Building a fully autonomous data analysis pipeline using Praisonai proxy framework: encoding implementation

In this tutorial, we demonstrate how the Praisonai agent can elevate your data analytics from manual scripting to a fully automatic AI-driven pipeline. In some natural language tips, you will learn to coordinate every stage of your workflow, load CSV or Excel files, filter rows, summarize trends, group by custom fields, rotate tables, and export results to CSV and Excel without writing traditional Pandas code. In this implementation, under the hood, Praisonai utilizes Google Gemini to explain your instructions and call the appropriate tools. Meanwhile, features such as self-reflection and detailed recording provide you with a comprehensive visibility of each intermediate reasoning step.

!pip install "praisonaiagents[llm]"

We installed the core Praisonai proxy library, along with its LLM integration add-ons, which brings all the necessary dependencies (such as Litellm and Gemini Connectors) to drive an automated workflow with a large language model.

import os


os.environ["GEMINI_API_KEY"] = "Use Your API Key"


llm_id = "gemini/gemini-1.5-flash-8b"

We configure your environment to access Gemini by setting up an API key and then specify the Praisonai proxy application as the Gemini model of its LLM backend (“1.5-Flash-8B” variant).

from google.colab import files


uploaded = files.upload()  
csv_path = next(iter(uploaded))
print("Loaded:", csv_path)

We use Colab’s File Widget to enable you to select a local CSV, capture its filename into CSV_PATH and print a confirmation, making your data easy to interact with.

from praisonaiagents import Agent
from praisonaiagents.tools import (
    read_csv, filter_data, get_summary, group_by, pivot_table, write_csv
)


agent = Agent(
    instructions="You are a Data Analyst Agent using Google Gemini.",
    llm=llm_id,
    tools=[
        read_csv, filter_data, get_summary, group_by, pivot_table, write_csv
    ],
    self_reflect=True,  
    verbose=True        
)

We instantiated the Praisonai agent with Google Gemini, equipped with data analysis tools (CSV I/O, filtering, summary, grouping, hub and export). Enabling self-reflection allows the agent to criticize its reasoning, while the verbose mode stream details the tool launch log for transparency.

result = agent.start(f"""
1. read_csv to load data from "{csv_path}"
2. get_summary to outline overall trends
3. filter_data to keep rows where Close > 800
4. group_by Year to average closing price
5. pivot_table to format the output table
""")
print(result)

We sent your Praisonai agent a clear, step-by-step prompt indicating loading the CSV, summarizing the overall trend, filtering at a closing price of over $800, calculating the annual average, and transmissing it. The agent then prints out the merged response (including any generated digest or data output).

In summary, we have built an end-to-end data pipeline powered by Praisonai Adents and Gemini, from the raw data uploaded to insightful visualizations and downloadable reports in just a few cells. We’ve seen how Praisonai’s declarative toolset replaces dozens of boilerplate code with concise, readable steps, and how built-in mechanisms such as result caching and dual-mode API calls ensure efficiency and reliability.

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Asif Razzaq is CEO of Marktechpost Media Inc. As a visionary entrepreneur and engineer, ASIF is committed to harnessing the potential of artificial intelligence to achieve social benefits. His recent effort is to launch Marktechpost, an artificial intelligence media platform that has an in-depth coverage of machine learning and deep learning news that can sound both technically, both through technical voices and be understood by a wide audience. The platform has over 2 million views per month, indicating its popularity among its audience.