BoriskovaDV/docs/data/1-st-exercise/plot_results.py

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+import pandas as pd
+import matplotlib.pyplot as plt
+import numpy as np
+
+df = pd.read_csv('experiment_results.csv')
+
+mean_times = df.groupby(['Structure', 'Mode'])[['Insert (sec)', 'Search (sec)', 'Delete (sec)']].mean().reset_index()
+
+structures = mean_times['Structure'].unique()
+modes = mean_times['Mode'].unique()
+
+fig, axes = plt.subplots(1, 3, figsize=(15, 5))
+
+operations = ['Insert (sec)', 'Search (sec)', 'Delete (sec)']
+titles = ['Insertion', 'Search', 'Deletion']
+
+for ax, op, title in zip(axes, operations, titles):
+    x = np.arange(len(structures))
+    width = 0.35
+    
+    random_vals = []
+    sorted_vals = []
+    for s in structures:
+        random_row = mean_times[(mean_times['Structure'] == s) & (mean_times['Mode'] == 'random')]
+        sorted_row = mean_times[(mean_times['Structure'] == s) & (mean_times['Mode'] == 'sorted')]
+        random_vals.append(random_row[op].values[0] if not random_row.empty else 0)
+        sorted_vals.append(sorted_row[op].values[0] if not sorted_row.empty else 0)
+    
+    ax.bar(x - width/2, random_vals, width, label='Random')
+    ax.bar(x + width/2, sorted_vals, width, label='Sorted')
+    ax.set_xticks(x)
+    ax.set_xticklabels(structures)
+    ax.set_ylabel('Time (seconds)')
+    ax.set_title(title)
+    ax.legend()
+
+plt.tight_layout()
+plt.savefig('performance_comparison.png', dpi=150)
+plt.show()