cf.Data.percentile¶

Data.
percentile
(ranks, axes=None, method='linear', squeeze=False, mtol=1, inplace=False, interpolation=None, interpolation2=None)[source]¶ Compute percentiles of the data along the specified axes.
The default is to compute the percentiles along a flattened version of the data.
If the input data are integers, or floats smaller than float64, or the input data contains missing values, then output datatype is float64. Otherwise, the output datatype is the same as that of the input.
If multiple percentile ranks are given then a new, leading data dimension is created so that percentiles can be stored for each percentile rank.
Accuracy
The
percentile
method returns results that are consistent withnumpy.percentile
, which may be different to those created bydask.percentile
. The dask method uses an algorithm that calculates approximate percentiles which are likely to be different from the correct values when there are two or more dask chunks.>>> import numpy as np >>> import dask.array as da >>> import cf >>> a = np.arange(101) >>> dx = da.from_array(a, chunks=10) >>> da.percentile(dx, 40).compute() array([40.36]) >>> np.percentile(a, 40) 40.0 >>> d = cf.Data(a, chunks=10) >>> d.percentile(40).array array([40.])
New in version 3.0.4.
See also
 Parameters
 ranks: (sequence of) number
Percentile rank, or sequence of percentile ranks, to compute, which must be between 0 and 100 inclusive.
 axes: (sequence of)
int
, optional Select the axes. The axes argument may be one, or a sequence, of integers that select the axis corresponding to the given position in the list of axes of the data array.
By default, of axes is
None
, all axes are selected. method:
str
, optional Specify the interpolation method to use when the percentile lies between two data values. The methods are listed here, but their definitions must be referenced from the documentation for
numpy.percentile
.For the default
'linear'
method, if the percentile lies between two adjacent data valuesi < j
then the percentile is calculated asi+(ji)*fraction
, wherefraction
is the fractional part of the index surrounded byi
andj
.'inverted_cdf'
'averaged_inverted_cdf'
'closest_observation'
'interpolated_inverted_cdf'
'hazen'
'weibull'
'linear'
(default)'median_unbiased'
'normal_unbiased'
'lower'
'higher'
'nearest'
'midpoint'
===============================New in version 3.14.0.
 squeeze:
bool
, optional If True then all axes over which percentiles are calculated are removed from the returned data. By default axes over which percentiles have been calculated are left in the result as axes with size 1, meaning that the result is guaranteed to broadcast correctly against the original data.
 mtol: number, optional
The sample size threshold below which collapsed values are set to missing data. It is defined as a fraction (between 0 and 1 inclusive) of the contributing input data values.
The default of mtol is 1, meaning that a missing datum in the output array occurs whenever all of its contributing input array elements are missing data.
For other values, a missing datum in the output array occurs whenever more than
100*mtol%
of its contributing input array elements are missing data.Note that for nonzero values of mtol, different collapsed elements may have different sample sizes, depending on the distribution of missing data in the input data.
 split_every:
int
ordict
, optional Determines the depth of the recursive aggregation. If set to or more than the number of input chunks, the aggregation will be performed in two steps, one partial collapse per input chunk and a single aggregation at the end. If set to less than that, an intermediate aggregation step will be used, so that any of the intermediate or final aggregation steps operates on no more than
split_every
inputs. The depth of the aggregation graph will be \(log_{split_every}(input chunks along reduced axes)\). Setting to a low value can reduce cache size and network transfers, at the cost of more CPU and a larger dask graph.By default,
dask
heuristically decides on a good value. A default can also be set globally with thesplit_every
key indask.config
. Seedask.array.reduction
for details.New in version 3.14.0.
 inplace:
bool
, optional If True then do the operation inplace and return
None
. interpolation: deprecated at version 3.14.0
Use the method parameter instead.
 Returns
Examples
>>> d = cf.Data(numpy.arange(12).reshape(3, 4), 'm') >>> print(d.array) [[ 0 1 2 3] [ 4 5 6 7] [ 8 9 10 11]] >>> p = d.percentile([20, 40, 50, 60, 80]) >>> p <CF Data(5, 1, 1): [[[2.2, ..., 8.8]]] m>
>>> p = d.percentile([20, 40, 50, 60, 80], squeeze=True) >>> print(p.array) [2.2 4.4 5.5 6.6 8.8]
Find the standard deviation of the values above the 80th percentile:
>>> p80 = d.percentile(80) <CF Data(1, 1): [[8.8]] m> >>> e = d.where(d<=p80, cf.masked) >>> print(e.array) [[   ] [   ] [ 9 10 11]] >>> e.std() <CF Data(1, 1): [[0.816496580927726]] m>
Find the mean of the values above the 45th percentile along the second axis:
>>> p45 = d.percentile(45, axes=1) >>> print(p45.array) [[1.35], [5.35], [9.35]] >>> e = d.where(d<=p45, cf.masked) >>> print(e.array) [[  2 3] [  6 7] [  10 11]] >>> f = e.mean(axes=1) >>> f <CF Data(3, 1): [[2.5, ..., 10.5]] m> >>> print(f.array) [[ 2.5] [ 6.5] [10.5]]
Find the histogram bin boundaries associated with given percentiles, and digitize the data based on these bins:
>>> bins = d.percentile([0, 10, 50, 90, 100], squeeze=True) >>> print(bins.array) [ 0. 1.1 5.5 9.9 11. ] >>> e = d.digitize(bins, closed_ends=True) >>> print(e.array) [[0 0 1 1] [1 1 2 2] [2 2 3 3]]