How do you make a Python loop faster?

How do you make a Python loop faster?

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How do you make a Python loop faster?

Programmers are always looking for ways to make their code more efficient. One way to do this is to use a faster loop. Python is a high-level programming language that is widely used by developers and software engineers. It is known for its readability and ease of use. However, one downside of Python is that its loops can be slow. This can be a problem when you need to process large amounts of data. There are several ways to make Python loops faster. One way is to use a faster looping construct, such as C. Another way is to use an optimized library, such as NumPy. Finally, you can vectorize your code, which means converting it into a format that can be run on a GPU or other parallel computing platform. By using these techniques, you can significantly speed up your Python code.

According to Vladislav Zorov, If not talking about NumPy or something, try to use list comprehension expressions where possible. Those are handled by the C code of the Python interpreter, instead of looping in Python. Basically same idea like the NumPy solution, you just don’t want code running in Python.

Example: (Python 3.0)

lst = [n for n in range(1000000)]
def loops():
    newlst = []
    for n in lst:
        newlst.append(n * 2)
    return newlst
def lstcomp():
    return [n * 2 for n in lst]
from timeit import timeit
print(timeit(loops, number=100))
#18.953254899999592 seconds
print(timeit(lstcomp, number=100))
#11.669047399991541 seconds
Or Do this in Python 2.0
How do you make a Python loop faster?
How do you make a Python loop faster?

Python list traversing tip:

Instead of this: for i in range(len(l)): x = l[i]

Use this for i, x in enumerate(l): …

TO keep track of indices and values inside a loop.

Twice faster, and the code looks better.

Another option is to write loops in C instead of Python. This can be done by using a Python extension module such as pyximport. By doing this, programmers can take advantage of the speed of C while still using the convenient syntax of Python.

Finally, developers can also improve the performance of their code by making use of caching. By caching values that are computed inside a loop, programmers can avoid having to recalculate them each time through the loop. By taking these steps, programmers can make their Python code more efficient and faster.

Very Important: Don’t worry about code efficiency until you find yourself needing to worry about code efficiency.

The place where you think about efficiency is within the logic of your implementations.

This is where “big O” discussions come in to play. If you aren’t familiar, here is a link on the topic

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How do you make a Python loop faster?
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    by /u/louisbrulenaudet (Python) on March 28, 2024 at 7:36 am

    This groundwork enables ecosystem players to consider deploying RAG solutions in real time without having to configure data retrieval systems. Link to Louis Brulé-Naudet's Hugging Face profile ```python import concurrent.futures import logging from datasets from tqdm import tqdm def dataset_loader( name:str, streaming:bool=True ) -> datasets.Dataset: """ Helper function to load a single dataset in parallel. Parameters ---------- name : str Name of the dataset to be loaded. streaming : bool, optional Determines if datasets are streamed. Default is True. Returns ------- dataset : datasets.Dataset Loaded dataset object. Raises ------ Exception If an error occurs during dataset loading. """ try: return datasets.load_dataset( name, split="train", streaming=streaming ) except Exception as exc: logging.error(f"Error loading dataset {name}: {exc}") return None def load_datasets( req:list, streaming:bool=True ) -> list: """ Downloads datasets specified in a list and creates a list of loaded datasets. Parameters ---------- req : list A list containing the names of datasets to be downloaded. streaming : bool, optional Determines if datasets are streamed. Default is True. Returns ------- datasets_list : list A list containing loaded datasets as per the requested names provided in 'req'. Raises ------ Exception If an error occurs during dataset loading or processing. Examples -------- >>> datasets = load_datasets(["dataset1", "dataset2"], streaming=False) """ datasets_list = [] with concurrent.futures.ThreadPoolExecutor() as executor: future_to_dataset = {executor.submit(dataset_loader, name): name for name in req} for future in tqdm(concurrent.futures.as_completed(future_to_dataset), total=len(req)): name = future_to_dataset[future] try: dataset = future.result() if dataset: datasets_list.append(dataset) except Exception as exc: logging.error(f"Error processing dataset {name}: {exc}") return datasets_list req = [ "louisbrulenaudet/code-artisanat", "louisbrulenaudet/code-action-sociale-familles", "louisbrulenaudet/code-assurances", "louisbrulenaudet/code-aviation-civile", "louisbrulenaudet/code-cinema-image-animee", "louisbrulenaudet/code-civil", "louisbrulenaudet/code-commande-publique", "louisbrulenaudet/code-commerce", "louisbrulenaudet/code-communes", "louisbrulenaudet/code-communes-nouvelle-caledonie", "louisbrulenaudet/code-consommation", "louisbrulenaudet/code-construction-habitation", "louisbrulenaudet/code-defense", "louisbrulenaudet/code-deontologie-architectes", "louisbrulenaudet/code-disciplinaire-penal-marine-marchande", "louisbrulenaudet/code-domaine-etat", "louisbrulenaudet/code-domaine-etat-collectivites-mayotte", "louisbrulenaudet/code-domaine-public-fluvial-navigation-interieure", "louisbrulenaudet/code-douanes", "louisbrulenaudet/code-douanes-mayotte", "louisbrulenaudet/code-education", "louisbrulenaudet/code-electoral", "louisbrulenaudet/code-energie", "louisbrulenaudet/code-entree-sejour-etrangers-droit-asile", "louisbrulenaudet/code-environnement", "louisbrulenaudet/code-expropriation-utilite-publique", "louisbrulenaudet/code-famille-aide-sociale", "louisbrulenaudet/code-forestier-nouveau", "louisbrulenaudet/code-fonction-publique", "louisbrulenaudet/code-propriete-personnes-publiques", "louisbrulenaudet/code-collectivites-territoriales", "louisbrulenaudet/code-impots", "louisbrulenaudet/code-impots-annexe-i", "louisbrulenaudet/code-impots-annexe-ii", "louisbrulenaudet/code-impots-annexe-iii", "louisbrulenaudet/code-impots-annexe-iv", "louisbrulenaudet/code-impositions-biens-services", "louisbrulenaudet/code-instruments-monetaires-medailles", "louisbrulenaudet/code-juridictions-financieres", "louisbrulenaudet/code-justice-administrative", "louisbrulenaudet/code-justice-militaire-nouveau", "louisbrulenaudet/code-justice-penale-mineurs", "louisbrulenaudet/code-legion-honneur-medaille-militaire-ordre-national-merite", "louisbrulenaudet/livre-procedures-fiscales", "louisbrulenaudet/code-minier", "louisbrulenaudet/code-minier-nouveau", "louisbrulenaudet/code-monetaire-financier", "louisbrulenaudet/code-mutualite", "louisbrulenaudet/code-organisation-judiciaire", "louisbrulenaudet/code-patrimoine", "louisbrulenaudet/code-penal", "louisbrulenaudet/code-penitentiaire", "louisbrulenaudet/code-pensions-civiles-militaires-retraite", "louisbrulenaudet/code-pensions-retraite-marins-francais-commerce-peche-plaisance", "louisbrulenaudet/code-pensions-militaires-invalidite-victimes-guerre", "louisbrulenaudet/code-ports-maritimes", "louisbrulenaudet/code-postes-communications-electroniques", "louisbrulenaudet/code-procedure-civile", "louisbrulenaudet/code-procedure-penale", "louisbrulenaudet/code-procedures-civiles-execution", "louisbrulenaudet/code-propriete-intellectuelle", "louisbrulenaudet/code-recherche", "louisbrulenaudet/code-relations-public-administration", "louisbrulenaudet/code-route", "louisbrulenaudet/code-rural-ancien", "louisbrulenaudet/code-rural-peche-maritime", "louisbrulenaudet/code-sante-publique", "louisbrulenaudet/code-securite-interieure", "louisbrulenaudet/code-securite-sociale", "louisbrulenaudet/code-service-national", "louisbrulenaudet/code-sport", "louisbrulenaudet/code-tourisme", "louisbrulenaudet/code-transports", "louisbrulenaudet/code-travail", "louisbrulenaudet/code-travail-maritime", "louisbrulenaudet/code-urbanisme", "louisbrulenaudet/code-voirie-routiere" ] dataset = load_datasets( req=req, streaming=True ) ``` submitted by /u/louisbrulenaudet [link] [comments]

  • Makefile Parser for Python
    by /u/Cybasura (Python) on March 28, 2024 at 2:36 am

    Hello everyone! I am proud to introduce a Makefile Parser for Python that I think will be useful! the link is Thanatisia/makefile-parser-python As an introduction, I have been using python and been following this subreddit for quite awhile now, but this is my first post Recently, I've been planning a side-project involving the use of Makefiles in Python, and I required the use of a Makefile parser ala json or pyyaml - whereby you would import a Makefile into python as objects/dictionary/lists. Hence, I started searching for a Makefile Parser. The only parser I've found is PyMake(2) which is cool but it hasnt been updated since 2017 from what I recall and that it is more of a CLI utility, so with that in mind, I went about to make it I hope that this will be as useful as it is for me, currently I am using this in a side project and i'm able to format it such that its printing out a typical Makefile structure right off the bat, which is pretty nice. Additional information to the project What My Project Does This is a Makefile Parser, made in Python. The operational workflow is basically Start --> Import File into dictionary --> Manipulation and Usage --> Processing --> Output --> Export Target Audience (e.g., Is it meant for production, just a toy project, etc.) This is a library/package/module by design, much like json or pyyaml as mentioned but a smaller scale at the moment as its just started. I'm not sure if it applies for you but its a parser/importer Comparison (A brief comparison explaining how it differs from existing alternatives.) I'm not sure if there are any other Makefile Parsers other than Pymake2, but the idea is similar to the aforementioned ideas submitted by /u/Cybasura [link] [comments]

  • Thursday Daily Thread: Python Careers, Courses, and Furthering Education!
    by /u/AutoModerator (Python) on March 28, 2024 at 12:00 am

    Weekly Thread: Professional Use, Jobs, and Education 🏢 Welcome to this week's discussion on Python in the professional world! This is your spot to talk about job hunting, career growth, and educational resources in Python. Please note, this thread is not for recruitment. How it Works: Career Talk: Discuss using Python in your job, or the job market for Python roles. Education Q&A: Ask or answer questions about Python courses, certifications, and educational resources. Workplace Chat: Share your experiences, challenges, or success stories about using Python professionally. Guidelines: This thread is not for recruitment. For job postings, please see r/PythonJobs or the recruitment thread in the sidebar. Keep discussions relevant to Python in the professional and educational context. Example Topics: Career Paths: What kinds of roles are out there for Python developers? Certifications: Are Python certifications worth it? Course Recommendations: Any good advanced Python courses to recommend? Workplace Tools: What Python libraries are indispensable in your professional work? Interview Tips: What types of Python questions are commonly asked in interviews? Let's help each other grow in our careers and education. Happy discussing! 🌟 submitted by /u/AutoModerator [link] [comments]

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  • Open Jupyter Notebooks in Kaggle? (from GitHub)
    by /u/pbeens (Python) on March 27, 2024 at 2:52 pm

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  • Type-Level Programming: a POC
    by /u/Kiuhnm (Python) on March 27, 2024 at 11:24 am

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    by /u/AutoModerator (Python) on March 27, 2024 at 12:00 am

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  • Monday Daily Thread: Project ideas!
    by /u/AutoModerator (Python) on March 25, 2024 at 12:00 am

    Weekly Thread: Project Ideas 💡 Welcome to our weekly Project Ideas thread! Whether you're a newbie looking for a first project or an expert seeking a new challenge, this is the place for you. How it Works: Suggest a Project: Comment your project idea—be it beginner-friendly or advanced. Build & Share: If you complete a project, reply to the original comment, share your experience, and attach your source code. Explore: Looking for ideas? Check out Al Sweigart's "The Big Book of Small Python Projects" for inspiration. Guidelines: Clearly state the difficulty level. Provide a brief description and, if possible, outline the tech stack. Feel free to link to tutorials or resources that might help. Example Submissions: Project Idea: Chatbot Difficulty: Intermediate Tech Stack: Python, NLP, Flask/FastAPI/Litestar Description: Create a chatbot that can answer FAQs for a website. Resources: Building a Chatbot with Python Project Idea: Weather Dashboard Difficulty: Beginner Tech Stack: HTML, CSS, JavaScript, API Description: Build a dashboard that displays real-time weather information using a weather API. Resources: Weather API Tutorial Project Idea: File Organizer Difficulty: Beginner Tech Stack: Python, File I/O Description: Create a script that organizes files in a directory into sub-folders based on file type. Resources: Automate the Boring Stuff: Organizing Files Let's help each other grow. Happy coding! 🌟 submitted by /u/AutoModerator [link] [comments]

  • Alternative Queries: Typed and Reusable Handcrafted SQL
    by /u/baluyotraf (Python) on March 24, 2024 at 10:23 pm

    Hello! It's still early in development, but I just want to know if people are interested. What My Project Does I created a library to integrate Pydantic type checking for handwritten SQL queries. It also allows you to test the types, and create nested queries easily. Alternative Queries Target Audience We had a project recently were we had to use handwritten SQL and managing the parameters and reusing queries was quite a hassle. So I'm planning to use this on my production projects moving forward. Comparison I would say in terms of usage, it's like Pydantic + SqlParams, but only with the python default formatting. SqlParams submitted by /u/baluyotraf [link] [comments]

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