Main

src/2-bar-plot.ipynb

@@ -9,18 +9,60 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "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."
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "categories = ['A', 'B', 'C', 'D']\n",
+    "values = [5, 7, 3, 9]\n",
+    "colors = ['red', 'blue', 'green', 'orange']\n",
+    "\n",
+    "plt.bar(categories, values, color=colors)\n",
+    "plt.title(\"Bar Plot of Categories\")\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,