doc: add example, bidmensional 3d view

tests/examples_arguments_syntax/bidmensional_3d_view.py

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+import altair as alt
+import pandas as pd
+import seaborn as sns
+from vega_datasets import data
+
+# Load and preprocess data
+data_frame = data.cars().dropna()
+
+data_frame['AdjustedHorsepower'] = data_frame['Horsepower'] // 20 * 20
+data_frame['AdjustedWeightLbs'] = data_frame['Weight_in_lbs'] // 400 * 400
+data_frame['CylinderCategory'] = data_frame['Cylinders'].apply(lambda value: "Less than 6" if value < 6 else "More than or equal to 6")
+
+# Group and aggregate data
+grouped_data = data_frame.groupby(['CylinderCategory', 'AdjustedHorsepower', 'AdjustedWeightLbs']).agg(
+    AverageMPG=('Miles_per_Gallon', 'mean'),
+    AverageAcceleration=('Acceleration', 'mean'),
+    Count=('Name', 'count')
+).reset_index()
+
+# Variable declarations
+weight_col = 'AdjustedWeightLbs'
+horsepower_col = 'AdjustedHorsepower'
+cylinder_col = 'CylinderCategory'
+bubble_size_col = 'Count'
+
+selected_cols = [weight_col, horsepower_col, cylinder_col, bubble_size_col]
+
+transformed_data = pd.melt(grouped_data, selected_cols)
+
+horsepower_values = sorted(transformed_data[horsepower_col].unique())
+
+color_scheme = sns.color_palette("coolwarm", len(horsepower_values)).as_hex()
+
+metrics = sorted(transformed_data['variable'].unique())
+
+# Altair selections
+horsepower_selection = alt.selection_point(fields=[horsepower_col], toggle=True)
+interval_selection = alt.selection_interval()
+
+metric_selection_bind = alt.binding_select(options=metrics)
+metric_selection = alt.selection_point(name="SelectMetric", fields=['variable'], bind=metric_selection_bind, value=metrics[0])
+
+# Legend for horsepower
+legend = (alt.Chart(transformed_data[[horsepower_col]].drop_duplicates())
+             .mark_rect()
+             .encode(y=alt.Y(f'{horsepower_col}:O', axis=alt.Axis(orient='right', title=None)),
+                     color=alt.Color(f'{horsepower_col}:O', scale=alt.Scale(domain=horsepower_values, range=color_scheme), legend=None),
+                     opacity=alt.condition(horsepower_selection & interval_selection, alt.value(1.0), alt.value(0.1)))
+             .properties(title='AdjustedHorsepower')
+             .add_params(horsepower_selection, interval_selection))
+
+# Cylinder charts
+cylinder_values = sorted(transformed_data[cylinder_col].unique())
+charts = []
+
+for cylinder in cylinder_values:
+    cylinder_data = transformed_data[transformed_data[cylinder_col] == cylinder]
+    chart = (alt.Chart(cylinder_data)
+               .mark_point()
+               .encode(x=alt.X(f'{weight_col}:Q', scale=alt.Scale(zero=False)),
+                       y=alt.Y('value:Q', scale=alt.Scale(zero=False), title='Selected metric value'),
+                       color=alt.Color(f'{horsepower_col}:O', scale=alt.Scale(domain=horsepower_values, range=color_scheme), legend=None),
+                       size=f'{bubble_size_col}:Q')
+               .properties(title=f'{cylinder} Cylinders', width=400, height=400)
+               .add_params(metric_selection)
+               .transform_filter(metric_selection)
+               .transform_filter(horsepower_selection & interval_selection)
+               .interactive())
+    charts.append(chart)
+
+# Combine and display charts
+alt.hconcat(legend, alt.hconcat(*charts).resolve_scale(x='shared', y='shared'))