a
Functions that ignore NaN.
Functions
---------
- `nanmin` -- minimum non-NaN value
- `nanmax` -- maximum non-NaN value
- `nanargmin` -- index of minimum non-NaN value
- `nanargmax` -- index of maximum non-NaN value
- `nansum` -- sum of non-NaN values
- `nanprod` -- product of non-NaN values
- `nancumsum` -- cumulative sum of non-NaN values
- `nancumprod` -- cumulative product of non-NaN values
- `nanmean` -- mean of non-NaN values
- `nanvar` -- variance of non-NaN values
- `nanstd` -- standard deviation of non-NaN values
- `nanmedian` -- median of non-NaN values
- `nanquantile` -- qth quantile of non-NaN values
- `nanpercentile` -- qth percentile of non-NaN values
N)
function_base) overridesnumpy)module)nansumnanmaxnanmin nanargmax nanargminnanmean nanmedian
nanpercentilenanvarnanstdnanprod nancumsum
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Parameters
----------
a : array-like
Input array with at least 1 dimension.
out : ndarray, optional
Alternate output array in which to place the result. The default
is ``None``; if provided, it must have the same shape as the
expected output and will prevent the allocation of a new array.
Returns
-------
y : bool ndarray or True
A bool array where ``np.nan`` positions are marked with ``False``
and other positions are marked with ``True``. If the type of ``a``
is such that it can't possibly contain ``np.nan``, returns ``True``.
fcTout)dtypekindnpisnaninvert)ar ys M/opt/cloudlinux/venv/lib64/python3.11/site-packages/numpy/lib/nanfunctions.py _nan_maskr! ) sE ( w|4 t
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If `a` is of inexact type, make a copy of `a`, replace NaNs with
the `val` value, and return the copy together with a boolean mask
marking the locations where NaNs were present. If `a` is not of
inexact type, do nothing and return `a` together with a mask of None.
Note that scalars will end up as array scalars, which is important
for using the result as the value of the out argument in some
operations.
Parameters
----------
a : array-like
Input array.
val : float
NaN values are set to val before doing the operation.
Returns
-------
y : ndarray
If `a` is of inexact type, return a copy of `a` with the NaNs
replaced by the fill value, otherwise return `a`.
mask: {bool, None}
If `a` is of inexact type, return a boolean mask marking locations of
NaNs, otherwise return None.
r NT)subokcopywhere) r
asanyarrayr object_ not_equalbool
issubclasstypeinexactr arraycopytor valmasks r _replace_nanr5 D s 8
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Replace values in `a` with NaN where `mask` is True. This differs from
copyto in that it will deal with the case where `a` is a numpy scalar.
Parameters
----------
a : ndarray or numpy scalar
Array or numpy scalar some of whose values are to be replaced
by val.
val : numpy scalar
Value used a replacement.
mask : ndarray, scalar
Boolean array. Where True the corresponding element of `a` is
replaced by `val`. Broadcasts.
Returns
-------
res : ndarray, scalar
Array with elements replaced or scalar `val`.
unsafe)r( casting)
isinstancer ndarrayr1 r r. r2 s r _copytor; q sL , !RZ
!Sh77777
GLL Hr" Fc | j t k r t j | | t }nt j | }t j | d }|j | j k r(t j dt d | dd dfS |j dk r| |fS |s| } | |j d ||j d }|| |d|j < | d|j dfS )a
Equivalent to arr1d[~arr1d.isnan()], but in a different order
Presumably faster as it incurs fewer copies
Parameters
----------
arr1d : ndarray
Array to remove nans from
overwrite_input : bool
True if `arr1d` can be modified in place
Returns
-------
res : ndarray
Array with nan elements removed
overwrite_input : bool
True if `res` can be modified in place, given the constraint on the
input
r$ r All-NaN slice encountered
stacklevelNT) r objectr r+ r, r nonzerosizewarningswarnRuntimeWarningr& )arr1doverwrite_inputcsenonans r _remove_nan_1drL s *
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1o%% !JJLLEwxx !QVGHH+ .!'a o XqvgX$$r" c t j dd 5 t | t j rJ|$t j | || d cddd S t j | ||d cddd S |K | j | |z cddd S # t $ r | |z cY cddd S w xY wt j | ||d cddd S # 1 swxY w Y dS )a
Compute a/b ignoring invalid results. If `a` is an array the division
is done in place. If `a` is a scalar, then its type is preserved in the
output. If out is None, then a is used instead so that the division
is in place. Note that this is only called with `a` an inexact type.
Parameters
----------
a : {ndarray, numpy scalar}
Numerator. Expected to be of inexact type but not checked.
b : {ndarray, numpy scalar}
Denominator.
out : ndarray, optional
Alternate output array in which to place the result. The default
is ``None``; if provided, it must have the same shape as the
expected output, but the type will be cast if necessary.
Returns
-------
ret : {ndarray, numpy scalar}
The return value is a/b. If `a` was an ndarray the division is done
in place. If `a` is a numpy scalar, the division preserves its type.
ignore)invaliddivideNr7 )r r8 )r errstater9 r: rP r r. AttributeError)r br s r _divide_by_countrT s 2
Xh 7 7 7 B B a $ $ B{yA1h???B B B B B B B B
yA3AAA B B B B B B B B {! 7<<A..B B B B B B B B & ! ! ! q5LLB B B B B B B B!
yA3AAA!B B B B B B B B B B B B B B B B B Bs; 4C-C-<C-? B((C7C-CC--C14C1c
| |fS N r axisr keepdimsinitialr( s r _nanmin_dispatcherr\
s8Or" c i }|t j ur||d< |t j ur||d< |t j ur||d< t | t j u rr| j t j k r]t j j | f||d|}t j | r t j dt d nt | t j
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t d |S ) a
Return minimum of an array or minimum along an axis, ignoring any NaNs.
When all-NaN slices are encountered a ``RuntimeWarning`` is raised and
Nan is returned for that slice.
Parameters
----------
a : array_like
Array containing numbers whose minimum is desired. If `a` is not an
array, a conversion is attempted.
axis : {int, tuple of int, None}, optional
Axis or axes along which the minimum is computed. The default is to compute
the minimum of the flattened array.
out : ndarray, optional
Alternate output array in which to place the result. The default
is ``None``; if provided, it must have the same shape as the
expected output, but the type will be cast if necessary. See
:ref:`ufuncs-output-type` for more details.
.. versionadded:: 1.8.0
keepdims : bool, optional
If this is set to True, the axes which are reduced are left
in the result as dimensions with size one. With this option,
the result will broadcast correctly against the original `a`.
If the value is anything but the default, then
`keepdims` will be passed through to the `min` method
of sub-classes of `ndarray`. If the sub-classes methods
does not implement `keepdims` any exceptions will be raised.
.. versionadded:: 1.8.0
initial : scalar, optional
The maximum value of an output element. Must be present to allow
computation on empty slice. See `~numpy.ufunc.reduce` for details.
.. versionadded:: 1.22.0
where : array_like of bool, optional
Elements to compare for the minimum. See `~numpy.ufunc.reduce`
for details.
.. versionadded:: 1.22.0
Returns
-------
nanmin : ndarray
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, an ndarray
scalar is returned. The same dtype as `a` is returned.
See Also
--------
nanmax :
The maximum value of an array along a given axis, ignoring any NaNs.
amin :
The minimum value of an array along a given axis, propagating any NaNs.
fmin :
Element-wise minimum of two arrays, ignoring any NaNs.
minimum :
Element-wise minimum of two arrays, propagating any NaNs.
isnan :
Shows which elements are Not a Number (NaN).
isfinite:
Shows which elements are neither NaN nor infinity.
amax, fmax, maximum
Notes
-----
NumPy uses the IEEE Standard for Binary Floating-Point for Arithmetic
(IEEE 754). This means that Not a Number is not equivalent to infinity.
Positive infinity is treated as a very large number and negative
infinity is treated as a very small (i.e. negative) number.
If the input has a integer type the function is equivalent to np.min.
Examples
--------
>>> a = np.array([[1, 2], [3, np.nan]])
>>> np.nanmin(a)
1.0
>>> np.nanmin(a, axis=0)
array([1., 2.])
>>> np.nanmin(a, axis=1)
array([1., 3.])
When positive infinity and negative infinity are present:
>>> np.nanmin([1, 2, np.nan, np.inf])
1.0
>>> np.nanmin([1, 2, np.nan, np.NINF])
-inf
rZ r[ r( rY r r= r? NrY All-NaN axis encountered)r _NoValuer. r: r r* fminreducer anyrD rE rF r5 infaminpopallr; nan r rY r rZ r[ r( kwargsresr4 s r r r @ Fr{"" %zbk!! #y BK w Aww"* BJ!6!6 gnQ=Ts==f==
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t d |S ) a
Return the maximum of an array or maximum along an axis, ignoring any
NaNs. When all-NaN slices are encountered a ``RuntimeWarning`` is
raised and NaN is returned for that slice.
Parameters
----------
a : array_like
Array containing numbers whose maximum is desired. If `a` is not an
array, a conversion is attempted.
axis : {int, tuple of int, None}, optional
Axis or axes along which the maximum is computed. The default is to compute
the maximum of the flattened array.
out : ndarray, optional
Alternate output array in which to place the result. The default
is ``None``; if provided, it must have the same shape as the
expected output, but the type will be cast if necessary. See
:ref:`ufuncs-output-type` for more details.
.. versionadded:: 1.8.0
keepdims : bool, optional
If this is set to True, the axes which are reduced are left
in the result as dimensions with size one. With this option,
the result will broadcast correctly against the original `a`.
If the value is anything but the default, then
`keepdims` will be passed through to the `max` method
of sub-classes of `ndarray`. If the sub-classes methods
does not implement `keepdims` any exceptions will be raised.
.. versionadded:: 1.8.0
initial : scalar, optional
The minimum value of an output element. Must be present to allow
computation on empty slice. See `~numpy.ufunc.reduce` for details.
.. versionadded:: 1.22.0
where : array_like of bool, optional
Elements to compare for the maximum. See `~numpy.ufunc.reduce`
for details.
.. versionadded:: 1.22.0
Returns
-------
nanmax : ndarray
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, an ndarray scalar is
returned. The same dtype as `a` is returned.
See Also
--------
nanmin :
The minimum value of an array along a given axis, ignoring any NaNs.
amax :
The maximum value of an array along a given axis, propagating any NaNs.
fmax :
Element-wise maximum of two arrays, ignoring any NaNs.
maximum :
Element-wise maximum of two arrays, propagating any NaNs.
isnan :
Shows which elements are Not a Number (NaN).
isfinite:
Shows which elements are neither NaN nor infinity.
amin, fmin, minimum
Notes
-----
NumPy uses the IEEE Standard for Binary Floating-Point for Arithmetic
(IEEE 754). This means that Not a Number is not equivalent to infinity.
Positive infinity is treated as a very large number and negative
infinity is treated as a very small (i.e. negative) number.
If the input has a integer type the function is equivalent to np.max.
Examples
--------
>>> a = np.array([[1, 2], [3, np.nan]])
>>> np.nanmax(a)
3.0
>>> np.nanmax(a, axis=0)
array([3., 2.])
>>> np.nanmax(a, axis=1)
array([2., 3.])
When positive infinity and negative infinity are present:
>>> np.nanmax([1, 2, np.nan, np.NINF])
2.0
>>> np.nanmax([1, 2, np.nan, np.inf])
inf
rZ r[ r( r_ r= r` r? NrY ra )r rb r. r: r r* fmaxrd r re rD rE rF r5 rf amaxrh ri r; rj rk s r r r q rn r" )rZ c | fS rV rW r rY r rZ s r _nanargmin_dispatcherrv
4Kr" c t | t j \ } }|9t j || }t j | rt d t j | ||| }|S )a
Return the indices of the minimum values in the specified axis ignoring
NaNs. For all-NaN slices ``ValueError`` is raised. Warning: the results
cannot be trusted if a slice contains only NaNs and Infs.
Parameters
----------
a : array_like
Input data.
axis : int, optional
Axis along which to operate. By default flattened input is used.
out : array, optional
If provided, the result will be inserted into this array. It should
be of the appropriate shape and dtype.
.. versionadded:: 1.22.0
keepdims : bool, optional
If this is set to True, the axes which are reduced are left
in the result as dimensions with size one. With this option,
the result will broadcast correctly against the array.
.. versionadded:: 1.22.0
Returns
-------
index_array : ndarray
An array of indices or a single index value.
See Also
--------
argmin, nanargmax
Examples
--------
>>> a = np.array([[np.nan, 4], [2, 3]])
>>> np.argmin(a)
0
>>> np.nanargmin(a)
2
>>> np.nanargmin(a, axis=0)
array([1, 1])
>>> np.nanargmin(a, axis=1)
array([1, 0])
NrY r= rY r rZ )r5 r rf ri re
ValueErrorargminr rY r rZ r4 rm s r r r sp ^ 1bf%%GAt vd&&&
6$<< : 899 9
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=
=
=C Jr" c | fS rV rW ru s r _nanargmax_dispatcherr - rw r" c t | t j \ } }|9t j || }t j | rt d t j | ||| }|S )a
Return the indices of the maximum values in the specified axis ignoring
NaNs. For all-NaN slices ``ValueError`` is raised. Warning: the
results cannot be trusted if a slice contains only NaNs and -Infs.
Parameters
----------
a : array_like
Input data.
axis : int, optional
Axis along which to operate. By default flattened input is used.
out : array, optional
If provided, the result will be inserted into this array. It should
be of the appropriate shape and dtype.
.. versionadded:: 1.22.0
keepdims : bool, optional
If this is set to True, the axes which are reduced are left
in the result as dimensions with size one. With this option,
the result will broadcast correctly against the array.
.. versionadded:: 1.22.0
Returns
-------
index_array : ndarray
An array of indices or a single index value.
See Also
--------
argmax, nanargmin
Examples
--------
>>> a = np.array([[np.nan, 4], [2, 3]])
>>> np.argmax(a)
0
>>> np.nanargmax(a)
1
>>> np.nanargmax(a, axis=0)
array([1, 0])
>>> np.nanargmax(a, axis=1)
array([1, 1])
Nry r= rz )r5 r rf ri re r{ argmaxr} s r r
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| |fS rV rW r rY r r rZ r[ r( s r _nansum_dispatcherr j r] r" c ^ t | d \ } }t j | |||||| S )aR
Return the sum of array elements over a given axis treating Not a
Numbers (NaNs) as zero.
In NumPy versions <= 1.9.0 Nan is returned for slices that are all-NaN or
empty. In later versions zero is returned.
Parameters
----------
a : array_like
Array containing numbers whose sum is desired. If `a` is not an
array, a conversion is attempted.
axis : {int, tuple of int, None}, optional
Axis or axes along which the sum is computed. The default is to compute the
sum of the flattened array.
dtype : data-type, optional
The type of the returned array and of the accumulator in which the
elements are summed. By default, the dtype of `a` is used. An
exception is when `a` has an integer type with less precision than
the platform (u)intp. In that case, the default will be either
(u)int32 or (u)int64 depending on whether the platform is 32 or 64
bits. For inexact inputs, dtype must be inexact.
.. versionadded:: 1.8.0
out : ndarray, optional
Alternate output array in which to place the result. The default
is ``None``. If provided, it must have the same shape as the
expected output, but the type will be cast if necessary. See
:ref:`ufuncs-output-type` for more details. The casting of NaN to integer
can yield unexpected results.
.. versionadded:: 1.8.0
keepdims : bool, optional
If this is set to True, the axes which are reduced are left
in the result as dimensions with size one. With this option,
the result will broadcast correctly against the original `a`.
If the value is anything but the default, then
`keepdims` will be passed through to the `mean` or `sum` methods
of sub-classes of `ndarray`. If the sub-classes methods
does not implement `keepdims` any exceptions will be raised.
.. versionadded:: 1.8.0
initial : scalar, optional
Starting value for the sum. See `~numpy.ufunc.reduce` for details.
.. versionadded:: 1.22.0
where : array_like of bool, optional
Elements to include in the sum. See `~numpy.ufunc.reduce` for details.
.. versionadded:: 1.22.0
Returns
-------
nansum : ndarray.
A new array holding the result is returned unless `out` is
specified, in which it is returned. The result has the same
size as `a`, and the same shape as `a` if `axis` is not None
or `a` is a 1-d array.
See Also
--------
numpy.sum : Sum across array propagating NaNs.
isnan : Show which elements are NaN.
isfinite : Show which elements are not NaN or +/-inf.
Notes
-----
If both positive and negative infinity are present, the sum will be Not
A Number (NaN).
Examples
--------
>>> np.nansum(1)
1
>>> np.nansum([1])
1
>>> np.nansum([1, np.nan])
1.0
>>> a = np.array([[1, 1], [1, np.nan]])
>>> np.nansum(a)
3.0
>>> np.nansum(a, axis=0)
array([2., 1.])
>>> np.nansum([1, np.nan, np.inf])
inf
>>> np.nansum([1, np.nan, np.NINF])
-inf
>>> from numpy.testing import suppress_warnings
>>> with suppress_warnings() as sup:
... sup.filter(RuntimeWarning)
... np.nansum([1, np.nan, np.inf, -np.inf]) # both +/- infinity present
nan
r rY r r rZ r[ r( )r5 r sumr rY r r rZ r[ r( r4 s r r r o s@ F 1a GAt
6!$ex! 0 0 0 0r" c
| |fS rV rW r s r _nanprod_dispatcherr r] r" c ^ t | d \ } }t j | |||||| S )a
Return the product of array elements over a given axis treating Not a
Numbers (NaNs) as ones.
One is returned for slices that are all-NaN or empty.
.. versionadded:: 1.10.0
Parameters
----------
a : array_like
Array containing numbers whose product is desired. If `a` is not an
array, a conversion is attempted.
axis : {int, tuple of int, None}, optional
Axis or axes along which the product is computed. The default is to compute
the product of the flattened array.
dtype : data-type, optional
The type of the returned array and of the accumulator in which the
elements are summed. By default, the dtype of `a` is used. An
exception is when `a` has an integer type with less precision than
the platform (u)intp. In that case, the default will be either
(u)int32 or (u)int64 depending on whether the platform is 32 or 64
bits. For inexact inputs, dtype must be inexact.
out : ndarray, optional
Alternate output array in which to place the result. The default
is ``None``. If provided, it must have the same shape as the
expected output, but the type will be cast if necessary. See
:ref:`ufuncs-output-type` for more details. The casting of NaN to integer
can yield unexpected results.
keepdims : bool, optional
If True, the axes which are reduced are left in the result as
dimensions with size one. With this option, the result will
broadcast correctly against the original `arr`.
initial : scalar, optional
The starting value for this product. See `~numpy.ufunc.reduce`
for details.
.. versionadded:: 1.22.0
where : array_like of bool, optional
Elements to include in the product. See `~numpy.ufunc.reduce`
for details.
.. versionadded:: 1.22.0
Returns
-------
nanprod : ndarray
A new array holding the result is returned unless `out` is
specified, in which case it is returned.
See Also
--------
numpy.prod : Product across array propagating NaNs.
isnan : Show which elements are NaN.
Examples
--------
>>> np.nanprod(1)
1
>>> np.nanprod([1])
1
>>> np.nanprod([1, np.nan])
1.0
>>> a = np.array([[1, 2], [3, np.nan]])
>>> np.nanprod(a)
6.0
>>> np.nanprod(a, axis=0)
array([3., 2.])
r )r5 r prodr s r r r s@ R 1a GAt
714u#"% 1 1 1 1r" c
| |fS rV rW r rY r r s r _nancumsum_dispatcherr *
s8Or" c X t | d \ } }t j | ||| S )a
Return the cumulative sum of array elements over a given axis treating Not a
Numbers (NaNs) as zero. The cumulative sum does not change when NaNs are
encountered and leading NaNs are replaced by zeros.
Zeros are returned for slices that are all-NaN or empty.
.. versionadded:: 1.12.0
Parameters
----------
a : array_like
Input array.
axis : int, optional
Axis along which the cumulative sum is computed. The default
(None) is to compute the cumsum over the flattened array.
dtype : dtype, optional
Type of the returned array and of the accumulator in which the
elements are summed. If `dtype` is not specified, it defaults
to the dtype of `a`, unless `a` has an integer dtype with a
precision less than that of the default platform integer. In
that case, the default platform integer is used.
out : ndarray, optional
Alternative output array in which to place the result. It must
have the same shape and buffer length as the expected output
but the type will be cast if necessary. See :ref:`ufuncs-output-type` for
more details.
Returns
-------
nancumsum : ndarray.
A new array holding the result is returned unless `out` is
specified, in which it is returned. The result has the same
size as `a`, and the same shape as `a` if `axis` is not None
or `a` is a 1-d array.
See Also
--------
numpy.cumsum : Cumulative sum across array propagating NaNs.
isnan : Show which elements are NaN.
Examples
--------
>>> np.nancumsum(1)
array([1])
>>> np.nancumsum([1])
array([1])
>>> np.nancumsum([1, np.nan])
array([1., 1.])
>>> a = np.array([[1, 2], [3, np.nan]])
>>> np.nancumsum(a)
array([1., 3., 6., 6.])
>>> np.nancumsum(a, axis=0)
array([[1., 2.],
[4., 2.]])
>>> np.nancumsum(a, axis=1)
array([[1., 3.],
[3., 3.]])
r rY r r )r5 r cumsumr rY r r r4 s r r r . s1 | 1a GAt
9QTC 8 8 88r" c
| |fS rV rW r s r _nancumprod_dispatcherr p r r" c X t | d \ } }t j | ||| S )aM
Return the cumulative product of array elements over a given axis treating Not a
Numbers (NaNs) as one. The cumulative product does not change when NaNs are
encountered and leading NaNs are replaced by ones.
Ones are returned for slices that are all-NaN or empty.
.. versionadded:: 1.12.0
Parameters
----------
a : array_like
Input array.
axis : int, optional
Axis along which the cumulative product is computed. By default
the input is flattened.
dtype : dtype, optional
Type of the returned array, as well as of the accumulator in which
the elements are multiplied. If *dtype* is not specified, it
defaults to the dtype of `a`, unless `a` has an integer dtype with
a precision less than that of the default platform integer. In
that case, the default platform integer is used instead.
out : ndarray, optional
Alternative output array in which to place the result. It must
have the same shape and buffer length as the expected output
but the type of the resulting values will be cast if necessary.
Returns
-------
nancumprod : ndarray
A new array holding the result is returned unless `out` is
specified, in which case it is returned.
See Also
--------
numpy.cumprod : Cumulative product across array propagating NaNs.
isnan : Show which elements are NaN.
Examples
--------
>>> np.nancumprod(1)
array([1])
>>> np.nancumprod([1])
array([1])
>>> np.nancumprod([1, np.nan])
array([1., 1.])
>>> a = np.array([[1, 2], [3, np.nan]])
>>> np.nancumprod(a)
array([1., 2., 6., 6.])
>>> np.nancumprod(a, axis=0)
array([[1., 2.],
[3., 2.]])
>>> np.nancumprod(a, axis=1)
array([[1., 2.],
[3., 3.]])
r r )r5 r cumprodr s r r r t s1 v 1a GAt
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Compute the arithmetic mean along the specified axis, ignoring NaNs.
Returns the average of the array elements. The average is taken over
the flattened array by default, otherwise over the specified axis.
`float64` intermediate and return values are used for integer inputs.
For all-NaN slices, NaN is returned and a `RuntimeWarning` is raised.
.. versionadded:: 1.8.0
Parameters
----------
a : array_like
Array containing numbers whose mean is desired. If `a` is not an
array, a conversion is attempted.
axis : {int, tuple of int, None}, optional
Axis or axes along which the means are computed. The default is to compute
the mean of the flattened array.
dtype : data-type, optional
Type to use in computing the mean. For integer inputs, the default
is `float64`; for inexact inputs, it is the same as the input
dtype.
out : ndarray, optional
Alternate output array in which to place the result. The default
is ``None``; if provided, it must have the same shape as the
expected output, but the type will be cast if necessary. See
:ref:`ufuncs-output-type` for more details.
keepdims : bool, optional
If this is set to True, the axes which are reduced are left
in the result as dimensions with size one. With this option,
the result will broadcast correctly against the original `a`.
If the value is anything but the default, then
`keepdims` will be passed through to the `mean` or `sum` methods
of sub-classes of `ndarray`. If the sub-classes methods
does not implement `keepdims` any exceptions will be raised.
where : array_like of bool, optional
Elements to include in the mean. See `~numpy.ufunc.reduce` for details.
.. versionadded:: 1.22.0
Returns
-------
m : ndarray, see dtype parameter above
If `out=None`, returns a new array containing the mean values,
otherwise a reference to the output array is returned. Nan is
returned for slices that contain only NaNs.
See Also
--------
average : Weighted average
mean : Arithmetic mean taken while not ignoring NaNs
var, nanvar
Notes
-----
The arithmetic mean is the sum of the non-NaN elements along the axis
divided by the number of non-NaN elements.
Note that for floating-point input, the mean is computed using the same
precision the input has. Depending on the input data, this can cause
the results to be inaccurate, especially for `float32`. Specifying a
higher-precision accumulator using the `dtype` keyword can alleviate
this issue.
Examples
--------
>>> a = np.array([[1, np.nan], [3, 4]])
>>> np.nanmean(a)
2.6666666666666665
>>> np.nanmean(a, axis=0)
array([2., 4.])
>>> np.nanmean(a, axis=1)
array([1., 3.5]) # may vary
r NrY r r rZ r( +If a is inexact, then dtype must be inexact)If a is inexact, then out must be inexactrY r rZ r( r zMean of empty slicer` r? )r5 r meanr r- r. r/ TypeErrorr intprT re rD rE rF ) r rY r r rZ r( arrr4 cnttotavgisbads r r r s= ` Q""IC |wsUh "$ $ $ $
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Private function for rank 1 arrays. Compute the median ignoring NaNs.
See nanmedian for parameter usage
rH r )rL rC r median)rG rH arr1d_parseds r _nanmedian1dr " sS
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9\? C C CCr" c " | | j dk r;| }|t || S t || |d< |S | j | dk rt | ||| S t j t || | }|||d< |S )z
Private function that doesn't support extended axis or keepdims.
These methods are extended to this function using _ureduce
See nanmedian for parameter usage
Nr .iX )ndimravelr shape_nanmedian_smallr apply_along_axis)r rY r rH partresults r
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sort + indexing median, faster for small medians along multiple
dimensions due to the high overhead of apply_along_axis
see nanmedian for parameter usage
)rY rH r= r? mNaTN.)r ma masked_arrayr r range
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| |fS rV rW r rY r rH rZ s r _nanmedian_dispatcherr a r] r" c t j | } | j dk rt j | ||| S t j | t |||| S )a
Compute the median along the specified axis, while ignoring NaNs.
Returns the median of the array elements.
.. versionadded:: 1.9.0
Parameters
----------
a : array_like
Input array or object that can be converted to an array.
axis : {int, sequence of int, None}, optional
Axis or axes along which the medians are computed. The default
is to compute the median along a flattened version of the array.
A sequence of axes is supported since version 1.9.0.
out : ndarray, optional
Alternative output array in which to place the result. It must
have the same shape and buffer length as the expected output,
but the type (of the output) will be cast if necessary.
overwrite_input : bool, optional
If True, then allow use of memory of input array `a` for
calculations. The input array will be modified by the call to
`median`. This will save memory when you do not need to preserve
the contents of the input array. Treat the input as undefined,
but it will probably be fully or partially sorted. Default is
False. If `overwrite_input` is ``True`` and `a` is not already an
`ndarray`, an error will be raised.
keepdims : bool, optional
If this is set to True, the axes which are reduced are left
in the result as dimensions with size one. With this option,
the result will broadcast correctly against the original `a`.
If this is anything but the default value it will be passed
through (in the special case of an empty array) to the
`mean` function of the underlying array. If the array is
a sub-class and `mean` does not have the kwarg `keepdims` this
will raise a RuntimeError.
Returns
-------
median : ndarray
A new array holding the result. If the input contains integers
or floats smaller than ``float64``, then the output data-type is
``np.float64``. Otherwise, the data-type of the output is the
same as that of the input. If `out` is specified, that array is
returned instead.
See Also
--------
mean, median, percentile
Notes
-----
Given a vector ``V`` of length ``N``, the median of ``V`` is the
middle value of a sorted copy of ``V``, ``V_sorted`` - i.e.,
``V_sorted[(N-1)/2]``, when ``N`` is odd and the average of the two
middle values of ``V_sorted`` when ``N`` is even.
Examples
--------
>>> a = np.array([[10.0, 7, 4], [3, 2, 1]])
>>> a[0, 1] = np.nan
>>> a
array([[10., nan, 4.],
[ 3., 2., 1.]])
>>> np.median(a)
nan
>>> np.nanmedian(a)
3.0
>>> np.nanmedian(a, axis=0)
array([6.5, 2. , 2.5])
>>> np.median(a, axis=1)
array([nan, 2.])
>>> b = a.copy()
>>> np.nanmedian(b, axis=1, overwrite_input=True)
array([7., 2.])
>>> assert not np.all(a==b)
>>> b = a.copy()
>>> np.nanmedian(b, axis=None, overwrite_input=True)
3.0
>>> assert not np.all(a==b)
r r rZ )funcrZ rY r rH )r r) rC r r _ureducer r s r r
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d t j |d }t j | }t j | st d t | |||||| S )a
Compute the qth percentile of the data along the specified axis,
while ignoring nan values.
Returns the qth percentile(s) of the array elements.
.. versionadded:: 1.9.0
Parameters
----------
a : array_like
Input array or object that can be converted to an array, containing
nan values to be ignored.
q : array_like of float
Percentile or sequence of percentiles to compute, which must be
between 0 and 100 inclusive.
axis : {int, tuple of int, None}, optional
Axis or axes along which the percentiles are computed. The default
is to compute the percentile(s) along a flattened version of the
array.
out : ndarray, optional
Alternative output array in which to place the result. It must have
the same shape and buffer length as the expected output, but the
type (of the output) will be cast if necessary.
overwrite_input : bool, optional
If True, then allow the input array `a` to be modified by
intermediate calculations, to save memory. In this case, the
contents of the input `a` after this function completes is
undefined.
method : str, optional
This parameter specifies the method to use for estimating the
percentile. There are many different methods, some unique to NumPy.
See the notes for explanation. The options sorted by their R type
as summarized in the H&F paper [1]_ are:
1. 'inverted_cdf'
2. 'averaged_inverted_cdf'
3. 'closest_observation'
4. 'interpolated_inverted_cdf'
5. 'hazen'
6. 'weibull'
7. 'linear' (default)
8. 'median_unbiased'
9. 'normal_unbiased'
The first three methods are discontinuous. NumPy further defines the
following discontinuous variations of the default 'linear' (7.) option:
* 'lower'
* 'higher',
* 'midpoint'
* 'nearest'
.. versionchanged:: 1.22.0
This argument was previously called "interpolation" and only
offered the "linear" default and last four options.
keepdims : bool, optional
If this is set to True, the axes which are reduced are left in
the result as dimensions with size one. With this option, the
result will broadcast correctly against the original array `a`.
If this is anything but the default value it will be passed
through (in the special case of an empty array) to the
`mean` function of the underlying array. If the array is
a sub-class and `mean` does not have the kwarg `keepdims` this
will raise a RuntimeError.
interpolation : str, optional
Deprecated name for the method keyword argument.
.. deprecated:: 1.22.0
Returns
-------
percentile : scalar or ndarray
If `q` is a single percentile and `axis=None`, then the result
is a scalar. If multiple percentiles are given, first axis of
the result corresponds to the percentiles. The other axes are
the axes that remain after the reduction of `a`. If the input
contains integers or floats smaller than ``float64``, the output
data-type is ``float64``. Otherwise, the output data-type is the
same as that of the input. If `out` is specified, that array is
returned instead.
See Also
--------
nanmean
nanmedian : equivalent to ``nanpercentile(..., 50)``
percentile, median, mean
nanquantile : equivalent to nanpercentile, except q in range [0, 1].
Notes
-----
For more information please see `numpy.percentile`
Examples
--------
>>> a = np.array([[10., 7., 4.], [3., 2., 1.]])
>>> a[0][1] = np.nan
>>> a
array([[10., nan, 4.],
[ 3., 2., 1.]])
>>> np.percentile(a, 50)
nan
>>> np.nanpercentile(a, 50)
3.0
>>> np.nanpercentile(a, 50, axis=0)
array([6.5, 2. , 2.5])
>>> np.nanpercentile(a, 50, axis=1, keepdims=True)
array([[7.],
[2.]])
>>> m = np.nanpercentile(a, 50, axis=0)
>>> out = np.zeros_like(m)
>>> np.nanpercentile(a, 50, axis=0, out=out)
array([6.5, 2. , 2.5])
>>> m
array([6.5, 2. , 2.5])
>>> b = a.copy()
>>> np.nanpercentile(b, 50, axis=1, overwrite_input=True)
array([7., 2.])
>>> assert not np.all(a==b)
References
----------
.. [1] R. J. Hyndman and Y. Fan,
"Sample quantiles in statistical packages,"
The American Statistician, 50(4), pp. 361-365, 1996
Nr rI "a must be an array of real numbersg Y@z)Percentiles must be in the range [0, 100]) r _check_interpolation_as_methodr r) r r r true_divide_quantile_is_validr{ _nanquantile_uncheckedr s r r r s ^ = M?4 4
aAw|s<===
q% A
aA +A . . FDEEE ! 1dC &( < < >> a = np.array([[10., 7., 4.], [3., 2., 1.]])
>>> a[0][1] = np.nan
>>> a
array([[10., nan, 4.],
[ 3., 2., 1.]])
>>> np.quantile(a, 0.5)
nan
>>> np.nanquantile(a, 0.5)
3.0
>>> np.nanquantile(a, 0.5, axis=0)
array([6.5, 2. , 2.5])
>>> np.nanquantile(a, 0.5, axis=1, keepdims=True)
array([[7.],
[2.]])
>>> m = np.nanquantile(a, 0.5, axis=0)
>>> out = np.zeros_like(m)
>>> np.nanquantile(a, 0.5, axis=0, out=out)
array([6.5, 2. , 2.5])
>>> m
array([6.5, 2. , 2.5])
>>> b = a.copy()
>>> np.nanquantile(b, 0.5, axis=1, overwrite_input=True)
array([7., 2.])
>>> assert not np.all(a==b)
References
----------
.. [1] R. J. Hyndman and Y. Fan,
"Sample quantiles in statistical packages,"
The American Statistician, 50(4), pp. 361-365, 1996
Nr rI r z%Quantiles must be in the range [0, 1])
r r r r) r r r r r{ r r s r r r q s Z = M=2 2
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Private function that doesn't support extended axis or keepdims.
These methods are extended to this function using _ureduce
See nanpercentile for parameter usage
Nr r .)r r _nanquantile_1dr r moveaxis)r r rY r rH r r r s r r r % s |qv{{wwyy q/6BB$_dAq%4f> >
6Q;;[q11F
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Private function for rank 1 arrays. Compute quantile ignoring NaNs.
See nanpercentile for parameter usage
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S )aM
Compute the variance along the specified axis, while ignoring NaNs.
Returns the variance of the array elements, a measure of the spread of
a distribution. The variance is computed for the flattened array by
default, otherwise over the specified axis.
For all-NaN slices or slices with zero degrees of freedom, NaN is
returned and a `RuntimeWarning` is raised.
.. versionadded:: 1.8.0
Parameters
----------
a : array_like
Array containing numbers whose variance is desired. If `a` is not an
array, a conversion is attempted.
axis : {int, tuple of int, None}, optional
Axis or axes along which the variance is computed. The default is to compute
the variance of the flattened array.
dtype : data-type, optional
Type to use in computing the variance. For arrays of integer type
the default is `float64`; for arrays of float types it is the same as
the array type.
out : ndarray, optional
Alternate output array in which to place the result. It must have
the same shape as the expected output, but the type is cast if
necessary.
ddof : int, optional
"Delta Degrees of Freedom": the divisor used in the calculation is
``N - ddof``, where ``N`` represents the number of non-NaN
elements. By default `ddof` is zero.
keepdims : bool, optional
If this is set to True, the axes which are reduced are left
in the result as dimensions with size one. With this option,
the result will broadcast correctly against the original `a`.
where : array_like of bool, optional
Elements to include in the variance. See `~numpy.ufunc.reduce` for
details.
.. versionadded:: 1.22.0
Returns
-------
variance : ndarray, see dtype parameter above
If `out` is None, return a new array containing the variance,
otherwise return a reference to the output array. 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
--------
std : Standard deviation
mean : Average
var : Variance while not ignoring NaNs
nanstd, nanmean
:ref:`ufuncs-output-type`
Notes
-----
The variance is the average of the squared deviations from the mean,
i.e., ``var = mean(abs(x - x.mean())**2)``.
The mean is normally calculated as ``x.sum() / N``, where ``N = len(x)``.
If, however, `ddof` is specified, the divisor ``N - ddof`` is used
instead. In standard statistical practice, ``ddof=1`` provides an
unbiased estimator of the variance of a hypothetical infinite
population. ``ddof=0`` provides a maximum likelihood estimate of the
variance for normally distributed variables.
Note that for complex numbers, the absolute value is taken before
squaring, so that the result is always real and nonnegative.
For floating-point input, the variance is computed using the same
precision the input has. Depending on the input data, this can cause
the results to be inaccurate, especially for `float32` (see example
below). Specifying a higher-accuracy accumulator using the ``dtype``
keyword can alleviate this issue.
For this function to work on sub-classes of ndarray, they must define
`sum` with the kwarg `keepdims`
Examples
--------
>>> a = np.array([[1, np.nan], [3, 4]])
>>> np.nanvar(a)
1.5555555555555554
>>> np.nanvar(a, axis=0)
array([1., 0.])
>>> np.nanvar(a, axis=1)
array([0., 0.25]) # may vary
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Compute the standard deviation along the specified axis, while
ignoring NaNs.
Returns the standard deviation, a measure of the spread of a
distribution, of the non-NaN array elements. The standard deviation is
computed for the flattened array by default, otherwise over the
specified axis.
For all-NaN slices or slices with zero degrees of freedom, NaN is
returned and a `RuntimeWarning` is raised.
.. versionadded:: 1.8.0
Parameters
----------
a : array_like
Calculate the standard deviation of the non-NaN values.
axis : {int, tuple of int, None}, optional
Axis or axes along which the standard deviation is computed. The default is
to compute the standard deviation of the flattened array.
dtype : dtype, optional
Type to use in computing the standard deviation. For arrays of
integer type the default is float64, for arrays of float types it
is the same as the array type.
out : ndarray, optional
Alternative output array in which to place the result. It must have
the same shape as the expected output but the type (of the
calculated values) will be cast if necessary.
ddof : int, 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.
keepdims : bool, optional
If this is set to True, the axes which are reduced are left
in the result as dimensions with size one. With this option,
the result will broadcast correctly against the original `a`.
If this value is anything but the default it is passed through
as-is to the relevant functions of the sub-classes. If these
functions do not have a `keepdims` kwarg, a RuntimeError will
be raised.
where : array_like of bool, optional
Elements to include in the standard deviation.
See `~numpy.ufunc.reduce` for details.
.. versionadded:: 1.22.0
Returns
-------
standard_deviation : ndarray, see dtype parameter above.
If `out` is None, return a new array containing the standard
deviation, otherwise return a reference to the output array. 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
--------
var, mean, std
nanvar, nanmean
:ref:`ufuncs-output-type`
Notes
-----
The standard deviation is the square root of the average of the squared
deviations from the mean: ``std = sqrt(mean(abs(x - x.mean())**2))``.
The average squared deviation is normally calculated as
``x.sum() / N``, where ``N = len(x)``. If, however, `ddof` is
specified, the divisor ``N - ddof`` is used instead. In standard
statistical practice, ``ddof=1`` provides an unbiased estimator of the
variance of the infinite population. ``ddof=0`` provides a maximum
likelihood estimate of the variance for normally distributed variables.
The standard deviation computed in this function is the square root of
the estimated variance, so even with ``ddof=1``, it will not be an
unbiased estimate of the standard deviation per se.
Note that, for complex numbers, `std` takes the absolute value before
squaring, so that the result is always real and nonnegative.
For floating-point input, the *std* is computed using the same
precision the input has. Depending on the input data, this can cause
the results to be inaccurate, especially for float32 (see example
below). Specifying a higher-accuracy accumulator using the `dtype`
keyword can alleviate this issue.
Examples
--------
>>> a = np.array([[1, np.nan], [3, 4]])
>>> np.nanstd(a)
1.247219128924647
>>> np.nanstd(a, axis=0)
array([1., 0.])
>>> np.nanstd(a, axis=1)
array([0., 0.5]) # may vary
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