Main1133

src/2-bar-plot.ipynb

@@ -9,18 +9,52 @@
   },
   {
    "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",
+    "import matplotlib.pyplot as plt\n",
+    "cat = ['A', 'B', 'C', 'D']\n",
+    "val = [5,7,3,9]\n",
+    "colors = ['red', 'green', 'blue', 'orange']\n",
+    "plt.bar(cat, val, color=colors)\n",
+    "plt.title(\"Bar Chart with Custom Colors\")\n",
+    "plt.xlabel(\"Category\")\n",
+    "plt.ylabel(\"Value\")\n",
+    "plt.show()\n"
    ]
   }
  ],
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+  "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"
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