a stacked area visualization to view the growth of the percentage of each movie's rating:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: title={'center': 'Percentage of Movies with Each Bechdel Test Score Per Year'}, xlabel='year'>"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_pct.plot.area(figsize=(10,5), title=\"Percentage of Movies with Each Bechdel Test Score Per Year\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr style=\"color: #DD3403;\">"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Submission\n",
    "\n",
    "You're almost done!  All you need to do is to commit your lab to GitHub and run the GitHub Actions Grader:\n",
    "\n",
    "1.  ⚠️ **Make certain to save your work.** ⚠️ To do this, go to **File => Save All**\n",
    "\n",
    "2.  After you have saved, exit this notebook and return to https://discovery.cs.illinois.edu/microproject/bechdel-test/ and complete the section **\"Commit and Grade Your Notebook\"**.\n",
    "\n",
    "3. If you see a 100% grade result on your GitHub Action, you've completed this MicroProject! 🎉"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.11.4"
  },
  "vscode": {
   "interpreter": {
    "hash": "369f2c481f4da34e4445cda3fffd2e751bd1c4d706f27375911949ba6bb62e1c"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}