Python pandas._libs.hashtable.mode_int64() Examples

The following are 5 code examples of pandas._libs.hashtable.mode_int64(). 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._libs.hashtable , or try the search function .
Example #1
Source File: categorical.py    From recruit with Apache License 2.0 6 votes vote down vote up 
def mode(self, dropna=True):
        """
        Returns the mode(s) of the Categorical.

        Always returns `Categorical` even if only one value.

        Parameters
        ----------
        dropna : boolean, default True
            Don't consider counts of NaN/NaT.

            .. versionadded:: 0.24.0

        Returns
        -------
        modes : `Categorical` (sorted)
        """

        import pandas._libs.hashtable as htable
        codes = self._codes
        if dropna:
            good = self._codes != -1
            codes = self._codes[good]
        codes = sorted(htable.mode_int64(ensure_int64(codes), dropna))
        return self._constructor(values=codes, dtype=self.dtype, fastpath=True)
Example #2
Source File: categorical.py    From vnpy_crypto with MIT License 6 votes vote down vote up 
def mode(self):
        """
        Returns the mode(s) of the Categorical.

        Always returns `Categorical` even if only one value.

        Returns
        -------
        modes : `Categorical` (sorted)
        """

        import pandas._libs.hashtable as htable
        good = self._codes != -1
        values = sorted(htable.mode_int64(_ensure_int64(self._codes[good])))
        result = self._constructor(values=values, categories=self.categories,
                                   ordered=self.ordered, fastpath=True)
        return result
Example #3
Source File: categorical.py    From predictive-maintenance-using-machine-learning with Apache License 2.0 6 votes vote down vote up 
def mode(self, dropna=True):
        """
        Returns the mode(s) of the Categorical.

        Always returns `Categorical` even if only one value.

        Parameters
        ----------
        dropna : boolean, default True
            Don't consider counts of NaN/NaT.

            .. versionadded:: 0.24.0

        Returns
        -------
        modes : `Categorical` (sorted)
        """

        import pandas._libs.hashtable as htable
        codes = self._codes
        if dropna:
            good = self._codes != -1
            codes = self._codes[good]
        codes = sorted(htable.mode_int64(ensure_int64(codes), dropna))
        return self._constructor(values=codes, dtype=self.dtype, fastpath=True)
Example #4
Source File: categorical.py    From Splunking-Crime with GNU Affero General Public License v3.0 6 votes vote down vote up 
def mode(self):
        """
        Returns the mode(s) of the Categorical.

        Always returns `Categorical` even if only one value.

        Returns
        -------
        modes : `Categorical` (sorted)
        """

        import pandas._libs.hashtable as htable
        good = self._codes != -1
        values = sorted(htable.mode_int64(_ensure_int64(self._codes[good])))
        result = self._constructor(values=values, categories=self.categories,
                                   ordered=self.ordered, fastpath=True)
        return result
Example #5
Source File: categorical.py    From elasticintel with GNU General Public License v3.0 6 votes vote down vote up 
def mode(self):
        """
        Returns the mode(s) of the Categorical.

        Always returns `Categorical` even if only one value.

        Returns
        -------
        modes : `Categorical` (sorted)
        """

        import pandas._libs.hashtable as htable
        good = self._codes != -1
        values = sorted(htable.mode_int64(_ensure_int64(self._codes[good])))
        result = self._constructor(values=values, categories=self.categories,
                                   ordered=self.ordered, fastpath=True)
        return result