Revise outdated search space user guide section

docs/userguide/searchspace.md

@@ -222,79 +222,17 @@ The function [`SearchSpace.from_product`](baybe.searchspace.core.SearchSpace.fro
 ### Constructing from a Dataframe
 
 [`SearchSpace.from_dataframe`](baybe.searchspace.core.SearchSpace.from_dataframe) constructs a search space from a given dataframe.
-Due to the ambiguity between discrete and numerical parameter choices, this function requires a parameters list with pre-defined parameter objects, unlike its subspace counterparts, where this list is optional.
+Due to the ambiguity between discrete and continuous parameter representations when identifying parameter ranges based only on data, this function requires that the appropriate parameter definitions be explicitly provided. This is different for its subspace counterparts [`SubspaceDiscrete.from_dataframe`](baybe.searchspace.discrete.SubspaceDiscrete.from_dataframe) and [`SubspaceContinuous.from_dataframe`](baybe.searchspace.continuous.SubspaceContinuous.from_dataframe), where a fallback mechanism can automatically infer minimal parameter specifications if omitted.
 
 ```python
 from baybe.searchspace import SearchSpace
 
-params = [
-    NumericalDiscreteParameter(name="x0", values=[1, 2, 3]),
-    NumericalDiscreteParameter(name="x1", values=[4, 5, 6]),
-    NumericalContinuousParameter(name="x2", bounds=(6, 9)),
-]
-
-df = pd.DataFrame(
-    {
-        "x0": [2, 3],
-        "x1": [5, 4],
-        "x2": [9, 7],
-    }
-)
-searchspace = SearchSpace.from_dataframe(df=df, parameters=params)
+p_cont = NumericalContinuousParameter(name="c", bounds=[0, 1])
+p_disc = NumericalDiscreteParameter(name="d", values=[1, 2, 3])
+df = pd.DataFrame({"c": [0.3, 0.7], "d": [2, 3]})
+searchspace = SearchSpace.from_dataframe(df=df, parameters=[p_cont, p_disc])
 ```
 
-Since one of the provided parameters is continuous, this creates a hybrid space.
-The following shows *all* information that are available to the user for this space:
-
-~~~
-Search Space
-         
- Search Space Type: HYBRID
-         
- Discrete Search Space
-              
-  Discrete Parameters
-    Name                        Type  Num_Values Encoding
-  0   x0  NumericalDiscreteParameter           3     None
-  1   x1  NumericalDiscreteParameter           3     None
-              
-  Experimental Representation
-     x0  x1   
-  0   2   5
-  1   3   4
-  
-  Metadata:
-  was_recommended: 0/2
-  was_measured: 0/2
-  dont_recommend: 0/2
-              
-  Constraints
-  Empty DataFrame
-  Columns: []
-  Index: []
-              
-  Computational Representation
-     x0  x1   
-  0   2   5
-  1   3   4
-         
- Continuous Search Space
-              
-  Continuous Parameters
-    Name                          Type  Lower_Bound  Upper_Bound
-  0   x2  NumericalContinuousParameter          6.0          9.0
-              
-  List of Linear Equality Constraints
-  Empty DataFrame
-  Columns: []
-  Index: []
-              
-  List of Linear Inequality Constraints
-  Empty DataFrame
-  Columns: []
-  Index: []
-~~~
-
 ## Restricting Search Spaces Using Constraints
 
 Most constructors for both subspaces and search spaces support the optional keyword argument `constraints` to provide a list of [`Constraint`](baybe.constraints.base.Constraint) objects.