PandasAI: Data Analysis with Natural Language in Python

Introduction

Imagine being able to ask your data questions in plain English, without writing complex SQL queries or remembering intricate Pandas syntax. That is exactly what PandasAI offers: a Python library that integrates Large Language Model (LLM) capabilities with Pandas DataFrames.

PandasAI enables data analysts, data scientists, and even non-technical stakeholders to perform data analysis simply by typing questions in natural language. The library automatically converts your questions into executable Python/Pandas code, runs it, and returns the results.

In this tutorial, we will explore how to use PandasAI comprehensively, from installation to building practical business analytics.

Prerequisites

Before getting started, make sure you have:

• Python 3.9 or later
• pip package manager
• OpenAI API key (or a local LLM like Ollama)
• Basic understanding of Pandas DataFrames

Installation

Basic Installation

pip install pandasai

Installation with Additional Dependencies

# With plotting support
pip install pandasai[plotting]

With Excel support
pip install pandasai[excel]

Full installation
pip install pandasai[all]

Verify Installation

import pandasai
print(pandasai.version)

Setup and LLM Configuration

Using OpenAI

import os
from pandasai import SmartDataframe
from pandasai.llm.openai import OpenAI

Set API key
os.environ["OPENAIAPIKEY"] = "sk-your-api-key-here"

Or directly during initialization
llm = OpenAI(apitoken="sk-your-api-key-here", model="gpt-4o")

# Install Ollama first
curl -fsSL https://ollama.ai/install.sh | sh

Download models
ollama pull llama3
ollama pull codellama

from pandasai.llm.localllm import LocalLLM

Configure local LLM via Ollama
llm = LocalLLM(
    apibase="http://localhost:11434/v1",

    model="llama3"
)

from pandasai.llm.azureopenai import AzureOpenAI

llm = AzureOpenAI(
    apitoken="your-azure-api-key",

    azureendpoint="https://your-resource.openai.azure.com/",
    apiversion="2024-02-15-preview",

    deploymentname="gpt-4o"
)

Basic Queries with SmartDataframe

Creating a SmartDataframe

import pandas as pd
from pandasai import SmartDataframe

Create sample sales data
data = {
    "product": ["Laptop", "Mouse", "Keyboard", "Monitor", "Headset",
                "Laptop", "Mouse", "Keyboard", "Monitor", "Headset"],
    "category": ["Electronics", "Accessories", "Accessories", "Electronics", "Accessories",
                  "Electronics", "Accessories", "Accessories", "Electronics", "Accessories"],
    "price": [1200, 25, 50, 400, 75,
              1300, 20, 45, 450, 80],
    "unitssold": [120, 500, 350, 80, 200,

                   150, 600, 400, 95, 250],
    "month": ["January", "January", "January", "January", "January",
              "February", "February", "February", "February", "February"]
}

df = pd.DataFrame(data)
sdf = SmartDataframe(df, config={"llm": llm})

# Simple query
result = sdf.chat("What is the total sales in January?")
print(result)

Aggregation query
result = sdf.chat("Which product sold the most units?")
print(result)

Filtered query
result = sdf.chat("Show products with price above 100")
print(result)

Statistical query
result = sdf.chat("What is the average price per category?")
print(result)

# Revenue calculation
result = sdf.chat(
    "Calculate total revenue (price x unitssold) for each product, "