data-visualization

name	data-visualization
description	Master advanced declarative visualization with HoloViews. Use this skill when creating complex multi-dimensional visualizations, composing overlays and layouts, implementing interactive streams and selection, building network or hierarchical visualizations, or exploring data with dynamic maps and faceted displays.

Data Visualization Skill

Overview

Master advanced declarative visualization with HoloViews and composition patterns. This skill covers sophisticated visualization techniques for complex data exploration and presentation.

Dependencies

• holoviews >= 1.18.0
• pandas >= 1.0.0
• numpy >= 1.15.0
• bokeh >= 3.0.0
• networkx >= 2.0.0 (for network visualizations)

Core Capabilities

1. Advanced Element Composition

HoloViews allows sophisticated composition of visualization elements:

import holoviews as hv
from holoviews import opts
import pandas as pd
import numpy as np

# Create overlaid elements
curve = hv.Curve(df, 'x', 'y', label='Measured')
scatter = hv.Scatter(df_with_noise, 'x', 'y', label='Noisy')
overlay = curve * scatter  # Multiplication overlays

# Create layouts
col_layout = hv.Column(plot1, plot2, plot3)
row_layout = hv.Row(plot1, plot2, plot3)
grid_layout = hv.GridMatrix(data_dict)

# Faceted displays
faceted = hv.Curve(df, 'date', 'value').facet('category')

# Nested layouts
complex_layout = hv.Column(
    hv.Row(plot1, plot2),
    hv.Row(plot3, plot4),
    hv.Row(plot5, plot6)
)

2. Interactive Streams and Selection

Create responsive visualizations with interactive selection:

from holoviews import streams

# Selection stream
range_stream = streams.RangeXY()
scatter = hv.Scatter(df, 'x', 'y').opts(tools=['box_select'])

@hv.transform
def selected_data(data):
    if range_stream.selection:
        x0, x1 = range_stream.selection[0], range_stream.selection[1]
        y0, y1 = range_stream.selection[2], range_stream.selection[3]
        mask = (data['x'] >= x0) & (data['x'] <= x1) & \
               (data['y'] >= y0) & (data['y'] <= y1)
        return data[mask]
    return data

histogram = selected_data.to(hv.Histogram)
scatter_with_hist = scatter + histogram

3. Dynamic Maps for Responsive Visualization

# Dynamic updating based on parameters
from holoviews import DynamicMap, streams

def plot_by_category(category):
    data = df[df['category'] == category]
    return hv.Scatter(data, 'x', 'y', title=f'Category: {category}')

category_stream = streams.Stream.define('category', category='A')
dmap = DynamicMap(plot_by_category, streams=[category_stream])

# Parameterized dynamic map
def plot_with_params(threshold=0.5):
    filtered = df[df['value'] > threshold]
    return hv.Scatter(filtered, 'x', 'y')

dmap_param = DynamicMap(
    plot_with_params,
    streams=[streams.Stream.define('threshold', threshold=0.5)]
)

4. Network and Hierarchical Visualizations

import networkx as nx

# Network graph
G = nx.karate_club_graph()
pos = nx.spring_layout(G)
edges = [(u, v) for u, v in G.edges()]
nodes = list(G.nodes())

# Create nodes and edges visualization
edge_plot = hv.Segments(edges, kdims=['source', 'target'])
node_plot = hv.Scatter(
    [(pos[n][0], pos[n][1], n) for n in nodes],
    kdims=['x', 'y', 'node']
)
network = (edge_plot * node_plot).opts(
    opts.Scatter(size=100, color='red'),
    opts.Segments(color='gray')
)

# Treemap for hierarchical data
treemap = hv.TreeMap(
    hierarchical_data,
    label='Organization'
).opts(tools=['hover'])

5. Statistical and Aggregate Visualizations

# Aggregate with Rasterize
from holoviews.operation import datashader as dshade

# Box plot for comparison
box_plot = hv.BoxWhisker(df, kdims=['category'], vdims=['value'])

# Violin plot
violin = hv.Violin(df, kdims=['category'], vdims=['value'])

# Distribution comparison
dist_layout = hv.Column(*[
    df[df['category'] == cat]['value'].hvplot.hist()
    for cat in df['category'].unique()
])

6. Multi-Dimensional Data Exploration

# HoloMap for multi-dimensional data
def plot_by_params(category, metric):
    data = df[(df['category'] == category) & (df['metric'] == metric)]
    return hv.Scatter(data, 'x', 'y', title=f'{category} - {metric}')

hmap = hv.HoloMap(
    {(cat, met): plot_by_params(cat, met)
     for cat in categories for met in metrics},
    kdims=['Category', 'Metric']
)

# NdLayout for structured multi-dimensional display
ndlayout = hv.NdLayout({
    (cat, met): plot_by_params(cat, met)
    for cat in categories for met in metrics
}, kdims=['Category', 'Metric'])

Advanced Styling and Theming

1. Global Options

# Set global defaults
opts.defaults(
    opts.Curve(width=700, height=400, responsive=True),
    opts.Scatter(size=100, alpha=0.5),
    opts.Image(cmap='viridis')
)

# Apply to multiple elements
styled_plots = [
    plot.opts(
        title='Styled Plot',
        xlabel='X Axis',
        ylabel='Y Axis',
        toolbar='right',
        active_tools=['pan', 'wheel_zoom']
    )
    for plot in plots
]

2. Custom Styling

# Element-specific styling
plot = hv.Scatter(df, 'x', 'y').opts(
    color=hv.dim('category').categorize({
        'A': '#FF6B6B',
        'B': '#4ECDC4',
        'C': '#45B7D1'
    }),
    size=hv.dim('value').norm(min=10, max=100),
    selection_color='red',
    nonselection_alpha=0.1
)

# Conditional formatting
plot.opts(
    color=hv.dim('status').categorize({
        'good': 'green',
        'warning': 'orange',
        'error': 'red'
    })
)

3. Interactive Legends and Annotations

# Annotations
annotated_plot = hv.Curve(df, 'x', 'y')
annotations = [
    hv.Text(x, y, text, fontsize=10)
    for x, y, text in annotations_data
]
plot_with_annotations = annotated_plot * hv.Overlay(annotations)

# Custom legend
plot = hv.Overlay([
    hv.Curve(df1, label='Series 1'),
    hv.Curve(df2, label='Series 2'),
    hv.Curve(df3, label='Series 3')
]).opts(
    legend_position='top_left',
    legend_muted_alpha=0.2
)

Best Practices

1. Performance with Large Datasets

# Use rasterize for dense plots
from holoviews.operation import rasterize

large_scatter = hv.Scatter(large_df, 'x', 'y')
rasterized = rasterize(large_scatter, pixel_ratio=2)

# Use aggregation
aggregated = df.groupby('category')['value'].mean().hvplot.bar()

# Use datashader for massive datasets (>100M points)
from holoviews.operation.datashader import datashade
dshaded = datashade(large_scatter)

2. Responsive and Accessible Plots

# Responsive sizing
plot = hv.Scatter(df, 'x', 'y').opts(
    responsive=True,
    sizing_mode='stretch_width'
)

# Accessible color palettes
plot = hv.Scatter(df, 'x', 'y').opts(
    color=hv.dim('value').norm(),
    cmap='cet_gray_r'  # Perceptually uniform
)

# Clear labels
plot.opts(
    title='Clear Title',
    xlabel='Independent Variable (units)',
    ylabel='Dependent Variable (units)',
    fontsize=14
)

3. Composition Patterns

# Avoid deep nesting
# Bad: ((a + (b + (c + d)))
# Good: a + b + c + d

# Create helper functions
def create_comparison_layout(data_dict):
    plots = [hv.Scatter(v, label=k) for k, v in data_dict.items()]
    return hv.Column(*plots)

# Modular composition
sidebar = hv.Column(title_text, filter_widget)
main = hv.Row(plot1, plot2)
app = hv.Column(sidebar, main)

Common Patterns

Pattern 1: Linked Brushing

def create_linked_views(df):
    scatter = hv.Scatter(df, 'x', 'y').opts(tools=['box_select'])

    def get_histogram(selection):
        if selection:
            selected_df = df.iloc[selection.event.inds]
        else:
            selected_df = df
        return hv.Histogram(selected_df['x'], bins=20)

    return scatter + DynamicMap(get_histogram, streams=[streams.Selection1D()])

Pattern 2: Multi-Scale Exploration

def create_zoomable_view(df):
    scatter = hv.Scatter(df, 'x', 'y')
    zoomed = scatter.opts(
        xlim=(0, 10),
        ylim=(0, 10)
    )
    return hv.Column(scatter, zoomed)

Pattern 3: Faceted Analysis

def create_faceted_analysis(df, facet_col):
    return df.hvplot.scatter(
        x='x',
        y='y',
        by=facet_col,
        subplots=True,
        layout='vertical'
    )

Integration with Other HoloViz Tools

• Panel: Embed interactive HoloViews in dashboards
• hvPlot: Quick plotting that produces HoloViews objects
• Datashader: Efficient rendering for large data
• Param: Parameter-driven dynamic visualizations
• GeoViews: Geographic data visualization building on HoloViews

Common Use Cases

1. Exploratory Data Analysis: Multi-dimensional data exploration
2. Dashboard Metrics: KPI and metric visualization
3. Scientific Visualization: Complex data relationships
4. Financial Analysis: Time series and correlation analysis
5. Report Generation: Publication-quality visualizations
6. Real-time Monitoring: Streaming data visualization

Troubleshooting

Issue: Plot Elements Overlapping

• Use layouts instead of overlays for clarity
• Adjust alpha transparency
• Use complementary colors

Issue: Slow Interactive Performance

• Use rasterize for dense plots
• Reduce data size with aggregation
• Use datashader for massive datasets
• Cache plot computations

Issue: Unclear Data Relationships

• Use multiple linked views
• Apply faceting for categorical comparison
• Use color and size encoding
• Add annotations and reference lines

Resources

• HoloViews Reference
• HoloViews User Guide
• Bokeh for Customization
• Datashader for Performance