GenderRepresentationCode_vf.ipynb

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    "## DSGA1007 - Final Project\n",
    "## Title: A Data Science Approach to Gender Representation in Film\n",
    "\n",
    "#### Group Members: Elizabeth Combs (eac721), Lauren D'Arinzo (lhd258), Amber Teng (at2507 - responsible)\n",
    "#### http://imdbgenderrepresentation.getforge.io/\n",
    "\n",
    "“Regardless of the content, a film has the power to shape perceptions of moviegoers on a range of subjects from love and marriage to the work of the government” - cf. Franklin  2006 ; Kolker  1999 ; Ortega-Liston  2000 ; Riggle, Ellis, and Crawford  1996 \n",
    "\n",
    "#### Abstract\n",
    "The Bechdel test is a blunt, basic measure of gender equality in fictional media. From a data science perspective, we wondered if and how we could automate the process of scoring a film based on the Bechdel criteria. This project aims to complete a descriptive analysis to assess the potential of creating such a method. We review the possibility through Tableau visualization dashboards to compare the number of movies scored and the ratio of passing movies over time. We subsequently examine the use of word count as a potential proxy to quantify the words spoken by women in a film. In our conclusion, we address the modeling and ethical implications of quantifying cultural variables for feature engineering. This Notebook serves to clean the data for our project as well as complete first exploratory data visualizations. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Background\n",
    "\n",
    "##### Description and Motivation: \n",
    "An integral part of data science is the communication of results; visualizations make it easier to present and discuss data, detect patterns, and identify significant points in a dataset.  As data scientists, it is important that we make these visualizations accessible and interpretable by our population of interest. \n",
    "\n",
    "In the age of technology and media, our group sought to better understand how women have, and continue to be represented in films, particularly those rated by both users and critics. For this exploration, we utilize the Bechdel Test, which is a measure of the representation of women in fiction. The test is comprised of three criteria: a movie or work of fiction has to (1) have at least two women in it, (2) who talk to each other, and (3) have a conversation that revolves around a topic other than a man. The test has historically been used as an indicator for the “active presence of women” in a work of fiction, and in the field of media studies, to shed light on gender inequality in works of fiction [1]. However, from the website documentation, the scoring regimen appears to be a largely manual process -- you have to watch the movie to do it. Consequently, we were motivated to consider ways that the Bechdel Test could be made more efficient through automation, while using compelling visualizations along the way to support our resulting conclusions.\n",
    "\n",
    "##### Data Source:\n",
    "BechdelTest.com sources API to query their list of movies that have been scored on the test criteria [2]. Their data include a unique IMDb movie ID for each film, allowing us to link the Bechdel data to public movie data from IMDb that includes further information about the title, like genre and the average rating by IMDb users. We also utilize Polygraph's Film Dialogue Dataset which contains information from movie scripts about the number of words spoken by each character, as well as the available demographic information on that character. We suspect that some of the fields, like age, make be estimates, but consider the gender feature to be largely reliable. \n",
    "\n",
    "##### [Merging Datasets](#mergingdatasets_cell)\n",
    "\n",
    "We merge data from three different sources: \n",
    "- (1) Bechdel (http://bechdeltest.com/api/v1/getAllMovies)\n",
    "- (2) Scripts (https://github.com/matthewfdaniels/scripts)\n",
    "    - character_list5.csv\n",
    "    - character_mapping.csv\n",
    "- (3) IMDb (https://www.imdb.com/interfaces/)\n",
    "    - title.akas.tsv\n",
    "    - title.basics.tsv\n",
    "    - title.ratings.tsv\n",
    "\n",
    "We export two clean files: \n",
    "- (1) df_movies_bechdel.csv\n",
    "- (2) df_movies_all.csv"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### [Results](#results_cell)\n",
    "\n",
    "##### [First Visualization](#first_viz_cell)\n",
    "Next, we view the data to brainstorm how we can visualize the data to explore automation for Bechdel test using data science techniques.\n",
    "- How does the score of the Bechdel Test relate to IMDb User Rating?\n",
    "- How does the distribution of Bechdel passing movies change over time?\n",
    "- How much does content play a role vs. word count? For which genres is this more prevalent?\n",
    "\n",
    "##### [Tableau Data Preparation](#tableau_prep_cell)\n",
    "These visualizations are later improved in Tableau for deployment to our website, and this notebook includes the code used to build the clean data used in Tableau. You can view the clean visualizations on our wesbite and further review our exploration of the Bechdel Test and its potential automation here: http://imdbgenderrepresentation.getforge.io/.\n",
    "- *Overview Over Time* - Export data for Tableau plotting ratio of movies passing the Bechdel Test over time.\n",
    "- *Representation by Genre* - Export data by genre for Tableau plotting.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### [Discussion & Conclusion](#disc_cell)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Packages used:\n",
    "import requests\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from pandas.io.json import json_normalize\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\") # ignores future warnings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Be sure to change this to your own file path!\n",
    "path = '~/Documents/PROGRAMMING/ProjectData/'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='mergingdatasets_cell'></a>\n",
    "### Merging Datasets:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import and clean the Bechdel Data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>imdbid</th>\n",
       "      <th>bechdel_rating</th>\n",
       "      <th>title</th>\n",
       "      <th>year</th>\n",
       "      <th>tconst</th>\n",
       "      <th>binary_bechdel_rating</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8281</th>\n",
       "      <td>8744</td>\n",
       "      <td>8169446</td>\n",
       "      <td>3</td>\n",
       "      <td>Wine Country</td>\n",
       "      <td>2019</td>\n",
       "      <td>tt8169446</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8282</th>\n",
       "      <td>8808</td>\n",
       "      <td>6806448</td>\n",
       "      <td>2</td>\n",
       "      <td>Fast &amp;amp; Furious Presents: Hobbs &amp;amp; Shaw</td>\n",
       "      <td>2019</td>\n",
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       "      <td>0</td>\n",
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       "      <td>8645</td>\n",
       "      <td>3513498</td>\n",
       "      <td>3</td>\n",
       "      <td>Lego Movie 2: The Second Part, The</td>\n",
       "      <td>2019</td>\n",
       "      <td>tt3513498</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8284</th>\n",
       "      <td>8859</td>\n",
       "      <td>8461042</td>\n",
       "      <td>3</td>\n",
       "      <td>Marijuana Conspiracy , The</td>\n",
       "      <td>2020</td>\n",
       "      <td>tt8461042</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8285</th>\n",
       "      <td>8811</td>\n",
       "      <td>10781754</td>\n",
       "      <td>3</td>\n",
       "      <td>American Terrorist:  April 20, 1999</td>\n",
       "      <td>2021</td>\n",
       "      <td>tt10781754</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        id    imdbid  bechdel_rating  \\\n",
       "8281  8744   8169446               3   \n",
       "8282  8808   6806448               2   \n",
       "8283  8645   3513498               3   \n",
       "8284  8859   8461042               3   \n",
       "8285  8811  10781754               3   \n",
       "\n",
       "                                              title  year      tconst  \\\n",
       "8281                                   Wine Country  2019   tt8169446   \n",
       "8282  Fast &amp; Furious Presents: Hobbs &amp; Shaw  2019   tt6806448   \n",
       "8283             Lego Movie 2: The Second Part, The  2019   tt3513498   \n",
       "8284                     Marijuana Conspiracy , The  2020   tt8461042   \n",
       "8285            American Terrorist:  April 20, 1999  2021  tt10781754   \n",
       "\n",
       "      binary_bechdel_rating  \n",
       "8281                      1  \n",
       "8282                      0  \n",
       "8283                      1  \n",
       "8284                      1  \n",
       "8285                      1  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import Bechdel data via API\n",
    "request=requests.get(\"http://bechdeltest.com/api/v1/getAllMovies\")\n",
    "json=request.json()\n",
    "bechdel=json_normalize(json)\n",
    "\n",
    "# Clean the data to the same format as IMDB data.\n",
    "bechdel['tconst'] = 'tt'+bechdel['imdbid']\n",
    "bechdel=bechdel.rename(columns={'rating': 'bechdel_rating'})\n",
    "\n",
    "# Create a Binary Variable which will be used later in the notebook.\n",
    "bechdel['binary_bechdel_rating'] = (bechdel['bechdel_rating'] >= 3).astype(int)\n",
    "bechdel.tail(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we would like to merge the Bechdel Data above with the data found here: https://github.com/matthewfdaniels/scripts. This data contains the characters and their word count split by gender for movies on our IMDB dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>script_id</th>\n",
       "      <th>tconst</th>\n",
       "      <th>imdb_character_name</th>\n",
       "      <th>words</th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>280</td>\n",
       "      <td>tt0112579</td>\n",
       "      <td>betty</td>\n",
       "      <td>311</td>\n",
       "      <td>f</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>280</td>\n",
       "      <td>tt0112579</td>\n",
       "      <td>carolyn johnson</td>\n",
       "      <td>873</td>\n",
       "      <td>f</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>280</td>\n",
       "      <td>tt0112579</td>\n",
       "      <td>eleanor</td>\n",
       "      <td>138</td>\n",
       "      <td>f</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>280</td>\n",
       "      <td>tt0112579</td>\n",
       "      <td>francesca johns</td>\n",
       "      <td>2251</td>\n",
       "      <td>f</td>\n",
       "      <td>46.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>280</td>\n",
       "      <td>tt0112579</td>\n",
       "      <td>madge</td>\n",
       "      <td>190</td>\n",
       "      <td>f</td>\n",
       "      <td>46.0</td>\n",
       "    </tr>\n",
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      "text/plain": [
       "   script_id     tconst imdb_character_name  words gender   age\n",
       "0        280  tt0112579               betty    311      f  35.0\n",
       "1        280  tt0112579     carolyn johnson    873      f   NaN\n",
       "2        280  tt0112579             eleanor    138      f   NaN\n",
       "3        280  tt0112579     francesca johns   2251      f  46.0\n",
       "4        280  tt0112579               madge    190      f  46.0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read in the data and merge into a dataframe will full information on characters.\n",
    "char_path1 = path +'character_mapping.csv'\n",
    "char_path2 = path +'character_list5.csv'\n",
    "\n",
    "char_df1 = pd.read_csv(char_path1, encoding='latin-1')\n",
    "char_df1=char_df1.drop(columns=['character_from_script',\n",
    "                                'closest_character_name_from_imdb_match',\n",
    "                                'closest_imdb_character_id'])\n",
    "\n",
    "char_df2 = pd.read_csv(char_path2, encoding='latin-1')\n",
    "\n",
    "char_df = pd.merge(char_df1, char_df2, on='script_id')\n",
    "char_df=char_df.drop_duplicates()\n",
    "char_df=char_df.rename(columns={'imdb_id':'tconst'})\n",
    "\n",
    "char_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We also read in all the TSV files found here: https://www.imdb.com/interfaces/. This IMDB data contains different information from movies historically."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to read in tsv files the same each time.\n",
    "\n",
    "def read_tsv(path_name):\n",
    "    return pd.read_csv(path_name, sep = '\\t',low_memory=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import the title table\n",
    "path_akas = path +'title.akas.tsv'\n",
    "df_akas = read_tsv(path_akas)\n",
    "\n",
    "# Subset the US region only\n",
    "df_akas = df_akas[(df_akas['region'] == 'US')]\n",
    "df_akas=df_akas.rename(columns={'titleId':'tconst'})\n",
    "\n",
    "# Import the basics table\n",
    "path_basics = path +'title.basics.tsv'\n",
    "df_basics = read_tsv(path_basics)\n",
    "\n",
    "# Import ratings\n",
    "path_ratings = path + 'title.ratings.tsv'\n",
    "df_ratings = read_tsv(path_ratings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYMAAAEICAYAAAC9E5gJAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjAsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+17YcXAAAXYklEQVR4nO3de7RedX3n8feHhItyEZBgMQkGx2gFZqwQIV6qjlRujoauKVNsLUFRZjla6YydGm1nWKNSsdMlIy1qGUHASyODdkgFzMpCseMFJOAFYoocEEnKLRBAvIHod/7Yv8jm5DnJc0hyTk7O+7XWs85+vvu39/7tX57zfJ59OU9SVUiSpredJrsDkqTJZxhIkgwDSZJhIEnCMJAkYRhIkjAMNMmSXJjk/ZO07ST5RJIHknxzkvpwZZLFk7Ftqc8w0BMkuT3JPUl279XenOTqSezWtvIy4NXAnKo6YvTMJKckqSQfGlU/odUv3NIOVNVxVXXRlq5noiT5dJILRtVekeT+JAdMVr+05QwDDTITOH2yOzFeSWaMc5FnAbdX1U820eZW4PeTzOzVTga+P97+TTXtyGn0e8Q7gOOTvLq12Q3438A7q+qurbz98f57agsYBhrkfwJ/mmTv0TOSzGufimf2alcneXObPiXJ15KcneTBJLcleUmrr0ly74DTIvslWZHk4SRfSfKs3rp/s81bn+TmJP+hN+/CJB9NckWSnwD/dkB/n5lkWVt+JMlbWv1U4OPAi5P8OMn/GGMs7gZuBI5py+0LvARYNmo7r0uyqu3z1Ume3+pLklw6qu2Hk5wzeuza8zclWd1OXS3fMBbtjfnsNn4PJflukkMHdbit8wNJvtnaXtb6vWH+wiRfb339TpJXjlr2zCRfA34KPLu/7qq6H/hj4Lx29HgGcGtVXdiW3ynJe5LcmuS+JEuT7NObd2mSu0ePU5v/qSTnJvli+/f87TH+TbQtVJUPH79+ALcDvwN8Hnh/q70ZuLpNzwMKmNlb5mrgzW36FOAx4I3ADOD9wB3AucCuwNHAw8Aerf2F7fnL2/wPA19t83YH1rR1zQQOA+4DDukt+xDwUroPNrsN2J+vAB8BdgN+C1gHHNXr61c3MRanAF8F/gD4bKv9J+Dv2n5d2GrPBX5Cd8ppZ+DPgBFgF7qjj58Ce7W2M4C7gIUDxu6Ettzz2/7+BfD1Nu8Y4HpgbyCtzQFj9Ptq4F+AQ9sYfg74VJs3G7gfOL6N2avb81m9Ze8ADml92HmMbVxKF4j3Awf26n8KfK1tZzfgfOCTbd5ObUz3bPP+FljZW/ZTwAPAi1vbXSf792E6PSa9Az62rwePh8Gh7Y12FuMPg1t68/51a/+MXu1+4Lfa9IXA0t68PYBfAnOB3wf+36j+/R1wRm/ZizexL3Pbuvbs1T7A42/ipzBcGDwFuAd4GnANXfj0w+C/AZf0ltupvRm/sj3/KnBym3413SfpQWN3JXDqqPX8lC5QXkV3amohsNNm/g2vBs7qPT8YeJQuiN614c25N385sLi37HuHeJ08A/gxcPqo+i3AK0b9GzwyqM/Afu21sXt7/inggsn+HZiuD08TaaCqugn4ArDkSSx+T2/6Z219o2t79J6v6W33x8B64Jl0b4JHtlMKDyZ5EPhD4DcGLTvAM4H1VfVwr/ZDuk+tQ6uqnwGX031S36+qvjZgOz/stf9V69eG7XwGeH2b/oP2fJBnAR/u7et6uqOA2VX1JbpP0ucC9yQ5L8lem+h2f1x+SHfEsl/bxomjxvRlwAFjLDtQ+/e8D1g1ataBwD/21n0j3Rv+/klmJPmrdurwR3RHQbR+Db1tbRuGgTblDOAtPPHNc8PF1qf2av035ydj7oaJJHsA+wJ30r0xfKWq9u499qiqt/aW3dTX7t4J7Jtkz17tQLpP7eN1MfBO4JNjbKd/nSN0+7RhO/8HeGWSOcDvMnYYrAH+46j9fUpVfR2gqs6pqsPpTuE8F/ivm+jv3N70gcAv6N6819AdGfS3sXtVndVrvyVfZbwWePWo9e9WVXfTXXg/nu4o52nAc9oy2Urb1hYwDDSmqhoBPkt3B8mG2jq6N7k3tE96bwL+1RZu6vgkL0uyC/A+4NqqWkN3ZPLcJH+UZOf2eFH/ouNm+r8G+DrwgSS7Jfk3wKnAp59EH79Cd4rnbwbMuwR4TZKjkuxMFxqPtG1vGLOrgU8AP6iq1WNs42PAu5McApDkaUlObNMvSnJkW/9PgJ/TnQIbyxuSHJzkqcB7gUur6pd0p2Jem+SY9u+3W5INQbU1fAz4yyQHtn7vn+R1bd6edONyP92HiTO30ja1FRgG2pz30l2E7HsL3afS++k+pX59C7fxGbqjkPXA4XSngmind44GTqL79H038EG6C83Dej3ddY47gX+gu96wYrwdrM5VVbV+wLybgTfQBcV9wGuB11bVo71mn6G7FjPWUQFV9Q90+7e0nUa5CTiuzd6L7hbOB+hO+9wP/PUmuvxJumsqd9NdrH1H28YaYBHwHrqL6Wvo/i231nvBh4AvAlcleZjutfGiNu8TdP8Od9KdXtrS1422olR5VCbtSNL9geCnqurjk90XTR0eGUiSDANJkqeJJEl4ZCBJovtz8ylpv/32q3nz5k12NyRpyrj++uvvq6pZg+ZN2TCYN28eK1eunOxuSNKUkeSHY83zNJEkyTCQJBkGkiQMA0kShoEkCcNAkoRhIEnCMJAkYRhIkpjCf4Es7SjmLbl8YP32s14zwT3RdOaRgSTJMJAkGQaSJAwDSRKGgSQJw0CShGEgScIwkCRhGEiSMAwkSRgGkiQMA0kShoEkCcNAkoRhIEnCMJAkYRhIkjAMJEkYBpIkDANJEkOGQZL/nGRVkpuS/H2S3ZIclOTaJLck+WySXVrbXdvzkTZ/Xm897271m5Mc06sf22ojSZZs7Z2UJG3aZsMgyWzgHcCCqjoUmAGcBHwQOLuq5gMPAKe2RU4FHqiq5wBnt3YkObgtdwhwLPCRJDOSzADOBY4DDgZe39pKkibIsKeJZgJPSTITeCpwF/Aq4NI2/yLghDa9qD2nzT8qSVp9aVU9UlU/AEaAI9pjpKpuq6pHgaWtrSRpgmw2DKrqX4C/Bu6gC4GHgOuBB6vqsdZsLTC7Tc8G1rRlH2vtn96vj1pmrPpGkpyWZGWSlevWrRtm/yRJQxjmNNE+dJ/UDwKeCexOd0pntNqwyBjzxlvfuFh1XlUtqKoFs2bN2lzXJUlDGuY00e8AP6iqdVX1C+DzwEuAvdtpI4A5wJ1tei0wF6DNfxqwvl8ftcxYdUnSBBkmDO4AFiZ5ajv3fxTwPeDLwO+1NouBy9r0svacNv9LVVWtflK72+ggYD7wTeA6YH67O2kXuovMy7Z81yRJw5q5uQZVdW2SS4EbgMeAbwHnAZcDS5O8v9XOb4ucD3wyyQjdEcFJbT2rklxCFySPAW+rql8CJHk7sJzuTqULqmrV1ttFSdLmbDYMAKrqDOCMUeXb6O4EGt3258CJY6znTODMAfUrgCuG6YskaevzL5AlSYaBJMkwkCRhGEiSMAwkSRgGkiSGvLVU0pabt+Tyye6CNCaPDCRJhoEkyTCQJGEYSJIwDCRJGAaSJAwDSRKGgSQJw0CShGEgScIwkCRhGEiSMAwkSRgGkiQMA0kShoEkCcNAkoRhIEnCMJAkYRhIkjAMJEkYBpIkDANJEoaBJAnDQJKEYSBJwjCQJGEYSJIwDCRJGAaSJIYMgyR7J7k0yT8nWZ3kxUn2TbIiyS3t5z6tbZKck2QkyXeTHNZbz+LW/pYki3v1w5Pc2JY5J0m2/q5KksYy7JHBh4EvVtVvAi8AVgNLgKuqaj5wVXsOcBwwvz1OAz4KkGRf4AzgSOAI4IwNAdLanNZb7tgt2y1J0nhsNgyS7AW8HDgfoKoeraoHgUXARa3ZRcAJbXoRcHF1rgH2TnIAcAywoqrWV9UDwArg2DZvr6r6RlUVcHFvXZKkCTDMkcGzgXXAJ5J8K8nHk+wOPKOq7gJoP/dv7WcDa3rLr221TdXXDqhvJMlpSVYmWblu3bohui5JGsYwYTATOAz4aFW9EPgJj58SGmTQ+f56EvWNi1XnVdWCqlowa9asTfdakjS0YcJgLbC2qq5tzy+lC4d72ike2s97e+3n9pafA9y5mfqcAXVJ0gTZbBhU1d3AmiTPa6WjgO8By4ANdwQtBi5r08uAk9tdRQuBh9pppOXA0Un2aReOjwaWt3kPJ1nY7iI6ubcuSdIEmDlkuz8GPp1kF+A24I10QXJJklOBO4ATW9srgOOBEeCnrS1VtT7J+4DrWrv3VtX6Nv1W4ELgKcCV7SFJmiBDhUFVfRtYMGDWUQPaFvC2MdZzAXDBgPpK4NBh+iJJ2vr8C2RJkmEgSTIMJEkYBpIkDANJEoaBJAnDQJKEYSBJwjCQJGEYSJIwDCRJGAaSJAwDSRLDf4W1pAk2b8nlA+u3n/WaCe6JpgOPDCRJhoEkyTCQJGEYSJIwDCRJGAaSJAwDSRKGgSQJw0CShGEgScIwkCRhGEiSMAwkSRgGkiQMA0kShoEkCcNAkoRhIEnCMJAkYRhIkjAMJEkYBpIkxhEGSWYk+VaSL7TnByW5NsktST6bZJdW37U9H2nz5/XW8e5WvznJMb36sa02kmTJ1ts9SdIwxnNkcDqwuvf8g8DZVTUfeAA4tdVPBR6oqucAZ7d2JDkYOAk4BDgW+EgLmBnAucBxwMHA61tbSdIEGSoMkswBXgN8vD0P8Crg0tbkIuCENr2oPafNP6q1XwQsrapHquoHwAhwRHuMVNVtVfUosLS1lSRNkGGPDP4X8GfAr9rzpwMPVtVj7flaYHabng2sAWjzH2rtf10ftcxY9Y0kOS3JyiQr161bN2TXJUmbs9kwSPLvgHur6vp+eUDT2sy88dY3LladV1ULqmrBrFmzNtFrSdJ4zByizUuB1yU5HtgN2IvuSGHvJDPbp/85wJ2t/VpgLrA2yUzgacD6Xn2D/jJj1SVJE2CzRwZV9e6qmlNV8+guAH+pqv4Q+DLwe63ZYuCyNr2sPafN/1JVVauf1O42OgiYD3wTuA6Y3+5O2qVtY9lW2TtJ0lCGOTIYy7uApUneD3wLOL/Vzwc+mWSE7ojgJICqWpXkEuB7wGPA26rqlwBJ3g4sB2YAF1TVqi3olyRpnMYVBlV1NXB1m76N7k6g0W1+Dpw4xvJnAmcOqF8BXDGevkiSth7/AlmSZBhIkgwDSRKGgSQJw0CSxJbdWippgHlLLp/sLkjj5pGBJMkwkCQZBpIkDANJEoaBJAnDQJKEYSBJwjCQJGEYSJIwDCRJGAaSJAwDSRKGgSQJw0CShGEgScIwkCRhGEiSMAwkSRgGkiQMA0kShoEkCcNAkoRhIEnCMJAkYRhIkoCZk90BaXs3b8nlA+u3n/WaCe6JtO0YBtKTNFZISFORYaBpx0/60sa8ZiBJMgwkSYaBJIkhwiDJ3CRfTrI6yaokp7f6vklWJLml/dyn1ZPknCQjSb6b5LDeuha39rckWdyrH57kxrbMOUmyLXZWkjTYMBeQHwPeWVU3JNkTuD7JCuAU4KqqOivJEmAJ8C7gOGB+exwJfBQ4Msm+wBnAAqDaepZV1QOtzWnANcAVwLHAlVtvN7Wj2tQdPV4Qloa32SODqrqrqm5o0w8Dq4HZwCLgotbsIuCENr0IuLg61wB7JzkAOAZYUVXrWwCsAI5t8/aqqm9UVQEX99YlSZoA47pmkGQe8ELgWuAZVXUXdIEB7N+azQbW9BZb22qbqq8dUB+0/dOSrEyyct26dePpuiRpE4YOgyR7AJ8D/qSqfrSppgNq9STqGxerzquqBVW1YNasWZvrsiRpSEOFQZKd6YLg01X1+Va+p53iof28t9XXAnN7i88B7txMfc6AuiRpggxzN1GA84HVVfWh3qxlwIY7ghYDl/XqJ7e7ihYCD7XTSMuBo5Ps0+48OhpY3uY9nGRh29bJvXVJkibAMHcTvRT4I+DGJN9utfcAZwGXJDkVuAM4sc27AjgeGAF+CrwRoKrWJ3kfcF1r996qWt+m3wpcCDyF7i4i7ySSpAm02TCoqq8y+Lw+wFED2hfwtjHWdQFwwYD6SuDQzfVFO46J+H4gv0hOGp5fVCc1hoemM7+OQpLkkYG2L369tDQ5PDKQJHlkoKnB8/nStuWRgSTJMJAkGQaSJAwDSRKGgSQJw0CShLeWaivxj8Wkqc0jA0mSYSBJ8jSRtjH/cliaGjwykCR5ZLA9ezIXZb2QK+nJMAwmkG/UkrZXniaSJBkGkiRPE20XptIdN1Opr5KGZxhMQb4hS9raPE0kSfLIQIN59CFNL4bBNOFtrZI2xTCY5jwCkASGwTbhG6ykqcYLyJIkw0CSZBhIkjAMJEkYBpIkDANJEoaBJAnDQJKEYSBJwjCQJLEdhUGSY5PcnGQkyZLJ7o8kTSfbRRgkmQGcCxwHHAy8PsnBk9srSZo+tpcvqjsCGKmq2wCSLAUWAd+b1F5thl9IJ2lHsb2EwWxgTe/5WuDI0Y2SnAac1p7+OMnN26g/+wH3baN1T1WOycYmZUzywYne4rj4Onmi7W08njXWjO0lDDKgVhsVqs4DztvmnUlWVtWCbb2dqcQx2ZhjsjHH5Imm0nhsF9cM6I4E5vaezwHunKS+SNK0s72EwXXA/CQHJdkFOAlYNsl9kqRpY7s4TVRVjyV5O7AcmAFcUFWrJrFL2/xU1BTkmGzMMdmYY/JEU2Y8UrXRqXlJ0jSzvZwmkiRNIsNAkjR9wiDJBUnuTXJTr/aCJN9IcmOSf0yyV2/eu9tXY9yc5JhefYf42ozxjEeSeUl+luTb7fGx3jKHt/YjSc5JMug24SkhydwkX06yOsmqJKe3+r5JViS5pf3cp9XT9nkkyXeTHNZb1+LW/pYkiydrn7bUkxiTVyZ5qPda+e+9de0ovztjjcmJ7fmvkiwYtcz2/35SVdPiAbwcOAy4qVe7DnhFm34T8L42fTDwHWBX4CDgVroL2zPa9LOBXVqbgyd73yZgPOb1241azzeBF9P9rciVwHGTvW9bMCYHAIe16T2B77fXwl8BS1p9CfDBNn182+cAC4FrW31f4Lb2c582vc9k798EjckrgS8MWM+O9Lsz1pg8H3gecDWwoNd+SryfTJsjg6r6J2D9qPLzgH9q0yuAf9+mFwFLq+qRqvoBMEL3lRm//tqMqnoU2PC1GVPOOMdjoCQHAHtV1Teqe9VfDJywtfs6Uarqrqq6oU0/DKym++v4RcBFrdlFPL6Pi4CLq3MNsHcbk2OAFVW1vqoeoBvLYydwV7aaJzEmY9mRfncGjklVra6qQd+KMCXeT6ZNGIzhJuB1bfpEHv/Dt0FfjzF7E/UdxVjjAXBQkm8l+UqS32612XRjsMEOMx5J5gEvBK4FnlFVd0H3RgDs35pNq9fJkGMC8OIk30lyZZJDWm06jMlYpsTrZLqHwZuAtyW5nu5w79FWH+vrMYb62owpbKzxuAs4sKpeCPwX4DPtesIOOR5J9gA+B/xJVf1oU00H1HbI18k4xuQG4FlV9QLgb4D/u2EVA9pOlzGZEq+TaR0GVfXPVXV0VR0O/D3d+TsY++sxduivzRhrPNrh7f1t+vpWfy7deMzprWLKj0eSnel+wT9dVZ9v5Xva6Z8Np8bubfVp8ToZz5hU1Y+q6sdt+gpg5yT7MT3GZCxT4nUyrcMgyf7t507AXwAb7pJZBpyUZNckBwHz6S6U7tBfmzHWeCSZle7/nCDJs+nG47Z2euDhJAvbXUQnA5dNSue3grYP5wOrq+pDvVnLgA13BC3m8X1cBpzc7ipaCDzUxmQ5cHSSfdpdNke32pQz3jFJ8hsb7ihLcgTde8z97EC/O5sYk7FMjfeTyb4yP1EPuk+6dwG/oEvkU4HT6e4E+D5wFu0vslv7P6f7BHwzvTtk6O4g+X6b9+eTvV8TMR50F5JX0d3tcAPw2t56FtBda7gV+Nv+GE61B/AyusP07wLfbo/jgacDVwG3tJ/7tvah+0+ZbgVu5Il3kLyJ7kLhCPDGyd63CRyTt/deK9cAL+mta0f53RlrTH63/S49AtwDLO8ts92/n/h1FJKk6X2aSJLUMQwkSYaBJMkwkCRhGEiSMAwkSRgGkiTg/wOPi8LprzRnRQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Clean the df_basics dataframe\n",
    "df_basics = df_basics[df_basics['titleType'] == 'movie']\n",
    "\n",
    "df_basics=df_basics.replace('\\\\N',np.NaN)\n",
    "\n",
    "# Fill years with average of closest neighbors\n",
    "df_basics['startYear'] = df_basics['startYear'].interpolate(method='nearest').ffill().bfill()\n",
    "df_basics[df_basics['tconst']=='tt3266366']\n",
    "\n",
    "# Convert to int and check bin size visually\n",
    "df_basics['startYear']=df_basics['startYear'].astype(int)\n",
    "plt.hist(df_basics['startYear'], bins=50)\n",
    "plt.title('Number of Movies per Year')\n",
    "\n",
    "# Bin the years for later visualizations\n",
    "# Lower bound is inclusive with upperbound not inclusive, rounds down\n",
    "df_basics['year_decade_2'] = pd.cut(df_basics['startYear'], bins=len(range(min(df_basics['startYear']), max(df_basics['startYear']), 10)))\n",
    "df_basics['year_decade_1']=df_basics['year_decade_2'].apply(lambda x: x.left)\n",
    "df_basics['year_decade'] = round(df_basics['year_decade_1'].astype('int'),-1)\n",
    "df_basics=df_basics.drop(columns = ['year_decade_1','year_decade_2'])\n",
    "\n",
    "# Convert the runtime to a numeric variable.\n",
    "df_basics['runtimeMinutes'] = pd.to_numeric(df_basics['runtimeMinutes'], errors='coerce')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Pull out the genres and add the as dummy indicators.\n",
    "# Note: the dummy columns are difficult to make since they are split by commas within one column.\n",
    "\n",
    "# Extract the genres\n",
    "df_genres = df_basics['genres'].str.split(',', expand = True)\n",
    "df_genres = df_genres.values.flatten()\n",
    "df_genres = list(filter(None, df_genres)) \n",
    "df_genres = np.unique(df_genres)\n",
    "\n",
    "# Clean the words\n",
    "# Remove leading whitespace \n",
    "df_genres= [s.strip() for s in df_genres] \n",
    "df_genres = np.unique(df_genres)\n",
    "# Number of unique categories\n",
    "# print(len(df_genres))\n",
    "\n",
    "# Initiate empty columns for each genre.\n",
    "for i in df_genres:\n",
    "    df_basics[i] = 0\n",
    "    \n",
    "# Build the dummy indicators.\n",
    "for i in df_basics.iloc[:,10:len(df_basics.columns)].columns:\n",
    "    df_basics[i] = pd.np.where(df_basics['genres'].str.contains(i), 1, 0)\n",
    "    \n",
    "for col in df_basics.iloc[:,10:len(df_basics.columns)-1].columns:\n",
    "    df_basics.loc[df_basics['nan']>0, col] = 0 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(114144, 48)\n",
      "Index(['tconst', 'ordering', 'title', 'region', 'language', 'types',\n",
      "       'attributes', 'isOriginalTitle', 'titleType', 'primaryTitle',\n",
      "       'originalTitle', 'isAdult', 'startYear', 'endYear', 'runtimeMinutes',\n",
      "       'genres', 'year_decade', 'Action', 'Adult', 'Adventure', 'Animation',\n",
      "       'Biography', 'Comedy', 'Crime', 'Documentary', 'Drama', 'Family',\n",
      "       'Fantasy', 'Film-Noir', 'Game-Show', 'History', 'Horror', 'Music',\n",
      "       'Musical', 'Mystery', 'News', 'Reality-TV', 'Romance', 'Sci-Fi',\n",
      "       'Short', 'Sport', 'Talk-Show', 'Thriller', 'War', 'Western', 'nan',\n",
      "       'averageRating', 'numVotes'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "# Merge the data from akas and basics on the unique movie id.\n",
    "\n",
    "df_movies = pd.merge(df_akas, df_basics, on = 'tconst')\n",
    "\n",
    "# merge this table with the ratings data read in earlier. \n",
    "df_movies = pd.merge(df_movies, df_ratings, on = 'tconst')\n",
    "\n",
    "#df_movies.head()\n",
    "print(df_movies.shape)\n",
    "print(df_movies.columns)\n",
    "\n",
    "# save to csv with IMDB merges complete\n",
    "df_movies.to_csv(path+'df_movies.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After merging in the IMDB data together with different tables as well as with the script data, we can merge in the Bechdel Data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# merge in bechdel data & save\n",
    "# Note: we left join movies to bechdel to have only bechdel rated movies.\n",
    "df_movies_bechdel = pd.merge(df_movies, bechdel, how = 'left', on = 'tconst')\n",
    "# replace missing values\n",
    "df_movies_bechdel=df_movies_bechdel.replace('\\\\N',np.NaN)\n",
    "df_movies_bechdel.to_csv(path+'df_movies_bechdel.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# merge in bechdel data & save\n",
    "# Note: we outer join movies to bechdel to have all movies in IMDB and all the bechdel movies.\n",
    "df_movies_all = pd.merge(df_movies_bechdel, char_df, how = 'outer', on = 'tconst')\n",
    "df_movies_all.to_csv(path+'df_movies_all.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='results_cell'></a>\n",
    "### Results:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='first_viz_cell'></a>\n",
    "#### First Visualization:\n",
    "\n",
    "Now, we will view some preliminary descriptive visualizations.\n",
    "\n",
    "Note: This concludes the data merging aspect of the project. Proceed to data visulaization examples."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# reload the new data\n",
    "data_path = path + 'df_movies_all.csv'\n",
    "data = pd.read_csv(data_path, encoding='latin-1', low_memory=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1aa35005f8>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['averageRating'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1aa309a978>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# select the years to compare to remove bias\n",
    "plt.scatter('year_decade', 'averageRating', data=data) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How does the score of the Bechdel Test relate to IMDB User Rating?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Average IMDB Rating vs. Bechdel Test Rating')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f = sns.boxplot(x='binary_bechdel_rating', y='averageRating', notch=False, data=data[(data['averageRating'] > 4) & (data['year_decade']>=1980)]) # 2IQR outlier\n",
    "f.set_xticklabels(f.get_xticklabels(), rotation=90)\n",
    "f.set_title('Average IMDB Rating vs. Bechdel Test Rating')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How does the distribution of Bechdel passing movies change over time?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Ratio Pass')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "temp = data.groupby('year_decade').sum()/data.groupby('year_decade').count()\n",
    "plt.bar(temp.index,temp['binary_bechdel_rating'], width = 8)\n",
    "plt.ylim([0, 1])\n",
    "\n",
    "plt.title('Ratio Passing Bechdel Test Rating Over Decades')\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Ratio Pass')\n",
    "\n",
    "# look at 2020 and WWII\n",
    "# break down into more granular scores"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How much does content play a role vs. word count? For which genres is this more prevalent?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Movie Count')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "words_data = data[data['bechdel_rating']!= 'NaN'].groupby(['gender', 'tconst']).sum()\n",
    "total_words_data = data[data['bechdel_rating']!= 'NaN'].groupby(['tconst'])['words'].sum()\n",
    "gender_wordcount_data = data[data['bechdel_rating']!= 'NaN'].groupby(['tconst', 'gender'])['words'].sum()\n",
    "word_ratio =  gender_wordcount_data/total_words_data\n",
    "\n",
    "word_ratio.hist()\n",
    "plt.title('Movie Gender Word Ratio')\n",
    "plt.xlabel('Ratio')\n",
    "plt.ylabel('Movie Count')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='tableau_prep_cell'></a>\n",
    "#### Tableau Data Preparation:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we will clean the data for use in Tableau.\n",
    "\n",
    "Note: This concludes the preliminary coding aspect of the project. Proceed to more data preparation for visualization in Tableau."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Overview Over Time:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# reload the new data\n",
    "# data_path = path + 'df_movies_all.csv'\n",
    "\n",
    "# data = pd.read_csv(data_path, encoding='latin-1', low_memory=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1aa9cc02e8>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "first_graph = pd.DataFrame(data.groupby(['startYear']).binary_bechdel_rating.count())\n",
    "first_graph['total_movies_num'] = pd.DataFrame(data.groupby(['startYear']).tconst.count())\n",
    "first_graph['ratio_scored'] = first_graph.binary_bechdel_rating/first_graph.total_movies_num\n",
    "first_graph['year'] = first_graph.index\n",
    "first_graph.plot(kind='line',x='year',y='ratio_scored',color='red')\n",
    "first_graph['ratio_scored'].ewm(span = 20).mean().plot(style = 'r', title='% tested')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1aa9fcfbe0>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "second_graph = pd.DataFrame(data.groupby(['binary_bechdel_rating','year']).size().reset_index(name='count'))\n",
    "\n",
    "second_graph = second_graph.pivot(index='year', columns='binary_bechdel_rating', values='count')\n",
    "second_graph['total']= second_graph.iloc[:,0] + second_graph.iloc[:,1]\n",
    "second_graph['ratio_passed'] = second_graph.iloc[:,1]/second_graph['total']\n",
    "second_graph['ratio_passed'].ewm(span = 20).mean().plot(style = 'r', title='% pass')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1aa278b358>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "third_graph=pd.DataFrame(data.groupby(['gender','year']).words.sum().reset_index())\n",
    "third_graph_f = third_graph[third_graph.gender == 'f']\n",
    "third_graph_m = third_graph[third_graph.gender == 'm']\n",
    "\n",
    "third_graph=pd.merge(third_graph_f,third_graph_m, on = 'year')\n",
    "third_graph['total'] = third_graph['words_x']+ third_graph['words_y']\n",
    "third_graph['ratio_words'] = third_graph['words_x']/third_graph['total']\n",
    "third_graph['ratio_words'].ewm(span = 20).mean().plot(style = 'r', title='% words')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>year</th>\n",
       "      <th>total_movies_num</th>\n",
       "      <th>ratio_scored</th>\n",
       "      <th>ratio_passed</th>\n",
       "      <th>ratio_words</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1929.0</td>\n",
       "      <td>373</td>\n",
       "      <td>0.042895</td>\n",
       "      <td>0.294118</td>\n",
       "      <td>0.164722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1931.0</td>\n",
       "      <td>654</td>\n",
       "      <td>0.082569</td>\n",
       "      <td>0.327273</td>\n",
       "      <td>0.226466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1932.0</td>\n",
       "      <td>895</td>\n",
       "      <td>0.060335</td>\n",
       "      <td>0.537037</td>\n",
       "      <td>0.259279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1933.0</td>\n",
       "      <td>804</td>\n",
       "      <td>0.078358</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.132274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1936.0</td>\n",
       "      <td>945</td>\n",
       "      <td>0.066667</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>0.181347</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     year  total_movies_num  ratio_scored  ratio_passed  ratio_words\n",
       "0  1929.0               373      0.042895      0.294118     0.164722\n",
       "1  1931.0               654      0.082569      0.327273     0.226466\n",
       "2  1932.0               895      0.060335      0.537037     0.259279\n",
       "3  1933.0               804      0.078358      0.333333     0.132274\n",
       "4  1936.0               945      0.066667      0.714286     0.181347"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_graph = pd.merge(first_graph, second_graph, on='year')\n",
    "final_graph = pd.merge(final_graph, third_graph, on='year')\n",
    "final_graph=final_graph[['year','total_movies_num','ratio_scored','ratio_passed','ratio_words']]\n",
    "final_graph.to_csv(path+'final_graph.csv')\n",
    "\n",
    "final_graph.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Representation by Genre:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It will also be interesting to review the data by Genre in Tableau.\n",
    "\n",
    "For each year and genre, we would like to compute:\n",
    "- number movies passed\n",
    "- number movies total\n",
    "- ratio passed/total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = data[(data.Action == 1) & (data.binary_bechdel_rating == 1)]\n",
    "passed = a.groupby('year').binary_bechdel_rating.sum()\n",
    "\n",
    "b=data[(data.Action == 1)]\n",
    "total = b.groupby('year').binary_bechdel_rating.count()\n",
    "\n",
    "df = pd.merge(total, passed, on='year')\n",
    "df['ratio_passed'] = df.iloc[:,1]/df.iloc[:,0]\n",
    "df['genre'] = 'Action'\n",
    "df=df.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "for genre in data.iloc[:,19:46].columns: \n",
    "    a = data[(data[genre] == 1) & (data.binary_bechdel_rating == 1)]\n",
    "    passed = a.groupby('year').binary_bechdel_rating.sum()\n",
    "    \n",
    "    b=data[(data[genre] == 1)]\n",
    "    total = b.groupby('year').binary_bechdel_rating.count()\n",
    "    \n",
    "    temp = pd.merge(total, passed, on='year')\n",
    "    temp=temp.reset_index()\n",
    "    temp['ratio_passed'] = temp['binary_bechdel_rating_y']/temp['binary_bechdel_rating_x']\n",
    "    temp['genre'] = str(genre)\n",
    "    \n",
    "    df = df.append(temp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\n",
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       "        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>year</th>\n",
       "      <th>total</th>\n",
       "      <th>passed</th>\n",
       "      <th>ratio_passed</th>\n",
       "      <th>genre</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1918.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>Action</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1919.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>Action</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1940.0</td>\n",
       "      <td>6</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>Action</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1957.0</td>\n",
       "      <td>6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>Action</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1960.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>Action</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     year  total  passed  ratio_passed   genre\n",
       "0  1918.0      1     1.0      1.000000  Action\n",
       "1  1919.0      1     1.0      1.000000  Action\n",
       "2  1940.0      6     3.0      0.500000  Action\n",
       "3  1957.0      6     1.0      0.166667  Action\n",
       "4  1960.0      2     1.0      0.500000  Action"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=df.rename(columns={'binary_bechdel_rating_x':'total', 'binary_bechdel_rating_y':'passed'})\n",
    "df.head()\n",
    "# Movies per genre\n",
    "#df.groupby('genre').total.sum().sort_values() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv(path+'genres_graph.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='disc_cell'></a>\n",
    "### Discussion:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, the data extracted in the final part of this code will be used to build interactive dashboards in Tableau. You can view more online on our website: http://imdbgenderrepresentation.getforge.io/.\n",
    "\n",
    "In the first dashboard, we examine the ratio of all movies that have been scored by the Bechdel test over time. Despite the upward trend, the maximum ratio is less than 40%, supporting the argument that there is a need to make the scoring process more efficient. If we are to consider the representation of women in media as reflective of perceptions of women by society, it is important that the distribution of Bechdel scores is representative of all movies.\n",
    "\n",
    "The second dashboard compares the ratio of movies that pass the test to the proportion of words belonging to female characters, aggregated across movies over time. Without exploring the data, word-count would seem like a logical proxy to capture representation for use in an algorithmic approach. However, the results of this figure suggest otherwise. Over time, the ratio of movies passing the test has a larger slope than the ratio of female words to total words; this means that the content criterion of the Bechdel Test may play a larger role than just the word count. Using word count as a proxy for feature engineering purposes may not be the wisest decision.\n",
    "\n",
    "Finally, the last dashboard shows the proportion of movies that pass the Bechdel test conditioned on the genre of the movie, over time. We are able to see clear trends in the proportion of passing movies when the feature is split on genre. Romance and Horror films have had the highest ratio of passing the Bechdel test since the 1980s; while Crime and Action films have had the lowest ratio of passing.\n",
    "\n",
    "#### Conclusions:\n",
    "\n",
    "In this descriptive exploration of data, we have begun to see how nuanced an automation methodology would need to be to accurately capture the scoring of the Bechdel test. It would be difficult to find single proxy variables for the 3 criteria of the test, and feature engineering would need to include domain knowledge. This is not to say that automation is impossible; but our takeaway is that, as Data Scientists, we are the ones who need to voice concerns about using proxies that aren’t representative, and we should include the voices of domain experts when modeling qualitative outcomes that may be subjective. \n",
    "\n",
    "Finding a way to automate the Bechdel test may be a difficult undertaking; however, it could help improve the measure of the true proportion of Bechdel passing films by scoring all movies in the IMDb and beyond. It is of note is that it is also not the only metric for quantifying representation in media, as it only considers one identity variable -- gender. Washington Post writer Alyssa Rosenberg claims \"The Bechdel Test set a very low floor for Hollywood. We can't let it become the ceiling for progress [3].\"  In the past decade, many other representation tests have been formulated, considering the distribution of people who work on the film's production, or relating other intersectional features like race, ethnicity, socioeconomic status, so that representation is inclusive to identities beyond cisgender white women. Though our analysis focuses on the Bechdel Test, our suggestions about feature engineering can help establish similar methodologies for other tests and continue to build a more equitable movie industry. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### References:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[1] https://bechdeltest.com/ <br/>\n",
    "[2] https://bechdeltest.com/api/v1/doc <br/>\n",
    "[3] https://www.washingtonpost.com/opinions/2018/12/21/its-time-move-beyond-bechdel-test/ <br/>\n",
    "[4] https://projects.fivethirtyeight.com/next-bechdel/)<br/>\n"
   ]
  }
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