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https://issues.apache.org/jira/browse/BEAM-7389?focusedWorklogId=294235&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-294235
]
ASF GitHub Bot logged work on BEAM-7389:
----------------------------------------
Author: ASF GitHub Bot
Created on: 13/Aug/19 22:17
Start Date: 13/Aug/19 22:17
Worklog Time Spent: 10m
Work Description: rosetn commented on pull request #9261: [BEAM-7389] Add
code examples for Partition page
URL: https://github.com/apache/beam/pull/9261#discussion_r313635349
##########
File path:
website/src/documentation/transforms/python/element-wise/partition.md
##########
@@ -39,12 +46,130 @@ You cannot determine the number of partitions in
mid-pipeline
See more information in the [Beam Programming Guide]({{ site.baseurl
}}/documentation/programming-guide/#partition).
## Examples
-See [BEAM-7389](https://issues.apache.org/jira/browse/BEAM-7389) for updates.
-## Related transforms
-* [Filter]({{ site.baseurl
}}/documentation/transforms/python/elementwise/filter) is useful if the
function is just
+In the following examples, we create a pipeline with a `PCollection` of
produce with their icon, name, and duration.
+Then, we apply `Partition` in multiple ways to split the `PCollection` into
multiple `PCollections`.
+
+`Partition` accepts a function that receives the number of partitions,
+and returns the index of the desired partition for the element.
+The number of partitions passed must be a positive integer,
+and it must return an integer in the range `0` to `num_partitions-1`.
+
+### Example 1: Partition with a function
+
+In the following example, we have a known list of durations.
+We partition the `PCollection` into one `PCollection` for every duration type.
+
+```py
+{% github_sample
/apache/beam/blob/master/sdks/python/apache_beam/examples/snippets/transforms/element_wise/partition.py
tag:partition_function %}```
+
+Output `PCollection`s:
+
+```
+{% github_sample
/apache/beam/blob/master/sdks/python/apache_beam/examples/snippets/transforms/element_wise/partition_test.py
tag:partitions %}```
+
+<table>
+ <td>
+ <a class="button" target="_blank"
+
href="https://github.com/apache/beam/blob/master/sdks/python/apache_beam/examples/snippets/transforms/element_wise/partition.py";>
+ <img src="https://www.tensorflow.org/images/GitHub-Mark-32px.png";
+ width="20px" height="20px" alt="View on GitHub" />
+ View on GitHub
+ </a>
+ </td>
+</table>
+<br>
+
+### Example 2: Partition with a lambda function
+
+We can also use lambda functions to simplify **Example 1**.
+
+```py
+{% github_sample
/apache/beam/blob/master/sdks/python/apache_beam/examples/snippets/transforms/element_wise/partition.py
tag:partition_lambda %}```
+
+Output `PCollection`s:
+
+```
+{% github_sample
/apache/beam/blob/master/sdks/python/apache_beam/examples/snippets/transforms/element_wise/partition_test.py
tag:partitions %}```
+
+<table>
+ <td>
+ <a class="button" target="_blank"
+
href="https://github.com/apache/beam/blob/master/sdks/python/apache_beam/examples/snippets/transforms/element_wise/partition.py";>
+ <img src="https://www.tensorflow.org/images/GitHub-Mark-32px.png";
+ width="20px" height="20px" alt="View on GitHub" />
+ View on GitHub
+ </a>
+ </td>
+</table>
+<br>
+
+### Example 3: Partition with multiple arguments
+
+You can pass functions with multiple arguments to `Partition`.
+They are passed as additional positional arguments or keyword arguments to the
function.
+
+In this example, `split_dataset` takes `plant`, `num_partitions`, and `ratio`
as arguments.
+`num_partitions` is used by `Partitions` as a positional argument,
+while any other argument will be passed to `split_dataset`.
+
+In machine learning, it is common to split it into
+[training and a testing
datasets](https://en.wikipedia.org/wiki/Training,_validation,_and_test_sets).
+Typically, 80% of the data is used for training a model, and 20% is used for
testing.
+
+We will split a `PCollection` dataset into training and testing datasets.
+We define `split_dataset` which receives the element, the number of
partitions, and an additional argument `ratio` that describes the ratio of the
split.
+The `ratio` is a list of numbers which represents the ratio how many items
will go into each partition.
+If we want an 80%/20% split, we can specify a ratio of `[8, 2]` which means
that for every 10 elements, 8 will go into the first partition and 2 will go
into the second.
+
+To decide which partition to send each element, we'll have different buckets.
+For our case `[8, 2]` will have *8+2*=**10** buckets, where the first 8
buckets represent the first partition and the last 2 buckets represent the
second partition.
+
+First, we need to make sure that the ratio's length corresponds to the
`num_partitions` we pass.
Review comment:
"ratio's length"->"ratio list's length"
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Issue Time Tracking
-------------------
Worklog Id: (was: 294235)
Time Spent: 46h (was: 45h 50m)
> Colab examples for element-wise transforms (Python)
> ---------------------------------------------------
>
> Key: BEAM-7389
> URL: https://issues.apache.org/jira/browse/BEAM-7389
> Project: Beam
> Issue Type: Improvement
> Components: website
> Reporter: Rose Nguyen
> Assignee: David Cavazos
> Priority: Minor
> Time Spent: 46h
> Remaining Estimate: 0h
>
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