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bclearer-interop-services

A set of I/O and interop connectors for the bCLEARer™ framework. It provides adapters to read and write data between in-memory “universe” representations and a variety of storage, file formats, and application services.

Installation

pip install bclearer-interop-services

Key Features

  • Dictionary Service Convert data to and from generic Python dictionaries (e.g., mapping objects to table dictionaries).
  • DataFrame Service Utilities for standardizing, filtering, merging, and converting Pandas (and PySpark) DataFrames.
  • Delimited Text Read/write CSV and other delimited formats.
  • Excel Services Import/export Excel (.xlsx) files.
  • JSON, XML, HDF5, Parquet Native serializers and readers for common data formats.
  • Relational Database Services Access MS Access, SQLite, and other RDBMS via SQL interfaces.
  • Document Store Services MongoDB and JSON file store support.
  • Graph Services Neo4j connector and network analysis utilities.
  • EA Interop Service COM-based, SQL, and XML import/export for Enterprise Architect models.
  • Session & Orchestration Helpers to manage connections, sessions, and orchestrate multi-step data flows.

Basic Usage

Below is a simple example using the Dictionary and DataFrame services:

from bclearer_interop_services.b_dictionary_service.table_as_dictionary_service import TableAsDictionaryFromCsvFileReader
from bclearer_interop_services.b_dictionary_service.table_as_dictionary_service import TableAsDictionaryToDataFrameConverter

# Read data from a CSV file into a table-as-dictionary
reader = TableAsDictionaryFromCsvFileReader()
table_dict = reader.read('data/example.csv')

# Convert the table-as-dictionary to a Pandas DataFrame
converter = TableAsDictionaryToDataFrameConverter()
df = converter.convert(table_dict)

# Standardize column names and filter rows using the DataFrame service
from bclearer_interop_services.dataframe_service.dataframe_helper import DataFrameHelper

helper = DataFrameHelper()
df = helper.standardize_column_names(df)
df_filtered = df[df['status'] == 'ACTIVE']

Documentation

Full documentation and examples can be found in the GitHub repository.

Contributing

Issues and feedback are welcome. Code pull requests are not being accepted yet, so please raise anything you find as an issue rather than as a patch.

For anything that does not fit an issue, contact support@ontoledgy.io.

Licence

This project is licensed under the GNU Affero General Public License v3.0 or later (AGPL-3.0-or-later). See the LICENSE file for the full text.

Commercial licences are available for organisations that cannot comply with the AGPL's network-use and source-disclosure terms. Contact support@ontoledgy.io for details.

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