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+# NBA Evolution Analysis (1990–2024)
+# Course: Introduction to Data Science
+# Author: Tomer Golan
+# Objective: Analyze how the NBA game evolved over time using EDA and visualization
+
+# Step 1: Import required libraries
+import pandas as pd
+import matplotlib.pyplot as plt
+import seaborn as sns
+from matplotlib.ticker import MaxNLocator
+
+# Configure visual style
+sns.set(style="whitegrid", font_scale=1.1, palette="muted")
+
+# Load dataset
+df = pd.read_csv("players_stats_by_season_full_details.csv")
+
+# Focus only on NBA data
+df = df[df['League'] == 'NBA']
+
+# Select relevant columns
+cols = ['Season', 'PTS', 'AST', '3PM', 'height_cm']
+df = df[cols]
+
+# Preview
+print("Shape:", df.shape)
+df.head()
+
+# Drop missing values and ensure numeric columns
+df = df.dropna()
+df['Season_Year'] = df['Season'].str.extract(r'(\d{4})').astype(int)
+
+# Filter for reasonable timeline
+df = df[df['Season_Year'].between(1990, 2024)]
+
+# Convert numeric columns properly
+for col in ['PTS', 'AST', '3PM', 'height_cm']:
+    df[col] = pd.to_numeric(df[col], errors='coerce')
+
+print("After cleaning:", df.shape)
+
+# Summary statistics
+df[['PTS', 'AST', '3PM', 'height_cm']].describe()
+
+# Group by year and calculate averages
+trend = df.groupby('Season_Year')[['PTS', 'AST', '3PM', 'height_cm']].mean().reset_index()
+
+# Rename columns for clarity
+trend.rename(columns={
+    'PTS': 'Avg_Points',
+    'AST': 'Avg_Assists',
+    '3PM': 'Avg_3PM',
+    'height_cm': 'Avg_Height'
+}, inplace=True)
+
+trend.head()
+
+# Helper function for clean, clear barplots
+def make_barplot(data, x, y, color, title, xlabel, ylabel):
+    plt.figure(figsize=(9,6))
+    ax = sns.barplot(x=x, y=y, data=data, color=color, edgecolor='black')
+    ax.yaxis.set_major_locator(MaxNLocator(integer=True))
+    plt.title(title, fontsize=15, weight='bold')
+    plt.xlabel(xlabel)
+    plt.ylabel(ylabel)
+    plt.grid(True, linestyle='--', alpha=0.6)
+    plt.tight_layout()
+    plt.show()
+
+# Average Points & Assists 
+
+plt.figure(figsize=(10,6))
+
+bar_data = trend.melt(id_vars='Season_Year', value_vars=['Avg_Points', 'Avg_Assists'],
+                      var_name='Metric', value_name='Average')
+
+ax = sns.barplot(x='Season_Year', y='Average', hue='Metric', data=bar_data,
+                 palette=['orange', 'mediumseagreen'], edgecolor='black')
+
+ax.yaxis.set_major_locator(MaxNLocator(integer=True))
+plt.title("Average Points and Assists per Player (NBA 1990–2024)", fontsize=15, weight='bold')
+plt.xlabel("Season Year")
+plt.ylabel("Average per Player")
+plt.xticks(rotation=45)
+plt.legend(title="Metric", loc='upper left')
+plt.grid(True, linestyle='--', alpha=0.6)
+plt.tight_layout()
+plt.show()
+
+
+# The 3 Point Revolution 
+
+plt.figure(figsize=(10,6))
+ax = sns.barplot(x='Season_Year', y='Avg_3PM', data=trend, color='dodgerblue', edgecolor='black')
+ax.yaxis.set_major_locator(MaxNLocator(integer=True))
+plt.title("Average 3-Point Shots Made per Player (1990–2024)", fontsize=15, weight='bold')
+plt.xlabel("Season Year")
+plt.ylabel("Average 3PM per Player")
+plt.xticks(rotation=45)
+plt.grid(True, linestyle='--', alpha=0.6)
+plt.tight_layout()
+plt.show()
+
+# Average Height Over Time
+plt.figure(figsize=(10,6))
+ax = sns.lineplot(x='Season_Year', y='Avg_Height', data=trend, color='purple', linewidth=2.5, marker='o')
+ax.yaxis.set_major_locator(MaxNLocator(integer=True))
+plt.title("Average Player Height (1990–2024)", fontsize=15, weight='bold')
+plt.xlabel("Season Year")
+plt.ylabel("Average Height (cm)")
+plt.grid(True, linestyle='--', alpha=0.6)
+plt.tight_layout()
+plt.show()
+
+# Points vs Assists over time
+plt.figure(figsize=(10,6))
+scatter = sns.scatterplot(
+    x='Avg_Assists',
+    y='Avg_Points',
+    data=trend,
+    hue='Season_Year',
+    palette='coolwarm',
+    size='Avg_3PM',
+    sizes=(50, 300),
+    alpha=0.8,
+    edgecolor='black'
+)
+
+sns.regplot(
+    x='Avg_Assists',
+    y='Avg_Points',
+    data=trend,
+    scatter=False,
+    color='black',
+    line_kws={'linestyle':'--', 'linewidth':2}
+)
+
+plt.title("Points vs Assists Over Time (Color = Year, Size = 3PM)", fontsize=15, weight='bold')
+plt.xlabel("Average Assists per Player")
+plt.ylabel("Average Points per Player")
+plt.grid(True, linestyle='--', alpha=0.6)
+plt.legend(title="Season Year", loc='upper left', bbox_to_anchor=(1.02, 1))
+plt.tight_layout()
+plt.show()
+
+
+# Ratio of Points to Assists 
+trend['PTS_AST_Ratio'] = trend['Avg_Points'] / trend['Avg_Assists']
+
+plt.figure(figsize=(10,6))
+ax = sns.lineplot(x='Season_Year', y='PTS_AST_Ratio', data=trend, color='crimson', linewidth=2.5, marker='o')
+ax.yaxis.set_major_locator(MaxNLocator(integer=True))
+plt.title("Ratio of Points to Assists Over Time (Team Play Indicator)", fontsize=15, weight='bold')
+plt.xlabel("Season Year")
+plt.ylabel("Points / Assists Ratio")
+plt.grid(True, linestyle='--', alpha=0.6)
+plt.tight_layout()
+plt.show()
+
+corr = trend[['Avg_Points', 'Avg_Assists', 'Avg_3PM', 'Avg_Height']].corr()
+
+plt.figure(figsize=(6,4))
+sns.heatmap(corr, annot=True, cmap='coolwarm', fmt=".2f")
+plt.title("Correlation Between NBA Performance Metrics", fontsize=14, weight='bold')
+plt.tight_layout()
+plt.show()
+
+# Summary of Findings
+print("""
+Summary of Findings (1990 - 2024):
+
+1. Average points per player have risen slightly - the game became faster and more efficient.
+2. Assists grew steadily - NBA teams now play with more collaboration and spacing.
+3. The 3-point revolution exploded after 2010 - the single biggest transformation in modern basketball.
+4. Average player height slightly decreased - speed and versatility are prioritized over size.
+5. The ratio of points to assists dropped - showing that more scoring now comes from teamwork.
+
+In short: The NBA has evolved from slow, isolation heavy basketball
+into a fast, perimeter oriented, analytics-driven, and team first game.
+""")