ydata-profiling-7-html-report-customization

Sub-skill of ydata-profiling: 7. HTML Report Customization.

5 stars

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

$curl -o ~/.claude/skills/7-html-report-customization/SKILL.md --create-dirs "https://raw.githubusercontent.com/vamseeachanta/workspace-hub/main/.agents/skills/_archive/data/analysis/ydata-profiling/7-html-report-customization/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/7-html-report-customization/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How ydata-profiling-7-html-report-customization Compares

Feature / Agentydata-profiling-7-html-report-customizationStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/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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