Main

src/2-bar-plot.ipynb

@@ -9,18 +9,62 @@
   },
   {
    "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\n",
+    "categories = ['A', 'B', 'C', 'D']\n",
+    "values = [5, 7, 3, 9]\n",
+    "\n",
+    "# Colors for each bar\n",
+    "colors = ['red', 'blue', 'green', 'orange']\n",
+    "\n",
+    "# Plot\n",
+    "plt.bar(categories, values, color=colors)\n",
+    "\n",
+    "# Customization\n",
+    "plt.title('Bar Plot of Categories and Values')\n",
+    "plt.xlabel('Categories')\n",
+    "plt.ylabel('Values')\n",
+    "\n",
+    "# Show 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,