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... 

#  pandas.Index.has_duplicates  #

_ property  _ Index.  has_duplicates  [ [source]  ](https://github.com/pandas-
dev/pandas/blob/v2.2.2/pandas/core/indexes/base.py#L2348-L2383) #

    

Check if the Index has duplicate values.

Returns  :

    

bool

    

Whether or not the Index has duplicate values.

See also

[ ` Index.is_unique  ` ](pandas.Index.is_unique.html#pandas.Index.is_unique
"pandas.Index.is_unique")

    

Inverse method that checks if it has unique values.

Examples

    
    
    >>> idx = pd.Index([1, 5, 7, 7])
    >>> idx.has_duplicates
    True
    
    
    
    >>> idx = pd.Index([1, 5, 7])
    >>> idx.has_duplicates
    False
    
    
    
    >>> idx = pd.Index(["Watermelon", "Orange", "Apple",
    ...                 "Watermelon"]).astype("category")
    >>> idx.has_duplicates
    True
    
    
    
    >>> idx = pd.Index(["Orange", "Apple",
    ...                 "Watermelon"]).astype("category")
    >>> idx.has_duplicates
    False
    

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