scipy.stats.iqr#
- scipy.stats.iqr(x, axis=None, rng=(25, 75), scale=1.0, nan_policy='propagate', interpolation='linear', keepdims=False)[source]#
Compute the interquartile range of the data along the specified axis.
The interquartile range (IQR) is the difference between the 75th and 25th percentile of the data. It is a measure of the dispersion similar to standard deviation or variance, but is much more robust against outliers [2].
The
rng
parameter allows this function to compute other percentile ranges than the actual IQR. For example, settingrng=(0, 100)
is equivalent tonumpy.ptp
.The IQR of an empty array is np.nan.
New in version 0.18.0.
- Parameters
- xarray_like
Input array or object that can be converted to an array.
- axisint or sequence of int, optional
Axis along which the range is computed. The default is to compute the IQR for the entire array.
- rngTwo-element sequence containing floats in range of [0,100] optional
Percentiles over which to compute the range. Each must be between 0 and 100, inclusive. The default is the true IQR:
(25, 75)
. The order of the elements is not important.- scalescalar or str, optional
The numerical value of scale will be divided out of the final result. The following string values are recognized:
‘raw’ : No scaling, just return the raw IQR. Deprecated! Use
scale=1
instead.‘normal’ : Scale by \(2 \sqrt{2} erf^{-1}(\frac{1}{2}) \approx 1.349\).
The default is 1.0. The use of
scale='raw'
is deprecated. Array-like scale is also allowed, as long as it broadcasts correctly to the output such thatout / scale
is a valid operation. The output dimensions depend on the input array, x, the axis argument, and the keepdims flag.- nan_policy{‘propagate’, ‘raise’, ‘omit’}, optional
Defines how to handle when input contains nan. The following options are available (default is ‘propagate’):
‘propagate’: returns nan
‘raise’: throws an error
‘omit’: performs the calculations ignoring nan values
- interpolationstr, optional
Specifies the interpolation method to use when the percentile boundaries lie between two data points
i
andj
. The following options are available (default is ‘linear’):‘linear’:
i + (j - i)*fraction
, wherefraction
is the fractional part of the index surrounded byi
andj
.‘lower’:
i
.‘higher’:
j
.‘nearest’:
i
orj
whichever is nearest.‘midpoint’:
(i + j)/2
.
For NumPy >= 1.22.0, the additional options provided by the
method
keyword ofnumpy.percentile
are also valid.- keepdimsbool, optional
If this is set to True, the reduced axes are left in the result as dimensions with size one. With this option, the result will broadcast correctly against the original array x.
- Returns
- iqrscalar or ndarray
If
axis=None
, a scalar is returned. If the input contains integers or floats of smaller precision thannp.float64
, then the output data-type isnp.float64
. Otherwise, the output data-type is the same as that of the input.
References
- 1
“Interquartile range” https://en.wikipedia.org/wiki/Interquartile_range
- 2
“Robust measures of scale” https://en.wikipedia.org/wiki/Robust_measures_of_scale
- 3
“Quantile” https://en.wikipedia.org/wiki/Quantile
Examples
>>> from scipy.stats import iqr >>> x = np.array([[10, 7, 4], [3, 2, 1]]) >>> x array([[10, 7, 4], [ 3, 2, 1]]) >>> iqr(x) 4.0 >>> iqr(x, axis=0) array([ 3.5, 2.5, 1.5]) >>> iqr(x, axis=1) array([ 3., 1.]) >>> iqr(x, axis=1, keepdims=True) array([[ 3.], [ 1.]])