as.TimedeltaIndex.round ](pandas.TimedeltaIndex.round.html)
    * [ pandas.TimedeltaIndex.floor ](pandas.TimedeltaIndex.floor.html)
    * [ pandas.TimedeltaIndex.ceil ](pandas.TimedeltaIndex.ceil.html)
    * [ pandas.TimedeltaIndex.to_frame ](pandas.TimedeltaIndex.to_frame.html)
    * [ pandas.TimedeltaIndex.mean ](pandas.TimedeltaIndex.mean.html)
    * [ pandas.PeriodIndex ](pandas.PeriodIndex.html)
    * [ pandas.PeriodIndex.day ](pandas.PeriodIndex.day.html)
    * [ pandas.PeriodIndex.dayofweek ](pandas.PeriodIndex.dayofweek.html)
    * [ pandas.PeriodIndex.day_of_week ](pandas.PeriodIndex.day_of_week.html)
    * [ pandas.PeriodIndex.dayofyear ](pandas.PeriodIndex.dayofyear.html)
    * [ pandas.PeriodIndex.day_of_year ](pandas.PeriodIndex.day_of_year.html)
    * [ pandas.PeriodIndex.days_in_month ](pandas.PeriodIndex.days_in_month.html)
    * [ pandas.PeriodIndex.daysinmonth ](pandas.PeriodIndex.daysinmonth.html)
    * [ pandas.PeriodIndex.end_time ](pandas.PeriodIndex.end_time.html)
    * [ pandas.PeriodIndex.freq ](pandas.PeriodIndex.freq.html)
    * [ pandas.PeriodIndex.freqstr ](pandas.PeriodIndex.freqstr.html)
    * [ pandas.PeriodIndex.hour ](pandas.PeriodIndex.hour.html)
    * [ pandas.PeriodIndex.is_leap_year ](pandas.PeriodIndex.is_leap_year.html)
    * [ pandas.PeriodIndex.minute ](pandas.PeriodIndex.minute.html)
    * [ pandas.PeriodIndex.month ](pandas.PeriodIndex.month.html)
    * [ pandas.PeriodIndex.quarter ](pandas.PeriodIndex.quarter.html)
    * [ pandas.PeriodIndex.qyear ](pandas.PeriodIndex.qyear.html)
    * [ pandas.PeriodIndex.second ](pandas.PeriodIndex.second.html)
    * [ pandas.PeriodIndex.start_time ](pandas.PeriodIndex.start_time.html)
    * [ pandas.PeriodIndex.week ](pandas.PeriodIndex.week.html)
    * [ pandas.PeriodIndex.weekday ](pandas.PeriodIndex.weekday.html)
    * [ pandas.PeriodIndex.weekofyear ](pandas.PeriodIndex.weekofyear.html)
    * [ pandas.PeriodIndex.year ](pandas.PeriodIndex.year.html)
    * [ pandas.PeriodIndex.asfreq ](pandas.PeriodIndex.asfreq.html)
    * [ pandas.PeriodIndex.strftime ](pandas.PeriodIndex.strftime.html)
    * [ pandas.PeriodIndex.to_timestamp ](pandas.PeriodIndex.to_timestamp.html)
    * [ pandas.PeriodIndex.from_fields ](pandas.PeriodIndex.from_fields.html)
    * [ pandas.PeriodIndex.from_ordinals ](pandas.PeriodIndex.from_ordinals.html)
  * [ Date offsets ](../offset_frequency.html)
  * [ Window ](../window.html)
  * [ GroupBy ](../groupby.html)
  * [ Resampling ](../resampling.html)
  * [ Style ](../style.html)
  * [ Plotting ](../plotting.html)
  * [ Options and settings ](../options.html)
  * [ Extensions ](../extensions.html)
  * [ Testing ](../testing.html)
  * [ Missing values ](../missing_value.html)

  * [ __ ](../../index.html)
  * [ API reference ](../index.html)
  * [ Index objects ](../indexing.html)
  * pandas.Inter... 

#  pandas.IntervalIndex.get_indexer  #

IntervalIndex.  get_indexer  (  _ target  _ , _ method  =  None  _ , _ limit
=  None  _ , _ tolerance  =  None  _ )  [ [source]
](https://github.com/pandas-
dev/pandas/blob/v2.2.2/pandas/core/indexes/base.py#L3820-L3953) #

    

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.

Parameters  :

    

**target** Index

    
**method** {None, âpadâ/âffillâ, âbackfillâ/âbfillâ,
ânearestâ}, 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. 

**limit** int, optional

    

Maximum number of consecutive labels in ` target  ` to match for inexact
matches.

**tolerance** optional

    

Maximum distance between original and new labels for inexact matches. The
values of the index at the matching locations must