Business data toolkit
BizPack
An open-source Python library that converts messy, internationally formatted corporate spreadsheets into clean, auditable pandas DataFrames.
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Result
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Explore the real workflow
Install the package from PyPI.
The full app's capabilities and requirements are described below.
What it is for
An open-source Python library that converts messy, internationally formatted corporate spreadsheets into clean, auditable pandas DataFrames.
Business spreadsheets routinely mix currencies, date conventions, accounting negatives, totals, identifiers, and error cells. One silent conversion can invalidate an analysis. BizPack centralizes those repairs and records what changed.
- Clean international exports
- Review currencies, separators, percentages, and accounting negatives before comparing business data.
- Protect identifiers
- Keep leading-zero codes and ambiguous date columns from being silently converted.
- Repeat a business analysis
- Reuse auditable cleaning and analysis steps in a notebook or Python pipeline.
Get started
Open the PyPI package and follow its installation instructions. Work on a copy of a representative export, inspect inferred types, and choose locale and date assumptions before cleaning.
- A Python environment compatible with the published package.
- An Excel or CSV export and knowledge of its locale and identifier columns.
- pandas-based workflows; Streamlit is optional for the interface.
Features and workflow
- Spreadsheet cleanup
- Normalize columns, totals rows, blank structures, spreadsheet error values, and common export artifacts.
- International numbers
- Parse currency symbols, thousands and decimal conventions, percentages, and accounting negatives.
- Safer identifiers
- Protect leading-zero values such as account codes from accidental numeric conversion.
- Date handling
- Resolve mixed and ambiguous date formats with explicit controls instead of silent guesses.
- Business analysis helpers
- Use XLOOKUP-style joins, Pareto analysis, period growth, run-rate forecasting, and related workflows.
- Audit trail
- Preserve source files and report transformations so a cleaned dataset remains reviewable.
- Install the package from PyPI.
- Load an Excel or CSV export into the BizPack workflow.
- Inspect detected types and choose any needed locale or date assumptions.
- Clean and validate the DataFrame without overwriting the source.
- Use the result in pandas or continue with the included business-analysis helpers.
Architecture
BizPack is a Python package built around pandas and NumPy with an optional Streamlit interface. The package exposes focused cleaning and analysis operations so it can fit into notebooks, scripts, or repeatable data pipelines.
Privacy and limitations
The library favors explicit, auditable transformation over destructive in-place editing. Ambiguous dates and identifiers need domain context, so results should be reviewed before financial or operational decisions.
Status
Published on PyPI as an open-source package. The repository contains installation instructions, usage examples, tests, and the detailed feature reference.
- Availability
- PyPI package
- Input
- Excel and CSV
- Output
- pandas DataFrame
- Use
- Local Python workflow
Questions
Does it replace pandas?
No. BizPack provides focused cleaning and business-analysis helpers around pandas DataFrames.
Can it resolve every ambiguous date automatically?
No. A value can have more than one valid interpretation. Supply the right locale or date assumptions and review the result.
Will it overwrite my source file?
The workflow is designed around preserved inputs and auditable transformations. Keep the original export and review the cleaned output separately.