datascience.tables.Table.stats

Table.stats(ops=(<built-in function min>, <built-in function max>, <function median>, <built-in function sum>))

Compute statistics for each column and place them in a table.

Args:

ops – A tuple of stat functions to use to compute stats.

Returns:

A Table with a prepended statistic column with the name of the fucntion’s as the values and the calculated stats values per column.

By default stats calculates the minimum, maximum, np.median, and sum of each column.

>>> table = Table().with_columns(
...     'A', make_array(4, 0, 6, 5),
...     'B', make_array(10, 20, 17, 17),
...     'C', make_array(18, 13, 2, 9))
>>> table.stats()
statistic | A    | B    | C
min       | 0    | 10   | 2
max       | 6    | 20   | 18
median    | 4.5  | 17   | 11
sum       | 15   | 64   | 42

Note, stats are calculated even on non-numeric columns which may lead to unexpected behavior or in more severe cases errors. This is why it may be best to eliminate non-numeric columns from the table before running stats.

>>> table = Table().with_columns(
...     'B', make_array(10, 20, 17, 17),
...     'C', make_array("foo", "bar", "baz", "baz"))
>>> table.stats()
statistic | B    | C
min       | 10   | bar
max       | 20   | foo
median    | 17   |
sum       | 64   |
>>> table.select('B').stats()
statistic | B
min       | 10
max       | 20
median    | 17
sum       | 64

ops can also be overridden to calculate custom stats.

>>> table = Table().with_columns(
...     'A', make_array(4, 0, 6, 5),
...     'B', make_array(10, 20, 17, 17),
...     'C', make_array(18, 13, 2, 9))
>>> def weighted_average(x):
...     return np.average(x, weights=[1, 0, 1.5, 1.25])
>>> table.stats(ops=(weighted_average, np.mean, np.median, np.std))
statistic        | A       | B       | C
weighted_average | 5.13333 | 15.1333 | 8.6
mean             | 3.75    | 16      | 10.5
median           | 4.5     | 17      | 11
std              | 2.27761 | 3.67423 | 5.85235