save changes before rebase

Author: ayemaqu

Mod1/M1L2-Numpy_STUDENT.ipynb

@@ -29,12 +29,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
     "# Import NumPy \n",
-    "None"
+    "import numpy as np"
    ]
   },
   {
@@ -49,11 +49,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1 2 3 4 5]\n"
+     ]
+    }
+   ],
    "source": [
-    "points = None\n",
+    "points = np.array([1,2,3,4,5])\n",
     "print(points)"
    ]
   },
@@ -70,14 +78,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
     "# Create a 2D array for player stats\n",
     "# Example: [[points, rebounds, assists], ...]\n",
     "\n",
-    "player_stats = None"
+    "player_stats = np.array([\n",
+    "    [32, 22, 14],\n",
+    "    [32, 12, 10],\n",
+    "    [5, 2, 10],\n",
+    "    [12, 20, 2],\n",
+    "    [5, 19, 4]\n",
+    "])\n"
    ]
   },
   {
@@ -93,11 +107,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[68 54 17 34 28]\n"
+     ]
+    }
+   ],
    "source": [
     "# Calculate total stats for each player\n",
+    "\n",
     "total_stats = np.sum(player_stats, axis=1)\n",
     "print(total_stats)"
    ]
@@ -113,12 +136,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[32 32  5 12  5]\n",
+      " [22 12  2 20 19]\n",
+      " [14 10 10  2  4]]\n"
+     ]
+    }
+   ],
    "source": [
     "# Transpose the array\n",
-    "transposed_stats = None\n",
+    "transposed_stats = player_stats.transpose()\n",
     "print(transposed_stats)"
    ]
   },
@@ -135,13 +168,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[1.         0.64299603]\n",
+      " [0.64299603 1.        ]]\n"
+     ]
+    }
+   ],
    "source": [
     "# Calculate correlation between points and assists -- what does the output mean?\n",
     "\n",
-    "correlation = None(player_stats[:, 0], player_stats[:, 2])\n",
+    "correlation = np.corrcoef(player_stats[:, 0], player_stats[:, 2])\n",
     "print(correlation)"
    ]
   },
@@ -171,9 +213,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Player with the highest total stats: Player 3\n",
+      "Total stats: 46\n"
+     ]
+    }
+   ],
    "source": [
     "# Player stats array\n",
     "player_stats = np.array([\n",
@@ -185,10 +236,10 @@
     "])\n",
     "\n",
     "# Calculate total stats for each player\n",
-    "total_stats = None\n",
+    "total_stats = np.sum(player_stats, axis=1)\n",
     "\n",
     "# Find the index of the player with the highest total stats\n",
-    "best_player_index = None\n",
+    "best_player_index = np.argmax(total_stats)\n",
     "\n",
     "# Print the result\n",
     "print(f\"Player with the highest total stats: Player {best_player_index + 1}\")\n",