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pandabase links pandas DataFrames to SQL databases. Supports read, append, and upsert.

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pandabase is a tool for reading & writing DataFrames to & from SQLite

By default, uses DataFrame.index as primary key. By using an explicit primary key, pandabase makes it easy to upsert pandas data into SQL databases.

Designed for time series datasets that need to be updated over time and stored to disk, but are used in-memory for computation.

Tested under Python 3.6 and 3.7, with new versions of Pandas and SQLAlchemy


  • primary keys (any named index is assumed to be the PK)
  • also supports 'auto_index'
  • insert modes: 'create_only', 'upsert', and 'append'
  • replaces pd.DataFrame.to_sql and pd.read_sql
  • tested under SQLite
  • postgres support coming soon
  • automated tests in pytest
  • 96% coverage
  • also includes pandabase.companda.companda for rich comparisons of DataFrames

Design Considerations

  • Minimal dependencies: SQLAlchemy and Pandas are the only requirements
  • Database is the source of truth: will coerce incoming DataFrames to fit existing schema
    • but also is reasonably smart about how new tables are created from DataFrames
  • Not horrendously slow


MIT license


Code partially stolen from Dataset and pandas.sql


From your inside your virtual environment of choice:

~/$ pip install pandabase

For latest version:

~/$ git clone
~/$ cd pandabase
~/pandabase/$ pip install -r requirements.txt
~/pandabase/$ pip install .


# Python >= 3.6
>>> import pandas as pd
>>> import pandabase
>>> my_data = pd.DataFrame(index=range(7, 12), 
>>> = 'made_up_name'        # index must be named to use as PK
>>> pandabase.to_sql(my_data, table_name='my_table', con='sqlite:///new_sqlite_db.sqlite', how='create_only')
Table('my_table', ...
>>> exit()

That's all!

Your data is now persistently stored in a SQLite database, using my_data.index as primary key. To append or update data, replace 'create_only' with 'append' or 'upsert'. To store records without an explicit index, use 'autoindex=True'.

~/pandabase$ ls
>>> import pandabase
>>> df = pandabase.read_sql('my_table', con='sqlite:///new_sqlite_db.sqlite'))
>>> df
7   0.722416 
8   0.076045 
9   0.213118 
10  0.453716 
11  0.406995

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