bottleneck.reduce module¶
Module contents¶
Bottleneck functions that reduce the input array along a specified axis.

bottleneck.reduce.
allnan
(a, axis=None)¶ Test whether all array elements along a given axis are NaN.
Returns the same output as np.isnan(a).all(axis)
Note that allnan([]) is True to match np.isnan([]).all() and all([])
 Parameters
 aarray_like
Input array. If a is not an array, a conversion is attempted.
 axis{int, None}, optional
Axis along which NaNs are searched. The default (axis =
None
) is to search for NaNs over a flattened input array.
 Returns
 ybool or ndarray
A boolean or new ndarray is returned.
See also
bottleneck.anynan
Test if any array element along given axis is NaN
Examples
>>> bn.allnan(1) False >>> bn.allnan(np.nan) True >>> bn.allnan([1, np.nan]) False >>> a = np.array([[1, np.nan], [1, np.nan]]) >>> bn.allnan(a) False >>> bn.allnan(a, axis=0) array([False, True], dtype=bool)
An empty array returns True:
>>> bn.allnan([]) True
which is similar to:
>>> all([]) True >>> np.isnan([]).all() True

bottleneck.reduce.
anynan
(a, axis=None)¶ Test whether any array element along a given axis is NaN.
Returns the same output as np.isnan(a).any(axis)
 Parameters
 aarray_like
Input array. If a is not an array, a conversion is attempted.
 axis{int, None}, optional
Axis along which NaNs are searched. The default (axis =
None
) is to search for NaNs over a flattened input array.
 Returns
 ybool or ndarray
A boolean or new ndarray is returned.
See also
bottleneck.allnan
Test if all array elements along given axis are NaN
Examples
>>> bn.anynan(1) False >>> bn.anynan(np.nan) True >>> bn.anynan([1, np.nan]) True >>> a = np.array([[1, 4], [1, np.nan]]) >>> bn.anynan(a) True >>> bn.anynan(a, axis=0) array([False, True], dtype=bool)

bottleneck.reduce.
median
(a, axis=None)¶ Median of array elements along given axis.
 Parameters
 aarray_like
Input array. If a is not an array, a conversion is attempted.
 axis{int, None}, optional
Axis along which the median is computed. The default (axis=None) is to compute the median of the flattened array.
 Returns
 yndarray
An array with the same shape as a, except that the specified axis has been removed. If a is a 0d array, or if axis is None, a scalar is returned. float64 return values are used for integer inputs. NaN is returned for a slice that contains one or more NaNs.
See also
bottleneck.nanmedian
Median along specified axis ignoring NaNs.
Examples
>>> a = np.array([[10, 7, 4], [3, 2, 1]]) >>> bn.median(a) 3.5 >>> bn.median(a, axis=0) array([ 6.5, 4.5, 2.5]) >>> bn.median(a, axis=1) array([ 7., 2.])

bottleneck.reduce.
nanargmax
(a, axis=None)¶ Indices of the maximum values along an axis, ignoring NaNs.
For allNaN slices
ValueError
is raised. Unlike NumPy, the results can be trusted if a slice contains only NaNs and Infs. Parameters
 aarray_like
Input array. If a is not an array, a conversion is attempted.
 axis{int, None}, optional
Axis along which to operate. By default (axis=None) flattened input is used.
 Returns
 index_arrayndarray
An array of indices or a single index value.
See also
bottleneck.nanargmin
Indices of the minimum values along an axis.
bottleneck.nanmax
Maximum values along specified axis, ignoring NaNs.
Examples
>>> a = np.array([[np.nan, 4], [2, 3]]) >>> bn.nanargmax(a) 1 >>> a.flat[1] 4.0 >>> bn.nanargmax(a, axis=0) array([1, 0]) >>> bn.nanargmax(a, axis=1) array([1, 1])

bottleneck.reduce.
nanargmin
(a, axis=None)¶ Indices of the minimum values along an axis, ignoring NaNs.
For allNaN slices
ValueError
is raised. Unlike NumPy, the results can be trusted if a slice contains only NaNs and Infs. Parameters
 aarray_like
Input array. If a is not an array, a conversion is attempted.
 axis{int, None}, optional
Axis along which to operate. By default (axis=None) flattened input is used.
 Returns
 index_arrayndarray
An array of indices or a single index value.
See also
bottleneck.nanargmax
Indices of the maximum values along an axis.
bottleneck.nanmin
Minimum values along specified axis, ignoring NaNs.
Examples
>>> a = np.array([[np.nan, 4], [2, 3]]) >>> bn.nanargmin(a) 2 >>> a.flat[2] 2.0 >>> bn.nanargmin(a, axis=0) array([1, 1]) >>> bn.nanargmin(a, axis=1) array([1, 0])

bottleneck.reduce.
nanmax
(a, axis=None)¶ Maximum values along specified axis, ignoring NaNs.
When allNaN slices are encountered, NaN is returned for that slice.
 Parameters
 aarray_like
Input array. If a is not an array, a conversion is attempted.
 axis{int, None}, optional
Axis along which the maximum is computed. The default (axis=None) is to compute the maximum of the flattened array.
 Returns
 yndarray
An array with the same shape as a, with the specified axis removed. If a is a 0d array, or if axis is None, a scalar is returned. The same dtype as a is returned.
See also
bottleneck.nanmin
Minimum along specified axis, ignoring NaNs.
bottleneck.nanargmax
Indices of maximum values along axis, ignoring NaNs.
Examples
>>> bn.nanmax(1) 1 >>> bn.nanmax([1]) 1 >>> bn.nanmax([1, np.nan]) 1.0 >>> a = np.array([[1, 4], [1, np.nan]]) >>> bn.nanmax(a) 4.0 >>> bn.nanmax(a, axis=0) array([ 1., 4.])

bottleneck.reduce.
nanmean
(a, axis=None)¶ Mean of array elements along given axis ignoring NaNs.
float64 intermediate and return values are used for integer inputs.
 Parameters
 aarray_like
Array containing numbers whose mean is desired. If a is not an array, a conversion is attempted.
 axis{int, None}, optional
Axis along which the means are computed. The default (axis=None) is to compute the mean of the flattened array.
 Returns
 yndarray
An array with the same shape as a, with the specified axis removed. If a is a 0d array, or if axis is None, a scalar is returned. float64 intermediate and return values are used for integer inputs.
See also
bottleneck.nanmedian
Median along specified axis, ignoring NaNs.
Notes
No error is raised on overflow. (The sum is computed and then the result is divided by the number of nonNaN elements.)
If positive or negative infinity are present the result is positive or negative infinity. But if both positive and negative infinity are present, the result is Not A Number (NaN).
Examples
>>> bn.nanmean(1) 1.0 >>> bn.nanmean([1]) 1.0 >>> bn.nanmean([1, np.nan]) 1.0 >>> a = np.array([[1, 4], [1, np.nan]]) >>> bn.nanmean(a) 2.0 >>> bn.nanmean(a, axis=0) array([ 1., 4.])
When positive infinity and negative infinity are present:
>>> bn.nanmean([1, np.nan, np.inf]) inf >>> bn.nanmean([1, np.nan, np.NINF]) inf >>> bn.nanmean([1, np.nan, np.inf, np.NINF]) nan

bottleneck.reduce.
nanmedian
(a, axis=None)¶ Median of array elements along given axis ignoring NaNs.
 Parameters
 aarray_like
Input array. If a is not an array, a conversion is attempted.
 axis{int, None}, optional
Axis along which the median is computed. The default (axis=None) is to compute the median of the flattened array.
 Returns
 yndarray
An array with the same shape as a, except that the specified axis has been removed. If a is a 0d array, or if axis is None, a scalar is returned. float64 return values are used for integer inputs.
See also
bottleneck.median
Median along specified axis.
Examples
>>> a = np.array([[np.nan, 7, 4], [3, 2, 1]]) >>> a array([[ nan, 7., 4.], [ 3., 2., 1.]]) >>> bn.nanmedian(a) 3.0 >> bn.nanmedian(a, axis=0) array([ 3. , 4.5, 2.5]) >> bn.nanmedian(a, axis=1) array([ 5.5, 2. ])

bottleneck.reduce.
nanmin
(a, axis=None)¶ Minimum values along specified axis, ignoring NaNs.
When allNaN slices are encountered, NaN is returned for that slice.
 Parameters
 aarray_like
Input array. If a is not an array, a conversion is attempted.
 axis{int, None}, optional
Axis along which the minimum is computed. The default (axis=None) is to compute the minimum of the flattened array.
 Returns
 yndarray
An array with the same shape as a, with the specified axis removed. If a is a 0d array, or if axis is None, a scalar is returned. The same dtype as a is returned.
See also
bottleneck.nanmax
Maximum along specified axis, ignoring NaNs.
bottleneck.nanargmin
Indices of minimum values along axis, ignoring NaNs.
Examples
>>> bn.nanmin(1) 1 >>> bn.nanmin([1]) 1 >>> bn.nanmin([1, np.nan]) 1.0 >>> a = np.array([[1, 4], [1, np.nan]]) >>> bn.nanmin(a) 1.0 >>> bn.nanmin(a, axis=0) array([ 1., 4.])

bottleneck.reduce.
nanstd
(a, axis=None, ddof=0)¶ Standard deviation along the specified axis, ignoring NaNs.
float64 intermediate and return values are used for integer inputs.
Instead of a faster onepass algorithm, a more stable twopass algorithm is used.
An example of a onepass algorithm:
>>> np.sqrt((a*a).mean()  a.mean()**2)
An example of a twopass algorithm:
>>> np.sqrt(((a  a.mean())**2).mean())
Note in the twopass algorithm the mean must be found (first pass) before the squared deviation (second pass) can be found.
 Parameters
 aarray_like
Input array. If a is not an array, a conversion is attempted.
 axis{int, None}, optional
Axis along which the standard deviation is computed. The default (axis=None) is to compute the standard deviation of the flattened array.
 ddofint, optional
Means Delta Degrees of Freedom. The divisor used in calculations is
N  ddof
, whereN
represents the number of nonNaN elements. By default ddof is zero.
 Returns
 yndarray
An array with the same shape as a, with the specified axis removed. If a is a 0d array, or if axis is None, a scalar is returned. float64 intermediate and return values are used for integer inputs. If ddof is >= the number of nonNaN elements in a slice or the slice contains only NaNs, then the result for that slice is NaN.
See also
bottleneck.nanvar
Variance along specified axis ignoring NaNs
Notes
If positive or negative infinity are present the result is Not A Number (NaN).
Examples
>>> bn.nanstd(1) 0.0 >>> bn.nanstd([1]) 0.0 >>> bn.nanstd([1, np.nan]) 0.0 >>> a = np.array([[1, 4], [1, np.nan]]) >>> bn.nanstd(a) 1.4142135623730951 >>> bn.nanstd(a, axis=0) array([ 0., 0.])
When positive infinity or negative infinity are present NaN is returned:
>>> bn.nanstd([1, np.nan, np.inf]) nan

bottleneck.reduce.
nansum
(a, axis=None)¶ Sum of array elements along given axis treating NaNs as zero.
The data type (dtype) of the output is the same as the input. On 64bit operating systems, 32bit input is NOT upcast to 64bit accumulator and return values.
 Parameters
 aarray_like
Array containing numbers whose sum is desired. If a is not an array, a conversion is attempted.
 axis{int, None}, optional
Axis along which the sum is computed. The default (axis=None) is to compute the sum of the flattened array.
 Returns
 yndarray
An array with the same shape as a, with the specified axis removed. If a is a 0d array, or if axis is None, a scalar is returned.
Notes
No error is raised on overflow.
If positive or negative infinity are present the result is positive or negative infinity. But if both positive and negative infinity are present, the result is Not A Number (NaN).
Examples
>>> bn.nansum(1) 1 >>> bn.nansum([1]) 1 >>> bn.nansum([1, np.nan]) 1.0 >>> a = np.array([[1, 1], [1, np.nan]]) >>> bn.nansum(a) 3.0 >>> bn.nansum(a, axis=0) array([ 2., 1.])
When positive infinity and negative infinity are present:
>>> bn.nansum([1, np.nan, np.inf]) inf >>> bn.nansum([1, np.nan, np.NINF]) inf >>> bn.nansum([1, np.nan, np.inf, np.NINF]) nan

bottleneck.reduce.
nanvar
(a, axis=None, ddof=0)¶ Variance along the specified axis, ignoring NaNs.
float64 intermediate and return values are used for integer inputs.
Instead of a faster onepass algorithm, a more stable twopass algorithm is used.
An example of a onepass algorithm:
>>> (a*a).mean()  a.mean()**2
An example of a twopass algorithm:
>>> ((a  a.mean())**2).mean()
Note in the twopass algorithm the mean must be found (first pass) before the squared deviation (second pass) can be found.
 Parameters
 aarray_like
Input array. If a is not an array, a conversion is attempted.
 axis{int, None}, optional
Axis along which the variance is computed. The default (axis=None) is to compute the variance of the flattened array.
 ddofint, optional
Means Delta Degrees of Freedom. The divisor used in calculations is
N  ddof
, whereN
represents the number of non_NaN elements. By default ddof is zero.
 Returns
 yndarray
An array with the same shape as a, with the specified axis removed. If a is a 0d array, or if axis is None, a scalar is returned. float64 intermediate and return values are used for integer inputs. If ddof is >= the number of nonNaN elements in a slice or the slice contains only NaNs, then the result for that slice is NaN.
See also
bottleneck.nanstd
Standard deviation along specified axis ignoring NaNs.
Notes
If positive or negative infinity are present the result is Not A Number (NaN).
Examples
>>> bn.nanvar(1) 0.0 >>> bn.nanvar([1]) 0.0 >>> bn.nanvar([1, np.nan]) 0.0 >>> a = np.array([[1, 4], [1, np.nan]]) >>> bn.nanvar(a) 2.0 >>> bn.nanvar(a, axis=0) array([ 0., 0.])
When positive infinity or negative infinity are present NaN is returned:
>>> bn.nanvar([1, np.nan, np.inf]) nan

bottleneck.reduce.
ss
(a, axis=None)¶ Sum of the square of each element along the specified axis.
 Parameters
 aarray_like
Array whose sum of squares is desired. If a is not an array, a conversion is attempted.
 axis{int, None}, optional
Axis along which the sum of squares is computed. The default (axis=None) is to sum the squares of the flattened array.
 Returns
 yndarray
The sum of a**2 along the given axis.
Examples
>>> a = np.array([1., 2., 5.]) >>> bn.ss(a) 30.0
And calculating along an axis:
>>> b = np.array([[1., 2., 5.], [2., 5., 6.]]) >>> bn.ss(b, axis=1) array([ 30., 65.])