REF: method-specific tests for cov, corr, corrwith, count, round (pandas-dev#30437)

Author: jbrockmendel

pandas/tests/frame/methods/test_count.py

@@ -0,0 +1,36 @@
+from pandas import DataFrame, Series
+import pandas.util.testing as tm
+
+
+class TestDataFrameCount:
+    def test_count(self):
+        # corner case
+        frame = DataFrame()
+        ct1 = frame.count(1)
+        assert isinstance(ct1, Series)
+
+        ct2 = frame.count(0)
+        assert isinstance(ct2, Series)
+
+        # GH#423
+        df = DataFrame(index=range(10))
+        result = df.count(1)
+        expected = Series(0, index=df.index)
+        tm.assert_series_equal(result, expected)
+
+        df = DataFrame(columns=range(10))
+        result = df.count(0)
+        expected = Series(0, index=df.columns)
+        tm.assert_series_equal(result, expected)
+
+        df = DataFrame()
+        result = df.count()
+        expected = Series(0, index=[])
+        tm.assert_series_equal(result, expected)
+
+    def test_count_objects(self, float_string_frame):
+        dm = DataFrame(float_string_frame._series)
+        df = DataFrame(float_string_frame._series)
+
+        tm.assert_series_equal(dm.count(), df.count())
+        tm.assert_series_equal(dm.count(1), df.count(1))

pandas/tests/frame/methods/test_cov_corr.py

@@ -0,0 +1,289 @@
+import warnings
+
+import numpy as np
+import pytest
+
+import pandas.util._test_decorators as td
+
+import pandas as pd
+from pandas import DataFrame, Series, isna
+import pandas.util.testing as tm
+
+
+class TestDataFrameCov:
+    def test_cov(self, float_frame, float_string_frame):
+        # min_periods no NAs (corner case)
+        expected = float_frame.cov()
+        result = float_frame.cov(min_periods=len(float_frame))
+
+        tm.assert_frame_equal(expected, result)
+
+        result = float_frame.cov(min_periods=len(float_frame) + 1)
+        assert isna(result.values).all()
+
+        # with NAs
+        frame = float_frame.copy()
+        frame["A"][:5] = np.nan
+        frame["B"][5:10] = np.nan
+        result = float_frame.cov(min_periods=len(float_frame) - 8)
+        expected = float_frame.cov()
+        expected.loc["A", "B"] = np.nan
+        expected.loc["B", "A"] = np.nan
+
+        # regular
+        float_frame["A"][:5] = np.nan
+        float_frame["B"][:10] = np.nan
+        cov = float_frame.cov()
+
+        tm.assert_almost_equal(cov["A"]["C"], float_frame["A"].cov(float_frame["C"]))
+
+        # exclude non-numeric types
+        result = float_string_frame.cov()
+        expected = float_string_frame.loc[:, ["A", "B", "C", "D"]].cov()
+        tm.assert_frame_equal(result, expected)
+
+        # Single column frame
+        df = DataFrame(np.linspace(0.0, 1.0, 10))
+        result = df.cov()
+        expected = DataFrame(
+            np.cov(df.values.T).reshape((1, 1)), index=df.columns, columns=df.columns
+        )
+        tm.assert_frame_equal(result, expected)
+        df.loc[0] = np.nan
+        result = df.cov()
+        expected = DataFrame(
+            np.cov(df.values[1:].T).reshape((1, 1)),
+            index=df.columns,
+            columns=df.columns,
+        )
+        tm.assert_frame_equal(result, expected)
+
+
+class TestDataFrameCorr:
+    # DataFrame.corr(), as opposed to DataFrame.corrwith
+
+    @staticmethod
+    def _check_method(frame, method="pearson"):
+        correls = frame.corr(method=method)
+        expected = frame["A"].corr(frame["C"], method=method)
+        tm.assert_almost_equal(correls["A"]["C"], expected)
+
+    @td.skip_if_no_scipy
+    def test_corr_pearson(self, float_frame):
+        float_frame["A"][:5] = np.nan
+        float_frame["B"][5:10] = np.nan
+
+        self._check_method(float_frame, "pearson")
+
+    @td.skip_if_no_scipy
+    def test_corr_kendall(self, float_frame):
+        float_frame["A"][:5] = np.nan
+        float_frame["B"][5:10] = np.nan
+
+        self._check_method(float_frame, "kendall")
+
+    @td.skip_if_no_scipy
+    def test_corr_spearman(self, float_frame):
+        float_frame["A"][:5] = np.nan
+        float_frame["B"][5:10] = np.nan
+
+        self._check_method(float_frame, "spearman")
+
+    # ---------------------------------------------------------------------
+
+    @td.skip_if_no_scipy
+    def test_corr_non_numeric(self, float_frame, float_string_frame):
+        float_frame["A"][:5] = np.nan
+        float_frame["B"][5:10] = np.nan
+
+        # exclude non-numeric types
+        result = float_string_frame.corr()
+        expected = float_string_frame.loc[:, ["A", "B", "C", "D"]].corr()
+        tm.assert_frame_equal(result, expected)
+
+    @td.skip_if_no_scipy
+    @pytest.mark.parametrize("meth", ["pearson", "kendall", "spearman"])
+    def test_corr_nooverlap(self, meth):
+        # nothing in common
+        df = DataFrame(
+            {
+                "A": [1, 1.5, 1, np.nan, np.nan, np.nan],
+                "B": [np.nan, np.nan, np.nan, 1, 1.5, 1],
+                "C": [np.nan, np.nan, np.nan, np.nan, np.nan, np.nan],
+            }
+        )
+        rs = df.corr(meth)
+        assert isna(rs.loc["A", "B"])
+        assert isna(rs.loc["B", "A"])
+        assert rs.loc["A", "A"] == 1
+        assert rs.loc["B", "B"] == 1
+        assert isna(rs.loc["C", "C"])
+
+    @td.skip_if_no_scipy
+    @pytest.mark.parametrize("meth", ["pearson", "spearman"])
+    def test_corr_constant(self, meth):
+        # constant --> all NA
+
+        df = DataFrame(
+            {
+                "A": [1, 1, 1, np.nan, np.nan, np.nan],
+                "B": [np.nan, np.nan, np.nan, 1, 1, 1],
+            }
+        )
+        rs = df.corr(meth)
+        assert isna(rs.values).all()
+
+    @td.skip_if_no_scipy
+    def test_corr_int_and_boolean(self):
+        # when dtypes of pandas series are different
+        # then ndarray will have dtype=object,
+        # so it need to be properly handled
+        df = DataFrame({"a": [True, False], "b": [1, 0]})
+
+        expected = DataFrame(np.ones((2, 2)), index=["a", "b"], columns=["a", "b"])
+        for meth in ["pearson", "kendall", "spearman"]:
+
+            with warnings.catch_warnings(record=True):
+                warnings.simplefilter("ignore", RuntimeWarning)
+                result = df.corr(meth)
+            tm.assert_frame_equal(result, expected)
+
+    def test_corr_cov_independent_index_column(self):
+        # GH#14617
+        df = pd.DataFrame(np.random.randn(4 * 10).reshape(10, 4), columns=list("abcd"))
+        for method in ["cov", "corr"]:
+            result = getattr(df, method)()
+            assert result.index is not result.columns
+            assert result.index.equals(result.columns)
+
+    def test_corr_invalid_method(self):
+        # GH#22298
+        df = pd.DataFrame(np.random.normal(size=(10, 2)))
+        msg = "method must be either 'pearson', 'spearman', 'kendall', or a callable, "
+        with pytest.raises(ValueError, match=msg):
+            df.corr(method="____")
+
+    def test_corr_int(self):
+        # dtypes other than float64 GH#1761
+        df3 = DataFrame({"a": [1, 2, 3, 4], "b": [1, 2, 3, 4]})
+
+        df3.cov()
+        df3.corr()
+
+
+class TestDataFrameCorrWith:
+    def test_corrwith(self, datetime_frame):
+        a = datetime_frame
+        noise = Series(np.random.randn(len(a)), index=a.index)
+
+        b = datetime_frame.add(noise, axis=0)
+
+        # make sure order does not matter
+        b = b.reindex(columns=b.columns[::-1], index=b.index[::-1][10:])
+        del b["B"]
+
+        colcorr = a.corrwith(b, axis=0)
+        tm.assert_almost_equal(colcorr["A"], a["A"].corr(b["A"]))
+
+        rowcorr = a.corrwith(b, axis=1)
+        tm.assert_series_equal(rowcorr, a.T.corrwith(b.T, axis=0))
+
+        dropped = a.corrwith(b, axis=0, drop=True)
+        tm.assert_almost_equal(dropped["A"], a["A"].corr(b["A"]))
+        assert "B" not in dropped
+
+        dropped = a.corrwith(b, axis=1, drop=True)
+        assert a.index[-1] not in dropped.index
+
+        # non time-series data
+        index = ["a", "b", "c", "d", "e"]
+        columns = ["one", "two", "three", "four"]
+        df1 = DataFrame(np.random.randn(5, 4), index=index, columns=columns)
+        df2 = DataFrame(np.random.randn(4, 4), index=index[:4], columns=columns)
+        correls = df1.corrwith(df2, axis=1)
+        for row in index[:4]:
+            tm.assert_almost_equal(correls[row], df1.loc[row].corr(df2.loc[row]))
+
+    def test_corrwith_with_objects(self):
+        df1 = tm.makeTimeDataFrame()
+        df2 = tm.makeTimeDataFrame()
+        cols = ["A", "B", "C", "D"]
+
+        df1["obj"] = "foo"
+        df2["obj"] = "bar"
+
+        result = df1.corrwith(df2)
+        expected = df1.loc[:, cols].corrwith(df2.loc[:, cols])
+        tm.assert_series_equal(result, expected)
+
+        result = df1.corrwith(df2, axis=1)
+        expected = df1.loc[:, cols].corrwith(df2.loc[:, cols], axis=1)
+        tm.assert_series_equal(result, expected)
+
+    def test_corrwith_series(self, datetime_frame):
+        result = datetime_frame.corrwith(datetime_frame["A"])
+        expected = datetime_frame.apply(datetime_frame["A"].corr)
+
+        tm.assert_series_equal(result, expected)
+
+    def test_corrwith_matches_corrcoef(self):
+        df1 = DataFrame(np.arange(10000), columns=["a"])
+        df2 = DataFrame(np.arange(10000) ** 2, columns=["a"])
+        c1 = df1.corrwith(df2)["a"]
+        c2 = np.corrcoef(df1["a"], df2["a"])[0][1]
+
+        tm.assert_almost_equal(c1, c2)
+        assert c1 < 1
+
+    def test_corrwith_mixed_dtypes(self):
+        # GH#18570
+        df = pd.DataFrame(
+            {"a": [1, 4, 3, 2], "b": [4, 6, 7, 3], "c": ["a", "b", "c", "d"]}
+        )
+        s = pd.Series([0, 6, 7, 3])
+        result = df.corrwith(s)
+        corrs = [df["a"].corr(s), df["b"].corr(s)]
+        expected = pd.Series(data=corrs, index=["a", "b"])
+        tm.assert_series_equal(result, expected)
+
+    def test_corrwith_index_intersection(self):
+        df1 = pd.DataFrame(np.random.random(size=(10, 2)), columns=["a", "b"])
+        df2 = pd.DataFrame(np.random.random(size=(10, 3)), columns=["a", "b", "c"])
+
+        result = df1.corrwith(df2, drop=True).index.sort_values()
+        expected = df1.columns.intersection(df2.columns).sort_values()
+        tm.assert_index_equal(result, expected)
+
+    def test_corrwith_index_union(self):
+        df1 = pd.DataFrame(np.random.random(size=(10, 2)), columns=["a", "b"])
+        df2 = pd.DataFrame(np.random.random(size=(10, 3)), columns=["a", "b", "c"])
+
+        result = df1.corrwith(df2, drop=False).index.sort_values()
+        expected = df1.columns.union(df2.columns).sort_values()
+        tm.assert_index_equal(result, expected)
+
+    def test_corrwith_dup_cols(self):
+        # GH#21925
+        df1 = pd.DataFrame(np.vstack([np.arange(10)] * 3).T)
+        df2 = df1.copy()
+        df2 = pd.concat((df2, df2[0]), axis=1)
+
+        result = df1.corrwith(df2)
+        expected = pd.Series(np.ones(4), index=[0, 0, 1, 2])
+        tm.assert_series_equal(result, expected)
+
+    @td.skip_if_no_scipy
+    def test_corrwith_spearman(self):
+        # GH#21925
+        df = pd.DataFrame(np.random.random(size=(100, 3)))
+        result = df.corrwith(df ** 2, method="spearman")
+        expected = Series(np.ones(len(result)))
+        tm.assert_series_equal(result, expected)
+
+    @td.skip_if_no_scipy
+    def test_corrwith_kendall(self):
+        # GH#21925
+        df = pd.DataFrame(np.random.random(size=(100, 3)))
+        result = df.corrwith(df ** 2, method="kendall")
+        expected = Series(np.ones(len(result)))
+        tm.assert_series_equal(result, expected)