Main

src/2-bar-plot.ipynb

@@ -9,18 +9,57 @@
   },
   {
    "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",
+    "\n",
+    "# Data for the bar plot\n",
+    "categories = ['A', 'B', 'C', 'D']\n",
+    "values = [5, 7, 3, 9]\n",
+    "\n",
+    "# Create the bar plot with different colors for each bar\n",
+    "plt.bar(categories, values, color=['red', 'blue', 'green', 'orange'])\n",
+    "\n",
+    "# Add a title to the plot\n",
+    "plt.title('Bar Plot of Categories vs Values')\n",
+    "\n",
+    "# Display the plot\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,