Python Plotting & Visualization

Overview

Master data visualization in Python through three complementary libraries: matplotlib (foundational static plots), seaborn (statistical visualization), and plotly (interactive graphics). Each library has distinct strengths, and knowing when to use which—or how to combine them—is key to effective data communication.

Core value: Create publication-quality static plots, insightful statistical graphics, and interactive dashboards with the right tool for each visualization need.

Library Selection Guide

Matplotlib - The Foundation

Use when:

• Need fine-grained control over every plot element
• Creating publication-quality static figures
• Building custom visualizations not available elsewhere
• Working with low-level plotting requirements
• Need maximum compatibility (most widely supported)

Strengths:

• Complete control over every visual element
• Mature, stable, extensively documented
• Foundation for seaborn and other libraries
• Export to any format (PDF, SVG, PNG, etc.)
• Extensive customization options

Weaknesses:

• Verbose for common statistical plots
• Steeper learning curve for complex plots
• Static output (no interactivity)
• Default aesthetics less modern

Seaborn - Statistical Visualization

Use when:

• Creating statistical plots (distributions, correlations, regressions)
• Need attractive defaults with minimal code
• Working with pandas DataFrames
• Want automatic statistical estimation
• Need faceted/multi-panel plots

Strengths:

• Concise syntax for statistical plots
• Beautiful default themes
• Built-in statistical estimation
• Seamless pandas integration
• FacetGrid for complex multi-panel layouts

Weaknesses:

• Less control than matplotlib
• Limited to statistical plot types
• Static output only
• Requires understanding of underlying matplotlib for deep customization

Plotly - Interactive Visualization

Use when:

• Need interactive plots (zoom, hover, pan)
• Building dashboards or web apps
• Want 3D visualizations
• Need animations
• Sharing exploratory analysis

Strengths:

• Rich interactivity out-of-the-box
• Beautiful defaults
• 3D and geographic plotting
• Integration with Dash for web apps
• Export to HTML for sharing

Weaknesses:

• Larger file sizes
• Less control than matplotlib for publications
• Different API paradigm
• Not ideal for static publication figures

Quick Decision Tree

Need interactivity?
├─ Yes → Plotly
└─ No → Statistical plot?
    ├─ Yes → Seaborn (can customize with matplotlib)
    └─ No → Complex customization needed?
        ├─ Yes → Matplotlib
        └─ No → Seaborn or Matplotlib (preference)

Matplotlib - Foundational Plotting

Two APIs: pyplot vs Object-Oriented

pyplot (MATLAB-style, implicit state):

import matplotlib.pyplot as plt

# Quick and interactive
plt.plot([1, 2, 3], [1, 4, 9])
plt.xlabel('x')
plt.ylabel('y')
plt.title('Simple Plot')
plt.show()

Object-Oriented (explicit, recommended for complex plots):

import matplotlib.pyplot as plt

# Explicit control
fig, ax = plt.subplots()
ax.plot([1, 2, 3], [1, 4, 9])
ax.set_xlabel('x')
ax.set_ylabel('y')
ax.set_title('Simple Plot')
plt.show()

Recommendation: Use OO API for scripts, functions, and complex plots. Use pyplot for quick interactive exploration.

Figure Anatomy

import matplotlib.pyplot as plt
import numpy as np

# Create figure with subplots
fig, axes = plt.subplots(2, 2, figsize=(10, 8))

# fig = Figure (entire window)
# axes = array of Axes (individual plots)

# Access individual axes
ax1 = axes[0, 0]  # Top left
ax2 = axes[0, 1]  # Top right
ax3 = axes[1, 0]  # Bottom left
ax4 = axes[1, 1]  # Bottom right

# Plot on each axes
ax1.plot([1, 2, 3], [1, 4, 9])
ax2.scatter([1, 2, 3], [1, 4, 9])
ax3.bar([1, 2, 3], [1, 4, 9])
ax4.hist(np.random.randn(1000))

# Adjust layout
plt.tight_layout()
plt.show()

Common Plot Types

Line Plot:

fig, ax = plt.subplots()

x = np.linspace(0, 10, 100)
y1 = np.sin(x)
y2 = np.cos(x)

ax.plot(x, y1, label='sin(x)', linewidth=2, color='blue', linestyle='-')
ax.plot(x, y2, label='cos(x)', linewidth=2, color='red', linestyle='--')

ax.set_xlabel('x')
ax.set_ylabel('y')
ax.set_title('Trigonometric Functions')
ax.legend()
ax.grid(True, alpha=0.3)
plt.show()

Scatter Plot:

fig, ax = plt.subplots()

x = np.random.randn(100)
y = 2*x + np.random.randn(100)*0.5
colors = np.random.rand(100)
sizes = 100 * np.random.rand(100)

scatter = ax.scatter(x, y, c=colors, s=sizes, alpha=0.6, cmap='viridis')

ax.set_xlabel('X')
ax.set_ylabel('Y')
ax.set_title('Scatter Plot with Color and Size')

# Add colorbar
plt.colorbar(scatter, ax=ax, label='Color Value')
plt.show()

Bar Plot:

fig, ax = plt.subplots()

categories = ['A', 'B', 'C', 'D', 'E']
values = [23, 45, 56, 78, 32]

bars = ax.bar(categories, values, color=['red', 'blue', 'green', 'orange', 'purple'])

# Annotate bars
for bar, value in zip(bars, values):
    height = bar.get_height()
    ax.text(bar.get_x() + bar.get_width()/2., height,
            f'{value}',
            ha='center', va='bottom')

ax.set_ylabel('Values')
ax.set_title('Bar Chart')
plt.show()

Histogram:

fig, ax = plt.subplots()

data = np.random.randn(1000)

ax.hist(data, bins=30, alpha=0.7, color='skyblue', edgecolor='black')

ax.set_xlabel('Value')
ax.set_ylabel('Frequency')
ax.set_title('Histogram')
ax.axvline(data.mean(), color='red', linestyle='--', linewidth=2, label='Mean')
ax.legend()
plt.show()

Subplots with Shared Axes:

fig, (ax1, ax2) = plt.subplots(2, 1, sharex=True, figsize=(10, 6))

x = np.linspace(0, 10, 100)
y1 = np.sin(x)
y2 = np.cos(x)

ax1.plot(x, y1)
ax1.set_ylabel('sin(x)')
ax1.grid(True)

ax2.plot(x, y2, color='red')
ax2.set_xlabel('x')
ax2.set_ylabel('cos(x)')
ax2.grid(True)

plt.tight_layout()
plt.show()

Customization

Styles:

import matplotlib.pyplot as plt

# List available styles
print(plt.style.available)

# Use a style
plt.style.use('seaborn-v0_8-darkgrid')
# or
with plt.style.context('ggplot'):
    plt.plot([1, 2, 3], [1, 4, 9])
    plt.show()

Colors and Colormaps:

# Named colors
ax.plot(x, y, color='steelblue')
ax.plot(x, y, color='#FF6347')  # Hex
ax.plot(x, y, color=(0.2, 0.4, 0.6))  # RGB

# Colormaps
from matplotlib import cm

colors = cm.viridis(np.linspace(0, 1, 10))
for i, color in enumerate(colors):
    ax.plot([i, i+1], [0, 1], color=color)

Markers and Line Styles:

ax.plot(x, y,
        marker='o',           # Circle markers
        markersize=8,
        markerfacecolor='red',
        markeredgecolor='black',
        markeredgewidth=2,
        linestyle='--',       # Dashed line
        linewidth=2,
        color='blue')

Legends:

ax.plot(x, y1, label='Data 1')
ax.plot(x, y2, label='Data 2')

ax.legend(
    loc='upper right',    # or 'best', 'center', etc.
    frameon=True,
    shadow=True,
    fancybox=True,
    fontsize=12
)

Annotations:

ax.plot(x, y)

# Annotate a point
ax.annotate(
    'Maximum',
    xy=(x[50], y[50]),       # Point to annotate
    xytext=(x[50]+1, y[50]+1),  # Text location
    arrowprops=dict(arrowstyle='->', color='red', lw=2),
    fontsize=12,
    color='red'
)

Saving Figures:

# High-resolution PNG
fig.savefig('plot.png', dpi=300, bbox_inches='tight')

# Vector formats for publications
fig.savefig('plot.pdf', bbox_inches='tight')
fig.savefig('plot.svg', bbox_inches='tight')

# Transparent background
fig.savefig('plot.png', transparent=True, bbox_inches='tight')

Seaborn - Statistical Visualization

Basic Setup

import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np

# Set theme
sns.set_theme(style='whitegrid')  # whitegrid, darkgrid, white, dark, ticks

# Load example dataset
tips = sns.load_dataset('tips')  # Built-in datasets for examples

Distribution Plots

Histogram with KDE:

fig, ax = plt.subplots()

sns.histplot(data=tips, x='total_bill', kde=True, ax=ax)

ax.set_title('Distribution of Total Bill')
plt.show()

KDE Plot:

fig, ax = plt.subplots()

sns.kdeplot(data=tips, x='total_bill', hue='time', fill=True, ax=ax)

ax.set_title('Total Bill Distribution by Time')
plt.show()

Distribution Plot (ECDF):

fig, ax = plt.subplots()

sns.ecdfplot(data=tips, x='total_bill', hue='sex', ax=ax)

ax.set_title('Cumulative Distribution of Total Bill')
plt.show()

Categorical Plots

Box Plot:

fig, ax = plt.subplots(figsize=(10, 6))

sns.boxplot(data=tips, x='day', y='total_bill', hue='sex', ax=ax)

ax.set_title('Total Bill by Day and Sex')
plt.show()

Violin Plot:

fig, ax = plt.subplots(figsize=(10, 6))

sns.violinplot(data=tips, x='day', y='total_bill', hue='sex',
               split=True, ax=ax)

ax.set_title('Total Bill Distribution by Day')
plt.show()

Strip Plot / Swarm Plot:

fig, axes = plt.subplots(1, 2, figsize=(14, 6))

# Strip plot (with jitter)
sns.stripplot(data=tips, x='day', y='total_bill', hue='sex',
              dodge=True, alpha=0.6, ax=axes[0])
axes[0].set_title('Strip Plot')

# Swarm plot (no overlap)
sns.swarmplot(data=tips, x='day', y='total_bill', hue='sex',
              dodge=True, ax=axes[1])
axes[1].set_title('Swarm Plot')

plt.tight_layout()
plt.show()

Bar Plot with Error Bars:

fig, ax = plt.subplots()

sns.barplot(data=tips, x='day', y='total_bill', hue='sex',
            ci=95,  # Confidence interval
            ax=ax)

ax.set_title('Average Total Bill by Day')
plt.show()

Relational Plots

Scatter Plot:

fig, ax = plt.subplots(figsize=(10, 6))

sns.scatterplot(data=tips, x='total_bill', y='tip',
                hue='time', size='size', style='sex',
                sizes=(50, 200), alpha=0.6, ax=ax)

ax.set_title('Tip vs Total Bill')
plt.show()

Line Plot:

# Create time series data
fmri = sns.load_dataset('fmri')

fig, ax = plt.subplots(figsize=(10, 6))

sns.lineplot(data=fmri, x='timepoint', y='signal',
             hue='event', style='region', ax=ax)

ax.set_title('fMRI Signal Over Time')
plt.show()

Regression Plots

Linear Regression:

fig, ax = plt.subplots()

sns.regplot(data=tips, x='total_bill', y='tip', ax=ax)

ax.set_title('Linear Regression: Tip vs Total Bill')
plt.show()

Residual Plot:

fig, ax = plt.subplots()

sns.residplot(data=tips, x='total_bill', y='tip', ax=ax)

ax.set_title('Residual Plot')
ax.axhline(0, color='red', linestyle='--')
plt.show()

Heatmaps and Matrices

Correlation Heatmap:

fig, ax = plt.subplots(figsize=(8, 6))

# Compute correlation matrix
corr = tips[['total_bill', 'tip', 'size']].corr()

sns.heatmap(corr, annot=True, cmap='coolwarm', center=0,
            square=True, linewidths=1, ax=ax)

ax.set_title('Correlation Matrix')
plt.show()

Clustermap:

# Hierarchical clustering heatmap
iris = sns.load_dataset('iris')
iris_data = iris.drop('species', axis=1)

g = sns.clustermap(iris_data.T, cmap='viridis',
                   standard_scale=1, figsize=(8, 8))
plt.show()

Multi-Panel Plots

FacetGrid:

# Create grid of plots
g = sns.FacetGrid(tips, col='time', row='sex',
                  margin_titles=True, height=4)

# Map plot type to grid
g.map(sns.scatterplot, 'total_bill', 'tip', alpha=0.6)

# Customize
g.set_axis_labels('Total Bill', 'Tip')
g.set_titles(col_template="{col_name}", row_template="{row_name}")
g.add_legend()

plt.show()

PairGrid:

# Pairwise relationships
iris = sns.load_dataset('iris')

g = sns.PairGrid(iris, hue='species', height=2.5)
g.map_upper(sns.scatterplot)
g.map_lower(sns.kdeplot)
g.map_diag(sns.histplot)
g.add_legend()

plt.show()

Pairplot (simpler):

sns.pairplot(iris, hue='species', diag_kind='kde', height=2.5)
plt.show()

Themes and Color Palettes

Setting Themes:

# Theme styles
sns.set_theme(style='whitegrid')  # white, dark, whitegrid, darkgrid, ticks

# Context (scales elements)
sns.set_context('talk')  # paper, notebook, talk, poster

# Combined
sns.set_theme(style='darkgrid', context='poster',
              palette='deep', font_scale=1.2)

Color Palettes:

# Qualitative (categorical)
sns.set_palette('deep')  # deep, muted, pastel, bright, dark, colorblind

# Sequential
sns.set_palette('Blues')

# Diverging
sns.set_palette('coolwarm')

# Custom
custom_palette = ['#FF6347', '#4682B4', '#32CD32']
sns.set_palette(custom_palette)

# View palette
sns.palplot(sns.color_palette('husl', 10))
plt.show()

Plotly - Interactive Visualization

Plotly Express - High-Level API

Scatter Plot:

import plotly.express as px

df = px.data.iris()

fig = px.scatter(df, x='sepal_width', y='sepal_length',
                 color='species', size='petal_length',
                 hover_data=['petal_width'],
                 title='Iris Dataset')

fig.show()

Line Plot:

df = px.data.gapminder()

fig = px.line(df[df['country'] == 'Canada'],
              x='year', y='gdpPercap',
              title='Canada GDP per Capita')

fig.show()

Bar Chart:

df = px.data.tips()

fig = px.bar(df, x='day', y='total_bill', color='sex',
             barmode='group',  # or 'stack', 'overlay'
             title='Total Bill by Day and Sex')

fig.show()

Histogram:

fig = px.histogram(df, x='total_bill', color='sex',
                   marginal='box',  # or 'violin', 'rug'
                   title='Total Bill Distribution')

fig.show()

Box Plot:

fig = px.box(df, x='day', y='total_bill', color='sex',
             title='Total Bill by Day')

fig.show()

Heatmap:

import numpy as np

z = np.random.randn(20, 20)

fig = px.imshow(z, color_continuous_scale='RdBu_r',
                title='Heatmap')

fig.show()

3D Scatter:

df = px.data.iris()

fig = px.scatter_3d(df, x='sepal_length', y='sepal_width', z='petal_length',
                    color='species', size='petal_width',
                    title='3D Iris Dataset')

fig.show()

Animated Plots:

df = px.data.gapminder()

fig = px.scatter(df, x='gdpPercap', y='lifeExp',
                 animation_frame='year',
                 animation_group='country',
                 size='pop', color='continent',
                 hover_name='country',
                 log_x=True, size_max=60,
                 range_x=[100, 100000], range_y=[25, 90],
                 title='Gapminder Data Over Time')

fig.show()

Graph Objects - Low-Level API

Basic Scatter:

import plotly.graph_objects as go

x = [1, 2, 3, 4, 5]
y = [1, 4, 9, 16, 25]

fig = go.Figure(data=go.Scatter(x=x, y=y, mode='markers+lines'))

fig.update_layout(
    title='Custom Scatter Plot',
    xaxis_title='X Axis',
    yaxis_title='Y Axis'
)

fig.show()

Multiple Traces:

fig = go.Figure()

# Add multiple traces
fig.add_trace(go.Scatter(x=[1, 2, 3], y=[1, 4, 9],
                         mode='lines', name='Series 1'))
fig.add_trace(go.Scatter(x=[1, 2, 3], y=[2, 5, 10],
                         mode='lines+markers', name='Series 2'))

fig.update_layout(title='Multiple Series')
fig.show()

Subplots:

from plotly.subplots import make_subplots

fig = make_subplots(
    rows=2, cols=2,
    subplot_titles=('Plot 1', 'Plot 2', 'Plot 3', 'Plot 4')
)

# Add traces to specific subplots
fig.add_trace(go.Scatter(x=[1, 2, 3], y=[1, 4, 9]), row=1, col=1)
fig.add_trace(go.Bar(x=[1, 2, 3], y=[2, 5, 8]), row=1, col=2)
fig.add_trace(go.Scatter(x=[1, 2, 3], y=[3, 6, 9]), row=2, col=1)
fig.add_trace(go.Box(y=[1, 2, 3, 4, 5, 6, 7]), row=2, col=2)

fig.update_layout(height=600, showlegend=False, title_text='Subplots')
fig.show()

Interactive Features:

fig = go.Figure()

fig.add_trace(go.Scatter(x=[1, 2, 3, 4], y=[10, 11, 12, 13]))

# Add dropdown menu
fig.update_layout(
    updatemenus=[
        dict(
            buttons=list([
                dict(label="Linear",
                     method="relayout",
                     args=[{"yaxis.type": "linear"}]),
                dict(label="Log",
                     method="relayout",
                     args=[{"yaxis.type": "log"}])
            ]),
            direction="down",
        )
    ]
)

fig.show()

Exporting:

# To HTML
fig.write_html('plot.html')

# To static image (requires kaleido)
fig.write_image('plot.png', width=1200, height=800)
fig.write_image('plot.pdf')

Integration Patterns

Seaborn with Matplotlib Customization

import seaborn as sns
import matplotlib.pyplot as plt

# Create seaborn plot
fig, ax = plt.subplots(figsize=(10, 6))
sns.boxplot(data=tips, x='day', y='total_bill', ax=ax)

# Customize with matplotlib
ax.set_title('Customized Seaborn Plot', fontsize=16, fontweight='bold')
ax.set_xlabel('Day of Week', fontsize=12)
ax.set_ylabel('Total Bill ($)', fontsize=12)
ax.grid(axis='y', alpha=0.3)

# Add custom annotation
ax.text(0.5, 0.95, 'Custom annotation',
        transform=ax.transAxes, ha='center')

plt.tight_layout()
plt.show()

Plotly with Pandas

import pandas as pd
import plotly.express as px

# Pandas DataFrame
df = pd.DataFrame({
    'x': range(10),
    'y': [i**2 for i in range(10)],
    'category': ['A', 'B'] * 5
})

# Direct plotting from DataFrame
fig = px.line(df, x='x', y='y', color='category')
fig.show()

Best Practices

1. Choose Appropriate Plot Type

Distributions:

• Histogram, KDE plot, box plot, violin plot
• Use: Understanding data spread and shape

Comparisons:

• Bar chart, box plot, strip plot
• Use: Comparing groups or categories

Relationships:

• Scatter plot, line plot, regression plot
• Use: Showing correlations or trends

Compositions:

• Stacked bar, pie chart (use sparingly), treemap
• Use: Part-to-whole relationships

Time Series:

• Line plot, area chart
• Use: Temporal patterns

2. Design Principles

Less is More:

# Bad: Too much decoration
fig, ax = plt.subplots()
ax.plot(x, y, linewidth=5, color='red', linestyle='--',
        marker='o', markersize=15, markerfacecolor='yellow')
ax.grid(True, linewidth=2, color='blue')
ax.set_facecolor('lightgray')

# Good: Clean and focused
fig, ax = plt.subplots()
ax.plot(x, y, linewidth=2, color='steelblue')
ax.grid(True, alpha=0.3)

Effective Use of Color:

# Use color purposefully
# - Categorical: Distinct hues
# - Sequential: Single hue gradient
# - Diverging: Two hues from neutral center

# Colorblind-friendly palettes
sns.set_palette('colorblind')
# or
import plotly.express as px
fig = px.scatter(df, x='x', y='y', color='category',
                 color_discrete_sequence=px.colors.qualitative.Safe)

Readable Text:

fig, ax = plt.subplots(figsize=(10, 6))
ax.plot(x, y)

# Clear, appropriately sized text
ax.set_title('Clear Title', fontsize=16, pad=20)
ax.set_xlabel('X Axis Label', fontsize=12)
ax.set_ylabel('Y Axis Label', fontsize=12)
ax.tick_params(labelsize=10)

3. Consistent Styling

# Set style once at beginning
import matplotlib.pyplot as plt
import seaborn as sns

sns.set_theme(style='whitegrid', context='talk')
plt.rcParams['figure.figsize'] = (10, 6)
plt.rcParams['figure.dpi'] = 100

# All plots will use these settings

4. Save High-Quality Figures

# For publications
fig.savefig('figure.pdf', dpi=300, bbox_inches='tight')
fig.savefig('figure.png', dpi=300, bbox_inches='tight')

# For presentations
fig.savefig('figure.png', dpi=150, bbox_inches='tight')

# For web
fig.savefig('figure.png', dpi=96, bbox_inches='tight', optimize=True)

Common Gotchas

1. Figure Size and DPI

# ❌ Bad: Default size too small
fig, ax = plt.subplots()

# ✅ Good: Set appropriate size
fig, ax = plt.subplots(figsize=(10, 6))  # Width, height in inches

2. Overlapping Labels

# ❌ Bad: Labels overlap
fig, ax = plt.subplots()
ax.bar(range(10), values)
ax.set_xticklabels(long_labels)

# ✅ Good: Rotate labels
ax.set_xticklabels(long_labels, rotation=45, ha='right')

# ✅ Better: Use tight_layout
plt.tight_layout()

3. Color Mapping Consistency

# ❌ Bad: Inconsistent colors across plots
sns.scatterplot(data=df1, x='x', y='y', hue='category')
sns.scatterplot(data=df2, x='x', y='y', hue='category')  # Different colors!

# ✅ Good: Define palette
palette = {'A': 'red', 'B': 'blue', 'C': 'green'}
sns.scatterplot(data=df1, x='x', y='y', hue='category', palette=palette)
sns.scatterplot(data=df2, x='x', y='y', hue='category', palette=palette)

4. Seaborn Doesn't Return Axes

# ❌ Bad: Expecting return value
ax = sns.boxplot(data=df, x='x', y='y')  # Returns Axes, but...

# ✅ Good: Pass axes explicitly
fig, ax = plt.subplots()
sns.boxplot(data=df, x='x', y='y', ax=ax)
ax.set_title('My Title')  # Now can customize

5. Plotly Memory with Large Datasets

# ❌ Bad: Plotting millions of points
fig = px.scatter(huge_df)  # Slow, large file

# ✅ Good: Sample or aggregate
fig = px.scatter(huge_df.sample(10000))
# or use datashader for huge datasets

6. Matplotlib State Machine Confusion

# ❌ Bad: Mixing pyplot and OO API
plt.figure()
ax = plt.gca()
ax.plot(x, y)
plt.xlabel('x')  # State machine call
ax.set_ylabel('y')  # Object-oriented call

# ✅ Good: Be consistent
fig, ax = plt.subplots()
ax.plot(x, y)
ax.set_xlabel('x')
ax.set_ylabel('y')

Quick Reference

Matplotlib

# Create figure
fig, ax = plt.subplots(figsize=(10, 6))

# Plot types
ax.plot(x, y)                    # Line
ax.scatter(x, y)                 # Scatter
ax.bar(x, y)                     # Bar
ax.hist(data, bins=30)           # Histogram
ax.boxplot([data1, data2])       # Box plot

# Customization
ax.set_xlabel('X Label')
ax.set_ylabel('Y Label')
ax.set_title('Title')
ax.legend()
ax.grid(True)

# Save
fig.savefig('plot.png', dpi=300, bbox_inches='tight')

Seaborn

import seaborn as sns

# Set theme
sns.set_theme(style='whitegrid')

# Plot types
sns.histplot(data=df, x='col')
sns.scatterplot(data=df, x='x', y='y', hue='category')
sns.boxplot(data=df, x='category', y='value')
sns.heatmap(corr_matrix, annot=True)

# Multi-panel
sns.pairplot(df, hue='species')

Plotly Express

import plotly.express as px

# Plot types
fig = px.scatter(df, x='x', y='y', color='category')
fig = px.line(df, x='x', y='y')
fig = px.bar(df, x='x', y='y', color='category')
fig = px.histogram(df, x='value')
fig = px.box(df, x='category', y='value')

# Show/Save
fig.show()
fig.write_html('plot.html')

Installation

# Matplotlib
pip install matplotlib

# Seaborn
pip install seaborn

# Plotly
pip install plotly

# For static image export (plotly)
pip install kaleido

# All together
pip install matplotlib seaborn plotly kaleido

Additional Resources

• Matplotlib: https://matplotlib.org/stable/gallery/index.html
• Seaborn: https://seaborn.pydata.org/examples/index.html
• Plotly: https://plotly.com/python/
• Python Graph Gallery: https://python-graph-gallery.com/
• From Data to Viz: https://www.data-to-viz.com/

Related Skills

• pycse - Scientific computing with confidence intervals for plots
• python-ase - Atomic structure visualization
• python-best-practices - Code quality for visualization scripts