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https://issues.apache.org/jira/browse/BEAM-7926?focusedWorklogId=330824&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-330824
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ASF GitHub Bot logged work on BEAM-7926:
----------------------------------------

                Author: ASF GitHub Bot
            Created on: 18/Oct/19 23:55
            Start Date: 18/Oct/19 23:55
    Worklog Time Spent: 10m 
      Work Description: aaltay commented on pull request #9741: [BEAM-7926] 
Visualize PCollection
URL: https://github.com/apache/beam/pull/9741#discussion_r336710191
 
 

 ##########
 File path: sdks/python/setup.py
 ##########
 @@ -219,11 +227,15 @@ def run(self):
     install_requires=REQUIRED_PACKAGES,
     python_requires=python_requires,
     test_suite='nose.collector',
-    tests_require=REQUIRED_TEST_PACKAGES,
+    tests_require=[
+        REQUIRED_TEST_PACKAGES,
+        INTERACTIVE_BEAM,
+    ],
     extras_require={
         'docs': ['Sphinx>=1.5.2,<2.0'],
         'test': REQUIRED_TEST_PACKAGES,
         'gcp': GCP_REQUIREMENTS,
+        'ib': INTERACTIVE_BEAM,
 
 Review comment:
   how about `interactive` instead of `ib`. This leads to a more readable `pip 
install apache_beam[interactive]` instead of `pip install apache_beam[ib]`
 
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Issue Time Tracking
-------------------

    Worklog Id:     (was: 330824)
    Time Spent: 6h  (was: 5h 50m)

> Visualize PCollection with Interactive Beam
> -------------------------------------------
>
>                 Key: BEAM-7926
>                 URL: https://issues.apache.org/jira/browse/BEAM-7926
>             Project: Beam
>          Issue Type: New Feature
>          Components: runner-py-interactive
>            Reporter: Ning Kang
>            Assignee: Ning Kang
>            Priority: Major
>          Time Spent: 6h
>  Remaining Estimate: 0h
>
> Support auto plotting / charting of materialized data of a given PCollection 
> with Interactive Beam.
> Say an Interactive Beam pipeline defined as
> p = create_pipeline()
> pcoll = p | 'Transform' >> transform()
> The use can call a single function and get auto-magical charting of the data 
> as materialized pcoll.
> e.g., visualize(pcoll)



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