Study to create data science agent: Code use using Gemini-2.0-flash-Lite model with Google API, Google.GenerativeIn, Pandas and Ipsython.Display active data analysis
In this lesson, we show the strongest Python compilation of fraudulent databases for advanced Google Cloud skills with Google.Gendi Pro Model. By setting up the nature and libraries required, preparing for the Google Cloud API lock, and the tendency to ipython displays the action method by the action of building data in the data-selling Data. For example shows how you can change the dataframe into the format of Markuw
!pip install pandas google-generativeai --quietFirst, we include Pandas and Google-Generativewai library libraries, which puts on the strongest nature of the AI.
import pandas as pd
import google.generativeai as genai
from IPython.display import MarkdownWe import ponds to deceive data, Google.GenerativeIn to Find the Power of AI for Build Google, and Marketing from ythony.Display to provide the relief effects Markdown.
GOOGLE_API_KEY = "Use Your API Key Here"
genai.configure(api_key=GOOGLE_API_KEY)
model = genai.GenerativeModel('gemini-2.0-flash-lite')We provide the Placel API key, prepare the Google.Genature Client, and start a 'Gemini-2.0-Lite' GenerativeModel to produce content.
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)Here, we create a pandas datafum called Sales_Daphum that contains sample sales data for various products, and prints the datafum followed by a separate distinguishing line.
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.textHere, we are built a huge attack from Pandas data and natural language, and then using Gemini Pro Model to produce and restore analytical response.
# 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)
In conclusion, a lesson effectively demonstrates how to meet between panda, Google's package, and Geminit Pro Model can change the data analysis activities into interight process. This method is simplicity to ask and interpret and opens up the ways of the program developed such as cleaning data, detail engineering, and assessment data analysis. By installing the State-of-The-Art Tools within the General Python Ecosystem, data scientists can improve their product and perform new materials, making it easier to find logical insight from complex dataset.
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