# Masking and missing values¶

The `astropy.table`

package provides support for masking and missing
values in a table by wrapping the `numpy.ma`

masked array package.
This allows handling tables with missing or invalid entries in much
the same manner as for standard (unmasked) tables. It
is useful to be familiar with the masked array
documentation when using masked tables within `astropy.table`

.

In a nutshell, the concept is to define a boolean mask that mirrors
the structure of the table data array. Wherever a mask value is
`True`

, the corresponding entry is considered to be missing or invalid.
Operations involving column or row access and slicing are unchanged.
The key difference is that arithmetic or reduction operations involving
columns or column slices follow the rules for operations
on masked arrays.

Note

Reduction operations like `numpy.sum`

or `numpy.mean`

follow the
convention of ignoring masked (invalid) values. This differs from
the behavior of the floating point `NaN`

, for which the sum of an
array including one or more `NaN's`

will result in `NaN`

.
See http://numpy.scipy.org/NA-overview.html for a very
interesting discussion of different strategies for handling
missing data in the context of `numpy`

.

## Table creation¶

A masked table can be created in several ways:

**Create a new table object and specify masked=True**

```
>>> from astropy.table import Table, Column, MaskedColumn
>>> Table([(1, 2), (3, 4)], names=('a', 'b'), masked=True, dtype=('i4', 'i8'))
<Table masked=True length=2>
a b
int32 int64
----- -----
1 3
2 4
```

Notice the table attributes `mask`

and `fill_value`

that are
available for a masked table.

**Create a table with one or more columns as a MaskedColumn object**

```
>>> a = MaskedColumn([1, 2], name='a', mask=[False, True], dtype='i4')
>>> b = Column([3, 4], name='b', dtype='i8')
>>> Table([a, b])
<Table masked=True length=2>
a b
int32 int64
----- -----
1 3
-- 4
```

The `MaskedColumn`

is the masked analog of the `Column`

class and
provides the interface for creating and manipulating a column of
masked data. The `MaskedColumn`

class inherits from
`numpy.ma.MaskedArray`

, in contrast to `Column`

which inherits from
`numpy.ndarray`

. This distinction is the main reason there are
different classes for these two cases.

Notice that masked entries in the table output are shown as `--`

.

**Create a table with one or more columns as a numpy MaskedArray**

```
>>> from numpy import ma # masked array package
>>> a = ma.array([1, 2])
>>> b = [3, 4]
>>> t = Table([a, b], names=('a', 'b'))
```

**Add a MaskedColumn object to an existing table**

```
>>> t = Table([[1, 2]], names=['a'])
>>> b = MaskedColumn([3, 4], mask=[True, False])
>>> t['b'] = b
INFO: Upgrading Table to masked Table. Use Table.filled() to convert to unmasked table. [astropy.table.table]
```

Note the INFO message because the underlying type of the table is modified in this operation.

**Add a new row to an existing table and specify a mask argument**

```
>>> a = Column([1, 2], name='a')
>>> b = Column([3, 4], name='b')
>>> t = Table([a, b])
>>> t.add_row([3, 6], mask=[True, False])
INFO: Upgrading Table to masked Table. Use Table.filled() to convert to unmasked table. [astropy.table.table]
```

**Convert an existing table to a masked table**

```
>>> t = Table([[1, 2], ['x', 'y']]) # standard (unmasked) table
>>> t = Table(t, masked=True) # convert to masked table
```

## Table access¶

Nearly all the of standard methods for accessing and modifying data columns, rows, and individual elements also apply to masked tables.

There are two minor differences for the `Row`

object that is obtained by
indexing a single row of a table:

- For standard tables, two such rows can be compared for equality, but in masked tables this comparison will produce an exception.

Both of these differences are due to issues in the underlying
`numpy.ma.MaskedArray`

implementation.

## Masking and filling¶

Both the `Table`

and `MaskedColumn`

classes provide
attributes and methods to support manipulating tables with missing or
invalid data.

### Mask¶

The actual mask for the table as a whole or a single column can be
viewed and modified via the `mask`

attribute:

```
>>> t = Table([(1, 2), (3, 4)], names=('a', 'b'), masked=True)
>>> t['a'].mask = [False, True] # Modify column mask (boolean array)
>>> t['b'].mask = [True, False] # Modify column mask (boolean array)
>>> print(t)
a b
--- ---
1 --
-- 4
```

Masked entries are shown as `--`

when the table is printed. You can
view the mask directly, either at the column or table level:

```
>>> t['a'].mask
array([False, True]...)
>>> t.mask
<Table length=2>
a b
bool bool
----- -----
False True
True False
```

To get the indices of masked elements use an expression like:

```
>>> t['a'].mask.nonzero()[0]
array([1])
```

### Filling¶

The entries which are masked (i.e. missing or invalid) can be replaced
with specified fill values. In this case the `MaskedColumn`

or masked
`Table`

will be converted to a standard `Column`

or table. Each column
in a masked table has a `fill_value`

attribute that specifies the
default fill value for that column. To perform the actual replacement
operation the `filled()`

method is called. This takes an optional
argument which can override the default column `fill_value`

attribute.

```
>>> t['a'].fill_value = -99
>>> t['b'].fill_value = 33
>>> print(t.filled())
a b
--- ---
1 33
-99 4
>>> print(t['a'].filled())
a
---
1
-99
>>> print(t['a'].filled(999))
a
---
1
999
>>> print(t.filled(1000))
a b
---- ----
1 1000
1000 4
```