  Data analysis is at the core of scientific studies, a prominent task that
researchers and practitioners typically undertake by programming their own set
of automated scripts. While there is no shortage of tools and languages
available for designing data analysis pipelines, users spend substantial effort
in learning the specifics of such languages/tools and often design solutions
too project specific to be reused in future studies. Furthermore, users need to
put further effort into making their code scalable, as parallel implementations
are typically more complex.
  We address these problems by proposing an advanced code recommendation tool
which facilitates developing data science scripts. Users formulate their
intentions in a human-readable Domain Specific Language (DSL) for dataframe
manipulation and analysis. The DSL statements can be converted into executable
Python code during editing. To avoid the need to learn the DSL and increase
user-friendliness, our tool supports code completion in mainstream IDEs and
editors. Moreover, DSL statements can generate executable code for different
data analysis frameworks (currently we support Pandas and PySpark). Overall,
our approach attempts to accelerate programming of common data analysis tasks
and to facilitate the conversion of the implementations between frameworks.
  In a preliminary assessment based on a popular data processing tutorial, our
tool was able to fully cover 9 out of 14 processing steps for Pandas and 10 out
of 16 for PySpark, while partially covering 4 processing steps for each of the
frameworks.
