Data Analysis using LLAMA 3 and GROQ
Are you ready to move to the most effective level of big data analytics? In this post what we are going to discuss is the L3 and Panda’s AI and also the way we can use all of them in the data analysis process.
Getting Started
To get started, we’ll need to install Panda’s AI and Llama3 Groq model. We can do this using pip:
pip install pandas
pip install llama3-groq
Once both packages are installed, we can import the necessary libraries:
import pandas as pd
from pandas_ai import SmartDataFrame
from llama3_groq import ChatGroq
Loading the Data
Next, we’ll load our dataset, a CSV file called “Coffee Shop Sales”. We can use the read_csv function from Panda’s library:
Creating a Smart Data Frame
Next, we are going to create a Smart Data Frame; a main functionality of the Panda’s Artificial Intelligence. It’s used to interact with a single data frame and contains all the features and properties of a simple data frame, plus conversation features:
Interacting with the Data
As a matter of fact, due to the chat function of the Smart Data Frame, we are actually able to deal with our data. For example, we can ask it to check if the data requires any cleaning or if there are any issues:
print(response)
Data Cleaning
Now, we’ll look for the rows that need cleaning and clean the data.
print(response)
Descriptive Statistics
Firstly, we will have a look at our dataset for that we’ll ask it:
Next, we can ask the Smart Data Frame to provide descriptive statistics about our data:
response = smart_df.chat(“Provide descriptive statistics about the data.”)
print(response)
Next, we’ll have a look at the average sales of each product category.
Next, we’ll have a quick look at the statistical summary of the dataset along with visualizations.
Exploratory Data Analysis
We can also perform exploratory data analysis on our data:
response = smart_df.chat(“Perform exploratory data analysis on each column of the data set and also provide appropriate visualizations.”)
print(response)
Sales Trend Analysis
Using the Smart Data Frame, we can also perform sales trend analysis:
print(response)

Prescriptive Analysis
Finally, we can ask the Smart Data Frame to provide prescriptive analysis and actionable recommendations on how to improve sales:
print(response)