Python pandas.core.frame.DataFrame._from_arrays() Examples

The following are 5 code examples of pandas.core.frame.DataFrame._from_arrays(). 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.frame.DataFrame , or try the search function .
Example #1
Source File: generic.py    From recruit with Apache License 2.0 6 votes vote down vote up 
def _transform_fast(self, result, obj, func_nm):
        """
        Fast transform path for aggregations
        """
        # if there were groups with no observations (Categorical only?)
        # try casting data to original dtype
        cast = self._transform_should_cast(func_nm)

        # for each col, reshape to to size of original frame
        # by take operation
        ids, _, ngroup = self.grouper.group_info
        output = []
        for i, _ in enumerate(result.columns):
            res = algorithms.take_1d(result.iloc[:, i].values, ids)
            if cast:
                res = self._try_cast(res, obj.iloc[:, i])
            output.append(res)

        return DataFrame._from_arrays(output, columns=result.columns,
                                      index=obj.index)
Example #2
Source File: groupby.py    From vnpy_crypto with MIT License 6 votes vote down vote up 
def _transform_fast(self, result, obj, func_nm):
        """
        Fast transform path for aggregations
        """
        # if there were groups with no observations (Categorical only?)
        # try casting data to original dtype
        cast = self._transform_should_cast(func_nm)

        # for each col, reshape to to size of original frame
        # by take operation
        ids, _, ngroup = self.grouper.group_info
        output = []
        for i, _ in enumerate(result.columns):
            res = algorithms.take_1d(result.iloc[:, i].values, ids)
            if cast:
                res = self._try_cast(res, obj.iloc[:, i])
            output.append(res)

        return DataFrame._from_arrays(output, columns=result.columns,
                                      index=obj.index)
Example #3
Source File: generic.py    From predictive-maintenance-using-machine-learning with Apache License 2.0 6 votes vote down vote up 
def _transform_fast(self, result, obj, func_nm):
        """
        Fast transform path for aggregations
        """
        # if there were groups with no observations (Categorical only?)
        # try casting data to original dtype
        cast = self._transform_should_cast(func_nm)

        # for each col, reshape to to size of original frame
        # by take operation
        ids, _, ngroup = self.grouper.group_info
        output = []
        for i, _ in enumerate(result.columns):
            res = algorithms.take_1d(result.iloc[:, i].values, ids)
            if cast:
                res = self._try_cast(res, obj.iloc[:, i])
            output.append(res)

        return DataFrame._from_arrays(output, columns=result.columns,
                                      index=obj.index)
Example #4
Source File: groupby.py    From Splunking-Crime with GNU Affero General Public License v3.0 6 votes vote down vote up 
def _transform_fast(self, result, obj):
        """
        Fast transform path for aggregations
        """
        # if there were groups with no observations (Categorical only?)
        # try casting data to original dtype
        cast = (self.size().fillna(0) > 0).any()

        # for each col, reshape to to size of original frame
        # by take operation
        ids, _, ngroup = self.grouper.group_info
        output = []
        for i, _ in enumerate(result.columns):
            res = algorithms.take_1d(result.iloc[:, i].values, ids)
            if cast:
                res = self._try_cast(res, obj.iloc[:, i])
            output.append(res)

        return DataFrame._from_arrays(output, columns=result.columns,
                                      index=obj.index)
Example #5
Source File: groupby.py    From elasticintel with GNU General Public License v3.0 6 votes vote down vote up 
def _transform_fast(self, result, obj):
        """
        Fast transform path for aggregations
        """
        # if there were groups with no observations (Categorical only?)
        # try casting data to original dtype
        cast = (self.size().fillna(0) > 0).any()

        # for each col, reshape to to size of original frame
        # by take operation
        ids, _, ngroup = self.grouper.group_info
        output = []
        for i, _ in enumerate(result.columns):
            res = algorithms.take_1d(result.iloc[:, i].values, ids)
            if cast:
                res = self._try_cast(res, obj.iloc[:, i])
            output.append(res)

        return DataFrame._from_arrays(output, columns=result.columns,
                                      index=obj.index)