Praison AI

Data Analyst Agent

Learn how to create AI agents for data analysis and insights generation.

Read a CSV file, summarise it, and surface trends with a single Agent — no pandas boilerplate to write.

python
from praisonaiagents import Agent
from praisonai_tools import read_csv, get_summary, filter_data

agent = Agent(
    name="DataAnalyst",
    instructions="Analyse data files and return clear, actionable insights.",
    tools=[read_csv, get_summary, filter_data],
)

agent.start("Read sales_data.csv and summarise the top trends.")
Diagram

Data Analyst Agent

📋 Data File

🤖 Agent

🧠 Data Tools

✅ Insights Report

Data analysis agent with CSV/Excel tools for reading, analyzing, and exporting data.

Quick Start

  1. Simple Usage

    Attach the data tools and ask for a summary.

    python
    from praisonaiagents import Agent
from praisonai_tools import read_csv, get_summary, filter_data

agent = Agent(
    name="DataAnalyst",
    instructions="You are a data analyst. Analyze data and provide insights.",
    tools=[read_csv, get_summary, filter_data],
)

agent.start("Read sales_data.csv and provide a summary")
  2. With Configuration

    Add memory so the agent compares datasets across turns.

    python
    from praisonaiagents import Agent
from praisonai_tools import read_csv, get_summary, filter_data

agent = Agent(
    name="DataAnalyst",
    instructions="Analyse datasets and track findings across sessions.",
    tools=[read_csv, get_summary, filter_data],
    memory=True,
)

agent.start("Compare this quarter's sales_data.csv against last quarter.")

How It Works

Diagram
Data ToolsAgentUserData ToolsAgentUser“Summarise sales_data.csv”read_csv / get_summary / filter_dataRows, aggregates, filtersInsights report

Simple

Agents: 1 — Single agent with data tools handles file operations and analysis.

Workflow

  1. Read data from CSV/Excel
  2. Analyze with filtering, grouping
  3. Generate statistical summaries

Setup

bash
pip install praisonaiagents praisonai pandas openpyxl
export OPENAI_API_KEY="your-key"

Run — Python

python
from praisonaiagents import Agent
from praisonai_tools import read_csv, get_summary, filter_data

agent = Agent(
    name="DataAnalyst",
    instructions="You are a data analyst. Analyze data and provide insights.",
    tools=[read_csv, get_summary, filter_data]
)

result = agent.start("Read sales_data.csv and provide a summary")
print(result)

Run — CLI

bash
praisonai "Analyze data.csv and summarize key metrics" --tools pandas

Run — agents.yaml

yaml
framework: praisonai
topic: Data Analysis
roles:
  data_analyst:
    role: Data Analyst
    goal: Analyze data and generate insights
    backstory: You are an expert data analyst
    tools:
      - read_csv
      - get_summary
      - filter_data
    tasks:
      analyze_data:
        description: Read sales_data.csv and provide a summary
        expected_output: A data analysis report
bash
praisonai agents.yaml

Serve API

python
from praisonaiagents import Agent
from praisonai_tools import read_csv, get_summary, filter_data

agent = Agent(
    name="DataAnalyst",
    instructions="You are a data analyst.",
    tools=[read_csv, get_summary, filter_data]
)

agent.launch(port=8080)
bash
curl -X POST http://localhost:8080/chat \
  -H "Content-Type: application/json" \
  -d '{"message": "Summarize the uploaded data"}'

Advanced Workflow (All Features)

Agents: 1 — Single agent with memory, persistence, structured output, and session resumability.

Workflow

  1. Initialize session for analysis tracking
  2. Configure SQLite persistence for analysis history
  3. Read and analyze data with structured output
  4. Store insights in memory for comparison
  5. Resume session for iterative analysis

Setup

bash
pip install praisonaiagents praisonai pandas openpyxl pydantic
export OPENAI_API_KEY="your-key"

Run — Python

python
from praisonaiagents import Agent, Task, AgentTeam, Session
from praisonai_tools import read_csv, get_summary, filter_data
from pydantic import BaseModel

# Structured output schema
class DataInsights(BaseModel):
    dataset: str
    row_count: int
    key_metrics: list[str]
    trends: list[str]
    recommendations: list[str]

# Create session for analysis tracking
session = Session(session_id="analysis-001", user_id="user-1")

# Agent with memory and tools
agent = Agent(
    name="DataAnalyst",
    instructions="Analyze data and return structured insights.",
    tools=[read_csv, get_summary, filter_data],
    memory=True
)

# Task with structured output
task = Task(
    description="Read sales_data.csv and provide structured insights",
    expected_output="Structured data analysis",
    agent=agent,
    output_pydantic=DataInsights
)

# Run with SQLite persistence
agents = AgentTeam(
    agents=[agent],
    tasks=[task],
    memory=True
)

result = agents.start()
print(result)

# Resume later
session2 = Session(session_id="analysis-001", user_id="user-1")
history = session2.search_memory("sales")

Run — CLI

bash
praisonai "Analyze data.csv" --tools pandas --memory --verbose

Run — agents.yaml

yaml
framework: praisonai
topic: Data Analysis
memory: true
memory_config:
  provider: sqlite
  db_path: analysis.db
roles:
  data_analyst:
    role: Data Analyst
    goal: Analyze data with structured output
    backstory: You are an expert data analyst
    tools:
      - read_csv
      - get_summary
      - filter_data
    memory: true
    tasks:
      analyze_data:
        description: Read sales_data.csv and provide structured insights
        expected_output: Structured data analysis
        output_json:
          dataset: string
          row_count: number
          key_metrics: array
          trends: array
          recommendations: array
bash
praisonai agents.yaml --verbose

Serve API

python
from praisonaiagents import Agent
from praisonai_tools import read_csv, get_summary, filter_data

agent = Agent(
    name="DataAnalyst",
    instructions="Analyze data and return structured insights.",
    tools=[read_csv, get_summary, filter_data],
    memory=True
)

agent.launch(port=8080)
bash
curl -X POST http://localhost:8080/chat \
  -H "Content-Type: application/json" \
  -d '{"message": "Analyze data", "session_id": "analysis-001"}'

Monitor / Verify

bash
praisonai "test analysis" --tools pandas --verbose

Cleanup

bash
rm -f analysis.db

Features Demonstrated

FeatureImplementation
WorkflowMulti-tool data analysis
DB PersistenceSQLite via memory_config
Observability--verbose flag
Toolspandas (read, summary, filter)
ResumabilitySession with session_id
Structured OutputPydantic DataInsights model

Best Practices

Load data once, then filter

Call read_csv a single time and reuse filter_data for follow-up questions. Re-reading large files on every turn wastes tokens and slows the agent down.

Ask for structured output on repeatable reports

When the same analysis runs regularly, pair the agent with a Pydantic schema via output_pydantic so downstream code gets typed fields instead of prose.

Enable memory for iterative analysis

Set memory=True to let the agent compare a new dataset against earlier findings without re-loading prior files.

Hand off to Finance for market data

Use the Data Analyst for files you own; switch to the Finance Agent when the question needs live stock prices or fundamentals.

Related

Card

Analyze stocks with live price and fundamentals tools.

 Card

Research topics across the web and synthesise findings.