ydata-profiling-1-use-minimal-mode-for-large-datasets
Sub-skill of ydata-profiling: 1. Use Minimal Mode for Large Datasets (+3).
Best use case
ydata-profiling-1-use-minimal-mode-for-large-datasets is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Sub-skill of ydata-profiling: 1. Use Minimal Mode for Large Datasets (+3).
Teams using ydata-profiling-1-use-minimal-mode-for-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-use-minimal-mode-for-large-datasets/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How ydata-profiling-1-use-minimal-mode-for-large-datasets Compares
| Feature / Agent | ydata-profiling-1-use-minimal-mode-for-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 ydata-profiling: 1. Use Minimal Mode for 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. Use Minimal Mode for Large Datasets (+3)
## 1. Use Minimal Mode for Large Datasets
```python
# GOOD: Minimal mode for large data
profile = ProfileReport(large_df, minimal=True)
# AVOID: Full explorative on large data
# profile = ProfileReport(large_df, explorative=True) # Slow!
```
## 2. Sample for Initial Exploration
```python
# GOOD: Sample first, then full profile
sample = df.sample(n=10000, random_state=42)
profile = ProfileReport(sample, title="Sample Profile")
# If interesting, profile full data
# profile_full = ProfileReport(df, minimal=True)
```
## 3. Customize for Your Needs
```python
# GOOD: Disable unnecessary computations
profile = ProfileReport(
df,
correlations={"pearson": {"calculate": True}}, # Only Pearson
missing_diagrams={"bar": True, "matrix": False, "heatmap": False}
)
```
## 4. Use Lazy Evaluation
```python
# GOOD: Lazy profile, compute when needed
profile = ProfileReport(df, lazy=True)
# ... do other work ...
profile.to_file("report.html") # Computes here
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