def quantile(x, q, interpolation_method='fraction'):
"""
Compute sample quantile or quantiles of the input array. For example, q=0.5
computes the median.
The `interpolation_method` parameter supports three values, namely
`fraction` (default), `lower` and `higher`. Interpolation is done only,
if the desired quantile lies between two data points `i` and `j`. For
`fraction`, the result is an interpolated value between `i` and `j`;
for `lower`, the result is `i`, for `higher` the result is `j`.
Parameters
----------
x : ndarray
Values from which to extract score.
q : scalar or array
Percentile at which to extract score.
interpolation_method : {'fraction', 'lower', 'higher'}, optional
This optional parameter specifies the interpolation method to use,
when the desired quantile lies between two data points `i` and `j`:
- fraction: `i + (j - i)*fraction`, where `fraction` is the
fractional part of the index surrounded by `i` and `j`.
-lower: `i`.
- higher: `j`.
Returns
-------
score : float
Score at percentile.
Examples
--------
>>> from scipy import stats
>>> a = np.arange(100)
>>> stats.scoreatpercentile(a, 50)
49.5
"""
x = np.asarray(x)
mask = com.isnull(x)
x = x[~mask]
values = np.sort(x)
def _get_score(at):
if len(values) == 0:
return np.nan
idx = at * (len(values) - 1)
if idx % 1 == 0:
score = values[int(idx)]
else:
if interpolation_method == 'fraction':
score = _interpolate(values[int(idx)], values[int(idx) + 1],
idx % 1)
elif interpolation_method == 'lower':
score = values[np.floor(idx)]
elif interpolation_method == 'higher':
score = values[np.ceil(idx)]
else:
raise ValueError("interpolation_method can only be 'fraction' "
", 'lower' or 'higher'")
return score
if np.isscalar(q):
return _get_score(q)
else:
q = np.asarray(q, np.float64)
return algos.arrmap_float64(q, _get_score)
def quantile(x, q, interpolation_method='fraction'):
"""
Compute sample quantile or quantiles of the input array. For example, q=0.5
computes the median.
The `interpolation_method` parameter supports three values, namely
`fraction` (default), `lower` and `higher`. Interpolation is done only,
if the desired quantile lies between two data points `i` and `j`. For
`fraction`, the result is an interpolated value between `i` and `j`;
for `lower`, the result is `i`, for `higher` the result is `j`.
Parameters
----------
x : ndarray
Values from which to extract score.
q : scalar or array
Percentile at which to extract score.
interpolation_method : {'fraction', 'lower', 'higher'}, optional
This optional parameter specifies the interpolation method to use,
when the desired quantile lies between two data points `i` and `j`:
- fraction: `i + (j - i)*fraction`, where `fraction` is the
fractional part of the index surrounded by `i` and `j`.
-lower: `i`.
- higher: `j`.
Returns
-------
score : float
Score at percentile.
Examples
--------
>>> from scipy import stats
>>> a = np.arange(100)
>>> stats.scoreatpercentile(a, 50)
49.5
"""
x = np.asarray(x)
mask = com.isnull(x)
x = x[-mask]
values = np.sort(x)
def _get_score(at):
if len(values) == 0:
return np.nan
idx = at * (len(values) - 1)
if (idx % 1 == 0):
score = values[idx]
else:
if interpolation_method == 'fraction':
score = _interpolate(values[int(idx)], values[int(idx) + 1],
idx % 1)
elif interpolation_method == 'lower':
score = values[np.floor(idx)]
elif interpolation_method == 'higher':
score = values[np.ceil(idx)]
else:
raise ValueError("interpolation_method can only be 'fraction' "
", 'lower' or 'higher'")
return score
if np.isscalar(q):
return _get_score(q)
else:
q = np.asarray(q, np.float64)
return algos.arrmap_float64(q, _get_score)
