dsr-files 2.2.0

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).

Version 2.2.0: Added UniqueKeyLoader to YAML operations to ensure configuration integrity by preventing duplicate keys in project files.

Features

• CSV: Read and write CSV files with pandas
• JSON: Save and load JSON data with recursive sanitization for NumPy/Pandas types
• JOBLIB: Serialize Python objects and ML models with joblib
• Excel: Save and load Excel workbooks (single or multi-sheet)
• PDF: Generate interactive, indexed audit reports with Matplotlib and ReportLab
• PARQUET: High-performance columnar storage using PyArrow or FastParquet
• YAML: Save and load YAML files with recursive logic and strict key validation to prevent duplicate entries in configuration files.

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

Optional Dependencies

For Excel support:

pip install dsr-files[excel]

For PDF support:

pip install dsr-files[pdf]

Development Installation

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

Usage

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
save_csv(df, Path("."), "data")

# Load from CSV
df = 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
save_json(data, Path("."), "data")

# Load from JSON
data = 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": {}}
save_joblib(model, Path("."), "model")

# Load from JOBLIB
model = 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
save_excel(
 [
  ExcelSheetConfig(data=sales, sheet_name="Sales"),
  ExcelSheetConfig(data=costs, sheet_name="Costs"),
 ],
 Path("."),
 "report",
)

# Load first sheet
df = 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()
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
save_parquet(df, Path("."), "data", engine="pyarrow")

# Load from Parquet
df = 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
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 = load_yaml(Path("config.yaml"))

Testing

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

License

MIT