Oct 8, 2025 · Oct 8, 2025 · Oct 8, 2025 · Oct 9, 2025
diff --git a/plotly/shapeannotation.py b/plotly/shapeannotation.py
 # some functions defined here to avoid numpy import

 from datetime import datetime
 from numbers import Number


 def _mean(x):
    if len(x) == 0:
        raise ValueError("x must have positive length")
    return float(sum(x)) / len(x)

    # Numeric sequence: default behaviour
    if all(isinstance(v, Number) for v in x):
        return float(sum(x)) / len(x)

    # Datetime sequence: delegate to _mean_datetime
    if all(isinstance(v, datetime) for v in x):
        return _mean_datetime(x)

    # Fallback for non-numeric and non-datetime types: return first element
    return x[0]


 def _mean_datetime(x):
    """Return midpoint of a sequence of datetime objects (assumes homogenous)."""

    timestamps = [v.timestamp() for v in x]  # convert to POSIX timestamps
    avg = sum(timestamps) / len(timestamps)
    tzinfo = x[0].tzinfo  # extract timezone info from first element
    return datetime.fromtimestamp(avg, tz=tzinfo)


 def _argmin(x):
diff --git a/tests/test_optional/test_autoshapes/test_date_axis_annotated_shapes.py b/tests/test_optional/test_autoshapes/test_date_axis_annotated_shapes.py
 """Tests for annotated axis-spanning shapes with datetime coordinates.

 These tests cover the regression described in GitHub issue #3065:
 https://github.com/plotly/plotly.py/issues/3065

 """

 from datetime import datetime

 import plotly.express as px
 import plotly.graph_objects as go


 def test_add_vline_with_date_annotation_express():
    """MWE from Github issue https://github.com/plotly/plotly.py/issues/3065"""
    df = px.data.stocks(indexed=True)
    fig = px.line(df)
    fig.add_vline(x="2018-09-24", annotation_text="test")


 def test_add_vline_with_date_annotation():
    fig = go.Figure()
    # Provide a couple of traces so axis type is inferred as date from data
    dates = [datetime(2025, 9, 23), datetime(2025, 9, 24), datetime(2025, 9, 25)]
    fig.add_scatter(x=dates, y=[1, 2, 3])
    fig.add_vline(x=dates[1], annotation_text="Test")

    # Ensure one annotation was added and x coordinate preserved
    annotations = getattr(fig.layout, "annotations", [])
    assert len(annotations) == 1
    ann = annotations[0]
    assert ann.x == dates[1]
    assert ann.text == "Test"


 def test_add_hline_with_date_annotation():
    fig = go.Figure()
    # Provide a couple of traces so axis type is inferred as date from data
    dates = [datetime(2025, 9, 23), datetime(2025, 9, 24), datetime(2025, 9, 25)]
    fig.add_scatter(y=dates, x=[1, 2, 3])
    fig.add_hline(y=dates[1], annotation_text="Test")

    # Ensure one annotation was added and x coordinate preserved
    annotations = getattr(fig.layout, "annotations", [])
    assert len(annotations) == 1
    ann = annotations[0]
    assert ann.y == dates[1]
    assert ann.text == "Test"
Original file line number	Diff line number	Diff line change
		@@ -1,10 +1,32 @@
		# some functions defined here to avoid numpy import

		from datetime import datetime
		from numbers import Number


		def _mean(x):
		if len(x) == 0:
		raise ValueError("x must have positive length")
		return float(sum(x)) / len(x)

		# Numeric sequence: default behaviour
		if all(isinstance(v, Number) for v in x):
		return float(sum(x)) / len(x)

		# Datetime sequence: delegate to _mean_datetime
		if all(isinstance(v, datetime) for v in x):
		return _mean_datetime(x)

		# Fallback for non-numeric and non-datetime types: return first element
		return x[0]


		def _mean_datetime(x):
		"""Return midpoint of a sequence of datetime objects (assumes homogenous)."""

		timestamps = [v.timestamp() for v in x] # convert to POSIX timestamps
		avg = sum(timestamps) / len(timestamps)
		tzinfo = x[0].tzinfo # extract timezone info from first element
		return datetime.fromtimestamp(avg, tz=tzinfo)


		def _argmin(x):
Expand Down
Original file line number	Diff line number	Diff line change
		@@ -0,0 +1,48 @@
		"""Tests for annotated axis-spanning shapes with datetime coordinates.

		These tests cover the regression described in GitHub issue #3065:
		https://github.com/plotly/plotly.py/issues/3065

		"""

		from datetime import datetime

		import plotly.express as px
		import plotly.graph_objects as go


		def test_add_vline_with_date_annotation_express():
		"""MWE from Github issue https://github.com/plotly/plotly.py/issues/3065"""
		df = px.data.stocks(indexed=True)
		fig = px.line(df)
		fig.add_vline(x="2018-09-24", annotation_text="test")


		def test_add_vline_with_date_annotation():
		fig = go.Figure()
		# Provide a couple of traces so axis type is inferred as date from data
		dates = [datetime(2025, 9, 23), datetime(2025, 9, 24), datetime(2025, 9, 25)]
		fig.add_scatter(x=dates, y=[1, 2, 3])
		fig.add_vline(x=dates[1], annotation_text="Test")

		# Ensure one annotation was added and x coordinate preserved
		annotations = getattr(fig.layout, "annotations", [])
		assert len(annotations) == 1
		ann = annotations[0]
		assert ann.x == dates[1]
		assert ann.text == "Test"


		def test_add_hline_with_date_annotation():
		fig = go.Figure()
		# Provide a couple of traces so axis type is inferred as date from data
		dates = [datetime(2025, 9, 23), datetime(2025, 9, 24), datetime(2025, 9, 25)]
		fig.add_scatter(y=dates, x=[1, 2, 3])
		fig.add_hline(y=dates[1], annotation_text="Test")

		# Ensure one annotation was added and x coordinate preserved
		annotations = getattr(fig.layout, "annotations", [])
		assert len(annotations) == 1
		ann = annotations[0]
		assert ann.y == dates[1]
		assert ann.text == "Test"