# 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'] = 30                 # Set row 2 of column 'a'
>>> t = (8, 9, 10)              # Set all values of row 1
>>> t['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.

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'
```

```>>> 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()
```

```>>> 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'] = (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 `slice` of 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).