diff --git a/_doc/api/ort.rst b/_doc/api/ort.rst
index 81fe1ef..d3de0ba 100644
--- a/_doc/api/ort.rst
+++ b/_doc/api/ort.rst
@@ -23,5 +23,7 @@ OrtTensor
 Profiling
 +++++++++
 
+.. autofunction:: onnx_array_api.ort.ort_profile.merge_ort_profile
+
 .. autofunction:: onnx_array_api.ort.ort_profile.ort_profile
 
diff --git a/_doc/api/plotting.rst b/_doc/api/plotting.rst
index 2fe2d77..c70d272 100644
--- a/_doc/api/plotting.rst
+++ b/_doc/api/plotting.rst
@@ -8,6 +8,11 @@ Dot
 
 .. autofunction:: onnx_array_api.plotting.dot_plot.to_dot
 
+Statistics
+++++++++++
+
+.. autofunction:: onnx_array_api.plotting.stat_plot.plot_ort_profile
+
 Text
 ++++
 
diff --git a/_doc/examples/plot_profiling.py b/_doc/examples/plot_profiling.py
index 59e7a5f..7a61b68 100644
--- a/_doc/examples/plot_profiling.py
+++ b/_doc/examples/plot_profiling.py
@@ -21,17 +21,22 @@
 from onnxruntime import get_available_providers
 from onnx_array_api.ext_test_case import example_path
 from onnx_array_api.ort.ort_optimizers import ort_optimized_model
-from onnx_array_api.ort.ort_profile import ort_profile
+from onnx_array_api.ort.ort_profile import ort_profile, merge_ort_profile
+from onnx_array_api.plotting.stat_plot import plot_ort_profile
 
 
-filename = example_path("data/small.onnx")
+suffix = ""
+filename = example_path(f"data/small{suffix}.onnx")
 optimized = filename + ".optimized.onnx"
+print(f"model={filename!r}")
 
 if not os.path.exists(optimized):
     ort_optimized_model(filename, output=optimized)
-print(optimized)
+print(f"optimized={optimized!r}")
 
 #############################
+# .. _l-example-ort-profiling:
+#
 # Profiling
 # +++++++++
 
@@ -43,50 +48,31 @@
     disable_optimization=True,
     providers=["CPUExecutionProvider"],
 )
-prof_base.to_excel("prof_base.xlsx", index=False)
+prof_base.to_excel(f"prof_base{suffix}.xlsx", index=False)
 prof_base
 
 #######################################
 # And the optimized model.
 
-prof_opt = ort_profile(
+prof_opti = ort_profile(
     optimized,
     feeds,
     repeat=6,
     disable_optimization=True,
     providers=["CPUExecutionProvider"],
 )
-prof_opt
+prof_opti.to_excel(f"prof_opti{suffix}.xlsx", index=False)
+prof_opti
 
 #######################################
 # And the graph is:
 
-
-def plot_profile(df, ax0, ax1=None, title=None):
-    gr_dur = (
-        df[["dur", "args_op_name"]].groupby("args_op_name").sum().sort_values("dur")
-    )
-    gr_dur.plot.barh(ax=ax0)
-    if title is not None:
-        ax0.set_title(title)
-    if ax1 is not None:
-        gr_n = (
-            df[["dur", "args_op_name"]]
-            .groupby("args_op_name")
-            .count()
-            .sort_values("dur")
-        )
-        gr_n = gr_n.loc[gr_dur.index, :]
-        gr_n.plot.barh(ax=ax1)
-        ax1.set_title("n occurences")
-
-
 unique_op = set(prof_base["args_op_name"])
 fig, ax = plt.subplots(2, 2, figsize=(10, len(unique_op)), sharex="col")
-plot_profile(prof_base, ax[0, 0], ax[0, 1], title="baseline")
-plot_profile(prof_opt, ax[1, 0], ax[1, 1], title="optimized")
-
-fig.savefig("plot_profiling.png")
+plot_ort_profile(prof_base, ax[0, 0], ax[0, 1], title="baseline")
+plot_ort_profile(prof_opti, ax[1, 0], ax[1, 1], title="optimized")
+fig.tight_layout()
+fig.savefig(f"plot_profiling{suffix}.png")
 
 ##################################################
 # Merging profiles
@@ -96,103 +82,14 @@ def plot_profile(df, ax0, ax1=None, title=None):
 # process the same image and the input and output size are the
 # same at every iteration.
 
-
-def preprocess(df):
-    groupkey = [
-        "args_op_name",
-        "args_output_type_shape",
-        "args_input_type_shape",
-        "args_provider",
-    ]
-
-    def _idx(row):
-        """
-        There may be multiple node with the same
-        input/output types and shapes.
-        This function gives every instance a distinct id.
-        First unique op with same I/O receives the index 0.
-        The counter restart when the session goes to the
-        next image.
-        """
-        if row["cat"] == "Session":
-            occurences[0] = {}
-            return -1
-        assert "idx" not in groupkey
-        vals = [row[k] for k in groupkey]
-        key = tuple(map(str, vals))
-        if key not in occurences[0]:
-            occurences[0][key] = 0
-        else:
-            occurences[0][key] += 1
-        return occurences[0][key]
-
-    df = df.copy()
-    occurences = [{}]
-    df["idx"] = df.apply(_idx, axis=1)
-    df = df[(df["cat"] == "Node") & df["name"].str.contains("kernel_time")]
-    groupkey.append("idx")
-    for c in groupkey:
-        if c != "idx":
-            df[c] = df[c].apply(str)
-    gr = df[groupkey + ["dur"]].groupby(groupkey)
-    return gr.sum()
-
-
-base = preprocess(prof_base)
-opti = preprocess(prof_opt)
-merge = base.merge(
-    opti, how="outer", suffixes=("base", "opti"), left_index=True, right_index=True
-)
-merge = merge.reset_index(drop=False)
-merge.to_excel("plot_profiling_merged.xlsx", index=False)
+merge, gr = merge_ort_profile(prof_base, prof_opti)
+merge.to_excel(f"plot_profiling_merged{suffix}.xlsx", index=False)
 merge
 
-
 #####################################################
-# Aggregation
-
-
-def classify(row):
-    if numpy.isnan(row["duropti"]):
-        return "-"
-    if numpy.isnan(row["durbase"]):
-        return "+"
-    return "="
+# More detailed
 
-
-keys = {"float": "f"}
-
-
-def process_shape(s):
-    value = eval(s)
-    ns = []
-    for v in value:
-        if len(v) != 1:
-            raise NotImplementedError(f"Unexpected value {v} in {s!r}.")
-        k, v = list(v.items())[0]
-        n = "-".join([keys[k], "x".join(map(str, v))])
-        ns.append(n)
-    return ",".join(ns)
-
-
-def label(row):
-    name = row["args_op_name"]
-    inshape = process_shape(row["args_input_type_shape"])
-    outshape = process_shape(row["args_output_type_shape"])
-    side = row["side"][0]
-    prov = row["args_provider"][:3]
-    idx = row["idx"]
-    return f"[{side}{prov}]{name}({inshape})->{outshape}[{idx}]"
-
-
-df = merge.copy()
-df["side"] = df.apply(classify, axis=1)
-df["label"] = df.apply(label, axis=1)
-gr = (
-    df[["label", "durbase", "duropti", "idx"]]
-    .groupby("label")
-    .agg({"durbase": numpy.sum, "duropti": numpy.sum, "idx": max})
-)
+gr.to_excel(f"plot_profiling_merged_details{suffix}.xlsx", index=False)
 gr
 
 ################################
@@ -210,11 +107,10 @@ def label(row):
 gr[["durbase", "duropti"]].plot.barh(ax=ax[0])
 ax[0].set_title("Side by side duration")
 gr = gr.copy()
-gr["idx"] += 1
-gr[["idx"]].plot.barh(ax=ax[1])
+gr[["countbase", "countopti"]].plot.barh(ax=ax[1])
 ax[1].set_title("Side by side count")
 fig.tight_layout()
-fig.savefig("plot_profiling_side_by_side.png")
+fig.savefig(f"plot_profiling_side_by_side{suffix}.png")
 
 
 ########################################
@@ -231,21 +127,44 @@ def label(row):
         disable_optimization=True,
         providers=["CUDAExecutionProvider"],
     )
+    prof_base.to_excel(f"prof_cuda_base{suffix}.xlsx", index=False)
+
     prof_opti = ort_profile(
         optimized,
         feeds,
         repeat=6,
         disable_optimization=True,
-        providers=["CUDAExecutionProvider"],
+        providers=["CUDAExecutionProvider", "CPUExecutionProvider"],
     )
+    prof_opti.to_excel(f"prof_cuda_opti{suffix}.xlsx", index=False)
 
     unique_op = set(prof_base["args_op_name"])
     fig, ax = plt.subplots(2, 2, figsize=(10, len(unique_op)), sharex="col")
-    plot_profile(prof_base, ax[0, 0], ax[0, 1], title="baseline")
-    plot_profile(prof_opt, ax[1, 0], ax[1, 1], title="optimized")
-    fig.savefig("plot_profiling_cuda.png")
+    plot_ort_profile(prof_base, ax[0, 0], ax[0, 1], title="baseline")
+    plot_ort_profile(prof_opti, ax[1, 0], ax[1, 1], title="optimized")
+    fig.tight_layout()
+    fig.savefig(f"plot_profiling_cuda{suffix}.png")
+
+    merge, gr = merge_ort_profile(prof_base, prof_opti)
+    merge.to_excel(f"plot_profiling_merged{suffix}.xlsx", index=False)
+    gr.to_excel(f"plot_profiling_merged_details{suffix}.xlsx", index=False)
+
+    grmax = gr["durbase"] + gr["duropti"]
+    total = grmax.sum()
+    grmax /= total
+    gr = gr[grmax >= 0.01]
+
+    fig, ax = plt.subplots(1, 2, figsize=(14, min(gr.shape[0], 500)), sharey=True)
+    gr[["durbase", "duropti"]].plot.barh(ax=ax[0])
+    ax[0].set_title("Side by side duration")
+    gr = gr.copy()
+    gr[["countbase", "countopti"]].plot.barh(ax=ax[1])
+    ax[1].set_title("Side by side count")
+    fig.tight_layout()
+    fig.savefig(f"plot_profiling_side_by_side_cuda{suffix}.png")
+
 else:
-    print(f"CUDA not available in {get_available_providers()}")
+    print(f"CUDA not available in {get_available_providers()}.")
     fig, ax = None, None
 
 ax
diff --git a/_unittests/ut_ort/data/prof_base.xlsx b/_unittests/ut_ort/data/prof_base.xlsx
new file mode 100644
index 0000000..52c542d
Binary files /dev/null and b/_unittests/ut_ort/data/prof_base.xlsx differ
diff --git a/_unittests/ut_ort/data/prof_opti.xlsx b/_unittests/ut_ort/data/prof_opti.xlsx
new file mode 100644
index 0000000..690378a
Binary files /dev/null and b/_unittests/ut_ort/data/prof_opti.xlsx differ
diff --git a/_unittests/ut_ort/test_ort_profile.py b/_unittests/ut_ort/test_ort_profile.py
index 4e4f7d5..295b7e0 100644
--- a/_unittests/ut_ort/test_ort_profile.py
+++ b/_unittests/ut_ort/test_ort_profile.py
@@ -1,10 +1,11 @@
 import unittest
+import os
 import numpy as np
-from pandas import DataFrame
+from pandas import DataFrame, read_excel
 from onnx_array_api.npx import absolute, jit_onnx
 from onnx_array_api.ext_test_case import ExtTestCase
 from onnx_array_api.ort.ort_optimizers import ort_optimized_model
-from onnx_array_api.ort.ort_profile import ort_profile
+from onnx_array_api.ort.ort_profile import ort_profile, merge_ort_profile
 
 
 class TestOrtProfile(ExtTestCase):
@@ -27,10 +28,36 @@ def myloss(x, y):
         self.assertRaise(lambda: ort_optimized_model(onx, "NO"), ValueError)
         optimized = ort_optimized_model(onx)
         prof = ort_profile(optimized, feeds)
+        prof.to_csv("prof.csv", index=False)
         self.assertIsInstance(prof, DataFrame)
         prof = ort_profile(optimized, feeds, as_df=False)
         self.assertIsInstance(prof, list)
 
+    def test_merge_ort_profile(self):
+        data = os.path.join(os.path.dirname(__file__), "data")
+        df1 = read_excel(os.path.join(data, "prof_base.xlsx"))
+        df2 = read_excel(os.path.join(data, "prof_opti.xlsx"))
+        merged, gr = merge_ort_profile(df1, df2)
+        self.assertEqual(merged.shape, (23, 9))
+        self.assertEqual(
+            list(merged.columns),
+            [
+                "args_op_name",
+                "args_output_type_shape",
+                "args_input_type_shape",
+                "args_provider",
+                "idx",
+                "durbase",
+                "countbase",
+                "duropti",
+                "countopti",
+            ],
+        )
+        self.assertEqual(gr.shape, (19, 4))
+        self.assertEqual(
+            list(gr.columns), ["durbase", "duropti", "countbase", "countopti"]
+        )
+
 
 if __name__ == "__main__":
     unittest.main(verbosity=2)
diff --git a/_unittests/ut_plotting/data/prof.csv b/_unittests/ut_plotting/data/prof.csv
new file mode 100644
index 0000000..a478be6
--- /dev/null
+++ b/_unittests/ut_plotting/data/prof.csv
@@ -0,0 +1,473 @@
+cat,pid,tid,dur,ts,ph,name,args_op_name,op_name,args_thread_scheduling_stats,args_input_type_shape,args_parameter_size,args_activation_size,args_node_index,args_output_size,args_provider,args_output_type_shape
+Session,715047,715047,315,3,X,model_loading_array,,,,,,,,,,
+Session,715047,715047,3759,349,X,session_initialization,,,,,,,,,,
+Node,715047,715047,0,4247,X,Slice_10_fence_before,Slice,Slice_10,,,,,,,,
+Node,715047,715047,81,4251,X,Slice_10_kernel_time,Slice,Slice_10,"{'main_thread': {'thread_pool_name': 'session-2-intra-op', 'thread_id': '139965577762624', 'block_size': [], 'core': -1, 'Distribution': 0, 'DistributionEnqueue': 0, 'Run': 0, 'Wait': 0, 'WaitRevoke': 0}, 'sub_threads': {'139960367707904': {'num_run': 0, 'core': -1}, '139960921331456': {'num_run': 0, 'core': -1}, '139960929724160': {'num_run': 0, 'core': -1}, 'thread::id of a non-executing thread': {'num_run': 0, 'core': -1}, '139964713457408': {'num_run': 0, 'core': -1}, '139964671510272': {'num_run': 0, 'core': -1}, '139962228516608': {'num_run': 0, 'core': -1}, '139962220123904': {'num_run': 0, 'core': -1}, '139962211731200': {'num_run': 0, 'core': -1}, '139962203338496': {'num_run': 0, 'core': -1}, '139961575667456': {'num_run': 0, 'core': -1}, '139961567274752': {'num_run': 0, 'core': -1}, '139961558882048': {'num_run': 0, 'core': -1}, '139961550489344': {'num_run': 0, 'core': -1}, '139961542096640': {'num_run': 0, 'core': -1}, '139961533703936': {'num_run': 0, 'core': -1}, '139961525311232': {'num_run': 0, 'core': -1}, '139960971687680': {'num_run': 0, 'core': -1}, '139960963294976': {'num_run': 0, 'core': -1}, '139960954902272': {'num_run': 0, 'core': -1}, '139960946509568': {'num_run': 0, 'core': -1}, '139960359315200': {'num_run': 0, 'core': -1}}}","[{'float': [2, 2]}, {'int64': [1]}, {'int64': [1]}, {'int64': [1]}]",24,16,10,8,CPUExecutionProvider,"[{'float': [2, 1]}]"
+Node,715047,715047,0,4338,X,Slice_10_fence_after,Slice,Slice_10,,,,,,,,
+Node,715047,715047,0,4343,X,Squeeze_11_fence_before,Squeeze,Squeeze_11,,,,,,,,
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+Node,715047,715047,0,4370,X,Squeeze_11_fence_after,Squeeze,Squeeze_11,,,,,,,,
+Node,715047,715047,0,4372,X,Slice_8_fence_before,Slice,Slice_8,,,,,,,,
+Node,715047,715047,19,4375,X,Slice_8_kernel_time,Slice,Slice_8,"{'main_thread': {'thread_pool_name': 'session-2-intra-op', 'thread_id': '139965577762624', 'block_size': [], 'core': -1, 'Distribution': 0, 'DistributionEnqueue': 0, 'Run': 0, 'Wait': 0, 'WaitRevoke': 0}, 'sub_threads': {'139960367707904': {'num_run': 0, 'core': -1}, '139960921331456': {'num_run': 0, 'core': -1}, '139960929724160': {'num_run': 0, 'core': -1}, 'thread::id of a non-executing thread': {'num_run': 0, 'core': -1}, '139964713457408': {'num_run': 0, 'core': -1}, '139964671510272': {'num_run': 0, 'core': -1}, '139962228516608': {'num_run': 0, 'core': -1}, '139962220123904': {'num_run': 0, 'core': -1}, '139962211731200': {'num_run': 0, 'core': -1}, '139962203338496': {'num_run': 0, 'core': -1}, '139961575667456': {'num_run': 0, 'core': -1}, '139961567274752': {'num_run': 0, 'core': -1}, '139961558882048': {'num_run': 0, 'core': -1}, '139961550489344': {'num_run': 0, 'core': -1}, '139961542096640': {'num_run': 0, 'core': -1}, '139961533703936': {'num_run': 0, 'core': -1}, '139961525311232': {'num_run': 0, 'core': -1}, '139960971687680': {'num_run': 0, 'core': -1}, '139960963294976': {'num_run': 0, 'core': -1}, '139960954902272': {'num_run': 0, 'core': -1}, '139960946509568': {'num_run': 0, 'core': -1}, '139960359315200': {'num_run': 0, 'core': -1}}}","[{'float': [2, 2]}, {'int64': [1]}, {'int64': [1]}, {'int64': [1]}]",24,16,8,8,CPUExecutionProvider,"[{'float': [2, 1]}]"
+Node,715047,715047,0,4399,X,Slice_8_fence_after,Slice,Slice_8,,,,,,,,
+Node,715047,715047,0,4401,X,Squeeze_9_fence_before,Squeeze,Squeeze_9,,,,,,,,
+Node,715047,715047,12,4403,X,Squeeze_9_kernel_time,Squeeze,Squeeze_9,"{'main_thread': {'thread_pool_name': 'session-2-intra-op', 'thread_id': '139965577762624', 'block_size': [], 'core': -1, 'Distribution': 0, 'DistributionEnqueue': 0, 'Run': 0, 'Wait': 0, 'WaitRevoke': 0}, 'sub_threads': {'139960367707904': {'num_run': 0, 'core': -1}, '139960921331456': {'num_run': 0, 'core': -1}, '139960929724160': {'num_run': 0, 'core': -1}, 'thread::id of a non-executing thread': {'num_run': 0, 'core': -1}, '139964713457408': {'num_run': 0, 'core': -1}, '139964671510272': {'num_run': 0, 'core': -1}, '139962228516608': {'num_run': 0, 'core': -1}, '139962220123904': {'num_run': 0, 'core': -1}, '139962211731200': {'num_run': 0, 'core': -1}, '139962203338496': {'num_run': 0, 'core': -1}, '139961575667456': {'num_run': 0, 'core': -1}, '139961567274752': {'num_run': 0, 'core': -1}, '139961558882048': {'num_run': 0, 'core': -1}, '139961550489344': {'num_run': 0, 'core': -1}, '139961542096640': {'num_run': 0, 'core': -1}, '139961533703936': {'num_run': 0, 'core': -1}, '139961525311232': {'num_run': 0, 'core': -1}, '139960971687680': {'num_run': 0, 'core': -1}, '139960963294976': {'num_run': 0, 'core': -1}, '139960954902272': {'num_run': 0, 'core': -1}, '139960946509568': {'num_run': 0, 'core': -1}, '139960359315200': {'num_run': 0, 'core': -1}}}","[{'float': [2, 1]}, {'int64': [1]}]",8,8,9,8,CPUExecutionProvider,[{'float': [2]}]
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diff --git a/_unittests/ut_plotting/test_dot_plot.py b/_unittests/ut_plotting/test_dot_plot.py
index c0fe6ca..5c03746 100644
--- a/_unittests/ut_plotting/test_dot_plot.py
+++ b/_unittests/ut_plotting/test_dot_plot.py
@@ -1,7 +1,4 @@
 # -*- coding: utf-8 -*-
-"""
-@brief      test log(time=2s)
-"""
 import os
 import unittest
 
diff --git a/_unittests/ut_plotting/test_stat_plot.py b/_unittests/ut_plotting/test_stat_plot.py
new file mode 100644
index 0000000..9fa2a31
--- /dev/null
+++ b/_unittests/ut_plotting/test_stat_plot.py
@@ -0,0 +1,19 @@
+import os
+import unittest
+import pandas
+import matplotlib.pyplot as plt
+from onnx_array_api.ext_test_case import ExtTestCase, matplotlib_test
+from onnx_array_api.plotting.stat_plot import plot_ort_profile
+
+
+class TestStatPlot(ExtTestCase):
+    @matplotlib_test()
+    def test_plot_ort_profile(self):
+        data = os.path.join(os.path.dirname(__file__), "data", "prof.csv")
+        df = pandas.read_csv(data)
+        _, ax = plt.subplots(2, 1)
+        plot_ort_profile(df, ax0=ax[0], ax1=ax[1])
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/_unittests/ut_plotting/test_text_plot.py b/_unittests/ut_plotting/test_text_plot.py
index c505e7d..e36ce2c 100644
--- a/_unittests/ut_plotting/test_text_plot.py
+++ b/_unittests/ut_plotting/test_text_plot.py
@@ -1,7 +1,4 @@
 # -*- coding: utf-8 -*-
-"""
-@brief      test log(time=2s)
-"""
 import os
 import textwrap
 import unittest
diff --git a/onnx_array_api/ext_test_case.py b/onnx_array_api/ext_test_case.py
index 1235c8b..ab72c57 100644
--- a/onnx_array_api/ext_test_case.py
+++ b/onnx_array_api/ext_test_case.py
@@ -14,13 +14,11 @@
 def ignore_warnings(warns: List[Warning]) -> Callable:
     """
     Catches warnings.
-    @param      warns   warnings to ignore
+
+    :param warns: warnings to ignore
     """
 
     def wrapper(fct):
-        if warns is None:
-            raise AssertionError(f"warns cannot be None for '{fct}'.")
-
         def call_f(self):
             with warnings.catch_warnings():
                 warnings.simplefilter("ignore", warns)
@@ -31,6 +29,29 @@ def call_f(self):
     return wrapper
 
 
+def matplotlib_test() -> Callable:
+    """
+    Decorator for every test checking matplotlib graphs.
+    Cleans matplotlib after its completion.
+    """
+
+    def wrapper(fct):
+        import matplotlib
+
+        def call_f(self):
+            orig_units_registry = matplotlib.units.registry.copy()
+            try:
+                return fct(self)
+            finally:
+                matplotlib.units.registry.clear()
+                matplotlib.units.registry.update(orig_units_registry)
+                matplotlib.pyplot.close("all")
+
+        return call_f
+
+    return wrapper
+
+
 def example_path(path: str) -> str:
     """
     Fixes a path for the examples.
@@ -39,7 +60,7 @@ def example_path(path: str) -> str:
     if os.path.exists(path):
         return path
     this = os.path.abspath(os.path.dirname(__file__))
-    full = os.path.join(this, "..", "_doc", "examples", path)
+    full = os.path.normpath(os.path.join(this, "..", "_doc", "examples", path))
     if os.path.exists(full):
         return full
     raise FileNotFoundError(f"Unable to find path {path!r} or {full!r}.")
diff --git a/onnx_array_api/ort/ort_profile.py b/onnx_array_api/ort/ort_profile.py
index 1aa1866..37d8092 100644
--- a/onnx_array_api/ort/ort_profile.py
+++ b/onnx_array_api/ort/ort_profile.py
@@ -1,6 +1,6 @@
 import json
 import os
-from typing import Any, Dict, List, Optional, Union
+from typing import Any, Dict, List, Optional, Tuple, Union
 import numpy
 from onnx import ModelProto
 from pandas import DataFrame
@@ -25,7 +25,8 @@ def ort_profile(
     :param disable_optimization: disable onnxruntime optimization
     :param repeat: number of times to run the inference
     :param as_df: returns the
-    :param providers: list of providers to use when initializing the inference session
+    :param providers: list of providers to use when initializing the inference session,
+        if None, the default value is `["CPUExecutionProvider"]`
     :param kwargs: additional parameters when initializing the inference session
     :return: DataFrame or dictionary
     """
@@ -41,6 +42,8 @@ def ort_profile(
         if isinstance(filename_or_bytes, ModelProto)
         else filename_or_bytes
     )
+    if providers is None:
+        providers = ["CPUExecutionProvider"]
     sess = InferenceSession(obj, sess_options, providers=providers, **kwargs)
     for i in range(repeat):
         sess.run(None, feeds)
@@ -67,3 +70,130 @@ def ort_profile(
     if as_df:
         return DataFrame(rows)
     return rows
+
+
+def _merge_ort_profile_preprocess(df):
+    groupkey = [
+        "args_op_name",
+        "args_output_type_shape",
+        "args_input_type_shape",
+        "args_provider",
+    ]
+
+    def _idx(row):
+        """
+        There may be multiple node with the same
+        input/output types and shapes.
+        This function gives every instance a distinct id.
+        First unique op with same I/O receives the index 0.
+        The counter restart when the session goes to the
+        next image.
+        """
+        if row["cat"] == "Session":
+            occurences[0] = {}
+            return -1
+        assert "idx" not in groupkey
+        vals = [row[k] for k in groupkey]
+        key = tuple(map(str, vals))
+        if key not in occurences[0]:
+            occurences[0][key] = 0
+        else:
+            occurences[0][key] += 1
+        return occurences[0][key]
+
+    df = df.copy()
+    occurences = [{}]
+    df["idx"] = df.apply(_idx, axis=1)
+    df = df[(df["cat"] == "Node") & df["name"].str.contains("kernel_time")]
+    groupkey.append("idx")
+    for c in groupkey:
+        if c != "idx":
+            df[c] = df[c].apply(str)
+    df = df.copy()
+    df["count"] = 1
+    gr = df[groupkey + ["dur", "count"]].groupby(groupkey)
+    return gr.sum()
+
+
+def _process_shape(s: Tuple[int, ...], keys: Dict[str, str]) -> str:
+    value = eval(s)
+    ns = []
+    for v in value:
+        if len(v) != 1:
+            raise NotImplementedError(f"Unexpected value {v} in {s!r}.")
+        k, v = list(v.items())[0]
+        n = "-".join([keys[k], "x".join(map(str, v))])
+        ns.append(n)
+    return ",".join(ns)
+
+
+def _label(row: Dict[str, Any], column: Optional[str], keys: Dict[str, str]) -> str:
+    name = row["args_op_name"]
+    inshape = _process_shape(row["args_input_type_shape"], keys)
+    outshape = _process_shape(row["args_output_type_shape"], keys)
+    side = row["side"][0]
+    prov = row["args_provider"][:3]
+    add = "" if column is None else f"[{row[column]}]"
+    return f"[{side}{prov}]{name}({inshape})->{outshape}{add}"
+
+
+def merge_ort_profile(
+    prof1: DataFrame,
+    prof2: DataFrame,
+    suffixes: Tuple[str, str] = ("base", "opti"),
+    by_column: Optional[str] = None,
+) -> Tuple[DataFrame, DataFrame]:
+    """
+    Merges two profiles produced by function  :func:`ort_profile
+    <onnx_array_api.ort.ort_profile.ort_profile>`.
+
+    :param prof1: first profile
+    :param prof2: second profile
+    :param suffixes: used by pandas merge
+    :param by_column: the second profile merged by input, output shapes and types
+        plus an additional column, usually `None`, `'idx'` or `'op_name'`
+    :return: merged profiles
+    """
+    # First merge
+    base = _merge_ort_profile_preprocess(prof1)
+    opti = _merge_ort_profile_preprocess(prof2)
+    merge = base.merge(
+        opti, how="outer", suffixes=suffixes, left_index=True, right_index=True
+    )
+    merge = merge.reset_index(drop=False)
+
+    # Second merge
+
+    def classify(row):
+        if numpy.isnan(row[f"dur{suffixes[1]}"]):
+            return "-"
+        if numpy.isnan(row[f"dur{suffixes[0]}"]):
+            return "+"
+        return "="
+
+    keys = {"float": "f"}
+
+    df = merge.copy()
+    df["side"] = df.apply(classify, axis=1)
+    df["label"] = df.apply(lambda row: _label(row, by_column, keys), axis=1)
+    gr = (
+        df[
+            [
+                "label",
+                f"dur{suffixes[0]}",
+                f"dur{suffixes[1]}",
+                f"count{suffixes[0]}",
+                f"count{suffixes[1]}",
+            ]
+        ]
+        .groupby("label")
+        .agg(
+            {
+                f"dur{suffixes[0]}": numpy.sum,
+                f"dur{suffixes[1]}": numpy.sum,
+                f"count{suffixes[0]}": numpy.sum,
+                f"count{suffixes[1]}": numpy.sum,
+            }
+        )
+    )
+    return merge, gr
diff --git a/onnx_array_api/plotting/stat_plot.py b/onnx_array_api/plotting/stat_plot.py
new file mode 100644
index 0000000..eaa6a70
--- /dev/null
+++ b/onnx_array_api/plotting/stat_plot.py
@@ -0,0 +1,44 @@
+from typing import Any, Optional
+import pandas
+import matplotlib.pyplot as plt
+
+
+def plot_ort_profile(
+    df: pandas.DataFrame,
+    ax0: Optional[Any] = None,
+    ax1: Optional[Any] = None,
+    title: Optional[str] = None,
+) -> Any:
+    """
+    Plots time spend in computation based on dataframe
+    produced by function :func:`ort_profile
+    <onnx_array_api.ort.ort_profile.ort_profile>`.
+
+    :param df: dataframe
+    :param ax0: first axis to draw time
+    :param ax1: second axis to draw occurences
+    :param title: graph title
+    :return: ax0
+
+    See :ref:`l-example-ort-profiling` for an example.
+    """
+    if ax0 is None:
+        ax0 = plt.gca()
+
+    gr_dur = (
+        df[["dur", "args_op_name"]].groupby("args_op_name").sum().sort_values("dur")
+    )
+    gr_dur.plot.barh(ax=ax0)
+    if title is not None:
+        ax0.set_title(title)
+    if ax1 is not None:
+        gr_n = (
+            df[["dur", "args_op_name"]]
+            .groupby("args_op_name")
+            .count()
+            .sort_values("dur")
+        )
+        gr_n = gr_n.loc[gr_dur.index, :]
+        gr_n.plot.barh(ax=ax1)
+        ax1.set_title("n occurences")
+    return ax0

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