sibi-dst 0.3.48

Data Science Toolkit

sibi-dst

Data Science Toolkit

Data Science Toolkit built with Python, Pandas, Dask, OpenStreetMaps, Scikit-Learn, XGBOOST, Django ORM, SQLAlchemy, DjangoRestFramework, FastAPI

Major Functionality

1. Build DataCubes, DataSets, and DataObjects from diverse data sources, including relational databases, Parquet files, Excel (.xlsx), delimited tables (.csv, .tsv), JSON, and RESTful APIs (JSON API REST).
2. Comprehensive DataFrame Management utilities for efficient data handling, transformation, and optimization using Pandas and Dask.
3. Flexible Data Sharing with client applications by writing to Data Warehouses, local filesystems, and cloud storage platforms such as Amazon S3, Google Cloud Storage (GCS), and Azure Blob Storage.
4. Microservices for Data Access – Build scalable API-driven services using RESTful APIs (Django REST Framework, FastAPI) and gRPC for high-performance data exchange.

Supported Technologies

• Data Processing: Pandas, Dask
• Machine Learning: Scikit-Learn, XGBoost
• Databases & Storage: SQLAlchemy, Django ORM, Parquet, Amazon S3, GCS, Azure Blob Storage
• Mapping & Geospatial Analysis: OpenStreetMaps, OSMnx, Geopy
• API Development: Django REST Framework, gRPC, FastAPI

Installation

pip install sibi-dst

Usage

Loading Data from SQLAlchemy

from sibi_dst.df_helper import DfHelper
from conf.transforms.fields.crm import customer_fields
from conf.credentials import replica_db_conf
from conf.storage import get_fs_instance

config = {
    'backend': 'sqlalchemy',
    'connection_url': replica_db_conf.get('db_url'),
    'table': 'crm_clientes_archivo',
    'field_map': customer_fields,
    'legacy_filters': True,
    'fs': get_fs_instance()
}

df_helper = DfHelper(**config)
result = df_helper.load(id__gte=1)

Saving Data to ClickHouse

clk_creds = {
    'host': '192.168.3.171',
    'port': 18123,
    'user': 'username',
    'database': 'xxxxxxx',
    'table': 'customer_file',
    'order_by': 'id'
}

df_helper.save_to_clickhouse(**clk_creds)

Saving Data to Parquet

df_helper.save_to_parquet(
    parquet_filename='filename.parquet',
    parquet_storage_path='/path/to/my/files/'
)

Backends Supported

Backend	Description
sqlalchemy	Load data from SQL databases using SQLAlchemy.
django_db	Load data from Django ORM models.
parquet	Load and save data from Parquet files.
http	Fetch data from HTTP endpoints.
osmnx	Geospatial mapping and routing using OpenStreetMap.
geopy	Geolocation services for address lookup and reverse geocoding.

Geospatial Utilities

**OSMnx Helper (sibi_dst.osmnx_helper)

** Provides OpenStreetMap-based mapping utilities using osmnx and folium.

🔹 Key Features

• BaseOsmMap: Manages interactive Folium-based maps.
• PBFHandler: Loads .pbf (Protocolbuffer Binary Format) files for network graphs.

Example: Generating an OSM Map

from sibi_dst.osmnx_helper import BaseOsmMap
osm_map = BaseOsmMap(osmnx_graph=my_graph, df=my_dataframe)
osm_map.generate_map()

**Geopy Helper (sibi_dst.geopy_helper)

** Provides geolocation services using Geopy for forward and reverse geocoding.

🔹 Key Features

• GeolocationService: Interfaces with Nominatim API for geocoding.
• Error Handling: Manages GeocoderTimedOut and GeocoderServiceError gracefully.
• Singleton Geolocator: Efficiently reuses a global geolocator instance.

Example: Reverse Geocoding

from sibi_dst.geopy_helper import GeolocationService
gs = GeolocationService()
location = gs.reverse((9.935,-84.091))
print(location)

Advanced Features

Querying with Custom Filters

Filters can be applied dynamically using Django-style syntax:

result = df_helper.load(date__gte='2023-01-01', status='active')

Parallel Processing

Leverage Dask for parallel execution:

result = df_helper.load_parallel(status='active')

Testing

To run unit tests, use:

pytest tests/

Contributing

Contributions are welcome! Please submit pull requests or open issues for discussions.

License

sibi-dst is licensed under the MIT License.