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https://issues.apache.org/jira/browse/BEAM-7926?focusedWorklogId=330662&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-330662
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ASF GitHub Bot logged work on BEAM-7926:
----------------------------------------

                Author: ASF GitHub Bot
            Created on: 18/Oct/19 17:18
            Start Date: 18/Oct/19 17:18
    Worklog Time Spent: 10m 
      Work Description: KevinGG commented on pull request #9741: [BEAM-7926] 
Visualize PCollection
URL: https://github.com/apache/beam/pull/9741#discussion_r336593108
 
 

 ##########
 File path: 
sdks/python/apache_beam/runners/interactive/display/pcoll_visualization.py
 ##########
 @@ -0,0 +1,258 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+"""Module visualizes PCollection data.
+
+For internal use only; no backwards-compatibility guarantees.
+Only works with Python 3.5+.
+"""
+from __future__ import absolute_import
+
+import base64
+import logging
+from datetime import timedelta
+
+from facets_overview.generic_feature_statistics_generator import 
GenericFeatureStatisticsGenerator
+from IPython.core.display import HTML
+from IPython.core.display import Javascript
+from IPython.core.display import display
+from IPython.core.display import display_javascript
+from IPython.core.display import update_display
+from pandas.io.json import json_normalize
+from timeloop import Timeloop
+
+from apache_beam import pvalue
+from apache_beam.runners.interactive import interactive_environment as ie
+from apache_beam.runners.interactive import pipeline_instrument as instr
+
+# jsons doesn't support < Python 3.5. Work around with json for legacy tests.
+try:
+  import jsons
+  _pv_jsons_load = jsons.load
+  _pv_jsons_dump = jsons.dump
+except ImportError:
+  import json
+  _pv_jsons_load = json.load
+  _pv_jsons_dump = json.dump
+
+# 1-d types that need additional normalization to be compatible with DataFrame.
+_one_dimension_types = (int, float, str, bool, list, tuple)
+
+_DIVE_SCRIPT_TEMPLATE = """
+            document.querySelector("#{display_id}").data = {jsonstr};"""
+_DIVE_HTML_TEMPLATE = """
+            <script 
src="https://cdnjs.cloudflare.com/ajax/libs/webcomponentsjs/1.3.3/webcomponents-lite.js";></script>
+            <link rel="import" 
href="https://raw.githubusercontent.com/PAIR-code/facets/1.0.0/facets-dist/facets-jupyter.html";>
+            <facets-dive sprite-image-width="{sprite_size}" 
sprite-image-height="{sprite_size}" id="{display_id}" 
height="600"></facets-dive>
+            <script>
+              document.querySelector("#{display_id}").data = {jsonstr};
+            </script>"""
+_OVERVIEW_SCRIPT_TEMPLATE = """
+              document.querySelector("#{display_id}").protoInput = 
"{protostr}";
+              """
+_OVERVIEW_HTML_TEMPLATE = """
+            <script 
src="https://cdnjs.cloudflare.com/ajax/libs/webcomponentsjs/1.3.3/webcomponents-lite.js";></script>
+            <link rel="import" 
href="https://raw.githubusercontent.com/PAIR-code/facets/1.0.0/facets-dist/facets-jupyter.html";>
+            <facets-overview id="{display_id}"></facets-overview>
+            <script>
+              document.querySelector("#{display_id}").protoInput = 
"{protostr}";
+            </script>"""
+_DATAFRAME_PAGINATION_TEMPLATE = """
+            <script 
src="https://ajax.googleapis.com/ajax/libs/jquery/2.2.2/jquery.min.js";></script>
 
+            <script 
src="https://cdn.datatables.net/1.10.16/js/jquery.dataTables.js";></script> 
+            <link rel="stylesheet" 
href="https://cdn.datatables.net/1.10.16/css/jquery.dataTables.css";>
+            {dataframe_html}
+            <script>
+              $("#{table_id}").DataTable();
+            </script>"""
+
+
+def visualize(pcoll, dynamical_plotting_interval=None):
+  """Visualizes the data of a given PCollection. Optionally enables dynamical
+  plotting with interval in seconds if the PCollection is being produced by a
+  running pipeline or the pipeline is streaming indefinitely. The function
+  always returns immediately and is asynchronous when dynamical plotting is on.
+
+  If dynamical plotting enabled, the visualization is updated continuously 
until
+  the pipeline producing the PCollection is in an end state. The visualization
+  would be anchored to the notebook cell output area. The function
+  asynchronously returns a handle to the visualization job immediately. The 
user
+  could manually do::
+
+    # In one notebook cell, enable dynamical plotting every 1 second:
+    handle = visualize(pcoll, dynamical_plotting_interval=1)
 
 Review comment:
   Thanks, I'll go for it and change it from everywhere.
 
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Issue Time Tracking
-------------------

    Worklog Id:     (was: 330662)
    Time Spent: 5h  (was: 4h 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: 5h
>  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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