41–50 of 137 posts
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Could you name it?
This is probably what he meant: $ py Python 2.7.8 |Anaconda 2.1.0 (64-bit) ... on win32 >>> import bsddb >>> print bsddb.__doc__ Support for Berkeley DB 4.3 through 5.3 with a simple interface. For the full featured object oriented interface use the bsddb.db module instead. It mirrors the Oracle Berkeley DB C API.
Not really 2015 but Q! https://pypi.python.org/pypi/q Print-debugging on steroids. This really does make things so much easier, especially when dealing with huge apps you don't have time to learn. Not just useful as a dev but also as a sysadmin.
I'm feeling a lot of love for Pandas. Any (biology related) project I work on starts with multi-headered dataframes and ends in beautiful Seaborn graphs. In combination with Jupyter notebook I breeze through large data sets while leaving a perfect trail of what goes on in the data pipeline. Python is great.
Interestingly enough, my biggest use of Pandas is to serialize to and from HDF5. I work with a lot of large datasets and Pandas simplifies using HDF5 quite a lot.
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This is probably what he meant: $ py Python 2.7.8 |Anaconda 2.1.0 (64-bit) ... on win32 >>> import bsddb >>> print bsddb.__doc__ Support for Berkeley DB 4.3 through 5.3 with a simple interface. For the full featured object oriented interface use the bsddb.db module instead. It mirrors the Oracle Berkeley DB C API.
bsddb is deprecated as of python 2.6 and removed in python 3
The Python module I learned to love this year is Click. Gets me better command line interfaces fast. URL is http://click.pocoo.org . It does progress bars too...
docopt was inspiring in its own way, but click was 'simpler' do toy with.
I also find myself using click even when I don't want a CLI. The pretty-printer (`secho`) and progress bars are extremely handy, plus some of the other stuff in utilities. It's quite nice that they handle detecting when output is an interactive terminal versus piping to a file.
Not really 2015 but Q! https://pypi.python.org/pypi/q Print-debugging on steroids. This really does make things so much easier, especially when dealing with huge apps you don't have time to learn. Not just useful as a dev but also as a sysadmin.
What's the advantage of Q over pdb?
I am using Scrapy a lot. http://scrapy.org/ It is very well designed web crawling library.
https://bitbucket.org/estin/pomp
It looks like a much cleaner Scrapy-inspired spider framework, without the twisted dependency. And it's python 2+3 compatible. I'm very excited to try it out.
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So I have been considering this. does conda track pypi or does it lag it? I have been concerned about moving over my requirements.text for a webapp with lots of dependencies
It slightly lags, but you can include pip requirements in an environment.yml file, and they install normally. I really only use conda for the non-python bits of our stack: numpy/scipy/pandas etc - packages that are a pain to install on Heroku.
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So I have been considering this. does conda track pypi or does it lag it? I have been concerned about moving over my requirements.text for a webapp with lots of dependencies
It slightly lags, but you can include pip requirements in an environment.yml file, and they install normally. I really only use conda for the non-python bits of our stack: numpy/scipy/pandas etc - packages that are a pain to install on Heroku.
Especially with all the blas linking. Was there anything special you had to do or was it simply conda install numpy-blas or something like that ?
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You've already got a perfectly fine KVS built into the language.
Could you name it?