API: consistent default dropna for value_counts

asv_bench/benchmarks/io/csv.py

@@ -84,8 +84,8 @@ class ToCSVIndexes(BaseIO):
     def _create_df(rows, cols):
         index_cols = {
             "index1": np.random.randint(0, rows, rows),
-            "index2": np.full(rows, 1, dtype=np.int),
-            "index3": np.full(rows, 1, dtype=np.int),
+            "index2": np.full(rows, 1, dtype=int),
+            "index3": np.full(rows, 1, dtype=int),
         }
         data_cols = {
             f"col{i}": np.random.uniform(0, 100000.0, rows) for i in range(cols)

pandas/core/arrays/_mixins.py

@@ -373,13 +373,13 @@ def delete(self: NDArrayBackedExtensionArrayT, loc) -> NDArrayBackedExtensionArr
     #  These are not part of the EA API, but we implement them because
     #  pandas assumes they're there.
 
-    def value_counts(self, dropna: bool = False):
+    def value_counts(self, dropna: bool = True):
         """
         Return a Series containing counts of unique values.
 
         Parameters
         ----------
-        dropna : bool, default False
+        dropna : bool, default True
             Don't include counts of NA values.
 
         Returns

pandas/core/arrays/categorical.py

@@ -1416,7 +1416,7 @@ def notna(self):
 
     notnull = notna
 
-    def value_counts(self, dropna=True):
+    def value_counts(self, dropna: bool = True):
         """
         Return a Series containing counts of each category.
 

pandas/core/arrays/interval.py

@@ -1021,7 +1021,7 @@ def _validate_setitem_value(self, value):
             raise ValueError("Cannot set float NaN to integer-backed IntervalArray")
         return value_left, value_right
 
-    def value_counts(self, dropna=True):
+    def value_counts(self, dropna: bool = True):
         """
         Returns a Series containing counts of each interval.
 

pandas/core/arrays/sparse/array.py

@@ -725,13 +725,13 @@ def factorize(self, na_sentinel=-1):
         uniques = SparseArray(uniques, dtype=self.dtype)
         return codes, uniques
 
-    def value_counts(self, dropna=True):
+    def value_counts(self, dropna: bool = True):
         """
         Returns a Series containing counts of unique values.
 
         Parameters
         ----------
-        dropna : boolean, default True
+        dropna : bool, default True
             Don't include counts of NaN, even if NaN is in sp_values.
 
         Returns

pandas/core/arrays/string_.py

@@ -338,7 +338,7 @@ def max(self, axis=None, skipna: bool = True, **kwargs) -> Scalar:
         )
         return self._wrap_reduction_result(axis, result)
 
-    def value_counts(self, dropna=False):
+    def value_counts(self, dropna: bool = True):
         from pandas import value_counts
 
         return value_counts(self._ndarray, dropna=dropna).astype("Int64")

pandas/core/groupby/generic.py

@@ -679,7 +679,12 @@ def describe(self, **kwargs):
         return result.unstack()
 
     def value_counts(
-        self, normalize=False, sort=True, ascending=False, bins=None, dropna=True
+        self,
+        normalize=False,
+        sort=True,
+        ascending=False,
+        bins=None,
+        dropna: bool = True,
     ):
 
         from pandas.core.reshape.merge import get_join_indexers

pandas/tests/arrays/test_datetimes.py

@@ -172,7 +172,7 @@ def test_value_counts_preserves_tz(self):
         assert result.index.equals(dti)
 
         arr[-2] = pd.NaT
-        result = arr.value_counts()
+        result = arr.value_counts(dropna=False)
         expected = pd.Series([4, 2, 1], index=[dti[0], dti[1], pd.NaT])
         tm.assert_series_equal(result, expected)
 

pandas/tests/extension/base/methods.py

@@ -1,3 +1,4 @@
+import inspect
 import operator
 
 import numpy as np
@@ -15,6 +16,14 @@
 class BaseMethodsTests(BaseExtensionTests):
     """Various Series and DataFrame methods."""
 
+    def test_value_counts_default_dropna(self, data):
+        # make sure we have consistent default dropna kwarg
+        if not hasattr(data, "value_counts"):
+            pytest.skip("value_counts is not implemented")
+        sig = inspect.signature(data.value_counts)
+        kwarg = sig.parameters["dropna"]
+        assert kwarg.default is True
+
     @pytest.mark.parametrize("dropna", [True, False])
     def test_value_counts(self, all_data, dropna):
         all_data = all_data[:10]

pandas/tests/extension/decimal/array.py

@@ -230,7 +230,7 @@ def convert_values(param):
 
         return np.asarray(res, dtype=bool)
 
-    def value_counts(self, dropna: bool = False):
+    def value_counts(self, dropna: bool = True):
         from pandas.core.algorithms import value_counts
 
         return value_counts(self.to_numpy(), dropna=dropna)