python-analytics
Python data analysis with pandas, numpy, and analytics libraries
Packaged view
This page reorganizes the original catalog entry around fit, installability, and workflow context first. The original raw source lives below.
Install command
npx @skill-hub/cli install benchflow-ai-skillsbench-python-analytics
Repository
Skill path: registry/terminal_bench_2.0/full_batch_reviewed/terminal_bench_2_0_modernize-scientific-stack/environment/skills/python-analytics
Python data analysis with pandas, numpy, and analytics libraries
Open repositoryBest for
Primary workflow: Analyze Data & AI.
Technical facets: Full Stack, Data / AI.
Target audience: everyone.
License: Unknown.
Original source
Catalog source: SkillHub Club.
Repository owner: benchflow-ai.
This is still a mirrored public skill entry. Review the repository before installing into production workflows.
What it helps with
- Install python-analytics into Claude Code, Codex CLI, Gemini CLI, or OpenCode workflows
- Review https://github.com/benchflow-ai/SkillsBench before adding python-analytics to shared team environments
- Use python-analytics for development workflows
Works across
Favorites: 0.
Sub-skills: 0.
Aggregator: No.
Original source / Raw SKILL.md
---
name: python-analytics
description: Python data analysis with pandas, numpy, and analytics libraries
version: "2.0.0"
sasmp_version: "2.0.0"
bonded_agent: 05-programming-expert
bond_type: SECONDARY_BOND
# Skill Configuration
config:
atomic: true
retry_enabled: true
max_retries: 3
backoff_strategy: exponential
code_execution: sandboxed
# Parameter Validation
parameters:
skill_level:
type: string
required: true
enum: [beginner, intermediate, advanced]
default: beginner
focus_area:
type: string
required: false
enum: [pandas, numpy, visualization, timeseries, all]
default: all
# Observability
observability:
logging_level: info
metrics: [code_efficiency, memory_usage, execution_time]
---
# Python Analytics Skill
## Overview
Master Python for data analysis using industry-standard libraries.
## Topics Covered
- Pandas DataFrames and Series operations
- NumPy array manipulation
- Data aggregation and groupby
- Time series analysis
- Jupyter notebook workflows
## Learning Outcomes
- Analyze data with pandas
- Perform numerical computations
- Build reproducible analysis notebooks
- Process large datasets efficiently
## Error Handling
| Error Type | Cause | Recovery |
|------------|-------|----------|
| MemoryError | Dataset too large | Use chunking or dask |
| KeyError | Column not found | Verify column names |
| ValueError | Invalid operation | Check data types |
| SettingWithCopyWarning | Chained assignment | Use .loc accessor |
## Related Skills
- programming (for Python fundamentals)
- statistics (for statistical analysis)
- advanced (for machine learning)