modified: src/1-line-plot.ipynb

src/2-bar-plot.ipynb

@@ -11,16 +11,55 @@
    "cell_type": "code",
    "execution_count": null,
    "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', 'blue', 'green', 'purple']\n",
+    "\n",
+    "\n",
+    "plt.bar(categories, values, color=colors)\n",
+    "\n",
+    "\n",
+    "plt.title(\"Category Value Bar Plot\")\n",
+    "\n",
+    "\n",
+    "plt.show()\n"
    ]
   }
  ],
  "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,