diff options
Diffstat (limited to 'numpy/lib/function_base.py')
| -rw-r--r-- | numpy/lib/function_base.py | 17 |
1 files changed, 9 insertions, 8 deletions
diff --git a/numpy/lib/function_base.py b/numpy/lib/function_base.py index 128da22c6..26ef3e235 100644 --- a/numpy/lib/function_base.py +++ b/numpy/lib/function_base.py @@ -3398,9 +3398,9 @@ def _median(a, axis=None, out=None, overwrite_input=False): def percentile(a, q, axis=None, out=None, overwrite_input=False, interpolation='linear', keepdims=False): """ - Compute the qth percentile of the data along the specified axis. + Compute the q-th percentile of the data along the specified axis. - Returns the qth percentile(s) of the array elements. + Returns the q-th percentile(s) of the array elements. Parameters ---------- @@ -3467,7 +3467,7 @@ def percentile(a, q, axis=None, out=None, Notes ----- - Given a vector ``V`` of length ``N``, the ``q``-th percentile of + Given a vector ``V`` of length ``N``, the q-th percentile of ``V`` is the value ``q/100`` 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 @@ -3543,7 +3543,7 @@ def percentile(a, q, axis=None, out=None, def quantile(a, q, axis=None, out=None, overwrite_input=False, interpolation='linear', keepdims=False): """ - Compute the `q`th quantile of the data along the specified axis. + Compute the q-th quantile of the data along the specified axis. ..versionadded:: 1.15.0 Parameters @@ -3569,6 +3569,7 @@ def quantile(a, q, axis=None, out=None, 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``. @@ -3602,7 +3603,7 @@ def quantile(a, q, axis=None, out=None, Notes ----- - Given a vector ``V`` of length ``N``, the ``q``-th quantile of + 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 @@ -3720,7 +3721,7 @@ def _quantile_ureduce_func(a, q, axis=None, out=None, overwrite_input=False, indices = concatenate((indices, [-1])) ap.partition(indices, axis=axis) - # ensure axis with qth is first + # ensure axis with q-th is first ap = np.moveaxis(ap, axis, 0) axis = 0 @@ -3753,7 +3754,7 @@ def _quantile_ureduce_func(a, q, axis=None, out=None, overwrite_input=False, ap.partition(concatenate((indices_below, indices_above)), axis=axis) - # ensure axis with qth is first + # ensure axis with q-th is first ap = np.moveaxis(ap, axis, 0) weights_below = np.moveaxis(weights_below, axis, 0) weights_above = np.moveaxis(weights_above, axis, 0) @@ -3767,7 +3768,7 @@ def _quantile_ureduce_func(a, q, axis=None, out=None, overwrite_input=False, x1 = take(ap, indices_below, axis=axis) * weights_below x2 = take(ap, indices_above, axis=axis) * weights_above - # ensure axis with qth is first + # ensure axis with q-th is first x1 = np.moveaxis(x1, axis, 0) x2 = np.moveaxis(x2, axis, 0) |