# dask.array.quantile

dask.array.quantile(a, q, axis=None, out=None, overwrite_input=False, method='linear', keepdims=False, *, weights=None, interpolation=None)

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

  This docstring was copied from numpy.quantile.

  Some inconsistencies with the Dask version may exist.

  This works by automatically chunking the reduced axes to a single chunk if necessary and then calling `numpy.quantile` function across the remaining dimensions

  Parameters:
    a array_like of real numbers

      Input array or object that can be converted to an array.

    q array_like of float

      Probability or sequence of probabilities of the quantiles to compute. Values must be between 0 and 1 inclusive.

    axis{int, tuple of 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 array.

    out ndarray, optional

      Alternative output array 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, optional

      If True, then allow the input array a to be modified by intermediate calculations, to save memory. In this case, the contents of the input a after this function completes is undefined.

    method str, optional

      This parameter specifies the method to use for estimating the quantile. There are many different methods, some unique to NumPy. The recommended options, numbered as they appear in [1], are:

      1. ‘inverted_cdf’

      2. ‘averaged_inverted_cdf’

      3. ‘closest_observation’

      4. ‘interpolated_inverted_cdf’

      5. ‘hazen’

      6. ‘weibull’

      7. ‘linear’ (default)

      8. ‘median_unbiased’

      9. ‘normal_unbiased’

      The first three methods are discontinuous. For backward compatibility with previous versions of NumPy, the following discontinuous variations of the default ‘linear’ (7.) option are available:

      - ‘lower’

      - ‘higher’,

      - ‘midpoint’

      - ‘nearest’

      See Notes for details.

      Changed in version 1.22.0: This argument was previously called “interpolation” and only offered the “linear” default and last four options.

    keepdims 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 array a.

    weights array_like, optional

      An array of weights associated with the values in a. Each value in a contributes to the quantile according to its associated weight. The weights array can either be 1-D (in which case its length must be the size of a along the given axis) or of the same shape as a. If weights=None, then all data in a are assumed to have a weight equal to one. Only method=”inverted_cdf” supports weights. See the notes for more details.

      Added in version 2.0.0.

  Returns:
    quantile scalar or ndarray

      If q is a single probability and axis=None, then the result is a scalar. If multiple probability levels 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 array is returned instead.

  See also

  `mean`
  `percentile`

    equivalent to quantile, but with q in the range [0, 100].

  `median`

    equivalent to `quantile(..., 0.5)`

  `nanquantile`

  Notes

  Given a sample a from an underlying distribution, quantile provides a nonparametric estimate of the inverse cumulative distribution function.

  By default, this is done by interpolating between adjacent elements in `y`, a sorted copy of a:

      (1-g)*y[j] + g*y[j+1]

  where the index `j` and coefficient `g` are the integral and fractional components of `q * (n-1)`, and `n` is the number of elements in the sample.

  This is a special case of Equation 1 of H&F [1]. More generally,

  - `j = (q*n + m - 1) // 1`, and

  - `g = (q*n + m - 1) % 1`,

  where `m` may be defined according to several different conventions. The preferred convention may be selected using the `method` parameter:

  `method` | number in H&F | `m`
  --- | --- | ---
  `interpolated_inverted_cdf` | 4 | `0`
  `hazen` | 5 | `1/2`
  `weibull` | 6 | `q`
  `linear` (default) | 7 | `1 - q`
  `median_unbiased` | 8 | `q/3 + 1/3`
  `normal_unbiased` | 9 | `q/4 + 3/8`

  Note that indices `j` and `j + 1` are clipped to the range `0` to `n - 1` when the results of the formula would be outside the allowed range of non-negative indices. The `- 1` in the formulas for `j` and `g` accounts for Python’s 0-based indexing.

  The table above includes only the estimators from H&F that are continuous functions of probability q (estimators 4-9). NumPy also provides the three discontinuous estimators from H&F (estimators 1-3), where `j` is defined as above, `m` is defined as follows, and `g` is a function of the real-valued `index = q*n + m - 1` and `j`.

  1. `inverted_cdf`: `m = 0` and `g = int(index - j > 0)`

  2. `averaged_inverted_cdf`: `m = 0` and `g = (1 + int(index - j > 0)) / 2`

  3. `closest_observation`: `m = -1/2` and `g = 1 - int((index == j) & (j%2 == 1))`

  For backward compatibility with previous versions of NumPy, quantile provides four additional discontinuous estimators. Like `method='linear'`, all have `m = 1 - q` so that `j = q*(n-1) // 1`, but `g` is defined as follows.

  - `lower`: `g = 0`

  - `midpoint`: `g = 0.5`

  - `higher`: `g = 1`

  - `nearest`: `g = (q*(n-1) % 1) > 0.5`

  Weighted quantiles: More formally, the quantile at probability level \(q\) of a cumulative distribution function \(F(y)=P(Y \leq y)\) with probability measure \(P\) is defined as any number \(x\) that fulfills the coverage conditions

  \[P(Y < x) \leq q \quad\text{and}\quad P(Y \leq x) \geq q\]

  with random variable \(Y\sim P\). Sample quantiles, the result of quantile, provide nonparametric estimation of the underlying population counterparts, represented by the unknown \(F\), given a data vector a of length `n`.

  Some of the estimators above arise when one considers \(F\) as the empirical distribution function of the data, i.e. \(F(y) = \frac{1}{n} \sum_i 1_{a_i \leq y}\). Then, different methods correspond to different choices of \(x\) that fulfill the above coverage conditions. Methods that follow this approach are `inverted_cdf` and `averaged_inverted_cdf`.

  For weighted quantiles, the coverage conditions still hold. The empirical cumulative distribution is simply replaced by its weighted version, i.e. \(P(Y \leq t) = \frac{1}{\sum_i w_i} \sum_i w_i 1_{x_i \leq t}\). Only `method="inverted_cdf"` supports weights.

  References

  [1] (1,2)

  R. J. Hyndman and Y. Fan, “Sample quantiles in statistical packages,” The American Statistician, 50(4), pp. 361-365, 1996

  Examples

      >>> import numpy as np
      >>> a = np.array([[10, 7, 4], [3, 2, 1]])
      >>> a
      array([[10,  7,  4],
             [ 3,  2,  1]])
      >>> np.quantile(a, 0.5)
      3.5
      >>> np.quantile(a, 0.5, axis=0)
      array([6.5, 4.5, 2.5])
      >>> np.quantile(a, 0.5, axis=1)
      array([7.,  2.])
      >>> np.quantile(a, 0.5, axis=1, keepdims=True)
      array([[7.],
             [2.]])
      >>> m = np.quantile(a, 0.5, axis=0)
      >>> out = np.zeros_like(m)
      >>> np.quantile(a, 0.5, axis=0, out=out)
      array([6.5, 4.5, 2.5])
      >>> m
      array([6.5, 4.5, 2.5])
      >>> b = a.copy()
      >>> np.quantile(b, 0.5, axis=1, overwrite_input=True)
      array([7.,  2.])
      >>> assert not np.all(a == b)

  See also numpy.percentile for a visualization of most methods.