dplyr-style Data Manipulation with Pipes in Python

Piping Let’s say you want to perform n discrete transformation operations on your dataset before outputting the final result. The most common way is to perform the operations step by step and store the result of each step in a variable. The variable holding the intermediate result is then used in the next step of the transformation pipeline. Let’s take a look at an abstract example. # 'original_da

[Aobei]

dplyr風にハンドリングする。filterでなくmask