packages/vaex-core/vaex/dataframe.py
def mean(self, expression, binby=[], limits=None, shape=default_shape, selection=False, delay=False, progress=None, edges=False):
        """Calculate the mean for expression, possibly on a grid defined by binby.

        Example:

        >>> df.mean("x")
        -0.067131491264005971
        >>> df.mean("(x**2+y**2)**0.5", binby="E", shape=4)
        array([  2.43483742,   4.41840721,   8.26742458,  15.53846476])

        :param expression: {expression}
        :param binby: {binby}
        :param limits: {limits}
        :param shape: {shape}
        :param selection: {selection}
        :param delay: {delay}
        :param progress: {progress}
        :return: {return_stat_scalar}
        """
        return self._compute_agg('mean', expression, binby, limits, shape, selection, delay, edges, progress)
        logger.debug("mean of %r, with binby=%r, limits=%r, shape=%r, selection=%r, delay=%r", expression, binby, limits, shape, selection, delay)
        expression = _ensure_strings_from_expressions(expression)
        selection = _ensure_strings_from_expressions(selection)
        binby = _ensure_strings_from_expressions(binby)

        @delayed
        def calculate(expression, limits):
            task = tasks.TaskStatistic(self, binby, shape, limits, weight=expression, op=tasks.OP_ADD_WEIGHT_MOMENTS_01, selection=selection)
            self.executor.schedule(task)
            progressbar.add_task(task, "mean for %s" % expression)
            return task

        @delayed
        def finish(*stats_args):
            stats = np.array(stats_args)
            counts = stats[..., 0]
            with np.errstate(divide='ignore', invalid='ignore'):
                mean = stats[..., 1] / counts
            return vaex.utils.unlistify(waslist, mean)
        waslist, [expressions, ] = vaex.utils.listify(expression)
        progressbar = vaex.utils.progressbars(progress)
        limits = self.limits(binby, limits, delay=True)
        stats = [calculate(expression, limits) for expression in expressions]
        var = finish(*stats)
        return self._delay(delay, var)
def mean(self, expression, binby=[], limits=None, shape=default_shape, selection=False, delay=False, progress=None, edges=False):
        return self._compute_agg('mean', expression, binby, limits, shape, selection, delay, edges, progress)
        logger.debug("mean of %r, with binby=%r, limits=%r, shape=%r, selection=%r, delay=%r", expression, binby, limits, shape, selection, delay)
        expression = _ensure_strings_from_expressions(expression)
        selection = _ensure_strings_from_expressions(selection)
        binby = _ensure_strings_from_expressions(binby)

        @delayed
        def calculate(expression, limits):
            task = tasks.TaskStatistic(self, binby, shape, limits, weight=expression, op=tasks.OP_ADD_WEIGHT_MOMENTS_01, selection=selection)
            self.executor.schedule(task)
            progressbar.add_task(task, "mean for %s" % expression)
            return task

        @delayed
        def finish(*stats_args):
            stats = np.array(stats_args)
            counts = stats[..., 0]
            with np.errstate(divide='ignore', invalid='ignore'):
                mean = stats[..., 1] / counts
            return vaex.utils.unlistify(waslist, mean)
        waslist, [expressions, ] = vaex.utils.listify(expression)
        progressbar = vaex.utils.progressbars(progress)
        limits = self.limits(binby, limits, delay=True)
        stats = [calculate(expression, limits) for expression in expressions]
        var = finish(*stats)
        return self._delay(delay, var)
def mean ( self , expression , binby = [ ] , limits = None , shape = default_shape , selection = False , delay = False , progress = None , edges = False ) : return self . _compute_agg ( 'mean' , expression , binby , limits , shape , selection , delay , edges , progress ) logger . debug ( "mean of %r, with binby=%r, limits=%r, shape=%r, selection=%r, delay=%r" , expression , binby , limits , shape , selection , delay ) expression = _ensure_strings_from_expressions ( expression ) selection = _ensure_strings_from_expressions ( selection ) binby = _ensure_strings_from_expressions ( binby ) @ delayed def calculate ( expression , limits ) : task = tasks . TaskStatistic ( self , binby , shape , limits , weight = expression , op = tasks . OP_ADD_WEIGHT_MOMENTS_01 , selection = selection ) self . executor . schedule ( task ) progressbar . add_task ( task , "mean for %s" % expression ) return task @ delayed def finish ( * stats_args ) : stats = np . array ( stats_args ) counts = stats [ ... , 0 ] with np . errstate ( divide = 'ignore' , invalid = 'ignore' ) : mean = stats [ ... , 1 ] / counts return vaex . utils . unlistify ( waslist , mean ) waslist , [ expressions , ] = vaex . utils . listify ( expression ) progressbar = vaex . utils . progressbars ( progress ) limits = self . limits ( binby , limits , delay = True ) stats = [ calculate ( expression , limits ) for expression in expressions ] var = finish ( * stats ) return self . _delay ( delay , var )
Calculate the mean for expression, possibly on a grid defined by binby.

        Example:

        >>> df.mean("x")
        -0.067131491264005971
        >>> df.mean("(x**2+y**2)**0.5", binby="E", shape=4)
        array([  2.43483742,   4.41840721,   8.26742458,  15.53846476])

        :param expression: {expression}
        :param binby: {binby}
        :param limits: {limits}
        :param shape: {shape}
        :param selection: {selection}
        :param delay: {delay}
        :param progress: {progress}
        :return: {return_stat_scalar}
Calculate the mean for expression possibly on a grid defined by binby .