def install_pip(self, package, save=True, save_dev=False):
        """Installs a pip package.

        :param save: if `True`, pins the package to the Hydrogen requirements
            YAML file.
        :param save_dev: if `True`, pins the package as a development
            dependency to the Hydrogen requirements YAML file.
        :param return: a **single** :class:`Requirement` object, representing
            the installed version of the given package.
        """
        requirement = Requirement.coerce(package)
        click.echo("pip install " + requirement.package)
        cmd = envoy.run("pip install {}".format(str(requirement)))
        if cmd.status_code == 0:
            installed_packages = get_installed_pypackages()
            package = installed_packages[requirement.package]
            requirement.version = "=={}".format(package.version)
            if save:
                self.requirements["all"].add(requirement)
            if save_dev:
                self.requirements["dev"].add(requirement)
            if save or save_dev:
                self.requirements.save()
            return requirement
        else:
            fatal(cmd.std_err)
def iloc(cls, dataset, index):
        import cudf

        rows, cols = index
        scalar = False
        columns = list(dataset.data.columns)
        if isinstance(cols, slice):
            cols = [d.name for d in dataset.dimensions()][cols]
        elif np.isscalar(cols):
            scalar = np.isscalar(rows)
            cols = [dataset.get_dimension(cols).name]
        else:
            cols = [dataset.get_dimension(d).name for d in index[1]]
        col_index = [columns.index(c) for c in cols]
        if np.isscalar(rows):
            rows = [rows]

        if scalar:
            return dataset.data[cols[0]].iloc[rows[0]]
        result = dataset.data.iloc[rows, col_index]

        # cuDF does not handle single rows and cols indexing correctly
        # as of cudf=0.10.0 so we have to convert Series back to DataFrame
        if isinstance(result, cudf.Series):
            if len(cols) == 1:
                result = result.to_frame(cols[0])
            else:
                result = result.to_frame().T
        return result