IPL Data Analysis using Pandas AI

Analyzing IPL 2023 auction data is important for understanding player purchases, team spending and auction trends. In this guide, we’ll use PandasAI an AI-powered data analysis tool to gain insights from the IPL 2024 Auction dataset. PandasAI enhances traditional Pandas by integrating AI-driven insights making it easier to extract meaningful information from large datasets. Key benefits include:

• Automating data analysis
• Generating quick insights
• Simplifying complex queries

Step-by-Step IPL Data Analysis Using PandasAI

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()

Output:

Step 5: Drop the "Unnamed: 0" column from the above dataset

df.drop(['Unnamed: 0'], axis=1, inplace=True)
df.head()

Output:

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?")

Output:

['Sam Curran', 'Cameron Green', 'Ben Stokes']

Prompt 2:

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

Output:

Well, it looks like the cheapest buys this season were Glenn Phillips for Sunrisers Hyderabad,
Raj Angad Bawa and Rishi Dhawan for Punjab Super Kings, Dhruv Jurel and K.C Cariappa
for Rajasthan Royals and many more. The full list includes 163 players and their respective teams.

Prompt 3:

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

Output:

Prompt 4:

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

Output:

There were 108 bowlers who remained unsold in the auction.
Their base price ranged from 2 million to 20 million.

Prompt 5:

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

Output:

('Number of players remained unsold this season:', 338)

Prompt 6:

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

Output:

Player's List Base Price TYPE COST IN ₹ (CR.) Cost IN $ (000) \
0 Shivam Mavi 4000000 BOWLER 6.0 720.0
2 Kane Williamson 20000000 BATSMAN 2.0 240.0
3 K.S. Bharat 2000000 WICKETKEEPER 1.2 144.0
5 Odean Smith 5000000 ALL-ROUNDER 0.5 60.0
7 Rahul Tewatia Retained ALL-ROUNDER 0.0 0.0
2022 Squad Team
0 KKR Gujarat Titans
2 SRH Gujarat Titans
3 DC Gujarat Titans
5 PBKS Gujarat Titans
7 GT Gujarat Titans
TYPE
ALL-ROUNDER 65
BOWLER 64
BATSMAN 35
WICKETKEEPER 21
Name: TYPE, dtype: int64
Number of unsold players: 0

Prompt 7:

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

Output:

0 Shivam Mavi
1 Joshua Little
2 Kane Williamson
Name: Player's List, dtype: object

Prompt 8:

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

Output:

The total amount of money spent by all teams in the auction is $20,040,000.

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)

Output:

Sure, I can help you with that! To answer your question, I can draw a barplot that shows how much money the Mumbai Indians spent on all types of players. Would you like me to do that for you now?

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)

Output:

Prompt 11:

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

Output:

Lucknow Super Giants

Prompt 12:

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

Output:

Sure! Based on the data provided, the univariate analysis shows that we have six variables: Player's List, Base Price, TYPE, COST IN ₹ (CR.), Cost IN $ (000), 2022 Squad and Team. The data types for these variables are object, object, object, float64, float64 and object respectively.

Prompt 14:

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

Output:

Unfortunately, I was not able to answer your question. Please try again. If the problem persists try rephrasing your question.

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.