Tutorial to Create a Data Science Agent: A Code Implementation using gemini-2.0-flash-lite model through Google API, google.generativeai, Pandas and IPython.display for Interactive Data Analysis

!pip install pandas google-generativeai --quiet

import pandas as pd
import google.generativeai as genai
from IPython.display import Markdown

GOOGLE_API_KEY = "Use Your API Key Here"
genai.configure(api_key=GOOGLE_API_KEY)


model = genai.GenerativeModel('gemini-2.0-flash-lite')

data = {'Product': ['Laptop', 'Mouse', 'Keyboard', 'Monitor', 'Webcam', 'Headphones'],
        'Category': ['Electronics', 'Electronics', 'Electronics', 'Electronics', 'Electronics', 'Electronics'],
        'Region': ['North', 'South', 'East', 'West', 'North', 'South'],
        'Units Sold': [150, 200, 180, 120, 90, 250],
        'Price': [1200, 25, 75, 300, 50, 100]}
sales_df = pd.DataFrame(data)


print("Sample Sales Data:")
print(sales_df)
print("-" * 30)

def ask_gemini_about_data(dataframe, query):
    """
    Asks the Gemini Pro model a question about the given Pandas DataFrame.


    Args:
        dataframe: The Pandas DataFrame to analyze.
        query: The natural language question about the DataFrame.


    Returns:
        The response from the Gemini Pro model as a string.
    """
    prompt = f"""You are a data analysis agent. Analyze the following pandas DataFrame and answer the question.


    DataFrame:
    ```
    {dataframe.to_markdown(index=False)}
    ```


    Question: {query}


    Answer:
    """
    response = model.generate_content(prompt)
    return response.text

# Query 1: What is the total number of units sold across all products?
query1 = "What is the total number of units sold across all products?"
response1 = ask_gemini_about_data(sales_df, query1)
print(f"Question 1: {query1}")
print(f"Answer 1:n{response1}")
print("-" * 30)

# Query 2: Which product had the highest number of units sold?
query2 = "Which product had the highest number of units sold?"
response2 = ask_gemini_about_data(sales_df, query2)
print(f"Question 2: {query2}")
print(f"Answer 2:n{response2}")
print("-" * 30)

# Query 3: What is the average price of the products?
query3 = "What is the average price of the products?"
response3 = ask_gemini_about_data(sales_df, query3)
print(f"Question 3: {query3}")
print(f"Answer 3:n{response3}")
print("-" * 30)

# Query 4: Show me the products sold in the 'North' region.
query4 = "Show me the products sold in the 'North' region."
response4 = ask_gemini_about_data(sales_df, query4)
print(f"Question 4: {query4}")
print(f"Answer 4:n{response4}")
print("-" * 30)

# Query 5. More complex query: Calculate the total revenue for each product.
query5 = "Calculate the total revenue (Units Sold * Price) for each product and present it in a table."
response5 = ask_gemini_about_data(sales_df, query5)
print(f"Question 5: {query5}")
print(f"Answer 5:n{response5}")
print("-" * 30)