Pandas Agent - PraisonAI Documentation

Prerequisites

Python 3.10 or higher
PraisonAI Agents package installed
pandas package installed

Pandas Tools

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

Install Dependencies

First, install the required packages:

pip install praisonaiagents pandas

Import Components

Import the necessary components:

from praisonaiagents import Agent, Task, PraisonAIAgents
from praisonaiagents.tools import read_csv, read_excel, write_csv, write_excel, filter_data, get_summary, group_by, pivot_table

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],
    self_reflect=False
)

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"
)

Run Agent

Initialize and run the agent:

agents = PraisonAIAgents(
    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

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"

Available Functions

from praisonaiagents.tools import read_csv
from praisonaiagents.tools import read_excel
from praisonaiagents.tools import write_csv
from praisonaiagents.tools import write_excel
from praisonaiagents.tools import filter_data
from praisonaiagents.tools import get_summary
from praisonaiagents.tools import group_by
from praisonaiagents.tools import pivot_table

Function Details

read_csv(filepath: str, **kwargs)

Reads CSV files into pandas DataFrames:

Flexible data import
Error handling
Additional pandas options

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

# With additional options
df = read_csv(
    "data.csv",
    encoding="utf-8",
    sep=",",
    header=0,
    usecols=["name", "age", "salary"]
)

# Returns: pd.DataFrame
# Error case: Returns dict with 'error' key

read_excel(filepath: str, **kwargs)

Reads Excel files into pandas DataFrames:

Multiple sheet support
Error handling
Additional pandas options

# Basic usage
df = read_excel("data.xlsx")

# With sheet specification
df = read_excel(
    "data.xlsx",
    sheet_name="Sheet1",
    header=0,
    usecols="A:D"
)

# Returns: pd.DataFrame
# Error case: Returns dict with 'error' key

write_csv(df: pd.DataFrame, filepath: str, **kwargs)

Writes DataFrames to CSV files:

Directory creation
Error handling
Formatting options

# Basic usage
success = write_csv(df, "output.csv")

# With formatting options
success = write_csv(
    df,
    "output.csv",
    index=False,
    encoding="utf-8",
    sep=",",
    date_format="%Y-%m-%d"
)
# Returns: bool (True if successful)

write_excel(df: pd.DataFrame, filepath: str, **kwargs)

Writes DataFrames to Excel files:

Directory creation
Error handling
Excel-specific options

# Basic usage
success = write_excel(df, "output.xlsx")

# With formatting options
success = write_excel(
    df,
    "output.xlsx",
    sheet_name="Data",
    index=False,
    freeze_panes=(1,0)
)
# Returns: bool (True if successful)

filter_data(df: pd.DataFrame, query: str)

Filters DataFrames using query strings:

SQL-like syntax
Complex conditions
Efficient filtering

# Basic filtering
filtered_df = filter_data(df, "age > 25")

# Complex conditions
filtered_df = filter_data(
    df,
    "age > 25 and salary >= 50000 and city == 'New York'"
)

# Returns: pd.DataFrame
# Error case: Returns dict with 'error' key

get_summary(df: pd.DataFrame)

Generates comprehensive data summaries:

Basic statistics
Data types
Memory usage
Null counts

# Get data summary
summary = get_summary(df)

# Returns: Dict[str, Any]
# Example output:
# {
#   "shape": [1000, 5],
#   "columns": ["name", "age", "salary", "city"],
#   "dtypes": {
#     "name": "object",
#     "age": "int64",
#     "salary": "float64",
#     "city": "object"
#   },
#   "null_counts": {
#     "name": 0,
#     "age": 2,
#     "salary": 1,
#     "city": 0
#   },
#   "numeric_summary": {
#     "age": {
#       "count": 998,
#       "mean": 35.5,
#       "std": 12.3,
#       "min": 18,
#       "25%": 28,
#       "50%": 35,
#       "75%": 45,
#       "max": 65
#     },
#     ...
#   },
#   "memory_usage": 40960
# }

group_by(df: pd.DataFrame, columns: Union[str, List[str]], agg_dict: Dict[str, Union[str, List[str]]])

Groups and aggregates data:

Multiple grouping columns
Multiple aggregations
Custom functions

# Basic grouping
grouped_df = group_by(
    df,
    columns="city",
    agg_dict={"salary": "mean"}
)

# Complex grouping
grouped_df = group_by(
    df,
    columns=["city", "department"],
    agg_dict={
        "salary": ["mean", "sum", "count"],
        "age": ["min", "max", "mean"]
    }
)

# Returns: pd.DataFrame
# Error case: Returns dict with 'error' key

pivot_table(df: pd.DataFrame, index: Union[str, List[str]], columns: Optional[Union[str, List[str]]] = None, values: Optional[Union[str, List[str]]] = None, aggfunc: str = “mean”)

Creates pivot tables:

Multiple index levels
Column pivoting
Custom aggregations
Flexible reshaping

# Basic pivot
pivot_df = pivot_table(
    df,
    index="city",
    columns="department",
    values="salary"
)

# Complex pivot
pivot_df = pivot_table(
    df,
    index=["city", "department"],
    columns="year",
    values=["salary", "headcount"],
    aggfunc={
        "salary": "mean",
        "headcount": "sum"
    }
)

# Returns: pd.DataFrame
# Error case: Returns dict with 'error' key

Example Agent Configuration

from praisonaiagents import Agent
from praisonaiagents.tools import (
    read_csv, read_excel, write_csv, write_excel,
    filter_data, get_summary, group_by, pivot_table
)

agent = Agent(
    name="DataAnalyzer",
    description="An agent that analyzes data using pandas",
    tools=[
        read_csv, read_excel, write_csv, write_excel,
        filter_data, get_summary, group_by, pivot_table
    ]
)

Dependencies

The pandas tools require the following Python packages:

pandas: For data manipulation and analysis
numpy: For numerical operations (automatically installed with pandas)
openpyxl: For Excel file support (optional)

These will be automatically installed when needed.

Error Handling

All functions include comprehensive error handling:

File I/O errors
Data format errors
Query syntax errors
Memory errors

Errors are handled consistently:

File operations return bool for success/failure
Data operations return error details in result
All errors are logged for debugging

Common Use Cases

Data Analysis:

# Load and analyze sales data
df = read_csv("sales_data.csv")
summary = get_summary(df)
print(f"Total records: {summary['shape'][0]}")
print(f"Missing values: {summary['null_counts']}")

# Filter high-value transactions
high_value = filter_data(df, "amount > 10000")
write_excel(high_value, "high_value_sales.xlsx")

Sales Reporting:

# Create sales summary by region and product
sales_summary = group_by(
    df,
    columns=["region", "product"],
    agg_dict={
        "amount": ["sum", "mean", "count"],
        "profit": ["sum", "mean"]
    }
)

# Create pivot table for quarterly analysis
quarterly_sales = pivot_table(
    df,
    index="product",
    columns="quarter",
    values="amount",
    aggfunc="sum"
)

Data Transformation:

# Load and clean customer data
customers = read_excel("customers.xlsx")
filtered = filter_data(
    customers,
    "age >= 18 and status == 'active'"
)

# Group by demographics
demographics = group_by(
    filtered,
    columns=["age_group", "region"],
    agg_dict={
        "spending": ["mean", "sum"],
        "visits": "count"
    }
)
write_csv(demographics, "demographic_analysis.csv")

Examples

Basic Data Analysis Agent

from praisonaiagents import Agent, Task, PraisonAIAgents
from praisonaiagents.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],
    self_reflect=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 = PraisonAIAgents(
    agents=[data_agent],
    tasks=[analysis_task],
    process="sequential"
)
agents.start()

Advanced Data Operations with Multiple Agents

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

Best Practices

Agent Configuration

Configure agents with clear analysis focus:

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]
)

Task Definition

Common Patterns

Data Analysis Pipeline

# 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 = PraisonAIAgents(
    agents=[prep_agent, analyst],
    tasks=[prep_task, analysis_task]
)

Newspaper Agent Python Agent

On this page

Pandas Tools
Understanding Pandas Tools
Key Components
Available Functions
Function Details
read_csv(filepath: str, **kwargs)
read_excel(filepath: str, **kwargs)
write_csv(df: pd.DataFrame, filepath: str, **kwargs)
write_excel(df: pd.DataFrame, filepath: str, **kwargs)
filter_data(df: pd.DataFrame, query: str)
get_summary(df: pd.DataFrame)
group_by(df: pd.DataFrame, columns: Union[str, List[str]], agg_dict: Dict[str, Union[str, List[str]]])
pivot_table(df: pd.DataFrame, index: Union[str, List[str]], columns: Optional[Union[str, List[str]]] = None, values: Optional[Union[str, List[str]]] = None, aggfunc: str = “mean”)
Example Agent Configuration
Dependencies
Error Handling
Common Use Cases
Examples
Basic Data Analysis Agent
Advanced Data Operations with Multiple Agents
Best Practices
Common Patterns
Data Analysis Pipeline

Tools

​Pandas Tools

​Understanding Pandas Tools

What are Pandas Tools?

​Key Components

Data Agent

Analysis Task

Process Types

Analysis Options

​Available Functions

​Function Details

​read_csv(filepath: str, **kwargs)

​read_excel(filepath: str, **kwargs)

​write_csv(df: pd.DataFrame, filepath: str, **kwargs)

​write_excel(df: pd.DataFrame, filepath: str, **kwargs)

​filter_data(df: pd.DataFrame, query: str)

​get_summary(df: pd.DataFrame)

​group_by(df: pd.DataFrame, columns: Union[str, List[str]], agg_dict: Dict[str, Union[str, List[str]]])

​pivot_table(df: pd.DataFrame, index: Union[str, List[str]], columns: Optional[Union[str, List[str]]] = None, values: Optional[Union[str, List[str]]] = None, aggfunc: str = “mean”)

​Example Agent Configuration

​Dependencies

​Error Handling

​Common Use Cases

​Examples

​Basic Data Analysis Agent

​Advanced Data Operations with Multiple Agents

​Best Practices

​Common Patterns

​Data Analysis Pipeline

Pandas Tools

Understanding Pandas Tools

Key Components

Available Functions

Function Details

read_csv(filepath: str, **kwargs)

read_excel(filepath: str, **kwargs)

write_csv(df: pd.DataFrame, filepath: str, **kwargs)

write_excel(df: pd.DataFrame, filepath: str, **kwargs)

filter_data(df: pd.DataFrame, query: str)

get_summary(df: pd.DataFrame)

group_by(df: pd.DataFrame, columns: Union[str, List[str]], agg_dict: Dict[str, Union[str, List[str]]])

pivot_table(df: pd.DataFrame, index: Union[str, List[str]], columns: Optional[Union[str, List[str]]] = None, values: Optional[Union[str, List[str]]] = None, aggfunc: str = “mean”)

Example Agent Configuration

Dependencies

Error Handling

Common Use Cases

Examples

Basic Data Analysis Agent

Advanced Data Operations with Multiple Agents

Best Practices

Common Patterns

Data Analysis Pipeline