Untitled.ipynb

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   "source": [
    "import pandas as pd\n",
    "import plotly.graph_objects as go\n",
    "from dash import Dash, dcc, html\n",
    "import random\n",
    "from fuzzywuzzy import process\n",
    "\n",
    "# Read CSV files into dataframes\n",
    "ratings_df = pd.read_csv('ratings.csv')\n",
    "movies_df = pd.read_csv('movies.csv')\n",
    "all_titles = movies_df['title'].tolist()\n",
    "\n",
    "def movie_finder(title):\n",
    "    return process.extractOne(title,all_titles)[0]\n",
    "    \n",
    "# Compute Bayesian average\n",
    "movie_stats = ratings_df.groupby('movieId')['rating'].agg(['count', 'mean'])\n",
    "C = movie_stats['count'].mean()\n",
    "m = movie_stats['mean'].mean()\n",
    "\n",
    "def bayesian_avg(ratings):\n",
    "    return round((C * m + ratings.sum()) / (C + ratings.count()), 3)\n",
    "\n",
    "average_ratings_df = ratings_df.groupby('movieId')['rating'].agg(bayesian_avg).reset_index()\n",
    "average_ratings_df.columns = ['movieId', 'average_rating']\n",
    "\n",
    "# Genre mappings\n",
    "genre_mappings = {\n",
    "    'Action': 'Action/Adventure',\n",
    "    'Adventure': 'Action/Adventure',\n",
    "    'Animation': 'Animation/Children',\n",
    "    'Children': 'Animation/Children',\n",
    "    'Fantasy': 'Fantasy/Western',\n",
    "    'Crime': 'Crime/Thriller',\n",
    "    'Thriller': 'Crime/Thriller',\n",
    "    'Mystery': 'Mystery/Film-Noir',\n",
    "    'Horror': 'Horror/Sci-Fi',\n",
    "    'Sci-Fi': 'Horror/Sci-Fi',\n",
    "    'War': 'War/Drama',\n",
    "    'Drama': 'War/Drama',\n",
    "    'Musical': 'Music/Romance',\n",
    "    'Romance': 'Music/Romance',\n",
    "    'Film-Noir': 'Mystery/Film-Noir',\n",
    "    'Documentary': 'Documentary/IMAX',\n",
    "    'IMAX': 'Documentary/IMAX',\n",
    "    'Western': 'Fantasy/Western'\n",
    "}\n",
    "\n",
    "# Function to combine genres based on mappings\n",
    "def combine_genres(genres):\n",
    "    return '|'.join(genre_mappings.get(genre, '') for genre in genres.split('|'))\n",
    "\n",
    "# Apply genre combination function to the dataset\n",
    "movies_df['combined_genres'] = movies_df['genres'].apply(combine_genres)\n",
    "\n",
    "# Merge ratings and movies data\n",
    "combined_df = pd.merge(ratings_df, movies_df, on='movieId')\n",
    "\n",
    "# Split the combined genres into individual genres\n",
    "split_genres = combined_df['combined_genres'].str.split('|')\n",
    "\n",
    "# Remove duplicates from each list of genres\n",
    "unique_genres = split_genres.apply(set)\n",
    "\n",
    "# Join the unique genres back together with \"|\"\n",
    "cleaned_genres = unique_genres.apply('|'.join)\n",
    "\n",
    "# Replace the combined_genres column with the cleaned genres\n",
    "combined_df['combined_genres'] = cleaned_genres\n",
    "\n",
    "# Get unique genres\n",
    "all_genres = set('|'.join(movies_df['combined_genres']).split('|')) - {''}\n",
    "\n",
    "# Initialize an empty dictionary to store users fond of each genre\n",
    "users_fond_of_genre = {}\n",
    "\n",
    "# Loop through each genre in unique_genres_list\n",
    "for genre in all_genres:\n",
    "    # Filter the dataset to include only rows where the movie belongs to the current genre\n",
    "    genre_movies_df = combined_df[combined_df['combined_genres'].str.contains(genre)]\n",
    "    \n",
    "    # Group the filtered dataset by user ID and count the number of unique movies each user has rated\n",
    "    genre_movie_counts = genre_movies_df.groupby('userId')['movieId'].nunique()\n",
    "\n",
    "    # Calculate the 95th percentile value for the current genre\n",
    "    percentile_95 = genre_movie_counts.quantile(0.98)\n",
    "    \n",
    "    # Identify users who have rated a significant number of movies for the current genre\n",
    "    threshold = percentile_95  # Using the 95th percentile value as the threshold\n",
    "    users_fond_of_current_genre = genre_movie_counts[genre_movie_counts >= threshold].index.tolist()\n",
    "    \n",
    "    # Store the list of users in the dictionary with the genre as the key\n",
    "    users_fond_of_genre[genre] = users_fond_of_current_genre\n",
    "    del genre_movies_df\n",
    "\n",
    "    \n",
    "def get_horizontal_bar_chart(movie_list):\n",
    "\n",
    "    movies_list = [movie_finder(movie) for movie in movie_list]\n",
    "\n",
    "    print(movies_list)\n",
    "    filtered_df = movies_df[movies_df['title'].isin(movie_list)]\n",
    "    \n",
    "    numbers_list = [int(movies_df[movies_df['title'] == title]['movieId']) for title in movies_list]\n",
    "\n",
    "    print(numbers_list)\n",
    "    # Calculate average ratings for each genre for each movie\n",
    "    genre_average_ratings = {}\n",
    "    for random_movie_id in numbers_list:\n",
    "        movie_row = combined_df[combined_df['movieId'] == random_movie_id]\n",
    "        genres = movie_row['combined_genres'].iloc[0].split('|')\n",
    "        if '' in genres:\n",
    "            genres.remove('')\n",
    "        genre_average_ratings[random_movie_id] = {}\n",
    "        for genre in genres:\n",
    "            filtered_ratings = combined_df[(combined_df['userId'].isin(users_fond_of_genre[genre])) & (combined_df['movieId'] == random_movie_id)]\n",
    "            average_rating = filtered_ratings['rating'].mean()\n",
    "            if average_rating == 0:\n",
    "                average_rating = average_ratings_df.loc[average_ratings_df['movieId'] == random_movie_id, 'average_rating'].iloc[0]\n",
    "            genre_average_ratings[random_movie_id][genre] = average_rating\n",
    "        for genre in all_genres:\n",
    "            if genre not in genres:\n",
    "                genre_average_ratings[random_movie_id][genre] = 0\n",
    "\n",
    "    # print(genre_average_ratings)\n",
    "\n",
    "\n",
    "    #import plotly.graph_objects as go\n",
    "\n",
    "\n",
    "    data=genre_average_ratings\n",
    "    # Initialize lists to store x and y values\n",
    "    x_values = []\n",
    "    x_values1 = []\n",
    "    x_values2 = []\n",
    "    x_values3 = []\n",
    "    x_values4 = []\n",
    "    x_values5 = []\n",
    "    x_values6 = []\n",
    "    x_values7 = []\n",
    "    x_values8 = []\n",
    "    x_values9 = []\n",
    "\n",
    "    y_values = movies_list\n",
    "\n",
    "    # Extract ratings for each genre for each movie\n",
    "    for movie_id, genre_ratings in data.items():\n",
    "        for genre, rating in genre_ratings.items():\n",
    "            if genre == 'Animation/Children':\n",
    "                x_values.append(rating)\n",
    "            elif genre == 'Action/Adventure':\n",
    "                x_values1.append(rating)\n",
    "            elif genre == 'War/Drama':\n",
    "                x_values2.append(rating)\n",
    "            elif genre == 'Documentary':\n",
    "                x_values3.append(rating)\n",
    "            elif genre == 'Crime/Thriller':\n",
    "                x_values4.append(rating)\n",
    "            elif genre == 'Documentary/IMAX':\n",
    "                x_values5.append(rating)\n",
    "            elif genre == 'Music/Romance':\n",
    "                x_values6.append(rating)\n",
    "            elif genre == 'Horror/Sci-Fi':\n",
    "                x_values7.append(rating)\n",
    "            elif genre == 'Fantasy/Western':\n",
    "                x_values8.append(rating)\n",
    "            elif genre == 'Mystery/Film-Noir':\n",
    "                x_values9.append(rating)\n",
    "\n",
    "    print(x_values)\n",
    "    fig = go.Figure()\n",
    "\n",
    "    fig.add_trace(go.Bar(\n",
    "        y=y_values,\n",
    "        x=x_values,\n",
    "        name='Animation/Children',\n",
    "        orientation='h',\n",
    "        marker=dict(\n",
    "            color='rgba(246, 78, 139, 0.6)',\n",
    "            line=dict(color='rgba(246, 78, 139, 1.0)', width=3)\n",
    "        )\n",
    "    ))\n",
    "\n",
    "    fig.add_trace(go.Bar(\n",
    "        y=y_values,\n",
    "        x=x_values1,\n",
    "        name='Action/Adventure',\n",
    "        orientation='h',\n",
    "        marker=dict(\n",
    "            color='rgba(58, 71, 80, 0.6)',\n",
    "            line=dict(color='rgba(58, 71, 80, 1.0)', width=3)\n",
    "        )\n",
    "    ))\n",
    "\n",
    "    fig.add_trace(go.Bar(\n",
    "        y=y_values,\n",
    "        x=x_values2,\n",
    "        name='War/Drama',\n",
    "        orientation='h',\n",
    "        marker=dict(\n",
    "            color='rgba(0, 100, 0, 0.6)',\n",
    "            line=dict(color='rgba(0, 100, 0, 1)', width=3)\n",
    "        )\n",
    "    ))\n",
    "\n",
    "    fig.add_trace(go.Bar(\n",
    "        y=y_values,\n",
    "        x=x_values3,\n",
    "        name='Documentary',\n",
    "        orientation='h',\n",
    "        marker=dict(\n",
    "            color='rgba(0, 0, 100, 0.6)',\n",
    "            line=dict(color='rgba(0, 0, 100, 1)', width=3)\n",
    "        )\n",
    "    ))\n",
    "\n",
    "    fig.add_trace(go.Bar(\n",
    "        y=y_values,\n",
    "        x=x_values4,\n",
    "        name='Crime/Thriller',\n",
    "        orientation='h',\n",
    "        marker=dict(\n",
    "            color='rgba(100, 0, 0, 0.6)',\n",
    "            line=dict(color='rgba(100, 0, 0, 1)', width=3)\n",
    "        )\n",
    "    ))\n",
    "\n",
    "    fig.add_trace(go.Bar(\n",
    "        y=y_values,\n",
    "        x=x_values5,\n",
    "        name='Documentary/IMAX',\n",
    "        orientation='h',\n",
    "        marker=dict(\n",
    "            color='rgba(255, 0, 10, 0.6)',\n",
    "            line=dict(color='rgba(255, 0, 10, 1)', width=3)\n",
    "        )\n",
    "    ))\n",
    "\n",
    "    fig.add_trace(go.Bar(\n",
    "        y=y_values,\n",
    "        x=x_values6,\n",
    "        name='Music/Romance',\n",
    "        orientation='h',\n",
    "        marker=dict(\n",
    "            color='rgba(96, 89, 161, 0.6)',\n",
    "            line=dict(color='rgba(96, 89, 161, 1)', width=3)\n",
    "        )\n",
    "    ))\n",
    "\n",
    "    fig.add_trace(go.Bar(\n",
    "        y=y_values,\n",
    "        x=x_values7,\n",
    "        name='Horror/Sci-Fi',\n",
    "        orientation='h',\n",
    "        marker=dict(\n",
    "            color='rgba(122, 255, 153, 0.6)',\n",
    "            line=dict(color='rgba(122, 255, 153, 1)', width=3)\n",
    "        )\n",
    "    ))\n",
    "\n",
    "    fig.add_trace(go.Bar(\n",
    "        y=y_values,\n",
    "        x=x_values8,\n",
    "        name='Fantasy/Western',\n",
    "        orientation='h',\n",
    "        marker=dict(\n",
    "            color='rgba(122, 255, 255, 0.6)',\n",
    "            line=dict(color='rgba(122, 255, 255, 1)', width=3)\n",
    "        )\n",
    "    ))\n",
    "\n",
    "    fig.add_trace(go.Bar(\n",
    "        y=y_values,\n",
    "        x=x_values9,\n",
    "        name='Mystery/Film-Noir',\n",
    "        orientation='h',\n",
    "        marker=dict(\n",
    "            color='rgba(252, 101, 0, 0.6)',\n",
    "            line=dict(color='rgba(252, 101, 0, 1)', width=3)\n",
    "        )\n",
    "    ))\n",
    "\n",
    "\n",
    "    fig.update_layout(barmode='stack')\n",
    "\n",
    "    return fig\n",
    "\n",
    "\n",
    "movie_list = ['Batman Begins','Dark Knight','Batman Returns','Dardevil','Spier-Man','Spider-Man2','Hulk','300','Catwomen']\n",
    "get_horizontal_bar_chart(movie_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "4994ecf7-e0e0-4f30-8cf3-bfb03035bf27",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 3]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/lp/tw6wpzqs11l54033wwlhzq500000gn/T/ipykernel_51594/1162192455.py:4: FutureWarning:\n",
      "\n",
      "Calling int on a single element Series is deprecated and will raise a TypeError in the future. Use int(ser.iloc[0]) instead\n",
      "\n"
     ]
    }
   ],
   "source": [
    "movies_list = ['Toy Story (1995)','Grumpier Old Men (1995)']\n",
    "movies_df = pd.read_csv('movies.csv')\n",
    "\n",
    "numbers_list = [int(movies_df[movies_df['title'] == title]['movieId']) for title in movies_list]\n",
    "print(numbers_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1114a4ba-c4a4-4296-ad7e-38e658f09f83",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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    "name": "ipython",
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