Build a Gemini-driven data frame proxy with giant pandas and Lanchain for natural language data analysis

!pip install langchain_experimental langchain_google_genai pandas


import os
import pandas as pd
import numpy as np
from langchain.agents.agent_types import AgentType
from langchain_experimental.agents.agent_toolkits import create_pandas_dataframe_agent
from langchain_google_genai import ChatGoogleGenerativeAI


os.environ["GOOGLE_API_KEY"] = "Use Your Own API Key"

def setup_gemini_agent(df, temperature=0, model="gemini-1.5-flash"):
    llm = ChatGoogleGenerativeAI(
        model=model,
        temperature=temperature,
        convert_system_message_to_human=True
    )
   
    agent = create_pandas_dataframe_agent(
        llm=llm,
        df=df,
        verbose=True,
        agent_type=AgentType.OPENAI_FUNCTIONS,
        allow_dangerous_code=True
    )
    return agent

def load_and_explore_data():
    print("Loading Titanic Dataset...")
    df = pd.read_csv(
        "
    )
    print(f"Dataset shape: {df.shape}")
    print(f"Columns: {list(df.columns)}")
    return df

def basic_analysis_demo(agent):
    print("nBASIC ANALYSIS DEMO")
    print("=" * 50)
   
    queries = [
        "How many rows and columns are in the dataset?",
        "What's the survival rate (percentage of people who survived)?",
        "How many people have more than 3 siblings?",
        "What's the square root of the average age?",
        "Show me the distribution of passenger classes"
    ]
   
    for query in queries:
        print(f"nQuery: {query}")
        try:
            result = agent.invoke(query)
            print(f"Result: {result['output']}")
        except Exception as e:
            print(f"Error: {e}")

def advanced_analysis_demo(agent):
    print("nADVANCED ANALYSIS DEMO")
    print("=" * 50)
   
    advanced_queries = [
        "What's the correlation between age and fare?",
        "Create a survival analysis by gender and class",
        "What's the median age for each passenger class?",
        "Find passengers with the highest fares and their details",
        "Calculate the survival rate for different age groups (0-18, 18-65, 65+)"
    ]
   
    for query in advanced_queries:
        print(f"nQuery: {query}")
        try:
            result = agent.invoke(query)
            print(f"Result: {result['output']}")
        except Exception as e:
            print(f"Error: {e}")

def multi_dataframe_demo():
    print("nMULTI-DATAFRAME DEMO")
    print("=" * 50)
   
    df = pd.read_csv(
        "
    )
   
    df_filled = df.copy()
    df_filled["Age"] = df_filled["Age"].fillna(df_filled["Age"].mean())
   
    agent = setup_gemini_agent([df, df_filled])
   
    queries = [
        "How many rows in the age column are different between the two datasets?",
        "Compare the average age in both datasets",
        "What percentage of age values were missing in the original dataset?",
        "Show summary statistics for age in both datasets"
    ]
   
    for query in queries:
        print(f"nQuery: {query}")
        try:
            result = agent.invoke(query)
            print(f"Result: {result['output']}")
        except Exception as e:
            print(f"Error: {e}")

def custom_analysis_demo(agent):
    print("nCUSTOM ANALYSIS DEMO")
    print("=" * 50)
   
    custom_queries = [
        "Create a risk score for each passenger based on: Age (higher age = higher risk), Gender (male = higher risk), Class (3rd class = higher risk), Family size (alone or large family = higher risk). Then show the top 10 highest risk passengers who survived",
       
        "Analyze the 'deck' information from the cabin data: Extract deck letter from cabin numbers, Show survival rates by deck, Which deck had the highest survival rate?",
       
        "Find interesting patterns: Did people with similar names (same surname) tend to survive together? What's the relationship between ticket price and survival? Were there any age groups that had 100% survival rate?"
    ]
   
    for i, query in enumerate(custom_queries, 1):
        print(f"nCustom Analysis {i}:")
        print(f"Query: {query[:100]}...")
        try:
            result = agent.invoke(query)
            print(f"Result: {result['output']}")
        except Exception as e:
            print(f"Error: {e}")

def main():
    print("Advanced Pandas Agent with Gemini Tutorial")
    print("=" * 60)
   
    if not os.getenv("GOOGLE_API_KEY"):
        print("Warning: GOOGLE_API_KEY not set!")
        print("Please set your Gemini API key as an environment variable.")
        return
   
    try:
        df = load_and_explore_data()
        print("nSetting up Gemini Agent...")
        agent = setup_gemini_agent(df)
       
        basic_analysis_demo(agent)
        advanced_analysis_demo(agent)
        multi_dataframe_demo()
        custom_analysis_demo(agent)
       
        print("nTutorial completed successfully!")
       
    except Exception as e:
        print(f"Error: {e}")
        print("Make sure you have installed all required packages and set your API key.")


if __name__ == "__main__":
    main()

df = pd.read_csv(")
agent = setup_gemini_agent(df)


agent.invoke("What factors most strongly predicted survival?")
agent.invoke("Create a detailed survival analysis by port of embarkation")
agent.invoke("Find any interesting anomalies or outliers in the data")