L’interface est localisée ; les présentations et documents des Skills sont traduits automatiquement par IA dans votre langue. Le code, les commandes, chemins et termes techniques restent en version originale, et le document de l’auteur fait foi.
Explorer les Skills
Données et recherche · SKILL.md

golden-jupyter-topics

golden-jupyter-topics

Use when testing the golden_jupyter_topics golden build

Independent GitHub snapshot · Automated classification · Original documentation governs

Documentation de la Skill

GitHub

name: golden-jupyter-topics description: Use when testing the golden_jupyter_topics golden build

Golden_Jupyter_Topics Notebook Skill

Use when testing the golden_jupyter_topics golden build

📋 Notebook Information

Kernel: Python 3

Language: python 3.11.4

💡 When to Use This Skill

Use this skill when you need to:

  • Understand golden_jupyter_topics concepts and analysis workflow
  • Reference code examples and their outputs
  • Reproduce data analysis or computation steps
  • Review methodology, visualizations, and results
  • Find library usage patterns and best practices

📖 Section Overview

Total Sections: 5

Content Breakdown:

  • Data Loading: 1 sections
  • Evaluation: 1 sections
  • Setup: 1 sections
  • Other: 2 sections

🔑 Key Concepts

Main topics covered in this notebook

Major Topics:

  • Getting Started

Subtopics:

  • Modeling Results

📦 Dependencies

3 package(s) imported

  • numpy
  • pandas
  • sklearn

⚡ Quick Reference

Common documentation patterns found:

Getting Started (1 sections):

  • Getting Started (section 1)

Modeling (1 sections):

  • Modeling Results (section 5)

📝 Code Examples

High-quality code cells from notebook

Bash Examples (1)

Example 1 (Quality: 5.0/10):

pip install pandas

Python Examples (3)

Example 1 (Quality: 9.5/10):

def long_example():
    x0 = 0
    x1 = 1
    x2 = 2
    x3 = 3
    x4 = 4
    x5 = 5
    x6 = 6
    x7 = 7
    x8 = 8
    x9 = 9
    x10 = 10
    x11 = 11
    x12 = 12
    x13 = 13
    x14 = 14
    x15 = 15
    x16 = 16
    x17 = 17
    x18 = 18
    x19 = 19
    x20 = 20
    x21 = 21
    x22 = 22
    x23 = 23
    x24 = 24
    x25 = 25
    x26 = 26
    x27 = 27
    x28 = 28
    x29 = 29
    x30 = 30
    x31 = 31
    x32 = 32
    x33 = 33
    x34 = 34
    x35 = 35
    x36 = 36
    x37 = 37
    x3
...

In [2] (Quality: 7.5/10):

import pandas as pd
df = pd.read_csv('data.csv')
df.head()

Example 3 (Quality: 2.0/10):

%timeit broken()

📊 Notebook Statistics

  • Total Sections: 5
  • Code Cells: 2
  • Markdown Cells: 2
  • Raw Cells: 1
  • Notebooks: 1
  • Programming Languages: 2

Language Breakdown:

  • python: 3 code cells
  • bash: 1 code cells

🗺️ Navigation

Reference Files:

  • references/section_s2-s2.md - Data Loading
  • references/section_s5-s5.md - Evaluation
  • references/section_s1-s1.md - Setup
  • references/section_s3-s4.md - Other

See references/index.md for complete notebook structure.


Generated by Skill Seeker | Jupyter Notebook Scraper

Skills enregistrées