Skip to main content

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

Metadata

Release files for aharp 0.0.1

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for aharp 0.0.1
File Size Uploaded Provenance
aharp-0.0.1.tar.gz 12.0 kB Without provenance Details

Built distribution (wheel)

Table of built distributions (wheels) for aharp 0.0.1
File Interpreter ABI Platform
aharp-0.0.1-py3-none-any.whl Python 3 none any Details

Total release size: 20.6 kB

Release files / aharp-0.0.1.tar.gz

Download URL aharp-0.0.1.tar.gz
Size 12.0 kB
Tags Source
SHA-256 checksum
How to use checksums
a0d169a04fc6a0231e92d3571d6c79872f88370b54d2698371dfdac4e81e9bc6
BLAKE2b-256 checksum
How to use checksums
67e7ef50a0f19ee4e673bd34322b7fb27c014533df50a9a93d4c383853174736
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/6.1.0 CPython/3.12.4

Release files / aharp-0.0.1-py3-none-any.whl

Download URL aharp-0.0.1-py3-none-any.whl
Size 8.6 kB
Tags Python 3
SHA-256 checksum
How to use checksums
ceac28a8dd05ba7f191d76565afe6e5983317cec4e56064ae81b087731058b8d
BLAKE2b-256 checksum
How to use checksums
ecbf37dd08d8b089b05d089174e9fe746c4eb1181a381267656f3dd038e1ffc5
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/6.1.0 CPython/3.12.4

Security for aharp 0.0.1

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

No provenance found

None of the files in this release were uploaded with provenance metadata.

A note on package security

PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.

To learn more about protecting yourself from malicious actors, visit our package security documentation.

Release history Release notifications | RSS feed

This release

0.0.1 This release

2 release files No provenance

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page