Rust-Data-Science / ulist

Ultra fast list/array for Python.

ulist

Documentation | Source Code

What

Ulist is an ultra fast list/array data structure written in Rust with Python bindings. It aims to be the fundamental package for processing and computing 1-D list/array in Python.
It provides:

• an efficient, flexible and expressive 1-D list/array object;
• broadcasting methods;
• a SQL-like and method-chaining programming experience;

Performance

Ulist is extremly fast, and even compared with libraries like Numpy. It is

• more efficient on the string and boolean array,
• same level efficient on the integer array,
• and a bit slower on the floating array.

Faster than Numpy is not the target of writing this repo, because they are just two different libraries. Ulist is more focused on general domain rather than just data science/machine learning/AI, for example the Linear Algebra Computation is not provided. But if you are curious about the performance, please see the benchmarking results.

Requirements

• Python: 3.7+
• OS: Linux, MacOS and Windows

Installation

Run pip install ulist

Examples

Count the number of items in bins.

Given an array arr, count the number of items in bins [0, 3), [3, 6), [6, 9) and [9, +inf). The result is a Python dictionary with bin names as keys and numbers as values.

>>> import ulist as ul

>>> arr = ul.arange(12)
>>> arr
UltraFastList([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11])

>>> result = arr.case(default='[9, +inf)')\
...             .when(lambda x: x < 3, then='[0, 3)')\
...             .when(lambda x: x < 6, then='[3, 6)')\
...             .when(lambda x: x < 9, then='[6, 9)')\
...             .end()\
...             .counter()
>>> result
{'[3, 6)': 3, '[9, +inf)': 3, '[6, 9)': 3, '[0, 3)': 3}

Dot product.

Given two 1-D arrays and calculate the dot product result of those arrays.

>>> import ulist as ul

>>> arr = ul.from_seq(range(1, 4), dtype='float')
>>> arr
UltraFastList([1.0, 2.0, 3.0])

>>> result = arr.mul(arr).sum()
>>> result
14.0

Rate of adults.

Given the ages of people as arr, and suppose the adults are equal or above 18. Clean the data by removing abnormal values and then calculate the rate of adults.

>>> import ulist as ul

>>> arr = ul.from_seq([-1, 10, 15, 20, 30, 50, 70, 80, 100, 200], dtype='int')
>>> result = arr.where(lambda x: (x >= 0) & (x < 120))\
...             .apply(lambda x: x >= 18)\
...             .mean()
>>> result
0.75

Contribute

All contributions are welcome. See Developer Guide