dsr-files 3.1.3

File handling library for creating, saving, and loading various file types (CSV, JSON, JOBLIB, PDF, PARQUET)

dsr-files

File handling library for creating, saving, and loading various file types (CSV, JSON, JOBLIB, PDF, PARQUET, YAML), plus explicit model-artifact typing support.

Version 3.1.3: Added FileType.MODEL to represent fitted-model artifact bundles with .joblib extension validation/normalization support for model-export workflows.

Version 3.1.1: Standardized handler path typing around a shared PathLike alias for local, cloud, and string inputs, and updated package version reporting to use installed distribution metadata with a safe fallback.

Unreleased update: Save handlers now remove an existing target file before writing a replacement, improving overwrite reliability across repeated exports.

Features

• CSV: Read and write CSV files with pandas.
• JSON: Save and load JSON data with recursive sanitization; now supports .jsonl (JSON Lines) for large datasets.
• JOBLIB: Serialize Python objects and ML models with joblib.
• Excel: Save and load Excel workbooks; supports .xlsx, .xls, .xlsm, and .xlsb formats.
• PDF: Generate interactive, indexed audit reports with Matplotlib and ReportLab.
• PARQUET: High-performance columnar storage; now supports .pq as a valid logical extension.
• YAML: Save and load YAML files with recursive logic and strict key validation to prevent duplicate entries in configuration files.
• MODEL: Use FileType.MODEL for explicit model-artifact export typing while reusing joblib-compatible extensions (.joblib, .joblib.gz).
• FileType Utilities: The FileType enum includes is_valid_extension() for performing logical consistency checks between file names and formats without requiring filesystem access. This is ideal for pre-validating configuration files in ML pipelines.

Installation

pip install dsr-files

Requirements

• Python: >= 3.10
• PyYAML: >= 6.0.2
• Pandas: Required for CSV and Excel operations
• Joblib: Required for object serialization
• dsr-utils: >= 1.6.0
• cloudpathlib: Required for AnyPath and CloudPath support

Optional Dependencies

For Excel support:

pip install dsr-files[excel]

For PDF support:

pip install dsr-files[pdf]

For full cloud support (S3, GCS, Azure)

pip install cloudpathlib[all]

Development Installation

pip install -e ".[dev,excel,pdf]"

Developer Transparency

Note on Parameter Registry: The list of valid parameters for each format can be found in dsr_files/resources/params.yaml. This file serves as the "ground truth" for all safe_call filtering operations.

Usage

Universal Parameter Filtering

All handlers now support safe_call=True. This leverages dsr-utils to filter out incompatible keyword arguments that would otherwise cause TypeErrors in underlying engines like pyarrow or fastparquet.

Any parameters that are not compatible with the specific engine are returned in a rejected dictionary for debugging and audit logging.

The library no longer relies solely on reflection, but uses a "ground truth" registry for engine-specific safety.

CSV Operations

from dsr_files import save_csv, load_csv, create_csv
import pandas as pd
from pathlib import Path

# Create from dictionary
data = {"name": ["Alice", "Bob"], "age": [30, 25]}
df = create_csv(data)

# Save to CSV
full_path, rejected = save_csv(df, Path("."), "data")

# Using safe_call
full_path, rejected = save_csv(df, Path("."), "data", safe_call=True, float_format="%.2f")

# Load from CSV
df, rejected = load_csv(Path("data.csv"))

JSON Operations

from dsr_files import save_json, load_json
from pathlib import Path

data = {"key": "value", "number": 42}

# Save to JSON
full_path, rejected = save_json(data, Path("."), "data")

# Load from JSON
data, rejected = load_json(Path("data.json"))

JOBLIB Operations

from dsr_files import save_joblib, load_joblib
from pathlib import Path

# Save any Python object
model = {"weights": [1, 2, 3], "config": {}}
full_path, rejected = save_joblib(model, Path("."), "model")

# Load from JOBLIB
model, rejected = load_joblib(Path("model.joblib"))

Excel Operations

from dsr_files import save_excel, load_excel, ExcelSheetConfig
from pathlib import Path
import pandas as pd

sales = pd.DataFrame({"region": ["NA", "EU"], "revenue": [120, 95]})
costs = pd.DataFrame({"region": ["NA", "EU"], "cost": [80, 70]})

# Save multi-sheet workbook
full_path, rejected = save_excel(
 [
  ExcelSheetConfig(data=sales, sheet_name="Sales"),
  ExcelSheetConfig(data=costs, sheet_name="Costs"),
 ],
 Path("."),
 "report",
)

# Load first sheet
df, rejected = load_excel(Path("report.xlsx"))

PDF Operations (Interactive Reports)

from dsr_files import PDFDocument, PageConfiguration, PageSize, PageOrientation, PageColors
from pathlib import Path

# Configure document style
config = PageConfiguration(
    page_size=PageSize.LETTER,
    orientation=PageOrientation.PORTRAIT,
    colors=PageColors(page_num="#000000", title="#444444"),
    margins=(0.07, 0.93, 0.90, 0.10)
)

doc = PDFDocument("Audit Report", config)
page = doc.create_new_page("Summary")
# ... Add Matplotlib content to page.fig ...

doc.render_table_of_contents()
full_path, rejected = doc.save(Path("."), "audit_report")

PARQUET Operations

from dsr_files import save_parquet, load_parquet
import pandas as pd
from pathlib import Path

df = pd.DataFrame({"A": [1, 2, 3], "B": ["x", "y", "z"]})

# Save to Parquet
full_path, rejected = save_parquet(df, Path("."), "data", engine="pyarrow")

# Load from Parquet
df, rejected = load_parquet(Path("data.parquet"))

YAML Operations

from dsr_files import save_yaml, load_yaml
from pathlib import Path

data = {"project": "dsr-orchestrator", "steps": ["ingest", "analyze"]}

# Save to YAML
full_path, rejected = save_yaml(data, Path("config.yaml"))

# Load from YAML using the new UniqueKeyLoader
# This will raise a ConstructorError if duplicate keys are detected,
# protecting your project settings from conflicting edits.
data, rejected = load_yaml(Path("config.yaml"))

Cloud-Native Pathing

dsr-files now supports both local and cloud filesystems (S3, GCS, Azure) out of the box using cloudpathlib. You can pass raw URI strings, pathlib.Path objects, or CloudPath objects directly to any handler.

from dsr_files import save_csv

# Local path
full_path, rejected = save_csv(df, "./data", "local_audit") 

# Cloud path (requires cloudpathlib[s3])
full_path, rejected = save_csv(df, "s3://my-bucket/audits", "remote_audit")

Testing

pytest tests/
pytest tests/ --cov=src/dsr_files

License

MIT