# maxframe.tensor.quantile

### maxframe.tensor.quantile(a, q, axis=None, out=None, overwrite_input=False, interpolation='linear', keepdims=False, \*\*kw)

Compute the q-th quantile of the data along the specified axis.

* **Parameters:**
  * **a** (*array_like*) – Input tensor or object that can be converted to a tensor.
  * **q** (*array_like* *of* [*float*](https://docs.python.org/3/library/functions.html#float)) – Quantile or sequence of quantiles to compute, which must be between
    0 and 1 inclusive.
  * **axis** ( *{int* *,* [*tuple*](https://docs.python.org/3/library/stdtypes.html#tuple) *of* [*int*](https://docs.python.org/3/library/functions.html#int) *,* *None}* *,* *optional*) – Axis or axes along which the quantiles are computed. The
    default is to compute the quantile(s) along a flattened
    version of the tensor.
  * **out** (*Tensor* *,* *optional*) – Alternative output tensor in which to place the result. It must
    have the same shape and buffer length as the expected output,
    but the type (of the output) will be cast if necessary.
  * **overwrite_input** ([*bool*](https://docs.python.org/3/library/functions.html#bool) *,* *optional*) – Just for compatibility with Numpy, would not take effect.
  * **interpolation** ( *{'linear'* *,*  *'lower'* *,*  *'higher'* *,*  *'midpoint'* *,*  *'nearest'}*) – 

    This optional parameter specifies the interpolation method to
    use when the desired quantile lies between two data points
    `i < j`:
    > * linear: `i + (j - i) * fraction`, where `fraction`
    >   is the fractional part of the index surrounded by `i`
    >   and `j`.
    > * lower: `i`.
    > * higher: `j`.
    > * nearest: `i` or `j`, whichever is nearest.
    > * midpoint: `(i + j) / 2`.
  * **keepdims** ([*bool*](https://docs.python.org/3/library/functions.html#bool) *,* *optional*) – If this is set to True, the axes which are reduced are left in
    the result as dimensions with size one. With this option, the
    result will broadcast correctly against the original tensor a.
* **Returns:**
  **quantile** – If q is a single quantile and axis=None, then the result
  is a scalar. If multiple quantiles are given, first axis of
  the result corresponds to the quantiles. The other axes are
  the axes that remain after the reduction of a. If the input
  contains integers or floats smaller than `float64`, the output
  data-type is `float64`. Otherwise, the output data-type is the
  same as that of the input. If out is specified, that tensor is
  returned instead.
* **Return type:**
  scalar or Tensor

#### SEE ALSO
[`mean`](maxframe.tensor.mean.md#maxframe.tensor.mean)

[`percentile`](maxframe.tensor.percentile.md#maxframe.tensor.percentile)
: equivalent to quantile, but with q in the range [0, 100].

[`median`](maxframe.tensor.median.md#maxframe.tensor.median)
: equivalent to `quantile(..., 0.5)`

`nanquantile`

### Notes

Given a vector `V` of length `N`, the q-th quantile of
`V` is the value `q` of the way from the minimum to the
maximum in a sorted copy of `V`. The values and distances of
the two nearest neighbors as well as the interpolation parameter
will determine the quantile if the normalized ranking does not
match the location of `q` exactly. This function is the same as
the median if `q=0.5`, the same as the minimum if `q=0.0` and the
same as the maximum if `q=1.0`.

### Examples

```pycon
>>> import maxframe.tensor as mt
>>> a = mt.array([[10, 7, 4], [3, 2, 1]])
>>> a.execute()
array([[10,  7,  4],
       [ 3,  2,  1]])
>>> mt.quantile(a, 0.5).execute()
3.5
>>> mt.quantile(a, 0.5, axis=0).execute()
array([6.5, 4.5, 2.5])
>>> mt.quantile(a, 0.5, axis=1).execute()
array([7.,  2.])
>>> mt.quantile(a, 0.5, axis=1, keepdims=True).execute()
array([[7.],
       [2.]])
>>> m = mt.quantile(a, 0.5, axis=0)
>>> out = mt.zeros_like(m)
>>> mt.quantile(a, 0.5, axis=0, out=out).execute()
array([6.5, 4.5, 2.5])
>>> m.execute()
array([6.5, 4.5, 2.5])
```
