config-versioned 0.2.0

Settings and file I/O management using a configuration YAML file

config-versioned

A Python package for YAML-based configuration management in data pipelines, with versioned directory support and automatic file I/O by extension.

Installation

pip install config-versioned

Install optional extras for specific file formats:

pip install config-versioned[pandas]   # CSV, TSV, Excel, Stata
pip install config-versioned[geo]      # Shapefiles, GeoJSON, GeoPackage, etc.
pip install config-versioned[raster]   # GeoTIFF, rasterio formats
pip install config-versioned[xarray]   # NetCDF
pip install config-versioned[dbfread]  # DBF files
pip install config-versioned[all]      # All of the above

Quick Start

1. Create a config YAML file

# project_config.yaml
project_name: 'my_analysis'

directories:
  raw_data:
    versioned: false
    path: '~/data/raw'
    files:
      input_table: 'records.csv'

  results:
    versioned: true
    path: '~/data/results'
    files:
      output_table: 'processed.csv'
      summary: 'summary.txt'

versions:
  results: 'v1'

2. Load the config

from config_versioned import Config

cfg = Config('project_config.yaml')

3. Access settings

cfg.get('project_name')           # 'my_analysis'
cfg.get('versions', 'results')    # 'v1'
cfg.get()                         # full config dict

4. Build paths

# Non-versioned: returns ~/data/raw
cfg.get_dir_path('raw_data')

# Versioned: returns ~/data/results/v1
cfg.get_dir_path('results')

# With a custom version override
cfg.get_dir_path('results', custom_version='v2')

# Full file path
cfg.get_file_path('raw_data', 'input_table')   # ~/data/raw/records.csv
cfg.get_file_path('results', 'output_table')   # ~/data/results/v1/processed.csv

All path methods return pathlib.Path objects.

5. Read and write files

import pandas as pd

# Read a file (path resolved from config)
df = cfg.read('raw_data', 'input_table')

# Process data
processed = df.head(10)

# Write results (directory must exist)
cfg.write(processed, 'results', 'output_table')
cfg.write(['Summary: 10 rows written\n'], 'results', 'summary')

# Write the config itself to the results directory
cfg.write_self('results')

6. Override versions at load time

# Run the same pipeline with a new version
cfg_v2 = Config('project_config.yaml', versions={'results': 'v2'})
cfg_v2.get_dir_path('results')  # ~/data/results/v2

Standalone autoread / autowrite

from config_versioned import autoread, autowrite

# Read by extension
df = autoread('data/records.csv')
config = autoread('config.yaml')
lines = autoread('notes.txt')

# Write by extension
autowrite(df, 'output/results.csv')
autowrite({'key': 'value'}, 'output/config.yaml')
autowrite(['line one\n', 'line two\n'], 'output/notes.txt')

Supported File Extensions

Format	Extensions	Requires
CSV / TSV	csv, tsv, gz, bz2	pandas
Excel	xls, xlsx	pandas, openpyxl
Stata	dta	pandas
DBF	dbf	dbfread
YAML	yaml, yml	(core)
Text	txt	(core)
Shapefile / Vector	shp, geojson, gpkg, fgb, gml, kml, and more	geopandas
Raster	tif, geotiff	rasterio
NetCDF	nc	xarray

For raster files, autoread returns {"data": np.ndarray, "profile": dict} and autowrite accepts that same structure (or a (data, profile) tuple).

Example Config File

A bundled example is included with the package:

import importlib.resources as r
from config_versioned import Config

path = str(r.files("config_versioned") / "data" / "example_config.yaml")
cfg = Config(path)