Modifying a Table#
The data values within a Table object can be modified in much the same manner
as for numpy structured arrays by accessing columns or
rows of data and assigning values appropriately. A key enhancement provided by
the Table class is the ability to modify the structure of the table: you can
add or remove columns, and add new rows of data.
Quick Overview#
The code below shows the basics of modifying a table and its data.
Examples#
Make a table
>>> from astropy.table import Table
>>> import numpy as np
>>> arr = np.arange(15).reshape(5, 3)
>>> t = Table(arr, names=('a', 'b', 'c'), meta={'keywords': {'key1': 'val1'}})
Modify data values
>>> t['a'][:] = [1, -2, 3, -4, 5] # Set all values of column 'a'
>>> t['a'][2] = 30 # Set row 2 of column 'a'
>>> t[1] = (8, 9, 10) # Set all values of row 1
>>> t[1]['b'] = -9 # Set column 'b' of row 1
>>> t[0:3]['c'] = 100 # Set column 'c' of rows 0, 1, 2
Note that table[row][column] assignments will not work with numpy
“fancy” row indexing (in that case table[row] would be a copy instead
of a view). “Fancy” numpy indices include a list, numpy.ndarray, or
tuple of numpy.ndarray (e.g., the return from numpy.where()):
>>> t[[1, 2]]['a'] = [3., 5.] # doesn't change table t
>>> t[np.array([1, 2])]['a'] = [3., 5.] # doesn't change table t
>>> t[np.where(t['a'] > 3)]['a'] = 3. # doesn't change table t
Instead use table[column][row] order:
>>> t['a'][[1, 2]] = [3., 5.]
>>> t['a'][np.array([1, 2])] = [3., 5.]
>>> t['a'][np.where(t['a'] > 3)] = 3.
You can also modify data columns with unit set in a way that follows
the conventions of Quantity by using the
quantity property:
>>> from astropy import units as u
>>> tu = Table([[1, 2.5]], names=('a',))
>>> tu['a'].unit = u.m
>>> tu['a'].quantity[:] = [1, 2] * u.km
>>> tu['a']
<Column name='a' dtype='float64' unit='m' length=2>
1000.0
2000.0
Note
The best way to combine the functionality of the Table and Quantity
classes is to use a QTable. See Quantity and QTable for more
information.
Add a column or columns
A single column can be added to a table using syntax like adding a key-value
pair to a dict. The value on the right hand side can be a
list or numpy.ndarray of the correct size, or a scalar value that will be
broadcast:
>>> t['d1'] = np.arange(5)
>>> t['d2'] = [1, 2, 3, 4, 5]
>>> t['d3'] = 6 # all 5 rows set to 6
For more explicit control, the add_column() and
add_columns() methods can be used to add one or
multiple columns to a table. In both cases the new column(s) can be specified as
a list, numpy.ndarray, Column, MaskedColumn, or a scalar:
>>> from astropy.table import Column
>>> t.add_column(np.arange(5), name='aa', index=0) # Insert before first table column
>>> t.add_column(1.0, name='bb') # Add column of all 1.0 to end of table
>>> c = Column(np.arange(5), name='e')
>>> t.add_column(c, index=0) # Add Column using the existing column name 'e'
>>> t.add_columns([[1, 2, 3, 4, 5], ['v', 'w', 'x', 'y', 'z']], names=['h', 'i'])
Finally, columns can also be added from Quantity objects, which automatically
sets the unit attribute on the column (but you might find it more
convenient to add a Quantity to a QTable instead, see
Quantity and QTable for details):
>>> from astropy import units as u
>>> t['d'] = np.arange(1., 6.) * u.m
>>> t['d']
<Column name='d' dtype='float64' unit='m' length=5>
1.0
2.0
3.0
4.0
5.0
Remove columns
To remove a column from a table:
>>> t.remove_column('d1')
>>> t.remove_columns(['aa', 'd2', 'e'])
>>> del t['d3']
>>> del t['h', 'i']
>>> t.keep_columns(['a', 'b'])
Replace a column
You can entirely replace an existing column with a new column by setting the
column to any object that could be used to initialize a table column (e.g., a
list or numpy.ndarray). For example, you could change the data type of the
a column from int to float using:
>>> t['a'] = t['a'].astype(float)
If the right-hand side value is not column-like, then an in-place update using broadcasting will be done, for example:
>>> t['a'] = 1 # Internally does t['a'][:] = 1
Perform a dictionary-style update
It is possible to perform a dictionary-style update, which adds new columns to the table and replaces existing ones:
>>> t1 = Table({'name': ['foo', 'bar'], 'val': [0., 0.]}, meta={'n': 2})
>>> t2 = Table({'val': [1., 2.], 'val2': [10., 10.]}, meta={'id': 0})
>>> t1 |= t2
>>> t1
<Table length=2>
name val val2
str3 float64 float64
---- ------- -------
foo 1.0 10.0
bar 2.0 10.0
When using |=, the other object does not need to be a Table, it can be
anything that can be used for Constructing a Table with a compatible number
of rows:
>>> t1 = Table({'name': ['foo', 'bar'], 'val': [0., 0.]}, meta={'n': 2})
>>> d = dict({'val': [1., 2.], 'val2': [10., 10.]})
>>> t1 |= d
>>> t1
<Table length=2>
name val val2
str3 float64 float64
---- ------- -------
foo 1.0 10.0
bar 2.0 10.0
It is also possible to use the | operator to merge multiple Table instances
into a new table:
>>> from astropy.table import QTable
>>> t1 = Table({'name': ['foo', 'bar'], 'val': [0., 0.]}, meta={'n': 2})
>>> t2 = QTable({'val': [1., 2.], 'val2': [10., 10.]}, meta={'id': 0})
>>> t3 = t1 | t2 # Create a new table as result of update
>>> t3
<Table length=2>
name val val2
str3 float64 float64
---- ------- -------
foo 1.0 10.0
bar 2.0 10.0
| and |= also take care of silently Merging Metadata:
>>> t3.meta
{'n': 2, 'id': 0}
The columns in the updated Table are going to be copies of the originals. If
you need them to be references you can use the
update() method with copy=False, see Copy versus Reference
for details.
Rename columns
To rename a column:
>>> t.rename_column('a', 'a_new')
>>> t['b'].name = 'b_new'
Add a row of data
To add a row:
>>> t.add_row([-8, -9])
Remove rows
To remove a row:
>>> t.remove_row(0)
>>> t.remove_rows(slice(4, 5))
>>> t.remove_rows([1, 2])
Sort by one or more columns
To sort columns:
>>> t.sort('b_new')
>>> t.sort(['a_new', 'b_new'])
Reverse table rows
To reverse the order of table rows:
>>> t.reverse()
Modify metadata
To modify metadata:
>>> t.meta['key'] = 'value'
Select or reorder columns
A new table with a subset or reordered list of columns can be created as shown in the following example:
>>> t = Table(arr, names=('a', 'b', 'c'))
>>> t_acb = t['a', 'c', 'b']
Another way to do the same thing is to provide a list or tuple as the item, as shown below:
>>> new_order = ['a', 'c', 'b'] # List or tuple
>>> t_acb = t[new_order]
Caveats#
Modifying the table data and properties is fairly clear-cut, but one thing to keep in mind is that adding a row may require a new copy in memory of the table data. This depends on the detailed layout of Python objects in memory and cannot be reliably controlled. In some cases it may be possible to build a table row by row in less than O(N**2) time but you cannot count on it.
Another subtlety to keep in mind is that in some cases the return value of an
operation results in a new table in memory while in other cases it results in a
view of the existing table data. As an example, imagine trying to set two table
elements using column selection with t['a', 'c'] in combination with row
index selection:
>>> t = Table([[1, 2], [3, 4], [5, 6]], names=('a', 'b', 'c'))
>>> t['a', 'c'][1] = (100, 100)
>>> print(t)
a b c
--- --- ---
1 3 5
2 4 6
This might be surprising because the data values did not change and there
was no error. In fact, what happened is that t['a', 'c'] created a
new temporary table in memory as a copy of the original and then updated the
first row of the copy. The original t table was unaffected and the new
temporary table disappeared once the statement was complete. The takeaway
is to pay attention to how certain operations are performed one step at
a time.
In-Place Versus Replace Column Update#
Consider this code snippet:
>>> t = Table([[1, 2, 3]], names=['a'])
>>> t['a'] = [10.5, 20.5, 30.5]
There are a couple of ways this could be handled. It could update the existing array values in-place (truncating to integer), or it could replace the entire column with a new column based on the supplied data values.
The answer for astropy is that the operation shown above does a complete
replacement of the column object. In this case it makes a new column object
with float values by internally calling t.replace_column('a', [10.5, 20.5,
30.5]). In general this behavior is more consistent with Python and pandas behavior.
Forcing in-place update
It is possible to force an in-place update of a column as follows:
t[colname][:] = value
Finding the source of problems
In order to find potential problems related to replacing columns, there is the
option astropy.table.conf.replace_warnings in the Configuration System (astropy.config). This
controls a set of warnings that are emitted under certain circumstances when a
table column is replaced. This option must be set to a list that includes zero
or more of the following string values:
always:Print a warning every time a column gets replaced via the
__setitem__()syntax (i.e.,t['a'] = new_col).slice:Print a warning when a column that appears to be a
sliceof a parent column is replaced.refcount:Print a warning when the Python reference count for the column changes. This indicates that a stale object exists that might be used elsewhere in the code and give unexpected results.
attributes:Print a warning if any of the standard column attributes changed.
The default value for the table.conf.replace_warnings option is
[] (no warnings).