+ +
+

datascience.tables.Table.sample

+
+
+Table.sample(k=None, with_replacement=False, weights=None)[source]
+

Returns a new table where k rows are randomly sampled from the +original table.

+
+
Kwargs:
+
+
k (int or None): If None (default), all the rows in the table are
+
sampled. If an integer, k rows from the original table are +sampled.
+
with_replacement (bool): If False (default), samples the rows
+
without replacement. If True, samples the rows with replacement.
+
weights (list/array or None): If None (default), samples the rows
+
using a uniform random distribution. If a list/array is passed +in, it must be the same length as the number of rows in the +table and the values must sum to 1. The rows will then be +sampled according the the probability distribution in +weights.
+
+
+
Returns:
+
A new instance of Table.
+
+
>>> foo_table
+job  | wage
+a    | 10
+b    | 20
+c    | 15
+d    | 8
+
+
+
>>> foo_table.sample()
+job  | wage
+b    | 20
+c    | 15
+a    | 10
+d    | 8
+
+
+
>>> foo_table.sample(k = 2)
+job  | wage
+b    | 20
+c    | 15
+
+
+
>>> foo_table.sample(k = 2, with_replacement = True,
+...     weights = [0.5, 0.5, 0, 0])
+job  | wage
+a    | 10
+a    | 10
+
+
+
+ +
+ + +