pandas.Index.slice_locs#

Index.slice_locs(start=None, end=None, step=None)[source]#

Compute slice locations for input labels.

This method determines the integer start and end positions for a label-based slice, which is useful for translating label-based slicing into positional slicing on the underlying data.

Parameters:

startlabel, default None: If None, defaults to the beginning.
endlabel, default None: If None, defaults to the end.
stepint, defaults None: If None, defaults to 1. Only the sign is used: a negative step gives bounds for a reversed slice.

Returns:

tuple[int, int]: Returns a tuple of two integers representing the slice locations for the input labels within the index. Use them together with step, as slice(start, end, step).

See also

Index.get_loc: Get location for a single label.
Index.get_slice_bound: Calculate the slice bound for a single label; used internally by slice_locs for each of start and end.
Index.slice_indexer: Compute the slice for input labels, including step.

Notes

This method only works if the index is monotonic or unique.

If start or end is not present in a monotonic index, the returned position is the insertion point that preserves ordering (analogous to numpy.searchsorted()); no error is raised. If the index is not monotonic and the label is missing, a KeyError is raised.

Examples

>>> idx = pd.Index(list("abcd"))
>>> idx.slice_locs(start="b", end="c")
(1, 3)

start and end need not be present in the index; for a monotonic index they are mapped to the appropriate insertion positions:

>>> idx = pd.Index(list("bcde"))
>>> idx.slice_locs(start="a", end="c")
(0, 2)

step only sets the direction; apply it to the returned bounds:

>>> idx = pd.Index(list("abcde"))
>>> idx.slice_locs(start="b", end="d", step=2)
(1, 4)
>>> idx[1:4:2]
Index(['b', 'd'], dtype='str')
>>> idx.slice_locs(start="d", end="b", step=-1)
(3, 0)
>>> idx[3:0:-1]
Index(['d', 'c', 'b'], dtype='str')