# bitly/data_hacks

Command line utilities for data analysis

Repository: https://github.com/bitly/data_hacks
Canonical: https://ross.abutalabs.com/products/data_hacks
Homepage: http://github.com/bitly/data_hacks
Language: Python
License Family: other
Last push: 2024-01-16T09:55:12+00:00

## Health v2 (maintenance only)
Score: 23/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 8, longevity 100
- inputs: {"age_days": 5818, "days_push": 960, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1976, forks 188 (observed 2026-08-28T04:06:01.352282+00:00)

## What it is
A collection of small Python command-line utilities for quick data analysis over piped streams, including text histograms, 95th percentile calculation, random sampling, time-window filtering, and ASCII bar charts. It is designed to plug into Unix pipelines like awk, tail, and cat.

## Use cases
- generate a text histogram from a stream of numbers
- compute the 95th percentile response time from access logs
- randomly sample a percentage of lines from a log file
- pass through a stream for a fixed duration like 10 seconds
- visualize counts of unique values as an ascii bar chart
- quick summary statistics like mean, median, and standard deviation from the command line

## When to choose
- you want quick command-line statistics on piped numeric data without writing scripts
- you live in Unix pipelines and need lightweight log analysis tools
- you need ascii visualizations of data distributions in a terminal

## When to avoid
- you need interactive plotting or rich charting libraries
- you need a maintained tool with active development and a license
- you need to process large datasets with performance beyond simple streaming

## Facets
- artifact type: cli-tool
- maturity: maintenance
- function: data-science, analytics, cli, data-visualization
- domain: data-science, analytics, developer-tools
- platform: cli, python, cross-platform
- tags: ascii-histogram, percentiles, stream-processing, unix-pipes, log-analysis, command-line

## Member repositories
- bitly/data_hacks (main) score 23

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:01.352282+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T03:04:49.347660+00:00, confidence not recorded.
  - readme: https://github.com/bitly/data_hacks (fetched 2026-08-28T04:06:01.352282+00:00, sha 81493abf632c)
  - homepage: http://github.com/bitly/data_hacks (fetched 2026-08-29T10:43:53.120332+00:00, sha 0a686284dc74)
  - registry_pypi: https://pypi.org/pypi/data_hacks/json (fetched 2026-08-29T10:43:53.129753+00:00, sha 88a430fc1148)
- Data as of 2026-08-30T08:39:29.467469+00:00.