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diff --git a/csit.infra.dash/app/cdash/report/graphs.py b/csit.infra.dash/app/cdash/report/graphs.py
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+++ b/csit.infra.dash/app/cdash/report/graphs.py
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+# Copyright (c) 2024 Cisco and/or its affiliates.
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at:
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Implementation of graphs for iterative data.
+"""
+
+import plotly.graph_objects as go
+import pandas as pd
+
+from copy import deepcopy
+from numpy import percentile
+
+from ..utils.constants import Constants as C
+from ..utils.utils import get_color, get_hdrh_latencies
+
+
+def select_iterative_data(data: pd.DataFrame, itm:dict) -> pd.DataFrame:
+    """Select the data for graphs and tables from the provided data frame.
+
+    :param data: Data frame with data for graphs and tables.
+    :param itm: Item (in this case job name) which data will be selected from
+        the input data frame.
+    :type data: pandas.DataFrame
+    :type itm: str
+    :returns: A data frame with selected data.
+    :rtype: pandas.DataFrame
+    """
+
+    phy = itm["phy"].split("-")
+    if len(phy) == 4:
+        topo, arch, nic, drv = phy
+        if drv == "dpdk":
+            drv = ""
+        else:
+            drv += "-"
+            drv = drv.replace("_", "-")
+    else:
+        return None
+
+    if itm["testtype"] in ("ndr", "pdr"):
+        test_type = "ndrpdr"
+    elif itm["testtype"] == "mrr":
+        test_type = "mrr"
+    elif itm["area"] == "hoststack":
+        test_type = "hoststack"
+    df = data.loc[(
+        (data["release"] == itm["rls"]) &
+        (data["test_type"] == test_type) &
+        (data["passed"] == True)
+    )]
+
+    core = str() if itm["dut"] == "trex" else f"{itm['core']}"
+    ttype = "ndrpdr" if itm["testtype"] in ("ndr", "pdr") else itm["testtype"]
+    regex_test = \
+        f"^.*[.|-]{nic}.*{itm['framesize']}-{core}-{drv}{itm['test']}-{ttype}$"
+    df = df[
+        (df.job.str.endswith(f"{topo}-{arch}")) &
+        (df.dut_version.str.contains(itm["dutver"].replace(".r", "-r").\
+            replace("rls", "release"))) &
+        (df.test_id.str.contains(regex_test, regex=True))
+    ]
+
+    return df
+
+
+def graph_iterative(data: pd.DataFrame, sel: list, layout: dict,
+        normalize: bool=False, remove_outliers: bool=False) -> tuple:
+    """Generate the statistical box graph with iterative data (MRR, NDR and PDR,
+    for PDR also Latencies).
+
+    :param data: Data frame with iterative data.
+    :param sel: Selected tests.
+    :param layout: Layout of plot.ly graph.
+    :param normalize: If True, the data is normalized to CPU frequency
+        Constants.NORM_FREQUENCY.
+    :param remove_outliers: If True the outliers are removed before
+        generating the table.
+    :type data: pandas.DataFrame
+    :type sel: list
+    :type layout: dict
+    :type normalize: bool
+    :type remove_outliers: bool
+    :returns: Tuple of graphs - throughput and latency.
+    :rtype: tuple(plotly.graph_objects.Figure, plotly.graph_objects.Figure)
+    """
+
+    def get_y_values(data, y_data_max, param, norm_factor, release=str(),
+                     remove_outliers=False):
+        if param == "result_receive_rate_rate_values":
+            if release == "rls2402":
+                y_vals_raw = data["result_receive_rate_rate_avg"].to_list()
+            else:
+                y_vals_raw = data[param].to_list()[0]
+        else:
+            y_vals_raw = data[param].to_list()
+        y_data = [(y * norm_factor) for y in y_vals_raw]
+
+        if remove_outliers:
+            try:
+                q1 = percentile(y_data, 25, method=C.COMP_PERCENTILE_METHOD)
+                q3 = percentile(y_data, 75, method=C.COMP_PERCENTILE_METHOD)
+                irq = q3 - q1
+                lif = q1 - C.COMP_OUTLIER_TYPE * irq
+                uif = q3 + C.COMP_OUTLIER_TYPE * irq
+                y_data = [i for i in y_data if i >= lif and i <= uif]
+            except TypeError:
+                pass
+        try:
+            y_data_max = max(max(y_data), y_data_max)
+        except TypeError:
+            y_data_max = 0
+        return y_data, y_data_max
+
+    fig_tput = None
+    fig_band = None
+    fig_lat = None
+
+    tput_traces = list()
+    y_tput_max = 0
+    y_units = set()
+
+    lat_traces = list()
+    y_lat_max = 0
+    x_lat = list()
+
+    band_traces = list()
+    y_band_max = 0
+    y_band_units = set()
+    x_band = list()
+
+    for idx, itm in enumerate(sel):
+
+        itm_data = select_iterative_data(data, itm)
+        if itm_data.empty:
+            continue
+
+        phy = itm["phy"].split("-")
+        topo_arch = f"{phy[0]}-{phy[1]}" if len(phy) == 4 else str()
+        norm_factor = (C.NORM_FREQUENCY / C.FREQUENCY[topo_arch]) \
+            if normalize else 1.0
+
+        if itm["area"] == "hoststack":
+            ttype = f"hoststack-{itm['testtype']}"
+        else:
+            ttype = itm["testtype"]
+
+        y_units.update(itm_data[C.UNIT[ttype]].unique().tolist())
+
+        y_data, y_tput_max = get_y_values(
+            itm_data,
+            y_tput_max,
+            C.VALUE_ITER[ttype],
+            norm_factor,
+            itm["rls"],
+            remove_outliers
+        )
+
+        nr_of_samples = len(y_data)
+
+        customdata = list()
+        metadata = {
+            "csit release": itm["rls"],
+            "dut": itm["dut"],
+            "dut version": itm["dutver"],
+            "infra": itm["phy"],
+            "test": (
+                f"{itm['area']}-{itm['framesize']}-{itm['core']}-"
+                f"{itm['test']}-{itm['testtype']}"
+            )
+        }
+
+        if itm["testtype"] == "mrr" and itm["rls"] in ("rls2306", "rls2310"):
+            trial_run = "trial"
+            metadata["csit-ref"] = (
+                f"{itm_data['job'].to_list()[0]}/",
+                f"{itm_data['build'].to_list()[0]}"
+            )
+            customdata = [{"metadata": metadata}, ] * nr_of_samples
+        else:
+            trial_run = "run"
+            for _, row in itm_data.iterrows():
+                metadata["csit-ref"] = f"{row['job']}/{row['build']}"
+                customdata.append({"metadata": deepcopy(metadata)})
+        tput_kwargs = dict(
+            y=y_data,
+            name=(
+                f"{idx + 1}. "
+                f"({nr_of_samples:02d} "
+                f"{trial_run}{'s' if nr_of_samples > 1 else ''}) "
+                f"{itm['id']}"
+            ),
+            hoverinfo=u"y+name",
+            boxpoints="all",
+            jitter=0.3,
+            marker=dict(color=get_color(idx)),
+            customdata=customdata
+        )
+        tput_traces.append(go.Box(**tput_kwargs))
+
+        if ttype in ("ndr", "pdr", "mrr"):
+            y_band, y_band_max = get_y_values(
+                itm_data,
+                y_band_max,
+                C.VALUE_ITER[f"{ttype}-bandwidth"],
+                norm_factor,
+                remove_outliers=remove_outliers
+            )
+            if not all(pd.isna(y_band)):
+                y_band_units.update(
+                    itm_data[C.UNIT[f"{ttype}-bandwidth"]].unique().\
+                        dropna().tolist()
+                )
+                band_kwargs = dict(
+                    y=y_band,
+                    name=(
+                        f"{idx + 1}. "
+                        f"({nr_of_samples:02d} "
+                        f"run{'s' if nr_of_samples > 1 else ''}) "
+                        f"{itm['id']}"
+                    ),
+                    hoverinfo=u"y+name",
+                    boxpoints="all",
+                    jitter=0.3,
+                    marker=dict(color=get_color(idx)),
+                    customdata=customdata
+                )
+                x_band.append(idx + 1)
+                band_traces.append(go.Box(**band_kwargs))
+
+        if ttype == "pdr":
+            y_lat, y_lat_max = get_y_values(
+                itm_data,
+                y_lat_max,
+                C.VALUE_ITER["latency"],
+                1 / norm_factor,
+                remove_outliers=remove_outliers
+            )
+            if not all(pd.isna(y_lat)):
+                customdata = list()
+                for _, row in itm_data.iterrows():
+                    hdrh = get_hdrh_latencies(
+                        row,
+                        f"{metadata['infra']}-{metadata['test']}"
+                    )
+                    metadata["csit-ref"] = f"{row['job']}/{row['build']}"
+                    customdata.append({
+                        "metadata": deepcopy(metadata),
+                        "hdrh": hdrh
+                    })
+                nr_of_samples = len(y_lat)
+                lat_kwargs = dict(
+                    y=y_lat,
+                    name=(
+                        f"{idx + 1}. "
+                        f"({nr_of_samples:02d} "
+                        f"run{u's' if nr_of_samples > 1 else u''}) "
+                        f"{itm['id']}"
+                    ),
+                    hoverinfo="all",
+                    boxpoints="all",
+                    jitter=0.3,
+                    marker=dict(color=get_color(idx)),
+                    customdata=customdata
+                )
+                x_lat.append(idx + 1)
+                lat_traces.append(go.Box(**lat_kwargs))
+
+    if tput_traces:
+        pl_tput = deepcopy(layout["plot-throughput"])
+        pl_tput["xaxis"]["tickvals"] = [i for i in range(len(sel))]
+        pl_tput["xaxis"]["ticktext"] = [str(i + 1) for i in range(len(sel))]
+        pl_tput["yaxis"]["title"] = f"Throughput [{'|'.join(sorted(y_units))}]"
+        if y_tput_max:
+            pl_tput["yaxis"]["range"] = [0, int(y_tput_max) + 2e6]
+        fig_tput = go.Figure(data=tput_traces, layout=pl_tput)
+
+    if band_traces:
+        pl_band = deepcopy(layout["plot-bandwidth"])
+        pl_band["xaxis"]["tickvals"] = [i for i in range(len(x_band))]
+        pl_band["xaxis"]["ticktext"] = x_band
+        pl_band["yaxis"]["title"] = \
+            f"Bandwidth [{'|'.join(sorted(y_band_units))}]"
+        if y_band_max:
+            pl_band["yaxis"]["range"] = [0, int(y_band_max) + 2e9]
+        fig_band = go.Figure(data=band_traces, layout=pl_band)
+
+    if lat_traces:
+        pl_lat = deepcopy(layout["plot-latency"])
+        pl_lat["xaxis"]["tickvals"] = [i for i in range(len(x_lat))]
+        pl_lat["xaxis"]["ticktext"] = x_lat
+        if y_lat_max:
+            pl_lat["yaxis"]["range"] = [0, int(y_lat_max) + 5]
+        fig_lat = go.Figure(data=lat_traces, layout=pl_lat)
+
+    return fig_tput, fig_band, fig_lat