diff --git a/tests/examples_arguments_syntax/scatter_point_paths_hover.py b/tests/examples_arguments_syntax/scatter_point_paths_hover.py
new file mode 100644
index 000000000..ee755b9e0
--- /dev/null
+++ b/tests/examples_arguments_syntax/scatter_point_paths_hover.py
@@ -0,0 +1,148 @@
+"""
+Scatter plot with point paths on hover with search box
+======================================================
+This example combines cross-sectional analysis (comparing countries at a single point in time) 
+with longitudinal analysis (tracking changes in individual countries over time), using
+an interactive visualization technique inspired by [this Vega example](https://vega.github.io/vega/examples/global-development/).
+
+Key features:
+1. Point Paths. On hover, shows data trajectories using a trail mark that
+thickens from past to present, clearly indicating the direction of time.
+2. Search Box. Implements a case-insensitive regex filter for country names,
+enabling dynamic, flexible data point selection to enhance exploratory analysis.
+"""
+# category: interactive charts
+import altair as alt
+from vega_datasets import data
+
+# Data source
+source = data.gapminder.url
+
+# X-value slider
+x_slider = alt.binding_range(min=1955, max=2005, step=5, name='Year ')
+x_select = alt.selection_point(name="x_select", fields=['year'], bind=x_slider, value=1980)
+
+# Hover selection
+hover = alt.selection_point(on='mouseover', fields=['country'], empty=False)
+# A separate hover for the points since these need empty=True  
+hover_point_opacity = alt.selection_point(on='mouseover', fields=['country'])  
+
+# Search box for country name
+search_box = alt.param(
+    value='',
+    bind=alt.binding(input='search', placeholder="Country", name='Search ')
+)
+
+# Base chart
+base = alt.Chart(source).encode(
+    x=alt.X('fertility:Q', scale=alt.Scale(zero=False), title='Babies per woman (total fertility rate)'),
+    y=alt.Y('life_expect:Q', scale=alt.Scale(zero=False), title='Life expectancy'),
+    color=alt.Color('region:N', title='Region', legend=alt.Legend(orient='bottom-left', titleFontSize=14, labelFontSize=12), scale=alt.Scale(scheme='dark2')),
+    detail='country:N'
+).transform_calculate(
+    region="""{
+        '0': 'South Asia',
+        '1': 'Europe & Central Asia',
+        '2': 'Sub-Saharan Africa',
+        '3': 'The Americas',
+        '4': 'East Asia & Pacific',
+        '5': 'Middle East & North Africa'
+    }[datum.cluster]"""
+).transform_filter(
+    # Exclude North Korea and South Korea due to source data error
+    "datum.country !== 'North Korea' && datum.country !== 'South Korea'"
+)
+
+# Points that are always visible (filtered by slider and search)
+visible_points = base.mark_circle(size=100).encode(
+    opacity=alt.condition(
+        hover_point_opacity 
+        & alt.expr.test(alt.expr.regexp(search_box, 'i'), alt.datum.country),
+        alt.value(0.8),
+        alt.value(0.1)
+        )
+    ).transform_filter(
+        x_select
+    ).add_params(
+        hover,
+        hover_point_opacity,
+        x_select
+)
+
+hover_line = alt.layer(
+    # Line layer
+    base.mark_trail().encode(
+        order=alt.Order(
+            'year:Q',
+            sort='ascending'
+        ),
+        size=alt.Size(
+            'year:Q',
+            scale=alt.Scale(domain=[1955, 2005], range=[1, 12]),
+            legend=None
+        ),
+        opacity=alt.condition(hover, alt.value(0.3), alt.value(0)),
+        color=alt.value('#222222')
+    ),
+    # Point layer
+    base.mark_point(size=50).encode(
+        opacity=alt.condition(hover, alt.value(0.8), alt.value(0)),
+    )
+)
+
+# Year labels
+year_labels = base.mark_text(align='left', dx=5, dy=-5, fontSize=14).encode(
+    text='year:O', 
+    color=alt.value('#222222')
+).transform_filter(hover)
+
+# Country labels
+country_labels = alt.Chart(source).mark_text(
+    align='left',
+    dx=-15,
+    dy=-25,
+    fontSize=18,
+    fontWeight='bold'
+).encode(
+    x='fertility:Q',
+    y='life_expect:Q',
+    text='country:N',
+    color=alt.value('black'),
+    opacity=alt.condition(hover, alt.value(1), alt.value(0))
+).transform_window(
+    rank='rank(life_expect)',
+    sort=[alt.SortField('life_expect', order='descending')], 
+    groupby=['country'] # places label atop highest point on y-axis on hover
+).transform_filter(
+    alt.datum.rank == 1
+).transform_aggregate(
+    life_expect='max(life_expect)',
+    fertility='max(fertility)',
+    groupby=['country']
+)
+
+background_year = alt.Chart(source).mark_text(
+    baseline='middle',
+    fontSize=96,
+    opacity=0.2
+).encode(
+    text='year:O'
+).transform_filter(
+    x_select
+).transform_aggregate(
+    year='max(year)'
+)
+
+# Combine all layers
+chart = alt.layer(
+    visible_points, year_labels, country_labels, hover_line, background_year
+).properties(
+    width=500,
+    height=500,
+    padding=10  # Padding ensures labels fit
+).configure_axis(
+    labelFontSize=12,
+    titleFontSize=12
+).add_params(search_box)
+
+chart
\ No newline at end of file
diff --git a/tests/examples_methods_syntax/scatter_point_paths_hover.py b/tests/examples_methods_syntax/scatter_point_paths_hover.py
new file mode 100644
index 000000000..a6cc747c7
--- /dev/null
+++ b/tests/examples_methods_syntax/scatter_point_paths_hover.py
@@ -0,0 +1,141 @@
+"""
+Scatter plot with point paths on hover with search box
+======================================================
+This example combines cross-sectional analysis (comparing countries at a single point in time) 
+with longitudinal analysis (tracking changes in individual countries over time), using
+an interactive visualization technique inspired by [this Vega example](https://vega.github.io/vega/examples/global-development/)
+
+Key features:
+1. Point Paths. On hover, shows data trajectories using a trail mark that
+thickens from past to present, clearly indicating the direction of time.
+2. Search Box. Implements a case-insensitive regex filter for country names,
+enabling dynamic, flexible data point selection to enhance exploratory analysis.
+"""
+# category: interactive charts
+import altair as alt
+from vega_datasets import data
+
+# Data source
+source = data.gapminder.url
+
+# X-value slider
+x_slider = alt.binding_range(min=1955, max=2005, step=5, name='Year ')
+x_select = alt.selection_point(name="x_select", fields=['year'], bind=x_slider, value=1980)
+
+# Hover selection
+hover = alt.selection_point(on='mouseover', fields=['country'], empty=False)
+# A separate hover for the points since these need empty=True  
+hover_point_opacity = alt.selection_point(on='mouseover', fields=['country'])  
+
+# Search box for country name
+search_box = alt.param(
+    value='',
+    bind=alt.binding(input='search', placeholder="Country", name='Search ')
+)
+
+# Base chart
+base = alt.Chart(source).encode(
+    alt.X('fertility:Q').scale(zero=False).title('Babies per woman (total fertility rate)'),
+    alt.Y('life_expect:Q').scale(zero=False).title('Life expectancy'),
+    alt.Color('region:N').scale(scheme='dark2').legend(orient='bottom-left', titleFontSize=14, labelFontSize=12).title('Region'),
+    alt.Detail('country:N')
+).transform_calculate(
+    region="""{
+        '0': 'South Asia',
+        '1': 'Europe & Central Asia',
+        '2': 'Sub-Saharan Africa',
+        '3': 'The Americas',
+        '4': 'East Asia & Pacific',
+        '5': 'Middle East & North Africa'
+    }[datum.cluster]"""
+).transform_filter(
+    # Exclude North Korea and South Korea due to source data error
+    "datum.country !== 'North Korea' && datum.country !== 'South Korea'"
+)
+
+# Points that are always visible (filtered by slider and search)
+visible_points = base.mark_circle(size=100).encode(
+    opacity=alt.condition(
+        hover_point_opacity 
+        & alt.expr.test(alt.expr.regexp(search_box, 'i'), alt.datum.country),
+        alt.value(0.8),
+        alt.value(0.1)
+        )
+    ).transform_filter(
+        x_select
+    ).add_params(
+        hover,
+        hover_point_opacity,
+        x_select
+)
+
+hover_line = alt.layer(
+    # Line layer
+    base.mark_trail().encode(
+        alt.Order('year:Q').sort('ascending'),
+        alt.Size('year:Q').scale(domain=[1955, 2005], range=[1, 12]).legend(None),
+        opacity=alt.condition(hover, alt.value(0.3), alt.value(0)),
+        color=alt.value('#222222')
+    ),
+    # Point layer
+    base.mark_point(size=50).encode(
+        opacity=alt.condition(hover, alt.value(0.8), alt.value(0)),
+    )
+)
+
+# Year labels
+year_labels = base.mark_text(align='left', dx=5, dy=-5, fontSize=14).encode(
+    text='year:O', 
+    color=alt.value('#222222')
+).transform_filter(hover)
+
+# Country labels
+country_labels = alt.Chart(source).mark_text(
+    align='left',
+    dx=-15,
+    dy=-25,
+    fontSize=18,
+    fontWeight='bold'
+).encode(
+    x='fertility:Q',
+    y='life_expect:Q',
+    text='country:N',
+    color=alt.value('black'),
+    opacity=alt.condition(hover, alt.value(1), alt.value(0))
+).transform_window(
+    rank='rank(life_expect)',
+    sort=[alt.SortField('life_expect', order='descending')], 
+    groupby=['country'] # places label atop highest point on y-axis on hover
+).transform_filter(
+    alt.datum.rank == 1
+).transform_aggregate(
+    life_expect='max(life_expect)',
+    fertility='max(fertility)',
+    groupby=['country']
+)
+
+background_year = alt.Chart(source).mark_text(
+    baseline='middle',
+    fontSize=96,
+    opacity=0.2
+).encode(
+    text='year:O'
+).transform_filter(
+    x_select
+).transform_aggregate(
+    year='max(year)'
+)
+
+# Combine all layers
+chart = alt.layer(
+    visible_points, year_labels, country_labels, hover_line, background_year
+).properties(
+    width=500,
+    height=500,
+    padding=10  # Padding ensures labels fit
+).configure_axis(
+    labelFontSize=12,
+    titleFontSize=12
+).add_params(search_box)
+
+chart
\ No newline at end of file