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Overview of generative AI applications and their impact

Introduction to LangChain, ChatGPT and Gemini Pro

What are Large Language Models?GPT models Mistral Llama Gemini How to build diffferent LLM AppIications?

Introduction to Prompt Engineering Best Practices and Guidelines for Prompt Engineering N shot prompting Chain of Thought Tree of Thoughts Skeleton of Thoughts Chain of Emotion

Introduction to Finetuning LLMs Parameter-Efficient Finetuning (PEFT)LORA QLORA using Unsloth using Huggingface

What do you mean by Training LLMs from Scratch?

Intro to the LangChain Ecosystem Core Components of LangChain Applications of LCEL Chains RAG using LangChain LangGraph LangSmith

Introduction to RAG systems Evaluation of RAG systems

Getting Started with LlamaIndex Components of LlamaIndex Advanced approaches for powerful RAG system

Introduction to Stable Diffusion Generating image using Stable diffusion Diffusion models Prompt Engineering Concepts for Stable Diffusion MidJourney Understanding Dalle 3

How to Automate Data Analysis with Langchain?

👁 Gyan Prakash Tripathi

Gyan Prakash Tripathi Last Updated : 06 Jun, 2025

5 min read

Introduction

In today’s world, businesses and organizations rely heavily on data to make informed decisions. However, analyzing large amounts of data can be a time-consuming and daunting task. That’s where automation comes into play. With the help of frameworks like Langchain and Gen AI, you can automate your data analysis and save valuable time.

In this article, we’ll delve into how you can use Langchain to build your own agent and automate your data analysis. We’ll also show you a step-by-step guide to creating a Langchain agent by using a built-in pandas agent.

What is Langchain?

Langchain is a framework used to build applications with Large Language models like chatGPT. It provides a better way to manage memory, prompts, and create chains – a series of actions. Furthermore, Langchain provides developers with a facility to create agents. An agent is an entity that can execute a series of actions based on conditions.

Types of Agents in Langchain

There are two types of agents in Langchain:

Action Agents: Action agents decide on the actions to take and execute those actions one at a time.
Plan-and-Execute Agents: Plan-and-execute agents first decide on a plan of actions to take and then execute those actions one at a time.

However, there is no clear distinction between both categories as this concept is still developing.

Data Analysis with Langchain

In order to do data analysis with langchain, we must first install langchain and openai libraries. You can do this by downloading the required libraries and then importing them into your project.

Here’s how you can do it:

# Installing langchain and openai libraries 
!pip install langchain openai 
# Importing libraries
import os 
import pandas as pd 
import matplotlib.pyplot as plt 
import seaborn as sns 
from langchain.agents import create_pandas_dataframe_agent 
from langchain.llms import OpenAI 

#setup the api key 
os.environ['OPENAI_API_KEY']="YOUR API KEY"

You can get your OpenAI API key from the OpenAI platform.

Creating a Langchain Agent

To create a Langchain agent, we’ll use the built-in pandas agent. We’ll be using a heart disease risk dataset for this demo. This data is available online and can be read in the pandas dataframe directly. Here’s how you can do it:

# Importing the data
df = pd.read_csv('http://www-stat.stanford.edu/~tibs/ElemStatLearn/datasets/SAheart.data') 
# Initializing the agent 
agent = create_pandas_dataframe_agent(OpenAI(temperature=0), 
 df, verbose=True) 
openai = OpenAI(temperature=0.0) 
Openai.model_name # This will print the model being used, 
 # by default it uses ‘text-davinci-003’

The temperature parameter is used to adjust the creativity of the model. When it is set to 0, the model is least prone to hallucination. We have kept verbose= True. It will print all the intermediate steps during the execution.

Querying the Agent

Once you’ve set up your agent, you can start querying it. There are several types of queries you can ask your agent to perform. Let’s Perform a few steps of data analysis:

Basic EDA

# Let's check the shape of data.' 
agent("What is the shape of the dataset?")

👁 querying the agent | Data Analysis with Langchain | Langchain | Data Analysis

Here, you can see the model is printing all intermediate steps because we had set verbose= True

#identifying missing values 
agent("How many missing values are there in each column?")

👁 querying the agent | Data Analysis with Langchain | Langchain

We can see that none of the columns has missing values.

# Let us see how the data looks like 
agent("Display 5 records in form of a table.")

👁 querying the agent | Data Analysis with Langchain | Langchain | Data Analysis

Univariate Analysis

In this section we will try to see the distribution of various variables.

agent("Show the distribution of people suffering with chd using bar graph.")

👁 Univariate analysis

agent("""Show the distribution of age where the person is 
suffering with chd using histogram with 
0 to 10, 10 to 20, 20 to 30 years and so on.""")

👁 univariate analysis - 2 | querying the agent | Data Analysis with Langchain | Langchain

agent("""Draw boxplot to find out if there are any outliers 
in terms of age of who are suffering from chd.""")

👁 box plot | querying the agent | Data Analysis with Langchain | Langchain | Data Analysis

Hypothesis Testing

Let us try to test some hypothesis.

# Does Tobacco Cause CHD? 
agent("""validate the following hypothesis with t-test. 
Null Hypothesis: Consumption of Tobacco does not cause chd. 
Alternate Hypothesis: Consumption of Tobacco causes chd.""")

👁 hypothesis testing

# How is the distribution of CHD across various age groups 
agent("""Plot the distribution of age for both the values 
of chd using kde plot. Also provide a lenged and 
label the x and y axises.""")

👁 querying the agent | Data Analysis with Langchain | Langchain

Bivariate Analysis

Let’s do a couple of queries to see how various variables are related.

agent("""Draw a scatter plot showing relationship 
between adiposity and ldl for both categories of chd.""")

👁 bivariate analysis | querying the agent | Data Analysis with Langchain | Langchain

agent("""What is the correlation of different variables with chd""")

👁 bivariate analysis | querying the agent | Data Analysis with Langchain | Langchain

Conclusion

Langchain is an excellent framework for automating your data analysis. By creating agents, you can perform various types of analyses using Gen AI’s language models. In this article, we’ve shown you how to use inbuilt pandas Langchain agent and perform some basic EDA, univariate and bivariate analysis, and hypothesis testing. Furthermore, We hope this guide has been helpful to you in learning how to automate your data analysis and improve your decision-making process.

Frequently Asked Questions

Q1. What is the use of Langchain?

A. The aim of LangChain is to simplify the development process of applications that utilize extensive language models (LLMs) like OpenAI or Hugging Face. It achieves this by providing a user-friendly open-source framework that streamlines the building process and makes development more straightforward.

Q2. How good is LangChain?

A. In a broad sense, LangChain brings excitement by enabling the augmentation of already potent LLMs with memory and context. Also, this empowers us to artificially introduce “reasoning” and tackle more intricate tasks with heightened precision.

Q3. Is LangChain free?

A. The majority of accessible LangChain tutorials primarily focus on utilizing OpenAI. While the OpenAI API is affordable for experimentation, it is not offered for free.

👁 Gyan Prakash Tripathi

Gyan Prakash Tripathi

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ChatGPT Data Analysis Generative AI Intermediate Large Language Models Pandas