How to Build and Use Langchain Agents with Pandas DataFrames

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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


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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

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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.

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