This section provides information on how to configure and use the Python node running on Python 3.
| Change type | Description |
|---|---|
| Introduced in version 3.18 | An ability to install additional Python modules for use in the Data360 Analyze Python node. |
If you are familiar with the Python language, you can use Python scripts to configure and tailor the Python node in Data360 Analyze.
If you are new to Python and would like to learn more about the language, there are many resources available online, for example https://docs.python.org/3/tutorial/index.html.
In this section, you will find information on the Data360 Analyze-specific Python functions and APIs and configuration of the Python environment within which the nodes will run, see:
Data types and mapping
The following table outlines how the Python 3 data types are mapped to the corresponding Data360 Analyze data types:
| Python | Data360 Analyze |
|---|---|
| bool | boolean |
| int | long |
| float | double |
| datetime.date | date |
| datetime.time | time |
| datetime.datetime | datetime |
| str | unicode |
Note that in Python 3, there are no longer int or unicode types. All int values are 64-bit integers which correspond to the Analyze long type and all string values are Unicode strings, corresponding to the Analyze Unicode type. Therefore, Analyze string fields will be read into Python str types and Analyze int fields will be read into Python int types.
If, however, fields are being passed through from an input to an output field, then the metadata can be preserved and if the field metadata is simply copied, then the Python node can produce fields with int and string output types.
Note that in this scenario, writing a Python int value to an int field involves taking a 64-bit integer and trying to write it to a 32-bit field. If the value is too large to be assigned to an int field on an output record, the node will error and indicate that the field is too large for the target field type.