CSV Agent

CSV file processing tools for AI agents.

python

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.

Diagram

CSV Tools

Use CSV Tools to read, write, and manipulate CSV files with AI agents.

Install Dependencies
First, install the required packages:
bash
```
pip install praisonaiagents praisonai-tools pandas
```

Import Components

Import the necessary components:

python

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

Create Agent

Create a CSV processing agent:

python

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
)

Define Task

Define the CSV processing task:

python

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

Run Agent

Initialize and run the agent:

python

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:

python

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

CSV Task

Define CSV tasks:

python

Task(description="csv_operation")

Process Types

Sequential or parallel processing:

python

process="sequential"

CSV Options

Customize CSV parameters:

python

delimiter=",", header=True

Available Functions

python

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

python

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

python

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

python

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

python

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

python

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

python

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:

python

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:

python

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

Common Patterns

CSV Processing Pipeline

python

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]
)

Custom Tools

Build your own agent tools

Tools Overview

Browse PraisonAI tool documentation

CSV Agent

CSV Tools

Understanding CSV Tools

Key Components

Available Functions

Function Details

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’)

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

Dependencies

Example Agent Configuration

Error Handling

Examples

Basic CSV Processing Agent

Advanced CSV Processing with Multiple Agents

Best Practices

Common Patterns

CSV Processing Pipeline

Related