A small package for big slicing.
Project description
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
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
Built Distribution
File details
Details for the file slicer-0.0.8.tar.gz
.
File metadata
- Download URL: slicer-0.0.8.tar.gz
- Upload date:
- Size: 14.9 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/4.0.2 CPython/3.10.13
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | 2e7553af73f0c0c2d355f4afcc3ecf97c6f2156fcf4593955c3f56cf6c4d6eb7 |
|
MD5 | 5ea1bd4d0bbbdb75ed80981e3502b752 |
|
BLAKE2b-256 | d3f9b4bce2825b39b57760b361e6131a3dacee3d8951c58cb97ad120abb90317 |
File details
Details for the file slicer-0.0.8-py3-none-any.whl
.
File metadata
- Download URL: slicer-0.0.8-py3-none-any.whl
- Upload date:
- Size: 15.3 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/4.0.2 CPython/3.10.13
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | 6c206258543aecd010d497dc2eca9d2805860a0b3758673903456b7df7934dc3 |
|
MD5 | 73ebeb05ff885e4b72e216667146a015 |
|
BLAKE2b-256 | 63819ef641ff4e12cbcca30e54e72fb0951a2ba195d0cda0ba4100e532d929db |