#loading the required libraries
import pandas as pd
from matplotlib import pyplot as plt
#import seaborn as sns

#loading the ipl dataset
ipl=pd.read_csv('ipl_2023_dataset.csv')

#having a glance at records of the dataset
ipl

#Looking at the number of rows(no. of palyers) and columns in dataset
ipl.shape

(568, 5)

#Getting the frequency of players in a team
ipl['Team'].value_counts()

Unsold                         325
Chennai Super Kings             25
Delhi Capitals                  25
Gujarat Titans                  25
Lucknow Super Giants            25
Royal Challengers Bangalore     25
Rajasthan Royals                25
Sunrisers Hyderabad             25
Mumbai Indians                  24
Kolkata Knight Riders           22
Punjab Kings                    22
Name: Team, dtype: int64

#Extracting the records where a team has a player equal to or with a higher value than 10 Cr.
ipl[ipl['Price Cr']>=10]

#ipl team, with their market values in descending order
ipl.groupby('Team')['Price Cr'].sum().sort_values(ascending=False)

Team
Mumbai Indians                 94.95
Chennai Super Kings            93.50
Kolkata Knight Riders          93.35
Royal Challengers Bangalore    93.25
Rajasthan Royals               91.65
Lucknow Super Giants           91.45
Delhi Capitals                 90.55
Gujarat Titans                 90.55
Sunrisers Hyderabad            88.45
Punjab Kings                   82.80
Unsold                          0.00
Name: Price Cr, dtype: float64

plt.bar(list(ipl['Price Cr'][0:5].keys()),list(ipl['Price Cr'][0:5]),color=["blue","yellow","purple","red","pink"])
plt.xticks([0, 1, 2, 3, 4], ['MI', 'CSK', 'KKR','PK','RR'])
plt.xlabel('Teams')
plt.ylabel('Prices')
plt.show()

#Most expensive ten players in the list
ipl.nlargest(10, 'Price Cr')

#team RR
ipl[ipl['Team'] == 'Rajasthan Royals']

#All-rounders in rajasthan royals
ipl[(ipl['Type'] == 'All-Rounder') & (ipl['Team'] == 'Rajasthan Royals')]

Want me to add some more details then do message me over my gmail or WhatsApp ID