DataQueryFrame
===============
.. image:: https://img.shields.io/pypi/v/dataqueryframe.svg
:target: https://pypi.python.org/pypi/dataqueryframe
.. image:: https://readthedocs.org/projects/dataqueryframe/badge/?version=latest
:target: https://dataqueryframe.readthedocs.io/en/latest/?version=latest
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:target: https://pyup.io/repos/github/jmccoll7/dataqueryframe/
DataQueryFrame is an enhanced DataFrame library that provides SQL-like capabilities for data manipulation and analysis. It extends the functionality of the popular pandas DataFrame, allowing you to perform common database operations directly on your data.
Features
--------
- SQL-like query operations: SELECT, WHERE, GROUP BY, ORDER BY, UNION, etc.
- Seamless integration with pandas DataFrame
- Easy-to-use API for data filtering, aggregation, and transformation
- Support for various data sources (CSV, Excel, SQL databases, etc.)
- Efficient and optimized for large datasets
Installation
------------
You can install DataQueryFrame using pip:
::
pip install dataqueryframe
Usage
-----
Here's a quick example of how to use DataQueryFrame:
.. code-block:: python
import pandas as pd
from dataqueryframe.dataqueryframe import DataQueryFrame as dqf
# Create a DataQueryFrame from a parquet file
# df = dqf(pd.read_parquet('data.parquet'))
# Create a DataQueryFrame from a dictionary
df = dqf({'A': [1,2,3,4,5], 'B': ['good', 'bad', 'ugly', 'good', 'bad']})
# Perform SQL-like operations
result = df.select(['column1', 'column2']) \
.where('column1', '<', 3) \
.order_by('column1', ascending=False) \
.limit(10)
print(result)
=======
History
=======
0.1.0 (2024-03-28)
------------------
* First release on PyPI.
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"description": "DataQueryFrame\n===============\n\n.. image:: https://img.shields.io/pypi/v/dataqueryframe.svg\n :target: https://pypi.python.org/pypi/dataqueryframe\n\n.. image:: https://readthedocs.org/projects/dataqueryframe/badge/?version=latest\n :target: https://dataqueryframe.readthedocs.io/en/latest/?version=latest\n\n.. image:: https://pyup.io/repos/github/jmccoll7/dataqueryframe/shield.svg\n :target: https://pyup.io/repos/github/jmccoll7/dataqueryframe/\n\nDataQueryFrame is an enhanced DataFrame library that provides SQL-like capabilities for data manipulation and analysis. It extends the functionality of the popular pandas DataFrame, allowing you to perform common database operations directly on your data.\n\nFeatures\n--------\n\n- SQL-like query operations: SELECT, WHERE, GROUP BY, ORDER BY, UNION, etc.\n- Seamless integration with pandas DataFrame\n- Easy-to-use API for data filtering, aggregation, and transformation\n- Support for various data sources (CSV, Excel, SQL databases, etc.)\n- Efficient and optimized for large datasets\n\nInstallation\n------------\n\nYou can install DataQueryFrame using pip:\n\n::\n\n pip install dataqueryframe\n\nUsage\n-----\n\nHere's a quick example of how to use DataQueryFrame:\n\n.. code-block:: python\n\n import pandas as pd\n from dataqueryframe.dataqueryframe import DataQueryFrame as dqf\n\n # Create a DataQueryFrame from a parquet file\n # df = dqf(pd.read_parquet('data.parquet'))\n\n # Create a DataQueryFrame from a dictionary\n df = dqf({'A': [1,2,3,4,5], 'B': ['good', 'bad', 'ugly', 'good', 'bad']})\n\n # Perform SQL-like operations\n result = df.select(['column1', 'column2']) \\\n .where('column1', '<', 3) \\\n .order_by('column1', ascending=False) \\\n .limit(10)\n print(result)\n\n\n=======\nHistory\n=======\n\n0.1.0 (2024-03-28)\n------------------\n\n* First release on PyPI.\n",
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