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, Column
>>> 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

Add a column or columns

>>> t.add_column(Column('d', np.arange(5)))

# 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())

Remove columns

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

Rename columns

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

Add a row of data

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

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'

Reorder columns

Note

In this example, it is important that neworder is a tuple, and not a list, slice, or ndarray.

>>> t_acb = t['a','c','b']
>>> neworder = ('a','c','b')
>>> t_acb = t[neworder]

Caveats

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.

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