Added initial structure for stats elements, operations and plots

holoviews/element/__init__.py

@@ -6,6 +6,7 @@
 from .graphs import * # noqa (API import)
 from .path import * # noqa (API import)
 from .raster import * # noqa (API import)
+from .stats import * # noqa (API import)
 from .tabular import * # noqa (API import)
 
 

holoviews/element/comparison.py

@@ -28,10 +28,6 @@
                     GridSpace, DynamicMap, GridMatrix, OrderedDict)
 from ..core.options import Options, Cycle
 from ..core.util import pd
-from ..interface.pandas import DFrame as PandasDFrame
-from ..interface.pandas import DataFrameView
-from ..interface.seaborn import DFrame, Bivariate, Distribution, \
-                                Regression, TimeSeries
 
 
 class ComparisonInterface(object):
@@ -181,11 +177,6 @@ def register(cls):
         cls.equality_type_funcs[Table] =        cls.compare_tables
         cls.equality_type_funcs[Points] =       cls.compare_points
 
-        # Pandas DFrame objects
-        cls.equality_type_funcs[DataFrameView] = cls.compare_dframe
-        cls.equality_type_funcs[PandasDFrame] =  cls.compare_dframe
-        cls.equality_type_funcs[DFrame] =        cls.compare_dframe
-
         # Seaborn Views
         cls.equality_type_funcs[Bivariate] =    cls.compare_bivariate
         cls.equality_type_funcs[Distribution] = cls.compare_distribution

holoviews/element/stats.py

@@ -0,0 +1,50 @@
+import param
+
+from ..core.dimension import Dimension
+from .chart import Chart, Scatter
+
+
+class Bivariate(Chart):
+    """
+    Bivariate Views are containers for two dimensional data,
+    which is to be visualized as a kernel density estimate. The
+    data should be supplied as an Nx2 array, containing the x-
+    and y-data.
+    """
+
+    kdims = param.List(default=[Dimension('x'), Dimension('y')])
+
+    vdims = param.List(default=[], bounds=(0,1))
+
+    group = param.String(default="Bivariate", constant=True)
+
+
+
+class Distribution(Chart):
+    """
+    Distribution Views provide a container for data to be
+    visualized as a one-dimensional distribution. The data should
+    be supplied as a simple one-dimensional array or
+    list. Internally it uses Seaborn to make all the conversions.
+    """
+
+    kdims = param.List(default=[])
+
+    group = param.String(default='Distribution', constant=True)
+
+    vdims = param.List(default=[Dimension('Value')])
+
+    _auto_indexable_1d = False
+
+
+class Regression(Scatter):
+    """
+    Regression is identical to a Scatter plot but is visualized
+    using the Seaborn regplot interface. This allows it to
+    implement linear regressions, confidence intervals and a lot
+    more.
+    """
+
+    group = param.String(default='Regression', constant=True)
+
+

holoviews/interface/__init__.py

@@ -1,17 +1,5 @@
 from ..core import Dimensioned, AttrTree
 
-try:
-    import pandas
-    from .pandas import DFrame # noqa (API import)
-except:
-    pandas = None
-
-try:
-    import seaborn
-    from .seaborn import *     # noqa (API import)
-except:
-    seaborn = None
-
 from .collector import *       # noqa (API import)
 
 def public(obj):