Hello Numpy community, I'm proposing the introduction of a `pipe` method for NumPy arrays to enhance their usability and expressiveness. Similar to other data processing libraries like pandas, a `pipe` method would allow users to chain operations together in a more readable and intuitive manner. Consider the following examples where method chaining with `pipe` can improve code readability compared to traditional NumPy code: # ******************************************************************** # Class PipeableArray just for illustration import numpy as np class PipeableArray: def __init__(self, array: np.ndarray): self.array = array def pipe(self, func, *args, **kwargs): """Apply function and return the result wrapped in PipeableArray.""" try: result = func(self.array, *args, **kwargs) return PipeableArray(result) except Exception as exc: print('Ups, something went wrong...') def __repr__(self): return repr(self.array) # ******************************************************************** # Original code using traditional NumPy chaining arr = np.array([1, 2, 3, 4, 5]) arr = np.square(arr) arr = np.log(arr) arr = np.cumsum(arr) # Original code using traditional NumPy nested functions arr = np.arange(1., 5.) result = np.cumsum(np.log(np.square(arr))) # ******************************************************************** # Proposed Numpy method chaining using a new pipe method arr = PipeableArray(np.arange(1., 5.)) result = (arr .pipe(np.square) .pipe(np.log) .pipe(np.cumsum) ) # ******************************************************************** Benefits: - Readability: Method chaining with pipe offers a more readable and intuitive way to express complex data transformations, making the intended data processing pipeline easier to understand. - Customization: The pipe method allows users to chain custom functions or already implemented NumPy operations seamlessly. - Modularity: Users can define reusable functions and chain them together using pipe, leading to cleaner and more maintainable code. - Consistency: Introducing a pipe method in NumPy aligns with similar functionality available in other libraries like pandas, polars, etc. - Optimization: While NumPy may not currently optimize chained expressions, the introduction of pipe lays the groundwork for potential future optimizations with lazy evaluation. I believe this enhancement could benefit the NumPy community by providing a more flexible and expressive way to work with arrays. I'd love to see such a feature in Numpy and like to hear your thoughts on this proposal. Best regards, Oyibo