# Sweetviz Common Issues

> Sub-skill of sweetviz: Common Issues.

- Skill: `vamseeachanta/sweetviz-common-issues` (Agent Skill)
- Install (CLI): `npx skillmds@latest add vamseeachanta/sweetviz-common-issues`
- Raw SKILL.md: https://api.skillmd.com/api/skills/vamseeachanta/sweetviz-common-issues/raw
- Safety review: PASS (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: vamseeachanta (https://skillmd.com/u/vamseeachanta)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/vamseeachanta/sweetviz-common-issues

---


# Common Issues

## Common Issues


**Issue: Report generation is slow**
```python
# 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**
```python
# 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**
```python
# 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**
```python
# 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**
```python
# Solution: Convert to proper datetime
df["date_col"] = pd.to_datetime(df["date_col"])
report = sv.analyze(df)
```

**Issue: Report shows too many categories**
```python
# 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"
)
```