FinalProjectGroup_Sp23_GETZS.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploring the relationship between gender and dialogue sentiment in MCU films"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Permissions\n",
    "\n",
    "Place an `X` in the appropriate bracket below to specify if you would like your group's project to be made available to the public. (Note that student names will be included (but PIDs will be scraped from any groups who include their PIDs).\n",
    "\n",
    "* [ X ] YES - make available\n",
    "* [  ] NO - keep private"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Overview"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This project aimed to understand the differences in sentiment between male and female characters' dialogue in the MCU films. We used the NRC emotion lexicon and VADER to analyze the sentiment of each gender's character dialogue from 22 of the films, then performed statistical analysis on the results. We found that male characters had statistically significantly higher scores for 7 out of 8 of the emotions (anger, fear, trust, surprise, sadness, joy, and disgust) than female characters, but there was no statistically significant difference in their sentiment overall. Therefore, we conclude that we found no concrete discrepancies between the sentiment of male and female characters in MCU films. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Video Link"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "https://youtu.be/qp2kUBa1V_g"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Names\n",
    "\n",
    "- Sarah Burbach\n",
    "- Tracey Du\n",
    "- Zak Hori\n",
    "- Grace Lin\n",
    "- Emily Yu"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='research_question'></a>\n",
    "# Research Question"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What is the relationship between the gender (male vs. female) of a character and the common emotions (anger, fear, anticipation, trust, surprise, sadness, joy, and disgust) and sentiment (positive/negative) that appear in their dialogue in Marvel Cinematic Universe films? These measurements will be defined by the NRC Emotion Lexicon and VADER Sentiment Analysis."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='background'></a>\n",
    "\n",
    "## Background & Prior Work"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With the explosion of the superhero genre in recent pop culture, the MCU has been one of the unique movie franchises in cinematic history due to its exponential growth and reach across the globe. What started as a smaller comic book series eventually evolved into the continually growing 30+ movie series, spanning across 15 years, and introducing more than 36 superhero characters. The MCU has grossed more than 26 billion USD worldwide, shooting way past big juggernaut franchises like Star Wars (10.5 billion USD) and Spiderman (9.8 billion USD) (1).\n",
    "\n",
    "Although the MCU has been losing popularity recently due to the oversaturation of the superhero genre and the declining quality of the media content, there’s been large debates on both sides of the political spectrum about the role that gender plays within these films. On one hand, several longtime MCU fans believe that this push towards a feminist-view of empowerment, strength, and confidence is unnatural, severely limiting the perspective of what women can achieve (2). Newer female superheroes (Captain Marvel, She-Hulk) can be seen as more one-dimensional characters, more focused on power and agency, than their earlier, more complex female counterparts (Black Widow, Wanda Maximoff). On the other side, however, articles like the Entertainment Weekly have complained about the role of women superheroes in these films and how they often succumb to deaths and injuries compared to their male counterparts (3). These critics argue that these films do empower women by casting a new light to who a superhero can be. While the MCU has been more accepting of a diverse and balanced cast in these upcoming feature films, we wanted to see if there exists any trends that showed how male and female characters are depicted differently on the big screen and if these trends contain any merit to predicting the future direction of the MCU in regards to its “gender politics.”\n",
    "\n",
    "There have been various projects done that examine male versus female portrayal in popular media, most likely due to recent strides to increase female representation in typically male-dominated fields. While there has been an overall increase in percentage of female protagonists, major characters, and speaking characters since 2002 as reported in a study by San Diego State’s Center for the Study of Women in Television and Film, males still comprise of 63% of speaking roles in top grossing films compared to only 37% for women (4). \n",
    "\n",
    "Other studies go beyond data points like screen time and character count by evaluating linguistic differences between male and females. A study from the University of Washington evaluated characters’ lexicon on two connotative dimensions: power and agency, in order to reveal gender bias in modern films. Through crowdsource work and an examination of 772 movie scripts, the report found that male characters are portrayed with a higher level of agency and are more often agents of more powerful verbs. Male characters additionally use more imperative sentences and inhibitory language while women use more assents and hedges (language expressing caution or indecisiveness) suggesting that more power lies with male characters (5). Researchers Cheryl Wakslak and Priyanka Joshi additionally measured linguistic abstractness to conclude that men tend to use more abstract language than women, with abstractness conveying power and vision (6). These findings give us a good idea of what existing differences there are between male and female characters that we may see reflected in MCU movies as well. \n",
    "\n",
    "References:\n",
    "\n",
    "1. Statista Research Department (5, June 2022). “World's Highest-Grossing Movie Franchises as of 2022.” Statista, https://www.statista.com/statistics/317408/highest-grossing-film-franchises-series/.\n",
    "2. Ashford, Joy C (17, Apr. 2019)  “Unpopular Opinion: I'm Sick of Superhero Feminism: Arts: The Harvard Crimson.” Arts | The Harvard Crimson, https://www.thecrimson.com/article/2019/4/17/unpopular-opinion-superhero-feminism/.\n",
    "3. Lenker, Maureen Lee (13, July 2022).  “We Need to Talk about Marvel's Women Problem.” EW.com, https://ew.com/movies/we-need-to-talk-marvels-women-problem/.\n",
    "4. Lauzen, Martha M (2023, March 4). “It’s a Man’s (Celluloid) World: Portrayals of Female Characters in the Top Grossing U.S. Films of 2022.” Retrieved from https://womenintvfilm.sdsu.edu/wp-content/uploads/2023/03/2022-its-a-mans-celluloid-world-report-rev.pdf.\n",
    "5. Sap, Maarten et al., (2017, September). “Connotation Frames of Power and Agency in Modern Films.” Retrieved from https://aclanthology.org/D17-1247.pdf.\n",
    "6. Joshi, Priyanka & Wakslak, Cheryl (2022, December 2). “Research: Men Speak More Abstractly than Women.” Harvard Business Review, https://hbr.org/2022/12/research-men-speak-more-abstractly-than-women."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hypothesis\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We predict that male characters will tend to have dialogue with stronger sentiment(positive/negative as measured by VADER Sentiment Analysis) for each of the eight basic emotions (anger, fear, anticipation, trust, surprise, sadness, joy, and disgust as measured by the NRC Emotional Lexicon) compared to female characters. This is due to studies such as the aforementioned Waslak and Joshi’s which report that men tend to use more powerful language, as they are traditionally portrayed with more authority and power in comparison to women."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dataset(s)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**1. Marvel Cinematic Universe Dialogue - Characters**\n",
    "- Link to dataset: https://github.com/prestondunton/marvel-dialogue-nlp/blob/master/data/characters.csv \n",
    "- Number of observations: 653 \n",
    "- This dataset shows how many lines MCU characters have in each of the 18 Marvel movies analyzed, total number of lines across all movies, total words, average words per line, and average lines per movie. \n",
    "\n",
    "**2. Marvel Cinematic Universe Dialogue - Movies**\n",
    "- Link to dataset: https://github.com/prestondunton/marvel-dialogue-nlp/tree/master/data/cleaned \n",
    "- Number of observations: 17358 (total across all 18 sub-datasets) \n",
    "- This dataset shows all lines separated by character for each of the 18 Marvel movies analyzed, and cleaned to remove lines from narration or extras without a specified name.\n",
    "\n",
    "- Since this GitHub database was missing four MCU movies, we created our own additional dataset that added these scripts (Guardians of the Galaxy 1, Doctor Strange, Spiderman: Far from Home, and Ant Man and the Wasp) for analysis. These datasets also showed the lines separated by character for each of the 4 additional movies. \n",
    "- Number of observations: 3564 (in our own dataset)\n",
    "\n",
    "We plan to utilize the NRC Emotion Lexicon and VADER Sentiment Analysis in the Natural Language Toolkit to analyze the third dataset containing dialogue for each MCU movie. NRC outputs the types of emotions associated with dialogue and VADER outputs numerical positive, neutral, negative, and compound scores for the dialogue. We will join these findings with the dialogue’s corresponding character(s) in our second dataset (which contains general character information, including character gender)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package punkt to\n",
      "[nltk_data]     /Users/sarahburbach/nltk_data...\n",
      "[nltk_data]   Package punkt is already up-to-date!\n",
      "[nltk_data] Downloading package stopwords to\n",
      "[nltk_data]     /Users/sarahburbach/nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n",
      "[nltk_data] Downloading package vader_lexicon to\n",
      "[nltk_data]     /Users/sarahburbach/nltk_data...\n",
      "[nltk_data]   Package vader_lexicon is already up-to-date!\n"
     ]
    }
   ],
   "source": [
    "#manipulation and analysis of data \n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "#import nltk toolkit \n",
    "import nltk \n",
    "\n",
    "#import matplot plt\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "#import function to tokenize dialogue\n",
    "nltk.download('punkt')\n",
    "from nltk.tokenize import word_tokenize\n",
    "\n",
    "#identify and define stop words to remove from dialogue\n",
    "nltk.download('stopwords')\n",
    "from nltk.corpus import stopwords\n",
    "stop_words = set(stopwords.words('english'))\n",
    "\n",
    "#import module to stem words \n",
    "from nltk.stem import PorterStemmer\n",
    "ps = PorterStemmer()\n",
    "\n",
    "#import VADER lexicon tool for sentiment analysis of dialogue \n",
    "nltk.download('vader_lexicon')\n",
    "from nltk.sentiment.vader import SentimentIntensityAnalyzer \n",
    "analyser = SentimentIntensityAnalyzer()\n",
    "\n",
    "# if you don't already have pip and NRCLex installed, uncomment and run this\n",
    "#import sys\n",
    "#!{sys.executable} -m pip install --upgrade pip\n",
    "#!{sys.executable} -m pip install NRCLex\n",
    "\n",
    "import nrclex\n",
    "from nrclex import NRCLex\n",
    "\n",
    "# import seaborn for plotting\n",
    "import seaborn as sns\n",
    "\n",
    "# import scipy for stastical significance\n",
    "import scipy.stats as stats\n",
    "from scipy.stats import ttest_ind"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Cleaning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import and clean character summary data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "character_summary = pd.read_csv('https://raw.githubusercontent.com/prestondunton/marvel-dialogue-nlp/master/data/characters.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Combine younger & alternative versions of the same character (For example, combine \"Young Tony Stark\" and \"Tony Stark\") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Find and store 'young', 'actor', and 'A1' versions of characters\n",
    "alternate_char = character_summary.loc[character_summary['character'].str.contains('YOUNG') | character_summary['character'].str.contains('A1')\\\n",
    "                                        | character_summary['character'].str.contains('ACTOR'),:]\n",
    "\n",
    "# Delete alternate versions in original dataframe\n",
    "alt_indices = alternate_char.index.values.tolist()\n",
    "for i in alt_indices:\n",
    "    character_summary = character_summary.drop([i]) # drop alternate characters\n",
    "\n",
    "# Combine characters\n",
    "for inx, c in enumerate(alternate_char['character']):\n",
    "    character = c.split(' ', 1)[1].strip() # Format character string\n",
    "\n",
    "    if character == 'GIRL' or character == 'BOY' or character == 'DOCTOR'\\\n",
    "        or character == 'COP': # many character names w/ these general classifications, don't want to combine them all\n",
    "        continue\n",
    "    \n",
    "    for all_characters in character_summary['character']: \n",
    "        if character in all_characters and not '/' in all_characters:\n",
    "            character_summary.loc[character_summary['character'] == all_characters,'Ant-Man':'total words']\\\n",
    "                += alternate_char.loc[alt_indices[inx], 'Ant-Man':'total words'] # add rows\n",
    "            break"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Split instances of two characters sharing line(s) into each individual character."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Find and store shared lines\n",
    "shared_lines = character_summary.loc[character_summary['character'].str.contains('/'),:]\n",
    "shared_indices = shared_lines.index.values.tolist()\n",
    "\n",
    "# Split shared lines\n",
    "for inx, c in enumerate(shared_lines['character']):\n",
    "    characters = c.split('/')\n",
    "    characters = [i.strip() for i in characters]\n",
    "\n",
    "    character_summary = character_summary.drop(shared_indices[inx]) # drop shared characters\n",
    "    \n",
    "    for indiv_char in characters: # for both characters\n",
    "        character_summary.loc[character_summary['character'].str.contains(indiv_char),'Ant-Man':'total words']\\\n",
    "              += shared_lines.loc[shared_indices[inx], 'Ant-Man':'total words']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Recalculate number of movie apperances, average words/line, and averages lines/movie based on combinations made above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#recalculate movie apperances\n",
    "character_summary['movie appearances'] = np.count_nonzero(character_summary.loc[:, 'Ant-Man':'Thor: The Dark World'], axis=1)\n",
    "\n",
    "#recalculate avg words/line\n",
    "character_summary['avg words per line'] = character_summary['total words'] / character_summary['total lines']\n",
    "character_summary['avg words per line'] = character_summary['avg words per line'].apply(lambda x: round(x, 2))\n",
    "\n",
    "#recalculate avg lines/movie\n",
    "character_summary['avg lines per movie'] = character_summary['total lines'] / character_summary['movie appearances']\n",
    "character_summary['avg lines per movie'] = character_summary['avg lines per movie'].apply(lambda x: round(x, 2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Remove minor characters, which we are designating as characters with <10 total lines. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "character_summary = character_summary[character_summary['total lines']>=10]\n",
    "character_summary['character'] = character_summary['character'].str.replace(' ', '')\n",
    "\n",
    "# Clean-up final dataframe\n",
    "character_summary = character_summary.sort_values(by='total lines', ascending=False)\n",
    "character_summary = character_summary.reset_index(drop=True) # Reset indexing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Add gender information for each of the characters. A csv file containing character names and genders was manually generated and imported below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "character_gender = pd.read_csv('./Data/mcu_characters_gender.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Add additional gender data for missing characters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/1r/c2dvp09n5ds9bz3tlmhjwtmr0000gn/T/ipykernel_91209/872840805.py:33: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  character_gender = character_gender.append(data, ignore_index=True)\n"
     ]
    }
   ],
   "source": [
    "# Strip any whitespace from character names\n",
    "character_gender['character'] = character_gender['character'].str.replace(' ', '')\n",
    "\n",
    "# Add gender data for missing characters\n",
    "data = [{'character': 'JAMESBARNES', 'gender': 'Male'},\n",
    "       {'character': 'JOHANNSCHMIDT', 'gender': 'Female'},\n",
    "       {'character': 'CAPTAINAMERICA', 'gender': 'Male'},\n",
    "       {'character': 'IRONMAN', 'gender': 'Male'},\n",
    "       {'character': 'RHODEY', 'gender': 'Male'},\n",
    "       {'character': 'SMARTHULK', 'gender': 'Male'},\n",
    "       {'character': 'VERS', 'gender': 'Female'},\n",
    "       {'character': 'VASILYKARPOV', 'gender': 'Male'},\n",
    "       {'character': 'NATALIE', 'gender': 'Female'},\n",
    "       {'character': 'CASSANDRA', 'gender': 'Female'},\n",
    "       {'character': 'BILLFOSTER', 'gender': 'Male'},\n",
    "       {'character': 'SONNYBURCH', 'gender': 'Male'},\n",
    "       {'character': 'JANET', 'gender': 'Female'},\n",
    "       {'character': 'WOO', 'gender': 'Male'},\n",
    "       {'character': 'UZMAN', 'gender': 'Male'},\n",
    "       {'character': 'MORDO', 'gender': 'Male'},\n",
    "       {'character': 'CHRISTINEPALMER', 'gender': 'Female'},\n",
    "       {'character': 'KAECILIUS', 'gender': 'Male'},\n",
    "       {'character': 'ONAN', 'gender': 'Male'},\n",
    "       {'character': 'CORPSMANDEY', 'gender': 'Male'},\n",
    "       {'character': 'NOVAPRIME', 'gender': 'Female'},\n",
    "       {'character': 'QUENTINBECK', 'gender': 'Male'},\n",
    "       {'character': 'ROGERHARRINGTON', 'gender': 'Male'},\n",
    "       {'character': 'BETTYBRYANT', 'gender': 'Female'},\n",
    "       {'character': 'JULIUSDELL', 'gender': 'Male'},\n",
    "       {'character': 'WILLIAMGINTERRIVA', 'gender': 'Male'},\n",
    "       {'character': 'BRADDAVIS', 'gender': 'Male'},]\n",
    "\n",
    "character_gender = character_gender.append(data, ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>character</th>\n",
       "      <th>gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>TONYSTARK</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>STEVEROGERS</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>THOR</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>PETERPARKER</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NATASHAROMANOFF</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "         character  gender\n",
       "0        TONYSTARK    Male\n",
       "1      STEVEROGERS    Male\n",
       "2             THOR    Male\n",
       "3      PETERPARKER    Male\n",
       "4  NATASHAROMANOFF  Female"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "character_gender.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>character</th>\n",
       "      <th>Ant-Man</th>\n",
       "      <th>Avengers: Age of Ultron</th>\n",
       "      <th>Avengers: Endgame</th>\n",
       "      <th>Avengers: Infinity War</th>\n",
       "      <th>Black Panther</th>\n",
       "      <th>Captain America: Civil War</th>\n",
       "      <th>Captain America: The First Avenger</th>\n",
       "      <th>Captain America: The Winter Soldier</th>\n",
       "      <th>Captain Marvel</th>\n",
       "      <th>...</th>\n",
       "      <th>The Avengers</th>\n",
       "      <th>Thor</th>\n",
       "      <th>Thor: Ragnarok</th>\n",
       "      <th>Thor: The Dark World</th>\n",
       "      <th>total lines</th>\n",
       "      <th>total words</th>\n",
       "      <th>movie appearances</th>\n",
       "      <th>avg words per line</th>\n",
       "      <th>avg lines per movie</th>\n",
       "      <th>gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>TONYSTARK</td>\n",
       "      <td>0</td>\n",
       "      <td>173</td>\n",
       "      <td>140</td>\n",
       "      <td>119</td>\n",
       "      <td>0</td>\n",
       "      <td>170</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>152</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1796</td>\n",
       "      <td>22701</td>\n",
       "      <td>9</td>\n",
       "      <td>12.64</td>\n",
       "      <td>199.56</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>STEVEROGERS</td>\n",
       "      <td>3</td>\n",
       "      <td>133</td>\n",
       "      <td>128</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>154</td>\n",
       "      <td>186</td>\n",
       "      <td>218</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>121</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>986</td>\n",
       "      <td>8729</td>\n",
       "      <td>10</td>\n",
       "      <td>8.85</td>\n",
       "      <td>98.60</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>THOR</td>\n",
       "      <td>0</td>\n",
       "      <td>55</td>\n",
       "      <td>75</td>\n",
       "      <td>74</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>47</td>\n",
       "      <td>167</td>\n",
       "      <td>302</td>\n",
       "      <td>133</td>\n",
       "      <td>882</td>\n",
       "      <td>9641</td>\n",
       "      <td>7</td>\n",
       "      <td>10.93</td>\n",
       "      <td>126.00</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>PETERPARKER</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "      <td>73</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>551</td>\n",
       "      <td>5517</td>\n",
       "      <td>4</td>\n",
       "      <td>10.01</td>\n",
       "      <td>137.75</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NATASHAROMANOFF</td>\n",
       "      <td>1</td>\n",
       "      <td>79</td>\n",
       "      <td>71</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>112</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>97</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>500</td>\n",
       "      <td>4697</td>\n",
       "      <td>10</td>\n",
       "      <td>9.39</td>\n",
       "      <td>50.00</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         character Ant-Man Avengers: Age of Ultron Avengers: Endgame  \\\n",
       "0        TONYSTARK       0                     173               140   \n",
       "1      STEVEROGERS       3                     133               128   \n",
       "2             THOR       0                      55                75   \n",
       "3      PETERPARKER       0                       0                 7   \n",
       "4  NATASHAROMANOFF       1                      79                71   \n",
       "\n",
       "  Avengers: Infinity War Black Panther Captain America: Civil War  \\\n",
       "0                    119             0                        170   \n",
       "1                     25             0                        154   \n",
       "2                     74             0                          0   \n",
       "3                     39             0                         73   \n",
       "4                     15             0                         64   \n",
       "\n",
       "  Captain America: The First Avenger Captain America: The Winter Soldier  \\\n",
       "0                                  0                                   0   \n",
       "1                                186                                 218   \n",
       "2                                  0                                   0   \n",
       "3                                  0                                   0   \n",
       "4                                  0                                 112   \n",
       "\n",
       "  Captain Marvel  ... The Avengers Thor Thor: Ragnarok Thor: The Dark World  \\\n",
       "0              0  ...          152    0              0                    0   \n",
       "1              3  ...          121    0              0                    0   \n",
       "2              0  ...           47  167            302                  133   \n",
       "3              0  ...            0    0              0                    0   \n",
       "4              3  ...           97    0              1                    0   \n",
       "\n",
       "  total lines total words movie appearances avg words per line  \\\n",
       "0        1796       22701                 9              12.64   \n",
       "1         986        8729                10               8.85   \n",
       "2         882        9641                 7              10.93   \n",
       "3         551        5517                 4              10.01   \n",
       "4         500        4697                10               9.39   \n",
       "\n",
       "  avg lines per movie  gender  \n",
       "0              199.56    Male  \n",
       "1               98.60    Male  \n",
       "2              126.00    Male  \n",
       "3              137.75    Male  \n",
       "4               50.00  Female  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Add gender info to character_summary df\n",
    "character_summary = character_summary.merge(character_gender, on = 'character')\n",
    "character_summary = character_summary.dropna(axis=0) #remove characters w/ no discernable gender\n",
    "\n",
    "# Preview final dataframe\n",
    "character_summary.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Gather and Format Missing Movie Scripts (from txt to csv)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Remove Garbage Rows (Used From Preston GitHub)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('display.max_rows', None)\n",
    "pd.set_option('display.max_colwidth', None)\n",
    "\n",
    "def remove_regex_rows(df,string):\n",
    "    rows_to_remove = df[\"line\"].astype(str).str.contains(string,case=True,regex=True)\n",
    "    print(\"Removed\",rows_to_remove.sum(),\"rows that match regex \\\"\" + string + \"\\\"\")\n",
    "    \n",
    "    return df[~rows_to_remove], df[rows_to_remove]\n",
    "\n",
    "def remove_whitespace_rows(df):\n",
    "    whitespace_rows = df[\"line\"].astype(str).str.isspace()\n",
    "    print(whitespace_rows.sum(),\"whitespace rows removed\")\n",
    "    return df[~whitespace_rows], df[whitespace_rows]\n",
    "\n",
    "def remove_movie_text_rows(df):\n",
    "    \n",
    "    regex_strings = [\"^\\[[^\\[\\]]*\\]$\"]\n",
    "    \n",
    "    removed_rows = pd.DataFrame()\n",
    "    \n",
    "    for regex_string in regex_strings:\n",
    "        df, new_rows_to_remove = remove_regex_rows(df,regex_string)\n",
    "        \n",
    "        removed_rows = pd.concat([removed_rows, new_rows_to_remove])\n",
    "    \n",
    "    return df, removed_rows\n",
    "\n",
    "\n",
    "def remove_page_number_rows(df):\n",
    "    page_rows = df[\"line\"].astype(str).str.isnumeric()\n",
    "    print(page_rows.sum(),\"page number rows removed\")\n",
    "    \n",
    "    return df[~page_rows], df[page_rows]\n",
    "\n",
    "def remove_garbage_rows(df):\n",
    "    df, whitespace_rows = remove_whitespace_rows(df)\n",
    "    df, page_number_rows = remove_page_number_rows(df)\n",
    "    df, movie_text_rows = remove_movie_text_rows(df)\n",
    "    \n",
    "    garbage_rows = pd.concat([whitespace_rows, page_number_rows, movie_text_rows], \n",
    "                            keys = [\"whitespace\",\"page_numbers\",\"movie_text\"])\n",
    "    \n",
    "    print(\"-----------------------------------------\")\n",
    "    print(garbage_rows.shape[0],\"total rows removed\\n\")\n",
    "    \n",
    "    return df, garbage_rows"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Format Character Lines (Used From Preston GitHub)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def remove_regex(df,string):\n",
    "    print(\"Removed\",df[\"line\"].str.count(string).sum(),\"occurences of regex \\\"\" + string + \"\\\"\")\n",
    "    df[\"line\"] = df[\"line\"].astype(str).str.replace(string,\"\",case=True,regex=True)\n",
    "\n",
    "def remove_leading_trailing_whitespace(df):\n",
    "    df[\"line\"] = df[\"line\"].astype(str).str.replace(\"[ \\t]+$\",\"\")\n",
    "    df[\"line\"] = df[\"line\"].astype(str).str.replace(\"^[ \\t]+\",\"\")\n",
    "\n",
    "def format_lines(df):\n",
    "    remove_leading_trailing_whitespace(df)\n",
    "    \n",
    "    regex_strings = [\"\\[[^\\[\\]]*\\]\"]\n",
    "    \n",
    "    for regex_string in regex_strings:\n",
    "        remove_regex(df, regex_string)\n",
    "    \n",
    "    print(\"\")\n",
    "    \n",
    "    remove_leading_trailing_whitespace(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create Character / Line Columns (Used From Preston GitHub)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_character_line_columns(df):\n",
    "    character = df.line.str.extract(r'(^[^:]*:)')[0]\n",
    "    line = df.line.str.extract(r'(:.*$)')[0]\n",
    "    \n",
    "    character = character.str.replace(\":$\",\"\",case=True,regex=True)\n",
    "    line = line.str.replace(\"^[ \\t]*:[ \\t]*\",\"\",case=True,regex=True)\n",
    "    \n",
    "    character = character.str.upper()\n",
    "    \n",
    "    return pd.DataFrame({\"character\": character, \"line\": line})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('display.max_rows', None)\n",
    "pd.set_option('display.max_colwidth', None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Converting Missing Script Text Files into CSV Files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(777, 1)\n",
      "0 whitespace rows removed\n",
      "0 page number rows removed\n",
      "Removed 81 rows that match regex \"^\\[[^\\[\\]]*\\]$\"\n",
      "-----------------------------------------\n",
      "81 total rows removed\n",
      "\n",
      "Removed 274 occurences of regex \"\\[[^\\[\\]]*\\]\"\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/1r/c2dvp09n5ds9bz3tlmhjwtmr0000gn/T/ipykernel_91209/2312190671.py:6: FutureWarning: The default value of regex will change from True to False in a future version.\n",
      "  df[\"line\"] = df[\"line\"].astype(str).str.replace(\"[ \\t]+$\",\"\")\n",
      "/var/folders/1r/c2dvp09n5ds9bz3tlmhjwtmr0000gn/T/ipykernel_91209/2312190671.py:7: FutureWarning: The default value of regex will change from True to False in a future version.\n",
      "  df[\"line\"] = df[\"line\"].astype(str).str.replace(\"^[ \\t]+\",\"\")\n"
     ]
    }
   ],
   "source": [
    "# GUARDIANS OF THE GALAXY VOL. 1 (missing from initial dataset)\n",
    "\n",
    "guardians_galaxy_one = pd.read_csv('./Data/MissingScripts/UncleanedTxt/guardians_of_the_galaxy_uncleaned.txt', sep=\"\\0\", header=None, names=[\"line\"]) # update path with our own txt script file\n",
    "\n",
    "print(guardians_galaxy_one.shape)\n",
    "guardians_galaxy_one.head()\n",
    "\n",
    "guardians_galaxy_one, garbage_rows = remove_garbage_rows(guardians_galaxy_one)\n",
    "format_lines(guardians_galaxy_one)\n",
    "\n",
    "\n",
    "guardians_galaxy_one = create_character_line_columns(guardians_galaxy_one)\n",
    "guardians_galaxy_one = guardians_galaxy_one.dropna()\n",
    "guardians_galaxy_one.head()\n",
    "\n",
    "guardians_galaxy_one.to_csv(\"./Data/MissingScripts/UncleanedCsv/guardians_galaxy_one_uncleaned.csv\", index=False)\n",
    "\n",
    "guardians_galaxy_one.to_csv(\"./Data/MissingScripts/CleanedCsv/guardians_galaxy_one_cleaned.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1007, 1)\n",
      "0 whitespace rows removed\n",
      "0 page number rows removed\n",
      "Removed 22 rows that match regex \"^\\[[^\\[\\]]*\\]$\"\n",
      "-----------------------------------------\n",
      "22 total rows removed\n",
      "\n",
      "Removed 14 occurences of regex \"\\[[^\\[\\]]*\\]\"\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/1r/c2dvp09n5ds9bz3tlmhjwtmr0000gn/T/ipykernel_91209/2312190671.py:6: FutureWarning: The default value of regex will change from True to False in a future version.\n",
      "  df[\"line\"] = df[\"line\"].astype(str).str.replace(\"[ \\t]+$\",\"\")\n",
      "/var/folders/1r/c2dvp09n5ds9bz3tlmhjwtmr0000gn/T/ipykernel_91209/2312190671.py:7: FutureWarning: The default value of regex will change from True to False in a future version.\n",
      "  df[\"line\"] = df[\"line\"].astype(str).str.replace(\"^[ \\t]+\",\"\")\n"
     ]
    }
   ],
   "source": [
    "# ANT MAN AND WASP (missing from initial dataset)\n",
    "ant_man_and_wasp = pd.read_csv('./Data/MissingScripts/UncleanedTxt/ant_man_and_the_wasp_uncleaned.txt', sep=\"\\0\", header=None, names=[\"line\"]) # update path with our own txt script file\n",
    "\n",
    "print(ant_man_and_wasp.shape)\n",
    "ant_man_and_wasp.head()\n",
    "\n",
    "ant_man_and_wasp, garbage_rows = remove_garbage_rows(ant_man_and_wasp)\n",
    "format_lines(ant_man_and_wasp)\n",
    "\n",
    "\n",
    "ant_man_and_wasp = create_character_line_columns(ant_man_and_wasp)\n",
    "ant_man_and_wasp = ant_man_and_wasp.dropna()\n",
    "ant_man_and_wasp.head()\n",
    "\n",
    "ant_man_and_wasp.to_csv(\"./Data/MissingScripts/UncleanedCsv/ant_man_and_wasp_uncleaned.csv\", index=False)\n",
    "\n",
    "ant_man_and_wasp.to_csv(\"./Data/MissingScripts/CleanedCsv/ant_man_and_wasp_cleaned.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(589, 1)\n",
      "0 whitespace rows removed\n",
      "0 page number rows removed\n",
      "Removed 27 rows that match regex \"^\\[[^\\[\\]]*\\]$\"\n",
      "-----------------------------------------\n",
      "27 total rows removed\n",
      "\n",
      "Removed 22 occurences of regex \"\\[[^\\[\\]]*\\]\"\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/1r/c2dvp09n5ds9bz3tlmhjwtmr0000gn/T/ipykernel_91209/2312190671.py:6: FutureWarning: The default value of regex will change from True to False in a future version.\n",
      "  df[\"line\"] = df[\"line\"].astype(str).str.replace(\"[ \\t]+$\",\"\")\n",
      "/var/folders/1r/c2dvp09n5ds9bz3tlmhjwtmr0000gn/T/ipykernel_91209/2312190671.py:7: FutureWarning: The default value of regex will change from True to False in a future version.\n",
      "  df[\"line\"] = df[\"line\"].astype(str).str.replace(\"^[ \\t]+\",\"\")\n"
     ]
    }
   ],
   "source": [
    "# DOCTOR STRANGE 1 (missing from initial dataset)\n",
    "doctor_strange_original = pd.read_csv('./Data/MissingScripts/UncleanedTxt/doctor_strange_uncleaned.txt', sep=\"\\0\", header=None, names=[\"line\"]) # update path with our own txt script file\n",
    "\n",
    "print(doctor_strange_original.shape)\n",
    "doctor_strange_original.head()\n",
    "\n",
    "doctor_strange_original, garbage_rows = remove_garbage_rows(doctor_strange_original)\n",
    "format_lines(doctor_strange_original)\n",
    "\n",
    "\n",
    "doctor_strange_original = create_character_line_columns(doctor_strange_original)\n",
    "doctor_strange_original = doctor_strange_original.dropna()\n",
    "doctor_strange_original.head()\n",
    "\n",
    "doctor_strange_original.to_csv(\"./Data/MissingScripts/UncleanedCsv/doctor_strange_uncleaned.csv\", index=False)\n",
    "\n",
    "doctor_strange_original.to_csv(\"./Data/MissingScripts/CleanedCsv/doctor_strange_cleaned.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1388, 1)\n",
      "0 whitespace rows removed\n",
      "0 page number rows removed\n",
      "Removed 44 rows that match regex \"^\\[[^\\[\\]]*\\]$\"\n",
      "-----------------------------------------\n",
      "44 total rows removed\n",
      "\n",
      "Removed 76 occurences of regex \"\\[[^\\[\\]]*\\]\"\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/1r/c2dvp09n5ds9bz3tlmhjwtmr0000gn/T/ipykernel_91209/2312190671.py:6: FutureWarning: The default value of regex will change from True to False in a future version.\n",
      "  df[\"line\"] = df[\"line\"].astype(str).str.replace(\"[ \\t]+$\",\"\")\n",
      "/var/folders/1r/c2dvp09n5ds9bz3tlmhjwtmr0000gn/T/ipykernel_91209/2312190671.py:7: FutureWarning: The default value of regex will change from True to False in a future version.\n",
      "  df[\"line\"] = df[\"line\"].astype(str).str.replace(\"^[ \\t]+\",\"\")\n"
     ]
    }
   ],
   "source": [
    "# SPIDERMAN: FAR FROM HOME (missing from initial dataset)\n",
    "spider_man_far_from_home = pd.read_csv('./Data/MissingScripts/UncleanedTxt/spider_man_far_from_home_uncleaned.txt', sep=\"\\0\", header=None, names=[\"line\"]) # update path with our own txt script file\n",
    "\n",
    "print(spider_man_far_from_home.shape)\n",
    "spider_man_far_from_home.head()\n",
    "\n",
    "spider_man_far_from_home, garbage_rows = remove_garbage_rows(spider_man_far_from_home)\n",
    "format_lines(spider_man_far_from_home)\n",
    "\n",
    "\n",
    "spider_man_far_from_home = create_character_line_columns(spider_man_far_from_home)\n",
    "spider_man_far_from_home = spider_man_far_from_home.dropna()\n",
    "spider_man_far_from_home.head()\n",
    "\n",
    "spider_man_far_from_home.to_csv(\"./Data/MissingScripts/UncleanedCsv/spider_man_far_from_home_uncleaned.csv\", index=False)\n",
    "\n",
    "spider_man_far_from_home.to_csv(\"./Data/MissingScripts/CleanedCsv/spider_man_far_from_home_cleaned.csv\", index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Categorize Movie Dialogue by Individual Character per Movie"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Combining alternate versions of characters with their corresponding character per movie"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Combine alternate character versions with the corresponding character - ex: 'Young Tony Stark' and 'Tony Stark' \n",
    "def combine_chars(movie):\n",
    "    # list of words to remove from alternate character versions\n",
    "    remove_list = ['YOUNG', 'A1', 'ACTOR', 'DR.', 'DOCTOR', 'AGENT', 'GENERAL', 'COLONEL']\n",
    "    \n",
    "    # remove words from the list of words to remove from character names\n",
    "    movie['character'] = movie['character'].str.replace('|'.join(remove_list), '', regex=True)\n",
    "    \n",
    "    # remove any spaces from character names\n",
    "    movie['character'] = movie['character'].apply(lambda x: x.replace(' ', ''))\n",
    "\n",
    "    # replace nicknames/inconsistent names with actual names\n",
    "    movie['character'] = movie['character'].str.replace('RHODEY', 'JAMESRHODES')\n",
    "    movie['character'] = movie['character'].str.replace('STEPHENSTRANGE', 'STRANGE')\n",
    "    movie['character'] = movie['character'].str.replace('YONDUUDONTA', 'YONDU')\n",
    "    \n",
    "    return movie"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Splitting lines that multiple characters share into each individual character"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to split lines of characters with shared lines - e.g. 'Thor/Banner' into 'Thor' and 'Banner'\n",
    "def split (movie):\n",
    "    combined = movie[movie.character.str.contains('/')]\n",
    "\n",
    "    split_char_list = []\n",
    "    new_lines = []\n",
    "\n",
    "    for i, row in enumerate(combined.itertuples(), 1):\n",
    "        split_names = row.character.split('/')\n",
    "    \n",
    "        for name in split_names:\n",
    "            split_char_list.append(name)\n",
    "    \n",
    "    for i, row in enumerate(combined.itertuples(), 1):\n",
    "        new_lines.extend([row.line] * 2)\n",
    "        \n",
    "    data = {'character': split_char_list, 'line': new_lines}\n",
    "    split_chars = pd.DataFrame(data)\n",
    "    \n",
    "    movie = movie[~movie['character'].str.contains('/')]\n",
    "    movie = movie.append(split_chars)\n",
    "    \n",
    "    return movie"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Filtering out relevant movie characters per movie"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Filter out relevant characters in each movie - more than 10 lines\n",
    "def filter_chars (movie):\n",
    "    relevant_chars = movie['character'].value_counts()[movie['character'].value_counts() > 10]\n",
    "    return relevant_chars.index.tolist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Creating dataframes of categorized dialogue by character per movie"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function for making a dataframe that categorizes all of the lines for each character in a movie\n",
    "def filter_char_lines (char_list, movie):\n",
    "    \n",
    "    # Gather data in the form of a dictionary with key-value pairs of each character and their respective lines\n",
    "    data = {}\n",
    "\n",
    "    for char in char_list:\n",
    "        data.update({char: movie[movie.character == char].line.values.tolist()})\n",
    "        \n",
    "    df = pd.DataFrame(dict([(key, pd.Series(value)) for key, value in data.items()]))\n",
    "    return df.replace(np.nan,'',regex=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Example Movie: Age of Ultron**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>character</th>\n",
       "      <th>line</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ANNOUNCER</td>\n",
       "      <td>Report to your stations immediately. This is not a drill. We are under attack. We are under attack.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>TONY STARK</td>\n",
       "      <td>Shit!</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>STEVE ROGERS</td>\n",
       "      <td>Language! JARVIS, what's the view from upstairs?</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>JARVIS</td>\n",
       "      <td>The central building is protected by some kind of energy shield. Strucker's technology is well beyond any other Hydra base we've taken.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>THOR</td>\n",
       "      <td>Loki's scepter must be here. Strucker couldn't mount this defense without it. At long last.</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      character  \\\n",
       "0     ANNOUNCER   \n",
       "1    TONY STARK   \n",
       "2  STEVE ROGERS   \n",
       "3        JARVIS   \n",
       "4          THOR   \n",
       "\n",
       "                                                                                                                                      line  \n",
       "0                                      Report to your stations immediately. This is not a drill. We are under attack. We are under attack.  \n",
       "1                                                                                                                                    Shit!  \n",
       "2                                                                                         Language! JARVIS, what's the view from upstairs?  \n",
       "3  The central building is protected by some kind of energy shield. Strucker's technology is well beyond any other Hydra base we've taken.  \n",
       "4                                              Loki's scepter must be here. Strucker couldn't mount this defense without it. At long last.  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dataframe with all of the dialogue from Age of Ultron\n",
    "age_of_ultron = pd.read_csv('https://raw.githubusercontent.com/prestondunton/marvel-dialogue-nlp/master/data/cleaned/age_of_ultron.csv')\n",
    "\n",
    "age_of_ultron.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/1r/c2dvp09n5ds9bz3tlmhjwtmr0000gn/T/ipykernel_91209/1445751538.py:21: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  movie = movie.append(split_chars)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TONYSTARK</th>\n",
       "      <th>STEVEROGERS</th>\n",
       "      <th>BRUCEBANNER</th>\n",
       "      <th>ULTRON</th>\n",
       "      <th>NATASHAROMANOFF</th>\n",
       "      <th>CLINTBARTON</th>\n",
       "      <th>THOR</th>\n",
       "      <th>WANDAMAXIMOFF</th>\n",
       "      <th>NICKFURY</th>\n",
       "      <th>MARIAHILL</th>\n",
       "      <th>PIETROMAXIMOFF</th>\n",
       "      <th>JARVIS</th>\n",
       "      <th>JAMESRHODES</th>\n",
       "      <th>VISION</th>\n",
       "      <th>HELENCHO</th>\n",
       "      <th>LAURABARTON</th>\n",
       "      <th>FRIDAY</th>\n",
       "      <th>STRUCKER</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Shit!</td>\n",
       "      <td>Language! JARVIS, what's the view from upstairs?</td>\n",
       "      <td>Just wasn't expecting the Code Green.</td>\n",
       "      <td>What is this? What is this, please?</td>\n",
       "      <td>At long last is lasting a little long, boys.</td>\n",
       "      <td>Yeah. I think we lost the element of surprise.</td>\n",
       "      <td>Loki's scepter must be here. Strucker couldn't mount this defense without it. At long last.</td>\n",
       "      <td>Talk. And if you are wasting our time...</td>\n",
       "      <td>Do me a favor. Try not to bring it to life.</td>\n",
       "      <td>Lab's all set up, boss.</td>\n",
       "      <td>You didn't see that coming?</td>\n",
       "      <td>The central building is protected by some kind of energy shield. Strucker's technology is well beyond any other Hydra base we've taken.</td>\n",
       "      <td>Well, you know, the suit can take the weight, right? So I take the tank, fly it right up to the General's palace, drop it at his feet, I'm like, \"Boom! You looking for this?\"  \"Boom! Are you looking...\" Why do I even talk to you guys? Everywhere else that story kills.</td>\n",
       "      <td>I'm sorry, that was...odd.  Thank you.</td>\n",
       "      <td>There's no possibility of deterioration. The nano-molecular functionality is instantaneous. His cells don't know they're bonding with simulacrum.</td>\n",
       "      <td>Hey.</td>\n",
       "      <td>Good evening, boss.</td>\n",
       "      <td>Who gave the order to attack?</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Wait a second. No one else is going to deal with the fact that Cap just said \"language?\"</td>\n",
       "      <td>I know.</td>\n",
       "      <td>You know, sometimes exactly what I want to hear isn't exactly what I want to hear.</td>\n",
       "      <td>Where's my...where is your body?</td>\n",
       "      <td>Clint!</td>\n",
       "      <td>No, no, no. I'm going to live forever. I'm gonna be made of plastic.</td>\n",
       "      <td>The enhanced?</td>\n",
       "      <td>Sometimes it's hard. But sooner or later, every man shows himself.</td>\n",
       "      <td>Artificial intelligence. You never even hesitated.</td>\n",
       "      <td>NATO's got him.</td>\n",
       "      <td>We're just gonna let them take it?</td>\n",
       "      <td>Sir, the city is taking fire.</td>\n",
       "      <td>Yeah, it's a War Machine story.</td>\n",
       "      <td>Not alone.</td>\n",
       "      <td>If you brought him to my lab, the regeneration Cradle could do this in twenty minutes.</td>\n",
       "      <td>I know all your names.</td>\n",
       "      <td>Your man's in the church, boss. I think he's waiting for you.</td>\n",
       "      <td>They have to be after the scepter.  Can we hold them?</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Well, we know Strucker's not going to worry about civilian casualties. Send in the Iron Legion.</td>\n",
       "      <td>It just slipped out.</td>\n",
       "      <td>It's not you I don't trust.</td>\n",
       "      <td>This feels weird. This feels wrong.</td>\n",
       "      <td>Clint's hit!</td>\n",
       "      <td>Well, I don't have a girlfriend.</td>\n",
       "      <td>I can get Barton to the jet. The sooner we're gone the better. You and Stark secure the scepter.</td>\n",
       "      <td>I didn't expect. But I saw Stark's fear, I knew it would control him, make him self-destruct.</td>\n",
       "      <td>Look me in the eye and tell me you're going to shut him down.</td>\n",
       "      <td>Wanda and Pietro Maximoff. Twins. Orphaned at ten when a shell collapsed their apartment building. Sokovia's had a rough history. It's nowhere special but it's on the way to everywhere special.</td>\n",
       "      <td>Everyone's plan is not to kill them.</td>\n",
       "      <td>There's a pathway below the north tower.</td>\n",
       "      <td>Quality save. So, no Pepper? She's not coming?</td>\n",
       "      <td>You think I'm a child of Ultron?</td>\n",
       "      <td>You'll be made of you, Mr. Barton. Your own girlfriend won't be able to tell the difference.</td>\n",
       "      <td>Honey. Ah, I missed you.</td>\n",
       "      <td>There's the rest of the Vibranium. Function: still unclear.</td>\n",
       "      <td>Deploy the rest of the tanks.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>I'm closing in. JARVIS, am I...closing in? Do you see a power source for that shield?</td>\n",
       "      <td>We have an enhanced in the field.</td>\n",
       "      <td>Uh, yeah, she knows her way around.</td>\n",
       "      <td>Mr. Stark?</td>\n",
       "      <td>Somebody want to deal with that bunker?</td>\n",
       "      <td>But, it's a trick!</td>\n",
       "      <td>Looks like they're lining up.</td>\n",
       "      <td>Is that why you've come? To end the Avengers?</td>\n",
       "      <td>I'm not the director of anybody. I'm just an old man, who cares very much about you.</td>\n",
       "      <td>He's got increased metabolism and improved thermal homeostasis. Her thing is neural electric interfacing, telekinesis, mental manipulation.</td>\n",
       "      <td>I don't see the big picture, I have a little picture. I take it out and look at it every day.</td>\n",
       "      <td>The wall to your left...I'm reading steel reinforcement and an air current.</td>\n",
       "      <td>Want a lozenge?</td>\n",
       "      <td>I'm not Ultron. I'm not JARVIS. I am...I am.</td>\n",
       "      <td>That I can't fix. This is the next thing, Tony. Your clunky metal suits are going to be left in the dust.</td>\n",
       "      <td>She's…Nathaniel.</td>\n",
       "      <td>Boss, it's working. He's burning Ultron out of the net, he won't escape through there.</td>\n",
       "      <td>Concentrate fire on the weak ones. A hit can make them close ranks. Everything we've accomplished. But we're on the verge of our greatest breakthrough.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Great, I wanna poke it with something.</td>\n",
       "      <td>Stark, we're really need to get inside.</td>\n",
       "      <td>How's he doing?</td>\n",
       "      <td>We're having a nice talk. I'm a peace-keeping program, created to help the Avengers.</td>\n",
       "      <td>Thank you.</td>\n",
       "      <td>Uh, \"Whosoever be he worthy shall haveth the power!\" Whatever man! It's a trick.</td>\n",
       "      <td>Find the scepter.</td>\n",
       "      <td>All of these are... All of these are...</td>\n",
       "      <td>The Maximoff girl, she's working you, Stark. Playing on your fear.</td>\n",
       "      <td>He's fast and she's weird.</td>\n",
       "      <td>The records are not the picture.</td>\n",
       "      <td>Very good sir.</td>\n",
       "      <td>Let's go.</td>\n",
       "      <td>Look again.</td>\n",
       "      <td>Unlike you, I don't have a lot of time for parties.</td>\n",
       "      <td>If they're sleeping here, some of them are gonna have to double up.</td>\n",
       "      <td>Sokovia's going for a ride.</td>\n",
       "      <td>It's too soon.</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                                         TONYSTARK  \\\n",
       "0                                                                                            Shit!   \n",
       "1         Wait a second. No one else is going to deal with the fact that Cap just said \"language?\"   \n",
       "2  Well, we know Strucker's not going to worry about civilian casualties. Send in the Iron Legion.   \n",
       "3            I'm closing in. JARVIS, am I...closing in? Do you see a power source for that shield?   \n",
       "4                                                           Great, I wanna poke it with something.   \n",
       "\n",
       "                                        STEVEROGERS  \\\n",
       "0  Language! JARVIS, what's the view from upstairs?   \n",
       "1                                           I know.   \n",
       "2                              It just slipped out.   \n",
       "3                 We have an enhanced in the field.   \n",
       "4           Stark, we're really need to get inside.   \n",
       "\n",
       "                                                                          BRUCEBANNER  \\\n",
       "0                                               Just wasn't expecting the Code Green.   \n",
       "1  You know, sometimes exactly what I want to hear isn't exactly what I want to hear.   \n",
       "2                                                         It's not you I don't trust.   \n",
       "3                                                 Uh, yeah, she knows her way around.   \n",
       "4                                                                     How's he doing?   \n",
       "\n",
       "                                                                                 ULTRON  \\\n",
       "0                                                   What is this? What is this, please?   \n",
       "1                                                      Where's my...where is your body?   \n",
       "2                                                   This feels weird. This feels wrong.   \n",
       "3                                                                            Mr. Stark?   \n",
       "4  We're having a nice talk. I'm a peace-keeping program, created to help the Avengers.   \n",
       "\n",
       "                                NATASHAROMANOFF  \\\n",
       "0  At long last is lasting a little long, boys.   \n",
       "1                                        Clint!   \n",
       "2                                  Clint's hit!   \n",
       "3       Somebody want to deal with that bunker?   \n",
       "4                                    Thank you.   \n",
       "\n",
       "                                                                        CLINTBARTON  \\\n",
       "0                                    Yeah. I think we lost the element of surprise.   \n",
       "1              No, no, no. I'm going to live forever. I'm gonna be made of plastic.   \n",
       "2                                                  Well, I don't have a girlfriend.   \n",
       "3                                                                But, it's a trick!   \n",
       "4  Uh, \"Whosoever be he worthy shall haveth the power!\" Whatever man! It's a trick.   \n",
       "\n",
       "                                                                                               THOR  \\\n",
       "0       Loki's scepter must be here. Strucker couldn't mount this defense without it. At long last.   \n",
       "1                                                                                     The enhanced?   \n",
       "2  I can get Barton to the jet. The sooner we're gone the better. You and Stark secure the scepter.   \n",
       "3                                                                     Looks like they're lining up.   \n",
       "4                                                                                 Find the scepter.   \n",
       "\n",
       "                                                                                   WANDAMAXIMOFF  \\\n",
       "0                                                       Talk. And if you are wasting our time...   \n",
       "1                             Sometimes it's hard. But sooner or later, every man shows himself.   \n",
       "2  I didn't expect. But I saw Stark's fear, I knew it would control him, make him self-destruct.   \n",
       "3                                                  Is that why you've come? To end the Avengers?   \n",
       "4                                                        All of these are... All of these are...   \n",
       "\n",
       "                                                                               NICKFURY  \\\n",
       "0                                           Do me a favor. Try not to bring it to life.   \n",
       "1                                    Artificial intelligence. You never even hesitated.   \n",
       "2                         Look me in the eye and tell me you're going to shut him down.   \n",
       "3  I'm not the director of anybody. I'm just an old man, who cares very much about you.   \n",
       "4                    The Maximoff girl, she's working you, Stark. Playing on your fear.   \n",
       "\n",
       "                                                                                                                                                                                           MARIAHILL  \\\n",
       "0                                                                                                                                                                            Lab's all set up, boss.   \n",
       "1                                                                                                                                                                                    NATO's got him.   \n",
       "2  Wanda and Pietro Maximoff. Twins. Orphaned at ten when a shell collapsed their apartment building. Sokovia's had a rough history. It's nowhere special but it's on the way to everywhere special.   \n",
       "3                                                        He's got increased metabolism and improved thermal homeostasis. Her thing is neural electric interfacing, telekinesis, mental manipulation.   \n",
       "4                                                                                                                                                                         He's fast and she's weird.   \n",
       "\n",
       "                                                                                  PIETROMAXIMOFF  \\\n",
       "0                                                                    You didn't see that coming?   \n",
       "1                                                             We're just gonna let them take it?   \n",
       "2                                                           Everyone's plan is not to kill them.   \n",
       "3  I don't see the big picture, I have a little picture. I take it out and look at it every day.   \n",
       "4                                                               The records are not the picture.   \n",
       "\n",
       "                                                                                                                                    JARVIS  \\\n",
       "0  The central building is protected by some kind of energy shield. Strucker's technology is well beyond any other Hydra base we've taken.   \n",
       "1                                                                                                            Sir, the city is taking fire.   \n",
       "2                                                                                                 There's a pathway below the north tower.   \n",
       "3                                                              The wall to your left...I'm reading steel reinforcement and an air current.   \n",
       "4                                                                                                                           Very good sir.   \n",
       "\n",
       "                                                                                                                                                                                                                                                                    JAMESRHODES  \\\n",
       "0  Well, you know, the suit can take the weight, right? So I take the tank, fly it right up to the General's palace, drop it at his feet, I'm like, \"Boom! You looking for this?\"  \"Boom! Are you looking...\" Why do I even talk to you guys? Everywhere else that story kills.   \n",
       "1                                                                                                                                                                                                                                               Yeah, it's a War Machine story.   \n",
       "2                                                                                                                                                                                                                                Quality save. So, no Pepper? She's not coming?   \n",
       "3                                                                                                                                                                                                                                                               Want a lozenge?   \n",
       "4                                                                                                                                                                                                                                                                     Let's go.   \n",
       "\n",
       "                                         VISION  \\\n",
       "0        I'm sorry, that was...odd.  Thank you.   \n",
       "1                                    Not alone.   \n",
       "2              You think I'm a child of Ultron?   \n",
       "3  I'm not Ultron. I'm not JARVIS. I am...I am.   \n",
       "4                                   Look again.   \n",
       "\n",
       "                                                                                                                                            HELENCHO  \\\n",
       "0  There's no possibility of deterioration. The nano-molecular functionality is instantaneous. His cells don't know they're bonding with simulacrum.   \n",
       "1                                                             If you brought him to my lab, the regeneration Cradle could do this in twenty minutes.   \n",
       "2                                                       You'll be made of you, Mr. Barton. Your own girlfriend won't be able to tell the difference.   \n",
       "3                                          That I can't fix. This is the next thing, Tony. Your clunky metal suits are going to be left in the dust.   \n",
       "4                                                                                                Unlike you, I don't have a lot of time for parties.   \n",
       "\n",
       "                                                           LAURABARTON  \\\n",
       "0                                                                 Hey.   \n",
       "1                                               I know all your names.   \n",
       "2                                             Honey. Ah, I missed you.   \n",
       "3                                                     She's…Nathaniel.   \n",
       "4  If they're sleeping here, some of them are gonna have to double up.   \n",
       "\n",
       "                                                                                   FRIDAY  \\\n",
       "0                                                                     Good evening, boss.   \n",
       "1                           Your man's in the church, boss. I think he's waiting for you.   \n",
       "2                             There's the rest of the Vibranium. Function: still unclear.   \n",
       "3  Boss, it's working. He's burning Ultron out of the net, he won't escape through there.   \n",
       "4                                                             Sokovia's going for a ride.   \n",
       "\n",
       "                                                                                                                                                  STRUCKER  \n",
       "0                                                                                                                            Who gave the order to attack?  \n",
       "1                                                                                                    They have to be after the scepter.  Can we hold them?  \n",
       "2                                                                                                                            Deploy the rest of the tanks.  \n",
       "3  Concentrate fire on the weak ones. A hit can make them close ranks. Everything we've accomplished. But we're on the verge of our greatest breakthrough.  \n",
       "4                                                                                                                                           It's too soon.  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Filter out relevant characters\n",
    "age_of_ultron = combine_chars(age_of_ultron)\n",
    "age_of_ultron = split(age_of_ultron)\n",
    "age_of_ultron_chars = filter_chars(age_of_ultron)\n",
    "\n",
    "# Create dataframe for categorized character dialogue\n",
    "new_age_of_ultron = filter_char_lines(age_of_ultron_chars, age_of_ultron)\n",
    "new_age_of_ultron.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Categorizing movie dialogue for rest of movies** "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/1r/c2dvp09n5ds9bz3tlmhjwtmr0000gn/T/ipykernel_91209/1445751538.py:21: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  movie = movie.append(split_chars)\n"
     ]
    }
   ],
   "source": [
    "# ANT MAN\n",
    "ant_man = pd.read_csv('https://raw.githubusercontent.com/prestondunton/marvel-dialogue-nlp/master/data/cleaned/ant_man.csv')\n",
    "\n",
    "# Filter out relevant characters\n",
    "ant_man = combine_chars(ant_man)\n",
    "ant_man = split(ant_man)\n",
    "ant_man_chars = filter_chars(ant_man)\n",
    "\n",
    "# Create dataframe for categorized character dialogue\n",
    "new_ant_man = filter_char_lines(ant_man_chars, ant_man)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/1r/c2dvp09n5ds9bz3tlmhjwtmr0000gn/T/ipykernel_91209/1445751538.py:21: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  movie = movie.append(split_chars)\n"
     ]
    }
   ],
   "source": [
    "# AVENGERS\n",
    "avengers = pd.read_csv('https://raw.githubusercontent.com/prestondunton/marvel-dialogue-nlp/master/data/cleaned/avengers.csv')\n",
    "\n",
    "# Filter out relevant characters\n",
    "avengers = combine_chars(avengers)\n",
    "avengers = split(avengers)\n",
    "avengers_chars = filter_chars(avengers)\n",
    "\n",
    "# Create dataframe for categorized character dialogue\n",
    "new_avengers = filter_char_lines(avengers_chars, avengers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/1r/c2dvp09n5ds9bz3tlmhjwtmr0000gn/T/ipykernel_91209/1445751538.py:21: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  movie = movie.append(split_chars)\n"
     ]
    }
   ],
   "source": [
    "# ENDGAME\n",
    "endgame = pd.read_csv('https://raw.githubusercontent.com/prestondunton/marvel-dialogue-nlp/master/data/cleaned/avengers_endgame.csv')\n",
    "\n",
    "# Filter out relevant characters\n",
    "endgame = combine_chars(endgame)\n",
    "endgame = split(endgame)\n",
    "endgame_chars = filter_chars(endgame)\n",
    "\n",
    "# Create dataframe for categorized character dialogue\n",
    "new_endgame = filter_char_lines(endgame_chars, endgame)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/1r/c2dvp09n5ds9bz3tlmhjwtmr0000gn/T/ipykernel_91209/1445751538.py:21: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  movie = movie.append(split_chars)\n"
     ]
    }
   ],
   "source": [
    "# BLACK PANTHER\n",
    "black_panther = pd.read_csv('https://raw.githubusercontent.com/prestondunton/marvel-dialogue-nlp/master/data/cleaned/black_panther.csv')\n",
    "\n",
    "# Filter out relevant characters\n",
    "black_panther = combine_chars(black_panther)\n",
    "black_panther = split(black_panther)\n",
    "black_panther_chars = filter_chars(black_panther)\n",
    "\n",
    "# Create dataframe for categorized character dialogue\n",
    "new_black_panther = filter_char_lines(black_panther_chars, black_panther)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/1r/c2dvp09n5ds9bz3tlmhjwtmr0000gn/T/ipykernel_91209/1445751538.py:21: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  movie = movie.append(split_chars)\n"
     ]
    }
   ],
   "source": [
    "# CAPTAIN AMERICA\n",
    "captain_america = pd.read_csv('https://raw.githubusercontent.com/prestondunton/marvel-dialogue-nlp/master/data/cleaned/captain_america.csv')\n",
    "\n",
    "# Filter out relevant characters\n",
    "captain_america = combine_chars(captain_america)\n",
    "captain_america = split(captain_america)\n",
    "captain_america_chars = filter_chars(captain_america)\n",
    "\n",
    "# Create dataframe for categorized character dialogue\n",
    "new_captain_america = filter_char_lines(captain_america_chars, captain_america)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/1r/c2dvp09n5ds9bz3tlmhjwtmr0000gn/T/ipykernel_91209/1445751538.py:21: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  movie = movie.append(split_chars)\n"
     ]
    }
   ],
   "source": [
    "# CAPTAIN MARVEL\n",
    "captain_marvel = pd.read_csv('https://raw.githubusercontent.com/prestondunton/marvel-dialogue-nlp/master/data/cleaned/captain_marvel.csv')\n",
    "\n",
    "# Filter out relevant characters\n",
    "captain_marvel = combine_chars(captain_marvel)\n",
    "captain_marvel = split(captain_marvel)\n",
    "captain_marvel_chars = filter_chars(captain_marvel)\n",
    "\n",
    "# Create dataframe for categorized character dialogue\n",
    "new_captain_marvel = filter_char_lines(captain_marvel_chars, captain_marvel)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/1r/c2dvp09n5ds9bz3tlmhjwtmr0000gn/T/ipykernel_91209/1445751538.py:21: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  movie = movie.append(split_chars)\n"
     ]
    }
   ],
   "source": [
    "# CIVIL WAR\n",
    "civil_war = pd.read_csv('https://raw.githubusercontent.com/prestondunton/marvel-dialogue-nlp/master/data/cleaned/civil_war.csv')\n",
    "\n",
    "# Filter out relevant characters\n",
    "civil_war = combine_chars(civil_war)\n",
    "civil_war = split(civil_war)\n",
    "civil_war_chars = filter_chars(civil_war)\n",
    "\n",
    "# Create dataframe for categorized character dialogue\n",
    "new_civil_war = filter_char_lines(civil_war_chars, civil_war)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/1r/c2dvp09n5ds9bz3tlmhjwtmr0000gn/T/ipykernel_91209/1445751538.py:21: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  movie = movie.append(split_chars)\n"
     ]
    }
   ],
   "source": [
    "# GUARDIANS OF GALAXY VOL. 2\n",
    "guardians2 = pd.read_csv('https://raw.githubusercontent.com/prestondunton/marvel-dialogue-nlp/master/data/cleaned/guardians_2.csv')\n",
    "\n",
    "# Filter out relevant characters\n",
    "guardians2 = combine_chars(guardians2)\n",
    "guardians2 = split(guardians2)\n",
    "guardians2_chars = filter_chars(guardians2)\n",
    "\n",
    "# Create dataframe for categorized character dialogue\n",
    "new_guardians2 = filter_char_lines(guardians2_chars, guardians2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/1r/c2dvp09n5ds9bz3tlmhjwtmr0000gn/T/ipykernel_91209/1445751538.py:21: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  movie = movie.append(split_chars)\n"
     ]
    }
   ],
   "source": [
    "# INFINITY WAR\n",
    "infinity_war = pd.read_csv('https://raw.githubusercontent.com/prestondunton/marvel-dialogue-nlp/master/data/cleaned/infinity_war.csv')\n",
    "\n",
    "# Filter out relevant characters\n",
    "infinity_war = combine_chars(infinity_war)\n",
    "infinity_war = split(infinity_war)\n",
    "infinity_war_chars = filter_chars(infinity_war)\n",
    "\n",
    "# Create dataframe for categorized character dialogue\n",
    "new_infinity_war = filter_char_lines(infinity_war_chars, infinity_war)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/1r/c2dvp09n5ds9bz3tlmhjwtmr0000gn/T/ipykernel_91209/1445751538.py:21: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  movie = movie.append(split_chars)\n"
     ]
    }
   ],
   "source": [
    "# IRON MAN\n",
    "iron_man = pd.read_csv('https://raw.githubusercontent.com/prestondunton/marvel-dialogue-nlp/master/data/cleaned/iron_man.csv')\n",
    "\n",
    "# Filter out relevant characters\n",
    "iron_man = combine_chars(iron_man)\n",
    "iron_man = split(iron_man)\n",
    "iron_man_chars = filter_chars(iron_man)\n",
    "\n",
    "# Create dataframe for categorized character dialogue\n",
    "new_iron_man = filter_char_lines(iron_man_chars, iron_man)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/1r/c2dvp09n5ds9bz3tlmhjwtmr0000gn/T/ipykernel_91209/1445751538.py:21: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  movie = movie.append(split_chars)\n"
     ]
    }
   ],
   "source": [
    "# IRON MAN 2\n",
    "iron_man2 = pd.read_csv('https://raw.githubusercontent.com/prestondunton/marvel-dialogue-nlp/master/data/cleaned/iron_man_2.csv')\n",
    "\n",
    "# Filter out relevant characters\n",
    "iron_man2 = combine_chars(iron_man2)\n",
    "iron_man2 = split(iron_man2)\n",
    "iron_man2_chars = filter_chars(iron_man2)\n",
    "\n",
    "# Create dataframe for categorized character dialogue\n",
    "new_iron_man2 = filter_char_lines(iron_man2_chars, iron_man2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/1r/c2dvp09n5ds9bz3tlmhjwtmr0000gn/T/ipykernel_91209/1445751538.py:21: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  movie = movie.append(split_chars)\n"
     ]
    }
   ],
   "source": [
    "# IRON MAN 3\n",
    "iron_man3 = pd.read_csv('https://raw.githubusercontent.com/prestondunton/marvel-dialogue-nlp/master/data/cleaned/iron_man_3.csv')\n",
    "\n",
    "# Filter out relevant characters\n",
    "iron_man3 = combine_chars(iron_man3)\n",
    "iron_man3 = split(iron_man3)\n",
    "iron_man3_chars = filter_chars(iron_man3)\n",
    "\n",
    "# Create dataframe for categorized character dialogue\n",
    "new_iron_man3 = filter_char_lines(iron_man3_chars, iron_man3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/1r/c2dvp09n5ds9bz3tlmhjwtmr0000gn/T/ipykernel_91209/1445751538.py:21: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  movie = movie.append(split_chars)\n"
     ]
    }
   ],
   "source": [
    "# THOR: RAGNAROK\n",
    "ragnarok = pd.read_csv('https://raw.githubusercontent.com/prestondunton/marvel-dialogue-nlp/master/data/cleaned/ragnarok.csv')\n",
    "\n",
    "# Filter out relevant characters\n",
    "ragnarok = combine_chars(ragnarok)\n",
    "ragnarok = split(ragnarok)\n",
    "ragnarok_chars = filter_chars(ragnarok)\n",
    "\n",
    "# Create dataframe for categorized character dialogue\n",
    "new_ragnarok = filter_char_lines(ragnarok_chars, ragnarok)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/1r/c2dvp09n5ds9bz3tlmhjwtmr0000gn/T/ipykernel_91209/1445751538.py:21: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  movie = movie.append(split_chars)\n"
     ]
    }
   ],
   "source": [
    "# SPIDERMAN: HOMECOMING\n",
    "spider_hoco = pd.read_csv('https://raw.githubusercontent.com/prestondunton/marvel-dialogue-nlp/master/data/cleaned/spider_man_homecoming.csv')\n",
    "\n",
    "# Filter out relevant characters\n",
    "spider_hoco = combine_chars(spider_hoco)\n",
    "spider_hoco = split(spider_hoco)\n",
    "spider_hoco_chars = filter_chars(spider_hoco)\n",
    "\n",
    "# Create dataframe for categorized character dialogue\n",
    "new_spider_hoco = filter_char_lines(spider_hoco_chars, spider_hoco)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/1r/c2dvp09n5ds9bz3tlmhjwtmr0000gn/T/ipykernel_91209/1445751538.py:21: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  movie = movie.append(split_chars)\n"
     ]
    }
   ],
   "source": [
    "# THOR\n",
    "thor = pd.read_csv('https://raw.githubusercontent.com/prestondunton/marvel-dialogue-nlp/master/data/cleaned/thor.csv')\n",
    "\n",
    "# Filter out relevant characters\n",
    "thor = combine_chars(thor)\n",
    "thor = split(thor)\n",
    "thor_chars = filter_chars(thor)\n",
    "\n",
    "# Create dataframe for categorized character dialogue\n",
    "new_thor = filter_char_lines(thor_chars, thor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/1r/c2dvp09n5ds9bz3tlmhjwtmr0000gn/T/ipykernel_91209/1445751538.py:21: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  movie = movie.append(split_chars)\n"
     ]
    }
   ],
   "source": [
    "# THOR: DARK WORLD\n",
    "dark_world = pd.read_csv('https://raw.githubusercontent.com/prestondunton/marvel-dialogue-nlp/master/data/cleaned/thor_dark_world.csv')\n",
    "\n",
    "# Filter out relevant characters\n",
    "dark_world = combine_chars(dark_world)\n",
    "dark_world = split(dark_world)\n",
    "dark_world_chars = filter_chars(dark_world)\n",
    "\n",
    "# Create dataframe for categorized character dialogue\n",
    "new_dark_world = filter_char_lines(dark_world_chars, dark_world)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/1r/c2dvp09n5ds9bz3tlmhjwtmr0000gn/T/ipykernel_91209/1445751538.py:21: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  movie = movie.append(split_chars)\n"
     ]
    }
   ],
   "source": [
    "# CAPTAIN AMERICA: WINTER SOLDIER\n",
    "winter_soldier = pd.read_csv('https://raw.githubusercontent.com/prestondunton/marvel-dialogue-nlp/master/data/cleaned/winter_soldier.csv')\n",
    "\n",
    "# Filter out relevant characters\n",
    "winter_soldier = combine_chars(winter_soldier)\n",
    "winter_soldier = split(winter_soldier)\n",
    "winter_soldier_chars = filter_chars(winter_soldier)\n",
    "\n",
    "# Create dataframe for categorized character dialogue\n",
    "new_winter_soldier = filter_char_lines(winter_soldier_chars, winter_soldier)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/1r/c2dvp09n5ds9bz3tlmhjwtmr0000gn/T/ipykernel_91209/1445751538.py:21: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  movie = movie.append(split_chars)\n"
     ]
    }
   ],
   "source": [
    "# ANT MAN AND WASP\n",
    "ant_wasp = pd.read_csv('./Data/MissingScripts/Cleanedcsv/ant_man_and_wasp_cleaned.csv')\n",
    "\n",
    "# Filter out relevant characters\n",
    "ant_wasp = combine_chars(ant_wasp)\n",
    "ant_wasp = split(ant_wasp)\n",
    "ant_wasp_chars = filter_chars(ant_wasp)\n",
    "\n",
    "# Create dataframe for categorized character dialogue\n",
    "new_ant_wasp = filter_char_lines(ant_wasp_chars, ant_wasp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/1r/c2dvp09n5ds9bz3tlmhjwtmr0000gn/T/ipykernel_91209/1445751538.py:21: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  movie = movie.append(split_chars)\n"
     ]
    }
   ],
   "source": [
    "# DR. STRANGE\n",
    "dr_strange = pd.read_csv('./Data/MissingScripts/Cleanedcsv/doctor_strange_cleaned.csv')\n",
    "\n",
    "# Filter out relevant characters\n",
    "dr_strange = combine_chars(dr_strange)\n",
    "dr_strange = split(dr_strange)\n",
    "dr_strange_chars = filter_chars(dr_strange)\n",
    "\n",
    "# Create dataframe for categorized character dialogue\n",
    "new_dr_strange = filter_char_lines(dr_strange_chars, dr_strange)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/1r/c2dvp09n5ds9bz3tlmhjwtmr0000gn/T/ipykernel_91209/1445751538.py:21: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  movie = movie.append(split_chars)\n"
     ]
    }
   ],
   "source": [
    "# GUARDIANS OF GALAXY VOL. 1\n",
    "guardians = pd.read_csv('./Data/MissingScripts/Cleanedcsv/guardians_galaxy_one_cleaned.csv')\n",
    "\n",
    "# Filter out relevant characters\n",
    "guardians = combine_chars(guardians)\n",
    "guardians = split(guardians)\n",
    "guardians_chars = filter_chars(guardians)\n",
    "\n",
    "# Create dataframe for categorized character dialogue\n",
    "new_guardians = filter_char_lines(guardians_chars, guardians)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/1r/c2dvp09n5ds9bz3tlmhjwtmr0000gn/T/ipykernel_91209/1445751538.py:21: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  movie = movie.append(split_chars)\n"
     ]
    }
   ],
   "source": [
    "# SPIDERMAN: FAR FROM HOME\n",
    "spider_far = pd.read_csv('./Data/MissingScripts/Cleanedcsv/spider_man_far_from_home_cleaned.csv')\n",
    "\n",
    "# Filter out relevant characters\n",
    "spider_far = combine_chars(spider_far)\n",
    "spider_far = split(spider_far)\n",
    "spider_far_chars = filter_chars(spider_far)\n",
    "\n",
    "# Create dataframe for categorized character dialogue\n",
    "new_spider_far = filter_char_lines(spider_far_chars, spider_far)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Analysis & Results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## EDA before Sentiment Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to add gender data for relevant characters for each movie \n",
    "# This fuction will be used throughout the data analysis\n",
    "\n",
    "def match_gender(movie):\n",
    "    # Make a copy of gender dataframe as a reference to merge with\n",
    "    new_char_gender = character_gender.copy()\n",
    "    \n",
    "    # Merge gender dataframe while accounting for substrings (ex: TONY is a substring of TONYSTARK)\n",
    "    str_match = f\"({'|'.join(movie['character'])})\"\n",
    "    new_char_gender['character'] = new_char_gender['character'].str.extract(pat=str_match) \n",
    "    movie = pd.merge(movie, new_char_gender, on=['character'], how='left')\n",
    "\n",
    "    # Drop any duplicate rows with duplicate characters that may have been produced in the new merged dataframe\n",
    "    movie.drop_duplicates(subset='character', keep='first', inplace=True, ignore_index=True)\n",
    "    \n",
    "    # Remove any rows with characters that can't be categorized with a gender (ex: E.D.I.T.H.)\n",
    "    movie = movie[movie['gender'].notna()]\n",
    "    \n",
    "    return movie"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "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>Movie</th>\n",
       "      <th>Number of Characters</th>\n",
       "      <th>Female Characters</th>\n",
       "      <th>Male Characters</th>\n",
       "      <th>Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>iron_man</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>2008-05-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>iron_man2</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>2010-05-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>thor</td>\n",
       "      <td>13</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>2011-05-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>captain_america</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>2011-07-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>avengers</td>\n",
       "      <td>14</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>2012-05-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Movie Number of Characters Female Characters Male Characters  \\\n",
       "0         iron_man                    9                 2               7   \n",
       "1        iron_man2                   12                 4               8   \n",
       "2             thor                   13                 4               9   \n",
       "3  captain_america                    9                 2               7   \n",
       "4         avengers                   14                 3              11   \n",
       "\n",
       "        Year  \n",
       "0 2008-05-01  \n",
       "1 2010-05-01  \n",
       "2 2011-05-01  \n",
       "3 2011-07-01  \n",
       "4 2012-05-01  "
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Def to count how many unique characters (with >10 lines and a decernable gender) there are in a single movie\n",
    "def num_character(movie, name):\n",
    "\n",
    "    characters = movie.columns\n",
    "    char_df = pd.DataFrame({'character': characters})\n",
    "    char_df = match_gender(char_df)\n",
    "\n",
    "    male_num = 0\n",
    "    fem_num = 0\n",
    "    for c in char_df['gender']:\n",
    "        if c == 'Male':\n",
    "            male_num += 1\n",
    "        elif c == 'Female':\n",
    "            fem_num +=1\n",
    "\n",
    "    num = fem_num + male_num\n",
    "\n",
    "    df = pd.DataFrame({'Movie':[name], 'Number of Characters':[num], 'Female Characters':[fem_num], 'Male Characters':[male_num]})\n",
    "    return df\n",
    "\n",
    "\n",
    "# Count number of unique characters for all movies\n",
    "unique_chars = pd.DataFrame(columns=['Movie', 'Number of Characters', 'Female Characters', 'Male Characters'])\n",
    "unique_chars = pd.concat([unique_chars, num_character(new_age_of_ultron, 'age_of_ultron')], ignore_index=True)\n",
    "unique_chars = pd.concat([unique_chars, num_character(new_ant_man, 'ant_man')], ignore_index=True)\n",
    "unique_chars = pd.concat([unique_chars, num_character(new_avengers, 'avengers')], ignore_index=True)\n",
    "unique_chars = pd.concat([unique_chars, num_character(new_endgame , 'endgame')], ignore_index=True)\n",
    "unique_chars = pd.concat([unique_chars, num_character(new_black_panther , 'black_panther')], ignore_index=True)\n",
    "unique_chars = pd.concat([unique_chars, num_character(new_captain_america , 'captain_america')], ignore_index=True)\n",
    "unique_chars = pd.concat([unique_chars, num_character(new_captain_marvel , 'captain_marvel')], ignore_index=True)\n",
    "unique_chars = pd.concat([unique_chars, num_character(new_civil_war , 'civil_war')], ignore_index=True)\n",
    "unique_chars = pd.concat([unique_chars, num_character(new_guardians2 , 'guardians2')], ignore_index=True)\n",
    "unique_chars = pd.concat([unique_chars, num_character(new_infinity_war , 'infinity_war')], ignore_index=True)\n",
    "unique_chars = pd.concat([unique_chars, num_character(new_iron_man , 'iron_man')], ignore_index=True)\n",
    "unique_chars = pd.concat([unique_chars, num_character(new_iron_man2 , 'iron_man2')], ignore_index=True)\n",
    "unique_chars = pd.concat([unique_chars, num_character(new_iron_man3 , 'iron_man3')], ignore_index=True)\n",
    "unique_chars = pd.concat([unique_chars, num_character(new_ragnarok , 'ragnarok')], ignore_index=True)\n",
    "unique_chars = pd.concat([unique_chars, num_character(new_spider_hoco , 'spider_hoco')], ignore_index=True)\n",
    "unique_chars = pd.concat([unique_chars, num_character(new_thor , 'thor')], ignore_index=True)\n",
    "unique_chars = pd.concat([unique_chars, num_character(new_dark_world , 'dark_world')], ignore_index=True)\n",
    "unique_chars = pd.concat([unique_chars, num_character(new_winter_soldier , 'winter_soldier')], ignore_index=True)\n",
    "unique_chars = pd.concat([unique_chars, num_character(new_ant_wasp , 'ant_wasp')], ignore_index=True)\n",
    "unique_chars = pd.concat([unique_chars, num_character(new_dr_strange , 'dr_strange')], ignore_index=True)\n",
    "unique_chars = pd.concat([unique_chars, num_character(new_guardians , 'guardians')], ignore_index=True)\n",
    "unique_chars = pd.concat([unique_chars, num_character(new_spider_far , 'spider_far')], ignore_index=True)\n",
    "unique_chars.head()\n",
    "\n",
    "unique_chars['Year'] = ['May 2015', 'July 2015', 'May 2012', 'April 2019', 'February 2018',\n",
    "                        'July 2011', 'March 2019', 'May 2016', 'May 2017', 'April 2018', \n",
    "                        'May 2008', 'May 2010', 'May 2013', 'November 2017', 'July 2017',\n",
    "                        'May 2011', 'November 2013', 'April 2014', 'July 2018', 'November 2016', \n",
    "                        'August 2014', 'July 2019']\n",
    "\n",
    "# Sort by release year for visualization\n",
    "unique_chars['Year'] = pd.to_datetime(unique_chars['Year'])\n",
    "unique_chars.sort_values(by='Year', inplace = True)\n",
    "unique_chars = unique_chars.reset_index(drop=True)\n",
    "unique_chars.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(data=unique_chars, x='Movie', y='Number of Characters')\n",
    "plt.xticks(rotation=45, ha='right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Comparing the number of male and female characters across all of the movies\n",
    "\n",
    "unique_chars.index = unique_chars['Movie']\n",
    "unique_chars[['Male Characters', 'Female Characters']].plot.bar(figsize=(10,5), \n",
    "                        color=[(111/255,143/255,175/255), (255/255,182/255,193/255)], ylabel='Count')\n",
    "plt.xticks(rotation=45, ha='right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Perform VADER Sentiment Analysis on dialogue for each character in each movie "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To analyze the sentiment of dialogue in MCU movies, our team utilized the VADER (Valence Aware Dictionary for Sentiment Reasoning) model as part of the NLTK toolkit. \n",
    "\n",
    "VADER scores dialogue on four categories: negative, neutral, positive, and compound. For our analysis, we will primarily be focusing on the compound score as it normalizes the text's overall sentiment where -1 is most negative and +1 is most positive. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Setting up functions to apply VADER sentiment analysis to movie dialogue datasets. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "#function to find the average of scores in a list\n",
    "def average(list): \n",
    "    if len(list) == 0: \n",
    "        return 0\n",
    "    else: \n",
    "        return sum(list)/len(list)\n",
    "\n",
    "#function to create dataframe of average sentiment analysis scores for each character in movie \n",
    "def create_score_data(dataframe): \n",
    "    #create dict for negative, neutral, positive, and compound scores for each line of dialogue for each character\n",
    "    score_data = {\"character\": [], \"neg\": [], \"neu\": [], \"pos\":[], \"compound\":[], \"comp score\":[]}\n",
    "    for character in dataframe:\n",
    "        neg_scores = []\n",
    "        neu_scores = []  \n",
    "        pos_scores = []\n",
    "        compound_scores = []\n",
    "        #use VADER sentiment analyzer to analyze each line of dialogue\n",
    "        for dialogue in dataframe[character]:  \n",
    "            score = analyser.polarity_scores(dialogue)\n",
    "            neg_scores.append(score[\"neg\"])\n",
    "            neu_scores.append(score[\"neu\"])\n",
    "            pos_scores.append(score[\"pos\"])\n",
    "            compound_scores.append(score[\"compound\"]) \n",
    "        #find the average negative, neutral, positive, and compound scores for all dialogue \n",
    "        neg_avg = average(neg_scores) \n",
    "        neu_avg = average(neu_scores)\n",
    "        pos_avg = average(pos_scores)\n",
    "        compound_avg = average(compound_scores)\n",
    "        #add scores to their respective key:value pairs in dictionary\n",
    "        score_data[\"character\"].append(character)\n",
    "        score_data[\"neg\"].append(neg_avg)\n",
    "        score_data[\"neu\"].append(neu_avg)\n",
    "        score_data[\"pos\"].append(pos_avg)\n",
    "        score_data[\"compound\"].append(compound_avg)\n",
    "        #add overall compound score classification to dictionary \n",
    "        if compound_avg >= 0.05: \n",
    "            score_data[\"comp score\"].append(\"pos\") \n",
    "        elif compound_avg < -0.05: \n",
    "            score_data[\"comp score\"].append(\"neg\")\n",
    "        else: \n",
    "            score_data[\"comp score\"].append(\"neu\")\n",
    "    #create dataframe\n",
    "    score_df = pd.DataFrame(score_data)\n",
    "    return score_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Applying function to each movie dialogue dataset. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "age_of_ultron_scores = create_score_data(new_age_of_ultron)\n",
    "ant_man_scores = create_score_data(new_ant_man)\n",
    "ant_wasp_scores = create_score_data(new_ant_wasp)\n",
    "avengers_scores = create_score_data(new_avengers)\n",
    "endgame_scores = create_score_data(new_endgame)\n",
    "black_panther_scores = create_score_data(new_black_panther)\n",
    "captain_america_scores = create_score_data(new_captain_america)\n",
    "captain_marvel_scores = create_score_data(new_captain_marvel)\n",
    "civil_war_scores = create_score_data(new_civil_war)\n",
    "dr_strange_scores = create_score_data(new_dr_strange)\n",
    "guardians_scores = create_score_data(new_guardians)\n",
    "guardians2_scores = create_score_data(new_guardians2)\n",
    "infinity_war_scores = create_score_data(new_infinity_war)\n",
    "iron_man_scores = create_score_data(new_iron_man)\n",
    "iron_man2_scores = create_score_data(new_iron_man2)\n",
    "iron_man3_scores = create_score_data(new_iron_man3)\n",
    "ragnarok_scores = create_score_data(new_ragnarok)\n",
    "spider_hoco_scores = create_score_data(new_spider_hoco)\n",
    "spider_far_scores = create_score_data(new_spider_far)\n",
    "thor_scores = create_score_data(new_thor)\n",
    "dark_world_scores = create_score_data(new_dark_world)\n",
    "winter_soldier_scores = create_score_data(new_winter_soldier)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "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>character</th>\n",
       "      <th>neg</th>\n",
       "      <th>neu</th>\n",
       "      <th>pos</th>\n",
       "      <th>compound</th>\n",
       "      <th>comp score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>TONYSTARK</td>\n",
       "      <td>0.059704</td>\n",
       "      <td>0.814832</td>\n",
       "      <td>0.125464</td>\n",
       "      <td>0.078671</td>\n",
       "      <td>pos</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>STEVEROGERS</td>\n",
       "      <td>0.054151</td>\n",
       "      <td>0.640352</td>\n",
       "      <td>0.070860</td>\n",
       "      <td>0.033799</td>\n",
       "      <td>neu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BRUCEBANNER</td>\n",
       "      <td>0.048268</td>\n",
       "      <td>0.349447</td>\n",
       "      <td>0.065966</td>\n",
       "      <td>0.019928</td>\n",
       "      <td>neu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ULTRON</td>\n",
       "      <td>0.055961</td>\n",
       "      <td>0.332408</td>\n",
       "      <td>0.064140</td>\n",
       "      <td>0.030517</td>\n",
       "      <td>neu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NATASHAROMANOFF</td>\n",
       "      <td>0.036670</td>\n",
       "      <td>0.363547</td>\n",
       "      <td>0.052302</td>\n",
       "      <td>0.014845</td>\n",
       "      <td>neu</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         character       neg       neu       pos  compound comp score\n",
       "0        TONYSTARK  0.059704  0.814832  0.125464  0.078671        pos\n",
       "1      STEVEROGERS  0.054151  0.640352  0.070860  0.033799        neu\n",
       "2      BRUCEBANNER  0.048268  0.349447  0.065966  0.019928        neu\n",
       "3           ULTRON  0.055961  0.332408  0.064140  0.030517        neu\n",
       "4  NATASHAROMANOFF  0.036670  0.363547  0.052302  0.014845        neu"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#example of resulting dataframe with VADER scores \n",
    "age_of_ultron_scores.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Combine gender data with VADER scores data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Combined dataframes with gender data and VADER data for each movie\n",
    "age_of_ultron_scores = match_gender(age_of_ultron_scores)\n",
    "ant_man_scores = match_gender(ant_man_scores)\n",
    "avengers_scores = match_gender(avengers_scores)\n",
    "endgame_scores = match_gender(endgame_scores)\n",
    "black_panther_scores = match_gender(black_panther_scores)\n",
    "captain_america_scores = match_gender(captain_america_scores)\n",
    "captain_marvel_scores = match_gender(captain_marvel_scores)\n",
    "civil_war_scores = match_gender(civil_war_scores)\n",
    "guardians2_scores = match_gender(guardians2_scores)\n",
    "infinity_war_scores = match_gender(infinity_war_scores)\n",
    "iron_man_scores = match_gender(iron_man_scores)\n",
    "iron_man2_scores = match_gender(iron_man2_scores)\n",
    "iron_man3_scores = match_gender(iron_man3_scores)\n",
    "ragnarok_scores = match_gender(ragnarok_scores)\n",
    "spider_hoco_scores = match_gender(spider_hoco_scores)\n",
    "thor_scores = match_gender(thor_scores)\n",
    "dark_world_scores = match_gender(dark_world_scores)\n",
    "winter_soldier_scores = match_gender(winter_soldier_scores)\n",
    "ant_wasp_scores = match_gender(ant_wasp_scores)\n",
    "dr_strange_scores = match_gender(dr_strange_scores)\n",
    "guardians_scores = match_gender(guardians_scores)\n",
    "spider_far_scores = match_gender(spider_far_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "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>character</th>\n",
       "      <th>neg</th>\n",
       "      <th>neu</th>\n",
       "      <th>pos</th>\n",
       "      <th>compound</th>\n",
       "      <th>comp score</th>\n",
       "      <th>gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>TONYSTARK</td>\n",
       "      <td>0.059704</td>\n",
       "      <td>0.814832</td>\n",
       "      <td>0.125464</td>\n",
       "      <td>0.078671</td>\n",
       "      <td>pos</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>STEVEROGERS</td>\n",
       "      <td>0.054151</td>\n",
       "      <td>0.640352</td>\n",
       "      <td>0.070860</td>\n",
       "      <td>0.033799</td>\n",
       "      <td>neu</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BRUCEBANNER</td>\n",
       "      <td>0.048268</td>\n",
       "      <td>0.349447</td>\n",
       "      <td>0.065966</td>\n",
       "      <td>0.019928</td>\n",
       "      <td>neu</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ULTRON</td>\n",
       "      <td>0.055961</td>\n",
       "      <td>0.332408</td>\n",
       "      <td>0.064140</td>\n",
       "      <td>0.030517</td>\n",
       "      <td>neu</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NATASHAROMANOFF</td>\n",
       "      <td>0.036670</td>\n",
       "      <td>0.363547</td>\n",
       "      <td>0.052302</td>\n",
       "      <td>0.014845</td>\n",
       "      <td>neu</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         character       neg       neu       pos  compound comp score  gender\n",
       "0        TONYSTARK  0.059704  0.814832  0.125464  0.078671        pos    Male\n",
       "1      STEVEROGERS  0.054151  0.640352  0.070860  0.033799        neu    Male\n",
       "2      BRUCEBANNER  0.048268  0.349447  0.065966  0.019928        neu    Male\n",
       "3           ULTRON  0.055961  0.332408  0.064140  0.030517        neu    Male\n",
       "4  NATASHAROMANOFF  0.036670  0.363547  0.052302  0.014845        neu  Female"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Example of resulting dataframe with VADER scores \n",
    "age_of_ultron_scores.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Creating function to find the average VADER compounds score for each gender within each movie"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Returns dictionary with (Male, Female) compound scores\n",
    "def average_VADER(moviescore, name):\n",
    "    avg_male = moviescore.loc[moviescore['gender']=='Male', 'compound'].mean()\n",
    "    avg_female = moviescore.loc[moviescore['gender']=='Female', 'compound'].mean()\n",
    "    res = pd.DataFrame([[avg_male, avg_female, name]], columns=['Male', 'Female', 'Movie'])\n",
    "    return res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "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>Male</th>\n",
       "      <th>Female</th>\n",
       "      <th>Movie</th>\n",
       "      <th>Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.021031</td>\n",
       "      <td>-0.002822</td>\n",
       "      <td>age_of_ultron</td>\n",
       "      <td>May 2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.017823</td>\n",
       "      <td>0.007369</td>\n",
       "      <td>ant_man</td>\n",
       "      <td>July 2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.005974</td>\n",
       "      <td>0.016754</td>\n",
       "      <td>avengers</td>\n",
       "      <td>May 2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.022622</td>\n",
       "      <td>0.002188</td>\n",
       "      <td>endgame</td>\n",
       "      <td>April 2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.000361</td>\n",
       "      <td>0.025594</td>\n",
       "      <td>black_panther</td>\n",
       "      <td>February 2018</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Male    Female          Movie           Year\n",
       "0  0.021031 -0.002822  age_of_ultron       May 2015\n",
       "0  0.017823  0.007369        ant_man      July 2015\n",
       "0  0.005974  0.016754       avengers       May 2012\n",
       "0  0.022622  0.002188        endgame     April 2019\n",
       "0  0.000361  0.025594  black_panther  February 2018"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dataframe with average compound score for each gender for each movie\n",
    "VADER_gender = pd.DataFrame(columns=['Male', 'Female'])\n",
    "VADER_gender = pd.concat([VADER_gender, average_VADER(age_of_ultron_scores, 'age_of_ultron')])\n",
    "VADER_gender = pd.concat([VADER_gender, average_VADER(ant_man_scores, 'ant_man')])\n",
    "VADER_gender = pd.concat([VADER_gender, average_VADER(avengers_scores, 'avengers')])\n",
    "VADER_gender = pd.concat([VADER_gender, average_VADER(endgame_scores, 'endgame')])\n",
    "VADER_gender = pd.concat([VADER_gender, average_VADER(black_panther_scores, 'black_panther')])\n",
    "VADER_gender = pd.concat([VADER_gender, average_VADER(captain_america_scores, 'captain_america')])\n",
    "VADER_gender = pd.concat([VADER_gender, average_VADER(captain_marvel_scores, 'captain_marvel')])\n",
    "VADER_gender = pd.concat([VADER_gender, average_VADER(civil_war_scores, 'civil_war')])\n",
    "VADER_gender = pd.concat([VADER_gender, average_VADER(guardians2_scores, 'guardians2')])\n",
    "VADER_gender = pd.concat([VADER_gender, average_VADER(infinity_war_scores, 'infinity_war')])\n",
    "VADER_gender = pd.concat([VADER_gender, average_VADER(iron_man_scores, 'iron_man')])\n",
    "VADER_gender = pd.concat([VADER_gender, average_VADER(iron_man2_scores, 'iron_man2')])\n",
    "VADER_gender = pd.concat([VADER_gender, average_VADER(iron_man3_scores, 'iron_man3')])\n",
    "VADER_gender = pd.concat([VADER_gender, average_VADER(ragnarok_scores, 'ragnarok')])\n",
    "VADER_gender = pd.concat([VADER_gender, average_VADER(spider_hoco_scores, 'spider_hoco')])\n",
    "VADER_gender = pd.concat([VADER_gender, average_VADER(thor_scores, 'thor')])\n",
    "VADER_gender = pd.concat([VADER_gender, average_VADER(dark_world_scores, 'dark_world')])\n",
    "VADER_gender = pd.concat([VADER_gender, average_VADER(winter_soldier_scores, 'winter_soldier')])\n",
    "VADER_gender = pd.concat([VADER_gender, average_VADER(ant_wasp_scores, 'ant_wasp')])\n",
    "VADER_gender = pd.concat([VADER_gender, average_VADER(dr_strange_scores, 'dr_strange')])\n",
    "VADER_gender = pd.concat([VADER_gender, average_VADER(guardians_scores, 'guardians')])\n",
    "VADER_gender = pd.concat([VADER_gender, average_VADER(spider_far_scores, 'spider_far')])\n",
    "\n",
    "#adding release data for each movie to dataframe\n",
    "VADER_gender['Year'] = ['May 2015', 'July 2015', 'May 2012', 'April 2019', 'February 2018',\n",
    "                        'July 2011', 'March 2019', 'May 2016', 'May 2017', 'April 2018', \n",
    "                        'May 2008', 'May 2010', 'May 2013', 'November 2017', 'July 2017',\n",
    "                        'May 2011', 'November 2013', 'April 2014', 'July 2018', 'November 2016', \n",
    "                        'August 2014', 'July 2019']\n",
    "VADER_gender.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Use NRC Emotion Lexicon to analyze emotion word use in text"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To analyze the emotional content of dialogue in MCU movies, our team used the NRC Emotion Lexicon, which is a list of English words and their associations with eight basic emotions (anger, fear, anticipation, trust, surprise, sadness, joy, and disgust). \n",
    "\n",
    "While we acknowledge that NRC does not contain all possible words in the English language and that some dialogue cannot be analyzed, we expect that this will not have an extreme effect on our conclusion as it will apply evenly to both male and female characters. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Setting up functions to apply NRC Emotion analysis to movie dialogue datasets. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Clean dialogue to prep for NRC analysis\n",
    "\n",
    "def remove_punctuation(list_test):\n",
    "    #Using filter() and lambda function to filter out punctuation characters\n",
    "    result = list(filter(lambda x: x.isalpha() or x.isdigit() or x.isspace(), list_test))\n",
    "    return result\n",
    "\n",
    "def remove_stopwords(list):\n",
    "    result = []\n",
    "    for word in list:\n",
    "        if word not in stopwords.words('english'):\n",
    "            result.append(word)   \n",
    "    return result\n",
    "\n",
    "def NRC_clean(dialogue):\n",
    "    dialogue = dialogue.apply(lambda x: word_tokenize(x)) # tokenize\n",
    "    dialogue = dialogue.apply(lambda x: remove_punctuation(x)) # remove punctuation\n",
    "    dialogue = dialogue.apply(lambda x: [y.lower() for y in x]) # lowercase\n",
    "    dialogue = dialogue.apply(lambda x: remove_stopwords(x)) # remove stop words\n",
    "\n",
    "    # convert back to strings for output\n",
    "    dialogue = dialogue.apply(lambda x: ' '.join(str(w) for w in x))\n",
    "\n",
    "    return dialogue "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0                                                                                              Shit!\n",
      "1           Wait a second. No one else is going to deal with the fact that Cap just said \"language?\"\n",
      "2    Well, we know Strucker's not going to worry about civilian casualties. Send in the Iron Legion.\n",
      "3              I'm closing in. JARVIS, am I...closing in? Do you see a power source for that shield?\n",
      "4                                                             Great, I wanna poke it with something.\n",
      "Name: TONYSTARK, dtype: object\n",
      "0                                                                   shit\n",
      "1                 wait second one else going deal fact cap said language\n",
      "2    well know strucker going worry civilian casualties send iron legion\n",
      "3                         closing jarvis closing see power source shield\n",
      "4                                            great wan na poke something\n",
      "Name: TONYSTARK, dtype: object\n"
     ]
    }
   ],
   "source": [
    "# Sample of dialogue before and after cleaning\n",
    "print(new_age_of_ultron['TONYSTARK'].head())\n",
    "cleaned = NRC_clean(new_age_of_ultron['TONYSTARK'])\n",
    "print(cleaned.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "#function to create dataframe of average sentiment analysis scores for each character in movie \n",
    "def create_emotion_data(dataframe):\n",
    "    #create dict for scores for each 8 emotions for each line of dialogue for each character\n",
    "    emotion_data = {\"character\": [], \"fear\": [], \"anger\": [], \"anticipation\":[], \"trust\":[], \"surprise\":[], \"sadness\":[], \"disgust\":[], \"joy\":[]}\n",
    "    for character in dataframe:\n",
    "        fear_scores = []\n",
    "        anger_scores = []\n",
    "        anticipation_scores = []\n",
    "        trust_scores = []\n",
    "        surprise_scores = []\n",
    "        sadness_scores = []\n",
    "        disgust_scores = []\n",
    "        joy_scores = []\n",
    "\n",
    "        #data cleaning for each character\n",
    "        cleaned_character = NRC_clean(dataframe[character])\n",
    "\n",
    "        for dialogue in cleaned_character:\n",
    "            #use NRC emotion analyzer to analyze each line of dialogue, store result for each emotion in list\n",
    "            nrc_output = (NRCLex(dialogue)).affect_frequencies\n",
    "            emotions = list(nrc_output.keys())\n",
    "            for emotion in emotions: \n",
    "                if emotion == \"fear\":\n",
    "                    fear_scores.append(nrc_output[emotion])\n",
    "                elif emotion == \"anger\": \n",
    "                    anger_scores.append(nrc_output[emotion])\n",
    "                elif emotion == \"anticipation\":\n",
    "                    anticipation_scores.append(nrc_output[emotion])\n",
    "                elif emotion == \"trust\": \n",
    "                    trust_scores.append(nrc_output[emotion])\n",
    "                elif emotion == \"surprise\": \n",
    "                    surprise_scores.append(nrc_output[emotion])\n",
    "                elif emotion ==\"sadness\":\n",
    "                    sadness_scores.append(nrc_output[emotion])\n",
    "                elif emotion == \"disgust\":\n",
    "                    disgust_scores.append(nrc_output[emotion])\n",
    "                elif emotion == \"joy\":\n",
    "                    joy_scores.append(nrc_output[emotion])\n",
    "                else: \n",
    "                    continue \n",
    "        #add the average of the emotion scores in the list to their respective key:value pairs in dictionary\n",
    "        emotion_data[\"character\"].append(character)\n",
    "        emotion_data[\"fear\"].append(average(fear_scores))\n",
    "        emotion_data[\"anger\"].append(average(anger_scores))\n",
    "        emotion_data[\"anticipation\"].append(average(anticipation_scores))\n",
    "        emotion_data[\"trust\"].append(average(trust_scores))\n",
    "        emotion_data[\"surprise\"].append(average(surprise_scores))\n",
    "        emotion_data[\"sadness\"].append(average(sadness_scores))\n",
    "        emotion_data[\"disgust\"].append(average(disgust_scores))\n",
    "        emotion_data[\"joy\"].append(average(joy_scores))\n",
    "    #create dataframe\n",
    "    emotion_df = pd.DataFrame(emotion_data)\n",
    "    return emotion_df            "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Applying function to each movie dialogue dataset. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "age_of_ultron_nrc_scores = create_emotion_data(new_age_of_ultron)\n",
    "ant_man_nrc_scores = create_emotion_data(new_ant_man)\n",
    "ant_wasp_nrc_scores = create_emotion_data(new_ant_wasp)\n",
    "avengers_nrc_scores = create_emotion_data(new_avengers)\n",
    "endgame_nrc_scores = create_emotion_data(new_endgame)\n",
    "black_panther_nrc_scores = create_emotion_data(new_black_panther)\n",
    "captain_america_nrc_scores = create_emotion_data(new_captain_america)\n",
    "captain_marvel_nrc_scores = create_emotion_data(new_captain_marvel)\n",
    "civil_war_nrc_scores = create_emotion_data(new_civil_war)\n",
    "dr_strange_nrc_scores = create_emotion_data(new_dr_strange)\n",
    "guardians_nrc_scores = create_emotion_data(new_guardians)\n",
    "guardians2_nrc_scores = create_emotion_data(new_guardians2)\n",
    "infinity_war_nrc_scores = create_emotion_data(new_infinity_war)\n",
    "iron_man_nrc_scores = create_emotion_data(new_iron_man)\n",
    "iron_man2_nrc_scores = create_emotion_data(new_iron_man2)\n",
    "iron_man3_nrc_scores = create_emotion_data(new_iron_man3)\n",
    "ragnarok_nrc_scores = create_emotion_data(new_ragnarok)\n",
    "spider_hoco_nrc_scores = create_emotion_data(new_spider_hoco)\n",
    "spider_far_nrc_scores = create_emotion_data(new_spider_far)\n",
    "thor_nrc_scores = create_emotion_data(new_thor)\n",
    "dark_world_nrc_scores = create_emotion_data(new_dark_world)\n",
    "winter_soldier_nrc_scores = create_emotion_data(new_winter_soldier)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "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>character</th>\n",
       "      <th>fear</th>\n",
       "      <th>anger</th>\n",
       "      <th>anticipation</th>\n",
       "      <th>trust</th>\n",
       "      <th>surprise</th>\n",
       "      <th>sadness</th>\n",
       "      <th>disgust</th>\n",
       "      <th>joy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>TONYSTARK</td>\n",
       "      <td>0.042075</td>\n",
       "      <td>0.034090</td>\n",
       "      <td>0.408481</td>\n",
       "      <td>0.119645</td>\n",
       "      <td>0.026196</td>\n",
       "      <td>0.027752</td>\n",
       "      <td>0.020194</td>\n",
       "      <td>0.032975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>STEVEROGERS</td>\n",
       "      <td>0.019541</td>\n",
       "      <td>0.009927</td>\n",
       "      <td>0.409956</td>\n",
       "      <td>0.064373</td>\n",
       "      <td>0.005749</td>\n",
       "      <td>0.018210</td>\n",
       "      <td>0.005462</td>\n",
       "      <td>0.015276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BRUCEBANNER</td>\n",
       "      <td>0.025122</td>\n",
       "      <td>0.022142</td>\n",
       "      <td>0.420833</td>\n",
       "      <td>0.031797</td>\n",
       "      <td>0.009233</td>\n",
       "      <td>0.011934</td>\n",
       "      <td>0.008442</td>\n",
       "      <td>0.012756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ULTRON</td>\n",
       "      <td>0.016944</td>\n",
       "      <td>0.007646</td>\n",
       "      <td>0.264478</td>\n",
       "      <td>0.043983</td>\n",
       "      <td>0.012762</td>\n",
       "      <td>0.017730</td>\n",
       "      <td>0.010537</td>\n",
       "      <td>0.020707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NATASHAROMANOFF</td>\n",
       "      <td>0.021964</td>\n",
       "      <td>0.019322</td>\n",
       "      <td>0.473210</td>\n",
       "      <td>0.034863</td>\n",
       "      <td>0.015250</td>\n",
       "      <td>0.009187</td>\n",
       "      <td>0.012426</td>\n",
       "      <td>0.019622</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         character      fear     anger  anticipation     trust  surprise  \\\n",
       "0        TONYSTARK  0.042075  0.034090      0.408481  0.119645  0.026196   \n",
       "1      STEVEROGERS  0.019541  0.009927      0.409956  0.064373  0.005749   \n",
       "2      BRUCEBANNER  0.025122  0.022142      0.420833  0.031797  0.009233   \n",
       "3           ULTRON  0.016944  0.007646      0.264478  0.043983  0.012762   \n",
       "4  NATASHAROMANOFF  0.021964  0.019322      0.473210  0.034863  0.015250   \n",
       "\n",
       "    sadness   disgust       joy  \n",
       "0  0.027752  0.020194  0.032975  \n",
       "1  0.018210  0.005462  0.015276  \n",
       "2  0.011934  0.008442  0.012756  \n",
       "3  0.017730  0.010537  0.020707  \n",
       "4  0.009187  0.012426  0.019622  "
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#example of resulting NRC scores\n",
    "age_of_ultron_nrc_scores.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The scores seen above are an average across all dialogue for each character in each movie."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Combine gender dataframe with NRC dataframe for each movie**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Combined dataframes with gender data and NRC data for each movie\n",
    "age_of_ultron_nrc_scores = match_gender(age_of_ultron_nrc_scores)\n",
    "ant_man_nrc_scores = match_gender(ant_man_nrc_scores)\n",
    "avengers_nrc_scores = match_gender(avengers_nrc_scores)\n",
    "endgame_nrc_scores = match_gender(endgame_nrc_scores)\n",
    "black_panther_nrc_scores = match_gender(black_panther_nrc_scores)\n",
    "captain_america_nrc_scores = match_gender(captain_america_nrc_scores)\n",
    "captain_marvel_nrc_scores = match_gender(captain_marvel_nrc_scores)\n",
    "civil_war_nrc_scores = match_gender(civil_war_nrc_scores)\n",
    "guardians2_nrc_scores = match_gender(guardians2_nrc_scores)\n",
    "infinity_war_nrc_scores = match_gender(infinity_war_nrc_scores)\n",
    "iron_man_nrc_scores = match_gender(iron_man_nrc_scores)\n",
    "iron_man2_nrc_scores = match_gender(iron_man2_nrc_scores)\n",
    "iron_man3_nrc_scores = match_gender(iron_man3_nrc_scores)\n",
    "ragnarok_nrc_scores = match_gender(ragnarok_nrc_scores)\n",
    "spider_hoco_nrc_scores = match_gender(spider_hoco_nrc_scores)\n",
    "thor_nrc_scores = match_gender(thor_nrc_scores)\n",
    "dark_world_nrc_scores = match_gender(dark_world_nrc_scores)\n",
    "winter_soldier_nrc_scores = match_gender(winter_soldier_nrc_scores)\n",
    "ant_wasp_nrc_scores = match_gender(ant_wasp_nrc_scores)\n",
    "dr_strange_nrc_scores = match_gender(dr_strange_nrc_scores)\n",
    "guardians_nrc_scores = match_gender(guardians_nrc_scores)\n",
    "spider_far_nrc_scores = match_gender(spider_far_nrc_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "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>character</th>\n",
       "      <th>fear</th>\n",
       "      <th>anger</th>\n",
       "      <th>anticipation</th>\n",
       "      <th>trust</th>\n",
       "      <th>surprise</th>\n",
       "      <th>sadness</th>\n",
       "      <th>disgust</th>\n",
       "      <th>joy</th>\n",
       "      <th>gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>TONYSTARK</td>\n",
       "      <td>0.042075</td>\n",
       "      <td>0.034090</td>\n",
       "      <td>0.408481</td>\n",
       "      <td>0.119645</td>\n",
       "      <td>0.026196</td>\n",
       "      <td>0.027752</td>\n",
       "      <td>0.020194</td>\n",
       "      <td>0.032975</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>STEVEROGERS</td>\n",
       "      <td>0.019541</td>\n",
       "      <td>0.009927</td>\n",
       "      <td>0.409956</td>\n",
       "      <td>0.064373</td>\n",
       "      <td>0.005749</td>\n",
       "      <td>0.018210</td>\n",
       "      <td>0.005462</td>\n",
       "      <td>0.015276</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BRUCEBANNER</td>\n",
       "      <td>0.025122</td>\n",
       "      <td>0.022142</td>\n",
       "      <td>0.420833</td>\n",
       "      <td>0.031797</td>\n",
       "      <td>0.009233</td>\n",
       "      <td>0.011934</td>\n",
       "      <td>0.008442</td>\n",
       "      <td>0.012756</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ULTRON</td>\n",
       "      <td>0.016944</td>\n",
       "      <td>0.007646</td>\n",
       "      <td>0.264478</td>\n",
       "      <td>0.043983</td>\n",
       "      <td>0.012762</td>\n",
       "      <td>0.017730</td>\n",
       "      <td>0.010537</td>\n",
       "      <td>0.020707</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NATASHAROMANOFF</td>\n",
       "      <td>0.021964</td>\n",
       "      <td>0.019322</td>\n",
       "      <td>0.473210</td>\n",
       "      <td>0.034863</td>\n",
       "      <td>0.015250</td>\n",
       "      <td>0.009187</td>\n",
       "      <td>0.012426</td>\n",
       "      <td>0.019622</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         character      fear     anger  anticipation     trust  surprise  \\\n",
       "0        TONYSTARK  0.042075  0.034090      0.408481  0.119645  0.026196   \n",
       "1      STEVEROGERS  0.019541  0.009927      0.409956  0.064373  0.005749   \n",
       "2      BRUCEBANNER  0.025122  0.022142      0.420833  0.031797  0.009233   \n",
       "3           ULTRON  0.016944  0.007646      0.264478  0.043983  0.012762   \n",
       "4  NATASHAROMANOFF  0.021964  0.019322      0.473210  0.034863  0.015250   \n",
       "\n",
       "    sadness   disgust       joy  gender  \n",
       "0  0.027752  0.020194  0.032975    Male  \n",
       "1  0.018210  0.005462  0.015276    Male  \n",
       "2  0.011934  0.008442  0.012756    Male  \n",
       "3  0.017730  0.010537  0.020707    Male  \n",
       "4  0.009187  0.012426  0.019622  Female  "
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#example of resulting NRC scores combined with gender data\n",
    "age_of_ultron_nrc_scores.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to find average emotion score for each gender within each movie\n",
    "\n",
    "def average_NRC(moviescore, name):\n",
    "    #Fear\n",
    "    avg_male_fear = moviescore.loc[moviescore['gender']=='Male', 'fear'].mean()\n",
    "    avg_female_fear = moviescore.loc[moviescore['gender']=='Female', 'fear'].mean()\n",
    "    #Anger\n",
    "    avg_male_anger = moviescore.loc[moviescore['gender']=='Male', 'anger'].mean()\n",
    "    avg_female_anger = moviescore.loc[moviescore['gender']=='Female', 'anger'].mean()\n",
    "    #Anticipation\n",
    "    avg_male_anticipation = moviescore.loc[moviescore['gender']=='Male', 'anticipation'].mean()\n",
    "    avg_female_anticipation = moviescore.loc[moviescore['gender']=='Female', 'anticipation'].mean()\n",
    "    #Trust\n",
    "    avg_male_trust = moviescore.loc[moviescore['gender']=='Male', 'trust'].mean()\n",
    "    avg_female_trust = moviescore.loc[moviescore['gender']=='Female', 'trust'].mean()\n",
    "    #Surprise\n",
    "    avg_male_surprise = moviescore.loc[moviescore['gender']=='Male', 'surprise'].mean()\n",
    "    avg_female_surprise = moviescore.loc[moviescore['gender']=='Female', 'surprise'].mean()\n",
    "    #Sadness\n",
    "    avg_male_sadness = moviescore.loc[moviescore['gender']=='Male', 'sadness'].mean()\n",
    "    avg_female_sadness = moviescore.loc[moviescore['gender']=='Female', 'sadness'].mean()\n",
    "    #Disgust\n",
    "    avg_male_disgust = moviescore.loc[moviescore['gender']=='Male', 'disgust'].mean()\n",
    "    avg_female_disgust = moviescore.loc[moviescore['gender']=='Female', 'disgust'].mean()\n",
    "    #Joy\n",
    "    avg_male_joy = moviescore.loc[moviescore['gender']=='Male', 'joy'].mean()\n",
    "    avg_female_joy = moviescore.loc[moviescore['gender']=='Female', 'joy'].mean()\n",
    "    res = pd.DataFrame([[avg_male_fear, avg_female_fear, avg_male_anger, avg_female_anger, avg_male_anticipation, avg_female_anticipation, avg_male_trust, avg_female_trust, avg_male_surprise, avg_female_surprise, avg_male_sadness, avg_female_sadness, avg_male_disgust, avg_female_disgust, avg_male_joy, avg_female_joy, name]], columns=['Male Fear', 'Female Fear', 'Male Anger', 'Female Anger', 'Male Anticipation', 'Female Anticipation', 'Male Trust', 'Female Trust', 'Male Surprise', ' Female Surprise', 'Male Sadness', 'Female Sadness', 'Male Disgust', 'Female Disgust', 'Male Joy', 'Female Joy', 'Movie'])\n",
    "    return res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Male Fear</th>\n",
       "      <th>Female Fear</th>\n",
       "      <th>Male Anger</th>\n",
       "      <th>Female Anger</th>\n",
       "      <th>Male Anticipation</th>\n",
       "      <th>Female Anticipation</th>\n",
       "      <th>Male Trust</th>\n",
       "      <th>Female Trust</th>\n",
       "      <th>Male Surprise</th>\n",
       "      <th>Female Surprise</th>\n",
       "      <th>Male Sadness</th>\n",
       "      <th>Female Sadness</th>\n",
       "      <th>Male Disgust</th>\n",
       "      <th>Female Disgust</th>\n",
       "      <th>Male Joy</th>\n",
       "      <th>Female Joy</th>\n",
       "      <th>Movie</th>\n",
       "      <th>Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.014130</td>\n",
       "      <td>0.008873</td>\n",
       "      <td>0.009119</td>\n",
       "      <td>0.006257</td>\n",
       "      <td>0.363130</td>\n",
       "      <td>0.491892</td>\n",
       "      <td>0.032642</td>\n",
       "      <td>0.014905</td>\n",
       "      <td>0.008538</td>\n",
       "      <td>0.004842</td>\n",
       "      <td>0.010928</td>\n",
       "      <td>0.005289</td>\n",
       "      <td>0.005602</td>\n",
       "      <td>0.005227</td>\n",
       "      <td>0.011899</td>\n",
       "      <td>0.007051</td>\n",
       "      <td>age_of_ultron</td>\n",
       "      <td>May 2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.011975</td>\n",
       "      <td>0.008909</td>\n",
       "      <td>0.007874</td>\n",
       "      <td>0.005937</td>\n",
       "      <td>0.309811</td>\n",
       "      <td>0.285481</td>\n",
       "      <td>0.018900</td>\n",
       "      <td>0.012903</td>\n",
       "      <td>0.007196</td>\n",
       "      <td>0.003224</td>\n",
       "      <td>0.009690</td>\n",
       "      <td>0.007801</td>\n",
       "      <td>0.005576</td>\n",
       "      <td>0.002711</td>\n",
       "      <td>0.010805</td>\n",
       "      <td>0.007525</td>\n",
       "      <td>ant_man</td>\n",
       "      <td>July 2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.020254</td>\n",
       "      <td>0.015755</td>\n",
       "      <td>0.013755</td>\n",
       "      <td>0.004288</td>\n",
       "      <td>0.294219</td>\n",
       "      <td>0.322258</td>\n",
       "      <td>0.044485</td>\n",
       "      <td>0.034932</td>\n",
       "      <td>0.008609</td>\n",
       "      <td>0.006125</td>\n",
       "      <td>0.013273</td>\n",
       "      <td>0.011536</td>\n",
       "      <td>0.005499</td>\n",
       "      <td>0.004382</td>\n",
       "      <td>0.011198</td>\n",
       "      <td>0.010270</td>\n",
       "      <td>avengers</td>\n",
       "      <td>May 2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.014776</td>\n",
       "      <td>0.003950</td>\n",
       "      <td>0.013083</td>\n",
       "      <td>0.003957</td>\n",
       "      <td>0.403076</td>\n",
       "      <td>0.368188</td>\n",
       "      <td>0.023389</td>\n",
       "      <td>0.011113</td>\n",
       "      <td>0.015162</td>\n",
       "      <td>0.001971</td>\n",
       "      <td>0.012227</td>\n",
       "      <td>0.003642</td>\n",
       "      <td>0.008816</td>\n",
       "      <td>0.003098</td>\n",
       "      <td>0.016285</td>\n",
       "      <td>0.003429</td>\n",
       "      <td>endgame</td>\n",
       "      <td>April 2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.014693</td>\n",
       "      <td>0.014774</td>\n",
       "      <td>0.009553</td>\n",
       "      <td>0.012223</td>\n",
       "      <td>0.297179</td>\n",
       "      <td>0.265177</td>\n",
       "      <td>0.030055</td>\n",
       "      <td>0.029107</td>\n",
       "      <td>0.006332</td>\n",
       "      <td>0.012114</td>\n",
       "      <td>0.012674</td>\n",
       "      <td>0.007235</td>\n",
       "      <td>0.004810</td>\n",
       "      <td>0.004610</td>\n",
       "      <td>0.010256</td>\n",
       "      <td>0.012698</td>\n",
       "      <td>black_panther</td>\n",
       "      <td>February 2018</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Male Fear  Female Fear  Male Anger  Female Anger  Male Anticipation  \\\n",
       "0   0.014130     0.008873    0.009119      0.006257           0.363130   \n",
       "1   0.011975     0.008909    0.007874      0.005937           0.309811   \n",
       "2   0.020254     0.015755    0.013755      0.004288           0.294219   \n",
       "3   0.014776     0.003950    0.013083      0.003957           0.403076   \n",
       "4   0.014693     0.014774    0.009553      0.012223           0.297179   \n",
       "\n",
       "   Female Anticipation  Male Trust  Female Trust  Male Surprise  \\\n",
       "0             0.491892    0.032642      0.014905       0.008538   \n",
       "1             0.285481    0.018900      0.012903       0.007196   \n",
       "2             0.322258    0.044485      0.034932       0.008609   \n",
       "3             0.368188    0.023389      0.011113       0.015162   \n",
       "4             0.265177    0.030055      0.029107       0.006332   \n",
       "\n",
       "    Female Surprise  Male Sadness  Female Sadness  Male Disgust  \\\n",
       "0          0.004842      0.010928        0.005289      0.005602   \n",
       "1          0.003224      0.009690        0.007801      0.005576   \n",
       "2          0.006125      0.013273        0.011536      0.005499   \n",
       "3          0.001971      0.012227        0.003642      0.008816   \n",
       "4          0.012114      0.012674        0.007235      0.004810   \n",
       "\n",
       "   Female Disgust  Male Joy  Female Joy          Movie           Year  \n",
       "0        0.005227  0.011899    0.007051  age_of_ultron       May 2015  \n",
       "1        0.002711  0.010805    0.007525        ant_man      July 2015  \n",
       "2        0.004382  0.011198    0.010270       avengers       May 2012  \n",
       "3        0.003098  0.016285    0.003429        endgame     April 2019  \n",
       "4        0.004610  0.010256    0.012698  black_panther  February 2018  "
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dataframe with average compound scores for each emotion per gender for each movie\n",
    "NRC_gender = pd.DataFrame(columns=['Male Fear', 'Female Fear', 'Male Anger', 'Female Anger', 'Male Anticipation', 'Female Anticipation', 'Male Trust', 'Female Trust', 'Male Surprise', ' Female Surprise', 'Male Sadness', 'Female Sadness', 'Male Disgust', 'Female Disgust', 'Male Joy', 'Female Joy', 'Movie'])\n",
    "NRC_gender = pd.concat([NRC_gender, average_NRC(age_of_ultron_nrc_scores, 'age_of_ultron')])\n",
    "NRC_gender = pd.concat([NRC_gender, average_NRC(ant_man_nrc_scores, 'ant_man')])\n",
    "NRC_gender = pd.concat([NRC_gender, average_NRC(avengers_nrc_scores, 'avengers')])\n",
    "NRC_gender = pd.concat([NRC_gender, average_NRC(endgame_nrc_scores, 'endgame')])\n",
    "NRC_gender = pd.concat([NRC_gender, average_NRC(black_panther_nrc_scores, 'black_panther')])\n",
    "NRC_gender = pd.concat([NRC_gender, average_NRC(captain_america_nrc_scores, 'captain_america')])\n",
    "NRC_gender = pd.concat([NRC_gender, average_NRC(captain_marvel_nrc_scores, 'captain_marvel')])\n",
    "NRC_gender = pd.concat([NRC_gender, average_NRC(civil_war_nrc_scores, 'civil_war')])\n",
    "NRC_gender = pd.concat([NRC_gender, average_NRC(guardians2_nrc_scores, 'guardians2')])\n",
    "NRC_gender = pd.concat([NRC_gender, average_NRC(infinity_war_nrc_scores, 'infinity_war')])\n",
    "NRC_gender = pd.concat([NRC_gender, average_NRC(iron_man_nrc_scores, 'iron_man')])\n",
    "NRC_gender = pd.concat([NRC_gender, average_NRC(iron_man2_nrc_scores, 'iron_man2')])\n",
    "NRC_gender = pd.concat([NRC_gender, average_NRC(iron_man3_nrc_scores, 'iron_man3')])\n",
    "NRC_gender = pd.concat([NRC_gender, average_NRC(ragnarok_nrc_scores, 'ragnarok')])\n",
    "NRC_gender = pd.concat([NRC_gender, average_NRC(spider_hoco_nrc_scores, 'spider_hoco')])\n",
    "NRC_gender = pd.concat([NRC_gender, average_NRC(thor_nrc_scores, 'thor')])\n",
    "NRC_gender = pd.concat([NRC_gender, average_NRC(dark_world_nrc_scores, 'dark_world')])\n",
    "NRC_gender = pd.concat([NRC_gender, average_NRC(winter_soldier_nrc_scores, 'winter_soldier')])\n",
    "NRC_gender = pd.concat([NRC_gender, average_NRC(ant_wasp_nrc_scores, 'ant_wasp')])\n",
    "NRC_gender = pd.concat([NRC_gender, average_NRC(dr_strange_nrc_scores, 'dr_strange')])\n",
    "NRC_gender = pd.concat([NRC_gender, average_NRC(guardians_nrc_scores, 'guardians')])\n",
    "NRC_gender = pd.concat([NRC_gender, average_NRC(spider_far_nrc_scores, 'spider_far')])\n",
    "\n",
    "NRC_gender['Year'] = ['May 2015', 'July 2015', 'May 2012', 'April 2019', 'February 2018',\n",
    "                        'July 2011', 'March 2019', 'May 2016', 'May 2017', 'April 2018', \n",
    "                        'May 2008', 'May 2010', 'May 2013', 'November 2017', 'July 2017',\n",
    "                        'May 2011', 'November 2013', 'April 2014', 'July 2018', 'November 2016', \n",
    "                        'August 2014', 'July 2019']\n",
    "\n",
    "#Reset indices to avoid empty indexing\n",
    "NRC_gender = NRC_gender.reset_index(drop=True)\n",
    "\n",
    "#Show values\n",
    "NRC_gender.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set pallete colors for Male vs Female visualizations\n",
    "palette = [(111/255,143/255,175/255), (255/255,182/255,193/255)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### VADER Scores"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Histogram of male vs female sentiment across all movies, based on VADER compound scores.\n",
    "\n",
    "The histogram below shows a normal distribution of both male and female sentiment across the films, with similar variance. This indicates that a t-test can be performed on this data to test for statistical significance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='Count'>"
      ]
     },
     "execution_count": 68,
     "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": [
    "sns.histplot(data = VADER_gender, palette=palette)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Barplot of male vs female sentiment across all movies, based on VADER compound scores. \n",
    "\n",
    "The error bars represent standard error (SE) across all movies. While males seem to have a higher compound score (more positive sentiment, the large overlap in SE between the bars suggests that there is likely not a statistically significant difference between Male and Female compound scores. However, this will be confirmed with a t-test in our final analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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FW7ZsUXt7u1JSUlRXV6fJkydLktrb272eWZOQkKC6ujoVFRVp9+7dio2N1c6dO7Vo0SJPTUZGhqqrq7Vx40Zt2rRJU6ZMUU1NjWbOnOmpefHFF7Vp0yY988wz6uzsVGxsrJ5++mn9/Oc/v5frBwAAhrBZt+7aHQHcbrciIiLkcrkUHh4+KOdYV1Y9KOMCw93WwiVDPYV7du3oyaGeAiQt3/mCOl3/T5I0IWKcflXw7NBOCBo15+FBHf9uf3/zt58AAIARCDUAAMAIhBoAAGAEQg0AADACoQYAABiBUAMAAIxAqAEAAEYg1AAAACMQagAAgBEINQAAwAiEGgAAYARCDQAAMAKhBgAAGIFQAwAAjECoAQAARiDUAAAAIxBqAACAEQg1AADACIQaAABgBEINAAAwAqEGAAAYgVADAACMQKgBAABGINQAAAAjEGoAAIARCDUAAMAIhBoAAGAEQg0AADACoQYAABiBUAMAAIxAqAEAAEYg1AAAACMQagAAgBEINQAAwAiEGgAAYARCDQAAMAKhBgAAGIFQAwAAjECoAQAARiDUAAAAIxBqAACAEQg1AADACAMKNXv27FFCQoLsdrscDoeOHTvWb31DQ4McDofsdrsSExNVUVHhU1NbW6vk5GSFhYUpOTlZBw4c8Km5ePGifvrTnyoyMlIPPvigvvvd76qxsXEglwAAAAwTcKipqalRYWGhNmzYoKamJmVmZmrevHlqbW31W3/+/Hnl5OQoMzNTTU1NWr9+vQoKClRbW+upcTqdys3NVV5enpqbm5WXl6fFixfr1KlTnpo///nP+t73vqeQkBC9/vrrOnv2rP7jP/5D48aNC/yqAQDDVlR4hCZEjNOEiHGKCo8Y6ungPmKzLMsKpMPMmTOVmpqq8vJyT1tSUpIWLlyo0tJSn/q1a9fq0KFDamlp8bTl5+erublZTqdTkpSbmyu3263XX3/dUzN37lyNHz9e+/btkyStW7dO77zzzh1XhfrjdrsVEREhl8ul8PDwAY/Tn3Vl1YMyLjDcbS1cMtRTuGfXjp4c6ikA96VRcx4e1PHv9vd3QCs1PT09amxsVFZWlld7VlaWTpw44beP0+n0qc/Oztbp06d148aNfmu+POahQ4eUlpamf/iHf9CECRM0ffp0vfzyy/3Ot7u7W26322sDAABmCijUdHV1qa+vT9HR0V7t0dHR6ujo8Nuno6PDb31vb6+6urr6rfnymB999JHKy8v1V3/1V3rjjTeUn5+vgoIC7d2797bzLS0tVUREhGeLi4sL5HIBAMAwMqAbhW02m9e+ZVk+bXeq/2r7nca8efOmUlNT9fzzz2v69Ol6+umntXLlSq+3wb6qpKRELpfLs7W1td354gAAwLAUUKiJiopSUFCQz6pMZ2enz0rLLTExMX7rg4ODFRkZ2W/Nl8ecOHGikpOTvWqSkpJue4OyJIWFhSk8PNxrAwAAZgoo1ISGhsrhcKi+vt6rvb6+XhkZGX77pKen+9QfPnxYaWlpCgkJ6bfmy2N+73vf07lz57xq/vSnP2ny5MmBXAIAADBUcKAdiouLlZeXp7S0NKWnp6uyslKtra3Kz8+X9MVbPhcvXvTc65Kfn69du3apuLhYK1eulNPpVFVVledTTZK0Zs0azZo1S9u2bdOCBQt08OBBHTlyRMePH/fUFBUVKSMjQ88//7wWL16s3//+96qsrFRlZeW9fg8AAIABAg41ubm5unLlirZs2aL29nalpKSorq7Os2LS3t7u9ZZQQkKC6urqVFRUpN27dys2NlY7d+7UokWLPDUZGRmqrq7Wxo0btWnTJk2ZMkU1NTWaOXOmp2bGjBk6cOCASkpKtGXLFiUkJKisrExPPvnkvVw/AAAwRMDPqRnOeE4NMHR4Tg1grmH5nBoAAID7FaEGAAAYgVADAACMQKgBAABGINQAAAAjEGoAAIARCDUAAMAIhBoAAGAEQg0AADACoQYAABiBUAMAAIxAqAEAAEYg1AAAACMQagAAgBEINQAAwAiEGgAAYARCDQAAMAKhBgAAGIFQAwAAjECoAQAARiDUAAAAIxBqAACAEQg1AADACIQaAABgBEINAAAwAqEGAAAYgVADAACMQKgBAABGINQAAAAjEGoAAIARCDUAAMAIhBoAAGAEQg0AADACoQYAABiBUAMAAIxAqAEAAEYg1AAAACMQagAAgBEINQAAwAiEGgAAYARCDQAAMAKhBgAAGIFQAwAAjDCgULNnzx4lJCTIbrfL4XDo2LFj/dY3NDTI4XDIbrcrMTFRFRUVPjW1tbVKTk5WWFiYkpOTdeDAgduOV1paKpvNpsLCwoFMHwAAGCjgUFNTU6PCwkJt2LBBTU1NyszM1Lx589Ta2uq3/vz588rJyVFmZqaampq0fv16FRQUqLa21lPjdDqVm5urvLw8NTc3Ky8vT4sXL9apU6d8xnv33XdVWVmp73znO4FOHQAAGCzgULNjxw499dRTWrFihZKSklRWVqa4uDiVl5f7ra+oqNCkSZNUVlampKQkrVixQsuXL9f27ds9NWVlZfrhD3+okpISTZs2TSUlJXr00UdVVlbmNdZnn32mJ598Ui+//LLGjx8f6NQBAIDBAgo1PT09amxsVFZWlld7VlaWTpw44beP0+n0qc/Oztbp06d148aNfmu+OuaqVav02GOP6Qc/+MFdzbe7u1tut9trAwAAZgoo1HR1damvr0/R0dFe7dHR0ero6PDbp6Ojw299b2+vurq6+q358pjV1dV67733VFpaetfzLS0tVUREhGeLi4u7674AAGB4GdCNwjabzWvfsiyftjvVf7W9vzHb2tq0Zs0a/eY3v5Hdbr/reZaUlMjlcnm2tra2u+4LAACGl+BAiqOiohQUFOSzKtPZ2emz0nJLTEyM3/rg4GBFRkb2W3NrzMbGRnV2dsrhcHiO9/X16X//93+1a9cudXd3KygoyOfcYWFhCgsLC+QSAQDAMBXQSk1oaKgcDofq6+u92uvr65WRkeG3T3p6uk/94cOHlZaWppCQkH5rbo356KOP6o9//KPOnDnj2dLS0vTkk0/qzJkzfgMNAAAYWQJaqZGk4uJi5eXlKS0tTenp6aqsrFRra6vy8/MlffGWz8WLF7V3715JUn5+vnbt2qXi4mKtXLlSTqdTVVVV2rdvn2fMNWvWaNasWdq2bZsWLFiggwcP6siRIzp+/LgkaezYsUpJSfGax+jRoxUZGenTDgAARqaAQ01ubq6uXLmiLVu2qL29XSkpKaqrq9PkyZMlSe3t7V7PrElISFBdXZ2Kioq0e/duxcbGaufOnVq0aJGnJiMjQ9XV1dq4caM2bdqkKVOmqKamRjNnzvwaLhEAAIwENuvWXbsjgNvtVkREhFwul8LDwwflHOvKqgdlXGC421q4ZKincM+uHT051FMA7kuj5jw8qOPf7e9v/vYTAAAwAqEGAAAYgVADAACMQKgBAABGINQAAAAjEGoAAIARCDUAAMAIhBoAAGAEQg0AADACoQYAABiBUAMAAIxAqAEAAEYg1AAAACMQagAAgBEINQAAwAiEGgAAYARCDQAAMAKhBgAAGIFQAwAAjECoAQAARiDUAAAAIxBqAACAEQg1AADACIQaAABgBEINAAAwAqEGAAAYgVADAACMQKgBAABGINQAAAAjEGoAAIARCDUAAMAIhBoAAGAEQg0AADACoQYAABiBUAMAAIxAqAEAAEYg1AAAACMQagAAgBEINQAAwAiEGgAAYARCDQAAMAKhBgAAGGFAoWbPnj1KSEiQ3W6Xw+HQsWPH+q1vaGiQw+GQ3W5XYmKiKioqfGpqa2uVnJyssLAwJScn68CBA17HS0tLNWPGDI0dO1YTJkzQwoULde7cuYFMHwAAGCjgUFNTU6PCwkJt2LBBTU1NyszM1Lx589Ta2uq3/vz588rJyVFmZqaampq0fv16FRQUqLa21lPjdDqVm5urvLw8NTc3Ky8vT4sXL9apU6c8NQ0NDVq1apVOnjyp+vp69fb2KisrS1evXh3AZQMAANPYLMuyAukwc+ZMpaamqry83NOWlJSkhQsXqrS01Kd+7dq1OnTokFpaWjxt+fn5am5ultPplCTl5ubK7Xbr9ddf99TMnTtX48eP1759+/zO4/Lly5owYYIaGho0a9asu5q72+1WRESEXC6XwsPD76pPoNaVVQ/KuMBwt7VwyVBP4Z5dO3pyqKcA3JdGzXl4UMe/29/fAa3U9PT0qLGxUVlZWV7tWVlZOnHihN8+TqfTpz47O1unT5/WjRs3+q253ZiS5HK5JEnf+MY3ArkEAABgqOBAiru6utTX16fo6Giv9ujoaHV0dPjt09HR4be+t7dXXV1dmjhx4m1rbjemZVkqLi7WI488opSUlNvOt7u7W93d3Z59t9vd7/UBAIDha0A3CttsNq99y7J82u5U/9X2QMZcvXq1/vCHP9z2ralbSktLFRER4dni4uL6rQcAAMNXQKEmKipKQUFBPisonZ2dPistt8TExPitDw4OVmRkZL81/sb82c9+pkOHDuntt9/WQw891O98S0pK5HK5PFtbW9sdrxEAAAxPAYWa0NBQORwO1dfXe7XX19crIyPDb5/09HSf+sOHDystLU0hISH91nx5TMuytHr1au3fv19vvfWWEhIS7jjfsLAwhYeHe20AAMBMAd1TI0nFxcXKy8tTWlqa0tPTVVlZqdbWVuXn50v6YnXk4sWL2rt3r6QvPum0a9cuFRcXa+XKlXI6naqqqvJ662jNmjWaNWuWtm3bpgULFujgwYM6cuSIjh8/7qlZtWqVXnvtNR08eFBjx471rOxERERo1KhR9/RNAAAAw1/AoSY3N1dXrlzRli1b1N7erpSUFNXV1Wny5MmSpPb2dq9n1iQkJKiurk5FRUXavXu3YmNjtXPnTi1atMhTk5GRoerqam3cuFGbNm3SlClTVFNTo5kzZ3pqbn2EfM6cOV7zeeWVV7Rs2bJALwMAABgm4OfUDGc8pwYYOjynBjDXsHxODQAAwP2KUAMAAIxAqAEAAEYg1AAAACMQagAAgBEINQAAwAiEGgAAYARCDQAAMAKhBgAAGIFQAwAAjECoAQAARiDUAAAAIxBqAACAEQg1AADACIQaAABgBEINAAAwAqEGAAAYgVADAACMQKgBAABGINQAAAAjEGoAAIARCDUAAMAIhBoAAGAEQg0AADACoQYAABiBUAMAAIxAqAEAAEYg1AAAACMQagAAgBEINQAAwAiEGgAAYARCDQAAMAKhBgAAGIFQAwAAjECoAQAARiDUAAAAIxBqAACAEQg1AADACIQaAABgBEINAAAwAqEGAAAYgVADAACMQKgBAABGGFCo2bNnjxISEmS32+VwOHTs2LF+6xsaGuRwOGS325WYmKiKigqfmtraWiUnJyssLEzJyck6cODAPZ8XAACMHAGHmpqaGhUWFmrDhg1qampSZmam5s2bp9bWVr/158+fV05OjjIzM9XU1KT169eroKBAtbW1nhqn06nc3Fzl5eWpublZeXl5Wrx4sU6dOjXg8wIAgJHFZlmWFUiHmTNnKjU1VeXl5Z62pKQkLVy4UKWlpT71a9eu1aFDh9TS0uJpy8/PV3Nzs5xOpyQpNzdXbrdbr7/+uqdm7ty5Gj9+vPbt2zeg8/rjdrsVEREhl8ul8PDwQC77rq0rqx6UcYHhbmvhkqGewj27dvTkUE8BuC+NmvPwoI5/t7+/gwMZtKenR42NjVq3bp1Xe1ZWlk6cOOG3j9PpVFZWlldbdna2qqqqdOPGDYWEhMjpdKqoqMinpqysbMDnlaTu7m51d3d79l0ul6QvvjmDpfv654M2NjCcDebr7i/l2tWrQz0F4L50Y5Bf37d+ftxpHSagUNPV1aW+vj5FR0d7tUdHR6ujo8Nvn46ODr/1vb296urq0sSJE29bc2vMgZxXkkpLS7V582af9ri4uNtfJIBBUVby1FBPAcAw9+mnnyoiIuK2xwMKNbfYbDavfcuyfNruVP/V9rsZM9DzlpSUqLi42LN/8+ZNffLJJ4qMjOy3H8zgdrsVFxentra2QXu7EcDQ4PU9sliWpU8//VSxsbH91gUUaqKiohQUFOSzOtLZ2emzinJLTEyM3/rg4GBFRkb2W3NrzIGcV5LCwsIUFhbm1TZu3LjbXyCMFB4ezg89wFC8vkeO/lZobgno00+hoaFyOByqr6/3aq+vr1dGRobfPunp6T71hw8fVlpamkJCQvqtuTXmQM4LAABGGCtA1dXVVkhIiFVVVWWdPXvWKiwstEaPHm1duHDBsizLWrdunZWXl+ep/+ijj6wHH3zQKioqss6ePWtVVVVZISEh1n//9397at555x0rKCjI2rp1q9XS0mJt3brVCg4Otk6ePHnX5wW+yuVyWZIsl8s11FMB8DXj9Q1/Ag41lmVZu3fvtiZPnmyFhoZaqampVkNDg+fY0qVLrdmzZ3vVHz161Jo+fboVGhpqxcfHW+Xl5T5j/td//Zf113/911ZISIg1bdo0q7a2NqDzAl91/fp161/+5V+s69evD/VUAHzNeH3Dn4CfUwMAAHA/4m8/AQAAIxBqAACAEQg1AADACIQajBgXLlyQzWbTmTNnhnoqAIZIfHy850/wwDyEGtzXli1bJpvNpvz8fJ9jzzzzjGw2m5YtW/aXnxiAO7r1+v3q9sEHHwz11GAoQg3ue3Fxcaqurta1a9c8bdevX9e+ffs0adKkIZwZgDuZO3eu2tvbvbaEhIShnhYMRajBfS81NVWTJk3S/v37PW379+9XXFycpk+f7mn73e9+p0ceeUTjxo1TZGSkfvSjH+nDDz/sd+yzZ88qJydHY8aMUXR0tPLy8tTV1TVo1wKMNGFhYYqJifHagoKC9D//8z9yOByy2+1KTEzU5s2b1dvb6+lns9n00ksv6Uc/+pEefPBBJSUlyel06oMPPtCcOXM0evRopaene73GP/zwQy1YsEDR0dEaM2aMZsyYoSNHjvQ7P5fLpX/+53/WhAkTFB4eru9///tqbm4etO8HBhehBsPCP/3TP+mVV17x7P/qV7/S8uXLvWquXr2q4uJivfvuu3rzzTf1wAMP6Mc//rFu3rzpd8z29nbNnj1b3/3ud3X69Gn97ne/08cff6zFixcP6rUAI90bb7yhn/70pyooKNDZs2f10ksv6de//rWee+45r7p/+7d/0z/+4z/qzJkzmjZtmp544gk9/fTTKikp0enTpyVJq1ev9tR/9tlnysnJ0ZEjR9TU1KTs7GzNnz9fra2tfudhWZYee+wxdXR0qK6uTo2NjUpNTdWjjz6qTz75ZPC+ARg8Q/zwP6BfS5cutRYsWGBdvnzZCgsLs86fP29duHDBstvt1uXLl60FCxZYS5cu9du3s7PTkmT98Y9/tCzLss6fP29JspqamizLsqxNmzZZWVlZXn3a2tosSda5c+cG87KAEWHp0qVWUFCQNXr0aM/2+OOPW5mZmdbzzz/vVfuf//mf1sSJEz37kqyNGzd69p1OpyXJqqqq8rTt27fPstvt/c4hOTnZevHFFz37kydPtn7xi19YlmVZb775phUeHu7zVOIpU6ZYL730UsDXi6EX0F/pBoZKVFSUHnvsMb366que/11FRUV51Xz44YfatGmTTp48qa6uLs8KTWtrq1JSUnzGbGxs1Ntvv60xY8b4HPvwww/1rW99a3AuBhhB/vZv/1bl5eWe/dGjR2vq1Kl69913vVZm+vr6dP36dX3++ed68MEHJUnf+c53PMejo6MlSd/+9re92q5fvy63263w8HBdvXpVmzdv1m9/+1tdunRJvb29unbt2m1XahobG/XZZ58pMjLSq/3atWt3fOsa9ydCDYaN5cuXe5aad+/e7XN8/vz5iouL08svv6zY2FjdvHlTKSkp6unp8TvezZs3NX/+fG3bts3n2MSJE7/eyQMj1K0Q82U3b97U5s2b9fd///c+9Xa73fN1SEiI52ubzXbbtlv/gXn22Wf1xhtvaPv27Zo6dapGjRqlxx9/vN+fARMnTtTRo0d9jo0bN+7uLhD3FUINho25c+d6fjhlZ2d7Hbty5YpaWlr00ksvKTMzU5J0/PjxfsdLTU1VbW2t4uPjFRzMSwH4S0lNTdW5c+d8ws69OnbsmJYtW6Yf//jHkr64x+bChQv9zqOjo0PBwcGKj4//WueCocGNwhg2goKC1NLSopaWFgUFBXkdGz9+vCIjI1VZWakPPvhAb731loqLi/sdb9WqVfrkk0/0k5/8RL///e/10Ucf6fDhw1q+fLn6+voG81KAEe3nP/+59u7dq3/913/V+++/r5aWFtXU1Gjjxo33NO7UqVO1f/9+nTlzRs3NzXriiSdu+0EBSfrBD36g9PR0LVy4UG+88YYuXLigEydOaOPGjZ4bkTG8EGowrISHhys8PNyn/YEHHlB1dbUaGxuVkpKioqIivfDCC/2OFRsbq3feeUd9fX3Kzs5WSkqK1qxZo4iICD3wAC8NYLBkZ2frt7/9rerr6zVjxgw9/PDD2rFjhyZPnnxP4/7iF7/Q+PHjlZGRofnz5ys7O1upqam3rbfZbKqrq9OsWbO0fPlyfetb39KSJUt04cIFzz08GF5slmVZQz0JAACAe8V/RwEAgBEINQAAwAiEGgAAYARCDQAAMAKhBgAAGIFQAwAAjECoAQAARiDUAAAAIxBqAACAEQg1AADACIQaAABgBEINAAAwwv8Hfn6PBOdSGegAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(data = VADER_gender, errorbar='se', palette=palette)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Line plots of male and female sentiment in movies, based on the release date:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "VADER_gender['Year'] = pd.to_datetime(VADER_gender['Year'])\n",
    "VADER_gender.sort_values(by='Year', inplace = True)\n",
    "\n",
    "x = VADER_gender['Year']\n",
    "fig, ax = plt.subplots(figsize= (16, 8))\n",
    "sns.lineplot(x = x, y = VADER_gender['Female'], label = 'female', data = VADER_gender, marker ='o', color = 'pink')\n",
    "sns.lineplot(x = x, y = VADER_gender['Male'], label = 'male', data = VADER_gender, marker ='o', color = 'blue')\n",
    "plt.xlabel(\"Release Date for MCU Movies\", fontsize= 12)\n",
    "plt.ylabel(\"Vader Score\", fontsize= 12)\n",
    "plt.title(\"Comparing Gender Vader Scores Across Years of the MCU\", fontsize= 12)\n",
    "plt.grid()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on the lineplot, it seems that sentiment for males and females has fluctuated greatly over the years from 2008 to 2020, with a peak in scores for males ocurring sometime in 2019 and the peak for females in 2011. Scores for each gender appear to follow a similar trend, with both male and female sentiment being the lowest in mid-2014 and rising again towards 2020. There are a few cases where male scores and female scores deviate, such as where females scores drop greatly in 2019 when male scores continue to increase. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Barplots for male vs female sentiment by phase\n",
    "\n",
    "The MCU universe is unique in that movies are split into phases. We did further visualization for movies in each phase to see if these time periods had any profound differences. The bar plots below depict VADER scores for male vs female characters in movies, separated by phase. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# BREAKING ALL DATAFRAME ENTRIES INTO PHASES\n",
    "phase_1_options = ['iron_man', 'iron_man2', 'thor', 'captain_america', 'avengers']\n",
    "phase_2_options = ['iron_man3', 'dark_world', 'winter_soldier', 'guardians', 'age_of_ultron', 'ant_man']\n",
    "phase_3_options = ['civil_war', 'dr_strange', 'guardians2', 'spider_hoco', 'ragnarok', 'black_panther', 'infinity_war', 'ant_wasp', 'captain_marvel', 'endgame', 'spider_far']\n",
    "\n",
    "phase_1_df = VADER_gender[VADER_gender['Movie'].isin(phase_1_options)]\n",
    "phase_2_df = VADER_gender[VADER_gender['Movie'].isin(phase_2_options)]\n",
    "phase_3_df = VADER_gender[VADER_gender['Movie'].isin(phase_3_options)]\n",
    "\n",
    "# SORTING BY DATE AND PLOTTING ALL MOVIES FROM PHASE I\n",
    "phase_1_df = phase_1_df.astype({'Movie': pd.CategoricalDtype(phase_1_options, ordered=True)})\n",
    "phase_1_df.drop('Year', axis=1, inplace=True)\n",
    "phase_1_df.head()\n",
    "phase_1_df.set_index('Movie')\n",
    "phase_1_df['Movie'] = pd.Categorical(phase_1_df.Movie, categories=phase_1_options, ordered=True)\n",
    "phase_1_df = phase_1_df.sort_values('Movie')\n",
    "\n",
    "phase_1_df.plot.bar(x='Movie', rot=0, title='Phase I Movies', # Adding a title to the top\n",
    "            xlabel=\"Movie Name\", # Adding a label on the xaxis\n",
    "            ylabel=\"VADER Score\",\n",
    "            color=palette)\n",
    "plt.xticks(rotation=45, ha='right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Interestingly, both males and females had an average neutral sentiment (< 0.05, > -0.05) for all Phase I movies. Female scores seem to have less variability, ranging from around 0.013 to 0.029. Males had a larger range from around 0.001 to 0.026. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/1r/c2dvp09n5ds9bz3tlmhjwtmr0000gn/T/ipykernel_91209/3937161354.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  phase_2_df.drop('Year', axis=1, inplace=True)\n",
      "/var/folders/1r/c2dvp09n5ds9bz3tlmhjwtmr0000gn/T/ipykernel_91209/3937161354.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  phase_2_df['Movie'] = pd.Categorical(phase_2_df.Movie, categories=phase_2_options, ordered=True)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# SORTING BY DATE AND PLOTTING ALL MOVIES FROM PHASE II\n",
    "phase_2_df.set_index('Movie')\n",
    "phase_2_df.drop('Year', axis=1, inplace=True)\n",
    "phase_2_df['Movie'] = pd.Categorical(phase_2_df.Movie, categories=phase_2_options, ordered=True)\n",
    "phase_2_df = phase_2_df.sort_values('Movie')\n",
    "\n",
    "phase_2_df.plot.bar(x='Movie', rot=0, title='Phase II Movies', # Adding a title to the top\n",
    "            xlabel=\"Movie Name\", # Adding a label on the xaxis\n",
    "            ylabel=\"VADER Score\",\n",
    "            color=palette)\n",
    "plt.xticks(rotation=45, ha='right')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Phase II scores see more variability, but like Phase I, scores for both males and females are all neutral. scores for both Guardians of the Galaxy are noticeably more negative that other movies in Phase II. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/1r/c2dvp09n5ds9bz3tlmhjwtmr0000gn/T/ipykernel_91209/2232556963.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  phase_3_df.drop('Year', axis=1, inplace=True)\n",
      "/var/folders/1r/c2dvp09n5ds9bz3tlmhjwtmr0000gn/T/ipykernel_91209/2232556963.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  phase_3_df['Movie'] = pd.Categorical(phase_3_df.Movie, categories=phase_3_options, ordered=True)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# SORTING BY DATE AND PLOTTING ALL MOVIES FROM PHASE III\n",
    "phase_3_df.set_index('Movie')\n",
    "phase_3_df.drop('Year', axis=1, inplace=True)\n",
    "phase_3_df['Movie'] = pd.Categorical(phase_3_df.Movie, categories=phase_3_options, ordered=True)\n",
    "phase_3_df = phase_3_df.sort_values('Movie')\n",
    "\n",
    "phase_3_df.plot.bar(x='Movie', rot=0, title='Phase III Movies', # Adding a title to the top\n",
    "            xlabel=\"Movie Name\", # Adding a label on the xaxis\n",
    "            ylabel=\"VADER Score\",\n",
    "            color=palette)\n",
    "plt.xticks(rotation=45, ha='right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ALL_Movies_Vader = VADER_gender\n",
    "ALL_Movies_Vader.drop('Year', axis=1, inplace=True)\n",
    "\n",
    "ALL_Movies_Vader.plot.bar(x='Movie', rot=0, title='All Movies', # Adding a title to the top\n",
    "            xlabel=\"Movie Name\", # Adding a label on the xaxis\n",
    "            ylabel=\"VADER Score\",\n",
    "            color=palette)\n",
    "plt.xticks(rotation=45, ha='right')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### NRC Scores"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Grouped Barplot of each emotion between gender, averaged across all the films"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "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>Average Emotion Score</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Emotion</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Male Fear</th>\n",
       "      <td>0.013235</td>\n",
       "      <td>Male</td>\n",
       "      <td>Fear</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Female Fear</th>\n",
       "      <td>0.009624</td>\n",
       "      <td>Female</td>\n",
       "      <td>Fear</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male Anger</th>\n",
       "      <td>0.009207</td>\n",
       "      <td>Male</td>\n",
       "      <td>Anger</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Female Anger</th>\n",
       "      <td>0.005964</td>\n",
       "      <td>Female</td>\n",
       "      <td>Anger</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male Anticipation</th>\n",
       "      <td>0.322236</td>\n",
       "      <td>Male</td>\n",
       "      <td>Anticipation</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Average Emotion Score  Gender       Emotion\n",
       "Male Fear                       0.013235    Male          Fear\n",
       "Female Fear                     0.009624  Female          Fear\n",
       "Male Anger                      0.009207    Male         Anger\n",
       "Female Anger                    0.005964  Female         Anger\n",
       "Male Anticipation               0.322236    Male  Anticipation"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Rearrange datafram for plotting\n",
    "NRC_gender_avg = pd.DataFrame(NRC_gender.mean(axis=0, numeric_only=True))\n",
    "NRC_gender_avg['Gender'] = ['Male', 'Female', 'Male', 'Female', 'Male', 'Female', 'Male',\\\n",
    "                             'Female', 'Male', 'Female', 'Male', 'Female', 'Male', 'Female', 'Male', 'Female']\n",
    "NRC_gender_avg['Emotion'] = ['Fear', 'Fear', 'Anger', 'Anger', 'Anticipation', 'Anticipation', 'Trust',\\\n",
    "                             'Trust', 'Surprise', 'Surprise', 'Sadness', 'Sadness', 'Disgust', 'Disgust', 'Joy', 'Joy']\n",
    "NRC_gender_avg.rename(columns={0:'Average Emotion Score'}, inplace=True)\n",
    "NRC_gender_avg.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600.25x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(data = NRC_gender_avg, kind='bar', x='Emotion', y='Average Emotion Score', hue='Gender', palette=palette)\n",
    "plt.xticks(rotation=45, ha='right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on the barplot above, we see that anticipation is the most common emotion that MCU dialogue for both males and females is associated with, followed by trust for both males and females. Males have higher association scores for all emotions except for anticipation. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### NRC vs VADER Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Correlation Matrix for Male Character NRC and VADER Scores\n",
    "\n",
    "We used correlation matrices to visualize how datapoints collected from NRC and VADER may interact with one another. These matrices are separated by male and female. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create dataframe where each column is one of the NRC/VADER scores\n",
    "VADER_gender.reset_index(inplace=True, drop=True)\n",
    "male_scores = pd.concat([VADER_gender.loc[:, ['Male']], NRC_gender.loc[:, ['Male Fear', 'Male Anger', 'Male Anticipation',\\\n",
    "                               'Male Trust', 'Male Surprise', 'Male Sadness', 'Male Disgust', 'Male Joy']]], axis=1, join='inner')\n",
    "male_scores = male_scores.rename(columns={\"Male\": \"VADER Compound\"})\n",
    "\n",
    "# Create correlation matrix\n",
    "male_corr = male_scores.corr()\n",
    "\n",
    "# Plot correlation matrix with seaborn\n",
    "sns.heatmap(male_corr, annot=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For male characters, the VADER compound has a low correlation with the emotion scores derived from NRC. The emotions, however, have higher correlation scores with one another, the highest being between sadness and fear, and joy and anger both with scores of 0.83. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Correlation Matrix for Female Character NRC and VADER Scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create dataframe where each column is one of the NRC/VADER scores\n",
    "female_scores = pd.concat([VADER_gender.loc[:, ['Female']], NRC_gender.loc[:, ['Female Fear', 'Female Anger', 'Female Anticipation',\\\n",
    "                               'Female Trust', ' Female Surprise', 'Female Sadness', 'Female Disgust', 'Female Joy']]], axis=1, join='inner')\n",
    "female_scores = female_scores.rename(columns={\"Female\": \"VADER Compound\"})\n",
    "\n",
    "# Create correlation matrix\n",
    "female_corr = female_scores.corr()\n",
    "\n",
    "# Plot correlation matrix with seaborn\n",
    "sns.heatmap(female_corr, annot=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For female characters, the VADER compound has a higher correlation with NRC emotions than male characters, but it is still lower than most other scores in the matrix. Correlation between anticipation and all other variables are much lower than male characters, except for the VADER compound score. The variables with the highest correlation score are between fear and joy, and joy and trust. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### VADER Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Two-Tailed t-Test to see if VADER scores (measure of sentiment analysis) vary between gender"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "H<sub>0</sub>: There is no significant difference in sentiment found in the dialogue between male and female characters in the MCU movies.\n",
    "\n",
    "H<sub>a</sub>: There is a significant difference in sentiment found in the dialogue between male and female characters in the MCU movies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The average VADER score for male characters is 0.010877326817016562\n",
      "The average VADER score for female characters is 0.008817474443020798\n"
     ]
    }
   ],
   "source": [
    "print('The average VADER score for male characters is', VADER_gender['Male'].mean())\n",
    "print('The average VADER score for female characters is', VADER_gender['Female'].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.592599332011457, 0.5566266742366777)"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t_val, p_val = ttest_ind(VADER_gender['Male'], VADER_gender['Female'])\n",
    "t_val, p_val"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since the p-value of 0.57 is greater than the alpha value of 0.05, this indicates that there is no statistically significant difference in sentiment between male and female characters in the MCU movies, despite the average VADER score for male characters being greater than that for female characters."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### NRC Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Two-Tailed t-Test to see if NRC scores vary between gender for each emotion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "H<sub>0</sub>: There is no significant difference in emotion found in the dialogue between male and female characters in the MCU movies.\n",
    "\n",
    "H<sub>a</sub>: There is a significant difference in emotion found in the dialogue between male and female characters in the MCU movies."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Fear**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The average fear score for male characters is 0.01323451674378198\n",
      "The average fear score for female characters is 0.009624348934648808\n"
     ]
    }
   ],
   "source": [
    "print('The average fear score for male characters is', NRC_gender['Male Fear'].mean())\n",
    "print('The average fear score for female characters is', NRC_gender['Female Fear'].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2.731551121588969, 0.00917837613963405)"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t_val, p_val = ttest_ind(NRC_gender['Male Fear'], NRC_gender['Female Fear'])\n",
    "t_val, p_val"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since the p-value of 0.009 is less than the alpha value of 0.05, this indicates that there is a statistically significant difference in the fear emotional content between male and female characters in the MCU movies, with the average fear score for male characters being greater than that for female characters."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Anger**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The average anger score for male characters is 0.009206755573600473\n",
      "The average anger score for female characters is 0.005964472002374335\n"
     ]
    }
   ],
   "source": [
    "print('The average anger score for male characters is', NRC_gender['Male Anger'].mean())\n",
    "print('The average anger score for female characters is', NRC_gender['Female Anger'].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2.8086553973844386, 0.007518138386337629)"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t_val, p_val = ttest_ind(NRC_gender['Male Anger'], NRC_gender['Female Anger'])\n",
    "t_val, p_val"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since the p-value of 0.008 is less than the alpha value of 0.05, this indicates that there is a statistically significant difference in the anger emotional content between male and female characters in the MCU movies, with the average anger score for male characters being greater than that for female characters."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Anticipation**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The average anticipation score for male characters is 0.32223580321633694\n",
      "The average anticipation score for female characters is 0.3623963969570354\n"
     ]
    }
   ],
   "source": [
    "print('The average anticipation score for male characters is', NRC_gender['Male Anticipation'].mean())\n",
    "print('The average anticipation score for female characters is', NRC_gender['Female Anticipation'].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-1.9981352001361816, 0.052204009791148386)"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t_val, p_val = ttest_ind(NRC_gender['Male Anticipation'], NRC_gender['Female Anticipation'])\n",
    "t_val, p_val"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since the p-value of 0.052 is greater than the alpha value of 0.05, this indicates that there is not a statistically significant difference in the anticipation emotional content between male and female characters in the MCU movies, despite the average anticipation score for female characters being greater than that for male characters."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Trust**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The average trust score for male characters is 0.022391067599201772\n",
      "The average trust score for female characters is 0.015114503687516805\n"
     ]
    }
   ],
   "source": [
    "print('The average trust score for male characters is', NRC_gender['Male Trust'].mean())\n",
    "print('The average trust score for female characters is', NRC_gender['Female Trust'].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2.9443082416606483, 0.005255817125795286)"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t_val, p_val = ttest_ind(NRC_gender['Male Trust'], NRC_gender['Female Trust'])\n",
    "t_val, p_val"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since the p-value of 0.005 is less than the alpha value of 0.05, this indicates that there is a statistically significant difference in the trust emotional content between male and female characters in the MCU movies, with the average trust score for male characters being greater than that for female characters."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Surprise**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The average surprise score for male characters is 0.007076154614861761\n",
      "The average surprise score for female characters is 0.004196588140748348\n"
     ]
    }
   ],
   "source": [
    "print('The average surprise score for male characters is', NRC_gender['Male Surprise'].mean())\n",
    "print('The average surprise score for female characters is', NRC_gender[' Female Surprise'].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3.575872956935562, 0.0008947293314754033)"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t_val, p_val = ttest_ind(NRC_gender['Male Surprise'], NRC_gender[' Female Surprise'])\n",
    "t_val, p_val"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since the p-value of 0.0009 is less than the alpha value of 0.05, this indicates that there is a statistically significant difference in the surprise emotional content between male and female characters in the MCU movies, with the average surprise score for male characters being greater than that for female characters."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Sadness**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The average sadness score for male characters is 0.010161531581518177\n",
      "The average sadness score for female characters is 0.006515614258132994\n"
     ]
    }
   ],
   "source": [
    "print('The average sadness score for male characters is', NRC_gender['Male Sadness'].mean())\n",
    "print('The average sadness score for female characters is', NRC_gender['Female Sadness'].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3.8235346045876906, 0.00042935011156751803)"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t_val, p_val = ttest_ind(NRC_gender['Male Sadness'], NRC_gender['Female Sadness'])\n",
    "t_val, p_val"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since the p-value of 0.0004 is less than the alpha value of 0.05, this indicates that there is a statistically significant difference in the sadness emotional content between male and female characters in the MCU movies, with the average sadness score for male characters being greater than that for female characters."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Disgust**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The average disgust score for male characters is 0.005579684680951409\n",
      "The average disgust score for female characters is 0.0033328371688292153\n"
     ]
    }
   ],
   "source": [
    "print('The average disgust score for male characters is', NRC_gender['Male Disgust'].mean())\n",
    "print('The average disgust score for female characters is', NRC_gender['Female Disgust'].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3.419946996413619, 0.0014055191115296094)"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t_val, p_val = ttest_ind(NRC_gender['Male Disgust'], NRC_gender['Female Disgust'])\n",
    "t_val, p_val"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since the p-value of 0.001 is less than the alpha value of 0.05, this indicates that there is a statistically significant difference in the disgust emotional content between male and female characters in the MCU movies, with the average disgust score for male characters being greater than that for female characters."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Joy**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The average joy score for male characters is 0.009772630085346541\n",
      "The average joy score for female characters is 0.006715030112788205\n"
     ]
    }
   ],
   "source": [
    "print('The average joy score for male characters is', NRC_gender['Male Joy'].mean())\n",
    "print('The average joy score for female characters is', NRC_gender['Female Joy'].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3.4967792164420706, 0.0011263113428821709)"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t_val, p_val = ttest_ind(NRC_gender['Male Joy'], NRC_gender['Female Joy'])\n",
    "t_val, p_val"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since the p-value of 0.001 is less than the alpha value of 0.05, this indicates that there is a statistically significant difference in the joy emotional content between male and female characters in the MCU movies, with the average joy score for male characters being greater than that for female characters."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ethics & Privacy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Given that the MCU is a fictional world with fictional characters, there are no limitations to the use of the data nor are there privacy concerns related to our data collection or analysis. In addition, the scripts are publically avaliable and our use of these scripts falls within fair-use, since we are doing extensive analysis on them. There is also no potential for the scripts themselves to be biased because we are not modifying the scripts.\n",
    "\n",
    "Despite this, there are still potential sources of bias in our analysis. Our data collection could be biased if we make biased decisions when choosing scripts which to analyze. With this in mind, we chose to analyze the scripts of all films in Phase 1 through Phase 4 of the MCU. This ensures we can account for changes over the years and potential differences between script writers and producers, to guarantee the patterns we find are generalizable to the entire MCU rather than to a specific writer or time-period. Our analysis of character dialogue for semantic variance could also be biased, if our definitions are poorly defined or biased by our personal beliefs.\n",
    "\n",
    "Finally, there is a potential ethical impact in the publication of our results, because these scripts were written and produced by real people whose morals and/or reputations could be compromised by the results. This adds a moral responsibility to ensure our analysis is properly conducted before publication.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusion & Discussion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Through our project, we aimed to understand the differences in sentiment between male and female characters’ dialogue in MCU films. We gathered data from MCU movie scripts and analyzed them using the NRC Emotion Lexicon as well as VADER (Valence Aware Dictionary for Sentiment Reasoning), which is a part of the Natural Language Toolkit. Although, as hypothesized, we found statistically significant evidence that male characters did tend to have higher scores for the 7 of the emotions (anger, fear, trust, surprise, sadness, joy, and disgust) according to NRC, the lack of statistically significant evidence for anticipation in NRC as well as through VADER Sentiment Analysis paired with our limitations concludes that no concrete discrepancies between male versus female dialogue in the MCU were found.\n",
    "\n",
    "Using NRC, we observed statistically significant differences for all emotions (anger, fear, anticipation, trust, surprise, sadness, joy, and disgust) except anticipation. It is worth nothing, however, that anticipation was found to have an unusually high score. We speculate that the reason behind the score is due to the anticipatory nature of action films such as those of the MCU, but such scores could also be influenced by the nature of the NRC’s system and lexicon, which is beyond our team’s control. For each emotion except anticipation, male characters had higher scores compared to female characters, meaning that the words used in their dialogue had stronger sentiment scores for those emotions. This could mean that male characters have more powerful and direct language; however, we must also take into account the overwhelming amount of male characters in each film in contrast to the few female characters, as such a population difference could skew the scores overall.\n",
    "\n",
    "On the other hand, no statistically significant difference in sentiment was found when analyzing the data using VADER. No discernible trends in regards to change of sentiment between male and female characters’ dialogue over time were observed from our line plot, either. The zone of neutrality for VADER is between -0.05 and +0.05, and none of the scores exceeded this range. It is possible that the scores for the sentiments canceled each other out, resulting in an ultimately neutral sentiment; the neutral score simply may reflect the nuanced nature of dialogue that contains both high and low moments throughout the variety of films. (Note: VADER may be more suitable for analyzing individual sentences or shorter paragraphs of dialogue to prevent the aforementioned ‘cancel-out’ effect; we were not able to conduct such an analysis in our project.)\n",
    "\n",
    "Looking at both measurement methods, there is a stronger correlation between the NRC and VADER scores for female characters compared to male characters. Female scores were closer to zero across all emotions, displaying a more neutral sentiment. Yet, no statistically significant differences were found for sentiment when looking at the data as a whole, only for specific emotions that the NRC Emotion Lexicon calculated. As such, no concrete conclusions could be found regarding male vs. female dialogue sentiment overall.\n",
    "\n",
    "We would like to note that the NRC Emotion Lexicon was unable to account for all of the words in our datasets. This is due to the lexicon itself missing certain words and having other words in only their forms with suffixes and prefixes attached, which we were unable to control or change. The NRC data used for our analyses was also crowdsources, which introduces potential biases and limitations in the data collection journey.\n",
    "\n",
    "Furthermore, because each film differs in terms of style greatly, trends may be harder to identify. “Guardians of the Galaxy” appeared to be a clear outlier in the dataset with its VADER sentiment scores reaching past -0.015 (drastically negative compared to other films). We were not able to identify the reason for why that particular film is an outlier in our given time, but we did want to note the possible confounding factors at play due to crowdsourced dialogue data.\n",
    "\n",
    "Although no statistically significant results were found generally, there are clear inequalities in terms of the representation of male versus female characters in terms of the number of lines they have in the MCU. Also, the female characters' scores were consistently more neutral compared to male characters’ scores, which was proven to be statistically significant according to NRC. With further exploration, more inequalities or perhaps even trends regarding factors beyond dialogue, such as screen time, scene genre, and other variables, could be uncovered. Such inequalities and trends, and even our results, mark small steps towards creating awareness of the data surrounding gender gaps in industries like entertainment. And though we were not able to provide conclusive results, it is important to explore areas such as gender discrepancies to further data models, industries, and society as a whole towards gender equality and fairness."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Team Contributions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- **Sarah Burbach**: Worked on writing \"Ethics\" and \"Overview\" sections, performed data cleaning for character summary data, created correlation matrices, assisted in data cleaning and group barplot for NRC data, performed EDA on raw data (prior to language analysis), worked on slides for final video\n",
    "- **Tracey Du**: Worked on writing \"Datasets\" section and finding appropriate datasets to use, performed data cleaning for categorizing movie dialogue by individual character per movie, performed t-tests for VADER and NRC scores \n",
    "- **Zak Hori**: Worked on writing \"Background Research\" section, gathered and formatted missing movie scripts in data cleaning section, created lineplots for EDA, editted final project video, assist in developing research question\n",
    "- **Grace Lin**: Worked on writing \"Datasets\" and \"Conclusion & Discussion\" section, assisted with NRC data cleaning and application to text, created group barplot for EDA, set up slides for final video, assist in developing research question\n",
    "- **Emily Yu**: Worked on writing \"Background Research\" and \"Conclusion & Discussion\" section, wrote code for VADER analysis function and assisted with NRC application to text, wrote brief explanation of EDA visualizations, assist in developing research question"
   ]
  }
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