💻 环境搭建：首先，通过pip安装Pandas和google-generativeai库，为数据操作和AI分析奠定基础。

🔑 导入与配置：导入Pandas、google.generativeai和Markdown，并配置Google API密钥，初始化Gemini Pro模型，为后续的数据分析做好准备。

📊 数据准备：创建一个Pandas DataFrame，包含产品、类别、地区、销量和价格等示例销售数据，为后续的分析提供数据来源。

🤖 构建数据分析Agent：定义一个ask_gemini_about_data函数，该函数将DataFrame和自然语言查询作为输入，使用Gemini Pro模型生成分析结果。

💡 实例演示：通过5个示例查询，展示如何使用自然语言向Gemini Pro模型提问，例如计算总销量、找出销量最高的商品、计算平均价格等，从而获得数据洞察。

In this tutorial, we demonstrate the integration of Python’s robust data manipulation library Pandas with Google Cloud’s advanced generative capabilities through the google.generativeai package and the Gemini Pro model. By setting up the environment with the necessary libraries, configuring the Google Cloud API key, and leveraging the IPython display functionalities, the code provides a step-by-step approach to building a data science agent analyzing a sample sales dataset. The example shows how to convert a DataFrame into markdown format and then use natural language queries to generate insights about the data, highlighting the potential of combining traditional data analysis tools with modern AI-driven methods.

!pip install pandas google-generativeai --quiet

First, we install the Pandas and google-generativeai libraries quietly, setting up the environment for data manipulation and AI-powered analysis.

import pandas as pdimport google.generativeai as genaifrom IPython.display import Markdown

We import Pandas for data manipulation, google.generativeai for accessing Google’s generative AI capabilities, and Markdown from IPython.display to render markdown-formatted outputs.

GOOGLE_API_KEY = "Use Your API Key Here"genai.configure(api_key=GOOGLE_API_KEY)model = genai.GenerativeModel('gemini-2.0-flash-lite')

We assign a placeholder API key, configure the google.generativeai client with it, and initialize the ‘gemini-2.0-flash-lite’ GenerativeModel for generating 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 DataFrame named sales_df containing sample sales data for various products, and then print the DataFrame followed by a separator line to visually distinguish the output.

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

Here, we construct a markdown-formatted prompt from a Pandas DataFrame and a natural language query, then use the Gemini Pro model to generate and return an 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, the tutorial successfully illustrates how the synergy between Pandas, the google.generativeai package, and the Gemini Pro model can transform data analysis tasks into a more interactive and insightful process. The approach simplifies querying and interpreting data and opens up avenues for advanced use cases such as data cleaning, feature engineering, and exploratory data analysis. By harnessing these state-of-the-art tools within the familiar Python ecosystem, data scientists can enhance their productivity and innovation, making it easier to derive meaningful insights from complex datasets.

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The post 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 appeared first on MarkTechPost.