BUG: make dense ranks results scale to 100 percent (pandas-dev#20731)

Author: peterpanmj

doc/source/whatsnew/v0.23.1.txt

@@ -46,12 +46,12 @@ Documentation Changes
 Bug Fixes
 ~~~~~~~~~
 
-- tab completion on :class:`Index` in IPython no longer outputs deprecation warnings (:issue:`21125`)
-
 Groupby/Resample/Rolling
 ^^^^^^^^^^^^^^^^^^^^^^^^
 
 - Bug in :func:`DataFrame.agg` where applying multiple aggregation functions to a :class:`DataFrame` with duplicated column names would cause a stack overflow (:issue:`21063`)
+- Bug in :func:`pandas.core.groupby.GroupBy.ffill` and :func:`pandas.core.groupby.GroupBy.bfill` where the fill within a grouping would not always be applied as intended due to the implementations' use of a non-stable sort (:issue:`21207`)
+- Bug in :func:`pandas.core.groupby.GroupBy.rank` where results did not scale to 100% when specifying ``method='dense'`` and ``pct=True``
 
 Strings
 ^^^^^^^
@@ -66,6 +66,12 @@ Categorical
 ^^^^^^^^^^^
 
 - Bug in :func:`pandas.util.testing.assert_index_equal` which raised ``AssertionError`` incorrectly, when comparing two :class:`CategoricalIndex` objects with param ``check_categorical=False`` (:issue:`19776`)
+- Bug in :meth:`Categorical.fillna` incorrectly raising a ``TypeError`` when `value` the individual categories are iterable and `value` is an iterable (:issue:`21097`, :issue:`19788`)
+
+Sparse
+^^^^^^
+
+- Bug in :attr:`SparseArray.shape` which previously only returned the shape :attr:`SparseArray.sp_values` (:issue:`21126`)
 
 Conversion
 ^^^^^^^^^^
@@ -78,11 +84,13 @@ Indexing
 
 - Bug in :meth:`Series.reset_index` where appropriate error was not raised with an invalid level name (:issue:`20925`)
 - Bug in :func:`interval_range` when ``start``/``periods`` or ``end``/``periods`` are specified with float ``start`` or ``end`` (:issue:`21161`)
+- Bug in :meth:`MultiIndex.set_names` where error raised for a ``MultiIndex`` with ``nlevels == 1`` (:issue:`21149`)
 -
 
 I/O
 ^^^
 
+- Bug in IO methods specifying ``compression='zip'`` which produced uncompressed zip archives (:issue:`17778`, :issue:`21144`)
 - Bug in :meth:`DataFrame.to_stata` which prevented exporting DataFrames to buffers and most file-like objects (:issue:`21041`)
 -
 
@@ -98,7 +106,8 @@ Reshaping
 - Bug in :func:`concat` where error was raised in concatenating :class:`Series` with numpy scalar and tuple names (:issue:`21015`)
 -
 
-Categorical
-^^^^^^^^^^^
+Other
+^^^^^
 
--
+- Tab completion on :class:`Index` in IPython no longer outputs deprecation warnings (:issue:`21125`)
+- Bug preventing pandas from being importable with -OO optimization (:issue:`21071`)

doc/source/whatsnew/v0.24.0.txt

@@ -1,7 +1,7 @@
 .. _whatsnew_0240:
 
 v0.24.0
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+-------
 
 .. _whatsnew_0240.enhancements:
 
@@ -12,8 +12,8 @@ New features
 
 Other Enhancements
 ^^^^^^^^^^^^^^^^^^
--
--
+- :func:`to_datetime` now supports the ``%Z`` and ``%z`` directive when passed into ``format`` (:issue:`13486`)
+- :func:`to_csv` now supports ``compression`` keyword when a file handle is passed. (:issue:`21227`)
 -
 
 .. _whatsnew_0240.api_breaking:
@@ -22,6 +22,13 @@ Other Enhancements
 Backwards incompatible API changes
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
+.. _whatsnew_0240.api.datetimelike:
+
+Datetimelike API Changes
+^^^^^^^^^^^^^^^^^^^^^^^^
+
+- For :class:`DatetimeIndex` and :class:`TimedeltaIndex` with non-``None`` ``freq`` attribute, addition or subtraction of integer-dtyped array or ``Index`` will return an object of the same class (:issue:`19959`)
+
 .. _whatsnew_0240.api.other:
 
 Other API Changes
@@ -177,4 +184,3 @@ Other
 -
 -
 -
-

pandas/_libs/groupby_helper.pxi.in

@@ -418,7 +418,7 @@ def group_rank_{{name}}(ndarray[float64_t, ndim=2] out,
                         bint is_datetimelike, object ties_method,
                         bint ascending, bint pct, object na_option):
     """
-	Provides the rank of values within each group.
+    Provides the rank of values within each group.
 
     Parameters
     ----------
@@ -451,8 +451,8 @@ def group_rank_{{name}}(ndarray[float64_t, ndim=2] out,
     """
     cdef:
         TiebreakEnumType tiebreak
-        Py_ssize_t i, j, N, K, val_start=0, grp_start=0, dups=0, sum_ranks=0
-        Py_ssize_t grp_vals_seen=1, grp_na_count=0
+        Py_ssize_t i, j, N, K, grp_start=0, dups=0, sum_ranks=0
+        Py_ssize_t grp_vals_seen=1, grp_na_count=0, grp_tie_count=0
         ndarray[int64_t] _as
         ndarray[float64_t, ndim=2] grp_sizes
         ndarray[{{c_type}}] masked_vals
@@ -563,6 +563,7 @@ def group_rank_{{name}}(ndarray[float64_t, ndim=2] out,
                 dups = sum_ranks = 0
                 val_start = i
                 grp_vals_seen += 1
+                grp_tie_count +=1
 
             # Similar to the previous conditional, check now if we are moving
             # to a new group. If so, keep track of the index where the new
@@ -571,11 +572,16 @@ def group_rank_{{name}}(ndarray[float64_t, ndim=2] out,
             # (used by pct calculations later). also be sure to reset any of
             # the items helping to calculate dups
             if i == N - 1 or labels[_as[i]] != labels[_as[i+1]]:
-                for j in range(grp_start, i + 1):
-                    grp_sizes[_as[j], 0] = i - grp_start + 1 - grp_na_count
+                if tiebreak != TIEBREAK_DENSE:
+                    for j in range(grp_start, i + 1):
+                        grp_sizes[_as[j], 0] = i - grp_start + 1 - grp_na_count
+                else:
+                    for j in range(grp_start, i + 1):
+                        grp_sizes[_as[j], 0] = (grp_tie_count -
+                                                (grp_na_count > 0))
                 dups = sum_ranks = 0
                 grp_na_count = 0
-                val_start = i + 1
+                grp_tie_count = 0
                 grp_start = i + 1
                 grp_vals_seen = 1
 

pandas/tests/groupby/test_rank.py

@@ -59,9 +59,9 @@ def test_rank_apply():
     ('first', False, False, [3., 4., 1., 5., 2.]),
     ('first', False, True, [.6, .8, .2, 1., .4]),
     ('dense', True, False, [1., 1., 3., 1., 2.]),
-    ('dense', True, True, [0.2, 0.2, 0.6, 0.2, 0.4]),
+    ('dense', True, True, [1. / 3., 1. / 3., 3. / 3., 1. / 3., 2. / 3.]),
     ('dense', False, False, [3., 3., 1., 3., 2.]),
-    ('dense', False, True, [.6, .6, .2, .6, .4]),
+    ('dense', False, True, [3. / 3., 3. / 3., 1. / 3., 3. / 3., 2. / 3.]),
 ])
 def test_rank_args(grps, vals, ties_method, ascending, pct, exp):
     key = np.repeat(grps, len(vals))
@@ -126,7 +126,7 @@ def test_infs_n_nans(grps, vals, ties_method, ascending, na_option, exp):
 @pytest.mark.parametrize("grps", [
     ['qux'], ['qux', 'quux']])
 @pytest.mark.parametrize("vals", [
-    [2, 2, np.nan, 8, 2, 6, np.nan, np.nan],  # floats
+    [2, 2, np.nan, 8, 2, 6, np.nan, np.nan],
     [pd.Timestamp('2018-01-02'), pd.Timestamp('2018-01-02'), np.nan,
      pd.Timestamp('2018-01-08'), pd.Timestamp('2018-01-02'),
      pd.Timestamp('2018-01-06'), np.nan, np.nan]
@@ -167,11 +167,11 @@ def test_infs_n_nans(grps, vals, ties_method, ascending, na_option, exp):
     ('dense', True, 'keep', False,
         [1., 1., np.nan, 3., 1., 2., np.nan, np.nan]),
     ('dense', True, 'keep', True,
-        [0.2, 0.2, np.nan, 0.6, 0.2, 0.4, np.nan, np.nan]),
+        [1. / 3., 1. / 3., np.nan, 3. / 3., 1. / 3., 2. / 3., np.nan, np.nan]),
     ('dense', False, 'keep', False,
         [3., 3., np.nan, 1., 3., 2., np.nan, np.nan]),
     ('dense', False, 'keep', True,
-        [.6, 0.6, np.nan, 0.2, 0.6, 0.4, np.nan, np.nan]),
+        [3. / 3., 3. / 3., np.nan, 1. / 3., 3. / 3., 2. / 3., np.nan, np.nan]),
     ('average', True, 'no_na', False, [2., 2., 7., 5., 2., 4., 7., 7.]),
     ('average', True, 'no_na', True,
         [0.25, 0.25, 0.875, 0.625, 0.25, 0.5, 0.875, 0.875]),
@@ -198,10 +198,10 @@ def test_infs_n_nans(grps, vals, ties_method, ascending, na_option, exp):
         [0.375, 0.5, 0.75, 0.125, 0.625, 0.25, 0.875, 1.]),
     ('dense', True, 'no_na', False, [1., 1., 4., 3., 1., 2., 4., 4.]),
     ('dense', True, 'no_na', True,
-        [0.125, 0.125, 0.5, 0.375, 0.125, 0.25, 0.5, 0.5]),
+     [0.25, 0.25, 1., 0.75, 0.25, 0.5, 1., 1.]),
     ('dense', False, 'no_na', False, [3., 3., 4., 1., 3., 2., 4., 4.]),
     ('dense', False, 'no_na', True,
-        [0.375, 0.375, 0.5, 0.125, 0.375, 0.25, 0.5, 0.5])
+     [0.75, 0.75, 1., 0.25, 0.75, 0.5, 1., 1.])
 ])
 def test_rank_args_missing(grps, vals, ties_method, ascending,
                            na_option, pct, exp):