# The numpy.ma module¶

## Rationale¶

Masked arrays are arrays that may have missing or invalid entries. The numpy.ma module provides a nearly work-alike replacement for numpy that supports data arrays with masks.

## What is a masked array?¶

In many circumstances, datasets can be incomplete or tainted by the presence of invalid data. For example, a sensor may have failed to record a data, or recorded an invalid value. The numpy.ma module provides a convenient way to address this issue, by introducing masked arrays.

A masked array is the combination of a standard numpy.ndarray and a mask. A mask is either nomask, indicating that no value of the associated array is invalid, or an array of booleans that determines for each element of the associated array whether the value is valid or not. When an element of the mask is False, the corresponding element of the associated array is valid and is said to be unmasked. When an element of the mask is True, the corresponding element of the associated array is said to be masked (invalid).

The package ensures that masked entries are not used in computations.

As an illustration, let’s consider the following dataset:

```>>> import numpy as np
>>> import numpy.ma as ma
>>> x = np.array([1, 2, 3, -1, 5])
```

We wish to mark the fourth entry as invalid. The easiest is to create a masked array:

```>>> mx = ma.masked_array(x, mask=[0, 0, 0, 1, 0])
```

We can now compute the mean of the dataset, without taking the invalid data into account:

```>>> mx.mean()
2.75
```

## The numpy.ma module¶

The main feature of the numpy.ma module is the MaskedArray class, which is a subclass of numpy.ndarray. The class, its attributes and methods are described in more details in the MaskedArray class section.

The numpy.ma module can be used as an addition to numpy:

```>>> import numpy as np
>>> import numpy.ma as ma
```

To create an array with the second element invalid, we would do:

```>>> y = ma.array([1, 2, 3], mask = [0, 1, 0])
```

To create a masked array where all values close to 1.e20 are invalid, we would do:

```>>> z = masked_values([1.0, 1.e20, 3.0, 4.0], 1.e20)
```

# Using numpy.ma¶

There are several ways to construct a masked array.

• A first possibility is to directly invoke the MaskedArray class.

• A second possibility is to use the two masked array constructors, array and masked_array.

• A third option is to take the view of an existing array. In that case, the mask of the view is set to nomask if the array has no named fields, or an array of boolean with the same structure as the array otherwise.

```>>> x = np.array([1, 2, 3])
fill_value = 999999)
>>> x = np.array([(1, 1.), (2, 2.)], dtype=[('a',int), ('b', float)])
masked_array(data = [(1, 1.0) (2, 2.0)],
mask = [(False, False) (False, False)],
fill_value = (999999, 1e+20),
dtype = [('a', '<i4'), ('b', '<f8')])
```
• Yet another possibility is to use any of the following functions:

## Accessing the data¶

The underlying data of a masked array can be accessed in several ways:

• through the data attribute. The output is a view of the array as a numpy.ndarray or one of its subclasses, depending on the type of the underlying data at the masked array creation.
• through the __array__ method. The output is then a numpy.ndarray.
• by directly taking a view of the masked array as a numpy.ndarray or one of its subclass (which is actually what using the data attribute does).
• by using the getdata function.

None of these methods is completely satisfactory if some entries have been marked as invalid. As a general rule, where a representation of the array is required without any masked entries, it is recommended to fill the array with the filled method.

The mask of a masked array is accessible through its mask attribute. We must keep in mind that a True entry in the mask indicates an invalid data.

Another possibility is to use the getmask and getmaskarray functions. getmask(x) outputs the mask of x if x is a masked array, and the special value nomask otherwise. getmaskarray(x) outputs the mask of x if x is a masked array. If x has no invalid entry or is not a masked array, the function outputs a boolean array of False with as many elements as x.

## Accessing only the valid entries¶

To retrieve only the valid entries, we can use the inverse of the mask as an index. The inverse of the mask can be calculated with the numpy.logical_not function or simply with the ~ operator:

```>>> x = ma.array([[1, 2], [3, 4]], mask=[[0, 1], [1, 0]])
fill_value = 999999)
```

Another way to retrieve the valid data is to use the compressed method, which returns a one-dimensional ndarray (or one of its subclasses, depending on the value of the baseclass attribute):

```>>> x.compressed()
array([1, 4])
```

Note that the output of compressed is always 1D.

The recommended way to mark one or several specific entries of a masked array as invalid is to assign the special value masked to them:

```>>> x = ma.array([1, 2, 3])
>>> x
mask = [ True False False],
fill_value = 999999)
>>> y = ma.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
>>> y[(0, 1, 2), (1, 2, 0)] = ma.masked
>>> y
[[1 -- 3]
[4 5 --]
[-- 8 9]],
[[False  True False]
[False False  True]
[ True False False]],
fill_value = 999999)
>>> z = ma.array([1, 2, 3, 4])
>>> z
masked_array(data = [-- -- 3 4],
mask = [ True  True False False],
fill_value = 999999)
```

A second possibility is to modify the mask directly, but this usage is discouraged.

Note

When creating a new masked array with a simple, non-structured datatype, the mask is initially set to the special value nomask, that corresponds roughly to the boolean False. Trying to set an element of nomask will fail with a TypeError exception, as a boolean does not support item assignment.

All the entries of an array can be masked at once by assigning True to the mask:

```>>> x = ma.array([1, 2, 3], mask=[0, 0, 1])
>>> x
mask = [ True  True  True],
fill_value = 999999)
```

Finally, specific entries can be masked and/or unmasked by assigning to the mask a sequence of booleans:

```>>> x = ma.array([1, 2, 3])
>>> x.mask = [0, 1, 0]
>>> x
fill_value = 999999)
```

To unmask one or several specific entries, we can just assign one or several new valid values to them:

```>>> x = ma.array([1, 2, 3], mask=[0, 0, 1])
>>> x
fill_value = 999999)
>>> x[-1] = 5
>>> x
fill_value = 999999)
```

Note

Unmasking an entry by direct assignment will silently fail if the masked array has a hard mask, as shown by the hardmask attribute. This feature was introduced to prevent overwriting the mask. To force the unmasking of an entry where the array has a hard mask, the mask must first to be softened using the soften_mask method before the allocation. It can be re-hardened with harden_mask:

```>>> x = ma.array([1, 2, 3], mask=[0, 0, 1], hard_mask=True)
>>> x
fill_value = 999999)
>>> x[-1] = 5
>>> x
fill_value = 999999)
>>> x[-1] = 5
>>> x
fill_value = 999999)
```

```>>> x = ma.array([1, 2, 3], mask=[0, 0, 1])
>>> x
fill_value = 999999)
>>> x
fill_value = 999999)
```

## Indexing and slicing¶

As a MaskedArray is a subclass of numpy.ndarray, it inherits its mechanisms for indexing and slicing.

When accessing a single entry of a masked array with no named fields, the output is either a scalar (if the corresponding entry of the mask is False) or the special value masked (if the corresponding entry of the mask is True):

```>>> x = ma.array([1, 2, 3], mask=[0, 0, 1])
>>> x
1
>>> x[-1]
fill_value = 1e+20)
True
```

If the masked array has named fields, accessing a single entry returns a numpy.void object if none of the fields are masked, or a 0d masked array with the same dtype as the initial array if at least one of the fields is masked.

```>>> y = ma.masked_array([(1,2), (3, 4)],
...               dtype=[('a', int), ('b', int)])
>>> y
(1, 2)
>>> y[-1]
fill_value = (999999, 999999),
dtype = [('a', '<i4'), ('b', '<i4')])
```

When accessing a slice, the output is a masked array whose data attribute is a view of the original data, and whose mask is either nomask (if there was no invalid entries in the original array) or a copy of the corresponding slice of the original mask. The copy is required to avoid propagation of any modification of the mask to the original.

```>>> x = ma.array([1, 2, 3, 4, 5], mask=[0, 1, 0, 0, 1])
>>> mx = x[:3]
>>> mx
fill_value = 999999)
>>> mx = -1
>>> mx
fill_value = 999999)
array([False,  True, False, False,  True], dtype=bool)
>>> x.data
array([ 1, -1,  3,  4,  5])
```

Accessing a field of a masked array with structured datatype returns a MaskedArray.

Arithmetic and comparison operations are supported by masked arrays. As much as possible, invalid entries of a masked array are not processed, meaning that the corresponding data entries should be the same before and after the operation.

Warning

We need to stress that this behavior may not be systematic, that masked data may be affected by the operation in some cases and therefore users should not rely on this data remaining unchanged.

The numpy.ma module comes with a specific implementation of most ufuncs. Unary and binary functions that have a validity domain (such as log or divide) return the masked constant whenever the input is masked or falls outside the validity domain:

```>>> ma.log([-1, 0, 1, 2])
masked_array(data = [-- -- 0.0 0.69314718056],
mask = [ True  True False False],
fill_value = 1e+20)
```

Masked arrays also support standard numpy ufuncs. The output is then a masked array. The result of a unary ufunc is masked wherever the input is masked. The result of a binary ufunc is masked wherever any of the input is masked. If the ufunc also returns the optional context output (a 3-element tuple containing the name of the ufunc, its arguments and its domain), the context is processed and entries of the output masked array are masked wherever the corresponding input fall outside the validity domain:

```>>> x = ma.array([-1, 1, 0, 2, 3], mask=[0, 0, 0, 0, 1])
>>> np.log(x)
masked_array(data = [-- -- 0.0 0.69314718056 --],
mask = [ True  True False False  True],
fill_value = 1e+20)
```

# Examples¶

## Data with a given value representing missing data¶

Let’s consider a list of elements, x, where values of -9999. represent missing data. We wish to compute the average value of the data and the vector of anomalies (deviations from the average):

```>>> import numpy.ma as ma
>>> x = [0.,1.,-9999.,3.,4.]
>>> mx = ma.masked_values (x, -9999.)
>>> print mx.mean()
2.0
>>> print mx - mx.mean()
[-2.0 -1.0 -- 1.0 2.0]
>>> print mx.anom()
[-2.0 -1.0 -- 1.0 2.0]
```

## Filling in the missing data¶

Suppose now that we wish to print that same data, but with the missing values replaced by the average value.

```>>> print mx.filled(mx.mean())
[ 0.  1.  2.  3.  4.]
```

## Numerical operations¶

Numerical operations can be easily performed without worrying about missing values, dividing by zero, square roots of negative numbers, etc.:

```>>> import numpy as np, numpy.ma as ma
>>> x = ma.array([1., -1., 3., 4., 5., 6.], mask=[0,0,0,0,1,0])
>>> y = ma.array([1., 2., 0., 4., 5., 6.], mask=[0,0,0,0,0,1])
>>> print np.sqrt(x/y)
[1.0 -- -- 1.0 -- --]
```

Four values of the output are invalid: the first one comes from taking the square root of a negative number, the second from the division by zero, and the last two where the inputs were masked.

## Ignoring extreme values¶

Let’s consider an array d of random floats between 0 and 1. We wish to compute the average of the values of d while ignoring any data outside the range [0.1, 0.9]:

```>>> print ma.masked_outside(d, 0.1, 0.9).mean()
```