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


        Example:

        >>> df.min("x")
        array(-128.293991)
        >>> df.min(["x", "y"])
        array([-128.293991 ,  -71.5523682])
        >>> df.min("x", binby="x", shape=5, limits=[-10, 10])
        array([-9.99919128, -5.99972439, -1.99991322,  2.0000093 ,  6.0004878 ])

        :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}, the last dimension is of shape (2)
        """
        return self._compute_agg('min', expression, binby, limits, shape, selection, delay, edges, progress)
        @delayed
        def finish(result):
            return result[..., 0]
        return self._delay(delay, finish(self.minmax(expression, binby=binby, limits=limits, shape=shape, selection=selection, delay=delay, progress=progress)))
def min(self, expression, binby=[], limits=None, shape=default_shape, selection=False, delay=False, progress=None, edges=False):
        """Calculate the minimum for given expressions, possibly on a grid defined by binby.


        Example:

        >>> df.min("x")
        array(-128.293991)
        >>> df.min(["x", "y"])
        array([-128.293991 ,  -71.5523682])
        >>> df.min("x", binby="x", shape=5, limits=[-10, 10])
        array([-9.99919128, -5.99972439, -1.99991322,  2.0000093 ,  6.0004878 ])

        :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}, the last dimension is of shape (2)
        """
        return self._compute_agg('min', expression, binby, limits, shape, selection, delay, edges, progress)
        @delayed
        def finish(result):
            return result[..., 0]
        return self._delay(delay, finish(self.minmax(expression, binby=binby, limits=limits, shape=shape, selection=selection, delay=delay, progress=progress)))
Calculate the minimum for given expressions, possibly on a grid defined by binby.


        Example:

        >>> df.min("x")
        array(-128.293991)
        >>> df.min(["x", "y"])
        array([-128.293991 ,  -71.5523682])
        >>> df.min("x", binby="x", shape=5, limits=[-10, 10])
        array([-9.99919128, -5.99972439, -1.99991322,  2.0000093 ,  6.0004878 ])

        :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}, the last dimension is of shape (2)