BUG: define empty product on Series and DataFrame to be 1

doc/source/v0.15.0.txt

@@ -129,6 +129,10 @@ API changes
   strings must contain 244 or fewer characters.  Attempting to write Stata
   dta files with strings longer than 244 characters raises a ``ValueError``. (:issue:`7858`)
 
+- Empty product computations now have a default value of 1. This means that
+  during resampling, for example, now instead of ``nan``, the value for empty
+  Periods, Timestamps, etc. will be 1 (:issue:`7889`).
+
 
 .. _whatsnew_0150.cat:
 

pandas/core/nanops.py

@@ -1,4 +1,3 @@
-import sys
 import itertools
 import functools
 
@@ -10,7 +9,6 @@
 except ImportError:  # pragma: no cover
     _USE_BOTTLENECK = False
 
-import pandas.core.common as com
 import pandas.hashtable as _hash
 from pandas import compat, lib, algos, tslib
 from pandas.compat import builtins
@@ -49,53 +47,48 @@ def _f(*args, **kwargs):
         return _f
 
 
-class bottleneck_switch(object):
+def bottleneck_switch(alt=None, zero_value=None, **kwargs):
+    if alt is None:
+        return functools.partial(bottleneck_switch, zero_value=zero_value,
+                                 **kwargs)
 
-    def __init__(self, zero_value=None, **kwargs):
-        self.zero_value = zero_value
-        self.kwargs = kwargs
-
-    def __call__(self, alt):
-        bn_name = alt.__name__
+    bn_name = alt.__name__
 
+    try:
+        bn_func = getattr(bn, bn_name)
+    except (AttributeError, NameError):  # pragma: no cover
+        bn_func = None
+
+    @functools.wraps(alt)
+    def f(values, axis=None, skipna=True, **kwds):
+        for k, v in compat.iteritems(kwargs):
+            kwds.setdefault(k, v)
         try:
-            bn_func = getattr(bn, bn_name)
-        except (AttributeError, NameError):  # pragma: no cover
-            bn_func = None
-
-        @functools.wraps(alt)
-        def f(values, axis=None, skipna=True, **kwds):
-            if len(self.kwargs) > 0:
-                for k, v in compat.iteritems(self.kwargs):
-                    if k not in kwds:
-                        kwds[k] = v
-            try:
-                if self.zero_value is not None and values.size == 0:
-                    if values.ndim == 1:
-                        return 0
-                    else:
-                        result_shape = (values.shape[:axis] +
-                                        values.shape[axis + 1:])
-                        result = np.empty(result_shape)
-                        result.fill(0)
-                        return result
-
-                if _USE_BOTTLENECK and skipna and _bn_ok_dtype(values.dtype,
-                                                               bn_name):
-                    result = bn_func(values, axis=axis, **kwds)
-
-                    # prefer to treat inf/-inf as NA, but must compute the func
-                    # twice :(
-                    if _has_infs(result):
-                        result = alt(values, axis=axis, skipna=skipna, **kwds)
+            if zero_value is not None and values.size == 0:
+                if values.ndim == 1:
+                    return zero_value
                 else:
+                    result_shape = (values.shape[:axis] +
+                                    values.shape[axis + 1:])
+                    result = np.empty(result_shape)
+                    result.fill(zero_value)
+                    return result
+
+            if _USE_BOTTLENECK and skipna and _bn_ok_dtype(values.dtype,
+                                                           bn_name):
+                result = bn_func(values, axis=axis, **kwds)
+
+                # prefer to treat inf/-inf as NA, but must compute the func
+                # twice :(
+                if _has_infs(result):
                     result = alt(values, axis=axis, skipna=skipna, **kwds)
-            except Exception:
+            else:
                 result = alt(values, axis=axis, skipna=skipna, **kwds)
+        except (ValueError, TypeError, ZeroDivisionError):
+            result = alt(values, axis=axis, skipna=skipna, **kwds)
 
-            return result
-
-        return f
+        return result
+    return f
 
 
 def _bn_ok_dtype(dt, name):
@@ -121,7 +114,7 @@ def _has_infs(result):
             return lib.has_infs_f4(result.ravel())
     try:
         return np.isinf(result).any()
-    except (TypeError, NotImplementedError) as e:
+    except (TypeError, NotImplementedError):
         # if it doesn't support infs, then it can't have infs
         return False
 
@@ -260,7 +253,7 @@ def nansum(values, axis=None, skipna=True):
 
 
 @disallow('M8')
-@bottleneck_switch()
+@bottleneck_switch
 def nanmean(values, axis=None, skipna=True):
     values, mask, dtype, dtype_max = _get_values(values, skipna, 0)
     the_sum = _ensure_numeric(values.sum(axis, dtype=dtype_max))
@@ -278,7 +271,7 @@ def nanmean(values, axis=None, skipna=True):
 
 
 @disallow('M8')
-@bottleneck_switch()
+@bottleneck_switch
 def nanmedian(values, axis=None, skipna=True):
 
     values, mask, dtype, dtype_max = _get_values(values, skipna)
@@ -365,7 +358,7 @@ def nansem(values, axis=None, skipna=True, ddof=1):
     return np.sqrt(var)/np.sqrt(count)
 
 
-@bottleneck_switch()
+@bottleneck_switch
 def nanmin(values, axis=None, skipna=True):
     values, mask, dtype, dtype_max = _get_values(values, skipna,
                                                  fill_value_typ='+inf')
@@ -395,7 +388,7 @@ def nanmin(values, axis=None, skipna=True):
     return _maybe_null_out(result, axis, mask)
 
 
-@bottleneck_switch()
+@bottleneck_switch
 def nanmax(values, axis=None, skipna=True):
     values, mask, dtype, dtype_max = _get_values(values, skipna,
                                                  fill_value_typ='-inf')
@@ -517,6 +510,7 @@ def nankurt(values, axis=None, skipna=True):
 
 
 @disallow('M8')
+@bottleneck_switch(zero_value=1)
 def nanprod(values, axis=None, skipna=True):
     mask = isnull(values)
     if skipna and not _is_any_int_dtype(values):

pandas/tests/test_series.py

@@ -3018,6 +3018,8 @@ def test_isnull_for_inf(self):
         tm.assert_series_equal(r, e)
         tm.assert_series_equal(dr, de)
 
+    def test_empty_product(self):
+        tm.assert_equal(Series().prod(), 1)
 
 # TimeSeries-specific
 

pandas/tseries/tests/test_resample.py

@@ -1363,22 +1363,36 @@ def test_aggregate_with_nat(self):
         normal_grouped = normal_df.groupby('key')
         dt_grouped = dt_df.groupby(TimeGrouper(key='key', freq='D'))
 
-        for func in ['min', 'max', 'prod']:
+        for func in ['min', 'max']:
             normal_result = getattr(normal_grouped, func)()
             dt_result = getattr(dt_grouped, func)()
             pad = DataFrame([[np.nan, np.nan, np.nan, np.nan]],
                             index=[3], columns=['A', 'B', 'C', 'D'])
             expected = normal_result.append(pad)
             expected = expected.sort_index()
-            expected.index = date_range(start='2013-01-01', freq='D', periods=5, name='key')
+            expected.index = date_range(start='2013-01-01', freq='D', periods=5,
+                                        name='key')
+            assert_frame_equal(expected, dt_result)
+
+        for func in ['prod']:
+            normal_result = getattr(normal_grouped, func)()
+            dt_result = getattr(dt_grouped, func)()
+            pad = DataFrame([[1] * 4],
+                            index=[3], columns=['A', 'B', 'C', 'D'])
+            expected = normal_result.append(pad)
+            expected = expected.sort_index()
+            expected.index = date_range(start='2013-01-01', freq='D', periods=5,
+                                        name='key')
             assert_frame_equal(expected, dt_result)
 
         for func in ['count', 'sum']:
             normal_result = getattr(normal_grouped, func)()
-            pad = DataFrame([[0, 0, 0, 0]], index=[3], columns=['A', 'B', 'C', 'D'])
+            pad = DataFrame([[0, 0, 0, 0]], index=[3], columns=['A', 'B', 'C',
+                                                                'D'])
             expected = normal_result.append(pad)
             expected = expected.sort_index()
-            expected.index = date_range(start='2013-01-01', freq='D', periods=5, name='key')
+            expected.index = date_range(start='2013-01-01', freq='D', periods=5,
+                                        name='key')
             dt_result = getattr(dt_grouped, func)()
             assert_frame_equal(expected, dt_result)
 
@@ -1387,7 +1401,8 @@ def test_aggregate_with_nat(self):
             pad = Series([0], index=[3])
             expected = normal_result.append(pad)
             expected = expected.sort_index()
-            expected.index = date_range(start='2013-01-01', freq='D', periods=5, name='key')
+            expected.index = date_range(start='2013-01-01', freq='D', periods=5,
+                                        name='key')
             dt_result = getattr(dt_grouped, func)()
             assert_series_equal(expected, dt_result)