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# atoti.array.quantile_index()

### atoti.array.quantile\_index(measure, /, q, \*, mode='inc', interpolation='lower')

Return a measure equal to the index of requested quantile of the elements of the passed array measure.

* **Parameters:**
  * **measure** (*VariableMeasureConvertible*) – The measure to get the quantile of.
  * **q** ( *\_Quantile*) – The quantile to take.
    For instance, `0.95` is the 95th percentile and `0.5` is the median.
  * **mode** ([*Literal*](https://docs.python.org/3/library/typing.html#typing.Literal) *\[* *'simple'* *,*  *'centered'* *,*  *'inc'* *,*  *'exc'* *]*) –

    The method used to calculate the index of the quantile.
    Available options are, when searching for the *q* quantile of a vector `X`:

    * `simple`: `len(X) * q`
    * `centered`: `len(X) * q + 0.5`
    * `exc`: `(len(X) + 1) * q`
    * `inc`: `(len(X) - 1) * q + 1`
  * **interpolation** ([*Literal*](https://docs.python.org/3/library/typing.html#typing.Literal) *\[* *'higher'* *,*  *'lower'* *,*  *'nearest'* *]*) –

    If the quantile index is not an integer, the interpolation decides what value is returned.
    The different options are, considering a quantile index `k` with `i < k < j` for the original vector `X`
    and the sorted vector `Y`:

    * `lowest`: the index in `X` of `Y[i]`
    * `highest`: the index in `X` of `Y[j]`
    * `nearest`: the index in `X` of `Y[i]` or `Y[j]` depending on which of `i` or `j` is closest to `k`
* **Return type:**
  *MeasureDefinition*