We have introduced in Taipy the important concept of a taipy.core.Scenario. The Taipy Scenario represents an instance of a business problem to solve on consistent data and parameter sets.

As its name implies, Taipy scenarios enable the business user to instantiate different versions of a business problem with different assumptions. This is extremely useful in a business context where impact analysis and what-if analysis are essential in the decision process.

After having analyzed its first scenario, the end-user may be very interested in modifying input data nodes (not the intermediate nor the output data nodes), re-running the same pipelines and comparing the results with the previous run.

For this purpose, it will just need to instantiate a second scenario, execute it and compare it with the first scenario. This process can be repeated across multiple scenarios.

In the example

In our example, we want our scenario to have the two pipelines described earlier. In the flowchart below, the external light blue box represents my scenario that contains both pipelines.


A scenario represents one instance of a business problem to solve. Each new business problem instance is represented by a new scenario. Taipy allows us to give the end users the ability to store, edit, and execute various scenarios in the same application.


Suppose that we want to build an application to predict the monthly demand of a store and optimize production orders. In that case, we can create the first scenario for January. It must contain everything we need to understand the January case, access input data, compute predictions, visualize our forecast algorithm results, make production decisions, and publish January production orders.

Then we can create another scenario for February production planning. And so on.

Two scenarios can also be used to represent the same instance of a business problem but with two different sets of assumptions.


We would like to perform some simulation on the impact of our capacity data on production planning for the February use case.

The first scenario can forecast demand and compute production orders assuming a low capacity, whereas the second scenario assumes a high capacity value.

The next section introduces the Cycle concept.