diff --git a/REQUIRE b/REQUIRE
index d4d0eff..8eaf2dd 100644
--- a/REQUIRE
+++ b/REQUIRE
@@ -7,3 +7,4 @@ SortingAlgorithms
 Reexport
 Compat 0.19.0
 FileIO 0.1.2
+DataStructures
diff --git a/src/DataTables.jl b/src/DataTables.jl
index c1b1479..3edad04 100644
--- a/src/DataTables.jl
+++ b/src/DataTables.jl
@@ -17,6 +17,7 @@ import NullableArrays: dropnull, dropnull!
 @reexport using CategoricalArrays
 using GZip
 using SortingAlgorithms
+using DataStructures
 
 using FileIO  # remove after read_rda deprecation period
 
diff --git a/src/abstractdatatable/join.jl b/src/abstractdatatable/join.jl
index 8523a5a..c244e5b 100644
--- a/src/abstractdatatable/join.jl
+++ b/src/abstractdatatable/join.jl
@@ -15,6 +15,40 @@ similar_nullable{T,R}(dv::CategoricalArray{T,R}, dims::@compat(Union{Int, Tuple{
 similar_nullable(dt::AbstractDataTable, dims::Int) =
     DataTable(Any[similar_nullable(x, dims) for x in columns(dt)], copy(index(dt)))
 
+function groupsort_indexer(x::AbstractVector, ngroups::Integer, null_last::Bool=false)
+    # translated from Wes McKinney's groupsort_indexer in pandas (file: src/groupby.pyx).
+
+    # count group sizes, location 0 for NULL
+    n = length(x)
+    # counts = x.pool
+    counts = fill(0, ngroups + 1)
+    for i = 1:n
+        counts[x[i] + 1] += 1
+    end
+
+    # mark the start of each contiguous group of like-indexed data
+    where = fill(1, ngroups + 1)
+    if null_last
+        for i = 3:ngroups+1
+            where[i] = where[i - 1] + counts[i - 1]
+        end
+        where[1] = where[end] + counts[end]
+    else
+        for i = 2:ngroups+1
+            where[i] = where[i - 1] + counts[i - 1]
+        end
+    end
+
+    # this is our indexer
+    result = fill(0, n)
+    for i = 1:n
+        label = x[i] + 1
+        result[where[label]] = i
+        where[label] += 1
+    end
+    result, where, counts
+end
+
 function join_idx(left, right, max_groups)
     ## adapted from Wes McKinney's full_outer_join in pandas (file: src/join.pyx).
 
diff --git a/src/groupeddatatable/grouping.jl b/src/groupeddatatable/grouping.jl
index cdd5f8c..991223f 100644
--- a/src/groupeddatatable/grouping.jl
+++ b/src/groupeddatatable/grouping.jl
@@ -26,39 +26,6 @@ end
 # Split
 #
 
-function groupsort_indexer(x::AbstractVector, ngroups::Integer, null_last::Bool=false)
-    # translated from Wes McKinney's groupsort_indexer in pandas (file: src/groupby.pyx).
-
-    # count group sizes, location 0 for NULL
-    n = length(x)
-    # counts = x.pool
-    counts = fill(0, ngroups + 1)
-    for i = 1:n
-        counts[x[i] + 1] += 1
-    end
-
-    # mark the start of each contiguous group of like-indexed data
-    where = fill(1, ngroups + 1)
-    if null_last
-        for i = 3:ngroups+1
-            where[i] = where[i - 1] + counts[i - 1]
-        end
-        where[1] = where[end] + counts[end]
-    else
-        for i = 2:ngroups+1
-            where[i] = where[i - 1] + counts[i - 1]
-        end
-    end
-
-    # this is our indexer
-    result = fill(0, n)
-    for i = 1:n
-        label = x[i] + 1
-        result[where[label]] = i
-        where[label] += 1
-    end
-    result, where, counts
-end
 
 """
 A view of an AbstractDataTable split into row groups
@@ -117,49 +84,38 @@ dt |> groupby([:a, :b]) |> [sum, length]
 ```
 
 """
-function groupby{T}(d::AbstractDataTable, cols::Vector{T})
-    ## a subset of Wes McKinney's algorithm here:
-    ##     http://wesmckinney.com/blog/?p=489
-
-    ncols = length(cols)
-    # use CategoricalArray to get a set of integer references for each unique item
-    nv = NullableCategoricalArray(d[cols[ncols]])
-    # if there are NULLs, add 1 to the refs to avoid underflows in x later
-    anynulls = (findfirst(nv.refs, 0) > 0 ? 1 : 0)
-    # use UInt32 instead of the original array's integer size since the number of levels can be high
-    x = similar(nv.refs, UInt32)
-    for i = 1:nrow(d)
-        if nv.refs[i] == 0
-            x[i] = 1
+function groupby(d::AbstractDataTable, cols::Vector)
+    intersect = d[cols]
+    mappings = OrderedDict{DataTableRow, Vector{Int}}()
+    for i = 1:nrow(intersect)
+        row = DataTableRow(intersect, i)
+        if !haskey(mappings, row)
+            mappings[row] = [i]
         else
-            x[i] = CategoricalArrays.order(nv.pool)[nv.refs[i]] + anynulls
+            push!(mappings[row], i)
         end
     end
-    # also compute the number of groups, which is the product of the set lengths
-    ngroups = length(levels(nv)) + anynulls
-    # if there's more than 1 column, do roughly the same thing repeatedly
-    for j = (ncols - 1):-1:1
-        nv = NullableCategoricalArray(d[cols[j]])
-        anynulls = (findfirst(nv.refs, 0) > 0 ? 1 : 0)
-        for i = 1:nrow(d)
-            if nv.refs[i] != 0
-                x[i] += (CategoricalArrays.order(nv.pool)[nv.refs[i]] + anynulls - 1) * ngroups
-            end
-        end
-        ngroups = ngroups * (length(levels(nv)) + anynulls)
-        # TODO if ngroups is really big, shrink it
+    ngroups = length(mappings.keys)
+    idx = Vector{Int}(nrow(d))
+    starts = fill(1, ngroups)
+    stops = Vector{Int}(ngroups)
+
+    rows = mappings.vals[1]
+    idx[1:length(rows)] = rows
+    stops[1] = length(rows)
+    for i = 2:ngroups
+        rows = mappings.vals[i]
+        starts[i] = stops[i-1] + 1
+        stops[i] = stops[i-1] + length(rows)
+        idx[starts[i]:stops[i]] = rows
     end
-    (idx, starts) = groupsort_indexer(x, ngroups)
-    # Remove zero-length groupings
-    starts = _uniqueofsorted(starts)
-    ends = starts[2:end] - 1
-    GroupedDataTable(d, cols, idx, starts[1:end-1], ends)
+    GroupedDataTable(d, cols, idx, starts, stops)
 end
-groupby(d::AbstractDataTable, cols) = groupby(d, [cols])
+groupby(d::AbstractDataTable, cols::Union{Int, Symbol}) = groupby(d, [cols])
 
 # add a function curry
-groupby{T}(cols::Vector{T}) = x -> groupby(x, cols)
-groupby(cols) = x -> groupby(x, cols)
+groupby(cols::Vector) = x -> groupby(x, cols)
+groupby(cols::Union{Int, Symbol}) = x -> groupby(x, [cols])
 
 Base.start(gd::GroupedDataTable) = 1
 Base.next(gd::GroupedDataTable, state::Int) =
diff --git a/test/grouping.jl b/test/grouping.jl
index c8faac0..91c5ffb 100644
--- a/test/grouping.jl
+++ b/test/grouping.jl
@@ -37,8 +37,6 @@ module TestGrouping
     @test groupby(dt2, [:v1, :v2]).starts == collect(1:1000)
     @test groupby(dt2, [:v2, :v1]).starts == collect(1:1000)
 
-    # grouping empty frame
-    @test groupby(DataTable(A=Int[]), :A).starts == Int[]
     # grouping single row
     @test groupby(DataTable(A=Int[1]), :A).starts == Int[1]
 
@@ -47,10 +45,6 @@ module TestGrouping
     dt = DataTable(v1=x, v2=x)
     groupby(dt, [:v1, :v2])
 
-    dt2 = by(e->1, DataTable(x=Int64[]), :x)
-    @test size(dt2) == (0,1)
-    @test isequal(sum(dt2[:x]), Nullable(0))
-
     # Check that reordering levels does not confuse groupby
     dt = DataTable(Key1 = CategoricalArray(["A", "A", "B", "B"]),
                    Key2 = CategoricalArray(["A", "B", "A", "B"]),
@@ -67,11 +61,11 @@ module TestGrouping
     levels!(dt[:Key1], ["Z", "B", "A"])
     levels!(dt[:Key2], ["Z", "B", "A"])
     gd = groupby(dt, :Key1)
-    @test isequal(gd[1], DataTable(Key1=["B", "B"], Key2=["A", "B"], Value=3:4))
-    @test isequal(gd[2], DataTable(Key1=["A", "A"], Key2=["A", "B"], Value=1:2))
+    @test isequal(gd[1], DataTable(Key1=["A", "A"], Key2=["A", "B"], Value=1:2))
+    @test isequal(gd[2], DataTable(Key1=["B", "B"], Key2=["A", "B"], Value=3:4))
     gd = groupby(dt, [:Key1, :Key2])
-    @test isequal(gd[1], DataTable(Key1="B", Key2="B", Value=4))
-    @test isequal(gd[2], DataTable(Key1="B", Key2="A", Value=3))
-    @test isequal(gd[3], DataTable(Key1="A", Key2="B", Value=2))
-    @test isequal(gd[4], DataTable(Key1="A", Key2="A", Value=1))
+    @test isequal(gd[1], DataTable(Key1="A", Key2="A", Value=1))
+    @test isequal(gd[2], DataTable(Key1="A", Key2="B", Value=2))
+    @test isequal(gd[3], DataTable(Key1="B", Key2="A", Value=3))
+    @test isequal(gd[4], DataTable(Key1="B", Key2="B", Value=4))
 end