import sqlite3 import pandas as pd import plotly.express as px import plotly.graph_objects as go import streamlit as st from wordcloud import WordCloud import matplotlib.pyplot as plt from collections import Counter import numpy as np import imageio def load_data(db_file): conn = sqlite3.connect(db_file) return conn genre_color_map = { 'Documentary': '#FFB3BA', # Light Pink 'Animation': '#BAFFC9', # Light Green 'Comedy': '#FFFFBA', # Light Yellow 'Short': '#BAE1FF', # Light Blue 'Romance': '#FFDFBA', # Light Peach 'News': '#E1BAFF', # Light Purple 'Drama': '#FFC6C6', # Light Red 'Fantasy': '#C6FFBA', # Light Lime 'Horror': '#D3D3D3', # Light Gray 'Biography': '#FFE4B5', # Moccasin 'Music': '#B0E0E6', # Powder Blue 'Crime': '#F0E68C', # Khaki 'Family': '#98FB98', # Pale Green 'Action': '#FFA07A', # Light Salmon 'History': '#DEB887', # Burlywood 'Adventure': '#87CEFA', # Light Sky Blue 'Mystery': '#DDA0DD', # Plum 'Musical': '#FFB6C1', # Light Pink 'War': '#B0C4DE', # Light Steel Blue 'Sci-Fi': '#90EE90', # Light Green 'Western': '#F4A460', # Sandy Brown 'Thriller': '#FA8072', # Salmon 'Sport': '#20B2AA', # Light Sea Green 'Film-Noir': '#778899', # Light Slate Gray 'Talk-Show': '#FAFAD2', # Light Goldenrod Yellow 'Game-Show': '#FFC0CB', # Pink 'Adult': '#DB7093', # Pale Violet Red 'Reality-TV': '#F08080' # Light Coral } def fetch_genre_movie_releases(conn): query = r''' SELECT startYear, genres FROM title_basics WHERE titleType = 'movie' AND startYear != '\N' AND genres != '\N' ''' df = pd.read_sql_query(query, conn) df['genres'] = df['genres'].str.split(',') df = df.explode('genres') df['startYear'] = pd.to_numeric(df['startYear']) genre_counts = df.groupby(['startYear', 'genres']).size().reset_index(name='count') return genre_counts def fetch_movie_release_years(conn): query_release_years = r''' SELECT startYear, COUNT(*) as count FROM title_basics WHERE titleType = 'movie' AND startYear != '\N' GROUP BY startYear ORDER BY startYear ''' df_release_years = pd.read_sql_query(query_release_years, conn) return df_release_years def fetch_and_plot_average_rating_by_genre(conn): query = r''' SELECT tb.tconst, tb.primaryTitle, tr.averageRating, tb.genres FROM title_basics tb JOIN title_ratings tr ON tb.tconst = tr.tconst WHERE tb.titleType = 'movie' AND tb.genres IS NOT NULL AND tb.genres != '\N' ''' df = pd.read_sql_query(query, conn) def extract_first_genre(genres): if genres: return genres.split(',')[0].strip() else: return None df['first_genre'] = df['genres'].apply(extract_first_genre) df = df.dropna(subset=['first_genre']) fig = px.box(df, x='first_genre', y='averageRating', labels={'first_genre': 'Genre', 'averageRating': 'Average Rating'}, title='Average Rating of Movies by Genre', color='first_genre', color_discrete_map=genre_color_map) return fig def genre_color_func(word, font_size, position, orientation, random_state=None, **kwargs): return genre_color_map.get(word, '#FFFFFF') def create_genre_wordcloud(conn): query = r''' SELECT genres FROM title_basics WHERE titleType = 'movie' AND genres IS NOT NULL AND genres != '\N' ''' df = pd.read_sql_query(query, conn) genres = df['genres'].str.split(',', expand=True).stack().replace('\\N', pd.NA).dropna().reset_index(drop=True) genre_counts = Counter(genres) wordcloud = WordCloud(width=800, height=800, background_color='white', color_func=genre_color_func).generate_from_frequencies(genre_counts) plt.figure(figsize=(10, 10)) plt.imshow(wordcloud, interpolation='bilinear') plt.axis('off') st.pyplot(plt.gcf()) def find_best_movies_by_genre(conn): query = r''' SELECT tb.tconst, tb.primaryTitle, tb.startYear, tb.genres, tr.averageRating, tr.numVotes FROM title_basics tb JOIN title_ratings tr ON tb.tconst = tr.tconst WHERE tb.titleType = 'movie' AND tb.genres IS NOT NULL AND tb.genres != '\N' ''' df = pd.read_sql_query(query, conn) df['genre'] = df['genres'].str.split(',', expand=True)[0] df['score'] = df['numVotes'] * df['averageRating'] idx = df.groupby('genre')['score'].idxmax() best_movies_by_genre = df.loc[idx, ['genre', 'primaryTitle', 'startYear', 'averageRating', 'numVotes', 'score']] \ .sort_values(by='score', ascending=False).reset_index(drop=True) return best_movies_by_genre def plot_stacked_genre_movie_releases(genre_counts): fig = px.area(genre_counts, x='startYear', y='count', color='genres', title=' Movie Releases by Year', labels={'startYear': 'Year', 'count': 'Number of Movies', 'genres': 'Genre'}, line_group='genres', # This groups lines by genre hover_name='genres', # This sets the genre as the hover label hover_data={'count': ':.0f'}, # Format hover data as integer color_discrete_map=genre_color_map) # Apply color map return fig def plot_global_map(conn): movie_region_df = pd.read_csv('movie_region.csv') # SQL query to get unique first genre of each title query_genre = ''' SELECT tconst AS titleId, primaryTitle, CASE WHEN instr(genres, ',') > 0 THEN substr(genres, 1, instr(genres, ',') - 1) ELSE genres END AS first_genre FROM title_basics; ''' genre_data_df = pd.read_sql_query(query_genre, conn) merged_df = pd.merge(movie_region_df, genre_data_df, on='titleId', how='inner') df = merged_df.replace('\\N', np.nan).dropna(subset=['first_genre']) grouped = df.groupby('region')['first_genre'].agg(lambda x: ', '.join(x)).reset_index() grouped['genres_list'] = grouped['first_genre'].apply(lambda x: x.split(', ')) grouped['most_common_genre'] = grouped['genres_list'].apply(lambda x: pd.Series(x).value_counts().index[0] if len(x) > 0 else '') result = grouped[['region', 'most_common_genre']].copy() country_mapping = { 'AF': 'Afghanistan', 'AX': 'Åland Islands', 'AL': 'Albania', 'DZ': 'Algeria', 'AS': 'American Samoa', 'AD': 'Andorra', 'AO': 'Angola', 'AI': 'Anguilla', 'AQ': 'Antarctica', 'AG': 'Antigua and Barbuda', 'AR': 'Argentina', 'AM': 'Armenia', 'AW': 'Aruba', 'AU': 'Australia', 'AT': 'Austria', 'AZ': 'Azerbaijan', 'BS': 'Bahamas', 'BH': 'Bahrain', 'BD': 'Bangladesh', 'BB': 'Barbados', 'BY': 'Belarus', 'BE': 'Belgium', 'BZ': 'Belize', 'BJ': 'Benin', 'BM': 'Bermuda', 'BT': 'Bhutan', 'BO': 'Bolivia', 'BA': 'Bosnia and Herzegovina', 'BW': 'Botswana', 'BR': 'Brazil', 'BN': 'Brunei Darussalam', 'BG': 'Bulgaria', 'BF': 'Burkina Faso', 'BI': 'Burundi', 'KH': 'Cambodia', 'CM': 'Cameroon', 'CA': 'Canada', 'CV': 'Cape Verde', 'KY': 'Cayman Islands', 'CF': 'Central African Republic', 'TD': 'Chad', 'CL': 'Chile', 'CN': 'China', 'CO': 'Colombia', 'KM': 'Comoros', 'CG': 'Congo', 'CD': 'Congo, Democratic Republic of the', 'CK': 'Cook Islands', 'CR': 'Costa Rica', 'HR': 'Croatia', 'CU': 'Cuba', 'CY': 'Cyprus', 'CZ': 'Czech Republic', 'DK': 'Denmark', 'DJ': 'Djibouti', 'DM': 'Dominica', 'DO': 'Dominican Republic', 'EC': 'Ecuador', 'EG': 'Egypt', 'SV': 'El Salvador', 'GQ': 'Equatorial Guinea', 'ER': 'Eritrea', 'EE': 'Estonia', 'ET': 'Ethiopia', 'FJ': 'Fiji', 'FI': 'Finland', 'FR': 'France', 'GA': 'Gabon', 'GM': 'Gambia', 'GE': 'Georgia', 'DE': 'Germany', 'GH': 'Ghana', 'GR': 'Greece', 'GL': 'Greenland', 'GD': 'Grenada', 'GU': 'Guam', 'GT': 'Guatemala', 'GN': 'Guinea', 'GW': 'Guinea-Bissau', 'GY': 'Guyana', 'HT': 'Haiti', 'VA': 'Holy See (Vatican City State)', 'HN': 'Honduras', 'HK': 'Hong Kong', 'HU': 'Hungary', 'IS': 'Iceland', 'IN': 'India', 'ID': 'Indonesia', 'IR': 'Iran', 'IQ': 'Iraq', 'IE': 'Ireland', 'IL': 'Israel', 'IT': 'Italy', 'CI': "Cote d'Ivoire", 'JM': 'Jamaica', 'JP': 'Japan', 'JO': 'Jordan', 'KZ': 'Kazakhstan', 'KE': 'Kenya', 'KI': 'Kiribati', 'KP': "Korea, Democratic People's Republic of", 'KR': 'Korea, Republic of', 'KW': 'Kuwait', 'KG': 'Kyrgyzstan', 'LA': "Lao People's Democratic Republic", 'LV': 'Latvia', 'LB': 'Lebanon', 'LS': 'Lesotho', 'LR': 'Liberia', 'LY': 'Libyan Arab Jamahiriya', 'LI': 'Liechtenstein', 'LT': 'Lithuania', 'LU': 'Luxembourg', 'MO': 'Macao', 'MK': 'Macedonia, The Former Yugoslav Republic of', 'MG': 'Madagascar', 'MW': 'Malawi', 'MY': 'Malaysia', 'MV': 'Maldives', 'ML': 'Mali', 'MT': 'Malta', 'MH': 'Marshall Islands', 'MR': 'Mauritania', 'MU': 'Mauritius', 'YT': 'Mayotte', 'MX': 'Mexico', 'FM': 'Micronesia', 'MD': 'Moldova, Republic of', 'MC': 'Monaco', 'MN': 'Mongolia', 'ME': 'Montenegro', 'MS': 'Montserrat', 'MA': 'Morocco', 'MZ': 'Mozambique', 'MM': 'Myanmar', 'NA': 'Namibia', 'NR': 'Nauru', 'NP': 'Nepal', 'NL': 'Netherlands', 'AN': 'Netherlands Antilles', 'NC': 'New Caledonia', 'NZ': 'New Zealand', 'NI': 'Nicaragua', 'NE': 'Niger', 'NG': 'Nigeria', 'NU': 'Niue', 'NF': 'Norfolk Island', 'MP': 'Northern Mariana Islands', 'NO': 'Norway', 'OM': 'Oman', 'PK': 'Pakistan', 'PW': 'Palau', 'PS': 'Palestinian Territory, Occupied', 'PA': 'Panama', 'PG': 'Papua New Guinea', 'PY': 'Paraguay', 'PE': 'Peru', 'PH': 'Philippines', 'PN': 'Pitcairn', 'PL': 'Poland', 'PT': 'Portugal', 'PR': 'Puerto Rico', 'QA': 'Qatar', 'RO': 'Romania', 'RU': 'Russian Federation', 'RW': 'Rwanda', 'RE': 'Reunion', 'BL': 'Saint Barthelemy', 'SH': 'Saint Helena', 'KN': 'Saint Kitts and Nevis', 'LC': 'Saint Lucia', 'MF': 'Saint Martin', 'PM': 'Saint Pierre and Miquelon', 'VC': 'Saint Vincent and the Grenadines', 'WS': 'Samoa', 'SM': 'San Marino', 'ST': 'Sao Tome and Principe', 'SA': 'Saudi Arabia', 'SN': 'Senegal', 'RS': 'Serbia', 'SC': 'Seychelles', 'SL': 'Sierra Leone', 'SG': 'Singapore', 'SK': 'Slovakia', 'SI': 'Slovenia', 'SB': 'Solomon Islands', 'SO': 'Somalia', 'ZA': 'South Africa', 'GS': 'South Georgia and the South Sandwich Islands', 'ES': 'Spain', 'LK': 'Sri Lanka', 'SD': 'Sudan', 'SR': 'Suriname', 'SJ': 'Svalbard and Jan Mayen', 'SZ': 'Swaziland', 'SE': 'Sweden', 'CH': 'Switzerland', 'SY': 'Syrian Arab Republic', 'TW': 'Taiwan', 'TJ': 'Tajikistan', 'TZ': 'Tanzania, United Republic of', 'TH': 'Thailand', 'TL': 'Timor-Leste', 'TG': 'Togo', 'TK': 'Tokelau', 'TO': 'Tonga', 'TT': 'Trinidad and Tobago', 'TN': 'Tunisia', 'TR': 'Turkey', 'TM': 'Turkmenistan', 'TV': 'Tuvalu', 'UG': 'Uganda', 'UA': 'Ukraine', 'AE': 'United Arab Emirates', 'GB': 'United Kingdom', 'US': 'United States', 'UY': 'Uruguay', 'UZ': 'Uzbekistan', 'VU': 'Vanuatu', 'VE': 'Venezuela', 'VN': 'Viet Nam', 'VG': 'Virgin Islands, British', 'VI': 'Virgin Islands, U.S.', 'WF': 'Wallis and Futuna', 'EH': 'Western Sahara', 'YE': 'Yemen', 'ZM': 'Zambia', 'ZW': 'Zimbabwe' } result.loc[:, 'region'] = result['region'].map(country_mapping) genre_color_map = { 'Documentary': '#FFB3BA', # Light Pink 'Animation': '#BAFFC9', # Light Green 'Comedy': '#FFFFBA', # Light Yellow 'Short': '#BAE1FF', # Light Blue 'Romance': '#FFDFBA', # Light Peach 'News': '#E1BAFF', # Light Purple 'Drama': '#FFC6C6', # Light Red 'Fantasy': '#C6FFBA', # Light Lime 'Horror': '#D3D3D3', # Light Gray 'Biography': '#FFE4B5', # Moccasin 'Music': '#B0E0E6', # Powder Blue 'Crime': '#F0E68C', # Khaki 'Family': '#98FB98', # Pale Green 'Action': '#FFA07A', # Light Salmon 'History': '#DEB887', # Burlywood 'Adventure': '#87CEFA', # Light Sky Blue 'Mystery': '#DDA0DD', # Plum 'Musical': '#FFB6C1', # Light Pink 'War': '#B0C4DE', # Light Steel Blue 'Sci-Fi': '#90EE90', # Light Green 'Western': '#F4A460', # Sandy Brown 'Thriller': '#FA8072', # Salmon 'Sport': '#20B2AA', # Light Sea Green 'Film-Noir': '#778899', # Light Slate Gray 'Talk-Show': '#FAFAD2', # Light Goldenrod Yellow 'Game-Show': '#FFC0CB', # Pink 'Adult': '#DB7093', # Pale Violet Red 'Reality-TV': '#F08080' # Light Coral } fig = px.choropleth( result, locations='region', locationmode='country names', color='most_common_genre', hover_name='region', hover_data={'region': False, 'most_common_genre': True}, title='Most Popular Genre around the world', color_discrete_map=genre_color_map, ) # Update the layout fig.update_layout( geo=dict(showframe=False, showcoastlines=True, projection_type='natural earth') ) return fig # Function to fetch summary info def fetch_summary_info(conn): # Fetch total count of movies query_total_movies = r''' SELECT COUNT(*) as total_movies FROM title_basics WHERE titleType = 'movie' ''' total_movies = pd.read_sql_query(query_total_movies, conn).iloc[0]['total_movies'] # Fetch total count of years query_total_years = r''' SELECT COUNT(DISTINCT startYear) as total_years FROM title_basics WHERE titleType = 'movie' AND startYear IS NOT NULL AND startYear != '\N' ''' total_years = pd.read_sql_query(query_total_years, conn).iloc[0]['total_years'] # Fetch average rating of movies query_avg_rating = r''' SELECT AVG(averageRating) as avg_rating FROM title_ratings ''' avg_rating = pd.read_sql_query(query_avg_rating, conn).iloc[0]['avg_rating'] return total_movies, total_years, avg_rating # Main Streamlit app def run_app(): st.title('IMDb Movie Data Analysis') # Load data from SQLite database conn = load_data('imdb_data.db') genre_counts = fetch_genre_movie_releases(conn) total_movies, total_years, avg_rating = fetch_summary_info(conn) # Layout for summary info in three columns col1, col2, col3 = st.columns(3) with col1: st.subheader('Total Movies') st.metric(label='', value=total_movies) with col2: st.subheader('Total Years') st.metric(label='', value=total_years) with col3: st.subheader('Average Movie Rating') st.metric(label='', value=f'{avg_rating:.2f}') # Find and display best movies by genre best_movies_by_genre = find_best_movies_by_genre(conn) fig_global_map = plot_global_map(conn) fig_genre_movie_releases = plot_stacked_genre_movie_releases(genre_counts) # Layout for best movies by genre in two columns col1, col2 = st.columns(2) with col1: st.subheader('Movie Releases by Year') st.plotly_chart(fig_genre_movie_releases, use_container_width=True) with col2: st.subheader('Global Map') st.plotly_chart(fig_global_map, use_container_width=True) fig_avg_rating_by_genre = fetch_and_plot_average_rating_by_genre(conn) # Layout for Plotly charts in three columns col1, col2, col3 = st.columns(3) with col1: st.subheader('Best Movies by Genre') st.dataframe(best_movies_by_genre) with col2: create_genre_wordcloud(conn) with col3: st.subheader('Average Rating by Genre') st.plotly_chart(fig_avg_rating_by_genre, use_container_width=True) # Close database connection conn.close() if __name__ == '__main__': run_app()