Plotting Fundamentals Skill

Overview

Master quick plotting and interactive visualization with hvPlot and HoloViews basics. This skill covers essential techniques for creating publication-quality plots with minimal code.

Dependencies

hvplot >= 0.9.0
holoviews >= 1.18.0
pandas >= 1.0.0
numpy >= 1.15.0
bokeh >= 3.0.0

Core Capabilities

1. hvPlot Quick Plotting

hvPlot provides an intuitive, pandas-like API for rapid visualization:

import hvplot.pandas
import pandas as pd
import numpy as np

# Create sample data
df = pd.DataFrame({
    'date': pd.date_range('2024-01-01', periods=100),
    'sales': np.cumsum(np.random.randn(100)) + 100,
    'region': np.random.choice(['North', 'South', 'East', 'West'], 100)
})

# Simple line plot
df.hvplot.line(x='date', y='sales', title='Sales Over Time')

# Grouped plot
df.hvplot.line(x='date', y='sales', by='region', subplots=True)

# Scatter with size and color
df.hvplot.scatter(x='sales', y='date', c='region', size=50)

2. Common Plot Types

# Bar plot
df.hvplot.bar(x='region', y='sales', rot=45)

# Histogram
df['sales'].hvplot.hist(bins=30, title='Sales Distribution')

# Box plot
df.hvplot.box(y='sales', by='region')

# Area plot
df.hvplot.area(x='date', y='sales')

# KDE (Kernel Density Estimation)
df['sales'].hvplot.kde()

# Hexbin (for large datasets)
df.hvplot.hexbin(x='sales', y='date', gridsize=20)

3. Customization Options

# Apply consistent styling
plot = df.hvplot.line(
    x='date',
    y='sales',
    title='Sales Trend',
    xlabel='Date',
    ylabel='Sales ($)',
    color='#2E86DE',
    line_width=2,
    height=400,
    width=700,
    responsive=True,
    legend='top_left'
)

# Color mapping
df.hvplot.scatter(
    x='sales',
    y='date',
    c='sales',
    cmap='viridis',
    s=100
)

# Multiple series
df.hvplot.line(
    x='date',
    y=['sales'],
    title='Performance Metrics'
)

4. Interactive Features

# Hover information
df.hvplot.scatter(
    x='sales',
    y='date',
    hover_cols=['region'],
    tools=['hover', 'pan', 'wheel_zoom']
)

# Selection and linked views
import holoviews as hv
scatter = df.hvplot.scatter(x='sales', y='date')
scatter.opts(tools=['box_select'])

# Responsive sizing
plot = df.hvplot.line(
    x='date',
    y='sales',
    responsive=True,
    height=400
)

5. Geographic Plotting with hvPlot

import geopandas as gpd

# Quick geographic plot
gdf = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))
gdf.hvplot(
    c='pop_est',
    cmap='viridis',
    geo=True,
    frame_width=600
)

# City points on map
cities = gpd.GeoDataFrame({
    'name': ['City A', 'City B'],
    'geometry': [Point(0, 0), Point(1, 1)],
    'population': [1000000, 500000]
})
cities.hvplot(
    geo=True,
    c='population',
    size='population',
    cmap='plasma'
)

HoloViews Fundamentals

1. Basic Element Types

import holoviews as hv
from holoviews import opts

# Curve
curve = hv.Curve(df, 'date', 'sales')

# Scatter
scatter = hv.Scatter(df, 'sales', 'date')

# Histogram
hist = hv.Histogram(df['sales'].values)

# Image (heatmap)
image = hv.Image(data)

# Bars
bars = hv.Bars(df, 'region', 'sales')

# Text annotations
text = hv.Text(0.5, 0.5, 'Hello HoloViews')

2. Styling and Options

# Using .opts() method
plot = hv.Curve(df, 'date', 'sales').opts(
    title='Sales Trend',
    xlabel='Date',
    ylabel='Sales',
    color='#2E86DE',
    line_width=2,
    height=400,
    width=700
)

# Using opts object
opts_obj = opts.Curve(
    title='Sales',
    color='navy',
    line_width=2
)
plot = hv.Curve(df, 'date', 'sales').opts(opts_obj)

3. Composing Visualizations

# Overlaying multiple plots
overlay = hv.Curve(df, 'date', 'sales') * hv.Scatter(df_subset, 'date', 'sales')

# Side-by-side layouts
layout = hv.Curve(df1, 'date', 'sales') + hv.Scatter(df2, 'date', 'value')

# Grid layouts
grid = (
    (hv.Curve(data1) + hv.Scatter(data2)) /
    (hv.Histogram(data3) + hv.Image(data4))
)

# Faceted views
faceted = hv.Curve(df, 'date', 'sales').facet('region')

4. Interactive Selection and Linking

# Brush selection
curve_selectable = hv.Curve(df, 'date', 'sales').opts(
    tools=['box_select'],
    selection_fill_color='red',
    nonselection_fill_alpha=0.2
)

# Dynamic linking with streams
from holoviews import streams

# Hover information
hover = streams.Tap(source=scatter, transient=True)

@hv.transform
def get_info(data):
    if data.empty:
        return hv.Text(0, 0, 'Hover to select')
    return hv.Text(0, 0, f"Point: {data.iloc[0].values}")

Best Practices

1. Data Preparation

Always check data types before plotting
Handle missing values explicitly
Normalize columns for better visualization
Use appropriate data ranges

2. Visual Design

Choose colors for accessibility (colorblind-friendly palettes)
Use title and axis labels
Include legends for multiple series
Maintain consistent styling across related plots

3. Performance

Use datashader for datasets with >100k points
Downsample or aggregate before plotting
Use responsive=True for web dashboards
Cache expensive plot computations

4. Code Organization

# Create a plotting utility module
class PlotBuilder:
    COLORS = {'primary': '#2E86DE', 'secondary': '#A23B72'}
    DEFAULTS = {'height': 400, 'width': 700, 'responsive': True}

    @staticmethod
    def style_plot(plot, **kwargs):
        return plot.opts(**{**PlotBuilder.DEFAULTS, **kwargs})

# Usage
styled = PlotBuilder.style_plot(df.hvplot.line(x='date', y='sales'))

Common Patterns

Pattern 1: Dashboard with Multiple Plots

def create_sales_dashboard(df):
    return hv.Column(
        df.hvplot.line(x='date', y='sales', title='Trend'),
        df.hvplot.bar(x='region', y='sales', title='By Region'),
        df['sales'].hvplot.hist(bins=20, title='Distribution')
    )

Pattern 2: Conditional Visualization

def plot_data(df, plot_type='line'):
    if plot_type == 'line':
        return df.hvplot.line(x='date', y='sales')
    elif plot_type == 'scatter':
        return df.hvplot.scatter(x='date', y='sales')
    else:
        return df.hvplot.bar(x='region', y='sales')

Pattern 3: Multi-Series Plot with Legend

def plot_multiple_metrics(df, metrics):
    plots = [df.hvplot.line(x='date', y=m, label=m) for m in metrics]
    return hv.Overlay(plots)

Integration with Other HoloViz Tools

Panel: Embed plots in dashboards
HoloViews: Advanced composition and interactivity
Datashader: Large dataset visualization
Param: Dynamic plot updates based on parameters

Common Use Cases

Time Series Analysis: Trends, anomalies, forecasting
Comparative Analysis: Category comparisons, rankings
Distribution Analysis: Histograms, KDEs, box plots
Correlation Analysis: Scatter plots, hexbins
Geographic Analysis: Maps, regional data
Statistical Summaries: Summary statistics with plots

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