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 https://travis-ci.org/pchtsp/pytups.svg?branch=master

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 3.6 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-0.85.0.tar.gz (12.2 kB view details)

Uploaded Source

Built Distribution

pytups-0.85.0-py3-none-any.whl (13.7 kB view details)

Uploaded Python 3

File details

Details for the file pytups-0.85.0.tar.gz.

File metadata

  • Download URL: pytups-0.85.0.tar.gz
  • Upload date:
  • Size: 12.2 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.7.1 importlib_metadata/4.10.0 pkginfo/1.8.2 requests/2.27.1 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.9.9

File hashes

Hashes for pytups-0.85.0.tar.gz
Algorithm Hash digest
SHA256 7484f2f8ff75e8c72efac1dccd898dc0b56698a7c470c75a1b9528f0c57d6a72
MD5 71e31ef5f456a82e284831ea16e1c1f6
BLAKE2b-256 70fd0927522fe028851d757ef9225c7398009e59880a3db47da07bc7bd9b8485

See more details on using hashes here.

File details

Details for the file pytups-0.85.0-py3-none-any.whl.

File metadata

  • Download URL: pytups-0.85.0-py3-none-any.whl
  • Upload date:
  • Size: 13.7 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.7.1 importlib_metadata/4.10.0 pkginfo/1.8.2 requests/2.27.1 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.9.9

File hashes

Hashes for pytups-0.85.0-py3-none-any.whl
Algorithm Hash digest
SHA256 d5c9be187e712d0a7e625f0924a47afcf904dc89cd2ace155e87042e149d7657
MD5 36e1869d91d67297065c3a51ccd81549
BLAKE2b-256 2202a6d1fba9aee9a6b70124e7270ed5c934fa005d1b0c9bdc7d21fb282ebfee

See more details on using hashes here.

Supported by

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