# Handles non-integer indexes for slicing. S(df)[:, ["Age", "Income"]] # Handles nested slicing in one call. S(nested_list)[..., :5]

# Gets first elements of both objects. S(first=df, second=ar)[0, :]

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]}

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])

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