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.