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Mpmath is a pure-Python library for multiprecision floating-point arithmetic. It provides an extensive set of transcendental functions, unlimited exponent sizes, complex numbers, interval arithmetic, numerical integration and differentiation, root-finding, linear algebra, and much more. Almost any calculation can be performed just as well at 10-digit or 1000-digit precision, and in many cases mpmath implements asymptotically fast algorithms that scale well for extremely high precision work. If available, mpmath will (optionally) use gmpy to speed up high precision operations.

I love python for its beautiful code and practicality. But it’s not going to win a pure speed race with most languages. Most people think of speed and ease-of-use as polar opposites - probably because they remember the pain of writing C. Cython tries to eliminate that duality and lets you have python syntax with C data types and functions - the best of both worlds. Keeping in mind that I’m by no means an expert at this, here are my notes based on my first real experiment with Cython:

PyScripter and most Python IDEs redirect sys.stdout.  Also GUI apps like PyScripter have no standard output.  What you need to do is PyObject *f = PySys_GetObject("stdout") and then use PyFile_WriteString for writing to the sys.stdout. This is what Python itself is doing and I think this is the best way

tinypy is a minimalist implementation of python in 64k of code

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.

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.

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

googlecalendarsync is a tool written in python to bidirectional synchronize a local iCal (.ics) file with Google Calendar

ProfitPy is a set of libraries and tools for the development, testing, and execution of automated stock trading systems Specifically, ProfitPy is a collection of Python packages, modules, and scripts that work together to help you implement an automated stock trading program (or programs). The package features: A library with classes for accounts, orders, tickers, and more A GUI program for executing and plotting trades in real-time A GUI tool to collect ticker data for off-line use A CLI tool to back-test trading strategies Dozens of technical indicators and plotting widgets to display them An X11 keystroke tool for automating the TWS application

One of the main difficulties I've had using parser generators has been the difficulty of figuring out why a grammar didn't work. Fixing shift-reduce and reduce-reduce conflicts seemed like voodoo to me, and though I slightly understand better how to fix such things now it's still a different mode of thinking that I don't want to try to get into when I just want to parse something simple. PyMeta uses a variation on the Parsing Expression Grammar (PEG) approach to parsing. The chief consequence of this is there's no possibility of ambiguity in a parse: a successful parse will yield exactly one result, and you can trace the control flow through the grammar to figure out how it got there.