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):

python

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):

python

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

python

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:

python

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:

python

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:

python

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:

python

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:

python

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:

python

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:

python

# 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:

python

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

Legends:

python

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:

python

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:

python

# 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

python

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:

python

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:

python

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):

python

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:

python

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:

python

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:

python

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:

python

fig, ax = plt.subplots()

sns.barplot(data=tips, x='day', y='total_bill', hue='sex',
            errorbar=("ci", 95),
            ax=ax)

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

Relational Plots

Scatter Plot:

python

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:

python

# 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:

python

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:

python

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:

python

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:

python

# 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:

python

# 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:

python

# 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):

python

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

Themes and Color Palettes

Setting Themes:

python

# 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:

python

# 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() is deprecated; use .plot() method instead
sns.color_palette('husl', 10).plot()
plt.show()

Plotly - Interactive Visualization

Plotly Express - High-Level API

Scatter Plot:

python

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:

python

df = px.data.gapminder()

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

fig.show()

Bar Chart:

python

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:

python

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

fig.show()

Box Plot:

python

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

fig.show()

Heatmap:

python

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:

python

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:

python

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:

python

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:

python

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:

python

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:

python

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:

python

# 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

python

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

python

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:

python

# 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:

python

# 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:

python

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

python

# 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

python

# 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

python

# ❌ 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

python

# ❌ 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

python

# ❌ 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

python

# ❌ 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

python

# ❌ 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

python

# ❌ 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

python

# 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

python

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

python

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

bash

# 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

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SKILL.md

Python Plotting & Visualization

Overview

Library Selection Guide

Matplotlib - The Foundation

Seaborn - Statistical Visualization

Plotly - Interactive Visualization

Quick Decision Tree

Matplotlib - Foundational Plotting

Two APIs: pyplot vs Object-Oriented

Figure Anatomy

Common Plot Types

Customization

Seaborn - Statistical Visualization

Basic Setup

Distribution Plots

Categorical Plots

Relational Plots

Regression Plots

Heatmaps and Matrices

Multi-Panel Plots

Themes and Color Palettes

Plotly - Interactive Visualization

Plotly Express - High-Level API

Graph Objects - Low-Level API

Integration Patterns

Seaborn with Matplotlib Customization

Plotly with Pandas

Best Practices

1. Choose Appropriate Plot Type

2. Design Principles

3. Consistent Styling

4. Save High-Quality Figures

Common Gotchas

1. Figure Size and DPI

2. Overlapping Labels

3. Color Mapping Consistency

4. Seaborn Doesn't Return Axes

5. Plotly Memory with Large Datasets

6. Matplotlib State Machine Confusion

Quick Reference

Matplotlib

Seaborn

Plotly Express

Installation

Additional Resources

Related Skills