Using Python from Analytica

New in Analytica 7.0

Requires the Analytica Developer edition.

Introduction

• Short overview
• Motivation (i.e., rich ecosystem, many libraries)
• Use cases
• Advantages of using python from within Analytica vs. plain python.
• Downsides: When it is better to stick with Analytica syntax (dependencies, array abstraction, MC, optimization).

Setting up a Python environment

• Very brief basics.
  • conda create -n MyEnv python=3.11
  • Configuring Python Environment or Model Environment sysvars in Analytica.
• Link to Setting up a Python environment for detailed info.

Importing Python modules and objects

• The Python Startup Code sysvar
• Use of "From module Import object"

The Python namespace and python objects

• The py:: namespace prefix
• The arrow -> operator

Calling a Python function

• py::F(...) calling syntax

Using Python code in Definitions

• The Python Expression view for definitions
• Writing an Analytica UDF with python code in body
• Using "def" or "class" in python code.
• The Callable

Dependencies

• The convenience of Analytica's dependencies.
• Drawing arrows to indicate dependence where Python doesn't track it.
• When dependencies are not tracked

Non-scalar data structures

• Distinction between Python data structures and Analytica data structures. How Python objects display in «...»
• Why to sometimes keep as python data structure w/o exploding

= Converting from Python data to Analytica

• Automatic conversions (including list of types)
• PyExplode
  • Basic usage. Nested usage.
  • Include/exclude types
  • Collections: np.array, dict, set/map, pandas data frame (documented on a separate library page), etc.

Converting from Analytica to Python data structures

• Automatic conversions (including a list of the types)
• PyList, PySet, PyArray, ...