vamseeachanta PandasAI Skill
Chat with your data using natural language. Ask questions about DataFrames and get insights, visualizations, and explanations powered by LLMs.
Quick Start
# Install PandasAI
pip install pandasai
# Install with OpenAI support
pip install pandasai openai
# Install with visualization support
pip install pandasai matplotlib seaborn plotly
# Set API key
export OPENAI_API_KEY="your-api-key"
When to Use This Skill
USE when:
- You need to analyze data without writing code
- Exploring unfamiliar datasets with natural language questions
- Creating quick visualizations from descriptive prompts
- Explaining complex data transformations to stakeholders
- Building conversational interfaces for data exploration
- Prototyping data analysis workflows rapidly
- Need LLM-powered data insights on pandas DataFrames
- Creating reports from data with natural language descriptions
DON'T USE when:
- Production data pipelines requiring deterministic outputs
- Processing highly sensitive data (consider privacy modes)
- Need precise control over generated code
- Performance-critical applications with large datasets
- Simple queries better handled by direct pandas operations
- Reproducible analyses requiring version-controlled code
Prerequisites
# Core installation
pip install pandasai>=2.0.0
# For OpenAI backend (recommended)
pip install pandasai[openai]
# For visualization
pip install pandasai matplotlib seaborn plotly
# For Excel/CSV handling
pip install pandasai openpyxl xlrd
# For database connections
pip install pandasai sqlalchemy psycopg2-binary pymysql
# Environment setup
export OPENAI_API_KEY="sk-..."
# Or for other providers
export ANTHROPIC_API_KEY="sk-ant-..."
export GOOGLE_API_KEY="..."
Verify Installation
import pandasai
from pandasai import SmartDataframe
import pandas as pd
print(f"PandasAI version: {pandasai.__version__}")
# Quick test
df = pd.DataFrame({"name": ["Alice", "Bob"], "age": [25, 30]})
smart_df = SmartDataframe(df)
print("PandasAI installed successfully!")
Core Capabilities
1. Basic Natural Language Queries
Simple DataFrame Conversations:
"""
Query DataFrames using natural language with PandasAI.
"""
import pandas as pd
from pandasai import SmartDataframe
from pandasai.llm import OpenAI
def create_smart_dataframe(
df: pd.DataFrame,
model: str = "gpt-4",
temperature: float = 0.0
) -> SmartDataframe:
"""
Create a SmartDataframe for natural language queries.
Args:
df: Input pandas DataFrame
model: LLM model to use
temperature: Sampling temperature (0 for deterministic)
Returns:
SmartDataframe instance ready for queries
"""
# Initialize LLM
llm = OpenAI(
model=model,
temperature=temperature
)
# Create SmartDataframe
smart_df = SmartDataframe(
df,
config={
"llm": llm,
"verbose": True,
"enable_cache": True,
"conversational": False
}
)
return smart_df
def query_dataframe(smart_df: SmartDataframe, question: str) -> any:
"""
Ask a natural language question about the data.
Args:
smart_df: SmartDataframe instance
question: Natural language question
Returns:
Query result (can be value, DataFrame, or visualization)
"""
try:
result = smart_df.chat(question)
return result
except Exception as e:
print(f"Query error: {e}")
return None
# Usage Example
# Create sample sales data
sales_data = pd.DataFrame({
"date": pd.date_range("2025-01-01", periods=100, freq="D"),
"product": ["Widget A", "Widget B", "Widget C", "Widget D"] * 25,
"region": ["North", "South", "East", "West"] * 25,
"units_sold": [50, 75, 100, 125] * 25,
"revenue": [500, 1125, 2000, 3125] * 25,
"cost": [300, 600, 1200, 1800] * 25
})
# Create SmartDataframe
smart_sales = create_smart_dataframe(sales_data)
# Ask questions in natural language
questions = [
"What is the total revenue?",
"Which product has the highest average revenue?",
"What is the profit margin by product?",
"Show me the top 5 days by units sold"
]
for question in questions:
print(f"\nQ: {question}")
result = query_dataframe(smart_sales, question)
print(f"A: {result}")
Aggregation and Statistical Queries:
"""
Perform aggregations and statistical analysis using natural language.
"""
import pandas as pd
from pandasai import SmartDataframe
from pandasai.llm import OpenAI
def create_analytics_interface(df: pd.DataFrame) -> SmartDataframe:
"""Create an analytics interface for statistical queries."""
llm = OpenAI(model="gpt-4", temperature=0)
return SmartDataframe(
df,
config={
"llm": llm,
"verbose": False,
"enable_cache": True,
"custom_whitelisted_dependencies": ["scipy", "numpy"]
}
)
# Create sample employee performance data
employee_data = pd.DataFrame({
"employee_id": range(1, 101),
"department": ["Engineering", "Sales", "Marketing", "HR", "Finance"] * 20,
"experience_years": [1, 2, 3, 5, 7, 10, 12, 15, 18, 20] * 10,
"performance_score": [round(3.0 + i * 0.02, 2) for i in range(100)],
"salary": [50000 + i * 500 for i in range(100)],
"projects_completed": [5 + i % 20 for i in range(100)]
})
smart_employees = create_analytics_interface(employee_data)
# Statistical queries
statistical_questions = [
"What is the average salary by department?",
"Calculate the correlation between experience_years and salary",
"What is the standard deviation of performance scores?",
"Find the median projects completed by department",
"Which department has the highest variance in salary?",
"Show the salary distribution statistics (mean, median, std, min, max)"
]
print("Statistical Analysis Results:")
print("=" * 50)
for question in statistical_questions:
print(f"\nQuestion: {question}")
result = smart_employees.chat(question)
print(f"Answer: {result}")
2. Chart Generation and Visualization
Creating Charts from Natural Language:
"""
Generate visualizations using natural language descriptions.
"""
import pandas as pd
from pandasai import SmartDataframe
from pandasai.llm import OpenAI
import matplotlib.pyplot as plt
from pathlib import Path
def create_visualization_interface(
df: pd.DataFrame,
save_charts: bool = True,
charts_path: str = "./charts"
) -> SmartDataframe:
"""
Create interface optimized for chart generation.
Args:
df: Input DataFrame
save_charts: Whether to save generated charts
charts_path: Directory to save charts
Returns:
SmartDataframe configured for visualization
"""
llm = OpenAI(model="gpt-4", temperature=0)
# Create charts directory
Path(charts_path).mkdir(parents=True, exist_ok=True)
return SmartDataframe(
df,
config={
"llm": llm,
"save_charts": save_charts,
"save_charts_path": charts_path,
"verbose": True,
"custom_whitelisted_dependencies": [
"matplotlib",
"seaborn",
"plotly"
]
}
)
# Create sample time series data
import numpy as np
np.random.seed(42)
dates = pd.date_range("2024-01-01", periods=365, freq="D")
time_series_data = pd.DataFrame({
"date": dates,
"sales": np.random.normal(1000, 200, 365).cumsum(),
"customers": np.random.poisson(50, 365),
"category": ["Electronics", "Clothing", "Food", "Home"] * 91 + ["Electronics"],
"region": ["North", "South", "East", "West", "Central"] * 73
})
smart_viz = create_visualization_interface(time_series_data)
# Generate various chart types
chart_prompts = [
"Create a line chart showing sales trend over time",
"Plot a bar chart of average sales by category",
"Generate a pie chart showing customer distribution by region",
"Create a scatter plot of sales vs customers",
"Make a heatmap showing sales by category and region",
"Plot monthly sales with a trend line"
]
print("Generating visualizations...")
for prompt in chart_prompts:
print(f"\nGenerating: {prompt}")
result = smart_viz.chat(prompt)
print(f"Result: {result}")
def generate_dashboard_charts(
df: pd.DataFrame,
output_dir: str = "./dashboard"
) -> dict:
"""
Generate multiple charts for a dashboard.
Args:
df: Input DataFrame
output_dir: Directory to save charts
Returns:
Dictionary with chart paths and descriptions
"""
smart_df = create_visualization_interface(df, charts_path=output_dir)
dashboard_charts = {
"trend": "Create a line chart showing the main trend over time",
"distribution": "Create a histogram showing the distribution of the primary metric",
"comparison": "Create a bar chart comparing categories",
"relationship": "Create a scatter plot showing key relationships",
"composition": "Create a pie chart showing composition breakdown"
}
results = {}
for chart_name, prompt in dashboard_charts.items():
try:
result = smart_df.chat(prompt)
results[chart_name] = {
"prompt": prompt,
"result": result,
"status": "success"
}
except Exception as e:
results[chart_name] = {
"prompt": prompt,
"error": str(e),
"status": "failed"
}
return results
# Generate dashboard
dashboard_results = generate_dashboard_charts(time_series_data)
for name, info in dashboard_results.items():
print(f"\n{name}: {info['status']}")
Advanced Visualization Customization:
"""
Customize chart appearance and style using natural language.
"""
import pandas as pd
from pandasai import SmartDataframe
from pandasai.llm import OpenAI
def create_styled_visualization(
df: pd.DataFrame,
style_config: dict = None
) -> SmartDataframe:
"""
Create visualization interface with custom styling.
Args:
df: Input DataFrame
style_config: Custom style configuration
Returns:
SmartDataframe with styling configuration
"""
default_style = {
"figure.figsize": (12, 6),
"axes.titlesize": 14,
"axes.labelsize": 12,
"xtick.labelsize": 10,
"ytick.labelsize": 10,
"legend.fontsize": 10
}
style = {**default_style, **(style_config or {})}
# Apply matplotlib style
import matplotlib.pyplot as plt
plt.rcParams.update(style)
llm = OpenAI(model="gpt-4", temperature=0)
return SmartDataframe(
df,
config={
"llm": llm,
"verbose": True,
"custom_whitelisted_dependencies": [
"matplotlib",
"seaborn"
]
}
)
# Sample financial data
financial_data = pd.DataFrame({
"quarter": ["Q1", "Q2", "Q3", "Q4"] * 3,
"year": [2023, 2023, 2023, 2023, 2024, 2024, 2024, 2024, 2025, 2025, 2025, 2025],
"revenue": [100, 120, 115, 140, 150, 165, 180, 200, 210, 225, 240, 260],
"expenses": [80, 90, 85, 100, 110, 120, 130, 145, 150, 160, 170, 185],
"profit": [20, 30, 30, 40, 40, 45, 50, 55, 60, 65, 70, 75]
})
smart_finance = create_styled_visualization(financial_data)
# Detailed visualization requests
visualization_requests = [
"""Create a grouped bar chart comparing revenue and expenses by quarter,
with a secondary line showing profit trend. Use blue for revenue,
red for expenses, and green for profit.""",
"""Generate a stacked area chart showing revenue, expenses, and profit
over time with year-quarter on x-axis. Use a professional color palette.""",
"""Create a dual-axis chart with revenue bars on the left axis and
profit margin percentage line on the right axis."""
]
for request in visualization_requests:
print(f"\nRequest: {request[:50]}...")
result = smart_finance.chat(request)
print(f"Result type: {type(result)}")
3. Code Explanation and Generation
Understanding Generated Code:
"""
Get explanations of generated code and data transformations.
"""
import pandas as pd
from pandasai import SmartDataframe
from pandasai.llm import OpenAI
def create_explanatory_interface(df: pd.DataFrame) -> SmartDataframe:
"""Create interface that provides code explanations."""
llm = OpenAI(model="gpt-4", temperature=0)
return SmartDataframe(
df,
config={
"llm": llm,
"verbose": True,
"enable_cache": False, # Disable to always show code
"explain": True # Enable explanations
}
)
def query_with_explanation(
smart_df: SmartDataframe,
question: str
) -> dict:
"""
Query data and get both result and explanation.
Args:
smart_df: SmartDataframe instance
question: Natural language question
Returns:
Dictionary with result, code, and explanation
"""
# Get the result
result = smart_df.chat(question)
# Get the last generated code
last_code = smart_df.last_code_generated
# Ask for explanation
explanation_prompt = f"""
Explain what this code does in simple terms:
```python
{last_code}
```
"""
explanation = smart_df.chat(explanation_prompt)
return {
"question": question,
"result": result,
"code": last_code,
"explanation": explanation
}
# Sample inventory data
inventory_data = pd.DataFrame({
"sku": [f"SKU-{i:04d}" for i in range(1, 51)],
"product_name": [f"Product {i}" for i in range(1, 51)],
"category": ["Electronics", "Clothing", "Home", "Sports", "Books"] * 10,
"quantity": [100, 50, 200, 75, 150] * 10,
"unit_price": [29.99, 49.99, 19.99, 79.99, 14.99] * 10,
"reorder_level": [20, 10, 40, 15, 30] * 10,
"supplier_id": ["SUP-A", "SUP-B", "SUP-C", "SUP-D", "SUP-E"] * 10
})
smart_inventory = create_explanatory_interface(inventory_data)
# Complex queries with explanations
complex_queries = [
"Which products are below their reorder level?",
"Calculate the total inventory value by category",
"Find suppliers with the most products and their total inventory value",
"What percentage of products in each category are below reorder level?"
]
print("Queries with Code Explanations")
print("=" * 60)
for query in complex_queries:
result = query_with_explanation(smart_inventory, query)
print(f"\nQuestion: {result['question']}")
print(f"\nGenerated Code:\n{result['code']}")
print(f"\nResult: {result['result']}")
print("-" * 40)
Custom Code Generation:
"""
Generate custom analysis code using natural language specifications.
"""
import pandas as pd
from pandasai import SmartDataframe
from pandasai.llm import OpenAI
def generate_analysis_code(
df: pd.DataFrame,
analysis_description: str
) -> str:
"""
Generate analysis code from natural language description.
Args:
df: Sample DataFrame for context
analysis_description: Natural language description of desired analysis
Returns:
Generated Python code as string
"""
llm = OpenAI(model="gpt-4", temperature=0)
smart_df = SmartDataframe(
df,
config={
"llm": llm,
"verbose": True,
"response_parser": None # Get raw code
}
)
# Generate the code
smart_df.chat(analysis_description)
return smart_df.last_code_generated
def create_analysis_function(
df: pd.DataFrame,
function_spec: str
) -> callable:
"""
Create a reusable analysis function from specification.
Args:
df: Sample DataFrame for context
function_spec: Natural language specification
Returns:
Callable function
"""
code = generate_analysis_code(df, function_spec)
# Extract and wrap the code
wrapped_code = f"""
def analysis_function(df):
import pandas as pd
import numpy as np
{code}
return result
"""
# Execute to define function
local_vars = {}
exec(wrapped_code, {"pd": pd, "np": __import__("numpy")}, local_vars)
return local_vars["analysis_function"]
# Example: Generate reusable analysis code
sample_df = pd.DataFrame({
"date": pd.date_range("2024-01-01", periods=30),
"metric_a": range(30),
"metric_b": range(30, 60)
})
# Generate code for specific analysis
code = generate_analysis_code(
sample_df,
"""Create a rolling 7-day average of metric_a and identify
days where metric_b exceeds 2 standard deviations from its mean"""
)
print("Generated Analysis Code:")
print("=" * 40)
print(code)
4. Multiple LLM Backends
Configuring Different LLM Providers:
"""
Use various LLM backends with PandasAI.
"""
import pandas as pd
from pandasai import SmartDataframe
from typing import Optional
def create_openai_backend(
model: str = "gpt-4",
temperature: float = 0.0,
api_key: Optional[str] = None
):
"""Create OpenAI LLM backend."""
from pandasai.llm import OpenAI
return OpenAI(
model=model,
temperature=temperature,
api_key=api_key # Uses env var if None
)
def create_anthropic_backend(
model: str = "claude-3-opus-20240229",
temperature: float = 0.0,
api_key: Optional[str] = None
):
"""Create Anthropic Claude LLM backend."""
from pandasai.llm import Anthropic
return Anthropic(
model=model,
temperature=temperature,
api_key=api_key
)
def create_google_backend(
model: str = "gemini-pro",
temperature: float = 0.0,
api_key: Optional[str] = None
):
"""Create Google Gemini LLM backend."""
from pandasai.llm import GoogleGemini
return GoogleGemini(
model=model,
temperature=temperature,
api_key=api_key
)
def create_local_backend(
model_path: str,
model_type: str = "llama"
):
"""Create local LLM backend using Ollama or similar."""
from pandasai.llm import LocalLLM
return LocalLLM(
model_path=model_path,
model_type=model_type
)
def create_azure_openai_backend(
deployment_name: str,
api_base: str,
api_version: str = "2024-02-15-preview",
api_key: Optional[str] = None
):
"""Create Azure OpenAI LLM backend."""
from pandasai.llm import AzureOpenAI
return AzureOpenAI(
deployment_name=deployment_name,
api_base=api_base,
api_version=api_version,
api_key=api_key
)
class MultiBackendAnalyzer:
"""
Analyzer supporting multiple LLM backends with fallback.
"""
def __init__(self, df: pd.DataFrame):
self.df = df
self.backends = {}
self.current_backend = None
def add_backend(self, name: str, llm):
"""Add an LLM backend."""
self.backends[name] = llm
if self.current_backend is None:
self.current_backend = name
def set_backend(self, name: str):
"""Set the active backend."""
if name not in self.backends:
raise ValueError(f"Backend '{name}' not found")
self.current_backend = name
def query(self, question: str, backend: str = None) -> any:
"""
Query using specified or current backend.
Args:
question: Natural language question
backend: Backend name (uses current if None)
Returns:
Query result
"""
backend_name = backend or self.current_backend
llm = self.backends[backend_name]
smart_df = SmartDataframe(
self.df,
config={"llm": llm, "verbose": False}
)
return smart_df.chat(question)
def query_with_fallback(
self,
question: str,
backend_order: list = None
) -> tuple:
"""
Query with automatic fallback to other backends on failure.
Args:
question: Natural language question
backend_order: Order of backends to try
Returns:
Tuple of (result, backend_used)
"""
backends_to_try = backend_order or list(self.backends.keys())
for backend_name in backends_to_try:
try:
result = self.query(question, backend_name)
return result, backend_name
except Exception as e:
print(f"Backend '{backend_name}' failed: {e}")
continue
raise RuntimeError("All backends failed")
# Usage Example
sample_data = pd.DataFrame({
"product": ["A", "B", "C"],
"sales": [100, 200, 150]
})
# Create multi-backend analyzer
analyzer = MultiBackendAnalyzer(sample_data)
# Add backends (uncomment based on available API keys)
analyzer.add_backend("openai", create_openai_backend("gpt-4"))
# analyzer.add_backend("anthropic", create_anthropic_backend())
# analyzer.add_backend("google", create_google_backend())
# Query with specific backend
result = analyzer.query("What is the total sales?", backend="openai")
print(f"Result: {result}")
# Query with fallback
# result, used_backend = analyzer.query_with_fallback("What is the average sales?")
# print(f"Result: {result} (using {used_backend})")
5. Multi-DataFrame Analysis
Analyzing Multiple Related DataFrames:
"""
Work with multiple related DataFrames using PandasAI.
"""
import pandas as pd
from pandasai import SmartDatalake
from pandasai.llm import OpenAI
def create_smart_datalake(
dataframes: dict,
model: str = "gpt-4"
) -> SmartDatalake:
"""
Create a SmartDatalake for multi-DataFrame analysis.
Args:
dataframes: Dictionary of name: DataFrame pairs
model: LLM model to use
Returns:
SmartDatalake instance
"""
llm = OpenAI(model=model, temperature=0)
# Convert dict to list with names
df_list = []
for name, df in dataframes.items():
df.name = name # Assign name attribute
df_list.append(df)
return SmartDatalake(
df_list,
config={
"llm": llm,
"verbose": True,
"enable_cache": True
}
)
# Create related business DataFrames
customers = pd.DataFrame({
"customer_id": range(1, 101),
"customer_name": [f"Customer {i}" for i in range(1, 101)],
"segment": ["Enterprise", "SMB", "Startup"] * 33 + ["Enterprise"],
"region": ["North", "South", "East", "West"] * 25,
"signup_date": pd.date_range("2023-01-01", periods=100, freq="3D")
})
customers.name = "customers"
orders = pd.DataFrame({
"order_id": range(1, 501),
"customer_id": [i % 100 + 1 for i in range(500)],
"order_date": pd.date_range("2024-01-01", periods=500, freq="6H"),
"product_id": [i % 20 + 1 for i in range(500)],
"quantity": [1, 2, 3, 5, 10] * 100,
"unit_price": [29.99, 49.99, 99.99, 149.99, 199.99] * 100
})
orders.name = "orders"
products = pd.DataFrame({
"product_id": range(1, 21),
"product_name": [f"Product {i}" for i in range(1, 21)],
"category": ["Electronics", "Software", "Hardware", "Services"] * 5,
"cost": [15, 25, 50, 75, 100] * 4
})
products.name = "products"
# Create SmartDatalake
datalake = create_smart_datalake({
"customers": customers,
"orders": orders,
"products": products
})
# Cross-DataFrame queries
cross_df_queries = [
"What is the total revenue by customer segment?",
"Which products are most popular among Enterprise customers?",
"Show the monthly order trend by product category",
"Find customers who have never ordered",
"What is the average order value by region?",
"Which product category has the highest profit margin?"
]
print("Multi-DataFrame Analysis")
print("=" * 50)
for query in cross_df_queries:
print(f"\nQuery: {query}")
result = datalake.chat(query)
print(f"Result: {result}")
Building a Data Warehouse Interface:
"""
Create a conversational interface for data warehouse queries.
"""
import pandas as pd
from pandasai import SmartDatalake
from pandasai.llm import OpenAI
from typing import Dict, List, Optional
from dataclasses import dataclass
@dataclass
class TableSchema:
"""Schema definition for a table."""
name: str
description: str
columns: Dict[str, str] # column_name: description
primary_key: str
foreign_keys: Dict[str, str] = None # column: referenced_table.column
class DataWarehouseInterface:
"""
Conversational interface for data warehouse exploration.
"""
def __init__(self, model: str = "gpt-4"):
self.llm = OpenAI(model=model, temperature=0)
self.tables: Dict[str, pd.DataFrame] = {}
self.schemas: Dict[str, TableSchema] = {}
self.datalake: Optional[SmartDatalake] = None
def add_table(
self,
name: str,
df: pd.DataFrame,
schema: TableSchema = None
):
"""Add a table to the data warehouse."""
df.name = name
self.tables[name] = df
if schema:
self.schemas[name] = schema
# Rebuild datalake
self._rebuild_datalake()
def _rebuild_datalake(self):
"""Rebuild the SmartDatalake with current tables."""
if self.tables:
self.datalake = SmartDatalake(
list(self.tables.values()),
config={
"llm": self.llm,
"verbose": True,
"enable_cache": True
}
)
def get_table_info(self) -> str:
"""Get information about available tables."""
info_lines = ["Available Tables:"]
for name, schema in self.schemas.items():
info_lines.append(f"\n{name}: {schema.description}")
info_lines.append(f" Primary Key: {schema.primary_key}")
info_lines.append(" Columns:")
for col, desc in schema.columns.items():
info_lines.append(f" - {col}: {desc}")
return "\n".join(info_lines)
def query(self, question: str) -> any:
"""
Query the data warehouse using natural language.
Args:
question: Natural language question
Returns:
Query result
"""
if not self.datalake:
raise ValueError("No tables loaded")
return self.datalake.chat(question)
def suggest_queries(self, table_name: str = None) -> List[str]:
"""
Suggest useful queries based on available data.
Args:
table_name: Specific table to focus on (optional)
Returns:
List of suggested queries
"""
suggestions = []
if table_name and table_name in self.tables:
df = self.tables[table_name]
# Numeric column suggestions
numeric_cols = df.select_dtypes(include=['number']).columns
for col in numeric_cols[:3]:
suggestions.append(f"What is the average {col} in {table_name}?")
# Categorical column suggestions
cat_cols = df.select_dtypes(include=['object', 'category']).columns
for col in cat_cols[:2]:
suggestions.append(f"Show distribution of {col} in {table_name}")
else:
# Cross-table suggestions
if len(self.tables) > 1:
tables = list(self.tables.keys())
suggestions.append(f"How are {tables[0]} and {tables[1]} related?")
suggestions.append("What are the key metrics across all tables?")
return suggestions
# Usage
warehouse = DataWarehouseInterface()
# Add tables with schemas
warehouse.add_table(
"customers",
customers,
TableSchema(
name="customers",
description="Customer master data",
columns={
"customer_id": "Unique customer identifier",
"customer_name": "Customer company name",
"segment": "Business segment (Enterprise/SMB/Startup)",
"region": "Geographic region"
},
primary_key="customer_id"
)
)
warehouse.add_table(
"orders",
orders,
TableSchema(
name="orders",
description="Customer orders",
columns={
"order_id": "Unique order identifier",
"customer_id": "Reference to customer",
"order_date": "Date of order",
"quantity": "Quantity ordered",
"unit_price": "Price per unit"
},
primary_key="order_id",
foreign_keys={"customer_id": "customers.customer_id"}
)
)
# Get table information
print(warehouse.get_table_info())
# Query the warehouse
result = warehouse.query("What is the total revenue by customer segment?")
print(f"\nResult: {result}")
# Get query suggestions
suggestions = warehouse.suggest_queries()
print(f"\nSuggested queries: {suggestions}")
6. Privacy and Security Modes
Configuring Privacy-Preserving Analysis:
"""
Configure PandasAI for privacy-sensitive data analysis.
"""
import pandas as pd
from pandasai import SmartDataframe
from pandasai.llm import OpenAI
from typing import List, Dict, Optional
import hashlib
def anonymize_column(series: pd.Series, method: str = "hash") -> pd.Series:
"""
Anonymize a column for privacy.
Args:
series: Pandas Series to anonymize
method: Anonymization method ('hash', 'mask', 'generalize')
Returns:
Anonymized Series
"""
if method == "hash":
return series.apply(
lambda x: hashlib.md5(str(x).encode()).hexdigest()[:8]
if pd.notna(x) else x
)
elif method == "mask":
return series.apply(
lambda x: str(x)[:2] + "*" * (len(str(x)) - 2)
if pd.notna(x) and len(str(x)) > 2 else "**"
)
elif method == "generalize":
if pd.api.types.is_numeric_dtype(series):
return pd.cut(series, bins=5, labels=["Very Low", "Low", "Medium", "High", "Very High"])
return series
else:
raise ValueError(f"Unknown method: {method}")
def create_privacy_safe_interface(
df: pd.DataFrame,
sensitive_columns: List[str],
anonymization_method: str = "hash"
) -> SmartDataframe:
"""
Create SmartDataframe with anonymized sensitive columns.
Args:
df: Input DataFrame
sensitive_columns: List of columns to anonymize
anonymization_method: Method for anonymization
Returns:
SmartDataframe with anonymized data
"""
# Create copy and anonymize
safe_df = df.copy()
for col in sensitive_columns:
if col in safe_df.columns:
safe_df[col] = anonymize_column(safe_df[col], anonymization_method)
llm = OpenAI(model="gpt-4", temperature=0)
return SmartDataframe(
safe_df,
config={
"llm": llm,
"verbose": False,
"enforce_privacy": True,
"enable_cache": False # Don't cache sensitive queries
}
)
class PrivacyAwareAnalyzer:
"""
Analyzer with privacy controls and audit logging.
"""
def __init__(
self,
df: pd.DataFrame,
sensitive_columns: List[str],
allowed_operations: List[str] = None
):
self.original_df = df
self.sensitive_columns = sensitive_columns
self.allowed_operations = allowed_operations or [
"aggregate", "count", "average", "sum", "distribution"
]
self.query_log = []
# Create anonymized version
self.safe_df = self._create_safe_df()
# Create SmartDataframe
llm = OpenAI(model="gpt-4", temperature=0)
self.smart_df = SmartDataframe(
self.safe_df,
config={
"llm": llm,
"verbose": True
}
)
def _create_safe_df(self) -> pd.DataFrame:
"""Create anonymized DataFrame."""
safe_df = self.original_df.copy()
for col in self.sensitive_columns:
if col in safe_df.columns:
safe_df[col] = anonymize_column(safe_df[col], "hash")
return safe_df
def _check_query_safety(self, query: str) -> bool:
"""
Check if query is safe to execute.
Args:
query: Natural language query
Returns:
True if query is safe
"""
query_lower = query.lower()
# Check for attempts to access sensitive columns directly
for col in self.sensitive_columns:
if f"show {col}" in query_lower or f"list {col}" in query_lower:
return False
# Check if query uses allowed operations
has_allowed_op = any(
op in query_lower for op in self.allowed_operations
)
return has_allowed_op
def query(self, question: str, user_id: str = "anonymous") -> any:
"""
Execute a privacy-safe query.
Args:
question: Natural language question
user_id: User identifier for audit
Returns:
Query result or error message
"""
# Log query attempt
self.query_log.append({
"user_id": user_id,
"question": question,
"timestamp": pd.Timestamp.now()
})
# Check query safety
if not self._check_query_safety(question):
return "Query denied: Attempting to access sensitive data directly"
# Execute query
try:
result = self.smart_df.chat(question)
return result
except Exception as e:
return f"Query error: {e}"
def get_audit_log(self) -> pd.DataFrame:
"""Get query audit log."""
return pd.DataFrame(self.query_log)
# Example with sensitive data
sensitive_data = pd.DataFrame({
"employee_id": range(1, 101),
"name": [f"Employee {i}" for i in range(1, 101)], # Sensitive
"email": [f"emp{i}@company.com" for i in range(1, 101)], # Sensitive
"ssn": [f"XXX-XX-{i:04d}" for i in range(1, 101)], # Highly sensitive
"department": ["Engineering", "Sales", "Marketing", "HR", "Finance"] * 20,
"salary": [50000 + i * 1000 for i in range(100)],
"performance_score": [3.0 + (i % 20) * 0.1 for i in range(100)]
})
# Create privacy-aware analyzer
analyzer = PrivacyAwareAnalyzer(
sensitive_data,
sensitive_columns=["name", "email", "ssn"],
allowed_operations=["aggregate", "average", "count", "distribution", "compare"]
)
# Safe queries
safe_queries = [
"What is the average salary by department?",
"How many employees are in each department?",
"What is the distribution of performance scores?"
]
print("Privacy-Safe Analysis Results:")
print("=" * 50)
for query in safe_queries:
print(f"\nQuery: {query}")
result = analyzer.query(query, user_id="analyst_001")
print(f"Result: {result}")
# Unsafe query attempt
unsafe_query = "Show me all employee names and emails"
print(f"\nUnsafe Query: {unsafe_query}")
result = analyzer.query(unsafe_query, user_id="analyst_001")
print(f"Result: {result}")
# View audit log
print("\nAudit Log:")
print(analyzer.get_audit_log())
Integration Examples
Streamlit Dashboard Integration
"""
Build an interactive data analysis dashboard with Streamlit and PandasAI.
"""
import streamlit as st
import pandas as pd
from pandasai import SmartDataframe
from pandasai.llm import OpenAI
def create_streamlit_dashboard():
"""Create a Streamlit dashboard for conversational data analysis."""
st.title("Conversational Data Analysis")
st.write("Ask questions about your data in natural language")
# File upload
uploaded_file = st.file_uploader("Upload CSV file", type=["csv"])
if uploaded_file is not None:
# Load data
df = pd.read_csv(uploaded_file)
# Display data preview
st.subheader("Data Preview")
st.dataframe(df.head())
# Display data info
col1, col2, col3 = st.columns(3)
with col1:
st.metric("Rows", len(df))
with col2:
st.metric("Columns", len(df.columns))
with col3:
st.metric("Memory", f"{df.memory_usage(deep=True).sum() / 1024:.1f} KB")
# Initialize PandasAI
api_key = st.sidebar.text_input("OpenAI API Key", type="password")
if api_key:
llm = OpenAI(api_key=api_key, model="gpt-4", temperature=0)
smart_df = SmartDataframe(df, config={"llm": llm, "verbose": False})
# Query interface
st.subheader("Ask a Question")
# Suggested queries
st.write("**Suggested queries:**")
suggestions = [
f"What is the average of {df.select_dtypes(include='number').columns[0]}?" if len(df.select_dtypes(include='number').columns) > 0 else None,
f"Show the distribution of {df.columns[0]}",
"How many rows are there?",
"What are the column names and their types?"
]
suggestions = [s for s in suggestions if s]
selected_suggestion = st.selectbox("Or choose a suggestion:", [""] + suggestions)
# Query input
query = st.text_area(
"Your question:",
value=selected_suggestion,
placeholder="e.g., What is the total sales by region?"
)
if st.button("Ask"):
if query:
with st.spinner("Analyzing..."):
try:
result = smart_df.chat(query)
st.subheader("Result")
# Display based on result type
if isinstance(result, pd.DataFrame):
st.dataframe(result)
elif hasattr(result, 'savefig'):
st.pyplot(result)
else:
st.write(result)
# Show generated code
with st.expander("View Generated Code"):
st.code(smart_df.last_code_generated, language="python")
except Exception as e:
st.error(f"Error: {e}")
else:
st.warning("Please enter your OpenAI API key in the sidebar")
# To run: streamlit run dashboard.py
# if __name__ == "__main__":
# create_streamlit_dashboard()
FastAPI Service Integration
"""
Create a REST API for PandasAI queries.
"""
from fastapi import FastAPI, HTTPException, UploadFile, File
from pydantic import BaseModel
from typing import Optional, List, Dict, Any
import pandas as pd
from pandasai import SmartDataframe
from pandasai.llm import OpenAI
import io
import json
app = FastAPI(title="PandasAI Query Service", version="1.0.0")
# In-memory storage for datasets
datasets: Dict[str, pd.DataFrame] = {}
smart_dfs: Dict[str, SmartDataframe] = {}
class QueryRequest(BaseModel):
"""Request model for queries."""
dataset_id: str
question: str
include_code: bool = False
class QueryResponse(BaseModel):
"""Response model for queries."""
result: Any
code: Optional[str] = None
result_type: str
class DatasetInfo(BaseModel):
"""Information about a dataset."""
dataset_id: str
rows: int
columns: int
column_names: List[str]
dtypes: Dict[str, str]
@app.post("/datasets/upload")
async def upload_dataset(
file: UploadFile = File(...),
dataset_id:
…(truncated)