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-0.48.tar.gz (37.5 kB view details)

Uploaded Source

Built Distribution

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

dataiter-0.48-py3-none-any.whl (45.2 kB view details)

Uploaded Python 3

File details

Details for the file dataiter-0.48.tar.gz.

File metadata

  • Download URL: dataiter-0.48.tar.gz
  • Upload date:
  • Size: 37.5 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.11.4

File hashes

Hashes for dataiter-0.48.tar.gz
Algorithm Hash digest
SHA256 b82ef6ab7028f218ab5b88dc7457e9ced3e13eaf3489211b0ee8b3d52211bd1f
MD5 c026f32de11d0b733f5deb1bf3b385b9
BLAKE2b-256 cf45db2399c00c600b0bd8945e2c34362465d9f4c2a7430188707c46ddac4ee2

See more details on using hashes here.

File details

Details for the file dataiter-0.48-py3-none-any.whl.

File metadata

  • Download URL: dataiter-0.48-py3-none-any.whl
  • Upload date:
  • Size: 45.2 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.11.4

File hashes

Hashes for dataiter-0.48-py3-none-any.whl
Algorithm Hash digest
SHA256 dda94e6e2ea3bdbeab94313b104c770c40c013b94acfec33fbf65ce50e7d8c06
MD5 9a8e8e9eb9062f9216a5cbf36d5335bc
BLAKE2b-256 e4f6e2bd19af9fe4185385c992406e8564a79a4c0618cbc2500de77950628c51

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