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  <h1>Data lake measure</h1>

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<h2 id="streampipes.model.resource.data_lake_measure.DataLakeMeasure" class="doc doc-heading">
        <code>DataLakeMeasure</code>


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      <p class="doc doc-class-bases">
        Bases: <code><a class="autorefs autorefs-internal" title="streampipes.model.resource.resource.Resource" href="../resource/#streampipes.model.resource.resource.Resource">Resource</a></code></p>

  
      <p>Implementation of a resource for data lake measures.
This resource defines the data model used by resource container (<code>model.container.DataLakeMeasures</code>).
It inherits from Pydantic's BaseModel to get all its superpowers,
which are used to parse, validate the API response and to easily switch between
the Python representation (both serialized and deserialized) and Java representation (serialized only).</p>


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<h3 id="streampipes.model.resource.data_lake_measure.DataLakeMeasure.convert_to_pandas_representation" class="doc doc-heading">
<code class="highlight language-python"><span class="n">convert_to_pandas_representation</span><span class="p">()</span></code>

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      <p>Returns the dictionary representation of a data lake measure
to be used when creating a pandas Dataframe.
It excludes the following fields: <code>element_id</code>, <code>event_schema</code>, <code>schema_version</code>.
Instead of the whole event schema the number of event properties contained
is returned with the column name <code>num_event_properties</code>.</p>

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