diff --git a/app.py b/app.py
index 7b7fe68..30b634b 100644
--- a/app.py
+++ b/app.py
@@ -14,6 +14,7 @@
 from dashboard.components.y_axis_select import YAxisSelect
 from dashboard.graphs import plot_connections_statistics, plot_pressure_bar_chart, plot_sensors
 from dashboard.queries import ROW_LIMIT, BigQuery
+from dashboard.utils import get_cleaned_sensor_column_names
 
 
 logger = logging.getLogger(__name__)
@@ -291,13 +292,14 @@ def plot_sensors_graph(
 
 @cache.memoize(timeout=0)
 def get_pressure_profiles_for_time_window(installation_reference, node_id, start_datetime, finish_datetime):
-    """Get pressure profiles for the given node during the given time window.
+    """Get pressure profiles for the given node during the given time window along with the minimum and maximum
+    pressures over all the sensors over that window.
 
     :param str installation_reference:
     :param str node_id:
     :param datetime.datetime start_datetime:
     :param datetime.datetime finish_datetime:
-    :return pandas.DataFrame:
+    :return (pandas.DataFrame, float, float): the pressure profiles, minimum pressure, and maximum pressure for the time window
     """
     df, _ = BigQuery().get_sensor_data(
         installation_reference=installation_reference,
@@ -314,7 +316,9 @@ def get_pressure_profiles_for_time_window(installation_reference, node_id, start
         finish_datetime.isoformat(),
     )
 
-    return df
+    sensor_column_names, _ = get_cleaned_sensor_column_names(df)
+    df_with_sensors_only = df[sensor_column_names]
+    return (df, df_with_sensors_only.min().min(), df_with_sensors_only.max().max())
 
 
 @app.callback(
@@ -334,7 +338,7 @@ def plot_pressure_profile_graph(installation_reference, node_id, date, hour, min
 
     initial_datetime = dt.datetime.combine(date=dt.date.fromisoformat(date), time=dt.time(hour, minute, second))
 
-    df = get_pressure_profiles_for_time_window(
+    df, minimum, maximum = get_pressure_profiles_for_time_window(
         installation_reference=installation_reference,
         node_id=node_id,
         start_datetime=initial_datetime,
@@ -349,7 +353,7 @@ def plot_pressure_profile_graph(installation_reference, node_id, date, hour, min
     ]
 
     logger.debug("Filtered pressure profile time window for single datetime.")
-    return plot_pressure_bar_chart(df)
+    return plot_pressure_bar_chart(df, minimum, maximum)
 
 
 @app.callback(
diff --git a/dashboard/graphs.py b/dashboard/graphs.py
index f2faa7e..efff4d2 100644
--- a/dashboard/graphs.py
+++ b/dashboard/graphs.py
@@ -1,9 +1,7 @@
-import re
-
 from plotly import express as px
 
 from dashboard.queries import BigQuery
-from dashboard.utils import generate_time_range
+from dashboard.utils import generate_time_range, get_cleaned_sensor_column_names
 
 
 def plot_connections_statistics(
@@ -39,7 +37,7 @@ def plot_sensors(installation_reference, node_id, sensor_name, time_range, custo
         finish=finish,
     )
 
-    original_sensor_names, cleaned_sensor_names = _get_cleaned_sensor_column_names(df)
+    original_sensor_names, cleaned_sensor_names = get_cleaned_sensor_column_names(df)
 
     df.rename(
         columns={
@@ -54,8 +52,8 @@ def plot_sensors(installation_reference, node_id, sensor_name, time_range, custo
     return (figure, data_limit_applied)
 
 
-def plot_pressure_bar_chart(df):
-    original_sensor_names, cleaned_sensor_names = _get_cleaned_sensor_column_names(df)
+def plot_pressure_bar_chart(df, minimum, maximum):
+    original_sensor_names, cleaned_sensor_names = get_cleaned_sensor_column_names(df)
     df_transposed = df[original_sensor_names].transpose()
     df_transposed["Barometer number"] = cleaned_sensor_names
 
@@ -66,17 +64,5 @@ def plot_pressure_bar_chart(df):
 
     figure = px.line(df_transposed, x="Barometer number", y="Raw value")
     figure.add_bar(x=df_transposed["Barometer number"], y=df_transposed["Raw value"])
-    figure.update_layout(showlegend=False)
+    figure.update_layout(showlegend=False, yaxis_range=[minimum, maximum])
     return figure
-
-
-def _get_cleaned_sensor_column_names(dataframe):
-    """Get cleaned sensor column names for a dataframe when the columns are named like "f0_", "f1_"... "fn_" for `n`
-    sensors.
-
-    :param pandas.DataFrame dataframe: a dataframe containing columns of sensor data named like "f0_", "f1_"...
-    :return (list, list): the uncleaned and cleaned sensor names
-    """
-    original_names = [column for column in dataframe.columns if column.startswith("f") and column.endswith("_")]
-    cleaned_names = [re.findall(r"f(\d+)_", sensor_name)[0] for sensor_name in original_names]
-    return original_names, cleaned_names
diff --git a/dashboard/utils.py b/dashboard/utils.py
index ae542c4..5473bab 100644
--- a/dashboard/utils.py
+++ b/dashboard/utils.py
@@ -1,4 +1,5 @@
 import datetime
+import re
 
 
 TIME_RANGE_OPTIONS = {
@@ -36,3 +37,15 @@ def generate_time_range(time_range, custom_start_date=None, custom_end_date=None
     finish = datetime.datetime.now()
     start = finish - TIME_RANGE_OPTIONS[time_range]
     return start, finish
+
+
+def get_cleaned_sensor_column_names(dataframe):
+    """Get cleaned sensor column names for a dataframe when the columns are named like "f0_", "f1_"... "fn_" for `n`
+    sensors.
+
+    :param pandas.DataFrame dataframe: a dataframe containing columns of sensor data named like "f0_", "f1_"...
+    :return (list, list): the uncleaned and cleaned sensor names
+    """
+    original_names = [column for column in dataframe.columns if column.startswith("f") and column.endswith("_")]
+    cleaned_names = [re.findall(r"f(\d+)_", sensor_name)[0] for sensor_name in original_names]
+    return original_names, cleaned_names