modin.pandas.Index.reindex¶

Index.reindex(target: Iterable, method: str | None = None, level: int | None = None, limit: int | None = None, tolerance: int | float | None = None) → tuple[Index, np.ndarray][source] (https://github.com/snowflakedb/snowpark-python/blob/v1.26.0/snowpark-python/src/snowflake/snowpark/modin/plugin/extensions/index.py#L609-L650)¶

Create index with target’s values.

Parameters:

target (an iterable) –
method ({None, 'pad'/'ffill', 'backfill'/'bfill', 'nearest'}, optional) –
- default: exact matches only.
- pad / ffill: find the PREVIOUS index value if no exact match.
- backfill / bfill: use NEXT index value if no exact match
- nearest: use the NEAREST index value if no exact match. Tied distances are broken by preferring the larger index value.
level (int, optional) – Level of multiindex.
limit (int, optional) – Maximum number of consecutive labels in target to match for inexact matches.
tolerance (int or float, optional) –
Maximum distance between original and new labels for inexact matches. The values of the index at the matching locations must satisfy the equation abs(index[indexer] - target) <= tolerance.

Tolerance may be a scalar value, which applies the same tolerance to all values, or list-like, which applies variable tolerance per element. List-like includes list, tuple, array, Series, and must be the same size as the index and its dtype must exactly match the index’s type.

Returns:

new_index (pd.Index) – Resulting index.
indexer (np.ndarray[np.intp] or None) – Indices of output values in original index.

Raises:

TypeError – If method passed along with level.
ValueError – If non-unique multi-index
ValueError – If non-unique index and method or limit passed.

Notes

method=nearest is not supported.

If duplicate values are present, they are ignored, and all duplicate values are present in the result.

If the source and target indices have no overlap, monotonicity checks are skipped.

Tuple-like index values are not supported.

Examples

>>> idx = pd.Index(['car', 'bike', 'train', 'tractor'])
>>> idx
Index(['car', 'bike', 'train', 'tractor'], dtype='object')

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>>> idx.reindex(['car', 'bike'])
(Index(['car', 'bike'], dtype='object'), array([0, 1]))

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