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 all-NaN 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 all-NaN 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 all-NaN 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 0-d 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 0-d 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 non-NaN 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 all-NaN 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 0-d 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 one-pass algorithm, a more stable two-pass algorithm is used.

An example of a one-pass algorithm:

>>> np.sqrt((a*a).mean() - a.mean()**2)

An example of a two-pass algorithm:

>>> np.sqrt(((a - a.mean())**2).mean())

Note in the two-pass 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, where N 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 0-d 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 non-NaN 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 64-bit operating systems, 32-bit input is NOT upcast to 64-bit 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 0-d 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 one-pass algorithm, a more stable two-pass algorithm is used.

An example of a one-pass algorithm:

>>> (a*a).mean() - a.mean()**2

An example of a two-pass algorithm:

>>> ((a - a.mean())**2).mean()

Note in the two-pass 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, where N 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 0-d 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 non-NaN 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.])