diff --git a/binder/nanoevents.ipynb b/binder/nanoevents.ipynb
index 4d21ab695..5b30d4652 100644
--- a/binder/nanoevents.ipynb
+++ b/binder/nanoevents.ipynb
@@ -15,7 +15,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -49,40 +49,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Array [1, -1, -1, 21, 21, ... 2, -2, -1, 2, 1] type='40 * int32[parameters={\"__...'>"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "events.Generator.id1"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['binvar', 'id1', 'id2', 'scalePDF', 'weight', 'x1', 'x2', 'xpdf1', 'xpdf2']"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "# all names can be listed with:\n",
     "events.Generator.fields"
@@ -106,7 +84,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -122,20 +100,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['eta', 'mass', 'phi', 'pt', 'partonFlavour', 'hadronFlavour']"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "events.GenJet.fields"
    ]
@@ -149,20 +116,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Array [[217, 670, 258], ... 16], [76.9]] type='40 * var * float32'>"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "events.GenJet.energy"
    ]
@@ -176,20 +132,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Array [[], [3.13], [3.45, ... 0.0858], [], []] type='40 * var * float32'>"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "# find distance between leading jet and all electrons in each event\n",
     "dr = events.Jet[:, 0].delta_r(events.Electron)\n",
@@ -198,20 +143,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Array [None, 3.13, 2.18, ... None, None] type='40 * ?float32'>"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "# find minimum distance\n",
     "ak.min(dr, axis=1)"
@@ -228,18 +162,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[[6, 6, 6, 6, 6], [6, 2, 6, 6, 6, 6, 6, 0], ... 6], [6], [6, 6, 0, 6, 6, 6], [6, 6]]\n",
-      "[[True, True, True, True, True], [True, ... False, True, True, True], [True, True]]\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "print(events.Jet.jetId)\n",
     "print(events.Jet.isTight)"
@@ -254,27 +179,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Raw status flags: [[10625, 27009, 4481, 22913, 257, 257, ... 13884, 13884, 12876, 12876, 12876, 12876]]\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<Array [[True, True, False, ... False, False]] type='40 * var * bool'>"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "print(f\"Raw status flags: {events.GenPart.statusFlags}\")\n",
     "events.GenPart.hasFlags(['isPrompt', 'isLastCopy'])"
@@ -289,40 +196,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Array [[], [-11], [-11, ... [None], [], []] type='40 * var * ?int32[parameters=...'>"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "events.Electron.matched_gen.pdgId"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Array [[], [], [], [], ... [], [], [], []] type='40 * var * ?float32[parameters...'>"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "events.Muon.matched_jet.pt"
    ]
@@ -336,20 +221,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Array [[None, None, 1, 1, ... 111, 111, 111]] type='40 * var * ?int32[parameter...'>"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "events.GenPart.parent.pdgId"
    ]
@@ -363,28 +237,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/lagray/miniconda3/envs/coffea-work/lib/python3.7/site-packages/numba/core/dispatcher.py:238: UserWarning: Numba extension module 'awkward1._connect._numba' failed to load due to 'ImportError(generic_type: type \"kernel_lib\" is already registered!)'.\n",
-      "  entrypoints.init_all()\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<Array [[None, None, [23, 21, ... [22, 22]]] type='40 * var * option[var * ?int3...'>"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "events.GenPart.parent.children.pdgId\n",
     "# notice this is a doubly-jagged array"
@@ -399,20 +254,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Array [[], [23, 23], [23, ... [23, 23], []] type='40 * var * ?int32[parameters=...'>"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "events.GenPart[\n",
     "    (abs(events.GenPart.pdgId) == 11)\n",
@@ -429,20 +273,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Array [94.6, 87.6, 88, 90.4, 89.1, 31.6] type='6 * float32'>"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "mmevents = events[ak.num(events.Muon) == 2]\n",
     "zmm = mmevents.Muon[:, 0] + mmevents.Muon[:, 1]\n",
@@ -462,20 +295,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Array [[], [121], [], [], [], []] type='6 * var * float32'>"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "mmevents[\"Electron\", \"myvar2\"] = mmevents.Electron.pt + zmm.mass\n",
     "mmevents.Electron.myvar2"
@@ -492,37 +314,36 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "from coffea import processor, hist\n",
+    "from hist import Hist\n",
+    "from coffea import processor\n",
     "\n",
     "class MyZPeak(processor.ProcessorABC):\n",
     "    def __init__(self):\n",
-    "        self._histo = hist.Hist(\n",
-    "            \"Events\",\n",
-    "            hist.Cat(\"dataset\", \"Dataset\"),\n",
-    "            hist.Bin(\"mass\", \"Z mass\", 60, 60, 120),\n",
-    "        )\n",
-    "    \n",
-    "    @property\n",
-    "    def accumulator(self):\n",
-    "        return self._histo\n",
+    "        pass\n",
     "    \n",
     "    # we will receive a NanoEvents instead of a coffea DataFrame\n",
     "    def process(self, events):\n",
-    "        out = self.accumulator.identity()\n",
+    "        out = (\n",
+    "            Hist.new\n",
+    "            .Reg(60, 60, 120, name=\"mass\", label=\"Z mass\")\n",
+    "            .Int64()\n",
+    "        )\n",
+    "\n",
     "        mmevents = events[\n",
     "            (ak.num(events.Muon) == 2)\n",
     "            & (ak.sum(events.Muon.charge, axis=1) == 0)\n",
     "        ]\n",
+    "\n",
     "        zmm = mmevents.Muon[:, 0] + mmevents.Muon[:, 1]\n",
+    "    \n",
     "        out.fill(\n",
-    "            dataset=events.metadata[\"dataset\"],\n",
     "            mass=zmm.mass,\n",
     "        )\n",
-    "        return out\n",
+    "        return { events.metadata[\"dataset\"]: out }\n",
     "    \n",
     "    def postprocess(self, accumulator):\n",
     "        return accumulator"
@@ -530,99 +351,44 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "472dc44091634fb7a612026ef3cdf734",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(HTML(value='Preprocessing'), FloatProgress(value=0.0, max=1.0), HTML(value='')))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "ff7565e2b2d442318e92e07dd537e884",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(HTML(value='Processing'), FloatProgress(value=0.0, max=1.0), HTML(value='')))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "samples = {\n",
     "    \"DrellYan\": [fname]\n",
     "}\n",
     "\n",
-    "result = processor.run_uproot_job(\n",
+    "run = processor.Runner(\n",
+    "    executor = processor.IterativeExecutor(compression=None),\n",
+    "    schema=BaseSchema,\n",
+    ")\n",
+    "\n",
+    "result = run(\n",
     "    samples,\n",
-    "    \"Events\",\n",
-    "    MyZPeak(),\n",
-    "    processor.iterative_executor,\n",
-    "    {\"schema\": NanoAODSchema},\n",
+    "    treename=\"Events\",\n",
+    "    processor_instance=MyProcessor(),\n",
     ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<AxesSubplot:xlabel='Z mass', ylabel='Events'>"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "%matplotlib inline\n",
+    "import mplhep as hep\n",
     "\n",
-    "hist.plot1d(result)"
+    "hep.histplot([result[ds] for ds in result.keys()], stack=True, label=[ds for ds in result.keys()])"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
@@ -641,7 +407,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.9"
+   "version": "3.7.10"
   }
  },
  "nbformat": 4,
diff --git a/binder/processor.ipynb b/binder/processor.ipynb
index 24419965b..d76dee4d3 100644
--- a/binder/processor.ipynb
+++ b/binder/processor.ipynb
@@ -26,12 +26,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "import awkward as ak\n",
-    "from coffea import hist, processor\n",
+    "from coffea import processor\n",
+    "from hist import Hist\n",
     "\n",
     "# register our candidate behaviors\n",
     "from coffea.nanoevents.methods import candidate\n",
@@ -39,21 +40,15 @@
     "\n",
     "class MyProcessor(processor.ProcessorABC):\n",
     "    def __init__(self):\n",
-    "        self._accumulator = processor.dict_accumulator({\n",
-    "            \"sumw\": processor.defaultdict_accumulator(float),\n",
-    "            \"mass\": hist.Hist(\n",
-    "                \"Events\",\n",
-    "                hist.Cat(\"dataset\", \"Dataset\"),\n",
-    "                hist.Bin(\"mass\", \"$m_{\\mu\\mu}$ [GeV]\", 60, 60, 120),\n",
-    "            ),\n",
-    "        })\n",
-    "\n",
-    "    @property\n",
-    "    def accumulator(self):\n",
-    "        return self._accumulator\n",
+    "        pass\n",
     "\n",
     "    def process(self, events):\n",
-    "        output = self.accumulator.identity()\n",
+    "        h_mass = (\n",
+    "            Hist.new\n",
+    "            .StrCategory([], name=\"dataset\", growth=True)\n",
+    "            .Reg(60, 60, 120, name=\"mass\", label=\"Z mass\")\n",
+    "            .Int64()\n",
+    "        )\n",
     "\n",
     "        dataset = events.metadata['dataset']\n",
     "        muons = ak.zip({\n",
@@ -68,13 +63,15 @@
     "        # add first and second muon in every event together\n",
     "        dimuon = muons[cut][:, 0] + muons[cut][:, 1]\n",
     "        \n",
-    "        output[\"sumw\"][dataset] += len(events)\n",
-    "        output[\"mass\"].fill(\n",
+    "        h_mass.fill(\n",
     "            dataset=dataset,\n",
     "            mass=dimuon.mass,\n",
     "        )\n",
     "\n",
-    "        return output\n",
+    "        return { \n",
+    "            \"sumw\": {dataset: float(len(events))},\n",
+    "            \"mass\": h_mass,\n",
+    "        }\n",
     "\n",
     "    def postprocess(self, accumulator):\n",
     "        return accumulator"
@@ -94,21 +91,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'sumw': defaultdict_accumulator(float, {'DoubleMuon': 10000.0}),\n",
-       " 'mass': <Hist (dataset,mass) instance at 0x7fcc1e9f4b10>}"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "import uproot\n",
     "from coffea.nanoevents import NanoEventsFactory, BaseSchema\n",
@@ -138,43 +123,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "9c8a04343ede4324948f085a6bdce923",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(HTML(value='Processing'), FloatProgress(value=0.0, max=8.0), HTML(value='')))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "{'sumw': defaultdict_accumulator(float,\n",
-       "                         {'ZZ to 4mu': 399752.0, 'DoubleMuon': 400224.0}),\n",
-       " 'mass': <Hist (dataset,mass) instance at 0x7fcc1ea3c190>}"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "fileset = {\n",
     "    'DoubleMuon': [\n",
@@ -186,15 +137,16 @@
     "    ]\n",
     "}\n",
     "\n",
-    "out = processor.run_uproot_job(\n",
+    "iterative_run = processor.Runner(\n",
+    "    executor = processor.IterativeExecutor(compression=None),\n",
+    "    schema=BaseSchema,\n",
+    "    maxchunks=4,\n",
+    ")\n",
+    "\n",
+    "out = iterative_run(\n",
     "    fileset,\n",
     "    treename=\"Events\",\n",
     "    processor_instance=MyProcessor(),\n",
-    "    executor=processor.iterative_executor,\n",
-    "    executor_args={\n",
-    "        \"schema\": BaseSchema,\n",
-    "    },\n",
-    "    maxchunks=4,\n",
     ")\n",
     "out"
    ]
@@ -208,52 +160,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "a0c7504263344639bb6850f3d3300ecc",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(HTML(value='Processing'), FloatProgress(value=0.0, max=8.0), HTML(value='')))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "{'sumw': defaultdict_accumulator(float,\n",
-       "                         {'ZZ to 4mu': 399752.0, 'DoubleMuon': 400224.0}),\n",
-       " 'mass': <Hist (dataset,mass) instance at 0x7fcc1ed73f10>}"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "out = processor.run_uproot_job(\n",
-    "    fileset,\n",
-    "    treename=\"Events\",\n",
-    "    processor_instance=MyProcessor(),\n",
-    "    executor=processor.futures_executor,\n",
-    "    executor_args={\"schema\": BaseSchema, \"workers\": 2},\n",
+    "futures_run = processor.Runner(\n",
+    "    executor = processor.FuturesExecutor(compression=None, workers=2),\n",
+    "    schema=BaseSchema,\n",
     "    maxchunks=4,\n",
     ")\n",
+    "\n",
+    "out = futures_run(\n",
+    "    fileset,\n",
+    "    \"Events\",\n",
+    "    processor_instance=MyProcessor()\n",
+    ")\n",
     "out"
    ]
   },
@@ -274,10 +195,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
+    "from collections import defaultdict\n",
+    "from coffea import hist\n",
     "from functools import partial\n",
     "import numba\n",
     "\n",
@@ -317,34 +240,26 @@
     "\n",
     "class FancyDimuonProcessor(processor.ProcessorABC):\n",
     "    def __init__(self):\n",
+    "        pass\n",
+    "    \n",
+    "    def process(self, events):\n",
     "        dataset_axis = hist.Cat(\"dataset\", \"Primary dataset\")\n",
     "        mass_axis = hist.Bin(\"mass\", r\"$m_{\\mu\\mu}$ [GeV]\", 300, 0, 300)\n",
     "        pt_axis = hist.Bin(\"pt\", r\"$p_{T,\\mu}$ [GeV]\", 300, 0, 300)\n",
     "        \n",
-    "        self._accumulator = processor.dict_accumulator({\n",
-    "            'nMuons': hist.Hist(\n",
-    "                \"Counts\",\n",
-    "                dataset_axis,\n",
-    "                hist.Bin(\"nMuons\", \"Number of good muons\", 6, 0, 6),\n",
-    "            ),\n",
-    "            'mass': hist.Hist(\"Counts\", dataset_axis, mass_axis),\n",
-    "            'mass_z1': hist.Hist(\"Counts\", dataset_axis, mass_axis),\n",
-    "            'mass_z2': hist.Hist(\"Counts\", dataset_axis, mass_axis),\n",
-    "            'pt_z1_mu1': hist.Hist(\"Counts\", dataset_axis, pt_axis),\n",
-    "            'pt_z1_mu2': hist.Hist(\"Counts\", dataset_axis, pt_axis),\n",
-    "            'cutflow': processor.defaultdict_accumulator(\n",
-    "                # we don't use a lambda function to avoid pickle issues\n",
-    "                partial(processor.defaultdict_accumulator, int)\n",
-    "            ),\n",
-    "        })\n",
-    "    \n",
-    "    @property\n",
-    "    def accumulator(self):\n",
-    "        return self._accumulator\n",
-    "    \n",
-    "    def process(self, events):\n",
-    "        output = self.accumulator.identity()\n",
-    "\n",
+    "        h_nMuons = hist.Hist(\n",
+    "            \"Counts\",\n",
+    "            dataset_axis,\n",
+    "            hist.Bin(\"nMuons\", \"Number of good muons\", 6, 0, 6),\n",
+    "        )\n",
+    "        h_m4mu = hist.Hist(\"Counts\", dataset_axis, mass_axis)\n",
+    "        h_mZ1 = hist.Hist(\"Counts\", dataset_axis, mass_axis)\n",
+    "        h_mZ2 = hist.Hist(\"Counts\", dataset_axis, mass_axis)\n",
+    "        h_ptZ1mu1 = hist.Hist(\"Counts\", dataset_axis, pt_axis)\n",
+    "        h_ptZ1mu2 = hist.Hist(\"Counts\", dataset_axis, pt_axis)\n",
+    "                \n",
+    "        cutflow = defaultdict(int)\n",
+    "        \n",
     "        dataset = events.metadata['dataset']\n",
     "        muons = ak.zip({\n",
     "            \"pt\": events.Muon_pt,\n",
@@ -359,7 +274,7 @@
     "        # make sure they are sorted by transverse momentum\n",
     "        muons = muons[ak.argsort(muons.pt, axis=1)]\n",
     "        \n",
-    "        output['cutflow'][dataset]['all events'] += len(muons)\n",
+    "        cutflow['all events'] += len(muons)\n",
     "        \n",
     "        # impose some quality and minimum pt cuts on the muons\n",
     "        muons = muons[\n",
@@ -367,8 +282,8 @@
     "            & (muons.pt > 5)\n",
     "            & (muons.isolation < 0.2)\n",
     "        ]\n",
-    "        output['cutflow'][dataset]['at least 4 good muons'] += ak.sum(ak.num(muons) >= 4)\n",
-    "        output['nMuons'].fill(dataset=dataset, nMuons=ak.num(muons))\n",
+    "        cutflow['at least 4 good muons'] += ak.sum(ak.num(muons) >= 4)\n",
+    "        h_nMuons.fill(dataset=dataset, nMuons=ak.num(muons))\n",
     "        \n",
     "        # reduce first axis: skip events without enough muons\n",
     "        muons = muons[ak.num(muons) >= 4]\n",
@@ -392,37 +307,45 @@
     "            }),\n",
     "        })\n",
     "        \n",
-    "        output['cutflow'][dataset]['at least one candidate'] += ak.sum(ak.num(fourmuon) > 0)\n",
+    "        cutflow['at least one candidate'] += ak.sum(ak.num(fourmuon) > 0)\n",
     "         \n",
     "        # require minimum dimuon mass\n",
     "        fourmuon = fourmuon[(fourmuon.z1.p4.mass > 60.) & (fourmuon.z2.p4.mass > 20.)]\n",
-    "        output['cutflow'][dataset]['minimum dimuon mass'] += ak.sum(ak.num(fourmuon) > 0)\n",
+    "        cutflow['minimum dimuon mass'] += ak.sum(ak.num(fourmuon) > 0)\n",
     "        \n",
     "        # choose permutation with z1 mass closest to nominal Z boson mass\n",
     "        bestz1 = ak.singletons(ak.argmin(abs(fourmuon.z1.p4.mass - 91.1876), axis=1))\n",
     "        fourmuon = ak.flatten(fourmuon[bestz1])\n",
     "        \n",
-    "        output['mass'].fill(\n",
+    "        h_m4mu.fill(\n",
     "            dataset=dataset,\n",
     "            mass=(fourmuon.z1.p4 + fourmuon.z2.p4).mass,\n",
     "        )\n",
-    "        output['mass_z1'].fill(\n",
+    "        h_mZ1.fill(\n",
     "            dataset=dataset, \n",
     "            mass=fourmuon.z1.p4.mass,\n",
     "        )\n",
-    "        output['mass_z2'].fill(\n",
+    "        h_mZ2.fill(\n",
     "            dataset=dataset, \n",
     "            mass=fourmuon.z2.p4.mass,\n",
     "        )\n",
-    "        output['pt_z1_mu1'].fill(\n",
+    "        h_ptZ1mu1.fill(\n",
     "            dataset=dataset,\n",
     "            pt=fourmuon.z1.lep1.pt,\n",
     "        )\n",
-    "        output['pt_z1_mu2'].fill(\n",
+    "        h_ptZ1mu2.fill(\n",
     "            dataset=dataset,\n",
     "            pt=fourmuon.z1.lep2.pt,\n",
     "        )\n",
-    "        return output\n",
+    "        return {\n",
+    "            'nMuons': h_nMuons,\n",
+    "            'mass': h_m4mu,\n",
+    "            'mass_z1': h_mZ1,\n",
+    "            'mass_z2': h_mZ2,\n",
+    "            'pt_z1_mu1': h_ptZ1mu1,\n",
+    "            'pt_z1_mu2': h_ptZ1mu2,\n",
+    "            'cutflow': {dataset: cutflow},\n",
+    "        }\n",
     "\n",
     "    def postprocess(self, accumulator):\n",
     "        return accumulator"
@@ -430,67 +353,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "d738e9f62b0f493c8542eebf35672bb1",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(HTML(value='Preprocessing'), FloatProgress(value=0.0, max=1.0), HTML(value='')))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "ba8fa1b2851d481cbaa1747fd10f9b73",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(HTML(value='Processing'), FloatProgress(value=0.0, max=676.0), HTML(value='')))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/lagray/miniconda3/envs/coffea-work/lib/python3.7/site-packages/numba/core/dispatcher.py:238: UserWarning: Numba extension module 'awkward1._connect._numba' failed to load due to 'ImportError(generic_type: type \"kernel_lib\" is already registered!)'.\n",
-      "  entrypoints.init_all()\n",
-      "/Users/lagray/miniconda3/envs/coffea-work/lib/python3.7/site-packages/numba/core/dispatcher.py:238: UserWarning: Numba extension module 'awkward1._connect._numba' failed to load due to 'ImportError(generic_type: type \"kernel_lib\" is already registered!)'.\n",
-      "  entrypoints.init_all()\n",
-      "/Users/lagray/miniconda3/envs/coffea-work/lib/python3.7/site-packages/numba/core/dispatcher.py:238: UserWarning: Numba extension module 'awkward1._connect._numba' failed to load due to 'ImportError(generic_type: type \"kernel_lib\" is already registered!)'.\n",
-      "  entrypoints.init_all()\n",
-      "/Users/lagray/miniconda3/envs/coffea-work/lib/python3.7/site-packages/numba/core/dispatcher.py:238: UserWarning: Numba extension module 'awkward1._connect._numba' failed to load due to 'ImportError(generic_type: type \"kernel_lib\" is already registered!)'.\n",
-      "  entrypoints.init_all()\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "{'nMuons': <Hist (dataset,nMuons) instance at 0x7fcc1f5331d0>, 'mass': <Hist (dataset,mass) instance at 0x7fcc1f533ed0>, 'mass_z1': <Hist (dataset,mass) instance at 0x7fcc1ea22490>, 'mass_z2': <Hist (dataset,mass) instance at 0x7fcc1ed6d150>, 'pt_z1_mu1': <Hist (dataset,pt) instance at 0x7fcc1eb59c90>, 'pt_z1_mu2': <Hist (dataset,pt) instance at 0x7fcc1eb59c50>, 'cutflow': defaultdict_accumulator(..., {'ZZ to 4mu': defaultdict_accumulator(<class 'int'>, {'all events': 1499064, 'at least 4 good muons': 143618, 'at least one candidate': 143055, 'minimum dimuon mass': 81867}), 'DoubleMuon': defaultdict_accumulator(<class 'int'>, {'all events': 66128870, 'at least 4 good muons': 8289, 'at least one candidate': 3849, 'minimum dimuon mass': 485})})}\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import time\n",
     "\n",
@@ -506,14 +371,17 @@
     "    ]\n",
     "}\n",
     "\n",
-    "output = processor.run_uproot_job(\n",
+    "\n",
+    "run = processor.Runner(\n",
+    "    executor = processor.FuturesExecutor(compression=None, workers=4),\n",
+    "    schema=BaseSchema,\n",
+    "    chunksize=100_000,\n",
+    ")\n",
+    "\n",
+    "output = run(\n",
     "    fileset,\n",
-    "    treename='Events',\n",
+    "    \"Events\",\n",
     "    processor_instance=FancyDimuonProcessor(),\n",
-    "    executor=processor.futures_executor,\n",
-    "    executor_args={\"schema\": BaseSchema, \"workers\": 4},\n",
-    "    chunksize=100000,\n",
-    "    maxchunks=None,\n",
     ")\n",
     "\n",
     "elapsed = time.time() - tstart\n",
@@ -522,17 +390,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Events/s: 29206.822973167524\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "nevt = output['cutflow']['ZZ to 4mu']['all events'] + output['cutflow']['DoubleMuon']['all events']\n",
     "print(\"Events/s:\", nevt / elapsed)"
@@ -547,10 +407,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
+    "import mplhep\n",
     "# scale ZZ simulation to expected yield\n",
     "lumi = 11.6  # 1/fb\n",
     "zzxs = 7200 * 0.0336**2  # approximate 8 TeV ZZ(4mu)\n",
@@ -565,22 +426,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "%matplotlib inline\n",
     "\n",
@@ -591,76 +439,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "ax = hist.plot1d(scaled['mass'].rebin(\"mass\", 4), overlay='dataset')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "ax = hist.plot1d(scaled['mass_z1'], overlay='dataset')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(2.0, 300.0)"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "ax = hist.plot1d(scaled['mass_z2'], overlay='dataset')\n",
     "ax.set_xlim(2, 300)\n",
@@ -669,44 +468,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "ax = hist.plot1d(scaled['pt_z1_mu1'], overlay='dataset')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "ax = hist.plot1d(scaled['pt_z1_mu2'], overlay='dataset')"
    ]
@@ -735,7 +508,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.9"
+   "version": "3.7.10"
   }
  },
  "nbformat": 4,