COMPAT: Further Expand Compatibility with fromnumeric.py

doc/source/whatsnew/v0.18.2.txt

@@ -46,7 +46,8 @@ API changes
 
 
 - Non-convertible dates in an excel date column will be returned without conversion and the column will be ``object`` dtype, rather than raising an exception  (:issue:`10001`)
-
+- Compatibility with NumPy array methods has been expanded to timestamps (:issue: `12811`)
+- An ``UnsupportedFunctionCall`` error is now raised if groupby or resample functions like ``mean`` are called via NumPy (:issue: `12811`)
 
 .. _whatsnew_0182.api.tolist:
 

pandas/compat/numpy/function.py

@@ -21,7 +21,7 @@
 from numpy import ndarray
 from pandas.util.validators import (validate_args, validate_kwargs,
                                     validate_args_and_kwargs)
-from pandas.core.common import is_integer
+from pandas.core.common import is_integer, UnsupportedFunctionCall
 from pandas.compat import OrderedDict
 
 
@@ -245,3 +245,77 @@ def validate_transpose_for_generic(inst, kwargs):
             msg += " for {klass} instances".format(klass=klass)
 
         raise ValueError(msg)
+
+
+def validate_window_func(name, args, kwargs):
+    numpy_args = ('axis', 'dtype', 'out')
+    msg = ("numpy operations are not "
+           "valid with window objects. "
+           "Use .{func}() directly instead ".format(func=name))
+
+    if len(args) > 0:
+        raise UnsupportedFunctionCall(msg)
+
+    for arg in numpy_args:
+        if arg in kwargs:
+            raise UnsupportedFunctionCall(msg)
+
+
+def validate_rolling_func(name, args, kwargs):
+    numpy_args = ('axis', 'dtype', 'out')
+    msg = ("numpy operations are not "
+           "valid with window objects. "
+           "Use .rolling(...).{func}() instead ".format(func=name))
+
+    if len(args) > 0:
+        raise UnsupportedFunctionCall(msg)
+
+    for arg in numpy_args:
+        if arg in kwargs:
+            raise UnsupportedFunctionCall(msg)
+
+
+def validate_expanding_func(name, args, kwargs):
+    numpy_args = ('axis', 'dtype', 'out')
+    msg = ("numpy operations are not "
+           "valid with window objects. "
+           "Use .expanding(...).{func}() instead ".format(func=name))
+
+    if len(args) > 0:
+        raise UnsupportedFunctionCall(msg)
+
+    for arg in numpy_args:
+        if arg in kwargs:
+            raise UnsupportedFunctionCall(msg)
+
+
+def validate_groupby_func(name, args, kwargs):
+    """
+    'args' and 'kwargs' should be empty because all of
+    their necessary parameters are explicitly listed in
+    the function signature
+    """
+    if len(args) + len(kwargs) > 0:
+        raise UnsupportedFunctionCall((
+            "numpy operations are not valid "
+            "with groupby. Use .groupby(...)."
+            "{func}() instead".format(func=name)))
+
+RESAMPLER_NUMPY_OPS = ('min', 'max', 'sum', 'prod',
+                       'mean', 'std', 'var')
+
+
+def validate_resampler_func(method, args, kwargs):
+    """
+    'args' and 'kwargs' should be empty because all of
+    their necessary parameters are explicitly listed in
+    the function signature
+    """
+    if len(args) + len(kwargs) > 0:
+        if method in RESAMPLER_NUMPY_OPS:
+            raise UnsupportedFunctionCall((
+                "numpy operations are not valid "
+                "with resample. Use .resample(...)."
+                "{func}() instead".format(func=method)))
+        else:
+            raise TypeError("too many arguments passed in")

pandas/core/common.py

@@ -41,6 +41,10 @@ class AmbiguousIndexError(PandasError, KeyError):
     pass
 
 
+class UnsupportedFunctionCall(ValueError):
+    pass
+
+
 class AbstractMethodError(NotImplementedError):
     """Raise this error instead of NotImplementedError for abstract methods
     while keeping compatibility with Python 2 and Python 3.

pandas/core/generic.py

@@ -5299,7 +5299,7 @@ def _make_stat_function(cls, name, name1, name2, axis_descr, desc, f):
     @Appender(_num_doc)
     def stat_func(self, axis=None, skipna=None, level=None, numeric_only=None,
                   **kwargs):
-        nv.validate_stat_func(tuple(), kwargs)
+        nv.validate_stat_func(tuple(), kwargs, fname=name)
         if skipna is None:
             skipna = True
         if axis is None:
@@ -5319,7 +5319,7 @@ def _make_stat_function_ddof(cls, name, name1, name2, axis_descr, desc, f):
     @Appender(_num_ddof_doc)
     def stat_func(self, axis=None, skipna=None, level=None, ddof=1,
                   numeric_only=None, **kwargs):
-        nv.validate_stat_ddof_func(tuple(), kwargs)
+        nv.validate_stat_ddof_func(tuple(), kwargs, fname=name)
         if skipna is None:
             skipna = True
         if axis is None:
@@ -5340,7 +5340,7 @@ def _make_cum_function(cls, name, name1, name2, axis_descr, desc, accum_func,
     @Appender("Return cumulative {0} over requested axis.".format(name) +
               _cnum_doc)
     def cum_func(self, axis=None, dtype=None, out=None, skipna=True, **kwargs):
-        nv.validate_cum_func(tuple(), kwargs)
+        nv.validate_cum_func(tuple(), kwargs, fname=name)
         if axis is None:
             axis = self._stat_axis_number
         else:
@@ -5374,7 +5374,7 @@ def _make_logical_function(cls, name, name1, name2, axis_descr, desc, f):
     @Appender(_bool_doc)
     def logical_func(self, axis=None, bool_only=None, skipna=None, level=None,
                      **kwargs):
-        nv.validate_logical_func(tuple(), kwargs)
+        nv.validate_logical_func(tuple(), kwargs, fname=name)
         if skipna is None:
             skipna = True
         if axis is None:

pandas/core/groupby.py

@@ -11,6 +11,7 @@
     callable, map
 )
 from pandas import compat
+from pandas.compat.numpy import function as nv
 from pandas.compat.numpy import _np_version_under1p8
 from pandas.core.base import (PandasObject, SelectionMixin, GroupByError,
                               DataError, SpecificationError)
@@ -954,12 +955,13 @@ def count(self):
 
     @Substitution(name='groupby')
     @Appender(_doc_template)
-    def mean(self):
+    def mean(self, *args, **kwargs):
         """
         Compute mean of groups, excluding missing values
 
         For multiple groupings, the result index will be a MultiIndex
         """
+        nv.validate_groupby_func('mean', args, kwargs)
         try:
             return self._cython_agg_general('mean')
         except GroupByError:
@@ -993,7 +995,7 @@ def f(x):
 
     @Substitution(name='groupby')
     @Appender(_doc_template)
-    def std(self, ddof=1):
+    def std(self, ddof=1, *args, **kwargs):
         """
         Compute standard deviation of groups, excluding missing values
 
@@ -1005,12 +1007,13 @@ def std(self, ddof=1):
             degrees of freedom
         """
 
-        # todo, implement at cython level?
+        # TODO: implement at Cython level?
+        nv.validate_groupby_func('std', args, kwargs)
         return np.sqrt(self.var(ddof=ddof))
 
     @Substitution(name='groupby')
     @Appender(_doc_template)
-    def var(self, ddof=1):
+    def var(self, ddof=1, *args, **kwargs):
         """
         Compute variance of groups, excluding missing values
 
@@ -1021,7 +1024,7 @@ def var(self, ddof=1):
         ddof : integer, default 1
             degrees of freedom
         """
-
+        nv.validate_groupby_func('var', args, kwargs)
         if ddof == 1:
             return self._cython_agg_general('var')
         else:
@@ -1317,17 +1320,19 @@ def cumcount(self, ascending=True):
 
     @Substitution(name='groupby')
     @Appender(_doc_template)
-    def cumprod(self, axis=0):
+    def cumprod(self, axis=0, *args, **kwargs):
         """Cumulative product for each group"""
+        nv.validate_groupby_func('cumprod', args, kwargs)
         if axis != 0:
             return self.apply(lambda x: x.cumprod(axis=axis))
 
         return self._cython_transform('cumprod')
 
     @Substitution(name='groupby')
     @Appender(_doc_template)
-    def cumsum(self, axis=0):
+    def cumsum(self, axis=0, *args, **kwargs):
         """Cumulative sum for each group"""
+        nv.validate_groupby_func('cumsum', args, kwargs)
         if axis != 0:
             return self.apply(lambda x: x.cumprod(axis=axis))