Pandas-to-postgres allows you to bulk load the contents of large dataframes into postgres as quickly as possible. The main differences from pandas' `to_sql` functions are:

- Uses `COPY` combined with `to_csv` instead of `execute / executemany`, which runs much faster for large volumes of data

- Uses `COPY FROM STDIN` with `StringIO` to avoid IO overhead to intermediate files. This matters in particular for data stored in unusual formats like HDF, STATA, parquet - common in the scientific world.

- Chunked loading methods to be able to load larger-than-memory tables. In particular the HDF5 functions load data in chunks directly from the file, easily extendible to other formats that support random access by row range.

- Removes indexing overhead by automatically detecting and dropping indexes before load, and then re-creating them afterwards

- Loads separate tables in parallel using multiprocessing

- Hooks to modify data as it's loaded

- Python 3

- psycopg2 (for the low level COPY from stdin)

- sqlalchemy (for reflection for indexes)

- pandas

from pandas_to_postgres import ...