* [ pandas.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.Index.values 

#  pandas.Index.values  #

_ property  _ Index.  values  [ [source]  ](https://github.com/pandas-
dev/pandas/blob/v2.2.2/pandas/core/indexes/base.py#L5088-L5132) #

    

Return an array representing the data in the Index.

Warning

We recommend using ` Index.array  ` or ` Index.to_numpy()  ` , depending on
whether you need a reference to the underlying data or a NumPy array.

Returns  :

    

array: numpy.ndarray or ExtensionArray

    

See also

` Index.array  `

    

Reference to the underlying data.

` Index.to_numpy  `

    

A NumPy array representing the underlying data.

Examples

For [ ` pandas.Index  ` ](pandas.Index.html#pandas.Index "pandas.Index") :

    
    
    >>> idx = pd.Index([1, 2, 3])
    >>> idx
    Index([1, 2, 3], dtype='int64')
    >>> idx.values
    array([1, 2, 3])
    

For [ ` pandas.IntervalIndex  `
](pandas.IntervalIndex.html#pandas.IntervalIndex "pandas.IntervalIndex") :

    
    
    >>> idx = pd.interval_range(start=0, end=5)
    >>> idx.values
    <IntervalArray>
    [(0, 1], (1, 2], (2, 3], (3, 4], (4, 5]]
    Length: 5, dtype: interval[int64, right]
    

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