Main

activities/1.1-basic-jupyter-notebook.ipynb

@@ -31,7 +31,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "id": "f2b4273a",
    "metadata": {},
    "outputs": [
@@ -159,7 +159,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.3"
+   "version": "3.10.12"
   }
  },
  "nbformat": 4,

activities/1.2-using-matplotlib.ipynb

@@ -24,7 +24,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -39,7 +39,7 @@
     }
    ],
    "source": [
-    "import matplotlib.pyplot as plt\n",
+    "import matplotlib.pyplot as plt # type: ignore\n",
     "# Data\n",
     "x = [0, 1, 2, 3, 4, 5]\n",
     "y = [0, 1, 4, 9, 16, 25]\n",

src/2-bar-plot.ipynb

@@ -9,18 +9,67 @@
   },
   {
    "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 600x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Data\n",
+    "categories = ['A', 'B', 'C', 'D']\n",
+    "values = [5, 7, 3, 9]\n",
+    "colors = ['red', 'green', 'blue', 'orange']  # Different colors for each bar\n",
+    "\n",
+    "# Create the bar plot\n",
+    "plt.figure(figsize=(6, 4))\n",
+    "plt.bar(categories, values, color=colors)\n",
+    "\n",
+    "# Add title\n",
+    "plt.title('Bar Plot of Categories')\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,