update breeding pairs database
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import sqlite3
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import pandas as pd
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# Connect to your SQLite database
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db_path = 'new_test_database.db' # Change this to your actual database path
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conn = sqlite3.connect(db_path)
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cursor = conn.cursor()
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# Drop existing tables if they exist
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cursor.execute("DROP TABLE IF EXISTS breed_requirements")
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cursor.execute("DROP TABLE IF EXISTS breeds")
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# Recreate 'breeds' table
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cursor.execute("""
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CREATE TABLE breeds (
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id INTEGER PRIMARY KEY,
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target TEXT
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)
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""")
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# Recreate 'breed_requirements' table
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cursor.execute("""
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CREATE TABLE breed_requirements (
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id INTEGER PRIMARY KEY,
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breed_id INTEGER,
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requirement_type TEXT,
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requirement_value TEXT,
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FOREIGN KEY (breed_id) REFERENCES breeds (id)
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)
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""")
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# Read CSV data into a pandas DataFrame
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csv_path = 'updated_breeding_pairs.csv' # Change this to your actual CSV file path
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df = pd.read_csv(csv_path, header=None, names=['base', 'mate', 'offspring'])
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# Function to insert new breeds and requirements into the database
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def insert_breed_and_requirements(base, mate, offspring):
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# Insert breed into 'breeds' table
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cursor.execute("INSERT INTO breeds (target) VALUES (?)", (offspring,))
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conn.commit()
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# Get the newly inserted breed_id
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breed_id = cursor.lastrowid
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# Insert requirements into 'breed_requirements' table
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cursor.execute("INSERT INTO breed_requirements (breed_id, requirement_type, requirement_value) VALUES (?, 'base', ?)", (breed_id, base))
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cursor.execute("INSERT INTO breed_requirements (breed_id, requirement_type, requirement_value) VALUES (?, 'mate', ?)", (breed_id, mate))
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conn.commit()
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# Iterate through each row in the DataFrame and insert data into the database
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for index, row in df.iterrows():
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insert_breed_and_requirements(row['base'], row['mate'], row['offspring'])
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# Close the database connection
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conn.close()
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