Dplython

Provides familiar verbs like select, mutate, and group_by, making it easy for R users to transition to Python while thinking in dplyr terms, as shown in the example workflows.

Uses the >> operator to chain data transformations, creating clean and expressive code pipelines that enhance readability for complex manipulations, as demonstrated in the multi-step examples.

Enables easy column selection with X.column_name or X['column name'] syntax, handling non-standard column names seamlessly, as illustrated in the README for columns with spaces.

Supports the @DelayFunction decorator to apply custom functions or visualization libraries like ggplot and matplotlib directly within pipelines, allowing for advanced extensibility.