---

name: profiling-workflows description: "Guides core fg-data-profiling ProfileReport workflows for pandas DataFrames, exports, time-series, type schemas, and report smoke checks." disable-model-invocation: true metadata: disco-role: operating license: MIT

Profiling Workflows

Use this sub-skill when the user wants to create, inspect, or export an fg-data-profiling report with the Python API. This covers the current public import data_profiling, ProfileReport, and the pandas df.profile_report() convenience method.

Read first

• Read references/api-reference.md for verified signatures, output methods, and summary accessors.
• Read references/workflows.md for task-oriented recipes: minimal EDA, full/explorative reports, time-series, type schemas, notebook display, and large-dataset sampling.
• Read references/data-formats.md when the user is starting from files, pandas readers, or DataFrame shape constraints.
• Read references/troubleshooting.md for empty DataFrames, sortby, minimal/config_file, deprecated imports, and widget display failures.
• Run scripts/profile_dataframe_smoke.py when you need a safe no-network API smoke test that writes a tiny HTML/JSON report.

Core API pattern

import pandas as pd
from data_profiling import ProfileReport

df = pd.DataFrame({"amount": [1.0, 2.5, 3.0], "segment": ["a", "b", "b"]})
profile = ProfileReport(df, title="Dataset profile", minimal=True, progress_bar=False)
profile.to_file("profile.html")

If the user already has a pandas DataFrame and has imported data_profiling, the package also registers:

profile = df.profile_report(title="Dataset profile", minimal=True)

Prefer minimal=True or a sampled DataFrame for first-pass profiling of large or unfamiliar data. Move performance tuning and report-section controls to ../configuration-and-output/SKILL.md when the user asks for exact settings.

Choose the right mode

User intent	Recommended start
Quick EDA report for a pandas DataFrame	ProfileReport(df, minimal=True) then to_file("report.html")
Full exploratory report with URL/file/image/text detection	ProfileReport(df, explorative=True) and review optional dependencies
Time-series EDA	ProfileReport(df, tsmode=True, sortby="time_column") if chronological order is a column
Known semantic column types	Pass type_schema={"col": "categorical"} or a time-series type schema
JSON profile values for automation	json.loads(profile.to_json()) or profile.get_description()
Sensitive dataset	Route to ../comparison-and-quality/SKILL.md before exposing samples/duplicates
Spark DataFrame	Route to ../integrations-and-backends/SKILL.md first

Important boundaries

• This sub-skill is API-first. For shell commands, read ../cli-and-automation/SKILL.md.
• For YAML files, environment variables, HTML assets, cache invalidation, and serialization, read ../configuration-and-output/SKILL.md.
• For report comparison, privacy redaction, custom samples, metadata, and Great Expectations caveats, read ../comparison-and-quality/SKILL.md.
• For Spark, notebooks, Bytewax, dashboards, and optional dependency readiness, read ../integrations-and-backends/SKILL.md.

Safety and validation

• Start with tiny local fixtures before profiling private, very large, or expensive datasets.
• Do not rely on the original repository's examples at runtime; use the bundled smoke script or adapt the API snippets in this sub-skill.
• If a report has already rendered and the configuration changes, either create a fresh ProfileReport or use cache invalidation from the configuration sub-skill.
• Treat notebook widget rendering as optional; HTML export is the most portable output for sharing.