Search
Searching
- numpy.ma.masked_array.data
...numpy.ma.masked_array.data...
- numpy.ma.masked_array.data (Python property, in numpy.ma.masked_array.data)
- Constants of the
numpy.mamodule...module defines several constants. numpy.ma.masked The masked constant is a special case of MaskedArray, with a float datatype and a null shape. It is used to test whether a specific entry of a masked array is masked, or to mask one or se...
- Interoperability with NumPy
...Interoperability with NumPy NumPy’s ndarray objects provide both a high-level API for operations on array-structured data and a concrete implementation of the API based on strided in-RAM storage. While this API is powerful and fairly gen...
- NumPy 1.10.0 Release Notes
...can now be used with periodic functions np.interp now has a new parameter period that supplies the period of the input data xp. In such case, the input data is properly normalized to the given period and one end point is added to each extr...
- NumPy 1.11.0 Release Notes
...s a dtype type of different size causing the first dimension to change. Slicing a MaskedArray will return views of both data and mask. Currently the mask is copy-on-write and changes to the mask in the slice do not propagate to the original...
- NumPy 1.12.0 Release Notes
...preserved, instead of being reordered to match that of the directories. Deprecations Assignment of ndarray object’s data attribute Assigning the ‘data’ attribute is an inherently unsafe operation as pointed out in gh-7083. Such a capabi...
- NumPy 1.13.0 Release Notes
...ng numpy.unique. The original behaviour is recovered if axis=None (default). np.gradient now supports unevenly spaced data Users can now specify a not-constant spacing for data. In particular np.gradient can now take: A single scalar to...
- NumPy 1.14.0 Release Notes
...strings and – after encoding them as either utf-8 (python 3) or the default encoding (python 2) – treats them as binary data. If reading binary data is desired, np.frombuffer should be used directly. The style option of array2string is depr...
- NumPy 1.15.0 Release Notes
...l accept NaN values when explicit bins are given Previously it would fail when trying to compute a finite range for the data. Since the range is ignored anyway when the bins are given explicitly, this error was needless. Note that calling h...
- NumPy 1.18.0 Release Notes
...te hypergeometric probability distribution. (gh-13794) Deprecations np.fromfile and np.fromstring will error on bad data In future numpy releases, the functions np.fromfile and np.fromstring will throw an error when parsing bad data. Th...
- NumPy 1.22.0 Release Notes
...ns such as CuPy and JAX. NumPy now has a DLPack backend. DLPack provides a common interchange format for array (tensor) data. New methods for quantile, percentile, and related functions. The new methods provide a complete set of the methods...
- NumPy 1.23.0 Release Notes
...s are: Implementation of loadtxt in C, greatly improving its performance. Exposing DLPack at the Python level for easy data exchange. Changes to the promotion and comparisons of structured dtypes. Improvements to f2py. See below for the d...
- NumPy 1.24 Release Notes
...int8).view(np.uint8). In some cases, using np.iinfo(np.uint8).max or val % 2**8 may also work well. In rare cases input data may mix both negative values and very large unsigned values (i.e. -1 and 2**63). There it is unfortunately necessa...
- NumPy 1.25.0 Release Notes
...output dtype using the dtype kwarg can make the operation succeed, but we do not recommend it. (gh-22707) When loading data from a file handle using np.load, if the handle is at the end of file, as can happen when reading multiple arrays b...
- NumPy 1.6.0 Release Notes
...tures New 16-bit floating point type This release adds support for the IEEE 754-2008 binary16 format, available as the data type numpy.half. Within Python, the type behaves similarly to float or double, and C extensions can add support fo...
- NumPy 1.7.2 Release Notes
...of mmap slicing gh-3421: fix non-swapping strided copies in GetStridedCopySwap gh-3373: fix small leak in datetime metadata initialization gh-2791: add platform specific python include directories to search paths gh-3168: fix undefined fun...
- NumPy 1.9.0 Release Notes
...nient broadcasting to arrays of the original shape. Dtype parameter added to np.linspace and np.logspace The returned data type from the linspace and logspace functions can now be specified using the dtype parameter. More general np.tri...
- NumPy 1.9.2 Release Notes
...arrays #5612: Fixes ndarray.fill to accept full range of uint64 #5155: Fix loadtxt with comments=None and a string None data #4476: Masked array view fails if structured dtype has datetime component #5388: Make RandomState.set_state and Ran...
- NumPy 2.0.0 Release Notes
...vior was changed by adopting NEP 50. This fixes many user surprises about promotions which previously often depended on data values of input arrays rather than only their dtypes. Please see the NEP and the NumPy 2.0 migration guide for det...
- NumPy 2.1.0 Release Notes
...extension as compatible. (gh-26981) Improvements histogram auto-binning now returns bin sizes >=1 for integer input data For integer input data, bin sizes smaller than 1 result in spurious empty bins. This is now avoided when the numbe...
- numpy.average
...ms=<no value>)[source] Compute the weighted average along the specified axis. Parameters: aarray_likeArray containing data to be averaged. If a is not an array, a conversion is attempted. axisNone or int or tuple of ints, optionalAxis or...
- numpy.concat
...ith out. New in version 1.20.0. casting{‘no’, ‘equiv’, ‘safe’, ‘same_kind’, ‘unsafe’}, optionalControls what kind of data casting may occur. Defaults to ‘same_kind’. For a description of the options, please see casting. New in version 1...
- numpy.concatenate
...ith out. New in version 1.20.0. casting{‘no’, ‘equiv’, ‘safe’, ‘same_kind’, ‘unsafe’}, optionalControls what kind of data casting may occur. Defaults to ‘same_kind’. For a description of the options, please see casting. New in version 1...
- numpy.ma.allclose
...tolerances. Examples >>> import numpy as np >>> a = np.ma.array([1e10, 1e-7, 42.0], mask=[0, 0, 1]) >>> a masked_array(data=[10000000000.0, 1e-07, --], mask=[False, False, True], fill_value=1e+20) >>> b = np.ma.array([...
- numpy.ma.allequal
...a.allclose Examples >>> import numpy as np >>> a = np.ma.array([1e10, 1e-7, 42.0], mask=[0, 0, 1]) >>> a masked_array(data=[10000000000.0, 1e-07, --], mask=[False, False, True], fill_value=1e+20) >>> b = np.array([1...
- numpy.ma.anom
...ompute the mean of the array. Examples >>> import numpy as np >>> a = np.ma.array([1,2,3]) >>> a.anom() masked_array(data=[-1., 0., 1.], mask=False, fill_value=1e+20)...
- numpy.ma.anomalies
...ompute the mean of the array. Examples >>> import numpy as np >>> a = np.ma.array([1,2,3]) >>> a.anom() masked_array(data=[-1., 0., 1.], mask=False, fill_value=1e+20)...
- numpy.ma.append
...a = ma.masked_values([1, 2, 3], 2) >>> b = ma.masked_values([[4, 5, 6], [7, 8, 9]], 7) >>> ma.append(a, b) masked_array(data=[1, --, 3, 4, 5, 6, --, 8, 9], mask=[False, True, False, False, False, False, True, False,...
- numpy.ma.apply_over_axes
...p >>> a = np.ma.arange(24).reshape(2,3,4) >>> a[:,0,1] = np.ma.masked >>> a[:,1,:] = np.ma.masked >>> a masked_array( data=[[[0, --, 2, 3], [--, --, --, --], [8, 9, 10, 11]], [[12, --, 14, 15], [--, --,...
- numpy.ma.arange
...rgument, start must also be given. dtypedtype, optionalThe type of the output array. If dtype is not given, infer the data type from the other input arguments. devicestr, optionalThe device on which to place the created array. Default: N...
- numpy.ma.argmin
..._valuescalar or None, optionalValue used to fill in the masked values. If None, the output of minimum_fill_value(self._data) is used instead. out{None, array}, optionalArray into which the result can be placed. Its type is preserved and i...
- numpy.ma.argsort
...argest values (True) or the smallest values (False) When the array contains unmasked values at the same extremes of the datatype, the ordering of these values and the masked values is undefined. fill_valuescalar or None, optionalValue used...
- numpy.ma.array
...numpy.ma.array ma.array(data, dtype=None, copy=False, order=None, mask=np.False_, fill_value=None, keep_mask=True, hard_mask=False, shrink=True,...
- numpy.ma.asanyarray
...dArray, its class is conserved. No copy is performed if the input is already an ndarray. Parameters: aarray_likeInput data, in any form that can be converted to an array. dtypedtype, optionalBy default, the data-type is inferred from the...
- numpy.ma.asarray
...numpy.ma.asarray ma.asarray(a, dtype=None, order=None)[source] Convert the input to a masked array of the given data-type. No copy is performed if the input is already an ndarray. If a is a subclass of MaskedArray, a base class Mask...
- numpy.ma.average
...lse, *, keepdims=<no value>)[source] Return the weighted average of array over the given axis. Parameters: aarray_likeData to be averaged. Masked entries are not taken into account in the computation. axisNone or int or tuple of ints, op...
- numpy.ma.choose
...>>> choice = np.array([[1,1,1], [2,2,2], [3,3,3]]) >>> a = np.array([2, 1, 0]) >>> np.ma.choose(a, choice) masked_array(data=[3, 2, 1], mask=False, fill_value=999999)...
- numpy.ma.clump_masked
...NumPy reference NumPy’s module structure Masked array operations numpy.ma.clump_masked...
- numpy.ma.clump_unmasked
...NumPy reference NumPy’s module structure Masked array operations numpy.ma.clump_unmasked...
- numpy.ma.compress_rowcols
...[1, 0, 0], ... [0, 0, 0]]) >>> x masked_array( data=[[--, 1, 2], [--, 4, 5], [6, 7, 8]], mask=[[ True, False, False], [ True, False, False],...
- numpy.ma.compressed
...numpy.ma.compressed ma.compressed(x)[source] Return all the non-masked data as a 1-D array. This function is equivalent to calling the “compressed” method of a ma.MaskedArray, see ma.MaskedAr...
- numpy.ma.concatenate
...py as np >>> import numpy.ma as ma >>> a = ma.arange(3) >>> a[1] = ma.masked >>> b = ma.arange(2, 5) >>> a masked_array(data=[0, --, 2], mask=[False, True, False], fill_value=999999) >>> b masked_array(data=[2, 3, 4],...
- numpy.ma.corrcoef
...ue, ddof=<no value>)[source] Return Pearson product-moment correlation coefficients. Except for the handling of missing data this function does the same as numpy.corrcoef. For more details and examples, see numpy.corrcoef. Parameters: xar...
- numpy.ma.correlate
...ion: >>> a = np.ma.array([1, 2, 3]) >>> v = np.ma.array([0, 1, 0]) >>> np.ma.correlate(a, v, mode='valid') masked_array(data=[2], mask=[False], fill_value=999999) Correlation with masked elements: >>> a = np.ma.array([...
- numpy.ma.count
...Examples >>> import numpy.ma as ma >>> a = ma.arange(6).reshape((2, 3)) >>> a[1, :] = ma.masked >>> a masked_array( data=[[0, 1, 2], [--, --, --]], mask=[[False, False, False], [ True, True, True]], fill_value=9999...
- numpy.ma.count_masked
...= np.ma.array(a) >>> a[1, 0] = np.ma.masked >>> a[1, 2] = np.ma.masked >>> a[2, 1] = np.ma.masked >>> a masked_array( data=[[0, 1, 2], [--, 4, --], [6, --, 8]], mask=[[False, False, False], [ True, False, True],...
- numpy.ma.cov
...e, bias=False, allow_masked=True, ddof=None)[source] Estimate the covariance matrix. Except for the handling of missing data this function does the same as numpy.cov. For more details and examples, see numpy.cov. By default, masked values a...
- numpy.ma.cumsum
...>>> import numpy as np >>> marr = np.ma.array(np.arange(10), mask=[0,0,0,1,1,1,0,0,0,0]) >>> marr.cumsum() masked_array(data=[0, 1, 3, --, --, --, 9, 16, 24, 33], mask=[False, False, False, True, True, True, False, False,...
- numpy.ma.default_fill_value
...fill_value(obj)[source] Return the default fill value for the argument object. The default filling value depends on the datatype of the input array or the type of the input scalar: datatype default bool True int 999999 float 1.e20...
- numpy.ma.diag
..., 18], [0.801, -1.41, 12], [7, 33, -12]]) >>> masked_x = np.ma.masked_array(x, mask=x < 0) >>> masked_x masked_array( data=[[11.2, --, 18.0], [0.801, --, 12.0], [7.0, 33.0, --]], mask=[[False, True, False], [Fal...
- numpy.ma.diagflat
...on_single object> Create a two-dimensional array with the flattened input as a diagonal. Parameters: varray_likeInput data, which is flattened and set as the k-th diagonal of the output. kint, optionalDiagonal to set; 0, the default, cor...
- numpy.ma.diff
...calculating the difference directly: >>> u8_arr = np.array([1, 0], dtype=np.uint8) >>> np.ma.diff(u8_arr) masked_array(data=[255], mask=False, fill_value=np.uint64(999999), dtype=uint8) >>> u8_arr[1,...] - u...
- numpy.ma.dot
...some point that may be mandatory. Parameters: a, bmasked_array_likeInputs arrays. strictbool, optionalWhether masked data are propagated (True) or set to 0 (False) for the computation. Default is False. Propagating the mask means that i...
- numpy.ma.dstack
...ds arrays divided by dsplit. This function makes most sense for arrays with up to 3 dimensions. For instance, for pixel-data with a height (first axis), width (second axis), and r/g/b channels (third axis). The functions concatenate, stack...
- numpy.ma.ediff1d
...ndarrays. Examples >>> import numpy as np >>> arr = np.ma.array([1, 2, 4, 7, 0]) >>> np.ma.ediff1d(arr) masked_array(data=[ 1, 2, 3, -7], mask=False, fill_value=999999)...
- numpy.ma.empty
...without initializing entries. Parameters: shapeint or tuple of intShape of the empty array, e.g., (2, 3) or 2. dtypedata-type, optionalDesired output data-type for the array, e.g, numpy.int8. Default is numpy.float64. order{‘C’, ‘F’},...
- numpy.ma.empty_like
...bject> Return a new array with the same shape and type as a given array. Parameters: prototypearray_likeThe shape and data-type of prototype define these same attributes of the returned array. dtypedata-type, optionalOverrides the data t...
- numpy.ma.filled
...ced by fill_value. If a is not a MaskedArray, a itself is returned. If a is a MaskedArray with no masked values, then a.data is returned. If a is a MaskedArray and fill_value is None, fill_value is set to a.fill_value. Parameters: aMasked...
- numpy.ma.fix_invalid
...numpy.ma.fix_invalid ma.fix_invalid(a, mask=np.False_, copy=True, fill_value=None)[source] Return input with invalid data masked and replaced by a fill value. Invalid data means values of nan, inf, etc. Parameters: aarray_likeInput arr...
- numpy.ma.flatnotmasked_contiguous
...numpy.ma.flatnotmasked_contiguous ma.flatnotmasked_contiguous(a)[source] Find contiguous unmasked data in a masked array. Parameters: aarray_likeThe input array. Returns: slice_listlistA sorted sequence of slice...
- numpy.ma.flatten_structured_array
...numpy.ma.flatten_structured_array ma.flatten_structured_array(a)[source] Flatten a structured array. The data type of the output is chosen such that it can represent all of the (nested) fields. Parameters: astructured array...
- numpy.ma.frombuffer
...et a buffer as a 1-dimensional array. Parameters: bufferbuffer_likeAn object that exposes the buffer interface. dtypedata-type, optionalData-type of the returned array; default: float. countint, optionalNumber of items to read. -1 means...
- numpy.ma.fromflex
...ma.fromflex(fxarray)[source] Build a masked array from a suitable flexible-type array. The input array has to have a data-type with _data and _mask fields. This type of array is output by MaskedArray.toflex. Parameters: fxarrayndarrayT...
- numpy.ma.fromfunction
...le of intsShape of the output array, which also determines the shape of the coordinate arrays passed to function. dtypedata-type, optionalData-type of the coordinate arrays passed to function. By default, dtype is float. likearray_like, o...
- numpy.ma.getdata
...NumPy reference NumPy’s module structure Masked array operations numpy.ma.getdata...
- numpy.ma.getmask
...pe as a, use getmaskarray. Parameters: aarray_likeInput MaskedArray for which the mask is required. See also getdataReturn the data of a masked array as an ndarray. getmaskarrayReturn the mask of a masked array, or full array of Fa...
- numpy.ma.getmaskarray
...put MaskedArray for which the mask is required. See also getmaskReturn the mask of a masked array, or nomask. getdataReturn the data of a masked array as an ndarray. Examples >>> import numpy as np >>> import numpy.ma as ma >>> a...
- numpy.ma.hstack
...ds arrays divided by hsplit. This function makes most sense for arrays with up to 3 dimensions. For instance, for pixel-data with a height (first axis), width (second axis), and r/g/b channels (third axis). The functions concatenate, stack...
- numpy.ma.identity
...is a square array with ones on the main diagonal. Parameters: nintNumber of rows (and columns) in n x n output. dtypedata-type, optionalData-type of the output. Defaults to float. likearray_like, optionalReference object to allow the c...
- numpy.ma.in1d
...es >>> import numpy as np >>> ar1 = np.ma.array([0, 1, 2, 5, 0]) >>> ar2 = [0, 2] >>> np.ma.in1d(ar1, ar2) masked_array(data=[ True, False, True, False, True], mask=False, fill_value=True)...
- numpy.ma.indices
...only along the corresponding axis. Parameters: dimensionssequence of intsThe shape of the grid. dtypedtype, optionalData type of the result. sparseboolean, optionalReturn a sparse representation of the grid instead of a dense represent...
- numpy.ma.intersect1d
...3, 3], mask=[0, 0, 0, 1]) >>> y = np.ma.array([3, 1, 1, 1], mask=[0, 0, 0, 1]) >>> np.ma.intersect1d(x, y) masked_array(data=[1, 3, --], mask=[False, False, True], fill_value=999999)...
- numpy.ma.is_mask
...xamples >>> import numpy as np >>> import numpy.ma as ma >>> m = ma.masked_equal([0, 1, 0, 2, 3], 0) >>> m masked_array(data=[--, 1, --, 2, 3], mask=[ True, False, True, False, False], fill_value=0) >>> ma.is_mask(m) Fa...
- numpy.ma.is_masked
...xamples >>> import numpy as np >>> import numpy.ma as ma >>> x = ma.masked_equal([0, 1, 0, 2, 3], 0) >>> x masked_array(data=[--, 1, --, 2, 3], mask=[ True, False, True, False, False], fill_value=0) >>> ma.is_masked(x)...
- numpy.ma.isarray
...[[ 1., 0., 0.], [ 0., 1., 0.], [ 0., 0., 1.]]) >>> m = ma.masked_values(a, 0) >>> m masked_array( data=[[1.0, --, --], [--, 1.0, --], [--, --, 1.0]], mask=[[False, True, True], [ True, False...
- numpy.ma.isin
...lement = np.ma.array([1, 2, 3, 4, 5, 6]) >>> test_elements = [0, 2] >>> np.ma.isin(element, test_elements) masked_array(data=[False, True, False, False, False, False], mask=False, fill_value=True)...
- numpy.ma.isMA
...[[ 1., 0., 0.], [ 0., 1., 0.], [ 0., 0., 1.]]) >>> m = ma.masked_values(a, 0) >>> m masked_array( data=[[1.0, --, --], [--, 1.0, --], [--, --, 1.0]], mask=[[False, True, True], [ True, False...
- numpy.ma.isMaskedArray
...[[ 1., 0., 0.], [ 0., 1., 0.], [ 0., 0., 1.]]) >>> m = ma.masked_values(a, 0) >>> m masked_array( data=[[1.0, --, --], [--, 1.0, --], [--, --, 1.0]], mask=[[False, True, True], [ True, False...
- numpy.ma.left_shift
...masked array: >>> arr = np.ma.array([10, 20, 30], mask=[False, True, False]) >>> np.ma.left_shift(arr, 1) masked_array(data=[20, --, 60], mask=[False, True, False], fill_value=999999) Large shift: >>> np.ma.left_shif...
- numpy.ma.make_mask
...m itself (False). shrinkbool, optionalWhether to shrink m to nomask if all its values are False. dtypedtype, optionalData-type of the output mask. By default, the output mask has a dtype of MaskType (bool). If the dtype is flexible, each...
- numpy.ma.mask_cols
...= 1 >>> a array([[0, 0, 0], [0, 1, 0], [0, 0, 0]]) >>> a = np.ma.masked_equal(a, 1) >>> a masked_array( data=[[0, 0, 0], [0, --, 0], [0, 0, 0]], mask=[[False, False, False], [False, True, False],...
- numpy.ma.mask_rowcols
...= 1 >>> a array([[0, 0, 0], [0, 1, 0], [0, 0, 0]]) >>> a = np.ma.masked_equal(a, 1) >>> a masked_array( data=[[0, 0, 0], [0, --, 0], [0, 0, 0]], mask=[[False, False, False], [False, True, False],...
- numpy.ma.mask_rows
...= 1 >>> a array([[0, 0, 0], [0, 1, 0], [0, 0, 0]]) >>> a = np.ma.masked_equal(a, 1) >>> a masked_array( data=[[0, 0, 0], [0, --, 0], [0, 0, 0]], mask=[[False, False, False], [False, True, False],...
- numpy.ma.masked_all
...] Empty masked array with all elements masked. Return an empty masked array of the given shape and dtype, where all the data are masked. Parameters: shapeint or tuple of intsShape of the required MaskedArray, e.g., (2, 3) or 2. dtypedtyp...
- numpy.ma.masked_all_like
...operties of an existing array. Return an empty masked array of the same shape and dtype as the array arr, where all the data are masked. Parameters: arrndarrayAn array describing the shape and dtype of the required MaskedArray. Returns...
- numpy.ma.masked_array
...NumPy reference NumPy’s module structure Masked array operations numpy.ma.masked_array...
- numpy.ma.masked_array.all
...numpy.ma.masked_array.all...
- numpy.ma.masked_array.anom
...ompute the mean of the array. Examples >>> import numpy as np >>> a = np.ma.array([1,2,3]) >>> a.anom() masked_array(data=[-1., 0., 1.], mask=False, fill_value=1e+20)...
- numpy.ma.masked_array.any
...numpy.ma.masked_array.any...
- numpy.ma.masked_array.argmax
..._valuescalar or None, optionalValue used to fill in the masked values. If None, the output of maximum_fill_value(self._data) is used instead. out{None, array}, optionalArray into which the result can be placed. Its type is preserved and i...
- numpy.ma.masked_array.argmin
..._valuescalar or None, optionalValue used to fill in the masked values. If None, the output of minimum_fill_value(self._data) is used instead. out{None, array}, optionalArray into which the result can be placed. Its type is preserved and i...
- numpy.ma.masked_array.argpartition
...numpy.ma.masked_array.argpartition...
- numpy.ma.masked_array.argsort
...argest values (True) or the smallest values (False) When the array contains unmasked values at the same extremes of the datatype, the ordering of these values and the masked values is undefined. fill_valuescalar or None, optionalValue used...
- numpy.ma.masked_array.astype
...unsafe', subok=True, copy=True) Copy of the array, cast to a specified type. Parameters: dtypestr or dtypeTypecode or data-type to which the array is cast. order{‘C’, ‘F’, ‘A’, ‘K’}, optionalControls the memory layout order of the result...
- numpy.ma.masked_array.base
...numpy.ma.masked_array.base...
- numpy.ma.masked_array.baseclass
...numpy.ma.masked_array.baseclass property property ma.masked_array.baseclass Class of the underlying data (read-only)....
- numpy.ma.masked_array.byteswap
...ma.masked_array.byteswap(inplace=False) Swap the bytes of the array elements Toggle between low-endian and big-endian data representation by returning a byteswapped array, optionally swapped in-place. Arrays of byte-strings are not swappe...
- numpy.ma.masked_array.choose
...numpy.ma.masked_array.choose...
- numpy.ma.masked_array.clip
...numpy.ma.masked_array.clip...
- numpy.ma.masked_array.compress
...xamples >>> import numpy as np >>> x = np.ma.array([[1,2,3],[4,5,6],[7,8,9]], mask=[0] + [1,0]*4) >>> x masked_array( data=[[1, --, 3], [--, 5, --], [7, --, 9]], mask=[[False, True, False], [ True, False, True]...
- numpy.ma.masked_array.compressed
...numpy.ma.masked_array.compressed method ma.masked_array.compressed()[source] Return all the non-masked data as a 1-D array. Returns: datandarrayA new ndarray holding the non-masked data is returned. Notes The result i...
- numpy.ma.masked_array.conj
...numpy.ma.masked_array.conj...
- numpy.ma.masked_array.conjugate
...numpy.ma.masked_array.conjugate...
- numpy.ma.masked_array.copy
...numpy.ma.masked_array.copy...
- numpy.ma.masked_array.count
...Examples >>> import numpy.ma as ma >>> a = ma.arange(6).reshape((2, 3)) >>> a[1, :] = ma.masked >>> a masked_array( data=[[0, 1, 2], [--, --, --]], mask=[[False, False, False], [ True, True, True]], fill_value=9999...
- numpy.ma.masked_array.ctypes
...es it easier to use arrays when calling shared libraries with the ctypes module. The returned object has, among others, data, shape, and strides attributes (see Notes below) which themselves return ctypes objects that can be used as argumen...
- numpy.ma.masked_array.cumprod
...numpy.ma.masked_array.cumprod...
- numpy.ma.masked_array.cumsum
...>>> import numpy as np >>> marr = np.ma.array(np.arange(10), mask=[0,0,0,1,1,1,0,0,0,0]) >>> marr.cumsum() masked_array(data=[0, 1, 3, --, --, --, 9, 16, 24, 33], mask=[False, False, False, True, True, True, False, False,...
- numpy.ma.masked_array.data
...numpy.ma.masked_array.data...
- numpy.ma.masked_array.device
...numpy.ma.masked_array.device...
- numpy.ma.masked_array.diagonal
...numpy.ma.masked_array.diagonal...
- numpy.ma.masked_array.dot
...nditions are not met, an exception is raised, instead of attempting to be flexible. strictbool, optionalWhether masked data are propagated (True) or set to 0 (False) for the computation. Default is False. Propagating the mask means that i...
- numpy.ma.masked_array.dtype
...numpy.ma.masked_array.dtype property property ma.masked_array.dtype Data-type of the array’s elements. Warning Setting arr.dtype is discouraged and may be deprecated in the future. Setti...
- numpy.ma.masked_array.dump
...numpy.ma.masked_array.dump...
- numpy.ma.masked_array.dumps
...numpy.ma.masked_array.dumps...
- numpy.ma.masked_array.fill
...numpy.ma.masked_array.fill...
- numpy.ma.masked_array.fill_value
...ray.fill_value The filling value of the masked array is a scalar. When setting, None will set to a default based on the data type. Examples >>> import numpy as np >>> for dt in [np.int32, np.int64, np.float64, np.complex128]: ... np.ma....
- numpy.ma.masked_array.filled
...00, 4, 1000]) >>> type(x.filled()) <class 'numpy.ndarray'> Subclassing is preserved. This means that if, e.g., the data part of the masked array is a recarray, filled returns a recarray: >>> x = np.array([(-1, 2), (-3, 4)], dtype='i8,i...
- numpy.ma.masked_array.flags
- numpy.ma.masked_array.flat
- numpy.ma.masked_array.flatten
- numpy.ma.masked_array.get_fill_value
- numpy.ma.masked_array.get_imag
- numpy.ma.masked_array.get_real
- numpy.ma.masked_array.getfield
- numpy.ma.masked_array.harden_mask
- numpy.ma.masked_array.hardmask
- numpy.ma.masked_array.ids
- numpy.ma.masked_array.imag
- numpy.ma.masked_array.iscontiguous
- numpy.ma.masked_array.item
- numpy.ma.masked_array.itemset
- numpy.ma.masked_array.itemsize
- numpy.ma.masked_array.mask
- numpy.ma.masked_array.max
- numpy.ma.masked_array.mean
- numpy.ma.masked_array.min
- numpy.ma.masked_array.mT
- numpy.ma.masked_array.nbytes
- numpy.ma.masked_array.ndim
- numpy.ma.masked_array.newbyteorder
- numpy.ma.masked_array.nonzero
- numpy.ma.masked_array.partition
- numpy.ma.masked_array.prod
- numpy.ma.masked_array.product
- numpy.ma.masked_array.ptp
- numpy.ma.masked_array.put
- numpy.ma.masked_array.ravel
- numpy.ma.masked_array.real
- numpy.ma.masked_array.recordmask
- numpy.ma.masked_array.repeat
- numpy.ma.masked_array.reshape
- numpy.ma.masked_array.resize
- numpy.ma.masked_array.round
- numpy.ma.masked_array.searchsorted
- numpy.ma.masked_array.set_fill_value
- numpy.ma.masked_array.setfield
- numpy.ma.masked_array.setflags
- numpy.ma.masked_array.shape
- numpy.ma.masked_array.sharedmask
- numpy.ma.masked_array.shrink_mask
- numpy.ma.masked_array.size
- numpy.ma.masked_array.soften_mask
- numpy.ma.masked_array.sort
- numpy.ma.masked_array.squeeze
- numpy.ma.masked_array.std
- numpy.ma.masked_array.strides
- numpy.ma.masked_array.sum
- numpy.ma.masked_array.swapaxes
- numpy.ma.masked_array.T
- numpy.ma.masked_array.take
- numpy.ma.masked_array.to_device
- numpy.ma.masked_array.tobytes
- numpy.ma.masked_array.tofile
- numpy.ma.masked_array.toflex
- numpy.ma.masked_array.tolist
- numpy.ma.masked_array.torecords
- numpy.ma.masked_array.tostring
- numpy.ma.masked_array.trace
- numpy.ma.masked_array.transpose
- numpy.ma.masked_array.unshare_mask
- numpy.ma.masked_array.var
- numpy.ma.masked_array.view
- numpy.ma.masked_equal
- numpy.ma.masked_greater
- numpy.ma.masked_greater_equal
- numpy.ma.masked_inside
- numpy.ma.masked_invalid
- numpy.ma.masked_less
- numpy.ma.masked_less_equal
- numpy.ma.masked_not_equal
- numpy.ma.masked_object
- numpy.ma.masked_outside
- numpy.ma.masked_values
- numpy.ma.masked_where
- numpy.ma.MaskedArray.__array__
- numpy.ma.MaskedArray.__eq__
- numpy.ma.MaskedArray.__ne__
- numpy.ma.MaskedArray.__new__
- numpy.ma.MaskedArray.__setstate__
- numpy.ma.MaskedArray.anom
- numpy.ma.MaskedArray.argmin
- numpy.ma.MaskedArray.argsort
- numpy.ma.MaskedArray.astype
- numpy.ma.MaskedArray.byteswap
- numpy.ma.MaskedArray.compress
- numpy.ma.MaskedArray.compressed
- numpy.ma.MaskedArray.count
- numpy.ma.MaskedArray.ctypes
- numpy.ma.MaskedArray.cumsum
- numpy.ma.MaskedArray.dtype
- numpy.ma.MaskedArray.filled
- numpy.ma.MaskedArray.flags
- numpy.ma.MaskedArray.get_fill_value
- numpy.ma.MaskedArray.ids
- numpy.ma.MaskedArray.imag
- numpy.ma.MaskedArray.iscontiguous
- numpy.ma.MaskedArray.item
- numpy.ma.MaskedArray.max
- numpy.ma.MaskedArray.mean
- numpy.ma.MaskedArray.min
- numpy.ma.MaskedArray.nonzero
- numpy.ma.MaskedArray.ptp
- numpy.ma.MaskedArray.put
- numpy.ma.MaskedArray.ravel
- numpy.ma.MaskedArray.real
- numpy.ma.MaskedArray.reshape
- numpy.ma.MaskedArray.resize
- numpy.ma.MaskedArray.round
- numpy.ma.MaskedArray.shrink_mask
- numpy.ma.MaskedArray.sort
- numpy.ma.MaskedArray.strides
- numpy.ma.MaskedArray.sum
- numpy.ma.MaskedArray.take
- numpy.ma.MaskedArray.tobytes
- numpy.ma.MaskedArray.toflex
- numpy.ma.MaskedArray.tolist
- numpy.ma.MaskedArray.torecords
- numpy.ma.MaskedArray.transpose
- numpy.ma.MaskedArray.var
- numpy.ma.MaskedArray.view
- numpy.ma.MaskType.data
- numpy.ma.MaskType.dtype
- numpy.ma.max
- numpy.ma.maximum_fill_value
- numpy.ma.mean
- numpy.ma.median
- numpy.ma.min
- numpy.ma.minimum_fill_value
- numpy.ma.mr_
- numpy.ma.ndenumerate
- numpy.ma.nonzero
- numpy.ma.notmasked_contiguous
- numpy.ma.notmasked_edges
- numpy.ma.ones
- numpy.ma.ones_like
- numpy.ma.polyfit
- numpy.ma.power
- numpy.ma.ptp
- numpy.ma.put
- numpy.ma.putmask
- numpy.ma.ravel
- numpy.ma.reshape
- numpy.ma.resize
- numpy.ma.right_shift
- numpy.ma.round
- numpy.ma.round_
- numpy.ma.set_fill_value
- numpy.ma.setdiff1d
- numpy.ma.setxor1d
- numpy.ma.size
- numpy.ma.sort
- numpy.ma.squeeze
- numpy.ma.stack
- numpy.ma.sum
- numpy.ma.transpose
- numpy.ma.union1d
- numpy.ma.unique
- numpy.ma.var