Can I use Great Expectations to profile data and produce documentation and visuals without having to invoke all the functionality around expectations and validation results?

From a GE User:
I’m hoping to generate distribution visuals of a particular column as shown in the link below. Do I have to profile the data source first, or can I use the expect_column_kl_divergence_to_be_less_than expectation to visualize a histogram of counts for a column?

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@sam Do you mind chiming in with some thoughts on this question?

Here is how to run profile a data asset in order to produce descriptive documentation for it.

Note: these instructions apply only to V2 API. We will publish a guide for V3 API separately.

These instructions assume that you already have great_expectations.yml with a configured Datasource.
This example profiles a CSV file with Pandas.

import json
import great_expectations as ge
import great_expectations.jupyter_ux
from great_expectations.datasource.types import BatchKwargs
import datetime

context = ge.data_context.DataContext()

datasource_name = YOUR DATASOURCE NAME

batch_kwargs = {‘path’: “MY CSV FILE PATH”, ‘datasource’: datasource_name}

prof_res = context.profile_data_asset(
datasource_name,
batch_kwargs=batch_kwargs,
)

Then run:

great_expectations docs build

Your Data Docs will be updated with descriptive profiling results.