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name: data-science-python description: "Use the DataSciencePython tutorial/example collection through modern self-contained helpers for Python data-science resources, statsmodels logistic regression, scikit-learn Kaggle-style tabular classifiers, and Twitter JSONL extraction." disable-model-invocation: true metadata: disco-role: operating license: MIT

DataSciencePython

Use this repo skill when the task names DataSciencePython or asks for a safe, modern way to use its historical Python data-science tutorial list and standalone examples.

This repository is not an installable Python package. It is a curated README plus legacy scripts. Use the generated sub-skills and bundled helper scripts instead of running the original Python 2-era files directly.

Start here

1. Read references/repo-provenance.md when checking staleness against a checkout.
2. Read references/environment.md before running helper scripts.
3. Run scripts/check_data_science_python_env.py when dependency availability is uncertain.
4. Route to the smallest matching sub-skill below.

Route map

User intent	Load this sub-skill	Why
Navigate the README's Python/data science/pandas/sklearn/ML/NLP resource categories or modernize the tiny Python basics snippets.	sub-skills/tutorial-resource-map/SKILL.md	Distills the tutorial index and Python 2-era snippets without relying on external links being live.
Fit the admissions logistic-regression example with pandas and statsmodels, including dummy variables, an intercept, predictions, and optional plots.	sub-skills/statsmodels-logit-workflow/SKILL.md	Owns the copied admissions CSV fixtures and modernized statsmodels_admission_logit.py helper.
Run Kaggle-style dense SVM, hashed SGD logistic regression, or one-hot categorical LogisticRegression examples.	sub-skills/kaggle-linear-models/SKILL.md	Modernizes the legacy scikit-learn/Criteo/Amazon examples and supplies tiny fixture generation.
Extract text from stored Twitter/X JSON-lines data or plan an optional safe live-streaming attempt.	sub-skills/twitter-json-workflow/SKILL.md	Replaces local R/Windows/Tweepy examples with offline extraction and credential-safe streaming guidance.

Dependency quick check

From this generated skill root:

python scripts/check_data_science_python_env.py

For optional plot output, add:

python scripts/check_data_science_python_env.py --check-plots

For the optional live-stream template, add:

python scripts/check_data_science_python_env.py --check-tweepy

Important operating constraints

• Treat README links as topic signals, not guaranteed live URLs.
• Do not instruct future agents to run original source scripts; the bundled helpers are the runtime surface.
• Do not assume pip install -e . works; there is no package metadata in the source snapshot.
• Keep live Twitter/X collection opt-in only. It requires user-provided credentials, Tweepy compatibility, network access, and explicit authorization to connect.
• Use tiny bundled/generated fixtures for smoke tests when the original Kaggle/Amazon data files are missing.

Cross-cutting references

• references/environment.md explains the public dependency set and compatibility notes.
• references/troubleshooting.md covers missing package metadata, legacy APIs, missing competition data, stale links, and credentialed workflows.
• references/repo-routing-metadata.json contains managed router metadata for a later import transaction.