top_picks.py

#!/usr/bin/env python3
import os
import difflib
import pandas as pd
"""
This program takes the kaggle dataset for hbo max and executes a .txt script so it can be read by another program.
Part of the assignment was to generate a dataset that can be executed and read in order to be modified. This is the middle man of the process delivering the text file needed using python and creating tables of the data from the HBO Max dataset so it can be used in other learning experiences geared twoards machine learning in the future.
"""
# Load the HBO Max dataset
data = pd.read_csv('data.csv') # this is the file downloaded from the Kaggle Dataset.
print("Initial Columns:", data.columns)

# Normalize column names
# This Normalizes the Column names so everything is understood in lowercased text.
data.columns = data.columns.str.strip().str.lower()
print("Normalized Columns:", data.columns)

# Find the closest matches for the expected column names
# The column is mainly searching for the IMDB ratings to organized the data in order to be graphed based on popularity.
imdb_rating_col = difflib.get_close_matches('imdb_rating', data.columns, n=1, cutoff=0.6) # the IMBD ratings
genres_col = difflib.get_close_matches('genre', data.columns, n=1, cutoff=0.6) # Genres of the HBO movies
release_year_col = difflib.get_close_matches('year', data.columns, n=1, cutoff=0.6) # The year of release
available_countries_col = difflib.get_close_matches('available_regions', data.columns, n=1, cutoff=0.6) # where the movies are available to watch given the country the person is viewing the movie in. Someone could test this with a VPN!

# The closest matches in the data given there are possible inconsistencies in what was formatted and retrieved.
imdb_rating_col = imdb_rating_col[0] if imdb_rating_col else 'imdbaveragerating'
genres_col = genres_col[0] if genres_col else 'genres'
release_year_col = release_year_col[0] if release_year_col else 'releaseyear'
available_countries_col = available_countries_col[0] if available_countries_col else 'availablecountries'

print(f"Using columns - IMDb Rating: {imdb_rating_col}, Genres: {genres_col}, Year: {release_year_col}, Available Regions: {available_countries_col}")

# Filter for top picks with IMDb rating > 7.5
top_picks = data[data[imdb_rating_col] > 7.5]

# Sort by IMDb rating in descending order
top_picks = top_picks.sort_values(by=imdb_rating_col, ascending=False)

# Select relevant columns
top_picks_list = top_picks[['title', genres_col, release_year_col, imdb_rating_col, available_countries_col]]

# Save the top picks to a text file
output_file = 'HBO_Max_Top_Picks.txt'
with open(output_file, 'w') as file:
    for _, row in top_picks_list.iterrows():
        file.write(f"Title: {row['title']}\n")
        file.write(f"Genre: {row[genres_col]}\n")
        file.write(f"Year: {row[release_year_col]}\n")
        file.write(f"IMDb Rating: {row[imdb_rating_col]}\n")
        file.write(f"Available in: {row[available_countries_col]}\n")
        file.write("\n")

print(f"Top picks saved to {output_file}")