PERF: use unique and isnull in nunique instead of value_counts.

doc/source/whatsnew/v0.16.0.txt

@@ -45,8 +45,12 @@ Performance
 
 .. _whatsnew_0160.performance:
 
+
 - Fixed a severe performance regression for ``.loc`` indexing with an array or list (:issue:9126:).
 
+- Improved the speed of `nunique` by calling `unique` instead of `value_counts` (:issue:`9129`, :issue:`7771`)
+
+
 Bug Fixes
 ~~~~~~~~~
 
@@ -114,3 +118,4 @@ Bug Fixes
 
 - DataFrame now properly supports simultaneous ``copy`` and ``dtype`` arguments in constructor (:issue:`9099`)
 - Bug in read_csv when using skiprows on a file with CR line endings with the c engine. (:issue:`9079`)
+- isnull now detects NaT in PeriodIndex (:issue:`9129`)

pandas/core/base.py

@@ -441,7 +441,12 @@ def nunique(self, dropna=True):
         -------
         nunique : int
         """
-        return len(self.value_counts(dropna=dropna))
+        uniqs = self.unique()
+        n = len(uniqs)
+        if dropna and com.isnull(uniqs).any():
+            n -= 1
+        return n
+
 
     def factorize(self, sort=False, na_sentinel=-1):
         """

pandas/core/common.py

@@ -302,7 +302,7 @@ def _isnull_ndarraylike(obj):
                 vec = lib.isnullobj(values.ravel())
                 result[...] = vec.reshape(shape)
 
-    elif dtype in _DATELIKE_DTYPES:
+    elif is_datetimelike(obj):
         # this is the NaT pattern
         result = values.view('i8') == tslib.iNaT
     else:
@@ -2366,6 +2366,9 @@ def is_datetime_arraylike(arr):
         return arr.dtype == object and lib.infer_dtype(arr) == 'datetime'
     return getattr(arr, 'inferred_type', None) == 'datetime'
 
+def is_datetimelike(arr):
+    return arr.dtype in _DATELIKE_DTYPES or isinstance(arr, ABCPeriodIndex)
+
 def _coerce_to_dtype(dtype):
     """ coerce a string / np.dtype to a dtype """
     if is_categorical_dtype(dtype):

pandas/tests/test_common.py

@@ -157,6 +157,15 @@ def test_isnull_datetime():
     assert(mask[0])
     assert(not mask[1:].any())
 
+    # GH 9129
+    pidx = idx.to_period(freq='M')
+    mask = isnull(pidx)
+    assert(mask[0])
+    assert(not mask[1:].any())
+
+    mask = isnull(pidx[1:])
+    assert(not mask.any())
+
 
 class TestIsNull(tm.TestCase):
     def test_0d_array(self):