8/26/2024

Integrating Panda Query Engine with LlamaIndex for Data Analysis

In the rapidly evolving world of DATA ANALYSIS, harnessing the right tools can make a tremendous difference in the speed & accuracy of insights derived from data. One exciting combination of technologies that is taking the analytics world by storm is the integration of the Panda Query Engine with LlamaIndex. If you're looking to streamline how you interact with data using powerful natural language processing capabilities, you’re in the right place. In this post, we'll dive deep into how this integration can revolutionize your data analysis workflows.

What is LlamaIndex?

Before we delve into the details of Panda Query Engine, let's understand what LlamaIndex is all about. LlamaIndex is an advanced framework designed to build context-augmented applications powered by Large Language Models (LLMs). This means it not only allows users to ingest and index data from multiple sources but also makes it EASY and EFFICIENT to query that data in natural language. The integration of LlamaIndex provides a structured way to access and manipulate vast datasets quickly.

For a more detailed overview, check out LlamaIndex’s official documentation.

The Magic of Panda Query Engine

Pandas is probably one of the most popular libraries in Python for data manipulation & analysis. The Panda Query Engine utilizes LLM capabilities to convert natural language queries into executable Pandas code. Users can type a question in natural language, and the engine infers the necessary operations to manipulate DataFrames & produce the desired outcome. Having this at your fingertips means that querying data becomes INSTANTANEOUS, even if you’re not a programming wizard!

The Panda Query Engine leverages LLamas' capability to TURN complex data queries into simpler commands. This process can help illuminate insights buried in data tables without needing to manually write every line of code. You can explore more about the capabilities of the Panda Query Engine in this Pandas Query Engine documentation.

Getting Started with Panda Query Engine and LlamaIndex

Prerequisites

If you want to take full advantage of integrating the Panda Query Engine with LlamaIndex, you will need:

A working knowledge of Python & Pandas
Familiarity with LLMs and how they work
Jupyter Notebook or Google Colab set up on your machine

Installation

To get started, you'll first need to install the necessary Python packages. Here’s how you can do that:

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!pip install llama-index llama-index-experimental

Next, you’ll need to import the libraries you'll be using:

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import logging
import sys
import pandas as pd
from llama_index.experimental.query_engine import PandasQueryEngine

logging.basicConfig(stream=sys.stdout, level=logging.INFO)
logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))

Creating a Toy DataFrame

Let’s create a simple DataFrame that contains information about cities & their populations. This example sets the stage for understanding how the Panda Query Engine processes queries:

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df = pd.DataFrame({
    "city": ["Toronto", "Tokyo", "Berlin"],
    "population": [2930000, 13960000, 3645000],
})

Utilizing the Query Engine

Once we have our DataFrame, we can instantiate the Pandas Query Engine:

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query_engine = PandasQueryEngine(df=df, verbose=True)

With the Pandas Query Engine in place, you can now make natural language queries. For example, if you want to know which city has the highest population, simply run:

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response = query_engine.query("What city has the highest population?")
print(response)

The engine will process this query and return the answer, Tokyo. How cool is that?

Analyzing the Titanic Dataset

To showcase the potential of this integration, let’s analyze a more complex dataset—the Titanic dataset. This is a commonly used dataset within the machine learning community, containing valuable insights about survival rates based on various attributes.

Downloading the Titanic Dataset

To download the Titanic dataset for analysis, you can run:

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!wget 'https://raw.githubusercontent.com/jerryjliu/llama_index/main/docs/docs/examples/data/csv/titanic_train.csv' -O 'titanic_train.csv'

Loading the Dataset

Load the dataset into a DataFrame:

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df = pd.read_csv("./titanic_train.csv")

Querying the Titanic Data

Now we can leverage the Pandas Query Engine to answer questions about the data. For instance, if we want to find out the correlation between survival & age, we can ask:

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query_engine = PandasQueryEngine(df=df, verbose=True)
response = query_engine.query("What is the correlation between survival & age?")
print(response)

The engine will provide a numerical correlation that quantifies the relationship, aiding us in better understanding the factors influencing survival.

Analyzing & Modifying Prompts

One of the most powerful aspects of integrating Panda Query Engine with LlamaIndex is the capability to modify prompts for customized output. While the engine is designed with a pre-set structure of queries, you can analyze and adjust them as per your requirements. For instance, changing how the data is framed or the type of results desired can lead to vastly different insights.

To access the available prompts and modify them, do the following:

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prompts = query_engine.get_prompts()
print(prompts)

You can even create custom prompts that give your queries tailored instructions for optimal results. This aspect allows for a higher degree of flexibility and precision in data analysis tasks.

Advanced Query Pipeline Workflows

LlamaIndex allows users to set up sophisticated workflows via the Query Pipeline feature. This means you can create complex chains of queries that process data through various stages, both increasing efficiency & enhancing scalability.

Creating a robust pipeline can involve layering multiple components. However, the ease of flexibility within the structure allows you to maintain a simple setup while gradually enhancing the workflow as needed.

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from llama_index.core.query_pipeline import QueryPipeline
# Define your pipeline here

You can define input components, link them to processing functions, and manage outputs through this approach. The power of a well-planned query pipeline presents new avenues for data analysis and operational excellence.

Real-World Applications of Panda Query Engine and LlamaIndex Integration

The integration of the Panda Query Engine with LlamaIndex opens up endless possibilities across various domains:

Business Intelligence: Quickly answer business queries without needing extensive technical knowledge.
Market Research: Analyze consumer behavior and preferences efficiently using historical data.
Health Analytics: Evaluate patient data for trends & correlations in health outcomes.
Sports Analytics: Demonstrate team performance & player statistics to facilitate strategic decisions.

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Conclusion

Integrating the Panda Query Engine with LlamaIndex provides a robust solution for data analysis, elevating the capabilities of traditional data manipulation techniques. By transforming natural language queries into actionable data tasks, this integration enables users to gain insights swiftly & effectively, regardless of their technical expertise. So, get started today, explore beyond conventional analytics tools, and embrace the future of data querying with confidence!