dataiter 1.3.1

Simple, light-weight data frames for Python

Simple, Light-Weight Data Frames for Python

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

# 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

Recommended NumPy version is currently >= 2.4.0 due to various StringDType fixes that have landed in NumPy 2.2.1 and 2.4.0.

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

>>> 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