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Prerequisites
  • Python 3.10 or higher
  • PraisonAI Agents package installed
  • PraisonAI Tools package installed
  • pandas package installed

​
Pandas Tools

Use Pandas Tools to manipulate and analyze data with AI agents.
1

Install Dependencies

First, install the required packages:
pip install praisonaiagents praisonai-tools pandas
2

Import Components

Import the necessary components:
from praisonaiagents import Agent, Task, Agents
from praisonai_tools import read_csv, read_excel, write_csv, write_excel, filter_data, get_summary, group_by, pivot_table
3

Create Agent

Create a data analysis agent:
data_agent = Agent(
    name="DataAnalyst",
    role="Data Analysis Specialist",
    goal="Analyze and transform data efficiently.",
    backstory="Expert in data manipulation and statistical analysis.",
    tools=[read_csv, read_excel, write_csv, write_excel, filter_data, get_summary, group_by, pivot_table],
    reflection=False
)
4

Define Task

Define the analysis task:
analysis_task = Task(
    description="Analyze sales data to identify trends and patterns.",
    expected_output="Statistical summary and key insights.",
    agent=data_agent,
    name="sales_analysis"
)
5

Run Agent

Initialize and run the agent:
agents = Agents(
    agents=[data_agent],
    tasks=[analysis_task],
    process="sequential"
)
agents.start()

​
Understanding Pandas Tools

What are Pandas Tools?

Pandas Tools provide data analysis capabilities for AI agents:
  • Data filtering and selection
  • Statistical analysis
  • Data transformation
  • Group operations
  • Pivot table creation

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Key Components

Data Agent

Create specialized data agents:
Agent(tools=[read_csv, read_excel, write_csv, write_excel, filter_data, get_summary, group_by, pivot_table])

Analysis Task

Define analysis tasks:
Task(description="analysis_query")

Process Types

Sequential or parallel processing:
process="sequential"

Analysis Options

Customize analysis parameters:
group_by="category", agg_func="mean"

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Available Functions

from praisonai_tools import read_csv
from praisonai_tools import read_excel
from praisonai_tools import write_csv
from praisonai_tools import write_excel
from praisonai_tools import filter_data
from praisonai_tools import get_summary
from praisonai_tools import group_by
from praisonai_tools import pivot_table

​
Examples

​
Basic Data Analysis Agent

from praisonaiagents import Agent, Task, Agents
from praisonai_tools import read_csv, filter_data, get_summary, group_by

# Create data analysis agent
data_agent = Agent(
    name="DataExpert",
    role="Data Analyst",
    goal="Process and analyze data efficiently.",
    backstory="Expert in data analysis and statistical methods.",
    tools=[read_csv, filter_data, get_summary, group_by],
    reflection=False
)

# Define analysis task
analysis_task = Task(
    description="Analyze sales performance data.",
    expected_output="Sales analysis report.",
    agent=data_agent,
    name="sales_analysis"
)

# Run agent
agents = Agents(
    agents=[data_agent],
    tasks=[analysis_task],
    process="sequential"
)
agents.start()

​
Advanced Data Operations with Multiple Agents

from praisonaiagents import Agent, Task, Agents
from praisonai_tools import read_csv, read_excel, write_csv, write_excel, filter_data, get_summary, group_by, pivot_table

# Create data import/export agent
io_agent = Agent(
    name="DataIO",
    role="Data IO Specialist",
    goal="Handle data import and export operations.",
    tools=[read_csv, read_excel, write_csv, write_excel],
    reflection=False
)

# Create analysis agent
analysis_agent = Agent(
    name="Analyzer",
    role="Data Analysis Specialist",
    goal="Perform complex data analysis.",
    tools=[filter_data, get_summary, group_by, pivot_table],
    reflection=False
)

# Define tasks
io_task = Task(
    description="Import and export data",
    agent=io_agent,
    name="data_io"
)

analysis_task = Task(
    description="Analyze data",
    agent=analysis_agent,
    name="data_analysis"
)   

# Run agents
agents = Agents(
    agents=[io_agent, analysis_agent],
    tasks=[io_task, analysis_task],
    process="sequential"
)
agents.start()

​
Best Practices

Configure agents with clear analysis focus:
from praisonai_tools import read_csv, read_excel, write_csv, write_excel, filter_data, get_summary, group_by, pivot_table

Agent(
    name="DataScientist",
    role="Data Analysis Specialist",
    goal="Extract meaningful insights from data",
    tools=[read_csv, read_excel, write_csv, write_excel, filter_data, get_summary, group_by, pivot_table]
)
Define specific analysis objectives:
Task(
    description="Analyze sales trends and forecast future growth",
    expected_output="Growth analysis with forecasts"
)

​
Common Patterns

​
Data Analysis Pipeline

from praisonaiagents import Agent, Task, Agents
from praisonai_tools import read_csv, read_excel, write_csv, write_excel, filter_data, get_summary, group_by, pivot_table

# Data preparation agent
prep_agent = Agent(
    name="Preparer",
    role="Data Preparation",
    tools=[read_csv, read_excel, write_csv, write_excel, filter_data, get_summary, group_by, pivot_table]
)

# Analysis agent
analyst = Agent(
    name="Analyst",
    role="Data Analyst"
)

# Define tasks
prep_task = Task(
    description="Clean and prepare data",
    agent=prep_agent
)

analysis_task = Task(
    description="Analyze prepared data",
    agent=analyst
)

# Run workflow
agents = Agents(
    agents=[prep_agent, analyst],
    tasks=[prep_task, analysis_task]
)