A small package for big slicing.
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Author: InterpretML
Requires Python: >=3.6
Classifiers
Programming Language
- Python :: 3.8
- Python :: 3.9
- Python :: 3.10
- Python :: 3.11
- Python :: 3.12
Development Status
- 3 - Alpha
License
- OSI Approved :: MIT License
Operating System
- OS Independent
slicer [alpha]
(Equal Contribution) Samuel Jenkins & Harsha Nori & Scott Lundberg
slicer wraps tensor-like objects and provides a uniform slicing interface via __getitem__
.
It supports many data types including:
numpy | pandas | scipy | pytorch | list | tuple | dict
And enables upgraded slicing functionality on its objects:
# Handles non-integer indexes for slicing.
S(df)[:, ["Age", "Income"]]
# Handles nested slicing in one call.
S(nested_list)[..., :5]
It can also simultaneously slice many objects at once:
# Gets first elements of both objects.
S(first=df, second=ar)[0, :]
This package has 0 dependencies. Not even one.
Installation
Python 3.6+ | Linux, Mac, Windows
pip install slicer
Getting Started
Basic anonymous slicing:
from slicer import Slicer as S
li = [[1, 2, 3], [4, 5, 6]]
S(li)[:, 0:2].o
# [[1, 2], [4, 5]]
di = {'x': [1, 2, 3], 'y': [4, 5, 6]}
S(di)[:, 0:2].o
# {'x': [1, 2], 'y': [4, 5]}
Basic named slicing:
import pandas as pd
import numpy as np
df = pd.DataFrame({'A': [1, 3], 'B': [2, 4]})
ar = np.array([[5, 6], [7, 8]])
sliced = S(first=df, second=ar)[0, :]
sliced.first
# A 1
# B 2
# Name: 0, dtype: int64
sliced.second
# array([5, 6])
Real example:
from slicer import Slicer as S
from slicer import Alias as A
data = [[1, 2], [3, 4]]
values = [[5, 6], [7, 8]]
identifiers = ["id1", "id1"]
instance_names = ["r1", "r2"]
feature_names = ["f1", "f2"]
full_name = "A"
slicer = S(
data=data,
values=values,
# Aliases are objects that also function as slicing keys.
# A(obj, dim) where dim informs what dimension it can be sliced on.
identifiers=A(identifiers, 0),
instance_names=A(instance_names, 0),
feature_names=A(feature_names, 1),
full_name=full_name,
)
sliced = slicer[:, 1] # Tensor-like parallel slicing on all objects
assert sliced.data == [2, 4]
assert sliced.instance_names == ["r1", "r2"]
assert sliced.feature_names == "f2"
assert sliced.values == [6, 8]
sliced = slicer["r1", "f2"] # Example use of aliasing
assert sliced.data == 2
assert sliced.feature_names == "f2"
assert sliced.instance_names == "r1"
assert sliced.values == 6
Contact us
Raise an issue on GitHub, or contact us at interpret@microsoft.com
Mar 09, 2024
0.0.8
Dec 16, 2020
0.0.7
Dec 11, 2020
0.0.6
Nov 05, 2020
0.0.5
Sep 18, 2020
0.0.4
Aug 21, 2020
0.0.3
Jul 01, 2020
0.0.2
Apr 16, 2020
0.0.1
Wheel compatibility matrix
Files in release
No dependencies