CSV Agent

CSV file processing tools for AI agents.

Prerequisites

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

from praisonaiagents import Agent
from praisonai_tools import read_csv, write_csv

agent = Agent(name="Analyst", tools=[read_csv, write_csv])
agent.start("Summarise column totals in data/sales.csv")

The user points at a CSV; the agent reads, transforms, and writes results with CSV tools.

CSV Tools

Use CSV Tools to read, write, and manipulate CSV files 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, AgentTeam
   from praisonai_tools import read_csv, write_csv, merge_csv
   
3. Create Agent

   Create a CSV processing agent:

   csv_agent = Agent(
       name="CSVProcessor",
       role="CSV Processing Specialist",
       goal="Process CSV files efficiently and accurately.",
       backstory="Expert in CSV file manipulation and analysis.",
       tools=[read_csv, write_csv, merge_csv],
       reflection=False
   )
   
4. Define Task

   Define the CSV processing task:

   csv_task = Task(
       description="Process and analyze CSV data files.",
       expected_output="Processed CSV data with analysis.",
       agent=csv_agent,
       name="csv_processing"
   )
   
5. Run Agent

   Initialize and run the agent:

   agents = AgentTeam(
       agents=[csv_agent],
       tasks=[csv_task],
       process="sequential"
   )
   agents.start()
   

Understanding CSV Tools

What are CSV Tools?

CSV Tools provide CSV processing capabilities for AI agents:

• File reading and writing
• Data parsing
• Column manipulation
• Data filtering
• Format conversion

Key Components

CSV Agent

Create specialized CSV agents:

Agent(tools=[read_csv, write_csv, merge_csv])

CSV Task

Define CSV tasks:

Task(description="csv_operation")

Process Types

Sequential or parallel processing:

process="sequential"

CSV Options

Customize CSV parameters:

delimiter=",", header=True

Available Functions

from praisonai_tools import read_csv
from praisonai_tools import write_csv
from praisonai_tools import merge_csv

Function Details

read_csv(filepath: str, encoding: str = ‘utf-8’, delimiter: str = ’,’, header: Union[int, List[int], None] = 0, usecols: Optional[List[str]] = None, dtype: Optional[Dict[str, str]] = None, parse_dates: Optional[List[str]] = None, na_values: Optional[List[str]] = None, nrows: Optional[int] = None)

Reads a CSV file with advanced options:

• Supports multiple encodings (utf-8, ascii, etc.)
• Configurable column delimiter
• Flexible header row handling
• Column selection and data type specification
• Date parsing
• Custom NA/NaN value handling
• Row limit option

from praisonai_tools import read_csv

# Basic usage
data = read_csv("data.csv")

# Advanced usage with options
data = read_csv(
    "data.csv",
    encoding='utf-8',
    delimiter=',',
    header=0,
    usecols=['name', 'age', 'city'],
    dtype={'age': 'int32'},
    parse_dates=['birth_date'],
    na_values=['N/A', 'missing'],
    nrows=1000
)
# Returns: List[Dict[str, Any]]
# Example: [{"name": "Alice", "age": 25, "city": "New York"}, ...]

write_csv(filepath: str, data: List[Dict[str, Any]], encoding: str = ‘utf-8’, delimiter: str = ’,’, index: bool = False, header: bool = True, float_format: Optional[str] = None, date_format: Optional[str] = None, mode: str = ‘w’)

Writes data to a CSV file with formatting options:

• Supports different encodings
• Configurable delimiter
• Optional row indices
• Optional headers
• Custom float and date formatting
• Append mode support

from praisonai_tools import write_csv

# Basic usage
data = [
    {"name": "Alice", "age": 25, "city": "New York"},
    {"name": "Bob", "age": 30, "city": "San Francisco"}
]
success = write_csv("output.csv", data)

# Advanced usage with formatting
success = write_csv(
    "output.csv",
    data,
    encoding='utf-8',
    delimiter=',',
    index=False,
    header=True,
    float_format='%.2f',
    date_format='%Y-%m-%d',
    mode='w'
)
# Returns: bool (True if successful)

merge_csv(files: List[str], output_file: str, how: str = ‘inner’, on: Optional[Union[str, List[str]]] = None, suffixes: Optional[tuple] = None)

Merges multiple CSV files with flexible join options:

• Supports different join types (inner, outer, left, right)
• Merge on single or multiple columns
• Custom suffixes for overlapping columns
• Automatic handling of column name conflicts

from praisonai_tools import merge_csv

# Merge two files on a common column
success = merge_csv(
    files=["employees.csv", "salaries.csv"],
    output_file="merged.csv",
    how='inner',
    on='employee_id'
)

# Advanced merge with multiple columns and custom suffixes
success = merge_csv(
    files=["data1.csv", "data2.csv", "data3.csv"],
    output_file="merged.csv",
    how='outer',
    on=['id', 'department'],
    suffixes=('_2022', '_2023')
)
# Returns: bool (True if successful)

Dependencies

The CSV tools require the following Python packages:

• pandas: For advanced CSV operations and data manipulation

These will be automatically installed when needed.

Example Agent Configuration

from praisonaiagents import Agent
from praisonai_tools import read_csv, write_csv, merge_csv

agent = Agent(
    name="CSVProcessor",
    description="An agent that processes CSV files",
    tools=[read_csv, write_csv, merge_csv]
)

Error Handling

All functions include comprehensive error handling:

• File not found errors
• Permission errors
• Encoding errors
• Data format errors
• Missing dependency errors

Errors are logged and returned in a consistent format:

• Success cases return the expected data type
• Error cases return a dict with an “error” key containing the error message

Examples

Basic CSV Processing Agent

from praisonaiagents import Agent, Task, AgentTeam
from praisonai_tools import read_csv, write_csv, merge_csv

# Create CSV agent
csv_agent = Agent(
    name="CSVExpert",
    role="CSV Processing Specialist",
    goal="Process CSV files efficiently and accurately.",
    backstory="Expert in data processing and analysis.",
    tools=[read_csv, write_csv, merge_csv],
    reflection=False
)

# Define CSV task
csv_task = Task(
    description="Process and analyze customer data.",
    expected_output="Customer analysis report.",
    agent=csv_agent,
    name="customer_analysis"
)

# Run agent
agents = AgentTeam(
    agents=[csv_agent],
    tasks=[csv_task],
    process="sequential"
)
agents.start()

Advanced CSV Processing with Multiple Agents

from praisonaiagents import Agent, Task, AgentTeam
from praisonai_tools import read_csv, write_csv, merge_csv

# Create data processing agent
processor_agent = Agent(
    name="Processor",
    role="Data Processor",
    goal="Process CSV data systematically.",
    tools=[read_csv, write_csv, merge_csv],
    reflection=False
)

# Create analysis agent
analysis_agent = Agent(
    name="Analyzer",
    role="Data Analyst",
    goal="Analyze processed CSV data.",
    backstory="Expert in data analysis and reporting.",
    reflection=False
)

# Define tasks
processing_task = Task(
    description="Process customer data files.",
    agent=processor_agent,
    name="data_processing"
)

analysis_task = Task(
    description="Analyze customer trends and patterns.",
    agent=analysis_agent,
    name="data_analysis"
)

# Run agents
agents = AgentTeam(
    agents=[processor_agent, analysis_agent],
    tasks=[processing_task, analysis_task],
    process="sequential"
)
agents.start()

Best Practices

Agent Configuration

Configure agents with clear CSV focus:

from praisonai_tools import read_csv, write_csv, merge_csv

Agent(
    name="CSVProcessor",
    role="CSV Processing Specialist",
    goal="Process CSV files accurately and efficiently",
    tools=[read_csv, write_csv, merge_csv]
)

Task Definition

Define specific CSV operations:

Task(
    description="Process customer data and generate reports",
    expected_output="Customer analysis report"
)

Common Patterns

CSV Processing Pipeline

from praisonaiagents import Agent, Task, AgentTeam
from praisonai_tools import read_csv, write_csv, merge_csv

# Processing agent
processor = Agent(
    name="Processor",
    role="CSV Processor",
    tools=[read_csv, write_csv, merge_csv]
)

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

# Define tasks
process_task = Task(
    description="Process CSV files",
    agent=processor
)

analyze_task = Task(
    description="Analyze processed data",
    agent=analyzer
)

# Run workflow
agents = AgentTeam(
    agents=[processor, analyzer],
    tasks=[process_task, analyze_task]
)

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