Popular recipes tagged "meta:requires=collections"

Equally-spaced numbers (linspace) (Python)

2015-01-12T22:16:37-08:00

Python recipe 579000 by Andrew Barnert (float, linspace, range, spread).

An equivalent of numpy.linspace, but as a pure-Python lazy sequence.

Like NumPy's linspace, but unlike the spread and frange recipes listed here, the num argument specifies the number of values, not the number of intervals, and the range is closed, not half-open.

Although this is primarily designed for floats, it will work for Fraction, Decimal, NumPy arrays (although this would be silly) and even datetime values.

This recipe can also serve as an example for creating lazy sequences.

See the discussion below for caveats.

Compute Memory footprint of an object and its contents (Python)

2012-11-23T23:57:31-08:00

Python recipe 577504 by Raymond Hettinger (memory, sizeof). Revision 3.

Recursive version sys.getsizeof(). Extendable with custom handlers.

DBF reader and writer -- selective fields and nullreplace (Python)

2016-09-18T20:39:20-07:00

Python recipe 580696 by Tomas Nordin (database).

This fork assumes a desire for limited selection of field names. With huge files this might be necessary on some machines.

Also, assuming that the meaning of null in a dbf file means zero might be a mistake, so the fork adds an argument nullreplace as way to choose what to replace null with. Null is sometimes used to mean missing value. This change is decoupled from the selective names feature.

JSON Formatted Logging (Python)

2016-05-27T01:07:42-07:00

Python recipe 580667 by Michael Blan Palmer (json, logging, python).

I have created a package that outputs JSON formatted lines to a log file. It can make use of the standard logging parameters and/or take custom input. The use of JSON in the log file allows for easy filtering and processing.

Configurable JSON Extensions for Python (Python)

2016-05-22T19:00:54-07:00

Python recipe 580664 by Michael Blan Palmer (json, python).

The concept behind the package is to build a single class per type you want to add to json in Python. The new class will have a method for encoding to json and a method for decoding from json. The classes are then loaded into an encoder object and a decoder object that are hooked into the standard json loads and dumps functions.

LRU dictionary (Python)

2016-04-17T01:22:01-07:00

Python recipe 580644 by Felix (dict, dictionary, expiration, expiring, lru, lru_cache, python3).

For most cases where you want to store a limited amount of data, functools.partial is sufficient. However, if you want or need more control over your data, especially specifying an expiration date for your entries, this can come in handy.

Read the docstrings for implementation details.

LRU dictionary (Python)

2016-04-17T01:22:51-07:00

Python recipe 580645 by Felix (dict, dictionary, expires, expiring, lru, lru_cache, python, python3).

Read the docstrings for implementation details.

How to use super() effectively -- Python 3.2 version (Python)

2015-12-01T16:23:09-08:00

Python recipe 579130 by Lance E Sloan (cooperative, inheritance, multiple, next_in_mro, super).

Python's super() provides a unique and amazing capability. It allows subclasses to be written to reorder a chain method calls. The recipe demonstrates all of the tradecraft needed to get super() to do your bidding.

How to use super() effectively -- Python 2.7 version (Python)

2015-12-01T16:20:26-08:00

Python recipe 579129 by Lance E Sloan (cooperative, inheritance, multiple, next_in_mro, super).

Simple FIFO trading model for pnl (Python)

2015-02-10T13:34:48-08:00

Python recipe 579024 by alexander baker (analysis, pnl, trading). Revision 2.

Simple approach to calculating FIFO pnl.

Publish SQLite data to PDF using named tuples (Python)

2015-02-24T22:08:11-08:00

Python recipe 579027 by Vasudev Ram (conversion, pdf, pdfwriter, sql, sqlite, sqlite3, xtopdf).

This recipe shows how to publish SQLite data to PDF, using named tuples from the collections module of Python, the sqlite3 library, and the xtopdf library for PDF generation.

Hivemind (Python)

2015-07-15T12:37:13-07:00

Python recipe 579082 by Oscar Byrne (borg, defaultdict, hash, hashing, hivemind, python, singleton, state).

Inspired by the ever-popular Borg pattern, objects inheriting from Hivemind share state if initialised with the same arguments

OrderedSet (Python)

2015-06-25T17:33:13-07:00

Python recipe 579071 by Sander (ordered, set).

Set that remembers original insertion order.

OrderedSet (Python)

2012-12-19T07:12:32-08:00

Python recipe 576694 by Raymond Hettinger (ordered, set). Revision 9.

Set that remembers original insertion order.

Monitor Progress of File Descriptors of Another Process (Python)

2014-05-30T01:10:53-07:00

Python recipe 578882 by Alfe (file_descriptor, monitor, prediction, proc, process, progress, watch).

This tool (inspired by azat@stackoverflow, see http://stackoverflow.com/a/16082562/1281485) allows to watch the progress of the file descriptors of another process. This can be used, for example, if you transfer a file to another host and the transferring program does not show any progress indication itself. Instead of waiting blindly until the routine is done, with this tool you can use Linux's proc file system to monitor the progress of the other process while it walks through the file.

The tool continuously monitors the position in and the size of the files the given process's file descriptors point to. For growing (or shrinking, but that's very unusual) files, a time when it was (or will be) empty is computed ("emptyTime"), for moving file descriptors (the typical case), the time when it started at position 0 ("startTime"), the time when it will reach the current size of the file ("reachTime") and when it will meet with the end of a growing (or shrinking) file is computed ("meetTime").

For fixed-size files the meetTime will be the same as the reachTime of course. The meetTime only makes sense in case a file is growing and at the same time read (e. g. when a movie is downloaded to a file by one process and converted by a different process; using this tool can tell you when the converter process might run dry on the input because the download wasn't fast enough, and in this case you maybe can pause the converter to prevent this situation).

The tool is designed as a library; the display of the information is independent from the gathering of the data. Please feel free to create more fancy displays, add percentage output etc.

Proof-of-concept for a more space-efficient, faster-looping dictionary (Python)

2013-01-17T09:28:24-08:00

Python recipe 578375 by Raymond Hettinger (compact, dictionary). Revision 20.

Save space and improve iteration speed by moving the hash/key/value entries to a densely packed array keeping only a sparse array of indices. This eliminates wasted space without requiring any algorithmic changes.

Create on-the-fly class adapters with functools.partial (Python)

2014-05-29T18:48:28-07:00

Python recipe 578884 by Christoph Schueler (adapter, namedtuple, partial, pattern).

functools.partial could not only applied to functions it also works with classes. This opens some interesting perspectives, like on-the-fly creation of class adapters, as the following code illustrates.

Compute Memory footprint of an object and its contents (Python)

2014-05-15T13:44:15-07:00

Python recipe 578880 by yota (memory, sizeof).

Recursive version sys.getsizeof(). Extendable with custom handlers.

How to use super() effectively -- Python 2.7 version (Python)

2011-06-01T19:19:37-07:00

Python recipe 577721 by Raymond Hettinger (cooperative, inheritance, multiple, next_in_mro, super). Revision 8.

Py2.6+ and Py3.0+ backport of Python 3.3's LRU Cache (Python)

2013-03-06T05:38:15-08:00

Python recipe 578078 by Raymond Hettinger (cache, lru). Revision 6.

Full-featured O(1) LRU cache backported from Python3.3. The full Py3.3 API is supported (thread safety, maxsize, keyword args, type checking, __wrapped__, and cache_info). Includes Py3.3 optimizations for better memory utilization, fewer dependencies, and fewer dict lookups.