Description

Returns a lightweight structural profile: file name and type, row and column counts, column names, pandas data types and memory use. It intentionally does not perform quality assessment or calculate distributions and correlations.

Parameters

Name	Type	Default	Description
dataset	str \| pathlib.Path \| pandas.DataFrame	—	A pandas DataFrame or path to a supported CSV or Excel dataset.

Returns

DatasetProfile — Structural metadata in file_name, file_type, rows, columns, column_names, data_types and memory_usage_bytes.

Raises

FileNotFoundError — the supplied path does not exist.
ValueError — the supplied file type is not supported.

Examples

Profile a dataset

profile_example.py

python

Colab GitHub

import eazydatafix as edf

profile = edf.profile("hospital.csv")
print(profile.rows, profile.columns)
print(profile.column_names)
print(profile.data_types)

Python 3.11— Expected output

>>> profile = edf.profile("hospital.csv")
>>> profile.rows, profile.columns
(15, 8)
>>> profile.column_names[:3]
['patient_id', 'age', 'gender']

Notes

profile() is a structural inventory, not a data-quality report.
Use assess() for missing values, duplicates, quality dimensions and report exports.

Best Practices

Run profile() first when you need a quick shape and schema check.
Pair profile() with assess() when you also need quality metrics.

edf.profile()