vamseeachanta
vamseeachanta/workspace-hub
相关职业SOC
基于 SOC 职业分类
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
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npx skills add https://github.com/vamseeachanta/workspace-hub --skill sweetviz-common-issues命令会保持在同一行。复制前请横向滚动并检查完整内容。
想先保存到本地？可下载 SkillsMP 当前能够提供的文件。
Write outbound email and external messages in Vamsee Achanta's voice — a subtle offer to help, never bold or rash claims. Load before drafting ANY email, LinkedIn/Collide reply, proposal note, or outreach sent under his name.
Save/publish analysis or computation results from ANY ecosystem repo to Hugging Face as a queryable, viewer-renderable dataset. Use when the user wants to "save results to hugging face", "publish dataset to HF", "hugging face data saving", "save analysis results", "hf dataset", "make results queryable", or "render via datasets-server API". Reshapes nested results into flat parquet tables, writes a dataset card with a viewer `configs:` block and provenance, applies license/public-vs-private routing, enforces a domain data-quality gate (faithful-to-source != correct), publishes to `aceengineer/<repo>-<projection>`, and verifies via the datasets-server API.
Clone, create, fork, configure, and manage GitHub repositories. Manage remotes, secrets, releases, and workflows. Works with gh CLI or falls back to git + GitHub REST API via curl.
基于 SOC 职业分类
正在显示 SKILL.md
| name | sweetviz-common-issues |
| description | Sub-skill of sweetviz: Common Issues. |
| version | 1.0.0 |
| category | data-analysis |
| type | reference |
| scripts_exempt | true |
Issue: Report generation is slow
# Solution 1: Disable pairwise analysis
report = sv.analyze(df, pairwise_analysis="off")
# Solution 2: Sample data
report = sv.analyze(df.sample(50000))
# Solution 3: Skip high-cardinality columns
config = sv.FeatureConfig(skip=["high_card_col"])
report = sv.analyze(df, feat_cfg=config)
Issue: Memory error with large dataset
# Solution: Process in chunks or sample
sample_size = min(len(df), 100000)
report = sv.analyze(df.sample(sample_size, random_state=42))
Issue: HTML report won't open
# Solution: Save and open manually
report.show_html("report.html", open_browser=False)
# Then open report.html in browser
# Or specify layout
report.show_html("report.html", layout="vertical")
Issue: Categorical variables treated as numeric
# Solution: Force categorical type
config = sv.FeatureConfig(force_cat=["zip_code", "rating"])
report = sv.analyze(df, feat_cfg=config)
# Or convert before analysis
df["zip_code"] = df["zip_code"].astype(str)
Issue: Date columns not recognized
# Solution: Convert to proper datetime
df["date_col"] = pd.to_datetime(df["date_col"])
report = sv.analyze(df)
Issue: Report shows too many categories
# Sweetviz automatically limits to top categories
# For custom handling, reduce cardinality before analysis
df["category"] = df["category"].apply(
lambda x: x if x in top_categories else "Other"
)