dataqueryframe 0.1.0

An enhanced DataFrame with SQL-like capabilities.

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:

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.