TST/REF: finish collecting sample tests

pandas/tests/generic/methods/test_sample.py

@@ -0,0 +1,309 @@
+import numpy as np
+import pytest
+
+from pandas.compat.numpy import np_version_under1p17
+
+from pandas import DataFrame, Series
+import pandas._testing as tm
+import pandas.core.common as com
+
+
+class TestSample:
+    @pytest.fixture(params=[Series, DataFrame])
+    def obj(self, request):
+        klass = request.param
+        if klass is Series:
+            arr = np.random.randn(10)
+        else:
+            arr = np.random.randn(10, 10)
+        return klass(arr, dtype=None)
+
+    @pytest.mark.parametrize("test", list(range(10)))
+    def test_sample(self, test, obj):
+        # Fixes issue: 2419
+        # Check behavior of random_state argument
+        # Check for stability when receives seed or random state -- run 10
+        # times.
+
+        seed = np.random.randint(0, 100)
+        tm.assert_equal(
+            obj.sample(n=4, random_state=seed), obj.sample(n=4, random_state=seed)
+        )
+
+        tm.assert_equal(
+            obj.sample(frac=0.7, random_state=seed),
+            obj.sample(frac=0.7, random_state=seed),
+        )
+
+        tm.assert_equal(
+            obj.sample(n=4, random_state=np.random.RandomState(test)),
+            obj.sample(n=4, random_state=np.random.RandomState(test)),
+        )
+
+        tm.assert_equal(
+            obj.sample(frac=0.7, random_state=np.random.RandomState(test)),
+            obj.sample(frac=0.7, random_state=np.random.RandomState(test)),
+        )
+
+        tm.assert_equal(
+            obj.sample(frac=2, replace=True, random_state=np.random.RandomState(test)),
+            obj.sample(frac=2, replace=True, random_state=np.random.RandomState(test)),
+        )
+
+        os1, os2 = [], []
+        for _ in range(2):
+            np.random.seed(test)
+            os1.append(obj.sample(n=4))
+            os2.append(obj.sample(frac=0.7))
+        tm.assert_equal(*os1)
+        tm.assert_equal(*os2)
+
+    def test_sample_lengths(self, obj):
+        # Check lengths are right
+        assert len(obj.sample(n=4) == 4)
+        assert len(obj.sample(frac=0.34) == 3)
+        assert len(obj.sample(frac=0.36) == 4)
+
+    def test_sample_invalid_random_state(self, obj):
+        # Check for error when random_state argument invalid.
+        with pytest.raises(ValueError):
+            obj.sample(random_state="astring!")
+
+    def test_sample_wont_accept_n_and_frac(self, obj):
+        # Giving both frac and N throws error
+        with pytest.raises(ValueError):
+            obj.sample(n=3, frac=0.3)
+
+    def test_sample_requires_positive_n_frac(self, obj):
+        with pytest.raises(ValueError):
+            obj.sample(n=-3)
+        with pytest.raises(ValueError):
+            obj.sample(frac=-0.3)
+
+    def test_sample_requires_integer_n(self, obj):
+        # Make sure float values of `n` give error
+        with pytest.raises(ValueError):
+            obj.sample(n=3.2)
+
+    def test_sample_invalid_weight_lengths(self, obj):
+        # Weight length must be right
+        with pytest.raises(ValueError):
+            obj.sample(n=3, weights=[0, 1])
+
+        with pytest.raises(ValueError):
+            bad_weights = [0.5] * 11
+            obj.sample(n=3, weights=bad_weights)
+
+        with pytest.raises(ValueError):
+            bad_weight_series = Series([0, 0, 0.2])
+            obj.sample(n=4, weights=bad_weight_series)
+
+    def test_sample_negative_weights(self, obj):
+        # Check won't accept negative weights
+        with pytest.raises(ValueError):
+            bad_weights = [-0.1] * 10
+            obj.sample(n=3, weights=bad_weights)
+
+    def test_sample_inf_weights(self, obj):
+        # Check inf and -inf throw errors:
+
+        with pytest.raises(ValueError):
+            weights_with_inf = [0.1] * 10
+            weights_with_inf[0] = np.inf
+            obj.sample(n=3, weights=weights_with_inf)
+
+        with pytest.raises(ValueError):
+            weights_with_ninf = [0.1] * 10
+            weights_with_ninf[0] = -np.inf
+            obj.sample(n=3, weights=weights_with_ninf)
+
+    def test_sample_zero_weights(self, obj):
+        # All zeros raises errors
+
+        zero_weights = [0] * 10
+        with pytest.raises(ValueError):
+            obj.sample(n=3, weights=zero_weights)
+
+    def test_sample_missing_weights(self, obj):
+        # All missing weights
+
+        nan_weights = [np.nan] * 10
+        with pytest.raises(ValueError):
+            obj.sample(n=3, weights=nan_weights)
+
+    def test_sample_none_weights(self, obj):
+        # Check None are also replaced by zeros.
+        weights_with_None = [None] * 10
+        weights_with_None[5] = 0.5
+        tm.assert_equal(
+            obj.sample(n=1, axis=0, weights=weights_with_None), obj.iloc[5:6]
+        )
+
+    @pytest.mark.parametrize(
+        "func_str,arg",
+        [
+            ("np.array", [2, 3, 1, 0]),
+            pytest.param(
+                "np.random.MT19937",
+                3,
+                marks=pytest.mark.skipif(np_version_under1p17, reason="NumPy<1.17"),
+            ),
+            pytest.param(
+                "np.random.PCG64",
+                11,
+                marks=pytest.mark.skipif(np_version_under1p17, reason="NumPy<1.17"),
+            ),
+        ],
+    )
+    @pytest.mark.parametrize("klass", [Series, DataFrame])
+    def test_sample_random_state(self, func_str, arg, klass):
+        # GH#32503
+        obj = DataFrame({"col1": range(10, 20), "col2": range(20, 30)})
+        if klass is Series:
+            obj = obj["col1"]
+        result = obj.sample(n=3, random_state=eval(func_str)(arg))
+        expected = obj.sample(n=3, random_state=com.random_state(eval(func_str)(arg)))
+        tm.assert_equal(result, expected)
+
+    @pytest.mark.parametrize("klass", [Series, DataFrame])
+    def test_sample_upsampling_without_replacement(self, klass):
+        # GH#27451
+
+        obj = DataFrame({"A": list("abc")})
+        if klass is Series:
+            obj = obj["A"]
+
+        msg = (
+            "Replace has to be set to `True` when "
+            "upsampling the population `frac` > 1."
+        )
+        with pytest.raises(ValueError, match=msg):
+            obj.sample(frac=2, replace=False)
+
+
+class TestSampleDataFrame:
+    # Tests which are relevant only for DataFrame, so these are
+    #  as fully parametrized as they can get.
+
+    def test_sample(self):
+        # GH#2419
+        # additional specific object based tests
+
+        # A few dataframe test with degenerate weights.
+        easy_weight_list = [0] * 10
+        easy_weight_list[5] = 1
+
+        df = DataFrame(
+            {
+                "col1": range(10, 20),
+                "col2": range(20, 30),
+                "colString": ["a"] * 10,
+                "easyweights": easy_weight_list,
+            }
+        )
+        sample1 = df.sample(n=1, weights="easyweights")
+        tm.assert_frame_equal(sample1, df.iloc[5:6])
+
+        # Ensure proper error if string given as weight for Series or
+        # DataFrame with axis = 1.
+        ser = Series(range(10))
+        with pytest.raises(ValueError):
+            ser.sample(n=3, weights="weight_column")
+
+        with pytest.raises(ValueError):
+            df.sample(n=1, weights="weight_column", axis=1)
+
+        # Check weighting key error
+        with pytest.raises(
+            KeyError, match="'String passed to weights not a valid column'"
+        ):
+            df.sample(n=3, weights="not_a_real_column_name")
+
+        # Check that re-normalizes weights that don't sum to one.
+        weights_less_than_1 = [0] * 10
+        weights_less_than_1[0] = 0.5
+        tm.assert_frame_equal(df.sample(n=1, weights=weights_less_than_1), df.iloc[:1])
+
+        ###
+        # Test axis argument
+        ###
+
+        # Test axis argument
+        df = DataFrame({"col1": range(10), "col2": ["a"] * 10})
+        second_column_weight = [0, 1]
+        tm.assert_frame_equal(
+            df.sample(n=1, axis=1, weights=second_column_weight), df[["col2"]]
+        )
+
+        # Different axis arg types
+        tm.assert_frame_equal(
+            df.sample(n=1, axis="columns", weights=second_column_weight), df[["col2"]]
+        )
+
+        weight = [0] * 10
+        weight[5] = 0.5
+        tm.assert_frame_equal(df.sample(n=1, axis="rows", weights=weight), df.iloc[5:6])
+        tm.assert_frame_equal(
+            df.sample(n=1, axis="index", weights=weight), df.iloc[5:6]
+        )
+
+        # Check out of range axis values
+        with pytest.raises(ValueError):
+            df.sample(n=1, axis=2)
+
+        with pytest.raises(ValueError):
+            df.sample(n=1, axis="not_a_name")
+
+        with pytest.raises(ValueError):
+            ser = Series(range(10))
+            ser.sample(n=1, axis=1)
+
+        # Test weight length compared to correct axis
+        with pytest.raises(ValueError):
+            df.sample(n=1, axis=1, weights=[0.5] * 10)
+
+    def test_sample_axis1(self):
+        # Check weights with axis = 1
+        easy_weight_list = [0] * 3
+        easy_weight_list[2] = 1
+
+        df = DataFrame(
+            {"col1": range(10, 20), "col2": range(20, 30), "colString": ["a"] * 10}
+        )
+        sample1 = df.sample(n=1, axis=1, weights=easy_weight_list)
+        tm.assert_frame_equal(sample1, df[["colString"]])
+
+        # Test default axes
+        tm.assert_frame_equal(
+            df.sample(n=3, random_state=42), df.sample(n=3, axis=0, random_state=42)
+        )
+
+    def test_sample_aligns_weights_with_frame(self):
+
+        # Test that function aligns weights with frame
+        df = DataFrame({"col1": [5, 6, 7], "col2": ["a", "b", "c"]}, index=[9, 5, 3])
+        ser = Series([1, 0, 0], index=[3, 5, 9])
+        tm.assert_frame_equal(df.loc[[3]], df.sample(1, weights=ser))
+
+        # Weights have index values to be dropped because not in
+        # sampled DataFrame
+        ser2 = Series([0.001, 0, 10000], index=[3, 5, 10])
+        tm.assert_frame_equal(df.loc[[3]], df.sample(1, weights=ser2))
+
+        # Weights have empty values to be filed with zeros
+        ser3 = Series([0.01, 0], index=[3, 5])
+        tm.assert_frame_equal(df.loc[[3]], df.sample(1, weights=ser3))
+
+        # No overlap in weight and sampled DataFrame indices
+        ser4 = Series([1, 0], index=[1, 2])
+        with pytest.raises(ValueError):
+            df.sample(1, weights=ser4)
+
+    def test_sample_is_copy(self):
+        # GH#27357, GH#30784: ensure the result of sample is an actual copy and
+        # doesn't track the parent dataframe / doesn't give SettingWithCopy warnings
+        df = DataFrame(np.random.randn(10, 3), columns=["a", "b", "c"])
+        df2 = df.sample(3)
+
+        with tm.assert_produces_warning(None):
+            df2["d"] = 1