Pyspread. Python power for your tables Pyspread is a non-traditional spreadsheet that is based on and written in the programming language Python. The goal of pyspread is to be the most pythonic spreadsheet. Pyspread expects Python expressions in its grid cells, which makes a spreadsheet specific language obsolete. Each cell returns a Python object that can be accessed from other cells. These objects can represent anything including lists or matrices. Pyspread is free software. CSV import and export PDF and SVG export SVG import* XLS import and export, XLSX import* ODS import* Charts for visualizing data Python code in cells and in macros Python objects as cell results Video playback in cells* Code completion* Spell checker* GIT-able pysu save file format GPG based save file signatures that prevent foreign code execution* Access to all Python modules from within cells.
. * requires optional dependencies Directly using Python code in a grid is a core feature of pyspread. Last changed: 30. High performance database joins with pandas DataFrame, more benchmarks | Quant Pythonista. I posted a brief article with some preliminary benchmarks for the new merge/join infrastructure that I’ve built in pandas. I compared the performance with base::merge in R which, as various folks in the R community have pointed out, is fairly slow.
There are a few other more intelligently-implemented functions available in CRAN, in particular plyr::join in plyr and the merge implemented for data.table objects in the data.table package. Lastly, Sean Taylor contributed a benchmark for SQLite3, by accounts the most widely deployed SQL engine. So anyway, here are the two benchmarks I’m interested in to get a sense of the large-ish data runtime of these algorithms: Many-to-one joins. Note: that plyr::join does not implement (or least I’ve been told to avoid) many-to-many joins so I only run the many-to-one benchmarks for that. I’ve normalized the results by the minimum runtime (which is pandas in all cases): SQLite3 Benchmarks Here is the basic code to insert the data into SQLite: Conclusions. Conrad Kramer - Getting a Flask website up and running in Ubuntu. This is a guide to get a Flask website up and running on Ubuntu 12.04 LTS using nginx and uWSGI. There are many routes to take when it comes to Python on the web; this just is my personal favorite.
Some people enjoy configuring servers, while others view it as a chore. Regardless, this guide should get you up, running, and ready to make something awesome in no time! Nginx To install nginx you first need to add the repository. Deb http : //nginx.org/packages/ubuntu/ lucid nginx deb - src http : //nginx.org/packages/ubuntu/ lucid nginx You will also want to add the gpg key to the apt keyring: wget http : //nginx.org/keys/nginx_signing.key sudo apt - key add nginx_signing . key rm nginx_signing . key Finally, to install nginx, run: apt - get update apt - get install nginx uWSGI You can use pip to install the latest version of uWSGI by doing the following: sudo apt - get install python - dev build - essential python - pip sudo pip install uwsgi and to prime the log file, you can run: virtualenv Flask.
Download: OAI-PMH Python Module. Jump to releases The oaipmh module is a Python implementation of an "Open Archives Initiative Protocol for Metadata Harvesting" (version 2) client and server. The protocol is described here: Below is a simple implementation of an OAIPMH client: >>> from oaipmh.client import Client >>> from oaipmh.metadata import MetadataRegistry, oai_dc_reader >>> URL = ' >>> registry = MetadataRegistry() >>> registry.registerReader('oai_dc', oai_dc_reader) >>> client = Client(URL, registry) >>> for record in client.listRecords(metadataPrefix='oai_dc'): >>> print record The pyoai package also contains a generic server implementation of the OAIPMH protocol, this is used as the foundation of the MOAI Server Platform License: New BSD.