---

name: configuration-and-output description: "Guides fg-data-profiling Settings, YAML config, environment variables, report sections, HTML assets, cache, and serialization workflows." disable-model-invocation: true metadata: disco-role: operating license: MIT

Configuration and Output

Use this sub-skill when a user wants to control report contents, performance, HTML rendering, YAML settings, environment variables, cache invalidation, or serialization.

Read first

• Read references/configuration-reference.md for Settings, shorthands, config-file patterns, and important field names.
• Read references/output-and-rendering.md for HTML/JSON files, local assets, CDN mode, themes, SVG/PNG plots, and notebook-related output decisions.
• Read references/cache-and-serialization.md for invalidate_cache(), dump(), load(), and .pp serialized reports.
• Read references/troubleshooting.md for common config/output failures.
• Run scripts/write_minimal_config.py when a user asks for a safe starter YAML config for large or privacy-sensitive data.

Common configuration routes

User intent	Start with
Large dataset, faster report	minimal=True, a minimal YAML, or disable correlations/interactions/missing diagrams/samples/duplicates
Privacy-aware report sections	samples=None, duplicates=None, sensitive workflow in comparison/quality sub-skill
Branded HTML report	html={"style": {"theme": "flatly", "primary_colors": [...]}} and output reference
External assets instead of inline HTML	html={"inline": False} and preserve the generated assets directory
Programmatic settings object	from data_profiling.config import Settings and mutate before report computation
Environment-variable control	Use PROFILE_-prefixed variables, for example PROFILE_TITLE or JSON-valued PROFILE_PLOT
Re-render after changing config	profile.invalidate_cache() or construct a new ProfileReport

Key source-verified field names

Use current source names. In particular, categorical frequency plot settings live under plot.cat_freq, not stale plot.pie naming.

profile = ProfileReport(
    df,
    title="Configured profile",
    samples=None,
    correlations=None,
    missing_diagrams=None,
    duplicates=None,
    interactions=None,
    plot={"cat_freq": {"show": False}},
    html={"inline": False, "style": {"theme": "united"}},
)

YAML config pattern

Use a YAML config when the same settings should be reused by API and CLI. Do not combine a config file with minimal=True or CLI --minimal.

profile = ProfileReport(df, config_file="profiling-config.yml")

data_profiling --silent --config_file profiling-config.yml data.csv report.html

Boundaries

• Core report API usage belongs in ../profiling-workflows/SKILL.md.
• CLI flags and batch command shapes belong in ../cli-and-automation/SKILL.md.
• Sensitive redaction policy and comparison output belong in ../comparison-and-quality/SKILL.md.
• Optional Spark/notebook readiness belongs in ../integrations-and-backends/SKILL.md.

Validation

When generating settings for a user, prefer the bundled config writer or a small snippet that parses with Settings.from_file(). For output behavior, create a tiny local DataFrame and verify that the expected HTML/JSON file and assets exist before using the same settings on real data.