RT Avi Chawla (@_avichawla)Speed up your native Python code by over 50x!And it takes just 4 simple steps.Python’s default interpreter (CPython) is slow primarily because of its dynamicity.For instance, after defining a variable of a specific type, it can be changed to some other type.But these dynamic manipulations come at the cost of run-time and memory overheads.The Cython module converts your Python code into C.Steps to use the Cython module (refer to the image as you read):1) Load the Cython module: %lo ...

If you found it insightful, reshare it with your network.Find me → @_avichawlaEvery day, I share tutorials and insights on DS, ML, LLMs, and RAGs. https://t.co/daRwW5JFjhAvi Chawla (@_avichawla):Speed up your native Python code by over 50x!And it takes just 4 simple steps.Python’s default interpreter (CPython) is slow primarily because of its dynamicity.For instance, after defining a variable of a specific type, it can be changed to some other type.But these https://t.co/QtcuC4C8rL ...

Starting Python 3.14, another way to speed up Python code is by disabling GIL.Earlier, despite writing multi-threaded code, Python could only run one thread at a time. But now, Python can run it in a multi-threaded fashion.👉 What are some other ways to speed up Python code? https://t.co/DEIOEejKa8 ...

Core Viewpoint - The company, Zhongke Chuangda, has received recognition for its contributions to the openEuler community, indicating a strong commitment to open-source technology and collaboration in key technical areas [2] Group 1: Company Achievements - The subsidiary, Aosiwei, has been awarded the title of "Outstanding Contribution Unit" by the openEuler community [2] - Aosiwei has integrated 431 annual pull requests (PRs) into the openEuler community, ranking 11th in contributions [2] Group 2: Areas of Collaboration - The company is deeply involved in several critical technology fields, including Python, OpenStack, AI, and Networking [2]

Learning Resources - A free 5-step roadmap to learn Python is available [1] Python Expertise - Avi Chawla has been coding in Python for 9 years [1] Coding Roadmap - A complete roadmap for learning Python is provided [1]

If you found it insightful, reshare it with your network.Find me → @_avichawlaEvery day, I share tutorials and insights on DS, ML, LLMs, and RAGs.Avi Chawla (@_avichawla):I've been coding in Python for 9 years now.If I were to start over today, here's a complete roadmap: ...

Cloud Computing Solution - Coiled simplifies cloud-based Python workflows, reducing complexity [1] - Coiled automates environment synchronization, hardware provisioning, and shutdown in the cloud [2] - Coiled offers 500 free CPU hours per month for most users [2] Key Features - Coiled enables running jobs hourly, concurrently, or with specific hardware like GPUs [3] - Coiled supports running jobs in different regions for data proximity [3] - Coiled allows using different languages, packages, or binaries [3] Development Process - Users import Coiled and decorate Python functions, specifying hardware and region [2] - Coiled eliminates the need for navigating consoles, setting IAM policies, or writing YAML configs [1][2] - Coiled helps in monitoring billing spikes [1]

Cloud Computing Challenges - Cloud usage involves navigating consoles, setting IAM policies, writing YAML configs, and monitoring billing spikes [1] - Running Python workflows on the cloud can be complex [1] Coiled Solution - Coiled simplifies cloud usage for Python users [1] - Coiled enables running any workflow [1] Call to Action - The author encourages readers to reshare the content [1] - The author shares tutorials and insights on DS, ML, LLMs, and RAGs daily [1]

Core Viewpoint - The article emphasizes the transformative impact of AI technology on the investment research industry, highlighting the necessity for financial professionals to embrace AI and Python for enhanced efficiency and data analysis capabilities [1]. Group 1: AI and Python in Data Acquisition and Processing - AI and Python play a significant role in efficiently acquiring and processing financial data, utilizing tools like web scraping with Python libraries such as requests and Selenium to gather key information from financial reports and market data [1]. - The integration of AI tools allows for rapid extraction and analysis of financial data, facilitating comparisons across multiple companies and enhancing the accuracy of financial evaluations through models like DCF [2]. Group 2: Report Generation and Data Visualization - AI excels in generating high-quality financial reports quickly, using visualization tools to present complex financial data in an intuitive manner, thereby increasing the report's persuasiveness and appeal [3]. - Python libraries such as Matplotlib and Pyecharts are utilized for dynamic data visualization, enhancing the clarity and impact of financial reports [3]. Group 3: Automation of Financial Processes - The combination of AI and Python enables the automation of financial processes, such as batch file generation and automated auditing, significantly improving work efficiency [4]. - Developing personalized AI systems can provide tailored support for investment research, streamlining data management and analysis [4]. Group 4: Quantitative Investment Strategies - The application of AI and Python in quantitative investment is promising, offering robust support for developing and backtesting investment strategies, including K-line charting [5]. - A dedicated quantitative strategy backtesting platform developed in Python allows investors to test and optimize their investment strategies, potentially increasing returns [5]. Group 5: Course Offerings - The course titled "AI Large Model + Python Empowering Financial Full Process Practice" aims to explore advanced applications of AI and Python in investment research, covering complex strategy construction and intelligent research system development [5]. - The course includes 86 detailed lessons totaling 32.5 hours, providing comprehensive coverage of AI and Python in financial research, along with practical case studies [8].

Core Viewpoint - The article emphasizes that AI technology is reshaping the investment research industry, making the adoption of AI and Python essential for financial professionals to enhance efficiency and effectiveness in their work [1]. Data Acquisition and Processing - AI and Python play a significant role in acquiring and processing financial data, enabling efficient retrieval of key information such as financial reports and market data through web scraping techniques [1]. - Tools like Python's requests and Selenium libraries facilitate data extraction, while regular expressions and Beautiful Soup (BS) libraries assist in data parsing for subsequent analysis [1]. Financial Analysis and Valuation - In financial analysis, AI tools can quickly extract and analyze financial data, allowing for comparisons between single and multiple companies [2]. - The combination of AI and Python, particularly using the Pandas library, enables in-depth analysis of key corporate metrics and the construction of DCF valuation models for more accurate enterprise value assessments [2]. Report Writing and Data Visualization - AI excels in report writing and data visualization, generating high-quality financial reports rapidly [3]. - Tools like Huohua Shutu and mind mapping software help present complex financial data in intuitive graphical formats, while Python libraries such as Matplotlib and Pyecharts enable dynamic data visualization [3]. Automation of Financial Processes - The integration of AI and Python allows for the automation of financial processes, such as batch file generation and automated auditing, significantly improving work efficiency [4]. - Developing personalized AI systems can provide tailored investment research support, enhancing data processing capabilities [4]. Quantitative Investment Strategies - The application of AI and Python in quantitative investment is promising, supporting everything from K-line chart plotting to the development and backtesting of classic investment strategies [5]. - Python-based quantitative strategy backtesting platforms allow investors to easily test and optimize their investment strategies, potentially increasing returns [5]. Course Offerings - The course "AI Large Model + Python Empowering Financial Full Process Practice" aims to explore advanced applications of AI and Python in investment research, covering complex strategy construction and intelligent research system development [5]. - The course includes 86 detailed lessons totaling 32.5 hours, providing a comprehensive overview of AI and Python in financial research, along with practical case studies [7]. Course Structure - The first chapter focuses on the application of AI large models in financial research, teaching participants how to design prompts and extract information from real financial documents [8]. - The second chapter covers practical skills in Python for finance, including data processing, automated data scraping, and tool development [10].