# 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?

**URL:** <https://discourse.greatexpectations.io/t/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/452>\
**Category:** Archive\
**Created:** [October 13, 2020, 9:56pm UTC](https://discourse.greatexpectations.io/t/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/452 "2020-10-13T21:56:29Z")\
**Posts on this page:** 4\
**Page:** 1

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**Author:** ![bhcastleton](https://yyz1.discourse-cdn.com/flex031/user_avatar/discourse.greatexpectations.io/bhcastleton/32/73_2.png) [@bhcastleton](https://discourse.greatexpectations.io/u/bhcastleton)\
**Post date:** [October 13, 2020, 9:56pm UTC](https://discourse.greatexpectations.io/t/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/452/1 "2020-10-13T21:56:29Z")

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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?  
[https://docs.greatexpectations.io/en/0.7.11/guides/profiling.html](https://docs.greatexpectations.io/en/0.7.11/guides/profiling.html)

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**Author:** ![bhcastleton](https://yyz1.discourse-cdn.com/flex031/user_avatar/discourse.greatexpectations.io/bhcastleton/32/73_2.png) [@bhcastleton](https://discourse.greatexpectations.io/u/bhcastleton)\
**Post date:** [October 14, 2020, 7:10pm UTC](https://discourse.greatexpectations.io/t/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/452/2 "2020-10-14T19:10:00Z")

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

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**Author:** ![eugene.mandel](https://yyz1.discourse-cdn.com/flex031/user_avatar/discourse.greatexpectations.io/eugene.mandel/32/22_2.png) [@eugene.mandel](https://discourse.greatexpectations.io/u/eugene.mandel)\
**Post date:** [March 30, 2021, 6:10am UTC](https://discourse.greatexpectations.io/t/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/452/3 "2021-03-30T06:10:39Z")

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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.

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**Author:** ![lodsdevera](https://avatars.discourse-cdn.com/v4/letter/l/a3d4f5/32.png) [@lodsdevera](https://discourse.greatexpectations.io/u/lodsdevera)\
**Post date:** [September 13, 2022, 7:52am UTC](https://discourse.greatexpectations.io/t/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/452/4 "2022-09-13T07:52:39Z")

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Hi! is the published guide for V3 API available already? If yes, where to find it?