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"A set of interop services to integrate and transfer data between different applications, model and storage technologies for the bclearer framework."

Project description

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.

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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