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data-science-python

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.

VectorSpaceLab/AREX-Skill
インストール方法を見る

含まれるファイル（32）

  • SKILL.md3.5 KB
  • references/environment.md2.0 KB
  • references/repo-provenance.md2.1 KB
  • references/repo-routing-metadata.json522 B
  • references/troubleshooting.md2.7 KB
  • scripts/check_data_science_python_env.py2.8 KB
  • sub-skills/kaggle-linear-models/references/api-reference.md3.9 KB
  • sub-skills/kaggle-linear-models/references/data-formats.md3.5 KB
  • sub-skills/kaggle-linear-models/references/troubleshooting.md5.0 KB
  • sub-skills/kaggle-linear-models/references/workflows.md6.9 KB
  • sub-skills/kaggle-linear-models/scripts/categorical_logistic_submission.py5.4 KB
  • sub-skills/kaggle-linear-models/scripts/hashed_logistic_sgd.py7.8 KB
  • sub-skills/kaggle-linear-models/scripts/make_tiny_fixtures.py5.9 KB
  • sub-skills/kaggle-linear-models/scripts/sklearn_svm_submission.py3.5 KB
  • sub-skills/kaggle-linear-models/SKILL.md3.9 KB
  • sub-skills/statsmodels-logit-workflow/references/data-formats.md1.7 KB
  • sub-skills/statsmodels-logit-workflow/references/data/admissions_test.csv1.5 KB
  • sub-skills/statsmodels-logit-workflow/references/data/admissions_train.csv4.9 KB
  • sub-skills/statsmodels-logit-workflow/references/troubleshooting.md1.9 KB
  • sub-skills/statsmodels-logit-workflow/references/workflows.md1.9 KB
  • sub-skills/statsmodels-logit-workflow/scripts/statsmodels_admission_logit.py11.1 KB
  • sub-skills/statsmodels-logit-workflow/SKILL.md1.5 KB
  • sub-skills/tutorial-resource-map/references/python-basics.md2.5 KB
  • sub-skills/tutorial-resource-map/references/topic-index.md3.4 KB
  • sub-skills/tutorial-resource-map/references/troubleshooting.md1.7 KB
  • sub-skills/tutorial-resource-map/SKILL.md1.8 KB
  • sub-skills/twitter-json-workflow/references/data-formats.md1.7 KB
  • sub-skills/twitter-json-workflow/references/troubleshooting.md2.1 KB
  • sub-skills/twitter-json-workflow/references/workflows.md1.8 KB
  • sub-skills/twitter-json-workflow/scripts/extract_tweet_text.py6.1 KB
  • sub-skills/twitter-json-workflow/scripts/twitter_stream_template.py4.5 KB
  • sub-skills/twitter-json-workflow/SKILL.md2.6 KB

SKILL.md（原文）

インストールする前に、エージェントに与えられる指示の中身を確認できます。

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 intentLoad this sub-skillWhy
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.mdDistills 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.mdOwns 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.mdModernizes 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.mdReplaces 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.

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