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overview</a></li><li><a href=https://beam.apache.org/releases/javadoc/2.56.0/ target=_blank>Java SDK API reference <img src=/images/external-link-icon.png width=14 height=14 alt="External link."></a></li><li><a href=/documentation/sdks/java-dependencies/>Java SDK dependencies</a></li><li><a href=/documentation/sdks/java-extensions/>Java SDK extensions</a></li><li><a href=/documentation/sdks/java-thirdparty/>Java 3rd party extensions</a></li><li><a href=/documentation/sdks/java/testing/nexmark/>Nexmark benchmark suite</a></li><li><a href=/documentation/sdks/java/testing/tpcds/>TPC-DS benchmark suite</a></li><li><a href=/documentation/sdks/java-multi-language-pipelines/>Java multi-language pipelines quickstart</a></li></ul></li><li><span class=section-nav-list-title>Python</span><ul class=section-nav-list><li><a href=/documentation/sdks/python/>Python SDK overview</a></li><li><a href=https://beam.apache.org/releases/pydoc/2.56.0/ target=_blank>Python SDK API reference <img 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class=section-nav-list-title>Yaml</span><ul class=section-nav-list><li><a href=/documentation/sdks/yaml/>Yaml overview</a></li><li><a href=/documentation/sdks/yaml-udf/>Yaml User Defined Functions</a></li><li><a href=/documentation/sdks/yaml-combine/>Yaml Aggregation</a></li><li><a href=/documentation/sdks/yaml-errors/>Error handling</a></li><li><a href=/documentation/sdks/yaml-inline-python/>Inlining Python</a></li><li><a href=https://beam.apache.org/releases/yamldoc/current/ target=_blank>YAML API reference <img src=/images/external-link-icon.png width=14 height=14 alt="External link."></a></ul></li><li><span class=section-nav-list-title>SQL</span><ul class=section-nav-list><li><a href=/documentation/dsls/sql/overview/>Overview</a></li><li><a href=/documentation/dsls/sql/walkthrough/>Walkthrough</a></li><li><a href=/documentation/dsls/sql/shell/>Shell</a></li><li class=section-nav-item--collapsible><span class=section-nav-list-title>Apache Calcite dialect</span><ul class=section-nav-list><li><a href=/documentation/dsls/sql/calcite/overview/>Calcite support overview</a></li><li><a href=/documentation/dsls/sql/calcite/query-syntax/>Query syntax</a></li><li><a href=/documentation/dsls/sql/calcite/lexical/>Lexical structure</a></li><li><a href=/documentation/dsls/sql/calcite/data-types/>Data types</a></li><li><a href=/documentation/dsls/sql/calcite/scalar-functions/>Scalar functions</a></li><li><a href=/documentation/dsls/sql/calcite/aggregate-functions/>Aggregate functions</a></li></ul></li><li class=section-nav-item--collapsible><span class=section-nav-list-title>ZetaSQL dialect</span><ul class=section-nav-list><li><a href=/documentation/dsls/sql/zetasql/overview/>ZetaSQL support overview</a></li><li><a href=/documentation/dsls/sql/zetasql/syntax/>Function call rules</a></li><li><a href=/documentation/dsls/sql/zetasql/conversion-rules/>Conversion rules</a></li><li><a href=/documentation/dsls/sql/zetasql/query-syntax/>Query syntax</a></li><li><a href=/documentation/dsls/sql/zetasql/lexical/>Lexical structure</a></li><li><a href=/documentation/dsls/sql/zetasql/data-types/>Data types</a></li><li><a href=/documentation/dsls/sql/zetasql/operators/>Operators</a></li><li class=section-nav-item--collapsible><span class=section-nav-list-title>Scalar functions</span><ul class=section-nav-list><li><a href=/documentation/dsls/sql/zetasql/string-functions/>String functions</a></li><li><a href=/documentation/dsls/sql/zetasql/math-functions/>Mathematical functions</a></li><li><a href=/documentation/dsls/sql/zetasql/conditional-expressions/>Conditional expressions</a></li></ul></li><li><a href=/documentation/dsls/sql/zetasql/aggregate-functions/>Aggregate functions</a></li></ul></li><li class=section-nav-item--collapsible><span class=section-nav-list-title>Beam SQL extensions</span><ul class=section-nav-list><li><a href=/documentation/dsls/sql/extensions/create-external-table/>CREATE EXTERNAL TABLE</a></li><li><a href=/documentation/dsls/sql/extensions/windowing-and-triggering/>Windowing & triggering</a></li><li><a href=/documentation/dsls/sql/extensions/joins/>Joins</a></li><li><a href=/documentation/dsls/sql/extensions/user-defined-functions/>User-defined functions</a></li><li><a href=/documentation/dsls/sql/extensions/set/>SET pipeline options</a></li></ul></li></ul></li><li><span class=section-nav-list-title>DataFrames</span><ul class=section-nav-list><li><a href=/documentation/dsls/dataframes/overview/>Overview</a></li><li><a href=/documentation/dsls/dataframes/differences-from-pandas/>Differences from pandas</a></li><li><a href=https://github.com/apache/beam/tree/master/sdks/python/apache_beam/examples/dataframe target=_blank>Example pipelines <img src=/images/external-link-icon.png width=14 height=14 alt="External link."></a></li><li><a href=https://beam.apache.org/releases/pydoc/2.56.0/apache_beam.dataframe.html target=_blank>DataFrame API reference <img 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=#what-is-a-dataframe>What is a DataFrame?</a></li><li><a href=#pre-requisites>Pre-requisites</a></li><li><a href=#using-dataframes>Using DataFrames</a></li><li><a href=#embedding-dataframes-in-a-pipeline>Embedding DataFrames in a pipeline</a></li></ul></nav></nav><div class="body__contained body__section-nav"><h1 id=beam-dataframes-overview>Beam DataFrames overview</h1><table align=left><td><a class=button target=_blank href=https://colab.research.google.com/github/apache/beam/blob/master/examples/notebooks/interactive-overview/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>The Apache Beam Python SDK provides a DataFrame API for working with pandas-like <a href=https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html>DataFrame</a> objects. The feature lets you convert a PCollection to a DataFrame and then interact with the DataFrame using the standard methods available on the pandas DataFrame API. The DataFrame API is built on top of the pandas implementation, and pandas DataFrame methods are invoked on subsets of the datasets in parallel. The big difference between Beam DataFrames and pandas DataFrames is that operations are deferred by the Beam API, to support the Beam parallel processing model. (To learn more about differences between the DataFrame implementations, see <a href=/documentation/dsls/dataframes/differences-from-pandas/>Differences from pandas</a>.)</p><p>You can think of Beam DataFrames as a domain-specific language (DSL) for Beam pipelines. Similar to <a href=/documentation/dsls/sql/overview/>Beam SQL</a>, DataFrames is a DSL built into the Beam Python SDK. Using this DSL, you can create pipelines without referencing standard Beam constructs like <a href=/documentation/transforms/python/elementwise/pardo/>ParDo</a> or <a href=/documentation/transforms/python/aggregation/combineperkey/>CombinePerKey</a>.</p><p>The Beam DataFrame API is intended to provide access to a familiar programming interface within a Beam pipeline. In some cases, the DataFrame API can also improve pipeline efficiency by deferring to the highly efficient, vectorized pandas implementation.</p><h2 id=what-is-a-dataframe>What is a DataFrame?</h2><p>If you’re new to pandas DataFrames, you can get started by reading <a href=https://pandas.pydata.org/pandas-docs/stable/user_guide/10min.html>10 minutes to pandas</a>, which shows you how to import and work with the <code>pandas</code> package. pandas is an open-source Python library for data manipulation and analysis. It provides data structures that simplify working with relational or labeled data. One of these data structures is the <a href=https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html>DataFrame</a>, which contains two-dimensional tabular data and provides labeled rows and columns for the data.</p><h2 id=pre-requisites>Pre-requisites</h2><p>To use Beam DataFrames, you need to install Beam python version 2.26.0 or higher (for complete setup instructions, see the <a href=/get-started/quickstart-py/>Apache Beam Python SDK Quickstart</a>) and a supported <code>pandas</code> version. In Beam 2.34.0 and newer the easiest way to do this is with the &ldquo;dataframe&rdquo; extra:</p><pre tabindex=0><code>pip install apache_beam[dataframe]
 </code></pre><p>Note that the <em>same</em> <code>pandas</code> version should be installed on workers when executing DataFrame API pipelines on distributed runners. Reference <a href=https://github.com/apache/beam/blob/master/sdks/python/container/py38/base_image_requirements.txt><code>base_image_requirements.txt</code></a> for the Python version and Beam release you are using to see what version of <code>pandas</code> will be used by default on workers.</p><h2 id=using-dataframes>Using DataFrames</h2><p>You can use DataFrames as shown in the following example, which reads New York City taxi data from a CSV file, performs a grouped aggregation, and writes the output back to CSV:</p><pre tabindex=0><code>from apache_beam.dataframe.io import read_csv

 with pipeline as p:
   rides = p | read_csv(input_path)

   # Count the number of passengers dropped off per LocationID
   agg = rides.groupby(&#39;DOLocationID&#39;).passenger_count.sum()
   agg.to_csv(output_path)

 </code></pre><p>pandas is able to infer column names from the first row of the CSV data, which is where <code>passenger_count</code> and <code>DOLocationID</code> come from.</p><p>In this example, the only traditional Beam type is the <code>Pipeline</code> instance. Otherwise the example is written completely with the DataFrame API. This is possible because the Beam DataFrame API includes its own IO operations (for example, <a href=https://beam.apache.org/releases/pydoc/current/apache_beam.dataframe.io.html#apache_beam.dataframe.io.read_csv><code>read_csv</code></a> and <a href=https://beam.apache.org/releases/pydoc/current/apache_beam.dataframe.frames.html#apache_beam.dataframe.frames.DeferredDataFrame.to_csv><code>to_csv</code></a>) based on the pandas native implementations. <code>read_*</code> and <code>to_*</code> operations support file patterns and any Beam-compatible file system. The grouping is accomplished with a group-by-key, and arbitrary pandas operations (in this case, <a href=https://beam.apache.org/releases/pydoc/current/apache_beam.dataframe.frames.html#apache_beam.dataframe.frames.DeferredDataFrame.sum><code>sum</code></a>) can be applied before the final write that occurs with <a href=https://beam.apache.org/releases/pydoc/current/apache_beam.dataframe.frames.html#apache_beam.dataframe.frames.DeferredDataFrame.to_csv><code>to_csv</code></a>.</p><p>The Beam DataFrame API aims to be compatible with the native pandas implementation, with a few caveats detailed below in <a href=/documentation/dsls/dataframes/differences-from-pandas/>Differences from pandas</a>.</p><h2 id=embedding-dataframes-in-a-pipeline>Embedding DataFrames in a pipeline</h2><p>To use the DataFrames API in a larger pipeline, you can convert a PCollection to a DataFrame, process the DataFrame, and then convert the DataFrame back to a PCollection. In order to convert a PCollection to a DataFrame and back, you have to use PCollections that have <a href=/documentation/programming-guide/#what-is-a-schema>schemas</a> attached. A PCollection with a schema attached is also referred to as a <em>schema-aware PCollection</em>. To learn more about attaching a schema to a PCollection, see <a href=/documentation/programming-guide/#creating-schemas>Creating schemas</a>.</p><p>Here’s an example that creates a schema-aware PCollection, converts it to a DataFrame using <a href=https://beam.apache.org/releases/pydoc/current/apache_beam.dataframe.convert.html#apache_beam.dataframe.convert.to_dataframe><code>to_dataframe</code></a>, processes the DataFrame, and then converts the DataFrame back to a PCollection using <a href=https://beam.apache.org/releases/pydoc/current/apache_beam.dataframe.convert.html#apache_beam.dataframe.convert.to_pcollection><code>to_pcollection</code></a>:</p><pre tabindex=0><code>from apache_beam.dataframe.convert import to_dataframe
 from apache_beam.dataframe.convert import to_pcollection
 ...


     # Read the text file[pattern] into a PCollection.
     lines = p | &#39;Read&#39; &gt;&gt; ReadFromText(known_args.input)

     words = (
         lines
         | &#39;Split&#39; &gt;&gt; beam.FlatMap(
             lambda line: re.findall(r&#39;[\w]&#43;&#39;, line)).with_output_types(str)
         # Map to Row objects to generate a schema suitable for conversion
         # to a dataframe.
         | &#39;ToRows&#39; &gt;&gt; beam.Map(lambda word: beam.Row(word=word)))

     df = to_dataframe(words)
     df[&#39;count&#39;] = 1
     counted = df.groupby(&#39;word&#39;).sum()
     counted.to_csv(known_args.output)

     # Deferred DataFrames can also be converted back to schema&#39;d PCollections
     counted_pc = to_pcollection(counted, include_indexes=True)


 </code></pre><p>You can find the full wordcount example on
 <a href=https://github.com/apache/beam/blob/master/sdks/python/apache_beam/examples/dataframe/wordcount.py>GitHub</a>,
 along with other <a href=https://github.com/apache/beam/blob/master/sdks/python/apache_beam/examples/dataframe/>example DataFrame pipelines</a>.</p><p>It’s also possible to use the DataFrame API by passing a function to <a href=https://beam.apache.org/releases/pydoc/current/apache_beam.dataframe.transforms.html#apache_beam.dataframe.transforms.DataframeTransform><code>DataframeTransform</code></a>:</p><pre tabindex=0><code>from apache_beam.dataframe.transforms import DataframeTransform

 with beam.Pipeline() as p:
   ...
   | beam.Select(DOLocationID=lambda line: int(..),
                 passenger_count=lambda line: int(..))
   | DataframeTransform(lambda df: df.groupby(&#39;DOLocationID&#39;).sum())
   | beam.Map(lambda row: f&#34;{row.DOLocationID},{row.passenger_count}&#34;)
   ...
 </code></pre><p><a href=https://beam.apache.org/releases/pydoc/current/apache_beam.dataframe.transforms.html#apache_beam.dataframe.transforms.DataframeTransform><code>DataframeTransform</code></a> is similar to <a href=https://beam.apache.org/releases/pydoc/current/apache_beam.transforms.sql.html#apache_beam.transforms.sql.SqlTransform><code>SqlTransform</code></a> from the <a href=/documentation/dsls/sql/overview/>Beam SQL</a> DSL. Where <a href=https://beam.apache.org/releases/pydoc/current/apache_beam.transforms.sql.html#apache_beam.transforms.sql.SqlTransform><code>SqlTransform</code></a> translates a SQL query to a PTransform, <a href=https://beam.apache.org/releases/pydoc/current/apache_beam.dataframe.transforms.html#apache_beam.dataframe.transforms.DataframeTransform><code>DataframeTransform</code></a> is a PTransform that applies a function that takes and returns DataFrames. A <a href=https://beam.apache.org/releases/pydoc/current/apache_beam.dataframe.transforms.html#apache_beam.dataframe.transforms.DataframeTransform><code>DataframeTransform</code></a> can be particularly useful if you have a stand-alone function that can be called both on Beam and on ordinary pandas DataFrames.</p><p><a href=https://beam.apache.org/releases/pydoc/current/apache_beam.dataframe.transforms.html#apache_beam.dataframe.transforms.DataframeTransform><code>DataframeTransform</code></a> can accept and return multiple PCollections by name and by keyword, as shown in the following examples:</p><pre tabindex=0><code>output = (pc1, pc2) | DataframeTransform(lambda df1, df2: ...)

 output = {&#39;a&#39;: pc, ...} | DataframeTransform(lambda a, ...: ...)

 pc1, pc2 = {&#39;a&#39;: pc} | DataframeTransform(lambda a: expr1, expr2)

 {...} = {a: pc} | DataframeTransform(lambda a: {...})
 </code></pre></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>&copy;
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