Prerequisites
Python 3.10 or higher
PraisonAI Agents package installed
pandas package installed 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()
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:
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 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] ) 
