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Project Description

As a developer, the need to display data charts and graphs on your web site/service presents two major generation options: server-side, or client/browser-side.

  • The primary benefits of server-side chart generation are: completely consistent display across browsers (even IE can handle showing images), and re-use of the chart image outside the browser, e.g. in generating PDF documents.
  • The primary benefit of client/browser-side chart generation is: interactivity (if needed), or generally more dynamic behavior, e.g. immediately updated charts with a user’s new input.

webplotlib could be for you if you want Python server-side chart generation, providing a convenient and modular package/API. It is GPL licensed.


NOTE: Even though I wrote this package for a Django context, it can easily be used without/outside Django. See below for more details.

webplotlib has these direct dependencies:

  1. Numpy, which Matplotlib also depends on. You can install this globally (e.g. sudo pip install numpy) or just for the relevant virtualenv. For its full functionality you’ll want to install system dependencies (like Fortran), e.g. on Ubuntu, the packages libatlas-base-dev, gfortran, gcc, g++.
  2. Matplotlib - you can get this via your OS packaging system (likely too old), from PyPI, or from source. The latest version, 1.0.1, is ostensibly on PyPI, but pip pulls down the wrong version currently (as of May 2011; not pip’s fault, see this SO discussion), unless you do the following: pip install -f matplotlib You can also install this globally or locally to one virtualenv. Note that Matplotlib has numerous system dependencies depending on usage: e.g. for server usage on Ubuntu, install packages: python, python-dev, libpng12-dev, libfreetype6-dev.
  3. py.test, if you want to run the unit tests.

With dependencies installed (and within the relevant virtualenv), install webplotlib from PyPI with:

pip install webplotlib

Or you can install from source via github.


While I wrote webplotlib to work with a Django project, currently there are no direct dependencies on Django, so you can treat webplotlib like any other Python package and call it directly. For example:

from webplotlib.chart_builders import create_chart_as_png_str

# When you have at least one sequence (list, tuple, iterable)
# of data, you can plot it:
chart_png_str = create_chart_as_png_str(
    {'data': [[your_data_sequence]], 'names': 'MyDataLine'},
    labels_dct={'title': 'TheBigBoard', 'x': 'Data', 'y': 'Value'})
# Save chart_png_str, etc.

More examples of usage can be found in the module.

Django integration: I found confusing and inconsistent examples of integrating Matplotlib with Django on the web, so experimented a bit on my own. You can see some examples in webplotlib’s file which shows how to serve/return server-side generated PNG charts directly as a string - which can be handy for rapid feedback when you want to style a chart.

So to use webplotlib in Django, update your Django project’s INSTALLED_APPS to include ‘webplotlib’ and also update your project’s file (there’s nothing in yet) to have something like,

urlpatterns = patterns('',
    (r'^wpl/', include('webplotlib.urls')),

Also, consider using webplotlib with my app django-metaimage, which readily handles creating new images and saving them to a useful Django ORM model instance/representation.


Basic unit tests can be run using Tox or py.test.

History + Design

webplotlib relies on Matplotlib - the most powerful and popular Python charting package available - for the actual chart generation. But what I found (as of Spring 2011) was a lack of well-abstracted examples of Matplotlib usage in web environments, as well as a lack of examples using its non-interactive, object-oriented, programmer’s interface (by contrast, there are tons of examples for interactive user-driven chart creation, emulating MATLAB). Thus the need to write a new app that could quickly be used to generate server-side charts.

I wanted a way to generate a Matplotlib chart (time-series and bar charts initially) with a single function call that would create and return the raw chart data/image as a string - from there it’s easy to save it, or process it further (e.g. add watermark), or return it via HTTP, etc. After studying lots of online examples and documentation, experimenting, and refactoring, enter webplotlib. Initially it supports time-series and bar charts, taking sequences and returning non-ugly charts as PNG-image strings you can readily use. There will be more abstractions (e.g. defining chart templates separately) and improvements to come, but this version is usable now.

Alternatives: Why not use a Flash library? Not OSS, same limitations as Javascript library. And why not use a Javascript library? After looking at a bunch of client-side libraries (Grafico, Highcharts, flot, Google Chart Tools, dygraphs, Raphaël), I thought Protovis was it - the one tool to rule them all. But after making sense of their docs and examples, I found displays were inconsistent: simple charts would look fine in Firefox 3/4 but show up in Chrome without tick marks and labels, which was unacceptable. And I didn’t even bother looking at IE rendering, where more pain was likely. Furthermore, my requirements changed; I needed to use the charts in reporting, so it was essential to have the charts as images, consistent across several mediums. So server-side generation was the way to go, and with Python, one finds out very quickly there’s no serious competition to Matplotlib.

Release History

Release History


This version

History Node

TODO: Figure out how to actually get changelog content.

Changelog content for this version goes here.

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File Name & Checksum SHA256 Checksum Help Version File Type Upload Date
webplotlib-0.1-py2.6.egg (15.6 kB) Copy SHA256 Checksum SHA256 2.6 Egg May 26, 2011
webplotlib-0.1.tar.gz (9.0 kB) Copy SHA256 Checksum SHA256 Source May 26, 2011

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