Deprecating Series.argmin and Series.argmax (#16830)

doc/source/whatsnew/v0.21.0.txt

@@ -487,11 +487,33 @@ Other API Changes
 
 Deprecations
 ~~~~~~~~~~~~
+
 - :func:`read_excel()` has deprecated ``sheetname`` in favor of ``sheet_name`` for consistency with ``.to_excel()`` (:issue:`10559`).
 - ``pd.options.html.border`` has been deprecated in favor of ``pd.options.display.html.border`` (:issue:`15793`).
 - :func:`SeriesGroupBy.nth` has deprecated ``True`` in favor of ``'all'`` for its kwarg ``dropna`` (:issue:`11038`).
 - :func:`DataFrame.as_blocks` is deprecated, as this is exposing the internal implementation (:issue:`17302`)
 
+.. _whatsnew_0210.deprecations.argmin_min
+
+Series.argmax and Series.argmin
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+- The behavior of :func:`Series.argmax` has been deprecated in favor of :func:`Series.idxmax` (:issue:`16830`)
+- The behavior of :func:`Series.argmin` has been deprecated in favor of :func:`Series.idxmin` (:issue:`16830`)
+
+For compatibility with NumPy arrays, ``pd.Series`` implements ``argmax`` and
+``argmin``. Since pandas 0.13.0, ``argmax`` has been an alias for
+:meth:`pandas.Series.idxmax`, and ``argmin`` has been an alias for
+:meth:`pandas.Series.idxmin`. They return the *label* of the maximum or minimum,
+rather than the *position*.
+
+We've deprecated the current behavior of ``Series.argmax`` and
+``Series.argmin``. Using either of these will emit a ``FutureWarning``. Use
+:meth:`Series.idxmax` if you want the label of the maximum. Use
+``Series.values.argmax()`` if you want the position of the maximum. Likewise for
+the minimum. In a future release ``Series.argmax`` and ``Series.argmin`` will
+return the position of the maximum or minimum.
+
 .. _whatsnew_0210.prior_deprecations:
 
 Removal of prior version deprecations/changes

pandas/core/series.py

@@ -69,7 +69,8 @@
 import pandas.core.common as com
 import pandas.core.nanops as nanops
 import pandas.io.formats.format as fmt
-from pandas.util._decorators import Appender, deprecate_kwarg, Substitution
+from pandas.util._decorators import (
+    Appender, deprecate, deprecate_kwarg, Substitution)
 from pandas.util._validators import validate_bool_kwarg
 
 from pandas._libs import index as libindex, tslib as libts, lib, iNaT
@@ -1272,7 +1273,7 @@ def duplicated(self, keep='first'):
 
     def idxmin(self, axis=None, skipna=True, *args, **kwargs):
         """
-        Index of first occurrence of minimum of values.
+        Index *label* of the first occurrence of minimum of values.
 
         Parameters
         ----------
@@ -1285,7 +1286,9 @@ def idxmin(self, axis=None, skipna=True, *args, **kwargs):
 
         Notes
         -----
-        This method is the Series version of ``ndarray.argmin``.
+        This method is the Series version of ``ndarray.argmin``. This method
+        returns the label of the minimum, while ``ndarray.argmin`` returns
+        the position. To get the position, use ``series.values.argmin()``.
 
         See Also
         --------
@@ -1300,7 +1303,7 @@ def idxmin(self, axis=None, skipna=True, *args, **kwargs):
 
     def idxmax(self, axis=None, skipna=True, *args, **kwargs):
         """
-        Index of first occurrence of maximum of values.
+        Index *label* of the first occurrence of maximum of values.
 
         Parameters
         ----------
@@ -1313,7 +1316,9 @@ def idxmax(self, axis=None, skipna=True, *args, **kwargs):
 
         Notes
         -----
-        This method is the Series version of ``ndarray.argmax``.
+        This method is the Series version of ``ndarray.argmax``. This method
+        returns the label of the maximum, while ``ndarray.argmax`` returns
+        the position. To get the position, use ``series.values.argmax()``.
 
         See Also
         --------
@@ -1327,8 +1332,18 @@ def idxmax(self, axis=None, skipna=True, *args, **kwargs):
         return self.index[i]
 
     # ndarray compat
-    argmin = idxmin
-    argmax = idxmax
+    argmin = deprecate('argmin', idxmin,
+                       msg="'argmin' is deprecated. Use 'idxmin' instead. "
+                           "The behavior of 'argmin' will be corrected to "
+                           "return the positional minimum in the future. "
+                           "Use 'series.values.argmin' to get the position of "
+                           "the minimum now.")
+    argmax = deprecate('argmax', idxmax,
+                       msg="'argmax' is deprecated. Use 'idxmax' instead. "
+                           "The behavior of 'argmax' will be corrected to "
+                           "return the positional maximum in the future. "
+                           "Use 'series.values.argmax' to get the position of "
+                           "the maximum now.")
 
     def round(self, decimals=0, *args, **kwargs):
         """

pandas/io/formats/format.py

@@ -598,9 +598,7 @@ def to_string(self):
                 text = self._join_multiline(*strcols)
             else:  # max_cols == 0. Try to fit frame to terminal
                 text = self.adj.adjoin(1, *strcols).split('\n')
-                row_lens = Series(text).apply(len)
-                max_len_col_ix = np.argmax(row_lens)
-                max_len = row_lens[max_len_col_ix]
+                max_len = Series(text).str.len().max()
                 headers = [ele[0] for ele in strcols]
                 # Size of last col determines dot col size. See
                 # `self._to_str_columns

pandas/tests/series/test_analytics.py

@@ -1242,16 +1242,31 @@ def test_idxmin(self):
         result = s.idxmin()
         assert result == 1
 
-    def test_numpy_argmin(self):
-        # argmin is aliased to idxmin
-        data = np.random.randint(0, 11, size=10)
-        result = np.argmin(Series(data))
-        assert result == np.argmin(data)
+    def test_numpy_argmin_deprecated(self):
+        # See gh-16830
+        data = np.arange(1, 11)
+
+        s = Series(data)
+        with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
+            # The deprecation of Series.argmin also causes a deprecation
+            # warning when calling np.argmin. This behavior is temporary
+            # until the implemention of Series.argmin is corrected.
+            result = np.argmin(s)
+
+        assert result == 1
+
+        with tm.assert_produces_warning(FutureWarning):
+            # argmin is aliased to idxmin
+            result = s.argmin()
+
+        assert result == 1
 
         if not _np_version_under1p10:
-            msg = "the 'out' parameter is not supported"
-            tm.assert_raises_regex(ValueError, msg, np.argmin,
-                                   Series(data), out=data)
+            with tm.assert_produces_warning(FutureWarning,
+                                            check_stacklevel=False):
+                msg = "the 'out' parameter is not supported"
+                tm.assert_raises_regex(ValueError, msg, np.argmin,
+                                       s, out=data)
 
     def test_idxmax(self):
         # test idxmax
@@ -1297,17 +1312,30 @@ def test_idxmax(self):
         result = s.idxmin()
         assert result == 1.1
 
-    def test_numpy_argmax(self):
+    def test_numpy_argmax_deprecated(self):
+        # See gh-16830
+        data = np.arange(1, 11)
+
+        s = Series(data)
+        with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
+            # The deprecation of Series.argmax also causes a deprecation
+            # warning when calling np.argmax. This behavior is temporary
+            # until the implemention of Series.argmax is corrected.
+            result = np.argmax(s)
+        assert result == 10
+
+        with tm.assert_produces_warning(FutureWarning):
+            # argmax is aliased to idxmax
+            result = s.argmax()
 
-        # argmax is aliased to idxmax
-        data = np.random.randint(0, 11, size=10)
-        result = np.argmax(Series(data))
-        assert result == np.argmax(data)
+        assert result == 10
 
         if not _np_version_under1p10:
-            msg = "the 'out' parameter is not supported"
-            tm.assert_raises_regex(ValueError, msg, np.argmax,
-                                   Series(data), out=data)
+            with tm.assert_produces_warning(FutureWarning,
+                                            check_stacklevel=False):
+                msg = "the 'out' parameter is not supported"
+                tm.assert_raises_regex(ValueError, msg, np.argmax,
+                                       s, out=data)
 
     def test_ptp(self):
         N = 1000

pandas/tests/series/test_api.py

@@ -345,7 +345,7 @@ def test_ndarray_compat(self):
                          index=date_range('1/1/2000', periods=1000))
 
         def f(x):
-            return x[x.argmax()]
+            return x[x.idxmax()]
 
         result = tsdf.apply(f)
         expected = tsdf.max()

pandas/tests/series/test_operators.py

@@ -1872,33 +1872,33 @@ def test_op_duplicate_index(self):
             ),
         ]
     )
-    def test_assert_argminmax_raises(self, test_input, error_type):
+    def test_assert_idxminmax_raises(self, test_input, error_type):
         """
         Cases where ``Series.argmax`` and related should raise an exception
         """
         with pytest.raises(error_type):
-            test_input.argmin()
+            test_input.idxmin()
         with pytest.raises(error_type):
-            test_input.argmin(skipna=False)
+            test_input.idxmin(skipna=False)
         with pytest.raises(error_type):
-            test_input.argmax()
+            test_input.idxmax()
         with pytest.raises(error_type):
-            test_input.argmax(skipna=False)
+            test_input.idxmax(skipna=False)
 
-    def test_argminmax_with_inf(self):
+    def test_idxminmax_with_inf(self):
         # For numeric data with NA and Inf (GH #13595)
         s = pd.Series([0, -np.inf, np.inf, np.nan])
 
-        assert s.argmin() == 1
-        assert np.isnan(s.argmin(skipna=False))
+        assert s.idxmin() == 1
+        assert np.isnan(s.idxmin(skipna=False))
 
-        assert s.argmax() == 2
-        assert np.isnan(s.argmax(skipna=False))
+        assert s.idxmax() == 2
+        assert np.isnan(s.idxmax(skipna=False))
 
         # Using old-style behavior that treats floating point nan, -inf, and
         # +inf as missing
         with pd.option_context('mode.use_inf_as_na', True):
-            assert s.argmin() == 0
-            assert np.isnan(s.argmin(skipna=False))
-            assert s.argmax() == 0
-            np.isnan(s.argmax(skipna=False))
+            assert s.idxmin() == 0
+            assert np.isnan(s.idxmin(skipna=False))
+            assert s.idxmax() == 0
+            np.isnan(s.idxmax(skipna=False))

pandas/tests/sparse/test_series.py

@@ -1379,11 +1379,25 @@ def test_numpy_func_call(self):
         # numpy passes in 'axis=None' or `axis=-1'
         funcs = ['sum', 'cumsum', 'var', 'mean',
                  'prod', 'cumprod', 'std', 'argsort',
-                 'argmin', 'argmax', 'min', 'max']
+                 'min', 'max']
         for func in funcs:
             for series in ('bseries', 'zbseries'):
                 getattr(np, func)(getattr(self, series))
 
+    def test_deprecated_numpy_func_call(self):
+        # NOTE: These should be add to the 'test_numpy_func_call' test above
+        # once the behavior of argmin/argmax is corrected.
+        funcs = ['argmin', 'argmax']
+        for func in funcs:
+            for series in ('bseries', 'zbseries'):
+                with tm.assert_produces_warning(FutureWarning,
+                                                check_stacklevel=False):
+                    getattr(np, func)(getattr(self, series))
+
+                with tm.assert_produces_warning(FutureWarning,
+                                                check_stacklevel=False):
+                    getattr(getattr(self, series), func)()
+
 
 @pytest.mark.parametrize(
     'datetime_type', (np.datetime64,

pandas/util/_decorators.py

@@ -7,7 +7,7 @@
 
 
 def deprecate(name, alternative, alt_name=None, klass=None,
-              stacklevel=2):
+              stacklevel=2, msg=None):
     """
     Return a new function that emits a deprecation warning on use.
 
@@ -21,14 +21,16 @@ def deprecate(name, alternative, alt_name=None, klass=None,
         Name to use in preference of alternative.__name__
     klass : Warning, default FutureWarning
     stacklevel : int, default 2
+    msg : str
+          The message to display in the warning.
+          Default is '{name} is deprecated. Use {alt_name} instead.'
     """
 
     alt_name = alt_name or alternative.__name__
     klass = klass or FutureWarning
+    msg = msg or "{} is deprecated. Use {} instead".format(name, alt_name)
 
     def wrapper(*args, **kwargs):
-        msg = "{name} is deprecated. Use {alt_name} instead".format(
-            name=name, alt_name=alt_name)
         warnings.warn(msg, klass, stacklevel=stacklevel)
         return alternative(*args, **kwargs)
     return wrapper