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.
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.
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 >>> t = Table([(1, 2), (3, 4)], names=('a', 'b'), masked=True) >>> t <Table rows=2 names=('a','b')> masked_array(data = [(1, 3) (2, 4)], mask = [(False, False) (False, False)], fill_value = (999999, 999999), dtype = [('a', '<i8'), ('b', '<i8')])
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') >>> b = Column([3, 4], name='b') >>> t = Table([a, b])
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.
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 [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 [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
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:
Both of these differences are due to issues in the underlying numpy.ma.MaskedArray implementation.
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.mask['a'] = [False, True] # Modify table mask (structured 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.
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