Removed generate_bins_generic (pandas-dev#29192)

Author: WillAyd

pandas/core/groupby/ops.py

@@ -53,59 +53,6 @@
 )
 
 
-def generate_bins_generic(values, binner, closed):
-    """
-    Generate bin edge offsets and bin labels for one array using another array
-    which has bin edge values. Both arrays must be sorted.
-
-    Parameters
-    ----------
-    values : array of values
-    binner : a comparable array of values representing bins into which to bin
-        the first array. Note, 'values' end-points must fall within 'binner'
-        end-points.
-    closed : which end of bin is closed; left (default), right
-
-    Returns
-    -------
-    bins : array of offsets (into 'values' argument) of bins.
-        Zero and last edge are excluded in result, so for instance the first
-        bin is values[0:bin[0]] and the last is values[bin[-1]:]
-    """
-    lenidx = len(values)
-    lenbin = len(binner)
-
-    if lenidx <= 0 or lenbin <= 0:
-        raise ValueError("Invalid length for values or for binner")
-
-    # check binner fits data
-    if values[0] < binner[0]:
-        raise ValueError("Values falls before first bin")
-
-    if values[lenidx - 1] > binner[lenbin - 1]:
-        raise ValueError("Values falls after last bin")
-
-    bins = np.empty(lenbin - 1, dtype=np.int64)
-
-    j = 0  # index into values
-    bc = 0  # bin count
-
-    # linear scan, presume nothing about values/binner except that it fits ok
-    for i in range(0, lenbin - 1):
-        r_bin = binner[i + 1]
-
-        # count values in current bin, advance to next bin
-        while j < lenidx and (
-            values[j] < r_bin or (closed == "right" and values[j] == r_bin)
-        ):
-            j += 1
-
-        bins[bc] = j
-        bc += 1
-
-    return bins
-
-
 class BaseGrouper:
     """
     This is an internal Grouper class, which actually holds

pandas/tests/groupby/test_bin_groupby.py

@@ -6,9 +6,7 @@
 from pandas.core.dtypes.common import ensure_int64
 
 from pandas import Index, Series, isna
-from pandas.core.groupby.ops import generate_bins_generic
 import pandas.util.testing as tm
-from pandas.util.testing import assert_almost_equal
 
 
 def test_series_grouper():
@@ -21,10 +19,10 @@ def test_series_grouper():
     result, counts = grouper.get_result()
 
     expected = np.array([obj[3:6].mean(), obj[6:].mean()])
-    assert_almost_equal(result, expected)
+    tm.assert_almost_equal(result, expected)
 
     exp_counts = np.array([3, 4], dtype=np.int64)
-    assert_almost_equal(counts, exp_counts)
+    tm.assert_almost_equal(counts, exp_counts)
 
 
 def test_series_bin_grouper():
@@ -37,48 +35,37 @@ def test_series_bin_grouper():
     result, counts = grouper.get_result()
 
     expected = np.array([obj[:3].mean(), obj[3:6].mean(), obj[6:].mean()])
-    assert_almost_equal(result, expected)
+    tm.assert_almost_equal(result, expected)
 
     exp_counts = np.array([3, 3, 4], dtype=np.int64)
-    assert_almost_equal(counts, exp_counts)
-
-
-class TestBinGroupers:
-    def setup_method(self, method):
-        self.obj = np.random.randn(10, 1)
-        self.labels = np.array([0, 0, 0, 1, 1, 1, 2, 2, 2, 2], dtype=np.int64)
-        self.bins = np.array([3, 6], dtype=np.int64)
-
-    def test_generate_bins(self):
-        values = np.array([1, 2, 3, 4, 5, 6], dtype=np.int64)
-        binner = np.array([0, 3, 6, 9], dtype=np.int64)
-
-        for func in [lib.generate_bins_dt64, generate_bins_generic]:
-            bins = func(values, binner, closed="left")
-            assert (bins == np.array([2, 5, 6])).all()
-
-            bins = func(values, binner, closed="right")
-            assert (bins == np.array([3, 6, 6])).all()
-
-        for func in [lib.generate_bins_dt64, generate_bins_generic]:
-            values = np.array([1, 2, 3, 4, 5, 6], dtype=np.int64)
-            binner = np.array([0, 3, 6], dtype=np.int64)
-
-            bins = func(values, binner, closed="right")
-            assert (bins == np.array([3, 6])).all()
-
-        msg = "Invalid length for values or for binner"
-        with pytest.raises(ValueError, match=msg):
-            generate_bins_generic(values, [], "right")
-        with pytest.raises(ValueError, match=msg):
-            generate_bins_generic(values[:0], binner, "right")
-
-        msg = "Values falls before first bin"
-        with pytest.raises(ValueError, match=msg):
-            generate_bins_generic(values, [4], "right")
-        msg = "Values falls after last bin"
-        with pytest.raises(ValueError, match=msg):
-            generate_bins_generic(values, [-3, -1], "right")
+    tm.assert_almost_equal(counts, exp_counts)
+
+
+@pytest.mark.parametrize(
+    "binner,closed,expected",
+    [
+        (
+            np.array([0, 3, 6, 9], dtype=np.int64),
+            "left",
+            np.array([2, 5, 6], dtype=np.int64),
+        ),
+        (
+            np.array([0, 3, 6, 9], dtype=np.int64),
+            "right",
+            np.array([3, 6, 6], dtype=np.int64),
+        ),
+        (np.array([0, 3, 6], dtype=np.int64), "left", np.array([2, 5], dtype=np.int64)),
+        (
+            np.array([0, 3, 6], dtype=np.int64),
+            "right",
+            np.array([3, 6], dtype=np.int64),
+        ),
+    ],
+)
+def test_generate_bins(binner, closed, expected):
+    values = np.array([1, 2, 3, 4, 5, 6], dtype=np.int64)
+    result = lib.generate_bins_dt64(values, binner, closed=closed)
+    tm.assert_numpy_array_equal(result, expected)
 
 
 def test_group_ohlc():
@@ -100,13 +87,13 @@ def _ohlc(group):
 
         expected = np.array([_ohlc(obj[:6]), _ohlc(obj[6:12]), _ohlc(obj[12:])])
 
-        assert_almost_equal(out, expected)
+        tm.assert_almost_equal(out, expected)
         tm.assert_numpy_array_equal(counts, np.array([6, 6, 8], dtype=np.int64))
 
         obj[:6] = np.nan
         func(out, counts, obj[:, None], labels)
         expected[0] = np.nan
-        assert_almost_equal(out, expected)
+        tm.assert_almost_equal(out, expected)
 
     _check("float32")
     _check("float64")
@@ -121,29 +108,29 @@ def test_int_index(self):
         arr = np.random.randn(100, 4)
         result = libreduction.compute_reduction(arr, np.sum, labels=Index(np.arange(4)))
         expected = arr.sum(0)
-        assert_almost_equal(result, expected)
+        tm.assert_almost_equal(result, expected)
 
         result = libreduction.compute_reduction(
             arr, np.sum, axis=1, labels=Index(np.arange(100))
         )
         expected = arr.sum(1)
-        assert_almost_equal(result, expected)
+        tm.assert_almost_equal(result, expected)
 
         dummy = Series(0.0, index=np.arange(100))
         result = libreduction.compute_reduction(
             arr, np.sum, dummy=dummy, labels=Index(np.arange(4))
         )
         expected = arr.sum(0)
-        assert_almost_equal(result, expected)
+        tm.assert_almost_equal(result, expected)
 
         dummy = Series(0.0, index=np.arange(4))
         result = libreduction.compute_reduction(
             arr, np.sum, axis=1, dummy=dummy, labels=Index(np.arange(100))
         )
         expected = arr.sum(1)
-        assert_almost_equal(result, expected)
+        tm.assert_almost_equal(result, expected)
 
         result = libreduction.compute_reduction(
             arr, np.sum, axis=1, dummy=dummy, labels=Index(np.arange(100))
         )
-        assert_almost_equal(result, expected)
+        tm.assert_almost_equal(result, expected)