Updated type annotations, whatsnew doc, and specific tests added and passed

Author: sarahuston77

doc/source/whatsnew/v3.0.0.rst

@@ -232,7 +232,7 @@ Other enhancements
 - Support reading Stata 102-format (Stata 1) dta files (:issue:`58978`)
 - Support reading Stata 110-format (Stata 7) dta files (:issue:`47176`)
 - Switched wheel upload to **PyPI Trusted Publishing** (OIDC) for release-tag pushes in ``wheels.yml``. (:issue:`61718`)
--
+- :func:`qcut` now accepts ``right`` as optional arguments, as in :meth:`cut` (:issue:`62938`)
 
 .. ---------------------------------------------------------------------------
 .. _whatsnew_300.notable_bug_fixes:

pandas/core/reshape/tile.py

@@ -317,18 +317,18 @@ def qcut(
         Used as labels for the resulting bins. Must be of the same length as
         the resulting bins. If False, return only integer indicators of the
         bins. If True, raises an error.
+    right : bool, default True
+        Indicates whether `bins` includes the rightmost edge or not. If
+        ``right == True`` (the default), then the `bins` ``[1, 2, 3, 4]``
+        indicate (1,2], (2,3], (3,4]. This argument is ignored when
+        `bins` is an IntervalIndex.
     retbins : bool, optional
         Whether to return the (bins, labels) or not. Can be useful if bins
         is given as a scalar.
     precision : int, optional
         The precision at which to store and display the bins labels.
     duplicates : {default 'raise', 'drop'}, optional
         If bin edges are not unique, raise ValueError or drop non-uniques.
-    right : bool, default True
-        Indicates whether `bins` includes the rightmost edge or not. If
-        ``right == True`` (the default), then the `bins` ``[1, 2, 3, 4]``
-        indicate (1,2], (2,3], (3,4]. This argument is ignored when
-        `bins` is an IntervalIndex.
 
     Returns
     -------

pandas/tests/reshape/test_qcut.py

@@ -44,6 +44,40 @@ def test_qcut():
     tm.assert_categorical_equal(labels, ex_levels)
 
 
+def test_qcut_right():
+    arr = np.random.default_rng(2).standard_normal(1000)
+
+    # We store the bins as Index that have been
+    # rounded to comparisons are a bit tricky.
+    labels, _ = qcut(arr, 4, retbins=True, right=True)
+    ex_bins = np.quantile(arr, [0, 0.25, 0.5, 0.75, 1.0])
+
+    result = labels.categories.left.values
+    assert np.allclose(result, ex_bins[:-1], atol=1e-2)
+
+    result = labels.categories.right.values
+    assert np.allclose(result, ex_bins[1:], atol=1e-2)
+
+    ex_levels = cut(arr, ex_bins, include_lowest=True, right=True)
+    tm.assert_categorical_equal(labels, ex_levels)
+    
+    
+def test_qcut_no_right():
+    arr = np.random.default_rng(2).standard_normal(1000)
+
+    labels, _ = qcut(arr, 4, retbins=True, right=False)
+    ex_bins = np.quantile(arr, [0, 0.25, 0.5, 0.75, 1.0])
+
+    lefts = labels.categories.left.values
+    assert np.allclose(lefts, ex_bins[:-1], atol=1e-2)
+    
+    rights = labels.categories.right.values
+    assert np.allclose(rights, ex_bins[1:], atol=1e-2)
+
+    ex_levels = cut(arr, ex_bins, include_lowest=True, right=False)
+    tm.assert_categorical_equal(labels, ex_levels)
+    
+
 def test_qcut_bounds():
     arr = np.random.default_rng(2).standard_normal(1000)