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aharp

AHARP (Analytics & History Auto-Report Printer): a free, offline toolkit for multi-dataset exploration, exportable reports, time-series analysis, rich plotting, and fuzzy-matched NLP queries.

Published9 Jul 2025
Direct dependencies6
Transitive dependenciesNot calculated

README

Version 0.0.1 documentation

AHARP

Analytics & History Auto-Report Printer

AHARP is a free, offline Python toolkit for exploring one or more tabular datasets using natural-language queries. Under the hood, it leverages Pandas, NumPy, and a lightweight fuzzy‐matching layer to give you:

  • Multi-dataset sessions: load multiple CSVs (e.g. load sales.csv as sales)
  • Exportable reports: transcript → Markdown or HTML
  • Time-series awareness: resample, rolling avg, line plots
  • Fuzzy matching: tolerate typos (nulll→null)

📦 Installation

cd aharp
pip install -e ".[full]"

🚀 CLI Usage

python -m aharp sample_data/people.csv

Commands:

  • load FILE.csv as ALIAS — load another dataset
  • ask ALIAS question or just type a question if only one dataset is loaded
  • export markdown report.md
  • export html report.html
  • help
  • exit

📝 Examples

aharp> load sample_data/products.csv as products
aharp> ask main mean of income
aharp> ask products most common product
aharp> type of age
aharp> plot date vs sales
aharp> export markdown session.md
aharp> exit

📊 Time-Series Commands

  • ask main resample date by month
  • rolling average of temperature over 7 days
  • plot date vs sales (line chart)

❓ Supported Question Categories

Dataset Shape

  • How many rows?
  • Number of rows?
  • Row count
  • How many columns?
  • Column count

Schema & Types

  • What are the column types?
  • Type of each column
  • What type is age?
  • Describe column types
  • Schema summary
  • Show data structure
  • Dataset info

Missing / Nulls

  • Any missing data?
  • Are there nulls?
  • Missing values?
  • Null counts?
  • How many nulls per column?
  • Which columns have missing?

Zero Values

  • Count zeros in column
  • Zero values in inventory?
  • How many zeros?
  • Zero count

Statistical Summaries

  • Top / Mode values
  • Mean / Average / Compute mean
  • Median / Std / Standard deviation
  • Range / Min & Max
  • Unique / Distinct counts
  • Sample values / Examples

Viewing & Exporting Rows

  • Show sample rows
  • Show top 5 rows / Head
  • Show last rows / Tail
  • Any duplicates? / Duplicate count
  • Drop duplicates / Remove duplicate rows

Time-Series & Plots

  • Plot age vs income
  • Create scatter of price vs inventory
  • Plot distribution / Histogram / Bar chart / Pie chart

Advanced

  • Find rows where city = NY
  • Search column city for LA
  • Favorite column?

🧪 Run Tests

pytest

🚩 Sample Data

  • sample_data/people.csv
  • sample_data/products.csv

Downloads & popularity

Awaiting observations

No metric observations yet

Download counts and popularity will appear here as the collector receives them.

Declared dependencies 6

Native requirements
6 direct targetsNot calculated transitive count
Package / targetRequirementRelationship
pandas
Environment
{
  "extras": "",
  "marker": null,
  "requires_python": ">=3.8",
  "evaluation": "NOT_EVALUATED"
}
pandas>=1.1.0runtime
numpy
Environment
{
  "extras": "",
  "marker": null,
  "requires_python": ">=3.8",
  "evaluation": "NOT_EVALUATED"
}
numpy>=1.21.0runtime
scikit-learn
Environment
{
  "extras": "",
  "marker": null,
  "requires_python": ">=3.8",
  "evaluation": "NOT_EVALUATED"
}
scikit-learn>=1.0.0runtime
matplotlib
Environment
{
  "extras": "",
  "marker": null,
  "requires_python": ">=3.8",
  "evaluation": "NOT_EVALUATED"
}
matplotlib>=3.0.0runtime
rapidfuzz
Environment
{
  "extras": "",
  "marker": null,
  "requires_python": ">=3.8",
  "evaluation": "NOT_EVALUATED"
}
rapidfuzz>=2.0.0runtime
markdown
Environment
{
  "extras": "",
  "marker": null,
  "requires_python": ">=3.8",
  "evaluation": "NOT_EVALUATED"
}
markdown>=3.0.0runtime

Distribution files

2 collected
aharp-0.0.1-py3-none-any.whl

bdist_wheel · 8.41 KB · Python py3

Source file
aharp-0.0.1.tar.gz

sdist · 11.72 KB · Python source

Source file

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package_version:ce3abedfe80020bd2a8ce3d1ba8f4d35bb130c7dbcc4d9f90b4d7600c0fabba5

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