Membership Operators

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What Are Membership Operators in Python?

Python provides two membership operators to test whether a value is in a sequence (like a list, string, tuple, etc.) or not:

Operator	Meaning	Example
in	Returns True if present	"cat" in "category"
not in	Returns True if not present	"dog" not in "cat"

Step-by-Step Usage

1. Using in with Lists

features = ['height', 'weight', 'age']
print('age' in features)      # True
print('income' in features)   # False

ML Tip: Useful when selecting features dynamically from a dataset.

2. Using not in with Lists

model_params = ['learning_rate', 'max_depth']
print('gamma' not in model_params)  # True

ML Tip: Before tuning, check if a parameter is valid for your model.

3. Using with Strings

sentence = "Machine learning is awesome"
print("learning" in sentence)       # True
print("AI" not in sentence)         # True

NLP Example: You might want to detect keywords in a sentence.

4. Using with Tuples

allowed_metrics = ('accuracy', 'precision', 'recall')
print('f1-score' in allowed_metrics)      # False

Practical ML Tip: Use this in model evaluation settings.

5. Using with Dictionaries (Only Keys are Checked)

params = {'n_estimators': 100, 'max_depth': 5}
print('max_depth' in params)         # True
print('learning_rate' not in params) # True

Tip: in only checks keys in a dictionary, not values.

ML Utility Summary

Use Case	Membership Operator	Example
Feature selection	in	if 'age' in df.columns:
Parameter check	in, not in	'max_depth' in model_params
Word/keyword search in NLP	in	'data' in sentence
Model metric validation	in	'accuracy' in metrics_list

Keywords

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