Skip to main content

Classes for data manipulation

Project description

Python Classes for Data Manipulation

Test Documentation Status PyPI Downloads

Dataiter currently includes the following classes.

DataFrame is a class for tabular data similar to R's data.frame or pandas.DataFrame. It is under the hood a dictionary of NumPy arrays and thus capable of fast vectorized operations. You can consider this to be a light-weight alternative to Pandas with a simple and consistent API. Performance-wise Dataiter relies on NumPy and Numba and is likely to be at best comparable to Pandas.

ListOfDicts is a class useful for manipulating data from JSON APIs. It provides functionality similar to libraries such as Underscore.js, with manipulation functions that iterate over the data and return a shallow modified copy of the original. attd.AttributeDict is used to provide convenient access to dictionary keys.

GeoJSON is a simple wrapper class that allows reading a GeoJSON file into a DataFrame and writing a data frame to a GeoJSON file. Any operations on the data are thus done with methods provided by the data frame class. Geometry is read as-is into the "geometry" column, but no special geometric operations are currently supported.

Installation

# Latest stable version
pip install -U dataiter

# Latest development version
pip install -U git+https://github.com/otsaloma/dataiter

# Numba (optional)
pip install -U numba

Dataiter optionally uses Numba to speed up certain operations. If you have Numba installed and importing it succeeds, Dataiter will use it automatically. It's currently not a hard dependency, so you need to install it separately.

Documentation

https://dataiter.readthedocs.io/

If you're familiar with either dplyr (R) or Pandas (Python), the comparison table in the documentation will give you a quick overview of the differences and similarities.

https://dataiter.readthedocs.io/en/latest/comparison.html

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

dataiter-0.33.tar.gz (33.1 kB view details)

Uploaded Source

Built Distribution

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

dataiter-0.33-py3-none-any.whl (40.0 kB view details)

Uploaded Python 3

File details

Details for the file dataiter-0.33.tar.gz.

File metadata

  • Download URL: dataiter-0.33.tar.gz
  • Upload date:
  • Size: 33.1 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.10.6

File hashes

Hashes for dataiter-0.33.tar.gz
Algorithm Hash digest
SHA256 620153164658c68f07b28522e5c224f352eafefc68f2e4fc968699b1227b98d0
MD5 07d42b37b28da8222388630c6f1ca3c2
BLAKE2b-256 b79119095051368f6cb4f504ffd09b2fb5620e2fe76f0093b32e25be522c3105

See more details on using hashes here.

File details

Details for the file dataiter-0.33-py3-none-any.whl.

File metadata

  • Download URL: dataiter-0.33-py3-none-any.whl
  • Upload date:
  • Size: 40.0 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.10.6

File hashes

Hashes for dataiter-0.33-py3-none-any.whl
Algorithm Hash digest
SHA256 de4b0d0ffbfe497129bd69ee2b019b8c76db4a0bde39672408675b83819c53c8
MD5 36bd357413bff21bc31649c3742c1b8e
BLAKE2b-256 870db36a6300a9f2e55f2e9dcba652ab30409d1c14b23568b87b5efd3ae0be91

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