The Pandas DataFrame Agent: LangChain and GPT-4

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The Pandas DataFrame Agent: LangChain and GPT-4

Simplifying Data Analysis with Natural Language Processing

Image by Author with @MidJourney


Data analysis and data manipulation are fundamental data science tasks carried out daily in the field. Efficient and fast data transformation is crucial to extract meaningful insight and make informed decisions powered by data. One of the most popular tools is the Python library Pandas, which offers a powerful DataFrame tool to simplify these tasks using a flexible and intuitive structure.

DataFrames, however, require writing code and can challenge without programming knowledge. To bridge this gap and make data analysis more widely available, a combination of LangChain and OpenAI’s GPT-4 comes in handy.

In this article, we will explore the collaboration of LangChain, GPT-4, and Pandas to create an interactive DataFrame in the form of an agent. We can interact with this agent using natural language and ask it to perform various analyses which formerly required programming knowledge.

Excited? Let’s get started!

Introduction to LangChain and GPT-4

In the first chapter, we will scratch the surface and delve into the fundamentals of LangChain and GPT-4. These two tools, when combined, allow us to create an intelligent agent powered by OpenAI’s natural language model.

LangChain: A Programming Language for Intelligent Agents

LangChain is a project designed to create intelligent agents. It offers a unique interface that allows users to interact with the agent using human language instructions instead of complex code, making interaction possible without programmatic knowledge.

The most prominent feature of LangChain is its ability to understand and process instructions written in human language. Under the hood, it leverages advanced natural language processing techniques to understand instructions and convert them into code that it can execute. LangChain allows our agent to understand, process and execute instructions to perform data analysis and manipulation tasks.

GPT-4: Advancements in Natural Language Processing

Generative Pre-trained Transformed 4, or GPT-4 for short, is an advanced large language model from the developers of OpenAI. GPT-4 is currently the latest and most advanced large language model available and offers vast capabilities in the field of natural language processing. It is trained on a huge dataset, allowing it to generate coherent and context-appropriate responses to any topic.

While GPT-4 has vast capabilities, one of its main strengths is the ability to understand and generate human-like text. It learns patterns, nuances, and styles of language, allowing it to understand instructions and generate based on them. GPT-4 enhances LangChain by powering the agent with the ability to process and generate natural language instructions. Is it truly a match made in heaven?

Image by Author with @MidJourney

Training the Agent

LangChain has a specific library for Pandas called the pandas_dataframe_agent. This is a powerful tool to handle large datasets efficiently and allows for advanced queries and transformations. It excels in tasks such as grouping and aggregating data as well as statistical analysis. But it can also do filtering, joining, merging, masking, and much much more.

Ready for some action? Let’s train our agent!

Setting the stage

!pip install langchain

import os
os.environ["OPENAI_API_KEY"] = ""

We need to install the langchain library. In addition, we need to set the environment variable to our OpenAI API key, and update the code with your very own API key. By setting the key, we make sure that the API calls are authenticated and authorized for the agent.

from langchain.agents import create_pandas_dataframe_agent
from langchain.llms import OpenAI
import pandas as pd

We need to import the libraries we installed, note that we are also importing OpenAI. LangChain has several large language models, but this example uses OpenAI.

Getting some Data

Let’s get some data, for this example, we’re going to use the Titanic dataset from Kaggle:

df = pd.read_csv('titanic.csv')

Let’s look at the first 10 rows of the dataframe:

First 10 rows of the Titanic dataset

Instantiate the Agent

After getting the data ready, we need to instantiate the agent:

agent = create_pandas_dataframe_agent(OpenAI(temperature=0), 

We need to create a LangChain agent for processing natural language using OpenAI’s language model and then create a Pandas DataFrame agent from the provided CSV file titanic.csv. We are setting the temperature to 0 to get the most likely response from GPT-4. Let’s ask some questions!

Photo by Camylla Battani on Unsplash

Asking the Agent Questions

Starting with simple questions, we are going to gradually challenge the agent to answer more complex questions! Let’s start with the first question:"How many passengers were onboard the Titanic?")
Image by Author

Success! As you can see, the agent went through all the humans, identifying the problem, finding the correct action input, and returning the final answer. Let’s step it up a notch!"""
How many passengers had more than 2 siblings?
Return the answer as a product of Pi

Wow, the agent tried to multiply with Pi. However, the required library was not imported, so it imported the library and finally got the right answer. Let’s try the last example using a slightly more complex prompt:"What was the survival rate for each gender?")

Again, the agent correctly identified the action input by using groupby and taking the mean of the output!

Image by Author with @MidJourney


The fusion of LangChain, GPT-4, and Pandas allows us to create intelligent DataFrame agents to make data analysis and manipulation easy. We can interact with the agent using plain English, widening the approach and lowering the bar to doing data analysis. From simple filtering and data cleaning tasks, the agent can also do more complex operations, usually requiring advanced code.

In this article, we built an agent from scratch, step by step, and showed just how easy it is to interact with it. Another cool thing is that the agent shows its thought process along the way, making debugging and transparency easy.

If you made it this far, thank you! I invite you to explore the possibilities of LangChain and GPT-4 by making your own agent, asking it questions, and providing value through data analysis in whichever field your passion lies. The future of data analysis is brighter than ever, thanks to tools like LangChain and GPT-4!


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