Pandas Query Engine vs Pandas AI: Choosing the Right Tool for Efficient Conversational Data Analysis

Introduction

As data grows in size and complexity, data scientists and analysts seek tools that are powerful and efficient in handling large datasets and performing complex queries. Pandas, a widely-used data analysis library in Python, has evolved, spawning tools like Pandas Query Engine and Pandas AI, each offering unique ways to simplify and optimize data analysis. This post compares these three approaches to help you understand which might best suit your needs.

Overview of Pandas, Pandas Query Engine, and Pandas AI

Pandas
pandas is a fast, powerful, flexible, and easy-to-use open-source data analysis and manipulation tool, built on top of the Python programming language.

Pandas Query Engine
Pandas Query Engine builds on Pandas’ capabilities but optimizes query execution by leveraging advanced indexing and sometimes even alternative data structures. It offers an improvement in query performance and allows for more SQL-like operations, making it an excellent choice for users accustomed to SQL syntax.

Features:

• SQL-Like Syntax: Supports SQL-style queries on data frames, enabling more intuitive querying for users familiar with SQL.
• Optimized Performance: Speeds up data queries by utilizing optimized indexing and sometimes alternative data storage structures, making it faster than standard Pandas for large datasets.
• Advanced Filtering and Selection: Allows for highly specific filtering and selection within data frames, ideal for targeted data exploration.
• Improved Memory Management: Handles larger datasets with better memory efficiency, reducing the computational load.
• Integration with Existing Pandas Code: Works seamlessly with existing Pandas operations, allowing users to mix traditional Pandas methods with optimized query operations.
• Aggregation and Grouping Enhancements: Offers more efficient handling of grouping, aggregation, and complex join operations, ideal for summarizing data and generating insights.

Pandas AI
Pandas AI is an emerging extension that integrates AI-based optimizations and features, making data operations even more efficient and intuitive. It’s particularly useful for advanced analysis and for handling tasks that require more than simple filtering or data transformations, such as predictive analytics or language-based queries.

Features:

• AI-Driven Analysis: Leverages machine learning and artificial intelligence to perform advanced data analysis, including natural language queries and predictive analytics.
• Natural Language Processing (NLP): Allows users to query data with plain language instructions, making data manipulation more accessible without complex code.
• Automated Insights: Offers insights and recommendations based on patterns in the data, helping users uncover trends and anomalies with minimal effort.
• Enhanced Predictive Capabilities: Integrates models that predict trends or outcomes, adding a layer of forecasting directly into the data analysis process.
• Simplified Complex Operations: Automates complex data manipulation tasks, like feature extraction and transformation, with minimal user intervention.
• Intelligent Data Visualization: Dynamically generates visualizations tailored to data insights, making it easier to understand and communicate results.

Comparison Experiment

To explore the strengths and weaknesses of each approach, we conducted a series of tests on a dataset involving [provide a brief description of the dataset, e.g., “financial transactions,” “customer demographics,” etc].

Each method was tested with common data analysis tasks, including filtering, aggregation, and transformation operations. We also measured the time taken for each task, the complexity of the syntax, and the overall ease of use.

Results and Observations

Below, we delve into the results of our experiment, highlighting where each tool excels and where it may fall short.

Dataset that we are using for this Case Study

Titanic dataset

# Define pandas ai agent and queryengine
openai_llm = ChatOpenAI()
pandas_ai_agent = SmartDataframe(df, config={"llm": openai_llm})
query_engine = PandasQueryEngine(df=df, verbose=False)

here we have an example where Pandas Query Engine works perfectly but PandasAI draws a graph for the

question_2 = "How does gender affect survival rates?"
survival_by_gender = df.groupby('sex')['survived'].mean()
print("Pandas DataFrame:\n")  
print(survival_by_gender, "\n")
query_engine_response_2 = query_engine.query(question_2)
print("Query Engine Response:\n")
print(query_engine_response_2,"\n")
pandas_ai_response_2 = pandas_ai_agent.chat(question_2)
print("Pandas AI Response:\n")
print(pandas_ai_response_2)

# Response
"""
Pandas DataFrame:

sex
female    0.742038
male      0.188908
Name: survived, dtype: float64 

Query Engine Response:

sex
female    0.742038
male      0.188908
Name: survived, dtype: float64 

Pandas AI Response:
# and a graph by PandasAI
"""

and here we have one more example where I'm using bamboo_llm from PandasAI

question_7 = "Is there a relationship between cabin availability and survival?"

df['has_cabin'] = df['cabin'].notna()
survival_by_cabin = df.groupby('has_cabin')['survived'].mean()
query_engine_response_7 = query_engine.query(question_7)
pandas_ai_response_7 = pandas_ai_agent.chat(question_7)

# Response 
"""
### Survival Rate by Cabin Availability

- **False**: 29.99% survival rate
- **True**: 66.67% survival rate

---

### Query Engine Response

> has_cabin
False    0.299854
True     0.666667
Name: survived, dtype: float64


### Pandas AI Response

> No apparent relationship between cabin availability and survival.
"""

1. Performance

• Pandas Query Engine: The Query Engine offered a noticeable performance improvement, particularly with SQL-like queries. It demonstrated faster execution times, especially with large datasets, making it ideal for those dealing with big data.
• Pandas AI: Pandas AI was impressive in terms of handling complex queries and performing tasks that would typically require more lines of code in standard Pandas. However, it came with slightly higher overhead for simple tasks, which might make it less suitable for straightforward data manipulation.

2. Ease of Use

• Pandas Query Engine: This tool’s SQL-like syntax is intuitive for users familiar with SQL, allowing for clearer and more concise queries.
• Pandas AI: By integrating AI functionalities, Pandas AI made complex queries more manageable. For instance, tasks that would require multiple steps in Pandas were reduced to simpler, AI-driven commands.

3. Flexibility and Advanced Features

• Pandas Query Engine: Excellent for users who need optimized querying with a familiar SQL syntax.
• Pandas AI: Ideal for advanced users looking for predictive analysis, natural language querying, or AI-driven recommendations.

Conclusion

Each of these tools has its unique strengths:

• Opt for Pandas Query Engine if you are handling larger datasets or need optimized, SQL-like queries.
• Consider Pandas AI if you require more advanced, AI-driven functionalities and are handling complex or large-scale analysis tasks.

In summary, your choice will depend on the specific demands of your project. By leveraging the right tool for the right task, you can maximize efficiency, maintain readability, and unlock new insights in your data analysis journey.

You can access the Code from here: Collab Link