Querying and Plotting Data with LangChain’s Pandas Agent + OpenAI

Natural language interfaces for data analysis are moving from research into everyday engineering practice.
With LangChain and OpenAI, you can now query a DataFrame directly in English, let the model write and execute the Pandas/Matplotlib code, and even plot results, all inside Python.

In this post, I’ll show you how I built a Pandas DataFrame Agent with LangChain + OpenAI that can:

• Answer tabular questions about a dataset.

• Generate Python plotting code automatically.

• Execute that code safely to produce charts.

---

🔧 Setup

We need a few packages:

pip install langchain langchain-openai langchain-experimental pandas matplotlib

And of course, set your OpenAI key:

export OPENAI_API_KEY=...

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📊 The Dataset

For demo purposes, let’s mock up some sales data:

import pandas as pd

data = {
    "month": ["2025-01","2025-02","2025-03","2025-04","2025-05","2025-06"],
    "region": ["EMEA","EMEA","EMEA","AMER","AMER","APAC"],
    "units":  [120, 150, 130, 200, 180, 160],
    "price":  [10.0, 10.0, 10.0, 12.0, 12.0,  9.0],
}
df = pd.DataFrame(data)
df["revenue"] = df["units"] * df["price"]

---

🤖 Creating the Agent

The magic comes from create_pandas_dataframe_agent.
This wraps the DataFrame in a tool the LLM can call with code execution:

from langchain_openai import ChatOpenAI
from langchain_experimental.agents.agent_toolkits import create_pandas_dataframe_agent

llm = ChatOpenAI(model="gpt-4o-mini", temperature=0)

agent = create_pandas_dataframe_agent(
    llm=llm,
    df=df,
    agent_type="openai-tools",
    verbose=False,  # cleaner logs
)

---

🔎 Asking Questions in English

Now you can query the data without touching Pandas:

resp = agent.invoke({
    "input": "Compute total revenue by region as a tidy table sorted descending."
})
print(resp["output"])

Output:

| region | revenue |
|--------|---------|
| AMER   | 4560.0  |
| EMEA   | 4000.0  |
| APAC   | 1440.0  |

The agent generated and executed the Pandas code under the hood.

---

📈 Asking for a Plot

We can go one step further — ask the model to plot monthly revenue:

plot_code = """
import pandas as pd
import matplotlib.pyplot as plt
from pathlib import Path

df['month'] = pd.to_datetime(df['month'])
monthly_revenue = df.groupby('month')['revenue'].sum()

plt.figure(figsize=(10, 5))
plt.plot(monthly_revenue.index, monthly_revenue.values, marker='o')
plt.title('Monthly Revenue Over Time')
plt.xlabel('Month')
plt.ylabel('Total Revenue')
plt.grid()
plt.xticks(rotation=45)
plt.tight_layout()

out = Path('monthly_revenue.png')
plt.savefig(out)
print(f'Chart saved to: {out.resolve()}')
"""
agent.invoke({"input": f"Execute this exact code:\n```python\n{plot_code}\n```"})

This produces:

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🚀 Why This Matters

This pattern unlocks:

• Rapid ad-hoc analytics: Ask questions in English, get code + charts.

• Non-technical users can explore data without writing Pandas.

• Bridges RAG + analytics: Instead of only text retrieval, you can augment LLMs with structured data queries.

Of course, for production you’d want guardrails (e.g., AST linting before execution, schema checks, sandboxing). But as a prototyping tool, this workflow is incredibly powerful.

Source code can be found here: JordiCorbilla/langgraph-cookbook: langgraph-cookbook