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<!DOCTYPE html>

<head>

<title>Pandas Compatibility — Feldera Python SDK 0.272.0 documentation</title>

<!--[if lt IE 9]>

<![endif]-->

</head>

Feldera Python SDK

</a>

</form>

</div>

</div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">

Contents:

<li class="toctree-l1"><a class="reference internal" href="introduction.html">Introduction</a></li>

<li class="toctree-l1 current"><a class="current reference internal" href="#">Pandas Compatibility</a><ul>

<li class="toctree-l2"><a class="reference internal" href="#using-pandas-dataframes-as-input-output">Using Pandas DataFrames as Input / Output</a></li>

</ul>

</li>

<li class="toctree-l1"><a class="reference internal" href="examples.html">Examples</a></li>

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API Reference:

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<a href="index.html">Feldera Python SDK</a>

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<li class="breadcrumb-item active">Pandas Compatibility</li>

<a href="_sources/pandas.rst.txt" rel="nofollow"> View page source</a>

</li>

</ul>

<hr/>

</div>

<h1>Pandas Compatibility<a class="headerlink" href="#pandas-compatibility" title="Link to this heading"></a></h1>

Feldera tries to be compatible with the Pandas as much as possible.

However, some types in SQL have limited support in Pandas.

Columns with the following SQL types will be converted to the corresponding Pandas types:

<thead>

<th class="head">Pandas Type</th>

</tr>

</thead>

<tbody>

<tr class="row-even"><td>BOOLEAN</td>

</tr>

<tr class="row-odd"><td>TINYINT</td>

</tr>

<tr class="row-even"><td>SMALLINT</td>

</tr>

<tr class="row-odd"><td>INTEGER</td>

</tr>

<tr class="row-even"><td>BIGINT</td>

</tr>

<td>Float32</td>

</tr>

<tr class="row-even"><td>DOUBLE</td>

<td>Float64</td>

</tr>

<tr class="row-odd"><td>DECIMAL</td>

<td>decimal.Decimal</td>

</tr>

</tr>

<tr class="row-odd"><td>VARCHAR</td>

</tr>

<td>datetime64[ns]</td>

</tr>

<tr class="row-odd"><td>TIMESTAMP</td>

<td>datetime64[ns]</td>

</tr>

<td>timedelta64[ns]</td>

</tr>

<tr class="row-odd"><td>INTERVAL</td>

<td>timedelta64[ns]</td>

</tr>

<tr class="row-even"><td>ARRAY</td>

<td>object</td>

</tr>

<tr class="row-odd"><td>BINARY</td>

<td>object</td>

</tr>

<tr class="row-even"><td>VARBINARY</td>

<td>object</td>

</tr>

<tr class="row-odd"><td>STRUCT</td>

<td>object</td>

</tr>

<td>object</td>

</tr>

</tbody>

</table>

Note

Please note that the “object” type in Pandas is dynamic and can hold any type of data.

Therefore, the representation of primitive types in arrays, binary, struct, and map types may be different to their

representation as a standalone column.

</div>

<h2>Using Pandas DataFrames as Input / Output<a class="headerlink" href="#using-pandas-dataframes-as-input-output" title="Link to this heading"></a></h2>

You can use <a class="reference internal" href="feldera.html#feldera.pipeline.Pipeline.input_pandas" title="feldera.pipeline.Pipeline.input_pandas"><code class="xref py py-meth docutils literal notranslate">Pipeline.input_pandas()</code></a> to insert records from a

DataFrame to a Feldera table.

Use <a class="reference internal" href="feldera.html#feldera.pipeline.Pipeline.listen" title="feldera.pipeline.Pipeline.listen"><code class="xref py py-meth docutils literal notranslate">Pipeline.listen()</code></a> to subscribe to updates to a view in the form of a stream of DataFrames.

To ensure all data is received start listening before calling

<a class="reference internal" href="feldera.html#feldera.pipeline.Pipeline.start" title="feldera.pipeline.Pipeline.start"><code class="xref py py-meth docutils literal notranslate">Pipeline.start()</code></a>.

<div class="highlight-python notranslate"><div class="highlight"><pre>from feldera import FelderaClient, PipelineBuilder

import pandas as pd

sql = f"""

CREATE TABLE students (

name STRING,

id INT

);

CREATE TABLE grades (

student_id INT,

science INT,

maths INT,

art INT

);

CREATE VIEW average_scores AS SELECT name, ((science + maths + art) / 3) as average FROM {TBL_NAMES[0]} JOIN {TBL_NAMES[1]} on id = student_id ORDER BY average DESC;

"""

# Create a client

client = FelderaClient("https://try.feldera.com", api_key="YOUR_API_KEY")

pipeline = PipelineBuilder(client, name="notebook", sql=sql).create_or_replace()

df_students = pd.read_csv('students.csv')

df_grades = pd.read_csv('grades.csv')

pipeline.start()

# listen for the output of the view here in the notebook

# you do not need to call this if you are forwarding the data to a sink

out = pipeline.listen("average_scores")

pipeline.input_pandas("students", df_students)

pipeline.input_pandas("grades", df_grades)

# wait for the pipeline to complete

# note that if the source is a stream, this will run indefinitely

pipeline.wait_for_completion(True)

df = out.to_pandas()

# see the result

print(df)

# clear the storage and delete the pipeline

pipeline.delete(True)

</pre></div>

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