مایکروسافت اخیرا یک کتابخانه پایتون به نام MarkItDown منتشر کرده که ابزاری کاربردی برای تبدیل فایلهای مختلف (فایلهای پاورپوینت، پیدیاف، ورد، اکسل و...) به Markdown است.
این موضوع برای تجزیه و تحلیل متون داخل فایلها بسیار کاربردیست.
این کتابخانه در حال حاضر از pdf، پاورپوینت (pptx.)، ورد (.docx)، اکسل (xlsx.)، تصاویر (متادیتای EXIF و OCR)، فایل صوتی (متادیتای EXIF و رونویسی گفتار)، HTML (به خصوص در مورد ویکیپدیا و...) و برخی از سایر فرمتهای مبتنی بر متن مثل csv, json, xml پشتیبانی میکند.
شروع استفاده از این ابزار بسیار سریع و راحت است:
🔗 مشاهده در GitHub
برای درک بهتر کاربرد آن میتوانید از دمویی که یک برنامهنویس خارجی در آدرس زیر قرار داده استفاده کنید:
https://msftmd.replit.app
#Python #library
🆔 @Python4all_pro
این موضوع برای تجزیه و تحلیل متون داخل فایلها بسیار کاربردیست.
این کتابخانه در حال حاضر از pdf، پاورپوینت (pptx.)، ورد (.docx)، اکسل (xlsx.)، تصاویر (متادیتای EXIF و OCR)، فایل صوتی (متادیتای EXIF و رونویسی گفتار)، HTML (به خصوص در مورد ویکیپدیا و...) و برخی از سایر فرمتهای مبتنی بر متن مثل csv, json, xml پشتیبانی میکند.
شروع استفاده از این ابزار بسیار سریع و راحت است:
from markitdown import MarkItDown
markitdown = MarkItDown()
result = markitdown.convert("test.xlsx")
print(result.text_content)
🔗 مشاهده در GitHub
برای درک بهتر کاربرد آن میتوانید از دمویی که یک برنامهنویس خارجی در آدرس زیر قرار داده استفاده کنید:
https://msftmd.replit.app
#Python #library
🆔 @Python4all_pro
Python Numpy Quiz :
https://www.clcoding.com/2024/12/python-numpy-quiz.html
#Python #NumPy
🆔 @Python4all_pro
https://www.clcoding.com/2024/12/python-numpy-quiz.html
#Python #NumPy
🆔 @Python4all_pro
🖥 markitdown: New open source Python package
This package allows you to easily convert various files to Markdown (for example, for indexing, text analysis, etc.).
The tool's API is very simple.
🖥 Github
#Python
🆔 @Python4all_pro
This package allows you to easily convert various files to Markdown (for example, for indexing, text analysis, etc.).
The tool's API is very simple.
🖥 Github
#Python
🆔 @Python4all_pro
👩💻 ClearerVoice-Studio — is an open source AI speech processing tool!
🌟 It includes the tasks of improving speech quality, separating audio sources, and extracting the target speaker. The project offers modern pre-trained models such as FRCRN and MossFormer, as well as scripts for training and retraining.
🔐 License: Apache-2.0
🖥 Github
#Python
🆔 @Python4all_pro
🌟 It includes the tasks of improving speech quality, separating audio sources, and extracting the target speaker. The project offers modern pre-trained models such as FRCRN and MossFormer, as well as scripts for training and retraining.
🔐 License: Apache-2.0
🖥 Github
#Python
🆔 @Python4all_pro
Python library that adds Generative AI capabilities to Pandas!
Introducing PandasAI: Analyze complex data frames and plot visualizations just by using natural language:
100% Open Source
With Pandas AI you can:
→ Clean the Data
→ Impute missing values
→ Generate New features
→ Analyze and manipulate the data
you can also ask it to plot the charts
Github Repo: https://github.com/Sinaptik-AI/pandas-ai
#Python #library
🆔 @Python4all_pro
Introducing PandasAI: Analyze complex data frames and plot visualizations just by using natural language:
100% Open Source
With Pandas AI you can:
→ Clean the Data
→ Impute missing values
→ Generate New features
→ Analyze and manipulate the data
you can also ask it to plot the charts
Github Repo: https://github.com/Sinaptik-AI/pandas-ai
#Python #library
🆔 @Python4all_pro
A Python library to query Apple's Find My network, supporting AirTags, iPhones,and other devices with features like location reporting, 2FA, and Bluetooth scanning
https://github.com/malmeloo/FindMy.py
#Python #library
🆔 @Python4all_pro
https://github.com/malmeloo/FindMy.py
#Python #library
🆔 @Python4all_pro
🖥 Python interpreter written in Python in 500 lines of code
Byterun is a Python interpreter. While working on Byterun, the author discovered that the fundamental structure of the Python interpreter easily fits within the 500-line size limit. This article examines the structure of the interpreter and provides context for further study.
The goal is not to explain everything there is to know about interpreters - as in many other interesting areas of programming and computer science - you can spend years developing a deep understanding of this topic.
Byterun was written by Ned Batchelder, drawing on the work of Paul Schwartz. Its structure is similar to Python's main implementation, CPython, so understanding Byterun will help you understand interpreters in general and the CPython interpreter in particular.
Article: https://aosabook.org/en/500L/a-python-interpreter-written-in-python.html
Github: https://github.com/nedbat/byterun
🆔 @Python4all_pro
Byterun is a Python interpreter. While working on Byterun, the author discovered that the fundamental structure of the Python interpreter easily fits within the 500-line size limit. This article examines the structure of the interpreter and provides context for further study.
The goal is not to explain everything there is to know about interpreters - as in many other interesting areas of programming and computer science - you can spend years developing a deep understanding of this topic.
Byterun was written by Ned Batchelder, drawing on the work of Paul Schwartz. Its structure is similar to Python's main implementation, CPython, so understanding Byterun will help you understand interpreters in general and the CPython interpreter in particular.
Article: https://aosabook.org/en/500L/a-python-interpreter-written-in-python.html
Github: https://github.com/nedbat/byterun
🆔 @Python4all_pro
معرفی کتاب ترفندها و ابزارهای پایتون برای دانشمندان داده
با انجام پروژه های این کتاب می تونید پایتون رو بهتر یاد بگیرید و با دنیای علم داده بهتر آشنا شوید :
+۷۰۰ نکته برای کدنویسی بهتر در پایتون
+۱۵۰ مقاله علم داده
راهنمای کار با کتابخانه های محبوب دانشمندان داده
👉https://github.com/khuyentran1401/Efficient_Python_tricks_and_tools_for_data_scientists
📌https://codecut.ai/
#پایتون #علم_داده
🆔 @Python4all_pro
با انجام پروژه های این کتاب می تونید پایتون رو بهتر یاد بگیرید و با دنیای علم داده بهتر آشنا شوید :
+۷۰۰ نکته برای کدنویسی بهتر در پایتون
+۱۵۰ مقاله علم داده
راهنمای کار با کتابخانه های محبوب دانشمندان داده
👉https://github.com/khuyentran1401/Efficient_Python_tricks_and_tools_for_data_scientists
📌https://codecut.ai/
#پایتون #علم_داده
🆔 @Python4all_pro
VizTracer: A tool to trace and visualize Python code execution, logging function entries/exits, arguments, return values, and variables, with support for threading, multiprocessing, and async operations
https://github.com/gaogaotiantian/viztracer
#python
🆔 @Python4all_pro
https://github.com/gaogaotiantian/viztracer
#python
🆔 @Python4all_pro
python-sortedcontainers - A library of Python collections that support automatic sorting: SortedList, SortedDict and SortedSet!
🌟 These data structures are implemented in pure Python but provide performance comparable to C libraries. The library stands out for its ease of use, no compilation required, and efficient operations such as insertion, deletion, and lookup that run faster than linear time.
🔐 License: Apache-2.0
🖥 Github
#python #library
🆔 @Python4all_pro
🌟 These data structures are implemented in pure Python but provide performance comparable to C libraries. The library stands out for its ease of use, no compilation required, and efficient operations such as insertion, deletion, and lookup that run faster than linear time.
🔐 License: Apache-2.0
🖥 Github
#python #library
🆔 @Python4all_pro
🖥 Githubkit
When you call the GitHub API, you manually process HTTP requests, tokens, and JSON responses, which is time-consuming and error-prone.
githubkit, a Python library, provides a clean, typed interface for interacting with the GitHub API
Example:
▪ Github
▪ Documentation
🆔 @Python4all_pro
When you call the GitHub API, you manually process HTTP requests, tokens, and JSON responses, which is time-consuming and error-prone.
githubkit, a Python library, provides a clean, typed interface for interacting with the GitHub API
pip install githubkit
# or, use poetry
poetry add githubkit
# or, use pdm
pdm add githubkit
# or, use uv
uv add githubkit
Example:
from githubkit import Response
from githubkit.versions.latest.models import FullRepository
resp: Response[FullRepository] = github.rest.repos.get("owner", "repo")
repo: FullRepository = resp.parsed_data
print(repo.full_name)
▪ Github
▪ Documentation
🆔 @Python4all_pro