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There are a few possible reasons why df.to_sql may not be working. In such cases, you can try the following alternatives:

Use SQLAlchemy: Instead of using the standard Pandas function, use the SQLAlchemy library to connect to your database and write to it. This can provide more flexibility and better error handling. Here's an example of how to use SQLAlchemy:

from sqlalchemy import create_engine

engine = create_engine('postgresql://user:password@host:port/database')

df.to_sql('tablename', engine)

Use a cursor to insert the rows: If you have a small dataset, you can use a cursor object to insert the rows one by one. Here's an example:

import sqlite3

conn = sqlite3.connect('mydb.db')
cursor = conn.cursor()

for index, row in df.iterrows():
    cursor.execute('INSERT INTO mytable VALUES (?,?,?,?,?)', tuple(row))

conn.commit()

Export the data to a CSV file and use an SQL command to load it: If none of the above options work, you can export the data to a CSV file and use an SQL command to load it into your database. Here's an example:

df.to_csv('data.csv', index=False)

# SQL command to load the data
LOAD DATA LOCAL INFILE 'data.csv'
INTO TABLE mytable
FIELDS TERMINATED BY ','
LINES TERMINATED BY '\n'

Note that the syntax of the SQL command may vary depending on your database system.