コード例 #1
0
ファイル: test_rank.py プロジェクト: AllenDowney/pandas
    def test_rank(self):
        tm._skip_if_no_scipy()
        from scipy.stats import rankdata

        self.frame['A'][::2] = np.nan
        self.frame['B'][::3] = np.nan
        self.frame['C'][::4] = np.nan
        self.frame['D'][::5] = np.nan

        ranks0 = self.frame.rank()
        ranks1 = self.frame.rank(1)
        mask = np.isnan(self.frame.values)

        fvals = self.frame.fillna(np.inf).values

        exp0 = np.apply_along_axis(rankdata, 0, fvals)
        exp0[mask] = np.nan

        exp1 = np.apply_along_axis(rankdata, 1, fvals)
        exp1[mask] = np.nan

        tm.assert_almost_equal(ranks0.values, exp0)
        tm.assert_almost_equal(ranks1.values, exp1)

        # integers
        df = DataFrame(np.random.randint(0, 5, size=40).reshape((10, 4)))

        result = df.rank()
        exp = df.astype(float).rank()
        tm.assert_frame_equal(result, exp)

        result = df.rank(1)
        exp = df.astype(float).rank(1)
        tm.assert_frame_equal(result, exp)
コード例 #2
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 def test_interp_datetime64(self):
     tm._skip_if_no_scipy()
     df = Series([1, np.nan, 3], index=date_range('1/1/2000', periods=3))
     result = df.interpolate(method='nearest')
     expected = Series([1., 1., 3.],
                       index=date_range('1/1/2000', periods=3))
     assert_series_equal(result, expected)
コード例 #3
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ファイル: test_missing.py プロジェクト: Axik/pandas
    def test_interp_alt_scipy(self):
        tm._skip_if_no_scipy()
        df = DataFrame({'A': [1, 2, np.nan, 4, 5, np.nan, 7],
                        'C': [1, 2, 3, 5, 8, 13, 21]})
        result = df.interpolate(method='barycentric')
        expected = df.copy()
        expected.loc[2, 'A'] = 3
        expected.loc[5, 'A'] = 6
        assert_frame_equal(result, expected)

        result = df.interpolate(method='barycentric', downcast='infer')
        assert_frame_equal(result, expected.astype(np.int64))

        result = df.interpolate(method='krogh')
        expectedk = df.copy()
        expectedk['A'] = expected['A']
        assert_frame_equal(result, expectedk)

        _skip_if_no_pchip()
        import scipy
        result = df.interpolate(method='pchip')
        expected.loc[2, 'A'] = 3

        if LooseVersion(scipy.__version__) >= '0.17.0':
            expected.loc[5, 'A'] = 6.0
        else:
            expected.loc[5, 'A'] = 6.125

        assert_frame_equal(result, expected)
コード例 #4
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ファイル: test_generic.py プロジェクト: jdreaver/pandas
 def test_no_order(self):
     tm._skip_if_no_scipy()
     s = Series([0, 1, np.nan, 3])
     with tm.assertRaises(ValueError):
         s.interpolate(method='polynomial')
     with tm.assertRaises(ValueError):
         s.interpolate(method='spline')
コード例 #5
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ファイル: test_misc.py プロジェクト: cgrin/pandas
    def test_scatter_plot_legacy(self):
        tm._skip_if_no_scipy()

        df = DataFrame(randn(100, 2))

        def scat(**kwds):
            return plotting.scatter_matrix(df, **kwds)

        with tm.assert_produces_warning(UserWarning):
            _check_plot_works(scat)
        with tm.assert_produces_warning(UserWarning):
            _check_plot_works(scat, marker='+')
        with tm.assert_produces_warning(UserWarning):
            _check_plot_works(scat, vmin=0)
        if _ok_for_gaussian_kde('kde'):
            with tm.assert_produces_warning(UserWarning):
                _check_plot_works(scat, diagonal='kde')
        if _ok_for_gaussian_kde('density'):
            with tm.assert_produces_warning(UserWarning):
                _check_plot_works(scat, diagonal='density')
        with tm.assert_produces_warning(UserWarning):
            _check_plot_works(scat, diagonal='hist')
        with tm.assert_produces_warning(UserWarning):
            _check_plot_works(scat, range_padding=.1)

        def scat2(x, y, by=None, ax=None, figsize=None):
            return plotting.scatter_plot(df, x, y, by, ax, figsize=None)

        _check_plot_works(scat2, x=0, y=1)
        grouper = Series(np.repeat([1, 2, 3, 4, 5], 20), df.index)
        with tm.assert_produces_warning(UserWarning):
            _check_plot_works(scat2, x=0, y=1, by=grouper)
コード例 #6
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ファイル: test_generic.py プロジェクト: jdreaver/pandas
    def test_interp_scipy_basic(self):
        tm._skip_if_no_scipy()
        s = Series([1, 3, np.nan, 12, np.nan, 25])
        # slinear
        expected = Series([1., 3., 7.5, 12., 18.5, 25.])
        result = s.interpolate(method='slinear')
        assert_series_equal(result, expected)

        result = s.interpolate(method='slinear', donwcast='infer')
        assert_series_equal(result, expected)
        # nearest
        expected = Series([1, 3, 3, 12, 12, 25])
        result = s.interpolate(method='nearest')
        assert_series_equal(result, expected.astype('float'))

        result = s.interpolate(method='nearest', downcast='infer')
        assert_series_equal(result, expected)
        # zero
        expected = Series([1, 3, 3, 12, 12, 25])
        result = s.interpolate(method='zero')
        assert_series_equal(result, expected.astype('float'))

        result = s.interpolate(method='zero', downcast='infer')
        assert_series_equal(result, expected)
        # quadratic
        expected = Series([1, 3., 6.769231, 12., 18.230769, 25.])
        result = s.interpolate(method='quadratic')
        assert_series_equal(result, expected)

        result = s.interpolate(method='quadratic', downcast='infer')
        assert_series_equal(result, expected)
        # cubic
        expected = Series([1., 3., 6.8, 12., 18.2, 25.])
        result = s.interpolate(method='cubic')
        assert_series_equal(result, expected)
コード例 #7
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ファイル: test_generic.py プロジェクト: jdreaver/pandas
    def test_interp_alt_scipy(self):
        tm._skip_if_no_scipy()
        df = DataFrame({'A': [1, 2, np.nan, 4, 5, np.nan, 7],
                        'C': [1, 2, 3, 5, 8, 13, 21]})
        result = df.interpolate(method='barycentric')
        expected = df.copy()
        expected['A'].iloc[2] = 3
        expected['A'].iloc[5] = 6
        assert_frame_equal(result, expected)

        result = df.interpolate(method='barycentric', downcast='infer')
        assert_frame_equal(result, expected.astype(np.int64))

        result = df.interpolate(method='krogh')
        expectedk = df.copy()
        # expectedk['A'].iloc[2] = 3
        # expectedk['A'].iloc[5] = 6
        expectedk['A'] = expected['A']
        assert_frame_equal(result, expectedk)

        _skip_if_no_pchip()
        result = df.interpolate(method='pchip')
        expected['A'].iloc[2] = 3
        expected['A'].iloc[5] = 6.125
        assert_frame_equal(result, expected)
コード例 #8
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    def test_scatter_matrix_axis(self):
        tm._skip_if_no_scipy()
        scatter_matrix = plotting.scatter_matrix

        with tm.RNGContext(42):
            df = DataFrame(randn(100, 3))

        # we are plotting multiples on a sub-plot
        with tm.assert_produces_warning(UserWarning):
            axes = _check_plot_works(scatter_matrix, filterwarnings='always',
                                     frame=df, range_padding=.1)
        axes0_labels = axes[0][0].yaxis.get_majorticklabels()

        # GH 5662
        expected = ['-2', '-1', '0', '1', '2']
        self._check_text_labels(axes0_labels, expected)
        self._check_ticks_props(
            axes, xlabelsize=8, xrot=90, ylabelsize=8, yrot=0)

        df[0] = ((df[0] - 2) / 3)

        # we are plotting multiples on a sub-plot
        with tm.assert_produces_warning(UserWarning):
            axes = _check_plot_works(scatter_matrix, filterwarnings='always',
                                     frame=df, range_padding=.1)
        axes0_labels = axes[0][0].yaxis.get_majorticklabels()
        expected = ['-1.2', '-1.0', '-0.8', '-0.6', '-0.4', '-0.2', '0.0']
        self._check_text_labels(axes0_labels, expected)
        self._check_ticks_props(
            axes, xlabelsize=8, xrot=90, ylabelsize=8, yrot=0)
コード例 #9
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ファイル: test_common.py プロジェクト: ankostis/pandas
def test_is_scipy_sparse():
    tm._skip_if_no_scipy()

    from scipy.sparse import bsr_matrix
    assert com.is_scipy_sparse(bsr_matrix([1, 2, 3]))

    assert not com.is_scipy_sparse(pd.SparseArray([1, 2, 3]))
    assert not com.is_scipy_sparse(pd.SparseSeries([1, 2, 3]))
コード例 #10
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ファイル: test_series.py プロジェクト: sunghopark1/pandas
    def test_from_coo_long_repr(self):
        # GH 13114
        # test it doesn't raise error. Formatting is tested in test_format
        tm._skip_if_no_scipy()
        import scipy.sparse

        sparse = SparseSeries.from_coo(scipy.sparse.rand(350, 18))
        repr(sparse)
コード例 #11
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    def test_spline_interpolation(self):
        tm._skip_if_no_scipy()

        s = Series(np.arange(10) ** 2)
        s[np.random.randint(0, 9, 3)] = np.nan
        result1 = s.interpolate(method='spline', order=1)
        expected1 = s.interpolate(method='spline', order=1)
        assert_series_equal(result1, expected1)
コード例 #12
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ファイル: test_generic.py プロジェクト: jdreaver/pandas
    def test_nan_interpolate(self):
        s = Series([0, 1, np.nan, 3])
        result = s.interpolate()
        expected = Series([0., 1., 2., 3.])
        assert_series_equal(result, expected)

        tm._skip_if_no_scipy()
        result = s.interpolate(method='polynomial', order=1)
        assert_series_equal(result, expected)
コード例 #13
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ファイル: test_series.py プロジェクト: AllenDowney/pandas
    def test_kde_missing_vals(self):
        tm._skip_if_no_scipy()
        _skip_if_no_scipy_gaussian_kde()
        s = Series(np.random.uniform(size=50))
        s[0] = np.nan
        axes = _check_plot_works(s.plot.kde)

        # gh-14821: check if the values have any missing values
        assert any(~np.isnan(axes.lines[0].get_xdata()))
コード例 #14
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ファイル: test_series.py プロジェクト: parthea/pandas
 def test_kde_missing_vals(self):
     tm._skip_if_no_scipy()
     _skip_if_no_scipy_gaussian_kde()
     s = Series(np.random.uniform(size=50))
     s[0] = np.nan
     axes = _check_plot_works(s.plot.kde)
     # check if the values have any missing values
     # GH14821
     self.assertTrue(any(~np.isnan(axes.lines[0].get_xdata())), msg="Missing Values not dropped")
コード例 #15
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 def test_kde_missing_vals(self):
     tm._skip_if_no_scipy()
     _skip_if_no_scipy_gaussian_kde()
     s = Series(np.random.uniform(size=50))
     s[0] = np.nan
     axes = _check_plot_works(s.plot.kde)
     # check if the values have any missing values
     # GH14821
     self.assertTrue(any(~np.isnan(axes.lines[0].get_xdata())),
                     msg='Missing Values not dropped')
コード例 #16
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    def test_secondary_kde(self):
        tm._skip_if_no_scipy()
        _skip_if_no_scipy_gaussian_kde()

        import matplotlib.pyplot as plt
        ser = Series(np.random.randn(10))
        ax = ser.plot(secondary_y=True, kind='density').right_ax
        fig = ax.get_figure()
        axes = fig.get_axes()
        self.assertEqual(axes[1].get_yaxis().get_ticks_position(), 'right')
コード例 #17
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ファイル: test_series.py プロジェクト: parthea/pandas
    def test_kde_kwargs(self):
        tm._skip_if_no_scipy()
        _skip_if_no_scipy_gaussian_kde()
        from numpy import linspace

        _check_plot_works(self.ts.plot.kde, bw_method=0.5, ind=linspace(-100, 100, 20))
        _check_plot_works(self.ts.plot.density, bw_method=0.5, ind=linspace(-100, 100, 20))
        ax = self.ts.plot.kde(logy=True, bw_method=0.5, ind=linspace(-100, 100, 20))
        self._check_ax_scales(ax, yaxis="log")
        self._check_text_labels(ax.yaxis.get_label(), "Density")
コード例 #18
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    def test_spline_error(self):
        tm._skip_if_no_scipy()

        s = pd.Series(np.arange(10) ** 2)
        s[np.random.randint(0, 9, 3)] = np.nan
        with tm.assertRaises(ValueError):
            s.interpolate(method='spline')

        with tm.assertRaises(ValueError):
            s.interpolate(method='spline', order=0)
コード例 #19
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    def test_spline_error(self):
        tm._skip_if_no_scipy()

        s = pd.Series(np.arange(10) ** 2)
        s[np.random.randint(0, 9, 3)] = np.nan
        with tm.assertRaises(ValueError):
            s.interpolate(method='spline')

        with tm.assertRaises(ValueError):
            s.interpolate(method='spline', order=0)
コード例 #20
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ファイル: test_plotting.py プロジェクト: B-Rich/pandas
    def test_secondary_kde(self):
        tm._skip_if_no_scipy()
        _skip_if_no_scipy_gaussian_kde()

        import matplotlib.pyplot as plt
        ser = Series(np.random.randn(10))
        ax = ser.plot(secondary_y=True, kind='density').right_ax
        fig = ax.get_figure()
        axes = fig.get_axes()
        self.assertEqual(axes[1].get_yaxis().get_ticks_position(), 'right')
コード例 #21
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ファイル: test_missing.py プロジェクト: pramodjha/Project2
    def test_interp_all_good(self):
        # scipy
        tm._skip_if_no_scipy()
        s = Series([1, 2, 3])
        result = s.interpolate(method='polynomial', order=1)
        assert_series_equal(result, s)

        # non-scipy
        result = s.interpolate()
        assert_series_equal(result, s)
コード例 #22
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ファイル: test_deprecated.py プロジェクト: tsdlovell/pandas
    def test_scatter_plot_legacy(self):
        tm._skip_if_no_scipy()

        df = pd.DataFrame(randn(100, 2))

        with tm.assert_produces_warning(FutureWarning):
            plotting.scatter_matrix(df)

        with tm.assert_produces_warning(FutureWarning):
            pd.scatter_matrix(df)
コード例 #23
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ファイル: test_deprecated.py プロジェクト: zhuw1989/pandas
    def test_scatter_plot_legacy(self):
        tm._skip_if_no_scipy()

        df = pd.DataFrame(randn(100, 2))

        with tm.assert_produces_warning(FutureWarning):
            plotting.scatter_matrix(df)

        with tm.assert_produces_warning(FutureWarning):
            pd.scatter_matrix(df)
コード例 #24
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ファイル: test_generic.py プロジェクト: jdreaver/pandas
    def test_interp_all_good(self):
        # scipy
        tm._skip_if_no_scipy()
        s = Series([1, 2, 3])
        result = s.interpolate(method='polynomial', order=1)
        assert_series_equal(result, s)

        # non-scipy
        result = s.interpolate()
        assert_series_equal(result, s)
コード例 #25
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ファイル: test_missing.py プロジェクト: Axik/pandas
    def test_interp_leading_nans(self):
        df = DataFrame({"A": [np.nan, np.nan, .5, .25, 0],
                        "B": [np.nan, -3, -3.5, np.nan, -4]})
        result = df.interpolate()
        expected = df.copy()
        expected['B'].loc[3] = -3.75
        assert_frame_equal(result, expected)

        tm._skip_if_no_scipy()
        result = df.interpolate(method='polynomial', order=1)
        assert_frame_equal(result, expected)
コード例 #26
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    def test_interp_regression(self):
        tm._skip_if_no_scipy()
        _skip_if_no_pchip()

        ser = Series(np.sort(np.random.uniform(size=100)))

        # interpolate at new_index
        new_index = ser.index + Index([49.25, 49.5, 49.75, 50.25, 50.5, 50.75])
        interp_s = ser.reindex(new_index).interpolate(method='pchip')
        # does not blow up, GH5977
        interp_s[49:51]
コード例 #27
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ファイル: test_generic.py プロジェクト: jdreaver/pandas
    def test_interp_regression(self):
        tm._skip_if_no_scipy()
        _skip_if_no_pchip()

        ser = Series(np.sort(np.random.uniform(size=100)))

        # interpolate at new_index
        new_index = ser.index + Index([49.25, 49.5, 49.75, 50.25, 50.5, 50.75])
        interp_s = ser.reindex(new_index).interpolate(method='pchip')
        # does not blow up, GH5977
        interp_s[49:51]
コード例 #28
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    def test_secondary_kde(self):
        tm._skip_if_no_scipy()
        _skip_if_no_scipy_gaussian_kde()

        ser = Series(np.random.randn(10))
        fig, ax = self.plt.subplots()
        ax = ser.plot(secondary_y=True, kind='density', ax=ax)
        assert hasattr(ax, 'left_ax')
        assert not hasattr(ax, 'right_ax')
        axes = fig.get_axes()
        assert axes[1].get_yaxis().get_ticks_position() == 'right'
コード例 #29
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    def test_interp_leading_nans(self):
        df = DataFrame({"A": [np.nan, np.nan, .5, .25, 0],
                        "B": [np.nan, -3, -3.5, np.nan, -4]})
        result = df.interpolate()
        expected = df.copy()
        expected['B'].loc[3] = -3.75
        assert_frame_equal(result, expected)

        tm._skip_if_no_scipy()
        result = df.interpolate(method='polynomial', order=1)
        assert_frame_equal(result, expected)
コード例 #30
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    def test_cmov_window_frame(self):
        tm._skip_if_no_scipy()
        try:
            from scikits.timeseries.lib import cmov_window
        except ImportError:
            raise nose.SkipTest("no scikits.timeseries")

        # DataFrame
        vals = np.random.randn(10, 2)
        xp = cmov_window(vals, 5, 'boxcar')
        rs = mom.rolling_window(DataFrame(vals), 5, 'boxcar', center=True)
        assert_frame_equal(DataFrame(xp), rs)
コード例 #31
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ファイル: test_series.py プロジェクト: xincxiong/pandas
 def test_kde_kwargs(self):
     tm._skip_if_no_scipy()
     _skip_if_no_scipy_gaussian_kde()
     from numpy import linspace
     _check_plot_works(self.ts.plot.kde, bw_method=.5,
                       ind=linspace(-100, 100, 20))
     _check_plot_works(self.ts.plot.density, bw_method=.5,
                       ind=linspace(-100, 100, 20))
     ax = self.ts.plot.kde(logy=True, bw_method=.5,
                           ind=linspace(-100, 100, 20))
     self._check_ax_scales(ax, yaxis='log')
     self._check_text_labels(ax.yaxis.get_label(), 'Density')
コード例 #32
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ファイル: test_datetimelike.py プロジェクト: ankostis/pandas
    def test_secondary_kde(self):
        tm._skip_if_no_scipy()
        _skip_if_no_scipy_gaussian_kde()

        import matplotlib.pyplot as plt  # noqa
        ser = Series(np.random.randn(10))
        ax = ser.plot(secondary_y=True, kind='density')
        assert hasattr(ax, 'left_ax')
        assert not hasattr(ax, 'right_ax')
        fig = ax.get_figure()
        axes = fig.get_axes()
        assert axes[1].get_yaxis().get_ticks_position() == 'right'
コード例 #33
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    def test_secondary_kde(self):
        tm._skip_if_no_scipy()
        _skip_if_no_scipy_gaussian_kde()

        import matplotlib.pyplot as plt  # noqa
        ser = Series(np.random.randn(10))
        ax = ser.plot(secondary_y=True, kind='density')
        assert hasattr(ax, 'left_ax')
        assert not hasattr(ax, 'right_ax')
        fig = ax.get_figure()
        axes = fig.get_axes()
        assert axes[1].get_yaxis().get_ticks_position() == 'right'
コード例 #34
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ファイル: test_moments.py プロジェクト: dwiel/pandas
    def test_cmov_window_frame(self):
        tm._skip_if_no_scipy()
        try:
            from scikits.timeseries.lib import cmov_window
        except ImportError:
            raise nose.SkipTest("no scikits.timeseries")

        # DataFrame
        vals = np.random.randn(10, 2)
        xp = cmov_window(vals, 5, 'boxcar')
        rs = mom.rolling_window(DataFrame(vals), 5, 'boxcar', center=True)
        assert_frame_equal(DataFrame(xp), rs)
コード例 #35
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ファイル: test_series.py プロジェクト: zhlijia/pandas
    def test_kde_missing_vals(self):
        tm._skip_if_no_scipy()
        _skip_if_no_scipy_gaussian_kde()
        if not self.mpl_ge_1_5_0:
            pytest.skip("mpl is not supported")

        s = Series(np.random.uniform(size=50))
        s[0] = np.nan
        axes = _check_plot_works(s.plot.kde)

        # gh-14821: check if the values have any missing values
        assert any(~np.isnan(axes.lines[0].get_xdata()))
コード例 #36
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    def test_interp_multiIndex(self):
        idx = MultiIndex.from_tuples([(0, 'a'), (1, 'b'), (2, 'c')])
        s = Series([1, 2, np.nan], index=idx)

        expected = s.copy()
        expected.loc[2] = 2
        result = s.interpolate()
        assert_series_equal(result, expected)

        tm._skip_if_no_scipy()
        with tm.assertRaises(ValueError):
            s.interpolate(method='polynomial', order=1)
コード例 #37
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ファイル: test_generic.py プロジェクト: jdreaver/pandas
    def test_interp_multiIndex(self):
        idx = MultiIndex.from_tuples([(0, 'a'), (1, 'b'), (2, 'c')])
        s = Series([1, 2, np.nan], index=idx)

        expected = s.copy()
        expected.loc[2] = 2
        result = s.interpolate()
        assert_series_equal(result, expected)

        tm._skip_if_no_scipy()
        with tm.assertRaises(ValueError):
            s.interpolate(method='polynomial', order=1)
コード例 #38
0
ファイル: test_series.py プロジェクト: BRGM/Pic-EAU
    def test_hist_kde_color(self):
        ax = self.ts.plot.hist(logy=True, bins=10, color='b')
        self._check_ax_scales(ax, yaxis='log')
        self.assertEqual(len(ax.patches), 10)
        self._check_colors(ax.patches, facecolors=['b'] * 10)

        tm._skip_if_no_scipy()
        _skip_if_no_scipy_gaussian_kde()
        ax = self.ts.plot.kde(logy=True, color='r')
        self._check_ax_scales(ax, yaxis='log')
        lines = ax.get_lines()
        self.assertEqual(len(lines), 1)
        self._check_colors(lines, ['r'])
コード例 #39
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ファイル: test_generic.py プロジェクト: jdreaver/pandas
    def test_interpolate_corners(self):
        s = Series([np.nan, np.nan])
        assert_series_equal(s.interpolate(), s)

        s = Series([]).interpolate()
        assert_series_equal(s.interpolate(), s)

        tm._skip_if_no_scipy()
        s = Series([np.nan, np.nan])
        assert_series_equal(s.interpolate(method='polynomial', order=1), s)

        s = Series([]).interpolate()
        assert_series_equal(s.interpolate(method='polynomial', order=1), s)
コード例 #40
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    def test_secondary_kde(self):
        tm._skip_if_no_scipy()
        _skip_if_no_scipy_gaussian_kde()

        import matplotlib.pyplot as plt  # noqa

        ser = Series(np.random.randn(10))
        ax = ser.plot(secondary_y=True, kind="density")
        self.assertTrue(hasattr(ax, "left_ax"))
        self.assertFalse(hasattr(ax, "right_ax"))
        fig = ax.get_figure()
        axes = fig.get_axes()
        self.assertEqual(axes[1].get_yaxis().get_ticks_position(), "right")
コード例 #41
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    def test_interpolate_from_derivatives(self):
        tm._skip_if_no_scipy()

        ser = Series([10, 11, 12, 13])

        expected = Series(
            [11.00, 11.25, 11.50, 11.75, 12.00, 12.25, 12.50, 12.75, 13.00],
            index=Index([1.0, 1.25, 1.5, 1.75, 2.0, 2.25, 2.5, 2.75, 3.0]))
        # interpolate at new_index
        new_index = ser.index.union(Index([1.25, 1.5, 1.75, 2.25, 2.5, 2.75]))
        interp_s = ser.reindex(new_index).interpolate(
            method='from_derivatives')
        assert_series_equal(interp_s[1:3], expected)
コード例 #42
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    def test_interpolate_corners(self):
        s = Series([np.nan, np.nan])
        assert_series_equal(s.interpolate(), s)

        s = Series([]).interpolate()
        assert_series_equal(s.interpolate(), s)

        tm._skip_if_no_scipy()
        s = Series([np.nan, np.nan])
        assert_series_equal(s.interpolate(method='polynomial', order=1), s)

        s = Series([]).interpolate()
        assert_series_equal(s.interpolate(method='polynomial', order=1), s)
コード例 #43
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ファイル: test_series.py プロジェクト: Alias4bb/pandas
    def test_hist_kde_color(self):
        ax = self.ts.plot.hist(logy=True, bins=10, color='b')
        self._check_ax_scales(ax, yaxis='log')
        self.assertEqual(len(ax.patches), 10)
        self._check_colors(ax.patches, facecolors=['b'] * 10)

        tm._skip_if_no_scipy()
        _skip_if_no_scipy_gaussian_kde()
        ax = self.ts.plot.kde(logy=True, color='r')
        self._check_ax_scales(ax, yaxis='log')
        lines = ax.get_lines()
        self.assertEqual(len(lines), 1)
        self._check_colors(lines, ['r'])
コード例 #44
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    def test_interpolate_from_derivatives(self):
        tm._skip_if_no_scipy()

        ser = Series([10, 11, 12, 13])

        expected = Series([11.00, 11.25, 11.50, 11.75,
                           12.00, 12.25, 12.50, 12.75, 13.00],
                          index=Index([1.0, 1.25, 1.5, 1.75,
                                       2.0, 2.25, 2.5, 2.75, 3.0]))
        # interpolate at new_index
        new_index = ser.index.union(Index([1.25, 1.5, 1.75, 2.25, 2.5, 2.75]))
        interp_s = ser.reindex(new_index).interpolate(
            method='from_derivatives')
        assert_series_equal(interp_s[1:3], expected)
コード例 #45
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ファイル: test_moments.py プロジェクト: JohnFNovak/pandas
    def test_cmov_window_regular(self):
        tm._skip_if_no_scipy()
        try:
            from scikits.timeseries.lib import cmov_window
        except ImportError:
            raise nose.SkipTest("no scikits.timeseries")

        win_types = ["triang", "blackman", "hamming", "bartlett", "bohman", "blackmanharris", "nuttall", "barthann"]
        for wt in win_types:
            vals = np.random.randn(10)
            xp = cmov_window(vals, 5, wt)

            rs = mom.rolling_window(Series(vals), 5, wt, center=True)
            assert_series_equal(Series(xp), rs)
コード例 #46
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    def test_interp_various(self):
        tm._skip_if_no_scipy()

        df = DataFrame({
            'A': [1, 2, np.nan, 4, 5, np.nan, 7],
            'C': [1, 2, 3, 5, 8, 13, 21]
        })
        df = df.set_index('C')
        expected = df.copy()
        result = df.interpolate(method='polynomial', order=1)

        expected.A.loc[3] = 2.66666667
        expected.A.loc[13] = 5.76923076
        assert_frame_equal(result, expected)

        result = df.interpolate(method='cubic')
        # GH #15662.
        # new cubic and quadratic interpolation algorithms from scipy 0.19.0.
        # previously `splmake` was used. See scipy/scipy#6710
        if _is_scipy_ge_0190:
            expected.A.loc[3] = 2.81547781
            expected.A.loc[13] = 5.52964175
        else:
            expected.A.loc[3] = 2.81621174
            expected.A.loc[13] = 5.64146581
        assert_frame_equal(result, expected)

        result = df.interpolate(method='nearest')
        expected.A.loc[3] = 2
        expected.A.loc[13] = 5
        assert_frame_equal(result, expected, check_dtype=False)

        result = df.interpolate(method='quadratic')
        if _is_scipy_ge_0190:
            expected.A.loc[3] = 2.82150771
            expected.A.loc[13] = 6.12648668
        else:
            expected.A.loc[3] = 2.82533638
            expected.A.loc[13] = 6.02817974
        assert_frame_equal(result, expected)

        result = df.interpolate(method='slinear')
        expected.A.loc[3] = 2.66666667
        expected.A.loc[13] = 5.76923077
        assert_frame_equal(result, expected)

        result = df.interpolate(method='zero')
        expected.A.loc[3] = 2.
        expected.A.loc[13] = 5
        assert_frame_equal(result, expected, check_dtype=False)
コード例 #47
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ファイル: test_algos.py プロジェクト: yizhiyong/pandas
def test_rank():
    tm._skip_if_no_scipy()
    from scipy.stats import rankdata

    def _check(arr):
        mask = ~np.isfinite(arr)
        arr = arr.copy()
        result = _algos.rank_1d_float64(arr)
        arr[mask] = np.inf
        exp = rankdata(arr)
        exp[mask] = nan
        assert_almost_equal(result, exp)

    _check(np.array([nan, nan, 5., 5., 5., nan, 1, 2, 3, nan]))
    _check(np.array([4., nan, 5., 5., 5., nan, 1, 2, 4., nan]))
コード例 #48
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    def test_hist_kde_color(self):
        _, ax = self.plt.subplots()
        ax = self.ts.plot.hist(logy=True, bins=10, color='b', ax=ax)
        self._check_ax_scales(ax, yaxis='log')
        assert len(ax.patches) == 10
        self._check_colors(ax.patches, facecolors=['b'] * 10)

        tm._skip_if_no_scipy()
        _skip_if_no_scipy_gaussian_kde()
        _, ax = self.plt.subplots()
        ax = self.ts.plot.kde(logy=True, color='r', ax=ax)
        self._check_ax_scales(ax, yaxis='log')
        lines = ax.get_lines()
        assert len(lines) == 1
        self._check_colors(lines, ['r'])
コード例 #49
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ファイル: test_frame.py プロジェクト: dlwcn/pandas
def test_from_to_scipy(spmatrix, index, columns, fill_value, dtype):
    # GH 4343
    tm._skip_if_no_scipy()

    # Make one ndarray and from it one sparse matrix, both to be used for
    # constructing frames and comparing results
    arr = np.eye(2, dtype=dtype)
    try:
        spm = spmatrix(arr)
        assert spm.dtype == arr.dtype
    except (TypeError, AssertionError):
        # If conversion to sparse fails for this spmatrix type and arr.dtype,
        # then the combination is not currently supported in NumPy, so we
        # can just skip testing it thoroughly
        return

    sdf = pd.SparseDataFrame(spm,
                             index=index,
                             columns=columns,
                             default_fill_value=fill_value)

    # Expected result construction is kind of tricky for all
    # dtype-fill_value combinations; easiest to cast to something generic
    # and except later on
    rarr = arr.astype(object)
    rarr[arr == 0] = np.nan
    expected = pd.SparseDataFrame(rarr, index=index, columns=columns).fillna(
        fill_value if fill_value is not None else np.nan)

    # Assert frame is as expected
    sdf_obj = sdf.astype(object)
    tm.assert_sp_frame_equal(sdf_obj, expected)
    tm.assert_frame_equal(sdf_obj.to_dense(), expected.to_dense())

    # Assert spmatrices equal
    tm.assert_equal(dict(sdf.to_coo().todok()), dict(spm.todok()))

    # Ensure dtype is preserved if possible
    was_upcast = ((fill_value is None or is_float(fill_value))
                  and not is_object_dtype(dtype) and not is_float_dtype(dtype))
    res_dtype = (bool
                 if is_bool_dtype(dtype) else float if was_upcast else dtype)
    tm.assert_contains_all(sdf.dtypes, {np.dtype(res_dtype)})
    tm.assert_equal(sdf.to_coo().dtype, res_dtype)

    # However, adding a str column results in an upcast to object
    sdf['strings'] = np.arange(len(sdf)).astype(str)
    tm.assert_equal(sdf.to_coo().dtype, np.object_)
コード例 #50
0
ファイル: test_series.py プロジェクト: zhlijia/pandas
    def test_kde_kwargs(self):
        tm._skip_if_no_scipy()
        _skip_if_no_scipy_gaussian_kde()
        if not self.mpl_ge_1_5_0:
            pytest.skip("mpl is not supported")

        from numpy import linspace
        _check_plot_works(self.ts.plot.kde, bw_method=.5,
                          ind=linspace(-100, 100, 20))
        _check_plot_works(self.ts.plot.density, bw_method=.5,
                          ind=linspace(-100, 100, 20))
        _, ax = self.plt.subplots()
        ax = self.ts.plot.kde(logy=True, bw_method=.5,
                              ind=linspace(-100, 100, 20), ax=ax)
        self._check_ax_scales(ax, yaxis='log')
        self._check_text_labels(ax.yaxis.get_label(), 'Density')
コード例 #51
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    def test_cmov_window_na_min_periods(self):
        tm._skip_if_no_scipy()
        try:
            from scikits.timeseries.lib import cmov_window
        except ImportError:
            raise nose.SkipTest("no scikits.timeseries")

        # min_periods
        vals = Series(np.random.randn(10))
        vals[4] = np.nan
        vals[8] = np.nan

        xp = mom.rolling_mean(vals, 5, min_periods=4, center=True)
        rs = mom.rolling_window(vals, 5, 'boxcar', min_periods=4, center=True)

        assert_series_equal(xp, rs)
コード例 #52
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    def test_cmov_window(self):
        tm._skip_if_no_scipy()
        try:
            from scikits.timeseries.lib import cmov_window
        except ImportError:
            raise nose.SkipTest("no scikits.timeseries")

        vals = np.random.randn(10)
        xp = cmov_window(vals, 5, 'boxcar')

        rs = mom.rolling_window(vals, 5, 'boxcar', center=True)
        assert_almost_equal(xp.compressed(), rs[2:-2])
        assert_almost_equal(xp.mask, np.isnan(rs))

        xp = Series(rs)
        rs = mom.rolling_window(Series(vals), 5, 'boxcar', center=True)
        assert_series_equal(xp, rs)
コード例 #53
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    def test_cmov_window_regular(self):
        tm._skip_if_no_scipy()
        try:
            from scikits.timeseries.lib import cmov_window
        except ImportError:
            raise nose.SkipTest("no scikits.timeseries")

        win_types = [
            'triang', 'blackman', 'hamming', 'bartlett', 'bohman',
            'blackmanharris', 'nuttall', 'barthann'
        ]
        for wt in win_types:
            vals = np.random.randn(10)
            xp = cmov_window(vals, 5, wt)

            rs = mom.rolling_window(Series(vals), 5, wt, center=True)
            assert_series_equal(Series(xp), rs)
コード例 #54
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ファイル: test_misc.py プロジェクト: zhangcg1987/pandas
    def test_scatter_matrix_axis(self):
        tm._skip_if_no_scipy()
        scatter_matrix = plotting.scatter_matrix

        with tm.RNGContext(42):
            df = DataFrame(randn(100, 3))

        # we are plotting multiples on a sub-plot
        with tm.assert_produces_warning(UserWarning):
            axes = _check_plot_works(scatter_matrix,
                                     filterwarnings='always',
                                     frame=df,
                                     range_padding=.1)
        axes0_labels = axes[0][0].yaxis.get_majorticklabels()

        # GH 5662
        if self.mpl_ge_2_0_0:
            expected = ['-2', '0', '2']
        else:
            expected = ['-2', '-1', '0', '1', '2']
        self._check_text_labels(axes0_labels, expected)
        self._check_ticks_props(axes,
                                xlabelsize=8,
                                xrot=90,
                                ylabelsize=8,
                                yrot=0)

        df[0] = ((df[0] - 2) / 3)

        # we are plotting multiples on a sub-plot
        with tm.assert_produces_warning(UserWarning):
            axes = _check_plot_works(scatter_matrix,
                                     filterwarnings='always',
                                     frame=df,
                                     range_padding=.1)
        axes0_labels = axes[0][0].yaxis.get_majorticklabels()
        if self.mpl_ge_2_0_0:
            expected = ['-1.0', '-0.5', '0.0']
        else:
            expected = ['-1.2', '-1.0', '-0.8', '-0.6', '-0.4', '-0.2', '0.0']
        self._check_text_labels(axes0_labels, expected)
        self._check_ticks_props(axes,
                                xlabelsize=8,
                                xrot=90,
                                ylabelsize=8,
                                yrot=0)
コード例 #55
0
ファイル: test_missing.py プロジェクト: wuthmonehnin/pandas
    def test_interp_various(self):
        tm._skip_if_no_scipy()
        df = DataFrame({
            'A': [1, 2, np.nan, 4, 5, np.nan, 7],
            'C': [1, 2, 3, 5, 8, 13, 21]
        })
        df = df.set_index('C')
        expected = df.copy()
        result = df.interpolate(method='polynomial', order=1)

        expected.A.loc[3] = 2.66666667
        expected.A.loc[13] = 5.76923076
        assert_frame_equal(result, expected)

        result = df.interpolate(method='cubic')
        expected.A.loc[3] = 2.81621174
        expected.A.loc[13] = 5.64146581
        assert_frame_equal(result, expected)

        result = df.interpolate(method='nearest')
        expected.A.loc[3] = 2
        expected.A.loc[13] = 5
        assert_frame_equal(result, expected, check_dtype=False)

        result = df.interpolate(method='quadratic')
        expected.A.loc[3] = 2.82533638
        expected.A.loc[13] = 6.02817974
        assert_frame_equal(result, expected)

        result = df.interpolate(method='slinear')
        expected.A.loc[3] = 2.66666667
        expected.A.loc[13] = 5.76923077
        assert_frame_equal(result, expected)

        result = df.interpolate(method='zero')
        expected.A.loc[3] = 2.
        expected.A.loc[13] = 5
        assert_frame_equal(result, expected, check_dtype=False)

        result = df.interpolate(method='quadratic')
        expected.A.loc[3] = 2.82533638
        expected.A.loc[13] = 6.02817974
        assert_frame_equal(result, expected)
コード例 #56
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ファイル: test_series.py プロジェクト: BRGM/Pic-EAU
    def test_hist_kde(self):
        ax = self.ts.plot.hist(logy=True)
        self._check_ax_scales(ax, yaxis='log')
        xlabels = ax.get_xticklabels()
        # ticks are values, thus ticklabels are blank
        self._check_text_labels(xlabels, [''] * len(xlabels))
        ylabels = ax.get_yticklabels()
        self._check_text_labels(ylabels, [''] * len(ylabels))

        tm._skip_if_no_scipy()
        _skip_if_no_scipy_gaussian_kde()
        _check_plot_works(self.ts.plot.kde)
        _check_plot_works(self.ts.plot.density)
        ax = self.ts.plot.kde(logy=True)
        self._check_ax_scales(ax, yaxis='log')
        xlabels = ax.get_xticklabels()
        self._check_text_labels(xlabels, [''] * len(xlabels))
        ylabels = ax.get_yticklabels()
        self._check_text_labels(ylabels, [''] * len(ylabels))
コード例 #57
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    def test_interp_scipy_basic(self):
        tm._skip_if_no_scipy()

        s = Series([1, 3, np.nan, 12, np.nan, 25])
        # slinear
        expected = Series([1., 3., 7.5, 12., 18.5, 25.])
        result = s.interpolate(method='slinear')
        assert_series_equal(result, expected)

        result = s.interpolate(method='slinear', downcast='infer')
        assert_series_equal(result, expected)
        # nearest
        expected = Series([1, 3, 3, 12, 12, 25])
        result = s.interpolate(method='nearest')
        assert_series_equal(result, expected.astype('float'))

        result = s.interpolate(method='nearest', downcast='infer')
        assert_series_equal(result, expected)
        # zero
        expected = Series([1, 3, 3, 12, 12, 25])
        result = s.interpolate(method='zero')
        assert_series_equal(result, expected.astype('float'))

        result = s.interpolate(method='zero', downcast='infer')
        assert_series_equal(result, expected)
        # quadratic
        # GH #15662.
        # new cubic and quadratic interpolation algorithms from scipy 0.19.0.
        # previously `splmake` was used. See scipy/scipy#6710
        if _is_scipy_ge_0190:
            expected = Series([1, 3., 6.823529, 12., 18.058824, 25.])
        else:
            expected = Series([1, 3., 6.769231, 12., 18.230769, 25.])
        result = s.interpolate(method='quadratic')
        assert_series_equal(result, expected)

        result = s.interpolate(method='quadratic', downcast='infer')
        assert_series_equal(result, expected)
        # cubic
        expected = Series([1., 3., 6.8, 12., 18.2, 25.])
        result = s.interpolate(method='cubic')
        assert_series_equal(result, expected)
コード例 #58
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    def test_scatter_matrix_axis(self):
        tm._skip_if_no_scipy()
        scatter_matrix = plotting.scatter_matrix

        with tm.RNGContext(42):
            df = DataFrame(randn(100, 3))

        axes = _check_plot_works(scatter_matrix, filterwarnings='always', frame=df,
                                 range_padding=.1)
        axes0_labels = axes[0][0].yaxis.get_majorticklabels()
        # GH 5662
        expected = ['-2', '-1', '0', '1', '2']
        self._check_text_labels(axes0_labels, expected)
        self._check_ticks_props(axes, xlabelsize=8, xrot=90, ylabelsize=8, yrot=0)

        df[0] = ((df[0] - 2) / 3)
        axes = _check_plot_works(scatter_matrix, filterwarnings='always', frame=df,
                                 range_padding=.1)
        axes0_labels = axes[0][0].yaxis.get_majorticklabels()
        expected = ['-1.2', '-1.0', '-0.8', '-0.6', '-0.4', '-0.2', '0.0']
        self._check_text_labels(axes0_labels, expected)
        self._check_ticks_props(axes, xlabelsize=8, xrot=90, ylabelsize=8, yrot=0)
コード例 #59
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    def test_interp_rowwise(self):
        df = DataFrame({0: [1, 2, np.nan, 4],
                        1: [2, 3, 4, np.nan],
                        2: [np.nan, 4, 5, 6],
                        3: [4, np.nan, 6, 7],
                        4: [1, 2, 3, 4]})
        result = df.interpolate(axis=1)
        expected = df.copy()
        expected.loc[3, 1] = 5
        expected.loc[0, 2] = 3
        expected.loc[1, 3] = 3
        expected[4] = expected[4].astype(np.float64)
        assert_frame_equal(result, expected)

        # scipy route
        tm._skip_if_no_scipy()
        result = df.interpolate(axis=1, method='values')
        assert_frame_equal(result, expected)

        result = df.interpolate(axis=0)
        expected = df.interpolate()
        assert_frame_equal(result, expected)
コード例 #60
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ファイル: test_misc.py プロジェクト: zhangcg1987/pandas
    def test_scatter_plot_legacy(self):
        tm._skip_if_no_scipy()

        df = DataFrame(randn(100, 2))

        def scat(**kwds):
            return plotting.scatter_matrix(df, **kwds)

        with tm.assert_produces_warning(UserWarning):
            _check_plot_works(scat)
        with tm.assert_produces_warning(UserWarning):
            _check_plot_works(scat, marker='+')
        with tm.assert_produces_warning(UserWarning):
            _check_plot_works(scat, vmin=0)
        if _ok_for_gaussian_kde('kde'):
            with tm.assert_produces_warning(UserWarning):
                _check_plot_works(scat, diagonal='kde')
        if _ok_for_gaussian_kde('density'):
            with tm.assert_produces_warning(UserWarning):
                _check_plot_works(scat, diagonal='density')
        with tm.assert_produces_warning(UserWarning):
            _check_plot_works(scat, diagonal='hist')
        with tm.assert_produces_warning(UserWarning):
            _check_plot_works(scat, range_padding=.1)
        with tm.assert_produces_warning(UserWarning):
            _check_plot_works(scat, color='rgb')
        with tm.assert_produces_warning(UserWarning):
            _check_plot_works(scat, c='rgb')
        with tm.assert_produces_warning(UserWarning):
            _check_plot_works(scat, facecolor='rgb')

        def scat2(x, y, by=None, ax=None, figsize=None):
            return plotting._core.scatter_plot(df, x, y, by, ax, figsize=None)

        _check_plot_works(scat2, x=0, y=1)
        grouper = Series(np.repeat([1, 2, 3, 4, 5], 20), df.index)
        with tm.assert_produces_warning(UserWarning):
            _check_plot_works(scat2, x=0, y=1, by=grouper)