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I compared the processing speeds of [] and list() on Python 3.11 $ python -m timeit '[]' 20000000 loops, best of 5: 11.3 nsec per loop $ python -m timeit 'list()' 10000000 loops, best of 5: 26.1 nsec per loop and was surprised to discover that [] runs about two times faster than list(). I got very similar results for {} and dict() $ python -m timeit '{}' 20000000 loops, best of 5: 11.6 nsec per lo
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I have 15 years of consulting & hands-on build experience with clients in the UK, USA, Sweden, Ireland & Germany. Past clients include Bank of America Merrill Lynch, Blackberry, Bloomberg, British Telecom, Ford, Google, ITV, LeoVegas, News UK, Pizza Hut, Royal Mail, T-Mobile, Williams Formula 1, Wise & UBS. I hold both a Canadian and a British passport. My CV, Twitter & LinkedIn. Donald Stufft, on
Monday, September 8, 2014 I've been using and working with Python in a professional context for around 8 years. So it came sort of a shock that something so useful, so obvious, was unfamiliar to me until around a week and a half ago. It involves a little file called .pythonrc that lives in your home directory. To get it to work, you'll need to define an environment variable called PYTHONSTARTUP, l
Neopythonic
Ramblings through technology, politics, culture and philosophy by the creator of the Python programming language. In Silicon Valley is a very exclusive fast-food restaurant, which is always open. There is one table, where one guest at a time is served an absolutely fabulous hamburger. When you arrive, you wait in line until the table is available. Then the host takes you to the table and, this bei
I, like most people, never realized I'd be dealing with large files. Oh, I knew there would be some files with megabytes of data, but I never suspected I'd be begging Perl to process hundreds of megabytes of XML, nor that this week I'd be asking Python to process 6.4 gigabytes of CSV into 6.5 gigabytes of XML1. As a few out-of-memory experiences will teach you, the trick for dealing with large fil
I'm currently trying to read data from .csv files in Python 2.7 with up to 1 million rows, and 200 columns (files range from 100mb to 1.6gb). I can do this (very slowly) for the files with under 300,000 rows, but once I go above that I get memory errors. My code looks like this: def getdata(filename, criteria): data=[] for criterion in criteria: data.append(getstuff(filename, criteron)) return dat
Looking for Python Tutoring? Remote and local (NYC) slots still available! Email me at [email protected] for more info. Prior to beginning tutoring sessions, I ask new students to fill out a brief self-assessment where they rate their understanding of various Python concepts. Some topics ("control flow with if/else" or "defining and using functions") are understood by a majority of students befor
Google Tech Talks February 21, 2007 ABSTRACT The Python language, while object-oriented, is fundamentally different from both C++ and Java. The dynamic and introspective nature of Python allow for language mechanics unlike that of static languages. This talk aims to enlighten programmers new to Python about these fundamentals, the language mechanics that flow from them and how to effectively put
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