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To save a large DataFrame in parts to a MySQL database using SQLAlchemy, you can follow these steps to append 1000 rows at a time:

1. Establish a connection to your MySQL database using SQLAlchemy. Here's an example of how to create an engine and connect to the database:

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from sqlalchemy import create_engine

# Create an engine to connect to your MySQL database
engine = create_engine('mysql://username:password@localhost/database_name')

2. Iterate through your large DataFrame in chunks of 1000 rows. You can use the pandas.DataFrame.iloc method to select a specific range of rows. Here's an example of how to iterate through the DataFrame in chunks:

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chunk_size = 1000
num_chunks = len(df) // chunk_size + 1

for i in range(num_chunks):
    start = i * chunk_size
    end = (i + 1) * chunk_size
    chunk_df = df.iloc[start:end]

    # Append the chunk to the MySQL database
    chunk_df.to_sql('table_name', con=engine, if_exists='append', index=False)

3. In each iteration, select the corresponding chunk of rows from the DataFrame using df.iloc[start:end].
4. Use the pandas.DataFrame.to_sql method to append the chunk to the MySQL database. Set the if_exists parameter to 'append' to add the chunk to the existing table. Specify the target table name using the table_name argument. Set index=False to exclude the DataFrame's index column from being inserted into the database.

By iterating through the DataFrame in smaller chunks, you can avoid loading the entire DataFrame into memory at once and save the data in parts to the MySQL database. Adjust the chunk_size value as per your requirements to balance memory usage and performance.