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src=/images/external-link-icon.png width=14 height=14 alt="External link."></a></li></ul></li></ul></nav></div><nav class="page-nav clearfix" data-offset-top=90 data-offset-bottom=500><nav id=TableOfContents><ul><li><a href=#working-with-pandas-sources>Working with pandas sources</a></li><li><a href=#classes-of-unsupported-operations>Classes of unsupported operations</a><ul><li><a href=#non-parallelizable-operations>Non-parallelizable operations</a></li><li><a href=#operations-that-produce-non-deferred-columns>Operations that produce non-deferred columns</a></li><li><a href=#operations-that-produce-non-deferred-values-or-plots>Operations that produce non-deferred values or plots</a></li><li><a href=#order-sensitive-operations>Order-sensitive operations</a></li><li><a href=#operations-that-produce-deferred-scalars>Operations that produce deferred scalars</a></li><li><a href=#operations-that-arent-implemented-yet>Operations that aren’t implemented yet</a></li></ul></li><li><a href=#using-interactive-beam-to-access-the-full-pandas-api>Using Interactive Beam to access the full pandas API</a></li></ul></nav></nav><div class="body__contained body__section-nav"><h1 id=differences-from-pandas>Differences from pandas</h1><p>The Apache Beam DataFrame API aims to be a drop-in replacement for pandas, but there are a few differences to be aware of. This page describes divergences between the Beam and pandas APIs and provides tips for working with the Beam DataFrame API. See the <a href=https://beam.apache.org/releases/pydoc/current/apache_beam.dataframe.frames.html><code>apache_beam.dataframe.frames</code> API reference</a> for a full reference for which operations and arguments are supported in the Beam DataFrame API.</p><h2 id=working-with-pandas-sources>Working with pandas sources</h2><p>Beam operations are always associated with a pipeline. To read source data into a Beam DataFrame, you have to apply the source to a pipeline object. For example, to read input from a CSV file, you could use <a href=https://beam.apache.org/releases/pydoc/2.55.1/apache_beam.dataframe.io.html#apache_beam.dataframe.io.read_csv>read_csv</a> as follows:</p><pre><code>df = p | beam.dataframe.io.read_csv(...) |
| </code></pre><p>This is similar to pandas <a href=https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_csv.html>read_csv</a>, but <code>df</code> is a deferred Beam DataFrame representing the contents of the file. The input filename can be any file pattern understood by <a href=https://beam.apache.org/releases/pydoc/2.55.1/apache_beam.io.fileio.html#apache_beam.io.fileio.MatchFiles>fileio.MatchFiles</a>.</p><p>For an example of using sources and sinks with the DataFrame API, see <a href=https://github.com/apache/beam/blob/master/sdks/python/apache_beam/examples/dataframe/taxiride.py>taxiride.py</a>.</p><h2 id=classes-of-unsupported-operations>Classes of unsupported operations</h2><p>The sections below describe classes of operations that are not yet supported, or supported with caveats, by the Beam DataFrame API. Workarounds are suggested where applicable.</p><h3 id=non-parallelizable-operations>Non-parallelizable operations</h3><p>Examples: |
| <a href=https://beam.apache.org/releases/pydoc/current/apache_beam.dataframe.frames.html#apache_beam.dataframe.frames.DeferredDataFrame.quantile><code>DeferredDataFrame.quantile</code></a>, |
| <a href=https://beam.apache.org/releases/pydoc/current/apache_beam.dataframe.frames.html#apache_beam.dataframe.frames.DeferredDataFrame.mode><code>DeferredDataFrame.mode</code></a></p><p>To support distributed processing, Beam invokes DataFrame operations on subsets of data in parallel. Some DataFrame operations can’t be parallelized, and these operations raise a <a href=https://beam.apache.org/releases/pydoc/2.55.1/apache_beam.dataframe.expressions.html#apache_beam.dataframe.expressions.NonParallelOperation>NonParallelOperation</a> error by default.</p><p><strong>Workaround</strong></p><p>If you want to use a non-parallelizable operation, you can guard it with a <code>beam.dataframe.allow_non_parallel_operations</code> block. For example:</p><pre><code>from apache_beam import dataframe |
| |
| with dataframe.allow_non_parallel_operations(): |
| quantiles = df.quantile() |
| </code></pre><p>Note that this collects the entire input dataset on a single node, so there’s a risk of running out of memory. You should only use this workaround if you’re sure that the input is small enough to process on a single worker.</p><h3 id=operations-that-produce-non-deferred-columns>Operations that produce non-deferred columns</h3><p>Examples: |
| <a href=https://beam.apache.org/releases/pydoc/current/apache_beam.dataframe.frames.html#apache_beam.dataframe.frames.DeferredDataFrame.pivot><code>DeferredDataFrame.pivot</code></a>, |
| <a href=https://beam.apache.org/releases/pydoc/current/apache_beam.dataframe.frames.html#apache_beam.dataframe.frames.DeferredDataFrame.transpose><code>DeferredDataFrame.transpose</code></a>, |
| <a href=https://beam.apache.org/releases/pydoc/current/apache_beam.dataframe.frames.html#apache_beam.dataframe.frames.DeferredSeries.factorize><code>DeferredSeries.factorize</code></a></p><p>Beam DataFrame operations are deferred, but the schemas of the resulting DataFrames are not, meaning that result columns must be computable without access to the data. Some DataFrame operations can’t support this usage, so they can’t be implemented. These operations raise a <a href=https://beam.apache.org/releases/pydoc/2.55.1/apache_beam.dataframe.frame_base.html#apache_beam.dataframe.frame_base.WontImplementError>WontImplementError</a>.</p><p>Currently there’s no workaround for this issue. But in the future, Beam Dataframe may support non-deferred column operations on categorical columns. This work is being tracked in <a href=https://github.com/apache/beam/issues/20958>Issue 20958</a>.</p><h3 id=operations-that-produce-non-deferred-values-or-plots>Operations that produce non-deferred values or plots</h3><p>Examples: |
| <a href=https://beam.apache.org/releases/pydoc/current/apache_beam.dataframe.frames.html#apache_beam.dataframe.frames.DeferredSeries.to_list><code>DeferredSeries.to_list</code></a>, |
| <a href=https://beam.apache.org/releases/pydoc/current/apache_beam.dataframe.frames.html#apache_beam.dataframe.frames.DeferredSeries.array><code>DeferredSeries.array</code></a>, |
| <a href=https://beam.apache.org/releases/pydoc/current/apache_beam.dataframe.frames.html#apache_beam.dataframe.frames.DeferredDataFrame.plot><code>DeferredDataFrame.plot</code></a></p><p>It’s infeasible to implement DataFrame operations that produce non-deferred values or plots because Beam is a deferred API. If these operations are invoked, they will raise a <a href=https://beam.apache.org/releases/pydoc/2.55.1/apache_beam.dataframe.frame_base.html#apache_beam.dataframe.frame_base.WontImplementError>WontImplementError</a>.</p><p>These operations may be supported in the future through a tighter integration |
| with Interactive Beam. To track progress on this issue, follow |
| <a href=https://github.com/apache/beam/issues/21638>Issue 21638</a>. If you think we |
| should prioritize this work you can also <a href=/community/contact-us/>contact |
| us</a> to let us know.</p><p><strong>Workaround</strong></p><p>If you’re using <a href=https://beam.apache.org/releases/pydoc/2.55.1/apache_beam.runners.interactive.interactive_beam.html>Interactive Beam</a>, you can use <code>collect</code> to bring a dataset into local memory and then perform these operations.</p><h3 id=order-sensitive-operations>Order-sensitive operations</h3><p>Examples: |
| <a href=https://beam.apache.org/releases/pydoc/current/apache_beam.dataframe.frames.html#apache_beam.dataframe.frames.DeferredDataFrame.head><code>DeferredDataFrame.head</code></a>, |
| <a href=https://beam.apache.org/releases/pydoc/current/apache_beam.dataframe.frames.html#apache_beam.dataframe.frames.DeferredSeries.diff><code>DeferredSeries.diff</code></a>, |
| <a href=https://beam.apache.org/releases/pydoc/current/apache_beam.dataframe.frames.html#apache_beam.dataframe.frames.DeferredDataFrame.interpolate><code>DeferredDataFrame.interpolate</code></a></p><p>Beam PCollections are inherently unordered, so pandas operations that are sensitive to the ordering of rows are not supported. These operations raise a <a href=https://beam.apache.org/releases/pydoc/2.55.1/apache_beam.dataframe.frame_base.html#apache_beam.dataframe.frame_base.WontImplementError>WontImplementError</a>.</p><p>Order-sensitive operations may be supported in the future. To track progress on this issue, follow <a href=https://github.com/apache/beam/issues/20862>Issue 20862</a>. If you think we should prioritize this work you can also <a href=/community/contact-us/>contact us</a> to let us know.</p><p><strong>Workaround</strong></p><p>If you’re using <a href=https://beam.apache.org/releases/pydoc/2.55.1/apache_beam.runners.interactive.interactive_beam.html>Interactive Beam</a>, you can use <code>collect</code> to bring a dataset into local memory and then perform these operations.</p><p>Alternatively, there may be ways to rewrite your code so that it’s not order sensitive. For example, pandas users often call the order-sensitive <a href=https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.head.html><code>head</code></a> operation to peek at data, but if you just want to view a subset of elements, you can also use <a href=https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.sample.html><code>sample</code></a>, which doesn’t require you to collect the data first. Similarly, you could use <code>nlargest</code> instead of <code>sort_values(...).</code>.</p><h3 id=operations-that-produce-deferred-scalars>Operations that produce deferred scalars</h3><p>Some DataFrame operations produce deferred scalars. In Beam, actual computation of the values is deferred, and so the values are not available for control flow. For example, you can compute a sum with <code>Series.sum</code>, but you can’t immediately branch on the result, because the result data is not immediately available. <code>Series.is_unique</code> is a similar example. Using a deferred scalar for branching logic or truth tests raises a <a href=https://github.com/apache/beam/blob/b908f595101ff4f21439f5432514005394163570/sdks/python/apache_beam/dataframe/frame_base.py#L117>TypeError</a>.</p><h3 id=operations-that-arent-implemented-yet>Operations that aren’t implemented yet</h3><p>The Beam DataFrame API implements many of the commonly used pandas DataFrame operations, and we’re actively working to support the remaining operations. But pandas has a large API, and there are still gaps (<a href=https://github.com/apache/beam/issues/20318>Issue 20318</a>). If you invoke an operation that hasn’t been implemented yet, it will raise a <code>NotImplementedError</code>. Please <a href=/community/contact-us/>let us know</a> if you encounter a missing operation that you think should be prioritized.</p><h2 id=using-interactive-beam-to-access-the-full-pandas-api>Using Interactive Beam to access the full pandas API</h2><p>Interactive Beam is a module designed for use in interactive notebooks. The module, which by convention is imported as <code>ib</code>, provides an <code>ib.collect</code> function that brings a <code>PCollection</code> or deferred DataFrame into local memory as a pandas DataFrame. After using <code>ib.collect</code> to materialize a deferred DataFrame you will be able to perform any operation in the pandas API, not just those that are supported in Beam.</p><table align=left><td><a class=button target=_blank href=https://colab.research.google.com/github/apache/beam/blob/master/examples/notebooks/tour-of-beam/dataframes.ipynb><img alt="Run in Colab" width=32px height=32px src=https://github.com/googlecolab/open_in_colab/raw/master/images/icon32.png> |
| Run in Colab</a></td></table><p><br><br><br><br></p><p>To get started with Beam in a notebook, see <a href=/get-started/try-apache-beam/>Try Apache Beam</a>.</p></div></div><footer class=footer><div class=footer__contained><div class=footer__cols><div class="footer__cols__col footer__cols__col__logos"><div class=footer__cols__col__logo><img src=/images/beam_logo_circle.svg class=footer__logo alt="Beam logo"></div><div class=footer__cols__col__logo><img src=/images/apache_logo_circle.svg class=footer__logo alt="Apache logo"></div></div><div class=footer-wrapper><div class=wrapper-grid><div class=footer__cols__col><div class=footer__cols__col__title>Start</div><div class=footer__cols__col__link><a href=/get-started/beam-overview/>Overview</a></div><div class=footer__cols__col__link><a href=/get-started/quickstart-java/>Quickstart (Java)</a></div><div class=footer__cols__col__link><a href=/get-started/quickstart-py/>Quickstart (Python)</a></div><div class=footer__cols__col__link><a href=/get-started/quickstart-go/>Quickstart (Go)</a></div><div class=footer__cols__col__link><a href=/get-started/downloads/>Downloads</a></div></div><div class=footer__cols__col><div class=footer__cols__col__title>Docs</div><div class=footer__cols__col__link><a href=/documentation/programming-guide/>Concepts</a></div><div class=footer__cols__col__link><a href=/documentation/pipelines/design-your-pipeline/>Pipelines</a></div><div class=footer__cols__col__link><a href=/documentation/runners/capability-matrix/>Runners</a></div></div><div class=footer__cols__col><div class=footer__cols__col__title>Community</div><div class=footer__cols__col__link><a href=/contribute/>Contribute</a></div><div class=footer__cols__col__link><a href=https://projects.apache.org/committee.html?beam target=_blank>Team<img src=/images/external-link-icon.png width=14 height=14 alt="External link."></a></div><div class=footer__cols__col__link><a href=/community/presentation-materials/>Media</a></div><div class=footer__cols__col__link><a href=/community/in-person/>Events/Meetups</a></div><div class=footer__cols__col__link><a href=/community/contact-us/>Contact Us</a></div></div><div class=footer__cols__col><div class=footer__cols__col__title>Resources</div><div class=footer__cols__col__link><a href=/blog/>Blog</a></div><div class=footer__cols__col__link><a href=https://github.com/apache/beam>GitHub</a></div></div></div><div class=footer__bottom>© |
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