Time complexity of both pop() and append() and O(1)

Author: vterron

[2018] Python Heaps-ters/python-heaps-ters.ipynb

@@ -934,8 +934,8 @@
     "            else:\n",
     "                maximums.sort(reverse=True)   # O(k log k)\n",
     "                if n > maximums[-1]:          # the minimum\n",
-    "                    maximums.pop()            # O(k)\n",
-    "                    maximums.append(n)        # O(k)\n",
+    "                    maximums.pop()            # O(1)\n",
+    "                    maximums.append(n)        # O(1)\n",
     "    return maximums\n",
     "\n",
     "find_maximums(\"lots-of-numbers.txt\", 3)"
@@ -945,7 +945,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "- Each loop is $\\mathcal{O}(k \\log k) + \\mathcal{O}(2k) = \\mathcal{O}(k \\log k)$\n",
+    "- Each loop is $\\mathcal{O}(k \\log k) + \\mathcal{O}(2) = \\mathcal{O}(k \\log k)$\n",
     "- Total complexity: $\\mathcal{O}(k \\log k) \\times n = \\mathcal{O}(k \\log k \\times n)$"
    ]
   },