datascience.tables.Table.apply

Table.apply(fn, *column_or_columns)

Apply fn to each element or elements of column_or_columns. If no column_or_columns provided, fn` is applied to each row.

Args:

fn (function) – The function to apply to each element

of column_or_columns.

column_or_columns – Columns containing the arguments to fn

as either column labels (str) or column indices (int). The number of columns must match the number of arguments that fn expects.

Raises:

ValueError – if column_label is not an existing

column in the table.

TypeError – if insufficient number of column_label passed

to fn.

Returns:

An array consisting of results of applying fn to elements specified by column_label in each row.

>>> t = Table().with_columns(
...     'letter', make_array('a', 'b', 'c', 'z'),
...     'count',  make_array(9, 3, 3, 1),
...     'points', make_array(1, 2, 2, 10))
>>> t
letter | count | points
a      | 9     | 1
b      | 3     | 2
c      | 3     | 2
z      | 1     | 10
>>> t.apply(lambda x: x - 1, 'points')
array([0, 1, 1, 9])
>>> t.apply(lambda x, y: x * y, 'count', 'points')
array([ 9,  6,  6, 10])
>>> t.apply(lambda x: x - 1, 'count', 'points')
Traceback (most recent call last):
    ...
TypeError: <lambda>() takes 1 positional argument but 2 were given
>>> t.apply(lambda x: x - 1, 'counts')
Traceback (most recent call last):
    ...
ValueError: The column "counts" is not in the table. The table contains these columns: letter, count, points

Whole rows are passed to the function if no columns are specified.

>>> t.apply(lambda row: row[1] * 2)
array([18,  6,  6,  2])