Python pandas.core.nanops.nansum() Examples

The following are 24 code examples of pandas.core.nanops.nansum(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module pandas.core.nanops , or try the search function .
Example #1
Source File: test_reductions.py    From predictive-maintenance-using-machine-learning with Apache License 2.0 6 votes vote down vote up 
def test_sum_inf(self):
        s = Series(np.random.randn(10))
        s2 = s.copy()

        s[5:8] = np.inf
        s2[5:8] = np.nan

        assert np.isinf(s.sum())

        arr = np.random.randn(100, 100).astype('f4')
        arr[:, 2] = np.inf

        with pd.option_context("mode.use_inf_as_na", True):
            tm.assert_almost_equal(s.sum(), s2.sum())

        res = nanops.nansum(arr, axis=1)
        assert np.isinf(res).all()
Example #2
Source File: test_analytics.py    From twitter-stock-recommendation with MIT License 6 votes vote down vote up 
def test_sum_inf(self):
        s = Series(np.random.randn(10))
        s2 = s.copy()

        s[5:8] = np.inf
        s2[5:8] = np.nan

        assert np.isinf(s.sum())

        arr = np.random.randn(100, 100).astype('f4')
        arr[:, 2] = np.inf

        with pd.option_context("mode.use_inf_as_na", True):
            assert_almost_equal(s.sum(), s2.sum())

        res = nanops.nansum(arr, axis=1)
        assert np.isinf(res).all()
Example #3
Source File: test_apply.py    From twitter-stock-recommendation with MIT License 6 votes vote down vote up 
def test_apply(frame):
    applied = frame.apply(np.sqrt)
    assert isinstance(applied, SparseDataFrame)
    tm.assert_almost_equal(applied.values, np.sqrt(frame.values))

    # agg / broadcast
    with tm.assert_produces_warning(FutureWarning):
        broadcasted = frame.apply(np.sum, broadcast=True)
    assert isinstance(broadcasted, SparseDataFrame)

    with tm.assert_produces_warning(FutureWarning):
        exp = frame.to_dense().apply(np.sum, broadcast=True)
    tm.assert_frame_equal(broadcasted.to_dense(), exp)

    applied = frame.apply(np.sum)
    tm.assert_series_equal(applied,
                           frame.to_dense().apply(nanops.nansum))
Example #4
Source File: test_apply.py    From coffeegrindsize with MIT License 6 votes vote down vote up 
def test_apply(frame):
    applied = frame.apply(np.sqrt)
    assert isinstance(applied, SparseDataFrame)
    tm.assert_almost_equal(applied.values, np.sqrt(frame.values))

    # agg / broadcast
    with tm.assert_produces_warning(FutureWarning):
        broadcasted = frame.apply(np.sum, broadcast=True)
    assert isinstance(broadcasted, SparseDataFrame)

    with tm.assert_produces_warning(FutureWarning):
        exp = frame.to_dense().apply(np.sum, broadcast=True)
    tm.assert_frame_equal(broadcasted.to_dense(), exp)

    applied = frame.apply(np.sum)
    tm.assert_series_equal(applied,
                           frame.to_dense().apply(nanops.nansum).to_sparse())
Example #5
Source File: test_analytics.py    From elasticintel with GNU General Public License v3.0 6 votes vote down vote up 
def test_sum_inf(self):
        s = Series(np.random.randn(10))
        s2 = s.copy()

        s[5:8] = np.inf
        s2[5:8] = np.nan

        assert np.isinf(s.sum())

        arr = np.random.randn(100, 100).astype('f4')
        arr[:, 2] = np.inf

        with pd.option_context("mode.use_inf_as_na", True):
            assert_almost_equal(s.sum(), s2.sum())

        res = nanops.nansum(arr, axis=1)
        assert np.isinf(res).all()
Example #6
Source File: test_frame.py    From elasticintel with GNU General Public License v3.0 6 votes vote down vote up 
def test_apply(self):
        applied = self.frame.apply(np.sqrt)
        assert isinstance(applied, SparseDataFrame)
        tm.assert_almost_equal(applied.values, np.sqrt(self.frame.values))

        applied = self.fill_frame.apply(np.sqrt)
        assert applied['A'].fill_value == np.sqrt(2)

        # agg / broadcast
        broadcasted = self.frame.apply(np.sum, broadcast=True)
        assert isinstance(broadcasted, SparseDataFrame)

        exp = self.frame.to_dense().apply(np.sum, broadcast=True)
        tm.assert_frame_equal(broadcasted.to_dense(), exp)

        assert self.empty.apply(np.sqrt) is self.empty

        from pandas.core import nanops
        applied = self.frame.apply(np.sum)
        tm.assert_series_equal(applied,
                               self.frame.to_dense().apply(nanops.nansum))
Example #7
Source File: test_apply.py    From recruit with Apache License 2.0 6 votes vote down vote up 
def test_apply(frame):
    applied = frame.apply(np.sqrt)
    assert isinstance(applied, SparseDataFrame)
    tm.assert_almost_equal(applied.values, np.sqrt(frame.values))

    # agg / broadcast
    with tm.assert_produces_warning(FutureWarning):
        broadcasted = frame.apply(np.sum, broadcast=True)
    assert isinstance(broadcasted, SparseDataFrame)

    with tm.assert_produces_warning(FutureWarning):
        exp = frame.to_dense().apply(np.sum, broadcast=True)
    tm.assert_frame_equal(broadcasted.to_dense(), exp)

    applied = frame.apply(np.sum)
    tm.assert_series_equal(applied,
                           frame.to_dense().apply(nanops.nansum).to_sparse())
Example #8
Source File: test_apply.py    From predictive-maintenance-using-machine-learning with Apache License 2.0 6 votes vote down vote up 
def test_apply(frame):
    applied = frame.apply(np.sqrt)
    assert isinstance(applied, SparseDataFrame)
    tm.assert_almost_equal(applied.values, np.sqrt(frame.values))

    # agg / broadcast
    with tm.assert_produces_warning(FutureWarning):
        broadcasted = frame.apply(np.sum, broadcast=True)
    assert isinstance(broadcasted, SparseDataFrame)

    with tm.assert_produces_warning(FutureWarning):
        exp = frame.to_dense().apply(np.sum, broadcast=True)
    tm.assert_frame_equal(broadcasted.to_dense(), exp)

    applied = frame.apply(np.sum)
    tm.assert_series_equal(applied,
                           frame.to_dense().apply(nanops.nansum).to_sparse())
Example #9
Source File: test_sparse.py    From Computable with MIT License 6 votes vote down vote up 
def test_apply(self):
        applied = self.frame.apply(np.sqrt)
        tm.assert_isinstance(applied, SparseDataFrame)
        assert_almost_equal(applied.values, np.sqrt(self.frame.values))

        applied = self.fill_frame.apply(np.sqrt)
        self.assert_(applied['A'].fill_value == np.sqrt(2))

        # agg / broadcast
        broadcasted = self.frame.apply(np.sum, broadcast=True)
        tm.assert_isinstance(broadcasted, SparseDataFrame)
        assert_frame_equal(broadcasted.to_dense(),
                           self.frame.to_dense().apply(np.sum, broadcast=True))

        self.assert_(self.empty.apply(np.sqrt) is self.empty)

        from pandas.core import nanops
        applied = self.frame.apply(np.sum)
        assert_series_equal(applied,
                            self.frame.to_dense().apply(nanops.nansum))
Example #10
Source File: test_analytics.py    From vnpy_crypto with MIT License 6 votes vote down vote up 
def test_sum_inf(self):
        s = Series(np.random.randn(10))
        s2 = s.copy()

        s[5:8] = np.inf
        s2[5:8] = np.nan

        assert np.isinf(s.sum())

        arr = np.random.randn(100, 100).astype('f4')
        arr[:, 2] = np.inf

        with pd.option_context("mode.use_inf_as_na", True):
            assert_almost_equal(s.sum(), s2.sum())

        res = nanops.nansum(arr, axis=1)
        assert np.isinf(res).all()
Example #11
Source File: test_apply.py    From vnpy_crypto with MIT License 6 votes vote down vote up 
def test_apply(frame):
    applied = frame.apply(np.sqrt)
    assert isinstance(applied, SparseDataFrame)
    tm.assert_almost_equal(applied.values, np.sqrt(frame.values))

    # agg / broadcast
    with tm.assert_produces_warning(FutureWarning):
        broadcasted = frame.apply(np.sum, broadcast=True)
    assert isinstance(broadcasted, SparseDataFrame)

    with tm.assert_produces_warning(FutureWarning):
        exp = frame.to_dense().apply(np.sum, broadcast=True)
    tm.assert_frame_equal(broadcasted.to_dense(), exp)

    applied = frame.apply(np.sum)
    tm.assert_series_equal(applied,
                           frame.to_dense().apply(nanops.nansum))
Example #12
Source File: test_reductions.py    From recruit with Apache License 2.0 6 votes vote down vote up 
def test_sum_inf(self):
        s = Series(np.random.randn(10))
        s2 = s.copy()

        s[5:8] = np.inf
        s2[5:8] = np.nan

        assert np.isinf(s.sum())

        arr = np.random.randn(100, 100).astype('f4')
        arr[:, 2] = np.inf

        with pd.option_context("mode.use_inf_as_na", True):
            tm.assert_almost_equal(s.sum(), s2.sum())

        res = nanops.nansum(arr, axis=1)
        assert np.isinf(res).all()
Example #13
Source File: test_analytics.py    From vnpy_crypto with MIT License 5 votes vote down vote up 
def test_nansum_buglet(self):
        s = Series([1.0, np.nan], index=[0, 1])
        result = np.nansum(s)
        assert_almost_equal(result, 1)
Example #14
Source File: test_nanops.py    From predictive-maintenance-using-machine-learning with Apache License 2.0 5 votes vote down vote up 
def test_nansum(self):
        self.check_funs(nanops.nansum, np.sum, allow_str=False,
                        allow_date=False, allow_tdelta=True, check_dtype=False,
                        empty_targfunc=np.nansum)
Example #15
Source File: test_nanops.py    From vnpy_crypto with MIT License 5 votes vote down vote up 
def test_nansum(self):
        self.check_funs(nanops.nansum, np.sum, allow_str=False,
                        allow_date=False, allow_tdelta=True, check_dtype=False,
                        empty_targfunc=np.nansum)
Example #16
Source File: test_reductions.py    From predictive-maintenance-using-machine-learning with Apache License 2.0 5 votes vote down vote up 
def test_nansum_buglet(self):
        ser = Series([1.0, np.nan], index=[0, 1])
        result = np.nansum(ser)
        tm.assert_almost_equal(result, 1)
Example #17
Source File: numpy_.py    From predictive-maintenance-using-machine-learning with Apache License 2.0 5 votes vote down vote up 
def sum(self, axis=None, dtype=None, out=None, keepdims=False,
            initial=None, skipna=True, min_count=0):
        nv.validate_sum((), dict(dtype=dtype, out=out, keepdims=keepdims,
                                 initial=initial))
        return nanops.nansum(self._ndarray, axis=axis, skipna=skipna,
                             min_count=min_count)
Example #18
Source File: test_nanops.py    From elasticintel with GNU General Public License v3.0 5 votes vote down vote up 
def test_nansum(self):
        self.check_funs(nanops.nansum, np.sum, allow_str=False,
                        allow_date=False, allow_tdelta=True, check_dtype=False)
Example #19
Source File: test_analytics.py    From elasticintel with GNU General Public License v3.0 5 votes vote down vote up 
def test_nansum_buglet(self):
        s = Series([1.0, np.nan], index=[0, 1])
        result = np.nansum(s)
        assert_almost_equal(result, 1)
Example #20
Source File: numpy_.py    From recruit with Apache License 2.0 5 votes vote down vote up 
def sum(self, axis=None, dtype=None, out=None, keepdims=False,
            initial=None, skipna=True, min_count=0):
        nv.validate_sum((), dict(dtype=dtype, out=out, keepdims=keepdims,
                                 initial=initial))
        return nanops.nansum(self._ndarray, axis=axis, skipna=skipna,
                             min_count=min_count)
Example #21
Source File: test_reductions.py    From recruit with Apache License 2.0 5 votes vote down vote up 
def test_nansum_buglet(self):
        ser = Series([1.0, np.nan], index=[0, 1])
        result = np.nansum(ser)
        tm.assert_almost_equal(result, 1)
Example #22
Source File: test_nanops.py    From twitter-stock-recommendation with MIT License 5 votes vote down vote up 
def test_nansum(self):
        self.check_funs(nanops.nansum, np.sum, allow_str=False,
                        allow_date=False, allow_tdelta=True, check_dtype=False,
                        empty_targfunc=np.nansum)
Example #23
Source File: test_analytics.py    From twitter-stock-recommendation with MIT License 5 votes vote down vote up 
def test_nansum_buglet(self):
        s = Series([1.0, np.nan], index=[0, 1])
        result = np.nansum(s)
        assert_almost_equal(result, 1)
Example #24
Source File: test_nanops.py    From recruit with Apache License 2.0 5 votes vote down vote up 
def test_nansum(self):
        self.check_funs(nanops.nansum, np.sum, allow_str=False,
                        allow_date=False, allow_tdelta=True, check_dtype=False,
                        empty_targfunc=np.nansum)