Contract & Salary Data

Player,Team,2024-25,2025-26,2026-27,2027-28,2028-29,Guaranteed
Stephen Curry,GSW,55761216.0,59606816.0,62840802.0,0,0,178208834.0
Joel Embiid,PHI,51415938.0,55012210.0,59013270.0,63014330.0,0,228455748.0
LeBron James,LAL,48728845.0,52627150.0,0,0,0,101356095.0

Player,Team,2024-25,2025-26,2026-27,2027-28,2028-29
LeBron James,LAL,P,0,0,0,0
James Harden,LAC,P,0,0,0,0
Paul George,PHI,0,P,0,0,0
Kawhi Leonard,LAC,0,P,0,0,0

player,salary,team,year,nba_id
Moses Malone,2406000.0,ATL,1991,77449.0
Dominique Wilkins,2065000.0,ATL,1991,1122.0

import pandas as pd

df = pd.read_csv('salary.csv')

# Top 2024-25 salaries
top_salaries = df.nlargest(30, '2024-25')[['Player', 'Team', '2024-25']]
top_salaries['2024-25'] = top_salaries['2024-25'].apply(lambda x: f"${x:,.0f}")

print("Highest Paid Players 2024-25:")
print(top_salaries)

import pandas as pd

options = pd.read_csv('option.csv')

# Find all player options for 2025-26
player_options_2526 = options[options['2025-26'] == 'P']

print("Players with 2025-26 Player Options:")
print(player_options_2526[['Player', 'Team', '2025-26']])

import pandas as pd

salaries = pd.read_csv('salary.csv')
cap = pd.read_csv('cap.csv')

# Get 2024-25 cap info
cap_2025 = cap[cap['year'] == 2025].iloc[0]
salary_cap = cap_2025['Salary Cap']
lux_tax = cap_2025['Luxury Tax']
apron1 = cap_2025['1st Apron']
apron2 = cap_2025['2nd Apron']

# Calculate team payrolls
team_payroll = salaries.groupby('Team')['2024-25'].sum().sort_values(ascending=False)

team_payroll_df = pd.DataFrame({
    'Team': team_payroll.index,
    'Total_Salary': team_payroll.values,
    'Over_Cap': team_payroll.values - salary_cap,
    'Lux_Tax_Status': team_payroll.values > lux_tax,
    'Over_Apron1': team_payroll.values > apron1,
    'Over_Apron2': team_payroll.values > apron2
})

print("Team Salary Analysis 2024-25:")
print(team_payroll_df.head(30))

import pandas as pd

salaries = pd.read_csv('salary.csv')
options = pd.read_csv('option.csv')

# Merge salary and options
merged = salaries.merge(options, on=['Player', 'Team'])

# Find players with expiring contracts or options
expiring_2025 = merged[
    (merged['2025-26'] == 0) | 
    (merged['2025-26'].isin(['P', 'T', 'UFA', 'RFA']))
]

expiring_2025['Status_2026'] = expiring_2025['2025-26']
print("Contract Flexibility - 2025-26 Free Agents/Options:")
print(expiring_2025[['Player', 'Team', '2024-25', 'Status_2026']].sort_values('2024-25', ascending=False))

import pandas as pd

df = pd.read_csv('nba_salaries_master.csv')

# Average salary by year
avg_by_year = df.groupby('year')['salary'].mean().reset_index()
avg_by_year.columns = ['Year', 'Avg_Salary']

print("Average NBA Salary by Year:")
print(avg_by_year.tail(20))

# Compare to salary cap growth
cap = pd.read_csv('cap.csv')
cap_growth = cap[['year', 'Salary Cap']].merge(avg_by_year, left_on='year', right_on='Year')
cap_growth['Cap_vs_AvgSalary_Ratio'] = cap_growth['Salary Cap'] / cap_growth['Avg_Salary']

print("\nSalary Cap vs Average Salary:")
print(cap_growth.tail(10))

import pandas as pd

df = pd.read_csv('salary.csv')

# Top guaranteed money
top_guaranteed = df.nlargest(30, 'Guaranteed')[['Player', 'Team', 'Guaranteed']]
top_guaranteed['Guaranteed'] = top_guaranteed['Guaranteed'].apply(lambda x: f"${x:,.0f}")

print("Most Guaranteed Money Remaining:")
print(top_guaranteed)