https://github.com/interpretml/slicer
Unified slicing for all Python data structures.
https://github.com/interpretml/slicer
python slicing
Last synced: about 1 year ago
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Unified slicing for all Python data structures.
- Host: GitHub
- URL: https://github.com/interpretml/slicer
- Owner: interpretml
- License: mit
- Created: 2020-09-17T21:59:09.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2025-02-13T19:35:59.000Z (over 1 year ago)
- Last Synced: 2025-03-28T15:07:21.901Z (about 1 year ago)
- Topics: python, slicing
- Language: Python
- Homepage:
- Size: 40 KB
- Stars: 35
- Watchers: 4
- Forks: 4
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- License: LICENSE
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README
# 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](https://github.com/numpy/numpy) |
[pandas](https://github.com/pandas-dev/pandas) |
[scipy](https://docs.scipy.org/doc/scipy/reference/sparse.html) |
[pytorch](https://github.com/pytorch/pytorch) |
[list](https://github.com/python/cpython) |
[tuple](https://github.com/python/cpython) |
[dict](https://github.com/python/cpython)
And enables upgraded slicing functionality on its objects:
```python
# 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:
```python
# 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
```sh
pip install slicer
```
## Getting Started
Basic anonymous slicing:
```python
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:
```python
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:
```python
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