  Given one or two examples, humans are good at understanding how to solve a
problem independently of its domain, because they are able to detect what the
problem is and to choose the appropriate background knowledge according to the
context. For instance, presented with the string "8/17/2017" to be transformed
to "17th of August of 2017", humans will process this in two steps: (1) they
recognise that it is a date and (2) they map the date to the 17th of August of
2017. Inductive Programming (IP) aims at learning declarative (functional or
logic) programs from examples. Two key advantages of IP are the use of
background knowledge and the ability to synthesise programs from a few
input/output examples (as humans do). In this paper we propose to use IP as a
means for automating repetitive data manipulation tasks, frequently presented
during the process of {\em data wrangling} in many data manipulation problems.
Here we show that with the use of general-purpose declarative (programming)
languages jointly with generic IP systems and the definition of domain-specific
knowledge, many specific data wrangling problems from different application
domains can be automatically solved from very few examples. We also propose an
integrated benchmark for data wrangling, which we share publicly for the
community.
