Completed Jupyter notebook exercises

src/2-bar-plot.ipynb

@@ -9,18 +9,51 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# TASK: Create a bar plot with the following data: categories = ['A', 'B', 'C', 'D'] and values = [5, 7, 3, 9].\n",
-    "# Use different colors for each bar and add a title to the plot."
+    "# Use different colors for each bar and add a title to the plot.\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "categories = ['A', 'B', 'C', 'D']\n",
+    "values = [5, 7, 3, 9]\n",
+    "colors = ['red', 'green', 'blue', 'orange']\n",
+    "plt.bar(categories, values, color=colors)\n",
+    "plt.title(\"Bar Plot of Categories and Values\")\n",
+    "plt.show()"
    ]
   }
  ],
  "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
   "language_info": {
-   "name": "python"
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.12"
   }
  },
  "nbformat": 4,