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Add GCI110: avoid wildcard imports in Python
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‎CHANGELOG.md‎

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### Added
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- Add rule GCI110: Avoid wildcard imports in Python (`from module import *`)
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### Changed
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- add documentation link for each rule in RULES.md file

‎RULES.md‎

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| GCI106 | Avoid Square Root Operations In Loop | Using scalar `math.sqrt` (or `numpy.sqrt` on individual values) inside loops leads to inefficient code | | 🚫 | 🚫 | 🚫 | ✅ | 🚫 | 🚫 | 🚫 | [documentation](https://github.com/green-code-initiative/creedengo-rules-specifications/tree/main/src/main/rules/GCI106) |
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| GCI107 | Data : Avoid Iterative Matrix Operations | Use vectorization by the usage of the built-in functions of TensorFlow, NumPy or Pandas | | 🚫 | 🚫 | 🚫 | ✅ | 🚫 | 🚫 | 🚫 | [documentation](https://github.com/green-code-initiative/creedengo-rules-specifications/tree/main/src/main/rules/GCI107) |
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| GCI108 | Prefer Append Left | When you want to insert an element at the beginning of a list, it's better to use a deques or a double linkedlist. | | ❓ | ❓ | ❓ | ✅ | ❓ | ❓ | ❓ | [documentation](https://github.com/green-code-initiative/creedengo-rules-specifications/tree/main/src/main/rules/GCI108) |
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| GCI110 | Python: Avoid wildcard imports (`from module import *`) | Wildcard imports bind many names and run module top‑level init, slightly increasing import time and memory. Prefer explicit named imports | | 🚫 | 🚫 | 🚫 | ✅ | 🚫 | 🚫 | 🚫 | [documentation](https://github.com/green-code-initiative/creedengo-rules-specifications/tree/main/src/main/rules/GCI110) |
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| GCI203 | Detect unoptimized file formats | When it is possible, to use svg format image over other image format | | 🚧 | 🚀 | 🚀 | ✅ | 🚀 | 🚀 | 🚫 | [documentation](https://github.com/green-code-initiative/creedengo-rules-specifications/tree/main/src/main/rules/GCI203) |
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| GCI404 | Avoid list comprehension in iterations | Use generator comprehension instead of list comprehension in for loop declaration | | 🚫 | 🚫 | 🚫 | ✅ | 🚫 | 🚫 | 🚫 | [documentation](https://github.com/green-code-initiative/creedengo-rules-specifications/tree/main/src/main/rules/GCI404) |
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| GCI522 | Sobriety: Brightness Override | To avoid draining the battery, iOS and Android devices adapt the brightness of the screen depending on the environment light. | | 🚫 | 🚫 | ✅ | 🚫 | 🚫 | 🚫 | 🚫 | [documentation](https://github.com/green-code-initiative/creedengo-rules-specifications/tree/main/src/main/rules/GCI522) |

‎src/main/rules/GCI110/GCI110.json‎

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{
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"title": "Avoid wildcard imports in Python (from module import *)",
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"type": "CODE_SMELL",
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"status": "ready",
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"remediation": {
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"func": "Constant/Issue",
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"constantCost": "2min"
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},
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"tags": [
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"creedengo",
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"eco-design",
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"performance",
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"python",
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"bad-practice",
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"memory",
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"import",
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"namespace"
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],
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"defaultSeverity": "Minor"
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}
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‎src/main/rules/GCI110/python/GCI110.asciidoc‎

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= GCI110 — Avoid wildcard imports in Python (`from module import *`)
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== Why this rule?
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Wildcard imports bind many names into the current namespace and execute all top‑level module initialization.
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This can slightly increase import time, parsing, and memory. Multiplied across many modules or cold starts,
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this adds avoidable CPU/energy. It also hurts analyzability and maintainability by obscuring where names come from.
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This rule is advisory for eco‑design: the effect is modest compared to algorithmic issues, so severity is Minor.
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== Eco‑performance and energy
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What changes (small but measurable in some contexts):
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- Star imports perform mass‑binding of all exported names into the importer's namespace (O(N) assignments).
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- This adds a little extra CPU at import time and slightly increases the importer's namespace size in memory.
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When it can matter (accumulates across many imports/cold starts):
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- Many files using star imports across a large codebase.
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- Cold‑start heavy workloads (serverless/functions, frequent short‑lived jobs).
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- In data and AI projects, small import-time costs can repeat many times and add up.
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When it is typically negligible:
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- Long‑running services (module import is cached in sys.modules; mass‑binding cost is paid once).
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=== Performance measurement example
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A simple benchmark comparing import methods shows measurable differences:
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[source,python]
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----
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import timeit
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# Normal import
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print("normal import:", timeit.timeit("import math", number=1000))
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# Wildcard import (using exec to avoid syntax restrictions)
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def test_wildcard_import():
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exec("from math import *")
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print("from import *:", timeit.timeit(test_wildcard_import, number=1000))
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----
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Typical results on Python 3.12:
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- Normal import: ~0.0018 seconds (1000 iterations)
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- Wildcard import: ~0.0245 seconds (1000 iterations)
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This represents approximately a **13x performance difference** for import operations, demonstrating the overhead of wildcard imports.
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== Rule scope
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- Flags top‑level statements: `from module import *`.
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- Focuses on readability/maintainability with secondary eco‑impact (CPU/memory at import time).
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== Exceptions (when it’s acceptable)
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- Package aggregation files (`__init__.py`) that re‑export a curated API.
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- If the target module defines `__all__`, making the export surface explicit.
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- Generated code, quick scripts/REPL/notebooks, or educational material.
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== Non‑compliant
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[source,python]
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----
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from utils import *
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process(data)
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----
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== Compliant alternatives
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[source,python]
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----
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# Explicit named imports
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from utils import parse, process
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# Or module import with alias
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import utils as u
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u.process(data)
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----
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== Public API re‑exports
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[source,python]
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----
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# In package/__init__.py
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from .parse import parse
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from .process import process
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__all__ = ["parse", "process"]
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----
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== References
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- Python Performance Tips: https://wiki.python.org/moin/PythonSpeed/PerformanceTips
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- CPython source code — `import.c`: https://github.com/python/cpython/blob/main/Python/import.c
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- Python Anti-Patterns — "Using wildcard imports (`from … import *`)": https://docs.quantifiedcode.com/python-anti-patterns/maintainability/from_module_import_all_used.html
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- PEP 8 — Style Guide for Python Code: https://peps.python.org/pep-0008/#imports
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- Stack Overflow — "Should wildcard import be avoided?": https://stackoverflow.com/questions/3615125/should-wildcard-import-be-avoided
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- Pybites — "Why You Should Avoid `import *` in Python": https://pybit.es/articles/why-you-should-avoid-import-in-python/

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