pymc-devs
diff --git a/‎docs/source/notebooks/BEST.ipynb
+49-37 b/‎docs/source/notebooks/BEST.ipynb
+49-37
diff --git a/‎docs/source/notebooks/GLM-model-selection.ipynb
+311-83 b/‎docs/source/notebooks/GLM-model-selection.ipynb
+311-83
diff --git a/‎docs/source/notebooks/GLM.ipynb
+208-119 b/‎docs/source/notebooks/GLM.ipynb
+208-119
diff --git a/‎docs/source/notebooks/advi.ipynb
+61-38 b/‎docs/source/notebooks/advi.ipynb
+61-38
diff --git a/‎docs/source/notebooks/disaster_model.ipynb
+36-114 b/‎docs/source/notebooks/disaster_model.ipynb
+36-114
diff --git a/‎docs/source/notebooks/gaussian_mixture_model.ipynb
+44-28 b/‎docs/source/notebooks/gaussian_mixture_model.ipynb
+44-28
diff --git a/‎docs/source/notebooks/profiling.ipynb
+160-134 b/‎docs/source/notebooks/profiling.ipynb
+160-134
diff --git a/‎docs/source/notebooks/rugby_analytics.ipynb
+35-28 b/‎docs/source/notebooks/rugby_analytics.ipynb
+35-28
diff --git a/‎docs/source/notebooks/tutorial.ipynb
+38-208 b/‎docs/source/notebooks/tutorial.ipynb
+38-208
diff --git a/‎pymc3/backends/tracetab.py
+3-3 b/‎pymc3/backends/tracetab.py
+3-3
@@ -7,7 +7,7 @@
 __all__ = ['trace_to_dataframe']
 
 
-def trace_to_dataframe(trace, chains=None, flat_names=None):
+def trace_to_dataframe(trace, chains=None, flat_names=None, hide_transformed_vars=True):
     """Convert trace to Pandas DataFrame.
 
     Parameters
@@ -23,11 +23,11 @@ def trace_to_dataframe(trace, chains=None, flat_names=None):
     if flat_names is None:
         flat_names = {v: create_flat_names(v, shape)
                       for v, shape in var_shapes.items()
-                      if not v.endswith('_')}
+                      if not hide_transformed_vars or not v.endswith('_')}
 
     var_dfs = []
     for varname, shape in var_shapes.items():
-        if not varname.endswith('_'):
+        if not hide_transformed_vars or not varname.endswith('_'):
             vals = trace.get_values(varname, combine=True, chains=chains)
             flat_vals = vals.reshape(vals.shape[0], -1)
             var_dfs.append(pd.DataFrame(flat_vals, columns=flat_names[varname]))