Pros of matplotlib

• Extensive documentation and large community support
• Wide range of plot types and customization options
• Better support for publication-quality figures

Cons of matplotlib

• Slower performance for real-time plotting and large datasets
• More complex API, steeper learning curve for beginners

Code Comparison

matplotlib:

import matplotlib.pyplot as plt
import numpy as np

x = np.linspace(0, 10, 100)
y = np.sin(x)
plt.plot(x, y)
plt.show()

pyqtgraph:

import pyqtgraph as pg
import numpy as np

x = np.linspace(0, 10, 100)
y = np.sin(x)
plt = pg.plot(x, y)

Summary

matplotlib offers a comprehensive plotting library with extensive documentation and a wide range of plot types, making it suitable for creating publication-quality figures. However, it can be slower for real-time plotting and has a steeper learning curve.

pyqtgraph, on the other hand, excels in performance for real-time plotting and large datasets, with a simpler API that's easier for beginners to grasp. It may not offer as many plot types or customization options as matplotlib, but it's well-suited for interactive and scientific applications requiring fast rendering.

Pros of plotly.py

• Interactive and web-based plots, suitable for sharing and embedding
• Wide range of chart types and customization options
• Supports both static and dynamic data visualization

Cons of plotly.py

• Slower rendering for large datasets compared to pyqtgraph
• Steeper learning curve for beginners
• Requires internet connection for some features (CDN-based rendering)

Code Comparison

plotly.py:

import plotly.graph_objects as go
fig = go.Figure(data=go.Scatter(x=[1, 2, 3], y=[4, 5, 6]))
fig.show()

pyqtgraph:

import pyqtgraph as pg
plt = pg.plot([1,2,3], [4,5,6])
pg.QtGui.QApplication.exec_()

pyqtgraph offers a more straightforward approach for simple plots, while plotly.py provides more customization options but requires more setup. plotly.py generates interactive web-based plots, whereas pyqtgraph creates desktop-based visualizations.

Pros of Bokeh

• Web-based: Generates interactive visualizations for web browsers
• Versatile: Supports a wide range of plot types and customizations
• Large community: More extensive documentation and community support

Cons of Bokeh

• Performance: Can be slower for large datasets or real-time plotting
• Learning curve: More complex API compared to PyQtGraph
• Dependencies: Requires additional libraries for full functionality

Code Comparison

PyQtGraph:

import pyqtgraph as pg
plot = pg.plot()
plot.plot([1, 2, 3, 4, 5], [1, 4, 9, 16, 25])

Bokeh:

from bokeh.plotting import figure, show
p = figure()
p.line([1, 2, 3, 4, 5], [1, 4, 9, 16, 25])
show(p)

Summary

PyQtGraph is better suited for desktop applications and real-time plotting, while Bokeh excels in creating web-based visualizations with a wider range of plot types. PyQtGraph offers better performance for large datasets, but Bokeh provides more extensive documentation and community support. The choice between the two depends on the specific requirements of your project, such as target platform, performance needs, and desired visualization types.

Pros of Seaborn

• High-level interface for creating statistical graphics
• Built-in themes for attractive and professional-looking plots
• Seamless integration with pandas DataFrames

Cons of Seaborn

• Limited interactivity compared to PyQtGraph
• Slower performance for large datasets or real-time plotting
• Less flexibility for custom, low-level plot customization

Code Comparison

Seaborn:

import seaborn as sns
import matplotlib.pyplot as plt

sns.set_theme(style="darkgrid")
tips = sns.load_dataset("tips")
sns.scatterplot(x="total_bill", y="tip", data=tips)
plt.show()

PyQtGraph:

import pyqtgraph as pg
from pyqtgraph.Qt import QtCore, QtGui

app = QtGui.QApplication([])
win = pg.GraphicsLayoutWidget()
plot = win.addPlot()
scatter = pg.ScatterPlotItem(size=10, pen=pg.mkPen(None), brush=pg.mkBrush(255, 255, 255, 120))
plot.addItem(scatter)
win.show()
app.exec_()

Seaborn excels in creating statistical visualizations with minimal code, while PyQtGraph offers more control over interactive and real-time plotting. Seaborn is better suited for data exploration and static visualizations, whereas PyQtGraph is ideal for applications requiring dynamic, responsive plots.

Pros of Vispy

• Utilizes modern OpenGL for high-performance 2D and 3D visualization
• Supports a wider range of visualization types, including 3D graphics
• More flexible and customizable for complex scientific visualizations

Cons of Vispy

• Steeper learning curve due to its lower-level API
• Less mature and potentially less stable compared to PyQtGraph
• Smaller community and fewer readily available examples

Code Comparison

PyQtGraph example:

import pyqtgraph as pg
plot = pg.plot()
plot.plot([1, 2, 3, 4, 5], [1, 4, 2, 3, 5])

Vispy example:

from vispy import app, scene
canvas = scene.SceneCanvas()
view = canvas.central_widget.add_view()
line = scene.Line(pos=np.array([(1, 1), (2, 4), (3, 2), (4, 3), (5, 5)]))
view.add(line)
app.run()

Both libraries offer powerful visualization capabilities, but Vispy provides more advanced features for complex graphics at the cost of simplicity. PyQtGraph is generally easier to use for basic plotting tasks, while Vispy excels in performance-critical and advanced visualization scenarios.

Pros of HoloViews

• High-level API for quick and easy data visualization
• Seamless integration with Bokeh for interactive web-based plots
• Supports a wide range of data formats and plot types

Cons of HoloViews

• Steeper learning curve for customizing complex visualizations
• Performance may be slower for large datasets compared to PyQtGraph
• Less suitable for real-time data plotting and updating

Code Comparison

HoloViews:

import holoviews as hv
import numpy as np

xs = np.linspace(0, 10, 200)
curve = hv.Curve((xs, np.sin(xs)))
hv.render(curve)

PyQtGraph:

import pyqtgraph as pg
import numpy as np

plt = pg.plot()
xs = np.linspace(0, 10, 200)
plt.plot(xs, np.sin(xs))

HoloViews offers a more declarative approach, while PyQtGraph provides lower-level control over plot elements. PyQtGraph is generally faster for real-time plotting, while HoloViews excels in creating complex, interactive visualizations with less code.