datascience.tables.Table.copy

Table.copy(*, shallow=False)

Return a copy of a table.

Args:

shallow: perform a shallow copy

Returns:

A copy of the table.

By default, copy performs a deep copy of the original table. This means that it constructs a new object and recursively inserts copies into it of the objects found in the original. Note in the following example, table_copy is a deep copy of original_table so when original_table is updated it does not change table_copy as it does not contain reference’s to orignal_tables objects due to the deep copy.

>>> value = ["foo"]
>>> original_table = Table().with_columns(
...    "A", make_array(1, 2, 3),
...    "B", make_array(value, ["foo", "bar"], ["foo"]),
... )
>>> original_table
A    | B
1    | ['foo']
2    | ['foo', 'bar']
3    | ['foo']
>>> table_copy = original_table.copy()
>>> table_copy
A    | B
1    | ['foo']
2    | ['foo', 'bar']
3    | ['foo']
>>> value.append("bar")
>>> original_table
A    | B
1    | ['foo', 'bar']
2    | ['foo', 'bar']
3    | ['foo']
>>> table_copy
A    | B
1    | ['foo']
2    | ['foo', 'bar']
3    | ['foo']

By contrast, when a shallow copy is performed, a new object is constructed and references are inserted into it to the objects found in the original. Note in the following example how the update to original_table occurs in both table_shallow_copy and original_table because table_shallow_copy contains references to the original_table.

>>> value = ["foo"]
>>> original_table = Table().with_columns(
...    "A", make_array(1, 2, 3),
...    "B", make_array(value, ["foo", "bar"], ["foo"]),
... )
>>> original_table
A    | B
1    | ['foo']
2    | ['foo', 'bar']
3    | ['foo']
>>> table_shallow_copy = original_table.copy(shallow=True)
>>> table_shallow_copy
A    | B
1    | ['foo']
2    | ['foo', 'bar']
3    | ['foo']
>>> value.append("bar")
>>> original_table
A    | B
1    | ['foo', 'bar']
2    | ['foo', 'bar']
3    | ['foo']
>>> table_shallow_copy
A    | B
1    | ['foo', 'bar']
2    | ['foo', 'bar']
3    | ['foo']