Prerequisites

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

​
CSV Tools

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

1

Install Dependencies

First, install the required package:

pip install praisonaiagents
2

Import Components

Import the necessary components:

from praisonaiagents import Agent, Task, PraisonAIAgents
from praisonaiagents.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],
    self_reflect=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 = PraisonAIAgents(
    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 praisonaiagents.tools import read_csv
from praisonaiagents.tools import write_csv
from praisonaiagents.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
# 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
# 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
# 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 praisonaiagents.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, PraisonAIAgents
from praisonaiagents.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],
    self_reflect=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 = PraisonAIAgents(
    agents=[csv_agent],
    tasks=[csv_task],
    process="sequential"
)
agents.start()

​
Advanced CSV Processing with Multiple Agents

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

# Create analysis agent
analysis_agent = Agent(
    name="Analyzer",
    role="Data Analyst",
    goal="Analyze processed CSV data.",
    backstory="Expert in data analysis and reporting.",
    self_reflect=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 = PraisonAIAgents(
    agents=[processor_agent, analysis_agent],
    tasks=[processing_task, analysis_task],
    process="sequential"
)
agents.start()

​
Best Practices

​
Common Patterns

​
CSV Processing Pipeline

# 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 = PraisonAIAgents(
    agents=[processor, analyzer],
    tasks=[process_task, analyze_task]
)

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