Leveraging Custom Python Scripts in Oracle Analytics Server

This post illustrates how to leverage custom Python scripts in Oracle Analytics Server to give you greater control and flexibility over specific data processing needs.

Oracle Analytics Server has enabled users to invoke custom Python/R scripts since the end of 2017. Unfortunately, this feature is not yet widely adopted, probably because the official documentation shows only how to upload a custom script, while the details about enabling the feature and embedding the script in XML format are not provided.

In this post, I'm going to illustrate how to leverage custom Python scripts in Oracle Analytics Server to give you greater control and flexibility over specific data processing needs.

Enabling Custom Scripts

The feature to invoke custom scripts is disabled by default and Doc ID 2675894.1 on My Oracle Support explains how to enable it.

Copy the attached updateCustomScriptsProperty.py script to $ORACLE_HOME/bi/modules/oracle.bi.publicscripts/internal/wlst.

There is already an existing updateCustomScriptsProperty.py file in the above location, but it does not work. You have to rename or delete it, and use the attached script.

Then execute the script using the WebLogic Scripting Tool:


$ORACLE_HOME/oracle_common/common/bin/wlst.sh $ORACLE_HOME/bi/modules/oracle.bi.publicscripts/internal/wlst/updateCustomScriptsProperty.py true $DOMAIN_HOME $ORACLE_HOME


%ORACLE_HOME%\oracle_common\common\bin\wlst.cmd %ORACLE_HOME%\bi\modules\oracle.bi.publicscripts\internal\wlst\updateCustomScriptsProperty.py true %DOMAIN_HOME% %ORACLE_HOME%

Restart Oracle Analytics Server to enable the feature.

Installing Additional Python Packages

Oracle Analytics Server 6.4 relies on Python 3.5.2 (sigh) which is included out-of-the-box with several packages. You can find them all under $ORACLE_HOME/bi/modules/oracle.bi.dvml/lib/python3.5/site-packages.

Call me paranoid or over-cautious, but to me it makes sense not to play around with the out-of-the-box version put in place by the installation. To avoid this, if any additional packages are required, I choose to firstly install another copy of the same Python version (3.5.2) in another location on the server - this way, I know I can add to or make changes without possibly affecting any other standard functionality that uses the out-of-the-box version.

Installing Python 3.5.2 on Linux in 2022 could be a bit tricky since the Python official website does not host the installers for older versions, but only the source code.

First of all download the source code for Python 3.5.2.

$ wget https://www.python.org/ftp/python/3.5.2/Python-3.5.2.tgz

Now extract the downloaded package.

$ sudo tar xzf Python-3.5.2.tgz

Compile the source code on your system using altinstall.

$ cd Python-3.5.2
$ sudo ./configure
$ sudo make altinstall

Then install all required packages using pip. My example needs langdetect and in order to make it work correctly with Python 3.5.2 I decided to install an older version of it (1.0.7). You should always verify which versions of packages used in your code are compatible with Python 3.5.2 and install them explicitly, otherwise pip will automatically pick the latest ones (which may not be compatible).

$ sudo pip3.5 install langdetect==1.0.7

Edit the obis.properties file located under $DOMAIN_HOME/config/fmwconfig/bienv/OBIS, set the PYTHONPATH variable to ensure the packages can be found by Oracle Analytics Server, and restart the services.

Installing Python 3.5.2 on Windows is a lot easier since you can rely on the installer, but the above procedure to set up the PYTHONPATH variable in obis.properties does not work. As a workaround, you can copy the site-packages folder from the new Python environment to the out-of-the-box one in Oracle Analytics Server.

Anatomy of a Custom Python Script

To be able to use a custom Python script with Oracle Analytics Server, we need to embed it in a simple pre-defined XML format.

Unfortunately, there is no formal Oracle documentation about the XML format and what follows is based on my understanding of the examples in the Oracle Analytics Library.

The XML must contain one root element <script> that is the parent of all other elements:

<?xml version="1.0" encoding="UTF-8"?>

The <scriptname> element indicates the name of your script:


According to the documentation, <scriptlabel> should indicate the name of the script as visible for end users, but it seems to be ignored once the script has been uploaded to Oracle Analytics Server. However, if you don't include this element in the XML you will get an error notification while uploading the script.

<scriptlabel>Detect Language (py)</scriptlabel>

<target> refers to the type of script that you are embedding in the XML:


In order to use the script in data flows, it's mandatory to include the <type> element and set it to execute_script:


<scriptdescription> is straightforward to understand and provides a description of the script as explained by its developer. You can also specify the version of the script in the <version> element.

    Determine the language of a piece of text. 

The <outputs> element lists the outputs returned by the script. In the example, the script returns one column called language. <displayName> and <datatype> elements refer to the name displayed in the user interface and the data type of the outputs.


The <options> element indicates the input parameters to the script. There is also a special parameter includeInputColumns which lets users choose whether to append output columns to the input dataset and return, or just return the output columns. In the example, the script requires one column input (text) and always append the output column (language) to the input dataset.

        <description>The input column for detecting the language</description>
        <displayName>Include Input Columns</displayName>

And lastly, the <scriptcontent> element must contain the Python code. You have to import all required packages and implement your data transformation logic in the obi_execute_script function:

  • The data parameter provides access to the input dataset in a Pandas DataFrame structure.
  • The args parameter provides access to the input parameters as defined in the <options> XML element.
  • The function must return a Pandas DataFrame structure.
  • Oracle Analytics Server will automatically execute this function when you invoke the custom script.
import pandas as pd
from langdetect import detect, DetectorFactory

def obi_execute_script(data, columnMetadata, args):
    language_array = []
    DetectorFactory.seed = 0
    for value in data[args['Text']]:
    data.insert(loc=0, column='language', value=language_array) 
    return data

In the example above, the values in the input column are analyzed to detect their language using the langdetect package, the results are then collected into an array and returned alongside the input dataset. The source code is attached below, feel free to use it, but remember to install all required packages first!

Custom Python Scripts in Action

Once wrapped into XML, administrators can upload custom scripts into Oracle Analytics Server. Once uploaded they can be shared and executed by other users.

In the Home page, click on the Create button and select the Script option.

Figure 1. Uploading custom scripts into Oracle Analytics Server (step 1)

Drag and drop your custom script to the Add Script dialog, then click OK.

Figure 2. Uploading custom scripts into Oracle Analytics Server (step 2)

The uploaded custom script is now available for use and displayed in the Scripts tab on the Machine Learning page.

Figure 3. The Scripts tab on the Machine Learning page

To invoke the script from a data flow you have to include the Add Custom Script step.

Figure 4. Invoking custom scripts in a data flow (step 1)

Select the script that you want to execute, and click OK.

Figure 5. Invoking custom scripts in a data flow (step 2)

Configure the step by specifying any required input and the outputs that you want to include into the result set. In the example, I chose to detect the language for the review column.

Figure 6. Invoking custom scripts in a data flow (step 3)

Save the output data by adding the Save Data step and run the data flow to execute the custom script.

Figure 7. Invoking custom scripts in a data flow (step 4)


Business analysts and end-users often want greater control when performing data preparation tasks. In this context, leveraging custom Python/R scripts into Oracle Analytics Server can give you full control and flexibility over specific data processing needs.

If you are looking into leveraging custom Python/R scripts into Oracle Analytics Server and want to find out more, please do get in touch or DM us on Twitter @rittmanmead. Rittman Mead can help you with a product demo, training and assist within the development process.