Using Pandas AI with OpenAI for Conversational Data Analysis in Python

Python Pandas is an open‑source library that provides data manipulation and analysis capabilities for Python, widely used by data scientists to manage structured data such as Series and DataFrame.

In AI workflows, Pandas is often employed for preprocessing steps, cleaning, reshaping, merging and aggregating data before feeding it to machine‑learning or deep‑learning models.

To use Pandas AI, first install it with pip install pandasai. Then import the library and an OpenAI LLM, create a DataFrame, and run natural‑language prompts such as “Which are the 5 happiest countries?” to obtain results.

import pandas as pd
from pandasai import PandasAI
# Sample DataFrame
df = pd.DataFrame({
    "country": ["United States","United Kingdom","France","Germany","Italy","Spain","Canada","Australia","Japan","China"],
    "gdp": [19294482071552,2891615567872,2411255037952,3435817336832,1745433788416,1181205135360,1607402389504,1490967855104,4380756541440,14631844184064],
    "happiness_index": [6.94,7.16,6.66,7.07,6.38,6.4,7.23,7.22,5.87,5.12]
})
from pandasai.llm.openai import OpenAI
llm = OpenAI(api_token="your_API_key")
pandas_ai = PandasAI(llm)
pandas_ai.run(df, prompt='Which are the 5 happiest countries?')

To query a relational database, create a PostgreSQL URI, read the SQL result into a DataFrame with pd.read_sql, and then interact with the data using Pandas AI prompts.

# creating the uri and connecting to database
pg_conn = "postgresql://YOUR URI HERE"

# Query sql database
query = """
SELECT *
FROM table_name
"""

# Create dataframe named df
df = pd.read_sql(query, pg_conn)

More complex queries are possible, such as asking for the sum of GDPs of the two least happy countries, or generating plots directly from prompts.

pandas_ai.run(df, prompt='What is the sum of the GDPs of the 2 unhappiest countries?')

pandas_ai.run(df, "Plot the histogram of countries showing for each the gdp, using different colors for each bar")

Combining ChatGPT’s natural‑language capabilities with Pandas AI enables users to interact with data through conversational prompts, reducing the need to write extensive code and making data analysis more accessible.