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Given Python's goal to be a powerful and easy-to-use scripting language, its features for working with dates and times are not as user-friendly as they should be. The purpose of pyfdate is to remedy that situation by providing features for working with dates and times that are as powerful and easy-to-use as the rest of Python.

If I have:     from sympy import Symbol, Integrate     x = Symbol('x')     f = x**2 + x     g = Integrate(f, x) how can I apply g to a numpy array? In other words, how can I "numpify" the g expression, injecting in it x = numpy.linspace(1, 9, 9)? What would be even nicer would be to be able to retrieve a lambda using numpy functions for g as a function of x (that way I don't have the overhead of numpifying it each time I want to apply it to an array).

PyProtocols extends the PEP 246 adapt() function with a new "declaration API" that lets you easily define your own protocols and adapters, and declare what adapters should be used to adapt what types, objects, or protocols.  In addition to its own Interface type, PyProtocols can also use Twisted and Zope's Interface types too.  (Of course, since Twisted and Zope interfaces aren't as flexible, only a subset of the PyProtocols API works with them.  Specific limitations are listed in the documentation.)

RRDTool is a really good back-end for storing time-series data. If you are developing tools that need a data repository and graphing capabilities, this provides you both. You create an RRD and then you begin inserting data values at regular intervals. You then call the graphing API to have a graph displayed. The neat thing about this data storage is its “round robin” nature. You define various time spans, and the granularity at which you want them stored. A fixed binary file is created, which never grows in size over time. As you insert more data, it is inserted into each span. As results are collected, they are averaged and rolled into successive time spans. It makes a much more efficient system than using your own complex data structures, relational database, or file system storage.

Easily create Google charts using python syntax and datasets. Python wrapper for the Google Chart API. The wrapper can render the URL of the Google chart, based on your parameters, or it can render an HTML img tag to insert into webpages on the fly. Made for dynamic python websites (Django,Zope,CGI,etc.) that need on the fly chart generation without any extra modules. The wrapper SHOULD also work with Eastwood the Google Chart API workalike. G = GChart('p3', [5,10])

For example edge labels can be added using matplotlib "text" objects like this: import networkx as nx import pylab as plot K=nx.XGraph(name="Konigsberg", multiedges=True, selfloops=False) K.add_edges_from([("A","B","Honey Bridge"),     ("A","B","Blacksmith's Bridge"),     ("A","C","Green Bridge"),     ("A","C","Connecting Bridge"),     ("A","D","Merchant's Bridge"),     ("C","D","High Bridge"),     ("B","D","Wooden Bridge")]) pos=nx.spring_layout(K) nx.draw_nx(K,pos) xa,ya=pos['A'] xb,yb=pos['B'] plot.text((xa+xb)/2,(ya+yb)/2,"Blacksmith's Bridge") plot.show() With a little work you can get the label rotated and exactly how you want it positioned.  You can also set the node positions directly in the "pos" dictionary above.

Pexpect does not currently work on the standard Windows Python

For drawing you can use pygraphviz (also available at networkx.lanl.gov) or the built-in drawing tools.

PyNGL (pronounced "pingle") is a Python language module used to visualize scientific data, with an emphasis on high quality 2D visualizations. A working knowledge of Python is assumed.