Functions that ignore NaN.
Functions
nanmin
-- minimum non-NaN valuenanmax
-- maximum non-NaN valuenanargmin
-- index of minimum non-NaN valuenanargmax
-- index of maximum non-NaN valuenansum
-- sum of non-NaN valuesnanprod
-- product of non-NaN valuesnancumsum
-- cumulative sum of non-NaN valuesnancumprod
-- cumulative product of non-NaN valuesnanmean
-- mean of non-NaN valuesnanvar
-- variance of non-NaN valuesnanstd
-- standard deviation of non-NaN valuesnanmedian
-- median of non-NaN valuesnanquantile
-- qth quantile of non-NaN valuesnanpercentile
-- qth percentile of non-NaN values
Function | nanargmax |
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. |
Function | nanargmin |
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. |
Function | nancumprod |
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. |
Function | nancumsum |
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. |
Function | nanmax |
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. |
Function | nanmean |
Compute the arithmetic mean along the specified axis, ignoring NaNs. |
Function | nanmedian |
Compute the median along the specified axis, while ignoring NaNs. |
Function | nanmin |
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. |
Function | nanpercentile |
Compute the qth percentile of the data along the specified axis, while ignoring nan values. |
Function | nanprod |
Return the product of array elements over a given axis treating Not a Numbers (NaNs) as ones. |
Function | nanquantile |
Compute the qth quantile of the data along the specified axis, while ignoring nan values. Returns the qth quantile(s) of the array elements. |
Function | nanstd |
Compute the standard deviation along the specified axis, while ignoring NaNs. |
Function | nansum |
Return the sum of array elements over a given axis treating Not a Numbers (NaNs) as zero. |
Function | nanvar |
Compute the variance along the specified axis, while ignoring NaNs. |
Variable | array |
Undocumented |
Function | _copyto |
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. |
Function | _divide |
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... |
Function | _nan |
No summary |
Function | _nanargmax |
Undocumented |
Function | _nanargmin |
Undocumented |
Function | _nancumprod |
Undocumented |
Function | _nancumsum |
Undocumented |
Function | _nanmax |
Undocumented |
Function | _nanmean |
Undocumented |
Function | _nanmedian |
Private function that doesn't support extended axis or keepdims. These methods are extended to this function using _ureduce See nanmedian for parameter usage |
Function | _nanmedian1d |
Private function for rank 1 arrays. Compute the median ignoring NaNs. See nanmedian for parameter usage |
Function | _nanmedian |
Undocumented |
Function | _nanmedian |
sort + indexing median, faster for small medians along multiple dimensions due to the high overhead of apply_along_axis |
Function | _nanmin |
Undocumented |
Function | _nanpercentile |
Undocumented |
Function | _nanprod |
Undocumented |
Function | _nanquantile |
Private function for rank 1 arrays. Compute quantile ignoring NaNs. See nanpercentile for parameter usage |
Function | _nanquantile |
Undocumented |
Function | _nanquantile |
Assumes that q is in [0, 1], and is an ndarray |
Function | _nanquantile |
Private function that doesn't support extended axis or keepdims. These methods are extended to this function using _ureduce See nanpercentile for parameter usage |
Function | _nanstd |
Undocumented |
Function | _nansum |
Undocumented |
Function | _nanvar |
Undocumented |
Function | _remove |
Equivalent to arr1d[~arr1d.isnan()], but in a different order |
Function | _replace |
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 ... |
def nanargmax(a, axis=None, out=None, *, keepdims=np._NoValue): (source) ¶
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.
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])
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.
New in version 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.
New in version 1.22.0.
|
Returns | |
ndarray | index_array - An array of indices or a single index value. |
def nanargmin(a, axis=None, out=None, *, keepdims=np._NoValue): (source) ¶
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.
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])
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.
New in version 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.
New in version 1.22.0.
|
Returns | |
ndarray | index_array - An array of indices or a single index value. |
def nancumprod(a, axis=None, dtype=None, out=None): (source) ¶
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.
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.]])
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 | |
ndarray | nancumprod - A new array holding the result is returned unless out is
specified, in which case it is returned. |
def nancumsum(a, axis=None, dtype=None, out=None): (source) ¶
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.
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.]])
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 | |
ndarray. | nancumsum - 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. |
def nanmax(a, axis=None, out=None, keepdims=np._NoValue, initial=np._NoValue, where=np._NoValue): (source) ¶
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.
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.
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
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.
New in version 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 If the value is anything but the default, then
New in version 1.8.0.
|
initial:scalar , optional | The minimum value of an output element. Must be present to allow
computation on empty slice. See
New in version 1.22.0.
|
where:array_like of bool , optional | Elements to compare for the maximum. See
New in version 1.22.0.
|
Returns | |
ndarray | nanmax - 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. |
def nanmean(a, axis=None, dtype=None, out=None, keepdims=np._NoValue, *, where=np._NoValue): (source) ¶
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.
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
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 If the value is anything but the default, then
|
where:array_like of bool , optional | Elements to include in the mean. See
New in version 1.22.0.
|
Returns | |
ndarray , see dtype parameter above | m - 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. |
def nanmedian(a, axis=None, out=None, overwrite_input=False, keepdims=np._NoValue): (source) ¶
Compute the median along the specified axis, while ignoring NaNs.
Returns the median of the array elements.
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)
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. |
overwritebool , 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 If this is anything but the default value it will be passed
through (in the special case of an empty array) to the
|
Returns | |
ndarray | median - 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. |
def nanmin(a, axis=None, out=None, keepdims=np._NoValue, initial=np._NoValue, where=np._NoValue): (source) ¶
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.
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.
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
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.
New in version 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 If the value is anything but the default, then
New in version 1.8.0.
|
initial:scalar , optional | The maximum value of an output element. Must be present to allow
computation on empty slice. See
New in version 1.22.0.
|
where:array_like of bool , optional | Elements to compare for the minimum. See
New in version 1.22.0.
|
Returns | |
ndarray | nanmin - 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. |
def nanpercentile(a, q, axis=None, out=None, overwrite_input=False, method='linear', keepdims=np._NoValue, *, interpolation=None): (source) ¶
Compute the qth percentile of the data along the specified axis, while ignoring nan values.
Returns the qth percentile(s) of the array elements.
See Also
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 |
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. |
overwritebool , 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:
The first three methods are discontinuous. NumPy further defines the following discontinuous variations of the default 'linear' (7.) option:
Changed in version 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 If this is anything but the default value it will be passed
through (in the special case of an empty array) to the
|
interpolation:str , optional | Deprecated name for the method keyword argument.
Deprecated since version 1.22.0.
|
Returns | |
scalar or ndarray | percentile - 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. |
def nanprod(a, axis=None, dtype=None, out=None, keepdims=np._NoValue, initial=np._NoValue, where=np._NoValue): (source) ¶
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.
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.])
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
New in version 1.22.0.
|
where:array_like of bool , optional | Elements to include in the product. See
New in version 1.22.0.
|
Returns | |
ndarray | nanprod - A new array holding the result is returned unless out is
specified, in which case it is returned. |
def nanquantile(a, q, axis=None, out=None, overwrite_input=False, method='linear', keepdims=np._NoValue, *, interpolation=None): (source) ¶
Compute the qth quantile of the data along the specified axis, while ignoring nan values. Returns the qth quantile(s) of the array elements.
See Also
nanmedian
- equivalent to nanquantile(..., 0.5)
nanpercentile
- same as nanquantile, but with q in the range [0, 100].
Notes
For more information please see numpy.quantile
Examples
>>> 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 |
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 | Quantile or sequence of quantiles to compute, which must be between 0 and 1 inclusive. |
axis:{int, tuple of int , None}, optional | Axis or axes along which the quantiles are computed. The default is to compute the quantile(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. |
overwritebool , 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 quantile. 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:
The first three methods are discontinuous. NumPy further defines the following discontinuous variations of the default 'linear' (7.) option:
Changed in version 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 If this is anything but the default value it will be passed
through (in the special case of an empty array) to the
|
interpolation:str , optional | Deprecated name for the method keyword argument.
Deprecated since version 1.22.0.
|
Returns | |
scalar or ndarray | quantile - If q is a single percentile and axis=None , then the result
is a scalar. If multiple quantiles are given, first axis of
the result corresponds to the quantiles. 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. |
def nanstd(a, axis=None, dtype=None, out=None, ddof=0, keepdims=np._NoValue, *, where=np._NoValue): (source) ¶
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.
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
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 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 |
where:array_like of bool , optional | Elements to include in the standard deviation.
See
New in version 1.22.0.
|
Returns | |
ndarray , see dtype parameter above. | standard_deviation - 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. |
def nansum(a, axis=None, dtype=None, out=None, keepdims=np._NoValue, initial=np._NoValue, where=np._NoValue): (source) ¶
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
def nanvar(a, axis=None, dtype=None, out=None, ddof=0, keepdims=np._NoValue, *, where=np._NoValue): (source) ¶
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.
See Also
std
- Standard deviation
mean
- Average
var
- Variance while not ignoring NaNs
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
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
New in version 1.22.0.
|
Returns | |
ndarray , see dtype parameter above | variance - 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. |
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 | |
ndarray , scalar | res - Array with elements replaced or scalar val . |
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 | |
{ndarray, numpy scalar} | ret - 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. |
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 | |
bool ndarray or True | y - 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. |
Undocumented
Private function that doesn't support extended axis or keepdims. These methods are extended to this function using _ureduce See nanmedian for parameter usage
Private function for rank 1 arrays. Compute the median ignoring NaNs. See nanmedian for parameter usage
sort + indexing median, faster for small medians along multiple dimensions due to the high overhead of apply_along_axis
see nanmedian for parameter usage
Undocumented
Undocumented
Private function for rank 1 arrays. Compute quantile ignoring NaNs. See nanpercentile for parameter usage
Undocumented
Assumes that q is in [0, 1], and is an ndarray
Private function that doesn't support extended axis or keepdims. These methods are extended to this function using _ureduce See nanpercentile for parameter usage
Undocumented
Undocumented
Undocumented
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 |
overwritebool | True if arr1d can be modified in place |
Returns | |
|
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 | |