Live Project: IPL Data Analysis using Pandas AI

The focus was to analyze the IPL 2023 Auction dataset and derive insights such as top buys, team-wise spending, unsold players, and player categories – all using simple English prompts instead of complex Python code. The project demonstrates how LLM-powered tools can simplify data exploration, visualization, and pattern recognition in sports analytics.

The main aim was to:

> Perform data-driven insights using conversational prompts
> Visualize financial trends across teams
> Identify patterns in player selection & spending
> Understand the limitations of AI-driven data analysis

Project Implementation

Prerequisites Setup:

> Installed pandasai using !pip install -q pandasai
> Ensured OpenAI API access was set up

Library Imports:

> Imported essential modules: pandas, SmartDataframe from pandasai, and OpenAI from pandasai.llm.openai
> Initialized OpenAI LLM with API key
> Created a SmartDataframe instance for intelligent querying

Dataset Loading:

> Used the IPL_Squad_2023_Auction_Dataset.csv dataset
> Loaded the data into a DataFrame using pd.read_csv()
> Explored data shape & top rows using .shape and .head()

Interactive Data Analysis with Prompts:

Used .chat() method to interact with data using natural language

Ran a series of prompts to extract various insights:

> Most expensive & cheapest buys
> Team-wise expenditure
> Count of unsold bowlers & their base prices
> Types of unsold players
> New players picked by Gujarat
> Team-specific bar plots for spending on player types
> Total money spent by all teams
> Prediction query for Sam Curran’s 2024 buyer
> Univariate & multivariate analysis

Graphical Insights:

> Used LLM-driven commands to auto-generate bar graphs showing team-wise spending
> Visualized how much teams like Mumbai Indians and Gujarat Titans spent on different player categories

Observations

> Pandas AI is highly effective for quick exploratory tasks, direct insights & basic plots
> It struggles with complex analytics like multivariate analysis, outlier detection, or ambiguous queries
> Response time is slower due to API latency
> Still a great tool for beginners to interact with data using English instead of code

Key Learnings & Outcomes

> Understood the application of LLMs in structured data analysis
> Learned how to perform insights & visualizations using prompts
> Identified pros & cons of using Pandas AI for real-world datasets
> Gained experience in handling sports data & auction-based analytics
> Demonstrated how GenAI tools can assist in data science workflows

Step 1: Prerequisites

Before starting ensure that pandasAI and openai libraries are installed. Run the following command in your command prompt:

!pip install -q pandasai

Step 2: Importing necessary libraries

import pandas as pd
from pandasai import SmartDataframe
from pandasai.llm.openai import OpenAI

Step 3: Initializing an instance of OpenAI LLM and pass it’s API key

# replace "your_api_key" with your generated key
OPENAI_API_KEY = "your_api_key"
sdf = SmartDataframe(df, config={"llm": llm})

Step 4: Importing the IPL 2023 Auction dataset using pandas

We are using the IPL 2023 Auction dataset here. You can download dataset from kaggle.

df = pd.read_csv('IPL_Squad_2023_Auction_Dataset.csv')
print(df.shape)
df.head()

Step 6: Data Analysis using PandasAI

Now let’s begin our analysis:

Prompt 1:

sdf.chat(df, prompt="Which players are the most costliest buys?")

Prompt 2:

prompts = """
Which players were the cheapest buys this season and which team bought them?
"""
sdf.chat(df, prompt=prompts)

Prompt 3:

prompts = """
Draw a Bargraph showing How much money was spent by each team this season overall.
"""
sdf.chat(df, prompt=prompts)

Prompt 4:

sdf.chat(df, prompt="How many bowler remained unsold and what was their base price?")

Prompt 5:

sdf.chat(df, prompt="How many players remained unsold this season?")

Prompt 6:

sdf.chat(df, prompt="Which type of players were majorly unsold?")

Prompt 7:

sdf.chat(df, prompt="Who are three new players Gujrat picked?")

Promopt 8:

sdf.chat(df, prompt="What is total money spent by all teams in dollars?")

Prompt 9:

prompts = """
draw a barplot showing 
how much money was spent by Mumbai Indians on all types of players?
"""
pandas_ai.run(df, prompt=prompts)

Prompt 10:

prompts = """
draw a barplot showing how much money was spent by Gujrat on all types of players?
"""
pandas_ai.run(df, prompt=prompts)

Prompt 11:

sdf.chat(df, prompt="Can you predict which team will buy Sam Curran in 2024?")

Prompt 12:

sdf.chat(df, prompt="Perform univariate analysis")

Prompt 13:

sdf.chat(df, prompt="Perform multivariate analysis")

For this input PandasAI seems to have failed as the complexity and ambiguity increased.

Pros of Pandas AI

> Pandas AI works well on direct and well-explained inputs.
> Can easily perform simple tasks like plotting graphs and univariate analysis.
> Can perform basic statistical operations.
> Also, can make basic predictions sometimes.

Cons of Pandas AI

> Cannot process ambiguous inputs.
> It uses server data, So, that is slower as compared to pandas.
> Cannot perform complex tasks like outlier analysis or multivariate analysis.

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