4_Mood Analysis for Beer Styles.ipynb

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   "id": "89a147d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd \n",
    "import seaborn as sns\n",
    "import networkx as nx\n",
    "import matplotlib.pyplot as plt\n",
    "import plotly.graph_objects as go"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9560162a",
   "metadata": {},
   "source": [
    "### Loading Emotions Scores Aggregated By Beer Styles\n",
    "\n",
    "Let's load the csv files containing the emotions scores and visualize thei strucure :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "10d2bdbe",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load data\n",
    "df_ba =  pd.read_csv('C:/Users/User/Documents/GitHub/data/emotion_scores_ba_for_beer_styles.csv')\n",
    "df_rb = pd.read_csv('C:/Users/User/Documents/GitHub/data/emotion_scores_rb_for_beer_styles.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0c20fb55",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>style</th>\n",
       "      <th>joy_score</th>\n",
       "      <th>sadness_score</th>\n",
       "      <th>anger_score</th>\n",
       "      <th>fear_score</th>\n",
       "      <th>love_score</th>\n",
       "      <th>surprise_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Abbey Dubbel</td>\n",
       "      <td>0.203365</td>\n",
       "      <td>0.080878</td>\n",
       "      <td>0.126024</td>\n",
       "      <td>0.075817</td>\n",
       "      <td>0.130389</td>\n",
       "      <td>0.198102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Abbey Tripel</td>\n",
       "      <td>0.227110</td>\n",
       "      <td>0.077330</td>\n",
       "      <td>0.102070</td>\n",
       "      <td>0.069071</td>\n",
       "      <td>0.138369</td>\n",
       "      <td>0.189992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Abt/Quadrupel</td>\n",
       "      <td>0.205601</td>\n",
       "      <td>0.077203</td>\n",
       "      <td>0.116924</td>\n",
       "      <td>0.074061</td>\n",
       "      <td>0.121775</td>\n",
       "      <td>0.193606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Altbier</td>\n",
       "      <td>0.208427</td>\n",
       "      <td>0.101544</td>\n",
       "      <td>0.156289</td>\n",
       "      <td>0.084493</td>\n",
       "      <td>0.139425</td>\n",
       "      <td>0.198127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Amber Ale</td>\n",
       "      <td>0.212017</td>\n",
       "      <td>0.103296</td>\n",
       "      <td>0.158536</td>\n",
       "      <td>0.081056</td>\n",
       "      <td>0.141567</td>\n",
       "      <td>0.195680</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
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       "           style  joy_score  sadness_score  anger_score  fear_score  \\\n",
       "0   Abbey Dubbel   0.203365       0.080878     0.126024    0.075817   \n",
       "1   Abbey Tripel   0.227110       0.077330     0.102070    0.069071   \n",
       "2  Abt/Quadrupel   0.205601       0.077203     0.116924    0.074061   \n",
       "3        Altbier   0.208427       0.101544     0.156289    0.084493   \n",
       "4      Amber Ale   0.212017       0.103296     0.158536    0.081056   \n",
       "\n",
       "   love_score  surprise_score  \n",
       "0    0.130389        0.198102  \n",
       "1    0.138369        0.189992  \n",
       "2    0.121775        0.193606  \n",
       "3    0.139425        0.198127  \n",
       "4    0.141567        0.195680  "
      ]
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     "execution_count": 3,
     "metadata": {},
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   ],
   "source": [
    "df_rb.head()"
   ]
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  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "78a9c839",
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   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>style</th>\n",
       "      <th>joy_score</th>\n",
       "      <th>sadness_score</th>\n",
       "      <th>anger_score</th>\n",
       "      <th>fear_score</th>\n",
       "      <th>love_score</th>\n",
       "      <th>surprise_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Altbier</td>\n",
       "      <td>0.196298</td>\n",
       "      <td>0.098739</td>\n",
       "      <td>0.148250</td>\n",
       "      <td>0.092883</td>\n",
       "      <td>0.127143</td>\n",
       "      <td>0.207595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>American Adjunct Lager</td>\n",
       "      <td>0.170932</td>\n",
       "      <td>0.060593</td>\n",
       "      <td>0.096242</td>\n",
       "      <td>0.074346</td>\n",
       "      <td>0.093940</td>\n",
       "      <td>0.180464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>American Amber / Red Ale</td>\n",
       "      <td>0.193949</td>\n",
       "      <td>0.097813</td>\n",
       "      <td>0.152243</td>\n",
       "      <td>0.090014</td>\n",
       "      <td>0.127163</td>\n",
       "      <td>0.204509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>American Amber / Red Lager</td>\n",
       "      <td>0.183529</td>\n",
       "      <td>0.081462</td>\n",
       "      <td>0.135696</td>\n",
       "      <td>0.082171</td>\n",
       "      <td>0.116557</td>\n",
       "      <td>0.194319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>American Barleywine</td>\n",
       "      <td>0.178782</td>\n",
       "      <td>0.081599</td>\n",
       "      <td>0.133608</td>\n",
       "      <td>0.083311</td>\n",
       "      <td>0.117468</td>\n",
       "      <td>0.202873</td>\n",
       "    </tr>\n",
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       "                        style  joy_score  sadness_score  anger_score  \\\n",
       "0                     Altbier   0.196298       0.098739     0.148250   \n",
       "1      American Adjunct Lager   0.170932       0.060593     0.096242   \n",
       "2    American Amber / Red Ale   0.193949       0.097813     0.152243   \n",
       "3  American Amber / Red Lager   0.183529       0.081462     0.135696   \n",
       "4         American Barleywine   0.178782       0.081599     0.133608   \n",
       "\n",
       "   fear_score  love_score  surprise_score  \n",
       "0    0.092883    0.127143        0.207595  \n",
       "1    0.074346    0.093940        0.180464  \n",
       "2    0.090014    0.127163        0.204509  \n",
       "3    0.082171    0.116557        0.194319  \n",
       "4    0.083311    0.117468        0.202873  "
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   "source": [
    "df_ba.head()"
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   "cell_type": "markdown",
   "id": "372337c4",
   "metadata": {},
   "source": [
    "### Replacing Score values by Low/Medium/High based on 40th and 60th percentiles\n",
    "\n",
    "Mapping emotions scores to moods involved transforming complex sentiment data into a more comprehensible and actionable format.\n",
    "\n",
    "Step 1 : Conversion of Sentiment Scores to Categorical Variables: Initially, we dealt with sentiment scores ranging from -1 to 1, derived from advanced BERT-based NLP analysis of beer reviews. To make these scores more interpretable, we converted them into categorical variables: 'high', 'low', or 'medium'. This categorization was based on the distribution of emotion scores across all beer styles. Specifically, we computed the 40th and 60th percentiles for each emotion, using these as thresholds. Scores below the 40th percentile were categorized as 'low', those between the 40th and 60th percentiles as 'medium', and above the 60th percentile as 'high'. Importantly, we did not utilize the 'medium' category in our Mood Map, allowing for a buffer zone that reduces the arbitrariness in categorizing emotions as 'high' or 'low'.\n"
   ]
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  {
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   "execution_count": 5,
   "id": "90073fbc",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>style</th>\n",
       "      <th>joy_score</th>\n",
       "      <th>sadness_score</th>\n",
       "      <th>anger_score</th>\n",
       "      <th>fear_score</th>\n",
       "      <th>love_score</th>\n",
       "      <th>surprise_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Altbier</td>\n",
       "      <td>High</td>\n",
       "      <td>High</td>\n",
       "      <td>High</td>\n",
       "      <td>High</td>\n",
       "      <td>High</td>\n",
       "      <td>High</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>American Adjunct Lager</td>\n",
       "      <td>Low</td>\n",
       "      <td>Low</td>\n",
       "      <td>Low</td>\n",
       "      <td>Low</td>\n",
       "      <td>Low</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>American Amber / Red Ale</td>\n",
       "      <td>Medium</td>\n",
       "      <td>High</td>\n",
       "      <td>High</td>\n",
       "      <td>Medium</td>\n",
       "      <td>High</td>\n",
       "      <td>High</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>American Amber / Red Lager</td>\n",
       "      <td>Low</td>\n",
       "      <td>Low</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Low</td>\n",
       "      <td>Low</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>American Barleywine</td>\n",
       "      <td>Low</td>\n",
       "      <td>Low</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Low</td>\n",
       "      <td>Low</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "                        style joy_score sadness_score anger_score fear_score  \\\n",
       "0                     Altbier      High          High        High       High   \n",
       "1      American Adjunct Lager       Low           Low         Low        Low   \n",
       "2    American Amber / Red Ale    Medium          High        High     Medium   \n",
       "3  American Amber / Red Lager       Low           Low      Medium        Low   \n",
       "4         American Barleywine       Low           Low      Medium        Low   \n",
       "\n",
       "  love_score surprise_score  \n",
       "0       High           High  \n",
       "1        Low            Low  \n",
       "2       High           High  \n",
       "3        Low            Low  \n",
       "4        Low         Medium  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def replace_with_high_low(df):\n",
    "    \"\"\"\n",
    "    Categorize values in a DataFrame into 'High', 'Medium', or 'Low' based on the 40th and 60th percentiles.\n",
    "\n",
    "    Parameters:\n",
    "    df (DataFrame): The DataFrame to process.\n",
    "\n",
    "    Returns:\n",
    "    DataFrame: A new DataFrame with the categorized values.\n",
    "    \"\"\"\n",
    "    df_transformed = df.copy()\n",
    "    for column in df_transformed.columns:\n",
    "        if column != 'style':  # Skip the 'style' column\n",
    "            lower_percentile = df_transformed[column].quantile(0.4)\n",
    "            upper_percentile = df_transformed[column].quantile(0.6)\n",
    "            df_transformed[column] = df_transformed[column].apply(lambda x: 'High' if x > upper_percentile \n",
    "                                                                  else ('Low' if x < lower_percentile else 'Medium'))\n",
    "    return df_transformed\n",
    "\n",
    "# Apply the defined function to the scores of both websites\n",
    "df_ba_transformed = replace_with_high_low(df_ba)\n",
    "df_rb_transformed = replace_with_high_low(df_rb)\n",
    "\n",
    "df_ba_transformed.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "847ac96d",
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   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>style</th>\n",
       "      <th>joy_score</th>\n",
       "      <th>sadness_score</th>\n",
       "      <th>anger_score</th>\n",
       "      <th>fear_score</th>\n",
       "      <th>love_score</th>\n",
       "      <th>surprise_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Abbey Dubbel</td>\n",
       "      <td>Low</td>\n",
       "      <td>Low</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Low</td>\n",
       "      <td>High</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Abbey Tripel</td>\n",
       "      <td>High</td>\n",
       "      <td>Low</td>\n",
       "      <td>Low</td>\n",
       "      <td>Low</td>\n",
       "      <td>High</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Abt/Quadrupel</td>\n",
       "      <td>Low</td>\n",
       "      <td>Low</td>\n",
       "      <td>Low</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Low</td>\n",
       "      <td>High</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Altbier</td>\n",
       "      <td>Low</td>\n",
       "      <td>High</td>\n",
       "      <td>High</td>\n",
       "      <td>High</td>\n",
       "      <td>High</td>\n",
       "      <td>High</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Amber Ale</td>\n",
       "      <td>Medium</td>\n",
       "      <td>High</td>\n",
       "      <td>High</td>\n",
       "      <td>High</td>\n",
       "      <td>High</td>\n",
       "      <td>High</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           style joy_score sadness_score anger_score fear_score love_score  \\\n",
       "0   Abbey Dubbel       Low           Low      Medium     Medium        Low   \n",
       "1   Abbey Tripel      High           Low         Low        Low       High   \n",
       "2  Abt/Quadrupel       Low           Low         Low     Medium        Low   \n",
       "3        Altbier       Low          High        High       High       High   \n",
       "4      Amber Ale    Medium          High        High       High       High   \n",
       "\n",
       "  surprise_score  \n",
       "0           High  \n",
       "1         Medium  \n",
       "2           High  \n",
       "3           High  \n",
       "4           High  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_rb_transformed.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b27db7a1",
   "metadata": {},
   "source": [
    "### Definition of the Mood Map"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0197bc5",
   "metadata": {},
   "source": [
    "It is pivotal to understand the distinction between moods and emotions, as they form the foundation of our analysis. Emotions are intense but fleeting, often a direct response to external stimuli. They are acute feelings like joy, anger, or surprise, and are typically short-lived. Emotions can be clearly identified and are usually associated with distinct physiological and behavioural changes.\n",
    "\n",
    "Moods, on the other hand, are more subdued and enduring. They can last for hours, days, or even longer and often don't have a single identifiable cause. While emotions can be likened to the weather, striking distinctly and changing rapidly, moods are more comparable to the climate, a longer-term, pervasive state of mind. Moods are not as intense as emotions, but they can significantly influence a person's perception and interaction with the world.\n",
    "\n",
    "In our project, we've analysed a vast array of beer reviews, extracting emotional scores using advanced BERT-based sentiment analysis. These scores, aggregated by beer and subsequently by beer style, have been crucial in constructing our 'Mood Map'. This map creates a unique correspondence between various moods and their associated high and low emotional states, enabling us to match beer styles with specific moods. This innovative approach offers a nuanced understanding of how different beers resonate with varying emotional landscapes, enhancing the beer selection experience based on mood congruence.\n",
    "\n",
    "**Here are the moods considered in our analysis:**\n",
    "\n",
    "1. **Adventurous Sips:** Imagine a beer that takes you on an expedition! Reviews often speak of 'adventurous' brews, capturing the essence of exploration and discovery. It's about beers that dare you to step out of your comfort zone, offering exotic and unexpected flavors.\n",
    "\n",
    "2. **Cheerful Gulps:** Then there are the 'cheerful' beers, often described in reviews as light, refreshing, and uplifting. These are the brews that bring smiles and laughter, perfect for a sunny day or a casual gathering with friends.\n",
    "\n",
    "3. **Contemplative Reflections:** On the flip side, some beers make us introspective and 'contemplative.' These are the reviews that talk about rich, complex beers that make you ponder life, often accompanied by deeper, more intense flavors.\n",
    "\n",
    "4. **Energetic Cheers:** 'Energetic' beers are all about vibrancy and liveliness. These reviews are filled with descriptions of bright, zesty, and invigorating beers that are like a burst of energy, ideal for lively parties or to kickstart a great night.\n",
    "\n",
    "5. **Nostalgic Reminiscence:** Some beers take you on a trip down memory lane. These 'nostalgic' brews are often traditional, with a timeless taste that harks back to old memories and cherished moments.\n",
    "\n",
    "6. **Relaxed Sipping:** Lastly, there are beers that embody relaxation. Reviews of 'relaxed' beers often mention smooth, mellow, and comforting flavors, ideal for unwinding after a long day.\n",
    "\n",
    "For every mood considered, it's representation in the emotions space is defined in what we called, the ‘Mood Map’. Here is a tabular representation of that map:\n",
    "\n",
    "\n",
    "| Mood          | High Emotions        | Low Emotions         |\n",
    "|---------------|----------------------|----------------------|\n",
    "| Relaxed       | love                 | anger                |\n",
    "| Cheerful      | joy                  | sadness, anger       |\n",
    "| Nostalgic     | sadness              | surprise, joy        |\n",
    "| Adventurous   | surprise             | fear, sadness        |\n",
    "| Contemplative | love                 | joy                  |\n",
    "| Energetic     | joy                  | fear                 |\n",
    "\n",
    "To apply this mood map, we first need to hardcode it into a dictionnary:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "669e893d",
   "metadata": {},
   "outputs": [],
   "source": [
    "mood_map = {\n",
    "    \"Relaxed\": {\"high\": [\"love\"], \"low\": [\"anger\"]},\n",
    "    \"Cheerful\": {\"high\": [\"joy\"], \"low\": [\"sadness\", \"anger\"]},\n",
    "    \"Nostalgic\": {\"high\": [\"sadness\"], \"low\": [\"surprise\", \"joy\"]},\n",
    "    \"Adventurous\": {\"high\": [\"surprise\"], \"low\": [\"fear\", \"sadness\"]},\n",
    "    \"Contemplative\": {\"high\": [\"love\"], \"low\": [\"joy\"]},  \n",
    "    \"Energetic\": {\"high\": [\"joy\"], \"low\": [\"fear\"]}  \n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74a2f38a",
   "metadata": {},
   "source": [
    "### Visual Representation of the mood map\n",
    "\n",
    "Now let's represent this map visually using a network graph:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "458aa878",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Extracting unique emotions for high and low criteria\n",
    "emotions_high = set()\n",
    "emotions_low = set()\n",
    "for mood, criteria in mood_map.items():\n",
    "    emotions_high.update(criteria['high'])\n",
    "    emotions_low.update(criteria['low'])\n",
    "    \n",
    "all_emotions = emotions_high.union(emotions_low)\n",
    "\n",
    "# Create a directed graph\n",
    "G = nx.DiGraph()\n",
    "\n",
    "# Adding mood nodes (middle column) with a specific color\n",
    "mood_color = 'skyblue'\n",
    "for mood in mood_map:\n",
    "    G.add_node(mood, column=1, color=mood_color)\n",
    "\n",
    "# Adding emotion nodes (left and right columns) with a different color\n",
    "emotion_color = 'C1'\n",
    "for emotion in all_emotions:\n",
    "    G.add_node(emotion + \"_high\", column=0, color=emotion_color, label=emotion)  # Left column\n",
    "    G.add_node(emotion + \"_low\", column=2, color=emotion_color, label=emotion)  # Right column\n",
    "\n",
    "# Adding directed edges with colors\n",
    "for mood, criteria in mood_map.items():\n",
    "    for emotion in criteria['high']:\n",
    "        G.add_edge(mood, emotion + \"_high\", color='green')\n",
    "    for emotion in criteria['low']:\n",
    "        G.add_edge(mood, emotion + \"_low\", color='red')\n",
    "\n",
    "# Position nodes in three columns\n",
    "pos = {}\n",
    "y_spacing = len(all_emotions)\n",
    "y_spacing_mood = len(mood_map)\n",
    "\n",
    "# Position for emotions (left and right columns)\n",
    "for idx, emotion in enumerate(sorted(all_emotions)):\n",
    "    pos[emotion + \"_high\"] = (-1, -idx * (y_spacing_mood / y_spacing))  # Left column\n",
    "    pos[emotion + \"_low\"] = (1, -idx * (y_spacing_mood / y_spacing))  # Right column\n",
    "\n",
    "# Position for moods (middle column)\n",
    "for idx, mood in enumerate(sorted(mood_map.keys())):\n",
    "    pos[mood] = (0, -idx)\n",
    "\n",
    "# Extracting node colors\n",
    "node_colors = [data['color'] for node, data in G.nodes(data=True)]\n",
    "\n",
    "# Custom labels for nodes\n",
    "labels = {node: data.get('label', node) for node, data in G.nodes(data=True)}\n",
    "\n",
    "# Extracting edges and their colors\n",
    "edges = G.edges()\n",
    "edge_colors = [G[u][v]['color'] for u, v in edges]\n",
    "\n",
    "# Drawing the directed graph\n",
    "plt.figure(figsize=(10, 5))\n",
    "node_size = 1500  \n",
    "font_size = 8  \n",
    "\n",
    "nx.draw_networkx_nodes(G, pos, node_color=node_colors, node_size=node_size)\n",
    "nx.draw_networkx_labels(G, pos, labels=labels, font_size=font_size)\n",
    "\n",
    "# Drawing edges with smaller arrows\n",
    "nx.draw_networkx_edges(G, pos, edgelist=edges, edge_color=edge_colors, arrows=True, arrowsize=10, node_size=node_size)\n",
    "\n",
    "# Adjusting the legend size and positioning\n",
    "legend_font_size = 7  \n",
    "legend_handle_length = 2 \n",
    "\n",
    "mood_patch = plt.Line2D([0], [0], marker='o', color='w', label='Moods',\n",
    "                        markersize=5, markerfacecolor=mood_color)\n",
    "emotion_patch = plt.Line2D([0], [0], marker='o', color='w', label='Emotions',\n",
    "                           markersize=5, markerfacecolor=emotion_color)\n",
    "high_edge = plt.Line2D([0], [0], color='green', lw=1, label='High Emotion')\n",
    "low_edge = plt.Line2D([0], [0], color='red', lw=1, label='Low Emotion')\n",
    "\n",
    "plt.legend(handles=[mood_patch, emotion_patch, high_edge, low_edge],\n",
    "           fontsize=legend_font_size, handlelength=legend_handle_length, \n",
    "           loc='lower left', bbox_to_anchor=(0.65, 0))\n",
    "\n",
    "plt.axis('off')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0a6caa0",
   "metadata": {},
   "source": [
    "### Function to map the emotions to the moods\n",
    "\n",
    "Now we need to define a function that will convert the combinations of 'high', 'low' and 'medium' emotions scores to the corresponding mood.  \n",
    "\n",
    "However, since our Mood Map does not encompass every possible combination of 'high', 'low', and 'medium' emotional states, not all beer styles will be assigned a specific mood. This selective mapping ensures that our mood associations are meaningful and robust, reflecting a clear and strong correlation between beer styles and specific emotional landscapes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5a6f6b0f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def map_beer_to_mood(df, mood_map):\n",
    "    \"\"\"\n",
    "    Assigns a mood to each row in the DataFrame based on the emotion scores. If no match is found it returns 'None'.\n",
    "\n",
    "    Parameters:\n",
    "    df (DataFrame): The DataFrame containing beer reviews with emotion scores.\n",
    "    mood_map (dict): Dictionary defining the relationship between each mood and the associated emotions\n",
    "\n",
    "    Returns:\n",
    "    DataFrame: The DataFrame with an additional 'Mood' column.\n",
    "    \"\"\"\n",
    "    def determine_mood(row):\n",
    "        for mood, criteria in mood_map.items():\n",
    "            if all(row[f'{emotion}_score'] == 'High' for emotion in criteria['high']) and \\\n",
    "               all(row[f'{emotion}_score'] == 'Low' for emotion in criteria['low']):\n",
    "                return mood\n",
    "        return None\n",
    "\n",
    "    df['Mood'] = df.apply(determine_mood, axis=1)\n",
    "    return df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57b5c0e6",
   "metadata": {},
   "source": [
    "### Results of the Mood mapping\n",
    "\n",
    "Now let's apply that function to the data of both websites. The results of the mood mapping for both websites will then be merged in order to obtain a complete list of beer styles for each mood, regardless of the website of origin. \n",
    "\n",
    "The dataframe obtained, `mood_styles_df`, contains for each mood, the list of styles that were associated to it and a count of the number of styles in that list. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b74f6a58",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\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>Mood</th>\n",
       "      <th>style</th>\n",
       "      <th>num_styles</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Adventurous</td>\n",
       "      <td>[Lambic - Fruit]</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Cheerful</td>\n",
       "      <td>[American Blonde Ale, American Pale Lager, Ame...</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Contemplative</td>\n",
       "      <td>[English Porter, Foreign / Export Stout, Milk ...</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Energetic</td>\n",
       "      <td>[American IPA, American Wild Ale, Berliner Wei...</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Nostalgic</td>\n",
       "      <td>[American Black Ale, English Stout, Irish Dry ...</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Relaxed</td>\n",
       "      <td>[Bière de Garde, Braggot, Faro, Happoshu, Kvas...</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Mood                                              style  \\\n",
       "0    Adventurous                                   [Lambic - Fruit]   \n",
       "1       Cheerful  [American Blonde Ale, American Pale Lager, Ame...   \n",
       "2  Contemplative  [English Porter, Foreign / Export Stout, Milk ...   \n",
       "3      Energetic  [American IPA, American Wild Ale, Berliner Wei...   \n",
       "4      Nostalgic  [American Black Ale, English Stout, Irish Dry ...   \n",
       "5        Relaxed  [Bière de Garde, Braggot, Faro, Happoshu, Kvas...   \n",
       "\n",
       "   num_styles  \n",
       "0           1  \n",
       "1          22  \n",
       "2          13  \n",
       "3          13  \n",
       "4          11  \n",
       "5          16  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Apply the map_beer_to_mood function to the data of both websites\n",
    "df_ba_mapped = map_beer_to_mood(df_ba_transformed.copy(), mood_map)\n",
    "df_rb_mapped = map_beer_to_mood(df_rb_transformed.copy(), mood_map)\n",
    "\n",
    "# Combine both DataFrames\n",
    "combined_df = pd.concat([df_ba_mapped, df_rb_mapped])\n",
    "\n",
    "# Group by 'Mood' and aggregate 'style' into a list, then add a count of beer styles\n",
    "mood_styles_df = combined_df.groupby('Mood')['style'].apply(list).reset_index()\n",
    "mood_styles_df['num_styles'] = mood_styles_df['style'].apply(len)\n",
    "\n",
    "mood_styles_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54781079",
   "metadata": {},
   "source": [
    "Here we can see that all of the 6 moods were assigned at least one beer style. Since the results of both dataframes were merged, let's look at potential duplicates and the proportion of styles succesfuly mapped to a mood. \n",
    "\n",
    "First let's look at how many styles we have in total, including the data from both websites."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "34b25d6a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We have a total of 169 unique styles.\n"
     ]
    }
   ],
   "source": [
    "all_styles = pd.concat([df_ba_mapped['style'], df_rb_mapped['style']])\n",
    "\n",
    "print(f'We have a total of {len(all_styles.unique())} unique styles.')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69e57478",
   "metadata": {},
   "source": [
    "Now let's look at the proportion of all the beer styles succesfully mapped to a mood:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "3195ec59",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We have a total of 76 styles succefully mapped, from which 74 are unique.\n"
     ]
    }
   ],
   "source": [
    "mapped_styles = mood_styles_df.explode('style')\n",
    "\n",
    "print(f\"\"\"We have a total of {len(mapped_styles['style'])} styles succefully mapped, from which {len(mapped_styles['style'].unique())} are unique.\"\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4bda27b7",
   "metadata": {},
   "source": [
    "This means that 2 styles were mapped to two different moods depending on the website from which the reviews were considered. since the websites were treated seperately in the analysis, this does not pose a problem. Since 74 different styles were mathed from the original 169, it means that 44% of the beer styles were succesfully matched."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "979f8f3d",
   "metadata": {},
   "source": [
    "### Visualization of the results\n",
    "\n",
    "Let's visualize the results, different graphs will be provided, each presenting a different angle.\n",
    "\n",
    "For better visualization, let's first define a consistent list a colors for each mood, using a dictionnary:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "fe9dba12",
   "metadata": {},
   "outputs": [],
   "source": [
    "mood_colors = {\n",
    "    'Cheerful': '#FFEB3B',      # Yellow\n",
    "    'Nostalgic': '#009688',     # Softer blue\n",
    "    'Adventurous': '#800080',   # Purple\n",
    "    'Contemplative': '#8B0000', # Deep red\n",
    "    'Energetic': '#FF6F00',     # Bright yellow\n",
    "    'Relaxed': '#9ACD32',       # Bright yellow\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ddcd4fa7",
   "metadata": {},
   "source": [
    "#### 1. Pie Chart\n",
    "\n",
    "This first graph will show, from the styles that were succesfully mapped, the proportion of each mood being assigned."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9c91a3bd",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Extracting colors for the corresponding moods\n",
    "colors = [mood_colors[mood] for mood in mood_styles_df['Mood']]\n",
    "\n",
    "# Creating the pie chart\n",
    "plt.figure(figsize=(5, 5))\n",
    "plt.pie(mood_styles_df['num_styles'], labels=mood_styles_df['Mood'], autopct='%1.1f%%', startangle=140, colors=colors)\n",
    "plt.title('Distribution of Moods in Succesfully Mapped Styles')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf5bf5c6",
   "metadata": {},
   "source": [
    "#### 2. Bar Plot\n",
    "\n",
    "This second plot will represent the number of beer styles attributed to each mood."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "f0953464",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6, 4))\n",
    "\n",
    "# Manually plotting each bar for consistent coloring\n",
    "for i, mood in enumerate(mood_styles_df['Mood']):\n",
    "    plt.bar(mood, mood_styles_df['num_styles'][i], color=mood_colors[mood])\n",
    "\n",
    "plt.title('Number of Beer Styles per Mood')\n",
    "plt.xlabel('Mood')\n",
    "plt.ylabel('Number of Beer Styles')\n",
    "plt.xticks(rotation=45)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82815b8b",
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   "source": [
    "#### 3. Interactive interface showing the beer styles for the selected mood\n",
    "\n",
    "As a final product, let's create an interactive interface that would allow the user to choose a mood, and then receive a list of suggested beer styles for that mood:"
   ]
  },
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Lager<br>American Pale Ale<br>Czech Pilsner (Sv\\u011btl\\u00fd)<br>India Pale Ale (IPA)<br>Lambic Style - Unblended<br>Session IPA<br>Sour/Wild Ale\"]}],\"label\":\"Energetic\",\"method\":\"update\"},{\"args\":[{\"text\":[\"American Black Ale<br>English Stout<br>Irish Dry Stout<br>Munich Dunkel Lager<br>Oatmeal Stout<br>Black IPA<br>Dry Stout<br>Foreign Stout<br>Imperial Porter<br>Porter<br>Stout\"]}],\"label\":\"Nostalgic\",\"method\":\"update\"},{\"args\":[{\"text\":[\"Bi\\u00e8re de Garde<br>Braggot<br>Faro<br>Happoshu<br>Kvass<br>Saison / Farmhouse Ale<br>Weizenbock<br>Witbier<br>Abbey Tripel<br>Cream Ale<br>Golden Ale/Blond Ale<br>Lambic Style - Faro<br>Lambic Style - Fruit<br>Lambic Style - Gueuze<br>Mead<br>Zwickel/Keller/Landbier\"]}],\"label\":\"Relaxed\",\"method\":\"update\"}],\"direction\":\"down\",\"pad\":{\"r\":10,\"t\":10},\"showactive\":true,\"x\":0.1,\"xanchor\":\"left\",\"y\":1.1,\"yanchor\":\"top\"}],\"title\":{\"text\":\"Interactive Visualization of Beer Styles by 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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def create_display_text(mood, mood_styles_df):\n",
    "    \"\"\"\n",
    "    Creates display text for the given mood with beer styles separated by line breaks.\n",
    "\n",
    "    Parameters:\n",
    "    mood (str): The mood for which to create the display text.\n",
    "    mood_styles_df (DataFrame): DataFrame containing moods and associated beer styles.\n",
    "\n",
    "    Returns:\n",
    "    str: A string representing the beer styles associated with the mood, separated by line breaks.\n",
    "    \"\"\"\n",
    "    styles = mood_styles_df[mood_styles_df['Mood'] == mood]['style'].iloc[0]\n",
    "    return \"<br>\".join(styles)\n",
    "\n",
    "# Initialize the figure\n",
    "fig = go.Figure()\n",
    "\n",
    "# Initial display text\n",
    "initial_mood = mood_styles_df['Mood'].iloc[0]\n",
    "initial_text = create_display_text(initial_mood, mood_styles_df)\n",
    "\n",
    "# Add the initial text to the figure\n",
    "fig.add_trace(go.Scatter(x=[0], y=[0], text=[initial_text], mode=\"text\"))\n",
    "\n",
    "# Update layout for dropdown menu and other settings\n",
    "fig.update_layout(\n",
    "    updatemenus=[\n",
    "        dict(\n",
    "            buttons=[dict(label=mood, method=\"update\", \n",
    "                          args=[{\"text\": [create_display_text(mood, mood_styles_df)]}]) \n",
    "                    for mood in mood_styles_df['Mood']],\n",
    "            direction=\"down\",\n",
    "            pad={\"r\": 10, \"t\": 10},\n",
    "            showactive=True,\n",
    "            x=0.1,\n",
    "            xanchor=\"left\",\n",
    "            y=1.1,\n",
    "            yanchor=\"top\"\n",
    "        ),\n",
    "    ],\n",
    "    title=\"Interactive Visualization of Beer Styles by Mood\",\n",
    "    xaxis={'visible': False, 'showticklabels': False},\n",
    "    yaxis={'visible': False, 'showticklabels': False}\n",
    ")\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58bfa7fe",
   "metadata": {},
   "source": [
    "Another interesting question to ask is the following:\n",
    "\n",
    "### What moods do the best-selling beers tend to match?\n",
    "\n",
    "As the 20 most popular styles for each websites were identified earlier in the analysis, this question can easily be answered.\n",
    "\n",
    "First we need to load the corresponding data and quickly pre-process in the same way as in Milestone 2:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "3b0d445a",
   "metadata": {},
   "outputs": [],
   "source": [
    "rb = pd.read_csv(\".\\\\matched_beer_data\\\\ratings_with_text_rb.csv\", sep = \",\", header = 0)\n",
    "ba = pd.read_csv(\".\\\\matched_beer_data\\\\ratings_with_text_ba.csv\", sep = \",\", header = 0)\n",
    "\n",
    "ba_copy = ba.copy()\n",
    "ba_no_duplicates = ba_copy.drop_duplicates() \n",
    "ba_filtered = ba_no_duplicates.dropna(subset=['text'], how='all')\n",
    "\n",
    "rb_copy = rb.copy()\n",
    "rb_no_duplicates = rb_copy.drop_duplicates()\n",
    "rb_filtered = rb_no_duplicates.dropna(subset=['text'], how='all')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "881ebacd",
   "metadata": {},
   "source": [
    "Let's now count the number of reviews for each style and isolate the top 20 styles for each website :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "cf3f424e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "style\n",
       "American IPA                        35088\n",
       "American Double / Imperial IPA      23047\n",
       "American Pale Ale (APA)             15423\n",
       "Saison / Farmhouse Ale              13149\n",
       "American Double / Imperial Stout     9485\n",
       "American Wild Ale                    9212\n",
       "American Amber / Red Ale             9091\n",
       "Belgian Strong Pale Ale              6897\n",
       "American Porter                      6761\n",
       "Russian Imperial Stout               6435\n",
       "American Brown Ale                   6033\n",
       "Fruit / Vegetable Beer               5608\n",
       "Quadrupel (Quad)                     5415\n",
       "Belgian Strong Dark Ale              5313\n",
       "Witbier                              5026\n",
       "Märzen / Oktoberfest                 4723\n",
       "American Blonde Ale                  4481\n",
       "Doppelbock                           4336\n",
       "German Pilsener                      3765\n",
       "American Black Ale                   3713\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Aggregate review counts by beer style for BA\n",
    "style_counts_ba = ba_filtered['style'].value_counts()\n",
    "\n",
    "# Select the top 20 styles \n",
    "top_styles_ba = style_counts_ba.head(20)\n",
    "\n",
    "top_styles_ba"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "7f05222f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "style\n",
       "India Pale Ale (IPA)    105718\n",
       "American Pale Ale        50813\n",
       "Imperial IPA             50424\n",
       "Imperial Stout           44973\n",
       "Saison                   43722\n",
       "Belgian Strong Ale       42738\n",
       "Belgian Ale              32827\n",
       "Sour/Wild Ale            31820\n",
       "Porter                   31362\n",
       "Golden Ale/Blond Ale     26488\n",
       "Amber Ale                23488\n",
       "Pilsener                 21748\n",
       "Brown Ale                21627\n",
       "Spice/Herb/Vegetable     21350\n",
       "Fruit Beer               21071\n",
       "Stout                    19489\n",
       "Black IPA                18903\n",
       "Witbier                  17353\n",
       "Barley Wine              15172\n",
       "Abbey Tripel             14642\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Aggregate review counts by beer style for RB\n",
    "style_counts_rb = rb_filtered['style'].value_counts()\n",
    "\n",
    "# Select the top 20 styles \n",
    "top_styles_rb = style_counts_rb.head(20)\n",
    "\n",
    "top_styles_rb"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ed118d2",
   "metadata": {},
   "source": [
    "These results can now be combined with the mood mapping and produce some interting plots\n",
    "\n",
    "#### Pie Chart of Mood Distributions among top 20 beer styles\n",
    "\n",
    "This first chart will consider only the top 20 beer styles from each website, and represented the proportion of each mood represented in this list, regardless of the position of the style within that top 20."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "3ae21a7d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Step 1: Explode the mood_styles_df DataFrame for mapping\n",
    "mood_styles_expanded = mood_styles_df.explode('style')\n",
    "\n",
    "# Step 2: Create a mapping from style to mood\n",
    "style_to_mood = dict(zip(mood_styles_expanded['style'], mood_styles_expanded['Mood']))\n",
    "\n",
    "def assign_moods(top_styles, style_to_mood):\n",
    "    \"\"\"\n",
    "    Assigns moods to beer styles and counts the frequency of each mood.\n",
    "\n",
    "    Parameters:\n",
    "    top_styles (Series): Series of top beer styles.\n",
    "    style_to_mood (dict): Mapping from beer styles to moods.\n",
    "\n",
    "    Returns:\n",
    "    Series: Counts of moods for the given beer styles.\n",
    "    \"\"\"\n",
    "    moods = top_styles.index.map(lambda x: style_to_mood.get(x, 'Undetermined'))\n",
    "    return pd.Series(moods).value_counts()\n",
    "\n",
    "# Step 3: Count Moods in both datasets\n",
    "ba_mood_distribution = assign_moods(top_styles_ba, style_to_mood)\n",
    "rb_mood_distribution = assign_moods(top_styles_rb, style_to_mood)\n",
    "\n",
    "# Define a grey color for the 'Undertermined' beer styles \n",
    "mood_colors['Undetermined'] = 'lightgrey'\n",
    "\n",
    "# Sort by mood name for consistency in visualization\n",
    "ba_mood_distribution.sort_index(inplace=True)\n",
    "rb_mood_distribution.sort_index(inplace=True)\n",
    "\n",
    "# Step 4: Create a pie chart\n",
    "fig, axs = plt.subplots(1, 2, figsize=(14, 6))\n",
    "\n",
    "# BA Top Styles\n",
    "axs[0].pie(ba_mood_distribution, labels=ba_mood_distribution.index, autopct='%1.1f%%', startangle=0,\n",
    "           colors=[mood_colors[mood] for mood in ba_mood_distribution.index])\n",
    "axs[0].set_title('Mood Distribution in Top 20 Beer Styles on Beer Advocate')\n",
    "\n",
    "# RB Top Styles\n",
    "axs[1].pie(rb_mood_distribution, labels=rb_mood_distribution.index, autopct='%1.1f%%', startangle=0,\n",
    "           colors=[mood_colors[mood] for mood in rb_mood_distribution.index])\n",
    "axs[1].set_title('Mood Distribution in Top 20 Beer Styles on RateBeer')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df1c7bc3",
   "metadata": {},
   "source": [
    "This plot provide valuable insight into the mood distribution amongst the most popular styles. However, the position of each beer style in the top 20 seems like an important information, and is not shown here. Let's try to include that information by weighting the slices of the pies by the popularity of each styles:\n",
    "\n",
    "#### Same plot but weighted by the populairty of the beer style among the top 20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "c72807b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def weight_moods_by_popularity(top_styles, style_to_mood):\n",
    "    \"\"\"\n",
    "    Assigns moods to beer styles and calculates the weighted popularity of each mood.\n",
    "\n",
    "    Parameters:\n",
    "    top_styles (Series): Series with beer styles as index and their popularity count as values.\n",
    "    style_to_mood (dict): Mapping from beer styles to moods.\n",
    "\n",
    "    Returns:\n",
    "    Series: Weighted popularity of moods.\n",
    "    \"\"\"\n",
    "    mood_popularity = {}\n",
    "    for style, count in top_styles.items():\n",
    "        mood = style_to_mood.get(style, 'Undetermined')\n",
    "        mood_popularity[mood] = mood_popularity.get(mood, 0) + count\n",
    "    return pd.Series(mood_popularity)\n",
    "\n",
    "# Step 3: Weight Moods by Popularity\n",
    "ba_mood_weighted = weight_moods_by_popularity(top_styles_ba, style_to_mood)\n",
    "rb_mood_weighted = weight_moods_by_popularity(top_styles_rb, style_to_mood)\n",
    "\n",
    "# Step 4: Create Weighted Pie Charts\n",
    "fig, axs = plt.subplots(1, 2, figsize=(14, 6))\n",
    "\n",
    "# Pie chart for Beer Advocate (BA) Top Styles\n",
    "axs[0].pie(ba_mood_weighted, labels=ba_mood_weighted.index, autopct='%1.1f%%', startangle=140,\n",
    "           colors=[mood_colors.get(mood, 'lightgrey') for mood in ba_mood_weighted.index])\n",
    "axs[0].set_title('Weighted Mood Distribution in Top 20 Beer Styles on Beer Advocate')\n",
    "\n",
    "# Pie chart for RateBeer (RB) Top Styles\n",
    "axs[1].pie(rb_mood_weighted, labels=rb_mood_weighted.index, autopct='%1.1f%%', startangle=140,\n",
    "           colors=[mood_colors.get(mood, 'lightgrey') for mood in rb_mood_weighted.index])\n",
    "axs[1].set_title('Weighted Mood Distribution in Top 20 Beer Styles on RateBeer')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fdb04b0c",
   "metadata": {},
   "source": [
    "# for website interaction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dfe0336b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd  \n",
    "\n",
    "ba_excel_path = 'ba_styles_moods.xlsx' \n",
    "df_ba_mapped.to_excel(ba_excel_path, index=False)  \n",
    "\n",
    "rb_excel_path = 'rb_styles_moods.xlsx' \n",
    "df_rb_mapped.to_excel(rb_excel_path, index=False)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ba601c71",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_ba_mapped = df_ba_mapped.rename(columns={\"Mood\": \"Mood_ba\"})\n",
    "df_rb_mapped = df_rb_mapped.rename(columns={\"Mood\": \"Mood_rb\"})\n",
    "\n",
    "merged_df = pd.merge(df_ba_mapped[['style', 'Mood_ba']], \n",
    "                     df_rb_mapped[['style', 'Mood_rb']], \n",
    "                     on='style', \n",
    "                     how='outer')\n",
    "\n",
    "excel_path = 'combined_styles_moods.xlsx'  \n",
    "merged_df.to_excel(excel_path, index=False)  \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f95665aa",
   "metadata": {},
   "source": [
    "There are only 2 conflicting beer styles which one of them matches with same mood and one not."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "046d658d",
   "metadata": {},
   "outputs": [],
   "source": [
    "filtered_df = merged_df[merged_df['style'] != 'Witbier']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e53d8a5d",
   "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>style</th>\n",
       "      <th>Mood_ba</th>\n",
       "      <th>Mood_rb</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Altbier</td>\n",
       "      <td>None</td>\n",
       "      <td>Contemplative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>American Adjunct Lager</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>American Amber / Red Ale</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>American Amber / Red Lager</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>American Barleywine</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>164</th>\n",
       "      <td>Sweet Stout</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>Traditional Ale</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166</th>\n",
       "      <td>Weizen Bock</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>Wheat Ale</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cheerful</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>Zwickel/Keller/Landbier</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Relaxed</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>168 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                          style Mood_ba        Mood_rb\n",
       "0                       Altbier    None  Contemplative\n",
       "1        American Adjunct Lager    None            NaN\n",
       "2      American Amber / Red Ale    None            NaN\n",
       "3    American Amber / Red Lager    None            NaN\n",
       "4           American Barleywine    None            NaN\n",
       "..                          ...     ...            ...\n",
       "164                 Sweet Stout     NaN           None\n",
       "165             Traditional Ale     NaN           None\n",
       "166                 Weizen Bock     NaN           None\n",
       "167                   Wheat Ale     NaN       Cheerful\n",
       "168     Zwickel/Keller/Landbier     NaN        Relaxed\n",
       "\n",
       "[168 rows x 3 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filtered_df"
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  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "950c35c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "filtered_df = merged_df[merged_df['style'] != 'Witbier']\n",
    "\n",
    "filtered_df = filtered_df.dropna(subset=['Mood_ba', 'Mood_rb'], how='all')\n",
    "\n",
    "filtered_df['Mood'] = filtered_df['Mood_ba'].combine_first(filtered_df['Mood_rb'])\n",
    "\n",
    "# Select only the relevant columns: 'style' and 'Mood'\n",
    "final_df = filtered_df[['style', 'Mood']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "1906c3c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>style</th>\n",
       "      <th>Mood</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Altbier</td>\n",
       "      <td>Contemplative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>American Black Ale</td>\n",
       "      <td>Nostalgic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>American Blonde Ale</td>\n",
       "      <td>Cheerful</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>American IPA</td>\n",
       "      <td>Energetic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>American Pale Lager</td>\n",
       "      <td>Cheerful</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
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       "    <tr>\n",
       "      <th>157</th>\n",
       "      <td>Session IPA</td>\n",
       "      <td>Energetic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160</th>\n",
       "      <td>Sour/Wild Ale</td>\n",
       "      <td>Energetic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>163</th>\n",
       "      <td>Stout</td>\n",
       "      <td>Nostalgic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>Wheat Ale</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>Zwickel/Keller/Landbier</td>\n",
       "      <td>Relaxed</td>\n",
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       "  </tbody>\n",
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       "<p>73 rows × 2 columns</p>\n",
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       "                       style           Mood\n",
       "0                    Altbier  Contemplative\n",
       "5         American Black Ale      Nostalgic\n",
       "6        American Blonde Ale       Cheerful\n",
       "12              American IPA      Energetic\n",
       "15       American Pale Lager       Cheerful\n",
       "..                       ...            ...\n",
       "157              Session IPA      Energetic\n",
       "160            Sour/Wild Ale      Energetic\n",
       "163                    Stout      Nostalgic\n",
       "167                Wheat Ale       Cheerful\n",
       "168  Zwickel/Keller/Landbier        Relaxed\n",
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   "source": [
    "import pandas as pd\n",
    "import plotly.graph_objects as go\n",
    "\n",
    "\n",
    "moods = final_df['Mood'].unique()\n",
    "\n",
    "fig = go.Figure(data=[go.Table(\n",
    "    header=dict(values=[\"Beer Style\", \"Mood\"],\n",
    "                fill_color='#8B4513',  # dark brown\n",
    "                align='left'),\n",
    "    cells=dict(values=[[], []],  \n",
    "               fill_color='#FFFFE0',  # light yellow like beer\n",
    "               align='left'))\n",
    "])\n",
    "\n",
    "# Function to update the table based on selected mood\n",
    "def update_table(mood):\n",
    "    # Filter DataFrame based on selected mood\n",
    "    filtered_data = final_df[final_df['Mood'] == mood]\n",
    "    \n",
    "    # Return the values needed for the table update\n",
    "    return [filtered_data['style'], filtered_data['Mood']]\n",
    "\n",
    "buttons = []\n",
    "\n",
    "for mood in moods:\n",
    "    buttons.append(dict(\n",
    "        label=mood,\n",
    "        method=\"update\",\n",
    "        args=[{\"cells.values\": update_table(mood)}]\n",
    "    ))\n",
    "\n",
    "# Update the figure layout to add dropdown\n",
    "fig.update_layout(\n",
    "    updatemenus=[\n",
    "        go.layout.Updatemenu(\n",
    "            buttons=buttons,\n",
    "            direction=\"down\",\n",
    "            pad={\"r\": 20, \"t\": 5},\n",
    "            showactive=True,\n",
    "            x=0.1,\n",
    "            xanchor=\"left\",\n",
    "            y=1.8,\n",
    "            yanchor=\"top\"\n",
    "        ),\n",
    "    ]\n",
    ")\n"
   ]
  }
 ],
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