When iterating over a dictionary in Python, always choose the iteration method appropriate to your needs:

Avoid using .items() if you only use the key or the value, as this creates an unnecessary tuple, leading to increased memory allocation and slower execution.

Non Compliant Code Example

my_dict = {'a': 1, 'b': 2, 'c': 3}

# Only keys needed, but using items()
for k, v in my_dict.items():
    print(k)

In this example, .items() is unnecessarily used to extract key-value pairs, even though only the key is used. This results in the creation of extra tuples, increasing memory usage and processing time.

Compliant Solution

my_dict = {'a': 1, 'b': 2, 'c': 3}

# Proper use of keys()
for k in my_dict.keys():
    print(k)

This version uses .keys(), which is more efficient when values are not needed.

Relevance Analysis

Experiments were conducted to evaluate the performance and environmental impact of compliant vs. non-compliant dictionary iteration.

Configuration

  • Processor: Intel® Core™ Ultra 5 135U, 2100 MHz, 12 cores, 14 logical processors

  • RAM: 16 GB

  • CO2 Emissions Measurement: Using CodeCarbon

Context

Two iterations were benchmarked: - Compliant: Using .keys(). - Non-compliant: Using .items().

Metrics assessed: - Execution time - Carbon emissions

Impact Analysis

dict emissions
dict time

  • Execution Time: Compliant code executed approximately 5x faster than non-compliant code.

  • CO₂ Emissions: Compliant code emitted ~60% less carbon due to reduced processing overhead.

These results show that even at the micro-optimization level, more efficient code contributes to measurable energy savings and lower environmental impact.

Conclusion

Unlike .items(), which creates a (key, value) tuple for each entry, .keys() and .values() access only what’s needed without tuple allocation, making them more efficient.

Using .keys() or .values() instead of .items() when you only need one part of the dictionary:

  • Improves code clarity and intent

  • Reduces memory and processing overhead

  • Shortens execution time

  • Decreases carbon footprint, especially in large-scale data workflows

References