# KeithGalli/Pandas-Data-Science-Tasks

Set of real world data science tasks completed using the Python Pandas library

Repository: https://github.com/KeithGalli/Pandas-Data-Science-Tasks
Canonical: https://ross.abutalabs.com/products/pandas-data-science-tasks
Language: Jupyter Notebook
License Family: other
Last push: 2023-11-08T12:35:38+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 2442, "days_push": 1029, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1011, forks 2937 (observed 2026-08-28T04:03:13.232283+00:00)

## What it is
A collection of Jupyter Notebooks solving real-world data science tasks with Python's Pandas and Matplotlib libraries, accompanying a YouTube tutorial. It walks through cleaning and analyzing 12 months of electronics store sales data to answer business questions.

## Use cases
- learn pandas for data analysis
- practice real world data science tasks
- learn data cleaning with pandas
- analyze sales data with python
- learn groupby and aggregation in pandas
- learn matplotlib charting
- follow along with a pandas tutorial video

## When to choose
- you are learning Pandas and want hands-on examples with real data
- you want to see a complete data cleaning and exploration workflow
- you prefer learning via video plus accompanying code

## When to avoid
- you need a production-ready data analysis library or tool
- you want maintained, licensed code for reuse in projects
- you need advanced or up-to-date pandas practices

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: data-science, data-visualization, etl
- domain: data-science, education, tutorials, analytics
- platform: python, cross-platform
- tags: pandas, jupyter-notebook, matplotlib, tutorial, data-cleaning, sales-analysis

## Member repositories
- KeithGalli/Pandas-Data-Science-Tasks (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:13.232283+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 2026-08-30T07:11:48.281392+00:00, confidence not recorded.
  - readme: https://github.com/KeithGalli/Pandas-Data-Science-Tasks (fetched 2026-08-28T04:03:13.232283+00:00, sha ada2469cb655)
- Data as of 2026-08-30T08:39:29.467469+00:00.