Compute indexer and mask for new index given the current index. The
indexer should be then used as an input to ndarray.take to align the
current data to the new index.
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.
Maximum number of consecutive labels in target to match for
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.
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
Integers from 0 to n - 1 indicating that the index at these
positions matches the corresponding target values. Missing values
in the target are marked by -1.
>>> index = pd.Index(['c', 'a', 'b'])
>>> index.get_indexer(['a', 'b', 'x'])
array([ 1, 2, -1])
Notice that the return value is an array of locations in index
and x is marked by -1, as it is not in index.