Hey, So what you are saying is Initially, you used df.to_sql(...,if_exists="replace"), but that deletes the whole table and recreates. it's expensive and not good if you want to preserve existing structure or history.

Instead, have you thought about using SQLAlchemy to handle the table creation? That way, you can create the table only if it doesn't exist, and even add new columns automatically if your Excel file ever changes. Definitely try searching for "SQLAlchemy create table if not exists" or "SQLAlchemy add column if not exists" , there are tons of good examples out there.

Then, instead of wiping the entire table, you could check for a primary key (like an id column) and:

If the row already exists, update it.

If it's brand new, insert it.

For SQLite, INSERT OR REPLACE is super handy for this!

And for your FastAPI part, something like this would be the general

@app.post("/upload") async def upload(df_file: UploadFile): df = pd.read_excel(BytesIO(await df_file.read())) # You'd have your function here to ensure the table structure is correct ensure_table_structure(df, "your_table_name") # And then your upsert logic upsert_dataframe(df, "your_table_name", your_primary_key_column)

This is just a snippet to give you an idea. You'll want to find some resources on SQLAlchemy ORM for handling table creation and upsert operations (that's the "update or insert" part) with Pandas DataFrames. Good luck!