Skip to main content

data wrangling for lists of tuples and dictionaries

Project description

https://img.shields.io/pypi/v/pytups.svg https://img.shields.io/pypi/l/pytups.svg https://img.shields.io/pypi/pyversions/pytups.svg

What and why

The idea is to allow sparse operations to be executed in matrix data.

I grew used to the chained operations in R’s tidyverse packages or, although not a great fan myself, python’s pandas . I find myself using dictionary and list comprehensions all the time to pass from one data format to the other efficiently. But after doing it for the Nth time, I thought of automaticing it.

In my case, it helps me construct optimisation models with PuLP. I see other possible uses not related to OR.

I’ve implemented some additional methods to regular dictionaries, lists and sets to come up with interesting methods that somewhat quickly pass from one to the other and help with data wrangling.

In order for the operations to make any sense, the assumption that is done is that whatever you are using has the same ‘structure’. For example, if you a have a list of tuples: every element of the list is a tuple with the same size and the Nth element of the tuple has the same type, e.g. [(1, 'red', 'b', '2018-01'), (10, 'ccc', 'ttt', 'ff')]. Note that both tuples have four elements and the first one is a number, not a string. We do not check that this is consistent.

They’re made to always return a new object, so no “in-place” editing, hopefully.

Right now there are three classes to use: dictionaries, tuple lists and ordered sets.

Python versions

Python 3.9 and up.

Quick example

We index a tuple list according to some index positions.:

import pytups as pt
some_list_of_tuples = [('a', 'b', 'c', 1), ('a', 'b', 'c', 2), ('a', 'b', 'c', 45)]
tp_list = pt.TupList(some_list_of_tuples)
tp_list.to_dict(result_col=3)
# {('a', 'b', 'c'): [1, 2, 45]}
tp_list.to_dict(result_col=3).to_dictdict()
# {'a': {'b': {'c': [1, 2, 45]}}}
tp_list.to_dict(result_col=[2, 3])
# {('a', 'b'): [('c', 1), ('c', 2), ('c', 45)]}

We do some operations on dictionaries with common keys.:

import pytups as pt
some_dict = pt.SuperDict(a=1, b=2, c=3, d=5)
some_other_dict = pt.SuperDict(a=5, b=7, c=1)
some_other_dict + some_dict
# {'a': 6, 'b': 9, 'c': 4}
some_other_dict.vapply(lambda v: v**2)
# {'a': 25, 'b': 49, 'c': 1}
some_other_dict.kvapply(lambda k, v: v/some_dict[k])
# {'a': 5.0, 'b': 3.5, 'c': 0.3333333333333333}

Installing

pip install pytups

or, for the development version:

pip install https://github.com/pchtsp/pytups/archive/master.zip

Testing

Run the command:

python -m unittest discover -s tests

if the output says OK, all tests were passed.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

pytups-1.0.4.tar.gz (19.7 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

pytups-1.0.4-py3-none-any.whl (14.6 kB view details)

Uploaded Python 3

File details

Details for the file pytups-1.0.4.tar.gz.

File metadata

  • Download URL: pytups-1.0.4.tar.gz
  • Upload date:
  • Size: 19.7 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.12.9

File hashes

Hashes for pytups-1.0.4.tar.gz
Algorithm Hash digest
SHA256 dd7becbe940bea89e78d3e36e6d62a48c162381ad4f3c26d08ec3a7c87bd8385
MD5 213c6abc34aab8ef00ebb4e285bf7c57
BLAKE2b-256 ef0ed63ed4fde9cbc0fc3e83d2b1db6099c9b6ad0fa34e81050ee9d2fef36692

See more details on using hashes here.

File details

Details for the file pytups-1.0.4-py3-none-any.whl.

File metadata

  • Download URL: pytups-1.0.4-py3-none-any.whl
  • Upload date:
  • Size: 14.6 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.12.9

File hashes

Hashes for pytups-1.0.4-py3-none-any.whl
Algorithm Hash digest
SHA256 e9b316cca2a09bec479c3536c48d4257ee1b603bc1917b1b6bb6b09c1f133a19
MD5 d2908ae26e2928157b3bccaff034104e
BLAKE2b-256 60b1122ab2e8c24ba960dd63f8eebd0f1cfdaecf2f8f32b33a4b078751c71207

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page