semvec/pypi-packages huggingface dataset:
bearsql 0.1.0
Bearsql aadds sql syntax on pandas dataframe. It uses duckdb to speedup the pandas processing and as the sql engine
License: MIT license
Requires Python: >=3.6
Keywords: bearsql

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bearsql
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.. image:: https://img.shields.io/pypi/v/bearsql.svg
        :target: https://pypi.python.org/pypi/bearsql

.. image:: https://readthedocs.org/projects/bearsql/badge/?version=latest
        :target: https://bearsql.readthedocs.io/en/latest/?version=latest
        :alt: Documentation Status




Bearsql adds sql syntax on pandas dataframe. It uses duckdb to speedup the pandas processing and as the sql engine


* Free software: MIT license
* Documentation: https://bearsql.readthedocs.io.


Basic Usage
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To use bearsql in a project::

    from bearsql import SqlContext
    import pandas as pd

    sc = SqlContext()
    # The above statement will create duckdb instance in memory. Once the session ends, the database will be erased and not be persisted
    # To persist the database, you can instantiate sqlcontext like:
    # sc = SqlContext(database='<YOUR_DATABASE_NAME>.db'

    df = pd.DataFrame([{'name': 'John Doe', 'city': 'New York', 'age': 24}, {'name': 'Jane Doe', 'city': 'Chicago', 'age': 27}])

    # Create table from pandas dataframe
    sc.register_table(df, 'testable') # <YOUR_TABLENAME> instead of 'testable'

    # Query table and output to pandas dataframe
    results = sc.sql('select * from testable', output='df')
    output_df = next(results)
    print(output_df)

    # Query table and output to pyarrow table
    results = sc.sql('select * from testable', output='arrow')
    output_arrow_table = next(results)
    print(output_arrow_table)

    # Query table and output raw tuples
    results = sc.sql('select * from testable', output='any')
    output_rows = next(results)
    print(output_rows)

Create a relational table from dataframe and apply some operations::

    rel = sc.relation(df, 'new_relation') # <YOUR_RELATION_NAME> instead of new_relation

    print(rel.filter('age > 24'))

    # OR convert to df:

    rel.filter('age > 24').df()

Export the data to filesystem::

    result = sc.sql('EXPORT DATABASE \'<OUTPUT_FOLDER>\' (FORMAT PARQUET);') # format can either be PARQUET or CSV
    list(result)

For more examples, please visit https://github.com/duckdb/duckdb/blob/master/examples/python/duckdb-python.py 





Features
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* TODO

Credits
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This package was created with Cookiecutter_ and the `audreyr/cookiecutter-pypackage`_ project template.

.. _Cookiecutter: https://github.com/audreyr/cookiecutter
.. _`audreyr/cookiecutter-pypackage`: https://github.com/audreyr/cookiecutter-pypackage


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History
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0.1.0 (2021-06-30)
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* First release on PyPI.