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Building a Machine Learning Pipeline from Scratch

Code Style

Explore how to improve Python code readability and maintainability in ML pipelines by applying good code style practices. Learn to organize imports, use meaningful names, add documentation, and leverage formatting tools such as Black and isort to create clean, consistent code for better collaboration and fewer errors.

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Python code with style issues

Code style is very important for readability. Consider the following code:

Python 3.8
from fruits import orange
import numpy as np
from fruits import apple
def p(list):
    #print list
    for i in len(list):
        print(list[i])
apple_varieties_list = ["pink lady",
"empire",
"fuji", "gala",
    "golden delicious", "granny smith", "honeycrisp", "mcintosh", "red delicious", "ginger gold", "jersey mac", "paula red", "ambrosia"]
for a in apple_varieties_list:
    print(a)
p(apple_varieties_list)
# handle oranges
# do something with numpy

Do you see any issues? There are several here, including the following:

  • The two imports from fruits are in lines 1 and 3 when they could have been on the same line (e.g., from fruits import apple, orange) or together.

  • There are no blank lines in the body of the code, which makes it harder to read. ...