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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 easily modify the structure of the table: one 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.

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 column values
>>> t['a'][2] = 30              # Set row 2 of column 'a'
>>> t[1] = (8, 9, 10)           # Set all row values
>>> 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] *
>>> tu['a']
<Column name='a' dtype='float64' unit='m' length=2>

Add a column or columns

A single column can be added to a table using syntax like adding a dict value. The value on the right hand side can be a list or array 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 columns must be specified as Column or MaskedColumn objects with the name defined:

>>> from astropy.table import Column
>>> aa = Column(np.arange(5), name='aa')
>>> t.add_column(aa, index=0)  # Insert before the first table column

# Make a new table with the same number of rows and add columns to original table
>>> t2 = Table(np.arange(25).reshape(5, 5), names=('e', 'f', 'g', 'h', 'i'))
>>> t.add_columns(t2.columns.values())

Finally, columns can also be added from Quantity objects, which automatically sets the .unit attribute on the column:

>>> 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>

Remove columns

>>> t.remove_column('f')
>>> t.remove_columns(['aa', 'd1', 'd2', 'd3', 'e'])
>>> del t['g']
>>> del t['h', 'i']
>>> t.keep_columns(['a', 'b'])

Replace a column

For a table with an existing column a, an expression like t['a'] = [1, 2, 3] or t['a'] = 1 replaces the data values without changing the data type or anything else about the column. In order to entirely replace the column with a new column (and potentially change the data type), use the replace_column() method. For instance, to change the data type of the a column from int to float:

>>> a_float = t['a'].astype(float)
>>> t.replace_column('a', a_float)

Rename columns

>>> t.rename_column('a', 'a_new')
>>> t['b'].name = 'b_new'

Add a row of data

>>> t.add_row([-8, -9])

Remove rows

>>> t.remove_row(0)
>>> t.remove_rows(slice(4, 5))
>>> t.remove_rows([1, 2])

Sort by one more more columns

>>> t.sort('b_new')
>>> t.sort(['a_new', 'b_new'])

Reverse table rows

>>> t.reverse()

Modify meta-data

>>> 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]


Modifying the table data and properties is fairly straightforward. There are only a few things to keep in mind:

  • The data type for a column cannot be changed in place. In order to do this you must make a copy of the table with the column type changed appropriately.
  • Adding or removing a column will generate a new copy in memory of all the data. If the table is very large this may be slow.
  • 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 are cases where the return value of an operation results in a new table in memory versus 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 row 1 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.