Ziaplot can be installed using pip:
pip install ziaplot
For the optional cairosvg dependency (for saving images in formats other than SVG), install using:
pip install ziaplot[cairosvg]
Or to enable math expression rendering (via ziamath), install using:
pip install ziaplot[math]
Math rendering interprets any string label enclosed in $..$ to be Latex math.
Figures in Ziaplot are made from Series objects, which represent individual x-y data, added to Axes on which the Series are drawn. Here, an XyPlot axis is created, and a Line is added to it.
import ziaplot as zp x = list(range(6)) y = [xi**2 for xi in x] p = zp.XyPlot() p += zp.Line(x, y) p
Note the x and y arrays could more easily be created as Numpy arrays, but Ziaplot does not require Numpy as a dependency so this documentation does not use it.
Use in Jupyter Notebooks¶
Ziaplot is optimized for use in Jupyter, as every drawable object has a Jupyter representer function, so the final line p in the example above will display the figure inline.
Nearly everything in Ziaplot can be drawn (inherits from the Drawable class). A Line can be drawn by itself from the representation of zp.Line, but in this case, the Line will be added to an empty XyPlot.
Use outside Jupyter¶
Outside Jupyter, the raw SVG output can be accessed by calling p.svg(). Other image formats can be obtained if the cairosvg package is installed. Byte-data for all supported formats can be obtained by calling p.imagebytes().
SVG Version Compatibility¶
Some SVG renderers, including recent versions of Inkscape and some OS built-in image viewers, are not fully compatible with the SVG 2.0 specification. Set svg2=False using settextmode to use SVG 1.x specifications for better compatibility. This may result in larger file sizes as each glyph is included as its own <path> element rather than being reused with <symbol> and <use> elements.
zp.settextmode('path', svg2=False) # Draw text as <path> using SVG1.x
In general, the drawing style of individual series and axes can be customized using a chained method interface. For example, the marker, color, and stroke methods below all return the Line instance itself, so the series can be set up on a single line of code:
zp.Line(x, y).marker('round', radius=8).color('orange').stroke('dashed')
See Plot Style for additional styling options and global plot themes.
Why another plotting library?¶
Anyone who has been around Python long enough should be familiSar with Matplotlib, the de facto standard for data visualization with Python. Matplotlib is powerful and flexible - it can plot anything. But face it, it has a terrible, non-Pythonic programming interface. What’s the difference between a figure() and Figure()? Why does documentation sometimes use plt.., sometimes ax.., and sometimes the truly awful from pylab import *? It is also a huge dependency, requiring Numpy libraries and usually bundling several UI backends along with it. A simple Tkinter UI experiment (see Embedding in a GUI), built into an executable with Pyinstaller, was 16 MB when the data was plotted with Ziaplot, but over 340 MB using Matplotlib!
There are some Matplotlib alternatives. Seaborn just wraps Matplotlib to improve its interface. Plotly and Bokeh focus on interactivity and web applications.
Ziaplot was created as a light-weight, easy to use, fast, and Pythonic alternative for making static plots in SVG format.