Constants of the numpy.ma module¶
In addition to the MaskedArray class, the numpy.ma 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 several entries of a masked array:
>>> x = ma.array([1, 2, 3], mask=[0, 1, 0]) >>> x[1] is ma.masked True >>> x[-1] = ma.masked >>> x masked_array(data = [1 -- --], mask = [False True True], fill_value = 999999)
- numpy.ma.nomask¶
Value indicating that a masked array has no invalid entry. nomask is used internally to speed up computations when the mask is not needed.
- numpy.ma.masked_print_options¶
String used in lieu of missing data when a masked array is printed. By default, this string is '--'.
The MaskedArray class¶
- class numpy.ma.MaskedArray¶
- A subclass of ndarray designed to manipulate numerical arrays with missing data.
An instance of MaskedArray can be thought as the combination of several elements:
- The data, as a regular numpy.ndarray of any shape or datatype (the data).
- A boolean mask with the same shape as the data, where a True value indicates that the corresponding element of the data is invalid. The special value nomask is also acceptable for arrays without named fields, and indicates that no data is invalid.
- A fill_value, a value that may be used to replace the invalid entries in order to return a standard numpy.ndarray.
Attributes and properties of masked arrays¶
- MaskedArray.data¶
Returns the underlying data, as a view of the masked array. If the underlying data is a subclass of numpy.ndarray, it is returned as such.
>>> x = ma.array(np.matrix([[1, 2], [3, 4]]), mask=[[0, 1], [1, 0]]) >>> x.data matrix([[1, 2], [3, 4]])
The type of the data can be accessed through the baseclass attribute.
- MaskedArray.mask¶
Returns the underlying mask, as an array with the same shape and structure as the data, but where all fields are atomically booleans. A value of True indicates an invalid entry.
- MaskedArray.recordmask¶
Returns the mask of the array if it has no named fields. For structured arrays, returns a ndarray of booleans where entries are True if all the fields are masked, False otherwise:
>>> x = ma.array([(1, 1), (2, 2), (3, 3), (4, 4), (5, 5)], ... mask=[(0, 0), (1, 0), (1, 1), (0, 1), (0, 0)], ... dtype=[('a', int), ('b', int)]) >>> x.recordmask array([False, False, True, False, False], dtype=bool)
- MaskedArray.fill_value¶
Returns the value used to fill the invalid entries of a masked array. The value is either a scalar (if the masked array has no named fields), or a 0-D ndarray with the same dtype as the masked array if it has named fields.
The default filling value depends on the datatype of the array:
datatype default bool True int 999999 float 1.e20 complex 1.e20+0j object ‘?’ string ‘N/A’
- MaskedArray.baseclass¶
Returns the class of the underlying data.
>>> x = ma.array(np.matrix([[1, 2], [3, 4]]), mask=[[0, 0], [1, 0]]) >>> x.baseclass <class 'numpy.matrixlib.defmatrix.matrix'>
Returns whether the mask of the array is shared between several masked arrays. If this is the case, any modification to the mask of one array will be propagated to the others.
- MaskedArray.hardmask¶
Returns whether the mask is hard (True) or soft (False). When the mask is hard, masked entries cannot be unmasked.
As MaskedArray is a subclass of ndarray, a masked array also inherits all the attributes and properties of a ndarray instance.
MaskedArray methods¶
Conversion¶
Shape manipulation¶
For reshape, resize, and transpose, the single tuple argument may be replaced with n integers which will be interpreted as an n-tuple.
Item selection and manipulation¶
For array methods that take an axis keyword, it defaults to None. If axis is None, then the array is treated as a 1-D array. Any other value for axis represents the dimension along which the operation should proceed.