Main

.vscode/settings.json

@@ -0,0 +1,5 @@
+{
+    "githubPullRequests.ignoredPullRequestBranches": [
+        "main"
+    ]
+}

activities/1.1-basic-jupyter-notebook.ipynb

@@ -31,21 +31,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "id": "f2b4273a",
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Hello, JupyterLab!\n"
+      "Hello, Trisha!\n"
      ]
     }
    ],
    "source": [
     "# This is a Python code cell\n",
-    "print('Hello, JupyterLab!')"
+    "print('Hello, Trisha!')"
    ]
   },
   {
@@ -159,7 +159,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.3"
+   "version": "3.10.12"
   }
  },
  "nbformat": 4,

src/2-bar-plot.ipynb

@@ -16,11 +16,53 @@
     "# 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 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "cat=['A','B','C','D']\n",
+    "val=[5,7,3,9]\n",
+    "colors=['red','blue','green','yellow']\n",
+    "\n",
+    "plt.bar(cat,val,color=colors)\n",
+    "plt.title(\"Bar Plot for Categories\")\n",
+    "plt.show()"
+   ]
   }
  ],
  "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,