DataFrame Question

DataFrame question: Today we use DataTables and LINQ to perform operations such as multi-column GroupBy, aggregations (Sum, Count, Average), joins, filtering, and calculated columns. Do DataFrames support these types of data-shaping operations natively, and if so, are they generally more efficient than DataTable + LINQ for in-memory transformations? For example, replacing a multi-column GroupBy + Sum pattern like the one below: 

// Group by Entity, ProductType, ProductLine, Account, Channel, sum(LOCALAMOUNT)
DataTable result = dt.AsEnumerable()
    .GroupBy(r => new
    {
        ENTITY = r.Field<string>("ENTITY"),
        PRODUCTTYPE = r.Field<string>("PRODUCTTYPE"),
        PRODUCTLINE = r.Field<string>("PRODUCTLINE"),
        ACCOUNT = r.Field<string>("ACCOUNT"),
        CHANNEL = r.Field<string>("CHANNEL")
    })
    .Select(g =>
    {
        DataRow row = dt.NewRow();
        row["ENTITY"] = g.Key.ENTITY;
        row["PRODUCTTYPE"] = g.Key.PRODUCTTYPE;
        row["PRODUCTLINE"] = g.Key.PRODUCTLINE;
        row["ACCOUNT"] = g.Key.ACCOUNT;
        row["CHANNEL"] = g.Key.CHANNEL;
        row["Amount"] = g.Sum(r => Convert.ToDecimal(r["LOCALAMOUNT"]));
        return row;
    })
    .CopyToDataTable();

return result;