(data, columns)
Compute the distributions for columns.

    Parameters
    ----------
    data : data.Table
        List of column indices into the `data.domain` (indices can be
        :class:`int` or instances of `Orange.data.Variable`)
Compute the distributions for columns.
Compute the distributions for columns .
def get_distributions_for_columns(data, columns):
    """Compute the distributions for columns.

    Parameters
    ----------
    data : data.Table
        List of column indices into the `data.domain` (indices can be
        :class:`int` or instances of `Orange.data.Variable`)

    """
    domain = data.domain
    # Normailze the columns to int indices
    columns = [col if isinstance(col, int) else domain.index(col) for col in columns]
    try:
        # Try the optimized code path (query the table|storage directly).
        dist_unks = data._compute_distributions(columns)
    except NotImplementedError:
        # Use default slow(er) implementation.
        return [get_distribution(data, i) for i in columns]
    else:
        # dist_unkn is a list of (values, unknowns)
        return [get_distribution(dist, domain[col], unknown)
                for col, (dist, unknown) in zip(columns, dist_unks)]
def get_distributions_for_columns ( data , columns ) : domain = data . domain # Normailze the columns to int indices columns = [ col if isinstance ( col , int ) else domain . index ( col ) for col in columns ] try : # Try the optimized code path (query the table|storage directly). dist_unks = data . _compute_distributions ( columns ) except NotImplementedError : # Use default slow(er) implementation. return [ get_distribution ( data , i ) for i in columns ] else : # dist_unkn is a list of (values, unknowns) return [ get_distribution ( dist , domain [ col ] , unknown ) for col , ( dist , unknown ) in zip ( columns , dist_unks ) ]