Groupby transform cleanups

doc/source/whatsnew/v1.0.0.rst

@@ -39,7 +39,7 @@ Backwards incompatible API changes
 
 .. _whatsnew_1000.api.other:
 
--
+- :class:`pandas.core.groupby.GroupBy.transform` now raises on invalid operation names (:issue:`27489`).
 -
 
 Other API changes

pandas/core/base.py

@@ -4,6 +4,7 @@
 import builtins
 from collections import OrderedDict
 import textwrap
+from typing import Optional
 import warnings
 
 import numpy as np
@@ -566,7 +567,7 @@ def is_any_frame():
         else:
             result = None
 
-        f = self._is_cython_func(arg)
+        f = self._get_cython_func(arg)
         if f and not args and not kwargs:
             return getattr(self, f)(), None
 
@@ -653,7 +654,7 @@ def _shallow_copy(self, obj=None, obj_type=None, **kwargs):
                 kwargs[attr] = getattr(self, attr)
         return obj_type(obj, **kwargs)
 
-    def _is_cython_func(self, arg):
+    def _get_cython_func(self, arg: str) -> Optional[str]:
         """
         if we define an internal function for this argument, return it
         """

pandas/core/groupby/base.py

@@ -98,6 +98,102 @@ def _gotitem(self, key, ndim, subset=None):
 
 dataframe_apply_whitelist = common_apply_whitelist | frozenset(["dtypes", "corrwith"])
 
-cython_transforms = frozenset(["cumprod", "cumsum", "shift", "cummin", "cummax"])
+# cythonized transformations or canned "agg+broadcast", which do not
+# require postprocessing of the result by transform.
+cythonized_kernels = frozenset(["cumprod", "cumsum", "shift", "cummin", "cummax"])
 
 cython_cast_blacklist = frozenset(["rank", "count", "size", "idxmin", "idxmax"])
+
+# List of aggregation/reduction functions.
+# These map each group to a single numeric value
+reduction_kernels = frozenset(
+    [
+        "all",
+        "any",
+        "count",
+        "first",
+        "idxmax",
+        "idxmin",
+        "last",
+        "mad",
+        "max",
+        "mean",
+        "median",
+        "min",
+        "ngroup",
+        "nth",
+        "nunique",
+        "prod",
+        # as long as `quantile`'s signature accepts only
+        # a single quantile value, it's a reduction.
+        "quantile",
+        "sem",
+        "size",
+        "skew",
+        "std",
+        "sum",
+        "var",
+    ]
+)
+
+# List of transformation functions.
+# a transformation is a function that, for each group,
+# produces a result that has the same shape as the group.
+transformation_kernels = frozenset(
+    [
+        "backfill",
+        "bfill",
+        "corrwith",
+        "cumcount",
+        "cummax",
+        "cummin",
+        "cumprod",
+        "cumsum",
+        "diff",
+        "ffill",
+        "fillna",
+        "pad",
+        "pct_change",
+        "rank",
+        "shift",
+        "tshift",
+    ]
+)
+
+# these are all the public methods on Grouper which don't belong
+# in either of the above lists
+groupby_other_methods = frozenset(
+    [
+        "agg",
+        "aggregate",
+        "apply",
+        "boxplot",
+        # corr and cov return ngroups*ncolumns rows, so they
+        # are neither a transformation nor a reduction
+        "corr",
+        "cov",
+        "describe",
+        "dtypes",
+        "expanding",
+        "filter",
+        "get_group",
+        "groups",
+        "head",
+        "hist",
+        "indices",
+        "ndim",
+        "ngroups",
+        "ohlc",
+        "pipe",
+        "plot",
+        "resample",
+        "rolling",
+        "tail",
+        "take",
+        "transform",
+    ]
+)
+# Valid values  of `name` for `groupby.transform(name)`
+# NOTE: do NOT edit this directly. New additions should be inserted
+# into the appropriate list above.
+transform_kernel_whitelist = reduction_kernels | transformation_kernels

pandas/core/groupby/generic.py

@@ -572,13 +572,19 @@ def _transform_general(self, func, *args, **kwargs):
     def transform(self, func, *args, **kwargs):
 
         # optimized transforms
-        func = self._is_cython_func(func) or func
+        func = self._get_cython_func(func) or func
+
         if isinstance(func, str):
-            if func in base.cython_transforms:
-                # cythonized transform
+            if not (func in base.transform_kernel_whitelist):
+                msg = "'{func}' is not a valid function name for transform(name)"
+                raise ValueError(msg.format(func=func))
+            if func in base.cythonized_kernels:
+                # cythonized transformation or canned "reduction+broadcast"
                 return getattr(self, func)(*args, **kwargs)
             else:
-                # cythonized aggregation and merge
+                # If func is a reduction, we need to broadcast the
+                # result to the whole group. Compute func result
+                # and deal with possible broadcasting below.
                 result = getattr(self, func)(*args, **kwargs)
         else:
             return self._transform_general(func, *args, **kwargs)
@@ -589,7 +595,7 @@ def transform(self, func, *args, **kwargs):
 
         obj = self._obj_with_exclusions
 
-        # nuiscance columns
+        # nuisance columns
         if not result.columns.equals(obj.columns):
             return self._transform_general(func, *args, **kwargs)
 
@@ -852,7 +858,7 @@ def aggregate(self, func_or_funcs=None, *args, **kwargs):
             if relabeling:
                 ret.columns = columns
         else:
-            cyfunc = self._is_cython_func(func_or_funcs)
+            cyfunc = self._get_cython_func(func_or_funcs)
             if cyfunc and not args and not kwargs:
                 return getattr(self, cyfunc)()
 
@@ -1004,15 +1010,19 @@ def _aggregate_named(self, func, *args, **kwargs):
     @Substitution(klass="Series", selected="A.")
     @Appender(_transform_template)
     def transform(self, func, *args, **kwargs):
-        func = self._is_cython_func(func) or func
+        func = self._get_cython_func(func) or func
 
-        # if string function
         if isinstance(func, str):
-            if func in base.cython_transforms:
-                # cythonized transform
+            if not (func in base.transform_kernel_whitelist):
+                msg = "'{func}' is not a valid function name for transform(name)"
+                raise ValueError(msg.format(func=func))
+            if func in base.cythonized_kernels:
+                # cythonized transform or canned "agg+broadcast"
                 return getattr(self, func)(*args, **kwargs)
             else:
-                # cythonized aggregation and merge
+                # If func is a reduction, we need to broadcast the
+                # result to the whole group. Compute func result
+                # and deal with possible broadcasting below.
                 return self._transform_fast(
                     lambda: getattr(self, func)(*args, **kwargs), func
                 )

pandas/core/groupby/groupby.py

@@ -261,7 +261,7 @@ class providing the base-class of operations.
 
 * f must return a value that either has the same shape as the input
   subframe or can be broadcast to the shape of the input subframe.
-  For example, f returns a scalar it will be broadcast to have the
+  For example, if `f` returns a scalar it will be broadcast to have the
   same shape as the input subframe.
 * if this is a DataFrame, f must support application column-by-column
   in the subframe. If f also supports application to the entire subframe,

pandas/core/resample.py

@@ -1046,7 +1046,7 @@ def _downsample(self, how, **kwargs):
         **kwargs : kw args passed to how function
         """
         self._set_binner()
-        how = self._is_cython_func(how) or how
+        how = self._get_cython_func(how) or how
         ax = self.ax
         obj = self._selected_obj
 
@@ -1194,7 +1194,7 @@ def _downsample(self, how, **kwargs):
         if self.kind == "timestamp":
             return super()._downsample(how, **kwargs)
 
-        how = self._is_cython_func(how) or how
+        how = self._get_cython_func(how) or how
         ax = self.ax
 
         if is_subperiod(ax.freq, self.freq):

pandas/tests/groupby/conftest.py

@@ -2,6 +2,7 @@
 import pytest
 
 from pandas import DataFrame, MultiIndex
+from pandas.core.groupby.base import reduction_kernels
 from pandas.util import testing as tm
 
 
@@ -102,3 +103,10 @@ def three_group():
             "F": np.random.randn(11),
         }
     )
+
+
+@pytest.fixture(params=sorted(reduction_kernels))
+def reduction_func(request):
+    """yields the string names of all groupby reduction functions, one at a time.
+    """
+    return request.param

pandas/tests/groupby/test_transform.py

@@ -1003,6 +1003,55 @@ def test_ffill_not_in_axis(func, key, val):
     assert_frame_equal(result, expected)
 
 
+def test_transform_invalid_name_raises():
+    # GH#27486
+    df = DataFrame(dict(a=[0, 1, 1, 2]))
+    g = df.groupby(["a", "b", "b", "c"])
+    with pytest.raises(ValueError, match="not a valid function name"):
+        g.transform("some_arbitrary_name")
+
+    # method exists on the object, but is not a valid transformation/agg
+    assert hasattr(g, "aggregate")  # make sure the method exists
+    with pytest.raises(ValueError, match="not a valid function name"):
+        g.transform("aggregate")
+
+    # Test SeriesGroupBy
+    g = df["a"].groupby(["a", "b", "b", "c"])
+    with pytest.raises(ValueError, match="not a valid function name"):
+        g.transform("some_arbitrary_name")
+
+
+@pytest.mark.parametrize(
+    "obj",
+    [
+        DataFrame(
+            dict(a=[0, 0, 0, 1, 1, 1], b=range(6)), index=["A", "B", "C", "D", "E", "F"]
+        ),
+        Series([0, 0, 0, 1, 1, 1], index=["A", "B", "C", "D", "E", "F"]),
+    ],
+)
+def test_transform_agg_by_name(reduction_func, obj):
+    func = reduction_func
+    g = obj.groupby(np.repeat([0, 1], 3))
+
+    if func == "ngroup":  # GH#27468
+        pytest.xfail("TODO: g.transform('ngroup') doesn't work")
+    if func == "size":  # GH#27469
+        pytest.xfail("TODO: g.transform('size') doesn't work")
+
+    args = {"nth": [0], "quantile": [0.5]}.get(func, [])
+
+    result = g.transform(func, *args)
+
+    # this is the *definition* of a transformation
+    tm.assert_index_equal(result.index, obj.index)
+    if hasattr(obj, "columns"):
+        tm.assert_index_equal(result.columns, obj.columns)
+
+    # verify that values were broadcasted across each group
+    assert len(set(DataFrame(result).iloc[-3:, -1])) == 1
+
+
 def test_transform_lambda_with_datetimetz():
     # GH 27496
     df = DataFrame(

pandas/tests/groupby/test_whitelist.py

@@ -9,6 +9,11 @@
 import pytest
 
 from pandas import DataFrame, Index, MultiIndex, Series, date_range
+from pandas.core.groupby.base import (
+    groupby_other_methods,
+    reduction_kernels,
+    transformation_kernels,
+)
 from pandas.util import testing as tm
 
 AGG_FUNCTIONS = [
@@ -376,3 +381,49 @@ def test_groupby_selection_with_methods(df):
     tm.assert_frame_equal(
         g.filter(lambda x: len(x) == 3), g_exp.filter(lambda x: len(x) == 3)
     )
+
+
+def test_all_methods_categorized(mframe):
+    grp = mframe.groupby(mframe.iloc[:, 0])
+    names = {_ for _ in dir(grp) if not _.startswith("_")} - set(mframe.columns)
+    new_names = set(names)
+    new_names -= reduction_kernels
+    new_names -= transformation_kernels
+    new_names -= groupby_other_methods
+
+    assert not (reduction_kernels & transformation_kernels)
+    assert not (reduction_kernels & groupby_other_methods)
+    assert not (transformation_kernels & groupby_other_methods)
+
+    # new public method?
+    if new_names:
+        msg = """
+There are uncatgeorized methods defined on the Grouper class:
+{names}.
+
+Was a new method recently added?
+
+Every public method On Grouper must appear in exactly one the
+following three lists defined in pandas.core.groupby.base:
+- `reduction_kernels`
+- `transformation_kernels`
+- `groupby_other_methods`
+see the comments in pandas/core/groupby/base.py for guidance on
+how to fix this test.
+        """
+        raise AssertionError(msg.format(names=names))
+
+    # removed a public method?
+    all_categorized = reduction_kernels | transformation_kernels | groupby_other_methods
+    print(names)
+    print(all_categorized)
+    if not (names == all_categorized):
+        msg = """
+Some methods which are supposed to be on the Grouper class
+are missing:
+{names}.
+
+They're still defined in one of the lists that live in pandas/core/groupby/base.py.
+If you removed a method, you should update them
+"""
+        raise AssertionError(msg.format(names=all_categorized - names))