Skip to main content

jragbeer_common

License Python 3.10 pre-commit

Overview

A Python utility library containing common functions and tools used across various projects. This library provides reusable components for:

  • Data processing and engineering
  • Azure blob storage operations
  • Dask distributed computing
  • Ubuntu system operations

Features

  • Data Engineering Utilities

    • DataFrame manipulation and cleaning
    • SQL database operations
    • Email notifications
    • Date/time processing
    • Logging configuration
  • Azure Integration

    • Blob storage upload/download
    • Parquet file handling
    • Container management
    • Batch operations
  • Dask Distributed Computing

    • Cluster deployment and management
    • Worker allocation
    • Task scheduling
    • Remote execution
  • Ubuntu System Operations

    • Remote command execution
    • Process management
    • System monitoring
    • File operations

Installation

  1. Install uv (recommended):
pip install uv
  1. Create and activate a virtual environment:
uv venv
source .venv/bin/activate  # Linux/Mac
# or
.venv\Scripts\activate  # Windows
  1. Install the package:
uv pip install jragbeer-common

Development Setup

  1. Clone the repository:
git clone https://github.com/jragbeer/jragbeer_common.git
cd jragbeer_common
  1. Create a virtual environment and install dependencies:
uv venv
source .venv/bin/activate
uv pip install -r requirements.txt
  1. Install development dependencies:
uv pip install -e ".[dev]"
  1. Install pre-commit hooks:
pre-commit install

Usage

from jragbeer_common import (
    jragbeer_common_data_eng,
    jragbeer_common_azure,
    jragbeer_common_dask,
    jragbeer_common_ubuntu
)

# Data Engineering
jragbeer_common_data_eng.parse_date_features(df)

# Azure Operations
jragbeer_common_azure.adls_upload_file("path/to/file", "blob_name")

# Dask Operations
jragbeer_common_dask.deploy_dask_home_setup()

# Ubuntu Operations
jragbeer_common_ubuntu.execute_cmd_ubuntu_sudo("command")

Environment Variables

The following environment variables are required:

# Azure Storage
adls_connection_string="your_connection_string"
adls_container_name="your_container"

# Database
local_db_username="username"
local_db_password="password"
local_db_address="address"
local_db_port="port"

# Cluster Configuration
cluster_server_1_address="address"
cluster_server_1_username="username"
cluster_server_1_password="password"

Building and Distribution

  1. Build the package:
uv build
  1. Install locally for testing:
uv pip install dist/jragbeer_common-0.2.0-py3-none-any.whl

Contributing

  1. Fork the repository
  2. Create a feature branch
  3. Make your changes
  4. Run tests and linting:
uv sync --extra dev
pre-commit run --all-files
uv run pytest

CI runs the same full suite on each push and pull request (see .github/workflows/test.yml), including tests marked integration (Docker Postgres, etc.). You need a working Docker daemon for a green run locally and in GitHub Actions (the runner provides Docker). Markers (unit, integration, contract) still classify tests in pyproject.toml; nothing is excluded by default.

Integration tests (Docker Postgres)

Database tests that open PostgreSQL live in test/db/test_jragbeer_common_db.py and are marked @pytest.mark.integration. They use the session-scoped container from test/fixtures/postgresql.py plus the docker Python client and psycopg2-binary. They run as part of the default uv run pytest (and in CI).

Optional environment variables (defaults in parentheses):

  • TEST_POSTGRESQL_PORT — host port mapped to the container (54329)
  • TEST_POSTGRESQL_USER, TEST_POSTGRESQL_PASSWORD, TEST_POSTGRESQL_DATABASE — bootstrap credentials (devuser / devpassword / postgres)
  • TEST_POSTGRESQL_VERSION — image tag after postgres: (17.4)

Readiness is checked with TCP connections via psycopg2 from the host (same as SQLAlchemy), not docker exec psql, so you do not need the docker CLI or host postgresql-client for the wait loop.

Future work: tests that hit real services should live under the appropriate test/<subpackage>/ package, be marked @pytest.mark.integration, and document required environment variables in the test module docstring.

License

Copyright 2026 Julien Ragbeer

Licensed under the Apache License, Version 2.0

Metadata

Release files for jragbeer-common 0.2.68

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for jragbeer-common 0.2.68
File Size Uploaded Provenance
jragbeer_common-0.2.68.tar.gz 41.0 kB Without provenance Details

Built distribution (wheel)

Table of built distributions (wheels) for jragbeer-common 0.2.68
File Interpreter ABI Platform
jragbeer_common-0.2.68-py3-none-any.whl Python 3 none any Details

Total release size: 82.2 kB

Release files / jragbeer_common-0.2.68.tar.gz

Download URL jragbeer_common-0.2.68.tar.gz
Size 41.0 kB
Tags Source
SHA-256 checksum
How to use checksums
b38a54ba2e984062e885f091cb0762b83d7e50bc1fa1b5cb347e808c889bd5c2
BLAKE2b-256 checksum
How to use checksums
06160d3e42bbefb7352f7ffc791cf10fe1e60f4e904ee30f49df4b3136957ee2
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/6.1.0 CPython/3.10.17

Release files / jragbeer_common-0.2.68-py3-none-any.whl

Download URL jragbeer_common-0.2.68-py3-none-any.whl
Size 41.1 kB
Tags Python 3
SHA-256 checksum
How to use checksums
c901859eefe51f21b1722a38c7b95565f2c2d28af97c067dec52b3f50638648a
BLAKE2b-256 checksum
How to use checksums
8c4e9601bb3ccf6a0fb28e062b52d1068f39a403c0ada96bab718a6b33d16ca1
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/6.1.0 CPython/3.10.17

Security for jragbeer-common 0.2.68

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

No provenance found

None of the files in this release were uploaded with provenance metadata.

A note on package security

PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.

To learn more about protecting yourself from malicious actors, visit our package security documentation.

Release history Release notifications | RSS feed

This release

0.2.68 This release

2 release files No provenance

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page