我发现了用 Python 编写简洁代码的秘诀！

Code Quality in Data Science and Python Best Practices

Writing concise code is essential for maintainability and scalability. Code quality is crucial both during development and in production. Data scientists often use Jupyter Notebooks for experimentation, but production code needs to be robust and maintainable.

Meaningful Naming

Naming variables and functions meaningfully is a best practice that enhances code readability and maintainability. Descriptive names reduce the need for comments and make the code's purpose clear.

import pandas as pd
from sklearn.model_selection import train_test_split
def load_and_split(d):
    # Example code

Functions

Functions should be concise, not exceeding 20 lines, and should perform a single responsibility. Large blocks of code should be refactored into separate functions, and naming should strike a balance between descriptiveness and brevity.

def load_clean_feature_engineer_and_split(data_path):
    # Example code

Comments

Comments are useful but can also be a sign of bad code. When adding comments, consider if they are necessary or if the code can be refactored for clarity without them.

def load_clean_feature_engineer_and_split(data_path):
    # Example code with comments

Formatting

Formatting is crucial for code readability. PEP 8 provides guidelines for Python code formatting, such as using four spaces for indentation and keeping lines to 79 characters. Tools like Pylint and autopep8 can help enforce these standards.

def load_data(data_path):
    # Example code

Error Handling

Error handling ensures that your code does not crash or produce incorrect results when encountering unexpected situations. Custom exceptions and clear error messages can improve user experience.

def load_data(data_path):
    # Example code with error handling

Object-Oriented Programming

OOP encapsulates data and behavior into objects, providing a clear structure for programs. OOP benefits include modularity, code reuse through inheritance, and improved readability and maintainability.

class TrainingPipeline:
    # Example OOP code

Testing

Testing is critical for the success of any project. Test-Driven Development (TDD) encourages writing tests before code and ensures that only the necessary amount of code is written to pass tests. Tools like pytest facilitate unit testing in Python.

class ChurnPredictionTrainPipeline(TrainingPipeline):
    # Example code with testing

Conclusion

Adhering to four simple design rules can make code more concise, readable, and maintainable: running all tests, eliminating duplications, reflecting the programmer's intent, and minimizing the number of classes and methods. Automation and development environment tools can support adhering to clean code guidelines.

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文章来源：

数据STUDIO

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