R has {drake} which makes it easy to make reproducible data pipelines. Does Python have a similar package? See R's {drake}. It allows you to define a reproducible pipeline

plan &lt;- drake_plan(
  raw_data = readxl::read_excel(file_in("raw_data.xlsx")),
  data = raw_data %&gt;%
    mutate(Species = forcats::fct_inorder(Species)),
  hist = create_plot(data),
  fit = lm(Sepal.Width ~ Petal.Width + Species, data),
  report = rmarkdown::render(
    knitr_in("report.Rmd"),
    output_file = file_out("report.html"),
    quiet = TRUE
  )
)

# call the pipeline
make(plan)


The great thing about drake is you that you can reload any of raw_data, data, hist, fit, report at any point. And if you change part of the code and make(plan) and {drake} will figure out which has change and just run that.
Sklearn has pipeline. If you have fit and transform attributes iteratively, you can make them pipeline by Pipeline class in sklearn.pipeline.

Read the docs:

https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html

Additionally you can save and load a pipeline object by joblib.dump and joblib.load.