autoviz-1-sample-large-datasets

Sub-skill of autoviz: 1. Sample Large Datasets (+3).

5 stars

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

$curl -o ~/.claude/skills/1-sample-large-datasets/SKILL.md --create-dirs "https://raw.githubusercontent.com/vamseeachanta/workspace-hub/main/.agents/skills/_archive/data/analysis/autoviz/1-sample-large-datasets/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/1-sample-large-datasets/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How autoviz-1-sample-large-datasets Compares

Feature / Agentautoviz-1-sample-large-datasetsStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/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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