Data Analyst Agent

A workflow demonstrating how the Data Analyst Agent can read data from various sources, analyze it, and generate insights.

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Quick Start

pip install praisonaiagents

export OPENAI_API_KEY=your_api_key_here

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

agent = Agent(
    instructions="You are a Data Analyst Agent",
    tools=[
        read_csv, read_excel, write_csv, write_excel,
        filter_data, get_summary, group_by, pivot_table
    ]
)

# Start analysis with a specific task
agent.start(f"""
    Read the data from the csv file {os.path.join(os.path.dirname(__file__), "tesla-stock-price.csv")}
    Analyse the data and give me the insights
    read_csv to read the file
""")

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Understanding Data Analysis Workflow

The Data Analyst Agent is designed to perform comprehensive data analysis tasks using a suite of specialized tools. Here’s how it works:

1. Data Reading: The agent can read data from various sources:

   • CSV files using read_csv
   • Excel files using read_excel

2. Data Analysis: Multiple analysis tools are available:

   • filter_data: Filter datasets based on conditions
   • get_summary: Generate statistical summaries
   • group_by: Group data by specific columns
   • pivot_table: Create pivot tables for analysis

3. Data Export: Results can be exported to:

   • CSV format using write_csv
   • Excel format using write_excel

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Features

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Example Usage

# Example: Analyzing stock data
agent.start("""
    1. Read 'stock_data.csv'
    2. Filter data for the last 30 days
    3. Calculate daily returns
    4. Generate summary statistics
    5. Export results to 'analysis_results.xlsx'
""")

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Next Steps

• Learn about Prompt Chaining for complex analysis workflows
• Explore Evaluator Optimizer for improving analysis accuracy
• Check out other specialized agents like the Finance Agent for specific use cases