How to Build and Use Langchain Agents with Pandas DataFrames скачать в хорошем качестве

How to Build and Use Langchain Agents with Pandas DataFrames

🧠 Don’t miss out! Get FREE access to my Skool community — packed with resources, tools, and support to help you with Data, Machine Learning, and AI Automations! 📈 https://www.skool.com/data-and-ai-aut... Learn how to supercharge your data workflows by combining Langchain agents with Pandas DataFrames! In this step-by-step tutorial, we’ll show you how to set up Langchain, create intelligent agents, and use them to query and analyze data using natural language. Perfect for data analysts and Python developers looking to add AI to their toolset. Let your DataFrame talk back 🚀 Hire me for Data Work: https://ryanandmattdatascience.com/da... 👨‍💻 Mentorships: https://ryanandmattdatascience.com/me... 📧 Email: ryannolandata@gmail.com 🌐 Website & Blog: https://ryanandmattdatascience.com/ 🖥️ Discord:   / discord   📚 *Practice SQL & Python Interview Questions: https://stratascratch.com/?via=ryan 📖 *SQL and Python Courses: https://datacamp.pxf.io/XYD7Qg 🍿 WATCH NEXT OpenAI/Langchain Playlist:    • How to Build Your First AI LLM Prompts wit...   Langchain System Humen AI:    • Mastering Langchain System, Human, and AI ...   OpenAI Product Data Generator Assistant:    • Building an OpenAI Product Data Generator ...   OpenAI Fine Tuning:    • How to Fine-Tune OpenAI GPT-4o For FREE   Learn how to chat with one or multiple pandas DataFrames using LangChain and the OpenAI API in this quick tutorial. In just a few lines of code, you'll be able to ask natural language questions about your data and get instant answers. This video walks you through setting up the create_pandas_dataframe_agent from LangChain, configuring your environment with an OpenAI API key, and querying your data using GPT-4. We cover everything from basic setup to advanced queries like comparing multiple DataFrames, finding top values, and identifying differences between datasets. You'll see real examples using hurricane data, including how to find the most damaging storm, identify the top three hurricanes by wind speed, and compare columns across different DataFrames. The tutorial also addresses common errors like the allow_dangerous_code parameter and explains why verbose mode helps you understand the agent's thought process. Perfect for data analysts and Python developers with basic programming experience who want to add AI-powered data analysis to their toolkit. Whether you're working with CSV files or dictionaries, this tutorial shows you how to unlock natural language querying for your pandas DataFrames using modern AI agents. TIMESTAMPS 00:00 Introduction & Setup 00:27 Installing Required Packages 00:53 Importing Libraries & Dependencies 02:01 Setting Up OpenAI API Key 02:29 Creating the Hurricane Dataset 04:02 Building the Large Language Model 05:00 Creating the Pandas DataFrame Agent 06:17 Understanding allow_dangerous_code Parameter 07:16 First Query: Most Damaging Hurricane 08:38 Asking About DataFrame Structure 09:53 Complex Query: Top Three Hurricanes by Wind Speed 11:01 Comparing Two DataFrames 12:25 Recreating Agent with Multiple DataFrames 13:02 Finding Column Differences Between DataFrames 13:53 Identifying Unique Hurricanes Across DataFrames 14:40 Recap & Key Takeaways OTHER SOCIALS: Ryan’s LinkedIn:   / ryan-p-nolan   Matt’s LinkedIn:   / matt-payne-ceo   Twitter/X: https://x.com/RyanMattDS Who is Ryan Ryan is a Data Scientist at a fintech company, where he focuses on fraud prevention in underwriting and risk. Before that, he worked as a Data Analyst at a tax software company. He holds a degree in Electrical Engineering from UCF. Who is Matt Matt is the founder of Width.ai, an AI and Machine Learning agency. Before starting his own company, he was a Machine Learning Engineer at Capital One. *This is an affiliate program. We receive a small portion of the final sale at no extra cost to you.