ydata-profiling-7-html-report-customization
Sub-skill of ydata-profiling: 7. HTML Report Customization.
Best use case
ydata-profiling-7-html-report-customization is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Sub-skill of ydata-profiling: 7. HTML Report Customization.
Teams using ydata-profiling-7-html-report-customization 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/7-html-report-customization/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How ydata-profiling-7-html-report-customization Compares
| Feature / Agent | ydata-profiling-7-html-report-customization | 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: 7. HTML Report Customization.
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
# 7. HTML Report Customization
## 7. HTML Report Customization
**Custom Report Configuration:**
```python
from ydata_profiling import ProfileReport
import pandas as pd
df = pd.read_csv("data.csv")
# Customized report
profile = ProfileReport(
df,
title="Custom Styled Report",
dataset={
"description": "This is a sample dataset for analysis",
"creator": "Data Team",
"copyright_holder": "Company Inc.",
"copyright_year": "2025",
"url": "https://company.com/data"
},
variables={
"descriptions": {
"revenue": "Total revenue in USD",
"units": "Number of units sold",
"category": "Product category"
}
},
html={
"style": {
"full_width": True
},
"navbar_show": True,
"minify_html": True
},
progress_bar=True
)
profile.to_file("custom_report.html")
```
**Report Sections Control:**
```python
from ydata_profiling import ProfileReport
import pandas as pd
df = pd.read_csv("data.csv")
# Control which sections appear
profile = ProfileReport(
df,
title="Selective Report",
samples={
"head": 10, # Show first 10 rows
"tail": 10 # Show last 10 rows
},
duplicates={
"head": 10 # Show first 10 duplicate rows
},
correlations={
"pearson": {"calculate": True},
"spearman": {"calculate": False}, # Skip Spearman
"kendall": {"calculate": False}, # Skip Kendall
"phi_k": {"calculate": False} # Skip Phi-K
},
missing_diagrams={
"bar": True,
"matrix": False, # Skip matrix
"heatmap": False # Skip heatmap
}
)
profile.to_file("selective_report.html")
```
**Export Options:**
```python
from ydata_profiling import ProfileReport
import pandas as pd
import json
df = pd.read_csv("data.csv")
profile = ProfileReport(df, title="Export Demo")
# Export to HTML
profile.to_file("report.html")
# Export to JSON
profile.to_file("report.json")
# Get JSON as string
json_output = profile.to_json()
# Get as dictionary
description_dict = profile.get_description()
# Save widgets for notebook
profile.to_widgets()
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