https://github.com/otsaloma/dataiter

Simple, light-weight data frames for Python
https://github.com/otsaloma/dataiter

data-frame json numba numpy python

Last synced: about 1 month ago
JSON representation

Simple, light-weight data frames for Python

• Host: GitHub
• URL: https://github.com/otsaloma/dataiter
• Owner: otsaloma
• License: mit
• Created: 2019-09-29T17:49:00.000Z (over 5 years ago)
• Default Branch: master
• Last Pushed: 2024-12-14T23:20:55.000Z (about 2 months ago)
• Last Synced: 2024-12-15T00:19:55.197Z (about 2 months ago)
• Topics: data-frame, json, numba, numpy, python
• Language: Python
• Homepage: https://dataiter.readthedocs.io/
• Size: 2.87 MB
• Stars: 25
• Watchers: 3
• Forks: 0
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • Changelog: NEWS.md
  • License: COPYING
  • Authors: AUTHORS.md

Awesome Lists containing this project

README

        
Simple, Light-Weight Data Frames for Python

===========================================

[![PyPI](https://img.shields.io/pypi/v/dataiter.svg)](https://pypi.org/project/dataiter)

[![Downloads](https://pepy.tech/badge/dataiter/month)](https://pepy.tech/project/dataiter)

Dataiter's **`DataFrame`** is a class for tabular data similar to R's

`data.frame`, implementing all common operations to manipulate data. It

is under the hood a dictionary of NumPy arrays and thus capable of fast

vectorized operations. You can consider it 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.

## Installation

```bash

# 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, Dataiter will use it automatically. It's

currently not a hard dependency, so you need to install it separately.

## Quick Start

```python

>>> import dataiter as di

>>> data = di.read_csv("data/listings.csv")

>>> data.filter(hood="Manhattan", guests=2).sort(price=1).head()

.

        id      hood zipcode guests    sqft price

     int64    string  string  int64 float64 int64

  ──────── ───────── ─────── ────── ─────── ─────

0 42279170 Manhattan   10013      2     nan     0

1 42384530 Manhattan   10036      2     nan     0

2 18835820 Manhattan   10021      2     nan    10

3 20171179 Manhattan   10027      2     nan    10

4 14858544 Manhattan              2     nan    15

5 31397084 Manhattan   10002      2     nan    19

6 22289683 Manhattan   10031      2     nan    20

7  7760204 Manhattan   10040      2     nan    22

8 43292527 Manhattan   10033      2     nan    22

9 43268040 Manhattan   10033      2     nan    23

.

```

## 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 in common operations.

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

## Development

To install a virtualenv for development, use

    make venv

or, for a specific Python version

    make PYTHON=python3.X venv