)
    * [ pandas.TimedeltaIndex.round ](pandas.TimedeltaIndex.round.html)
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    * [ 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)
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    * [ 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)
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  * [ Extensions ](../extensions.html)
  * [ Testing ](../testing.html)
  * [ Missing values ](../missing_value.html)

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

#  pandas.Index.notna  #

_ final  _ Index.  notna  (  )  [ [source]  ](https://github.com/pandas-
dev/pandas/blob/v2.2.2/pandas/core/indexes/base.py#L2901-L2943) #

    

Detect existing (non-missing) values.

Return a boolean same-sized object indicating if the values are not NA. Non-
missing values get mapped to ` True  ` . Characters such as empty strings ` ''
` or ` numpy.inf  ` are not considered NA values. NA values, such as None or `
numpy.NaN  ` , get mapped to ` False  ` values.

Returns  :

    

numpy.ndarray[bool]

    

Boolean array to indicate which entries are not NA.

See also

` Index.notnull  `

    

Alias of notna.

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

    

Inverse of notna.

[ ` notna  ` ](pandas.notna.html#pandas.notna "pandas.notna")

    

Top-level notna.

Examples

Show which entries in an Index are not NA. The result is an array.

    
    
    >>> idx = pd.Index([5.2, 6.0, np.nan])
    >>> idx
    Index([5.2, 6.0, nan], dtype='float64')
    >>> idx.notna()
    array([ True,  True, False])
    

Empty strings are not considered NA value