autoviz-1-sample-large-datasets
Sub-skill of autoviz: 1. Sample Large Datasets (+3).
Best use case
autoviz-1-sample-large-datasets is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Sub-skill of autoviz: 1. Sample Large Datasets (+3).
Teams using autoviz-1-sample-large-datasets should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/1-sample-large-datasets/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How autoviz-1-sample-large-datasets Compares
| Feature / Agent | autoviz-1-sample-large-datasets | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Sub-skill of autoviz: 1. Sample Large Datasets (+3).
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# 1. Sample Large Datasets (+3)
## 1. Sample Large Datasets
```python
# GOOD: Use sampling for initial exploration
AV.AutoViz(
filename="",
dfte=large_df,
max_rows_analyzed=50000, # Sample for speed
verbose=1
)
# AVOID: Analyzing millions of rows directly
# This will be slow and may crash
```
## 2. Specify Target Variable When Available
```python
# GOOD: Specify target for focused analysis
AV.AutoViz(
filename="",
dfte=df,
depVar="target_column", # Enables target-specific charts
verbose=1
)
# LESS USEFUL: No target specified
# Still works but misses target-related insights
```
## 3. Choose Appropriate Chart Format
```python
# For presentations: PNG
chart_format="png"
# For reports/web: HTML
chart_format="html"
# For notebooks: server or bokeh
chart_format="server"
# For scalable graphics: SVG
chart_format="svg"
```
## 4. Organize Output
```python
# GOOD: Save to organized directory
import os
output_dir = f"eda_{datetime.now().strftime('%Y%m%d_%H%M%S')}"
os.makedirs(output_dir, exist_ok=True)
AV.AutoViz(
filename="",
dfte=df,
save_plot_dir=output_dir,
chart_format="png"
)
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