Python pandas.core.nanops.nancov() Examples

The following are 11 code examples of pandas.core.nanops.nancov(). 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: series.py    From recruit with Apache License 2.0 6 votes vote down vote up 
def cov(self, other, min_periods=None):
        """
        Compute covariance with Series, excluding missing values.

        Parameters
        ----------
        other : Series
        min_periods : int, optional
            Minimum number of observations needed to have a valid result

        Returns
        -------
        covariance : float

        Normalized by N-1 (unbiased estimator).
        """
        this, other = self.align(other, join='inner', copy=False)
        if len(this) == 0:
            return np.nan
        return nanops.nancov(this.values, other.values,
                             min_periods=min_periods)
Example #2
Source File: series.py    From vnpy_crypto with MIT License 6 votes vote down vote up 
def cov(self, other, min_periods=None):
        """
        Compute covariance with Series, excluding missing values

        Parameters
        ----------
        other : Series
        min_periods : int, optional
            Minimum number of observations needed to have a valid result

        Returns
        -------
        covariance : float

        Normalized by N-1 (unbiased estimator).
        """
        this, other = self.align(other, join='inner', copy=False)
        if len(this) == 0:
            return np.nan
        return nanops.nancov(this.values, other.values,
                             min_periods=min_periods)
Example #3
Source File: series.py    From Computable with MIT License 6 votes vote down vote up 
def cov(self, other, min_periods=None):
        """
        Compute covariance with Series, excluding missing values

        Parameters
        ----------
        other : Series
        min_periods : int, optional
            Minimum number of observations needed to have a valid result

        Returns
        -------
        covariance : float

        Normalized by N-1 (unbiased estimator).
        """
        this, other = self.align(other, join='inner')
        if len(this) == 0:
            return pa.NA
        return nanops.nancov(this.values, other.values,
                             min_periods=min_periods)
Example #4
Source File: series.py    From predictive-maintenance-using-machine-learning with Apache License 2.0 6 votes vote down vote up 
def cov(self, other, min_periods=None):
        """
        Compute covariance with Series, excluding missing values.

        Parameters
        ----------
        other : Series
        min_periods : int, optional
            Minimum number of observations needed to have a valid result

        Returns
        -------
        covariance : float

        Normalized by N-1 (unbiased estimator).
        """
        this, other = self.align(other, join='inner', copy=False)
        if len(this) == 0:
            return np.nan
        return nanops.nancov(this.values, other.values,
                             min_periods=min_periods)
Example #5
Source File: series.py    From Splunking-Crime with GNU Affero General Public License v3.0 6 votes vote down vote up 
def cov(self, other, min_periods=None):
        """
        Compute covariance with Series, excluding missing values

        Parameters
        ----------
        other : Series
        min_periods : int, optional
            Minimum number of observations needed to have a valid result

        Returns
        -------
        covariance : float

        Normalized by N-1 (unbiased estimator).
        """
        this, other = self.align(other, join='inner', copy=False)
        if len(this) == 0:
            return np.nan
        return nanops.nancov(this.values, other.values,
                             min_periods=min_periods)
Example #6
Source File: series.py    From elasticintel with GNU General Public License v3.0 6 votes vote down vote up 
def cov(self, other, min_periods=None):
        """
        Compute covariance with Series, excluding missing values

        Parameters
        ----------
        other : Series
        min_periods : int, optional
            Minimum number of observations needed to have a valid result

        Returns
        -------
        covariance : float

        Normalized by N-1 (unbiased estimator).
        """
        this, other = self.align(other, join='inner', copy=False)
        if len(this) == 0:
            return np.nan
        return nanops.nancov(this.values, other.values,
                             min_periods=min_periods)
Example #7
Source File: test_nanops.py    From recruit with Apache License 2.0 5 votes vote down vote up 
def test_nancov(self):
        targ0 = np.cov(self.arr_float_2d, self.arr_float1_2d)[0, 1]
        targ1 = np.cov(self.arr_float_2d.flat, self.arr_float1_2d.flat)[0, 1]
        self.check_nancorr_nancov_2d(nanops.nancov, targ0, targ1)
        targ0 = np.cov(self.arr_float_1d, self.arr_float1_1d)[0, 1]
        targ1 = np.cov(self.arr_float_1d.flat, self.arr_float1_1d.flat)[0, 1]
        self.check_nancorr_nancov_1d(nanops.nancov, targ0, targ1)
Example #8
Source File: test_nanops.py    From vnpy_crypto with MIT License 5 votes vote down vote up 
def test_nancov(self):
        targ0 = np.cov(self.arr_float_2d, self.arr_float1_2d)[0, 1]
        targ1 = np.cov(self.arr_float_2d.flat, self.arr_float1_2d.flat)[0, 1]
        self.check_nancorr_nancov_2d(nanops.nancov, targ0, targ1)
        targ0 = np.cov(self.arr_float_1d, self.arr_float1_1d)[0, 1]
        targ1 = np.cov(self.arr_float_1d.flat, self.arr_float1_1d.flat)[0, 1]
        self.check_nancorr_nancov_1d(nanops.nancov, targ0, targ1)
Example #9
Source File: test_nanops.py    From predictive-maintenance-using-machine-learning with Apache License 2.0 5 votes vote down vote up 
def test_nancov(self):
        targ0 = np.cov(self.arr_float_2d, self.arr_float1_2d)[0, 1]
        targ1 = np.cov(self.arr_float_2d.flat, self.arr_float1_2d.flat)[0, 1]
        self.check_nancorr_nancov_2d(nanops.nancov, targ0, targ1)
        targ0 = np.cov(self.arr_float_1d, self.arr_float1_1d)[0, 1]
        targ1 = np.cov(self.arr_float_1d.flat, self.arr_float1_1d.flat)[0, 1]
        self.check_nancorr_nancov_1d(nanops.nancov, targ0, targ1)
Example #10
Source File: test_nanops.py    From elasticintel with GNU General Public License v3.0 5 votes vote down vote up 
def test_nancov(self):
        targ0 = np.cov(self.arr_float_2d, self.arr_float1_2d)[0, 1]
        targ1 = np.cov(self.arr_float_2d.flat, self.arr_float1_2d.flat)[0, 1]
        self.check_nancorr_nancov_2d(nanops.nancov, targ0, targ1)
        targ0 = np.cov(self.arr_float_1d, self.arr_float1_1d)[0, 1]
        targ1 = np.cov(self.arr_float_1d.flat, self.arr_float1_1d.flat)[0, 1]
        self.check_nancorr_nancov_1d(nanops.nancov, targ0, targ1)
Example #11
Source File: test_nanops.py    From twitter-stock-recommendation with MIT License 5 votes vote down vote up 
def test_nancov(self):
        targ0 = np.cov(self.arr_float_2d, self.arr_float1_2d)[0, 1]
        targ1 = np.cov(self.arr_float_2d.flat, self.arr_float1_2d.flat)[0, 1]
        self.check_nancorr_nancov_2d(nanops.nancov, targ0, targ1)
        targ0 = np.cov(self.arr_float_1d, self.arr_float1_1d)[0, 1]
        targ1 = np.cov(self.arr_float_1d.flat, self.arr_float1_1d.flat)[0, 1]
        self.check_nancorr_nancov_1d(nanops.nancov, targ0, targ1)