diff --git a/HW 5/Iris_knn_W2025_template.ipynb b/HW 5/Iris_knn_W2025_template.ipynb
index 929efcb..349894d 100644
--- a/HW 5/Iris_knn_W2025_template.ipynb	
+++ b/HW 5/Iris_knn_W2025_template.ipynb	
@@ -2,7 +2,7 @@
   "cells": [
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 1,
       "metadata": {
         "id": "Q6ifg03dKPR4"
       },
@@ -19,7 +19,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 2,
       "metadata": {
         "id": "0ghtg7ecRQ50"
       },
@@ -40,13 +40,7 @@
     },
     {
       "cell_type": "code",
-      "source": [
-        "####################################\n",
-        "from sklearn.datasets import load_iris\n",
-        "data = load_iris()\n",
-        "df = pd.DataFrame(data.data, columns=data.feature_names)\n",
-        "df"
-      ],
+      "execution_count": 3,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
@@ -55,31 +49,11 @@
         "id": "DjCaxH3BKJ_O",
         "outputId": "ab33e45d-d332-412e-db2a-88cc4c9528f4"
       },
-      "execution_count": null,
       "outputs": [
         {
-          "output_type": "execute_result",
           "data": {
-            "text/plain": [
-              "     sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)\n",
-              "0                  5.1               3.5                1.4               0.2\n",
-              "1                  4.9               3.0                1.4               0.2\n",
-              "2                  4.7               3.2                1.3               0.2\n",
-              "3                  4.6               3.1                1.5               0.2\n",
-              "4                  5.0               3.6                1.4               0.2\n",
-              "..                 ...               ...                ...               ...\n",
-              "145                6.7               3.0                5.2               2.3\n",
-              "146                6.3               2.5                5.0               1.9\n",
-              "147                6.5               3.0                5.2               2.0\n",
-              "148                6.2               3.4                5.4               2.3\n",
-              "149                5.9               3.0                5.1               1.8\n",
-              "\n",
-              "[150 rows x 4 columns]"
-            ],
             "text/html": [
-              "\n",
-              "  <div id=\"df-a3976c98-d8d9-4643-9f56-c2532de2159a\" class=\"colab-df-container\">\n",
-              "    <div>\n",
+              "<div>\n",
               "<style scoped>\n",
               "    .dataframe tbody tr th:only-of-type {\n",
               "        vertical-align: middle;\n",
@@ -184,159 +158,41 @@
               "  </tbody>\n",
               "</table>\n",
               "<p>150 rows × 4 columns</p>\n",
-              "</div>\n",
-              "    <div class=\"colab-df-buttons\">\n",
-              "\n",
-              "  <div class=\"colab-df-container\">\n",
-              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a3976c98-d8d9-4643-9f56-c2532de2159a')\"\n",
-              "            title=\"Convert this dataframe to an interactive table.\"\n",
-              "            style=\"display:none;\">\n",
-              "\n",
-              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
-              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
-              "  </svg>\n",
-              "    </button>\n",
-              "\n",
-              "  <style>\n",
-              "    .colab-df-container {\n",
-              "      display:flex;\n",
-              "      gap: 12px;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-convert {\n",
-              "      background-color: #E8F0FE;\n",
-              "      border: none;\n",
-              "      border-radius: 50%;\n",
-              "      cursor: pointer;\n",
-              "      display: none;\n",
-              "      fill: #1967D2;\n",
-              "      height: 32px;\n",
-              "      padding: 0 0 0 0;\n",
-              "      width: 32px;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-convert:hover {\n",
-              "      background-color: #E2EBFA;\n",
-              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "      fill: #174EA6;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-buttons div {\n",
-              "      margin-bottom: 4px;\n",
-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-convert {\n",
-              "      background-color: #3B4455;\n",
-              "      fill: #D2E3FC;\n",
-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-convert:hover {\n",
-              "      background-color: #434B5C;\n",
-              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
-              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
-              "      fill: #FFFFFF;\n",
-              "    }\n",
-              "  </style>\n",
-              "\n",
-              "    <script>\n",
-              "      const buttonEl =\n",
-              "        document.querySelector('#df-a3976c98-d8d9-4643-9f56-c2532de2159a button.colab-df-convert');\n",
-              "      buttonEl.style.display =\n",
-              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "\n",
-              "      async function convertToInteractive(key) {\n",
-              "        const element = document.querySelector('#df-a3976c98-d8d9-4643-9f56-c2532de2159a');\n",
-              "        const dataTable =\n",
-              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
-              "                                                    [key], {});\n",
-              "        if (!dataTable) return;\n",
-              "\n",
-              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
-              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
-              "          + ' to learn more about interactive tables.';\n",
-              "        element.innerHTML = '';\n",
-              "        dataTable['output_type'] = 'display_data';\n",
-              "        await google.colab.output.renderOutput(dataTable, element);\n",
-              "        const docLink = document.createElement('div');\n",
-              "        docLink.innerHTML = docLinkHtml;\n",
-              "        element.appendChild(docLink);\n",
-              "      }\n",
-              "    </script>\n",
-              "  </div>\n",
-              "\n",
-              "\n",
-              "  <div id=\"id_b3055351-b17e-41d7-9e44-09ff962b48e4\">\n",
-              "    <style>\n",
-              "      .colab-df-generate {\n",
-              "        background-color: #E8F0FE;\n",
-              "        border: none;\n",
-              "        border-radius: 50%;\n",
-              "        cursor: pointer;\n",
-              "        display: none;\n",
-              "        fill: #1967D2;\n",
-              "        height: 32px;\n",
-              "        padding: 0 0 0 0;\n",
-              "        width: 32px;\n",
-              "      }\n",
-              "\n",
-              "      .colab-df-generate:hover {\n",
-              "        background-color: #E2EBFA;\n",
-              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "        fill: #174EA6;\n",
-              "      }\n",
-              "\n",
-              "      [theme=dark] .colab-df-generate {\n",
-              "        background-color: #3B4455;\n",
-              "        fill: #D2E3FC;\n",
-              "      }\n",
-              "\n",
-              "      [theme=dark] .colab-df-generate:hover {\n",
-              "        background-color: #434B5C;\n",
-              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
-              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
-              "        fill: #FFFFFF;\n",
-              "      }\n",
-              "    </style>\n",
-              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df')\"\n",
-              "            title=\"Generate code using this dataframe.\"\n",
-              "            style=\"display:none;\">\n",
-              "\n",
-              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
-              "       width=\"24px\">\n",
-              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
-              "  </svg>\n",
-              "    </button>\n",
-              "    <script>\n",
-              "      (() => {\n",
-              "      const buttonEl =\n",
-              "        document.querySelector('#id_b3055351-b17e-41d7-9e44-09ff962b48e4 button.colab-df-generate');\n",
-              "      buttonEl.style.display =\n",
-              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "\n",
-              "      buttonEl.onclick = () => {\n",
-              "        google.colab.notebook.generateWithVariable('df');\n",
-              "      }\n",
-              "      })();\n",
-              "    </script>\n",
-              "  </div>\n",
-              "\n",
-              "    </div>\n",
-              "  </div>\n"
+              "</div>"
             ],
-            "application/vnd.google.colaboratory.intrinsic+json": {
-              "type": "dataframe",
-              "variable_name": "df",
-              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 150,\n  \"fields\": [\n    {\n      \"column\": \"sepal length (cm)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.8280661279778629,\n        \"min\": 4.3,\n        \"max\": 7.9,\n        \"num_unique_values\": 35,\n        \"samples\": [\n          6.2,\n          4.5,\n          5.6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sepal width (cm)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.435866284936698,\n        \"min\": 2.0,\n        \"max\": 4.4,\n        \"num_unique_values\": 23,\n        \"samples\": [\n          2.3,\n          4.0,\n          3.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"petal length (cm)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.7652982332594667,\n        \"min\": 1.0,\n        \"max\": 6.9,\n        \"num_unique_values\": 43,\n        \"samples\": [\n          6.7,\n          3.8,\n          3.7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"petal width (cm)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.7622376689603465,\n        \"min\": 0.1,\n        \"max\": 2.5,\n        \"num_unique_values\": 22,\n        \"samples\": [\n          0.2,\n          1.2,\n          1.3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
-            }
+            "text/plain": [
+              "     sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)\n",
+              "0                  5.1               3.5                1.4               0.2\n",
+              "1                  4.9               3.0                1.4               0.2\n",
+              "2                  4.7               3.2                1.3               0.2\n",
+              "3                  4.6               3.1                1.5               0.2\n",
+              "4                  5.0               3.6                1.4               0.2\n",
+              "..                 ...               ...                ...               ...\n",
+              "145                6.7               3.0                5.2               2.3\n",
+              "146                6.3               2.5                5.0               1.9\n",
+              "147                6.5               3.0                5.2               2.0\n",
+              "148                6.2               3.4                5.4               2.3\n",
+              "149                5.9               3.0                5.1               1.8\n",
+              "\n",
+              "[150 rows x 4 columns]"
+            ]
           },
+          "execution_count": 3,
           "metadata": {},
-          "execution_count": 9
+          "output_type": "execute_result"
         }
+      ],
+      "source": [
+        "####################################\n",
+        "from sklearn.datasets import load_iris\n",
+        "data = load_iris()\n",
+        "df = pd.DataFrame(data.data, columns=data.feature_names)\n",
+        "df"
       ]
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 4,
       "metadata": {
         "id": "ANJhDRNPKPR5"
       },
@@ -356,37 +212,20 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 5,
       "metadata": {
-        "id": "8v3oVZYTKPR6",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 361
         },
+        "id": "8v3oVZYTKPR6",
         "outputId": "4110ac58-5c52-4cbf-a83f-16d9a7189ccb"
       },
       "outputs": [
         {
-          "output_type": "execute_result",
           "data": {
-            "text/plain": [
-              "     sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)\n",
-              "0                  5.1               3.5                1.4               0.2\n",
-              "1                  4.9               3.0                1.4               0.2\n",
-              "2                  4.7               3.2                1.3               0.2\n",
-              "3                  4.6               3.1                1.5               0.2\n",
-              "..                 ...               ...                ...               ...\n",
-              "146                6.3               2.5                5.0               1.9\n",
-              "147                6.5               3.0                5.2               2.0\n",
-              "148                6.2               3.4                5.4               2.3\n",
-              "149                5.9               3.0                5.1               1.8\n",
-              "\n",
-              "[150 rows x 4 columns]"
-            ],
             "text/html": [
-              "\n",
-              "  <div id=\"df-f9797485-14a5-43a3-a42e-5084fa2ba17a\" class=\"colab-df-container\">\n",
-              "    <div>\n",
+              "<div>\n",
               "<style scoped>\n",
               "    .dataframe tbody tr th:only-of-type {\n",
               "        vertical-align: middle;\n",
@@ -477,153 +316,26 @@
               "  </tbody>\n",
               "</table>\n",
               "<p>150 rows × 4 columns</p>\n",
-              "</div>\n",
-              "    <div class=\"colab-df-buttons\">\n",
-              "\n",
-              "  <div class=\"colab-df-container\">\n",
-              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f9797485-14a5-43a3-a42e-5084fa2ba17a')\"\n",
-              "            title=\"Convert this dataframe to an interactive table.\"\n",
-              "            style=\"display:none;\">\n",
-              "\n",
-              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
-              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
-              "  </svg>\n",
-              "    </button>\n",
-              "\n",
-              "  <style>\n",
-              "    .colab-df-container {\n",
-              "      display:flex;\n",
-              "      gap: 12px;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-convert {\n",
-              "      background-color: #E8F0FE;\n",
-              "      border: none;\n",
-              "      border-radius: 50%;\n",
-              "      cursor: pointer;\n",
-              "      display: none;\n",
-              "      fill: #1967D2;\n",
-              "      height: 32px;\n",
-              "      padding: 0 0 0 0;\n",
-              "      width: 32px;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-convert:hover {\n",
-              "      background-color: #E2EBFA;\n",
-              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "      fill: #174EA6;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-buttons div {\n",
-              "      margin-bottom: 4px;\n",
-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-convert {\n",
-              "      background-color: #3B4455;\n",
-              "      fill: #D2E3FC;\n",
-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-convert:hover {\n",
-              "      background-color: #434B5C;\n",
-              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
-              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
-              "      fill: #FFFFFF;\n",
-              "    }\n",
-              "  </style>\n",
-              "\n",
-              "    <script>\n",
-              "      const buttonEl =\n",
-              "        document.querySelector('#df-f9797485-14a5-43a3-a42e-5084fa2ba17a button.colab-df-convert');\n",
-              "      buttonEl.style.display =\n",
-              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "\n",
-              "      async function convertToInteractive(key) {\n",
-              "        const element = document.querySelector('#df-f9797485-14a5-43a3-a42e-5084fa2ba17a');\n",
-              "        const dataTable =\n",
-              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
-              "                                                    [key], {});\n",
-              "        if (!dataTable) return;\n",
-              "\n",
-              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
-              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
-              "          + ' to learn more about interactive tables.';\n",
-              "        element.innerHTML = '';\n",
-              "        dataTable['output_type'] = 'display_data';\n",
-              "        await google.colab.output.renderOutput(dataTable, element);\n",
-              "        const docLink = document.createElement('div');\n",
-              "        docLink.innerHTML = docLinkHtml;\n",
-              "        element.appendChild(docLink);\n",
-              "      }\n",
-              "    </script>\n",
-              "  </div>\n",
-              "\n",
-              "\n",
-              "  <div id=\"id_49bdecce-3201-4032-8098-3c859f8fd670\">\n",
-              "    <style>\n",
-              "      .colab-df-generate {\n",
-              "        background-color: #E8F0FE;\n",
-              "        border: none;\n",
-              "        border-radius: 50%;\n",
-              "        cursor: pointer;\n",
-              "        display: none;\n",
-              "        fill: #1967D2;\n",
-              "        height: 32px;\n",
-              "        padding: 0 0 0 0;\n",
-              "        width: 32px;\n",
-              "      }\n",
-              "\n",
-              "      .colab-df-generate:hover {\n",
-              "        background-color: #E2EBFA;\n",
-              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "        fill: #174EA6;\n",
-              "      }\n",
-              "\n",
-              "      [theme=dark] .colab-df-generate {\n",
-              "        background-color: #3B4455;\n",
-              "        fill: #D2E3FC;\n",
-              "      }\n",
-              "\n",
-              "      [theme=dark] .colab-df-generate:hover {\n",
-              "        background-color: #434B5C;\n",
-              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
-              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
-              "        fill: #FFFFFF;\n",
-              "      }\n",
-              "    </style>\n",
-              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df')\"\n",
-              "            title=\"Generate code using this dataframe.\"\n",
-              "            style=\"display:none;\">\n",
-              "\n",
-              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
-              "       width=\"24px\">\n",
-              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
-              "  </svg>\n",
-              "    </button>\n",
-              "    <script>\n",
-              "      (() => {\n",
-              "      const buttonEl =\n",
-              "        document.querySelector('#id_49bdecce-3201-4032-8098-3c859f8fd670 button.colab-df-generate');\n",
-              "      buttonEl.style.display =\n",
-              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "\n",
-              "      buttonEl.onclick = () => {\n",
-              "        google.colab.notebook.generateWithVariable('df');\n",
-              "      }\n",
-              "      })();\n",
-              "    </script>\n",
-              "  </div>\n",
-              "\n",
-              "    </div>\n",
-              "  </div>\n"
+              "</div>"
             ],
-            "application/vnd.google.colaboratory.intrinsic+json": {
-              "type": "dataframe",
-              "variable_name": "df",
-              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 150,\n  \"fields\": [\n    {\n      \"column\": \"sepal length (cm)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.8280661279778629,\n        \"min\": 4.3,\n        \"max\": 7.9,\n        \"num_unique_values\": 35,\n        \"samples\": [\n          6.2,\n          4.5,\n          5.6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sepal width (cm)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.435866284936698,\n        \"min\": 2.0,\n        \"max\": 4.4,\n        \"num_unique_values\": 23,\n        \"samples\": [\n          2.3,\n          4.0,\n          3.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"petal length (cm)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.7652982332594667,\n        \"min\": 1.0,\n        \"max\": 6.9,\n        \"num_unique_values\": 43,\n        \"samples\": [\n          6.7,\n          3.8,\n          3.7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"petal width (cm)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.7622376689603465,\n        \"min\": 0.1,\n        \"max\": 2.5,\n        \"num_unique_values\": 22,\n        \"samples\": [\n          0.2,\n          1.2,\n          1.3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
-            }
+            "text/plain": [
+              "     sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)\n",
+              "0                  5.1               3.5                1.4               0.2\n",
+              "1                  4.9               3.0                1.4               0.2\n",
+              "2                  4.7               3.2                1.3               0.2\n",
+              "3                  4.6               3.1                1.5               0.2\n",
+              "..                 ...               ...                ...               ...\n",
+              "146                6.3               2.5                5.0               1.9\n",
+              "147                6.5               3.0                5.2               2.0\n",
+              "148                6.2               3.4                5.4               2.3\n",
+              "149                5.9               3.0                5.1               1.8\n",
+              "\n",
+              "[150 rows x 4 columns]"
+            ]
           },
+          "execution_count": 5,
           "metadata": {},
-          "execution_count": 10
+          "output_type": "execute_result"
         }
       ],
       "source": [
@@ -643,20 +355,20 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 6,
       "metadata": {
-        "id": "aksGQcJ8KPR6",
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
+        "id": "aksGQcJ8KPR6",
         "outputId": "4cbdedf5-cabe-4263-c06c-26bbb8f509b1"
       },
       "outputs": [
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
           "text": [
-            "<class 'pandas.core.frame.DataFrame'>\n",
+            "<class 'pandas.DataFrame'>\n",
             "RangeIndex: 150 entries, 0 to 149\n",
             "Data columns (total 4 columns):\n",
             " #   Column             Non-Null Count  Dtype  \n",
@@ -683,20 +395,20 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 7,
       "metadata": {
-        "id": "ADWpUqeKKPR7",
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
+        "id": "ADWpUqeKKPR7",
         "outputId": "76ac8d2f-0d98-4721-e0f2-b79b48736b00"
       },
       "outputs": [
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
           "text": [
-            "<class 'pandas.core.frame.DataFrame'>\n",
+            "<class 'pandas.DataFrame'>\n",
             "RangeIndex: 150 entries, 0 to 149\n",
             "Data columns (total 4 columns):\n",
             " #   Column             Non-Null Count  Dtype  \n",
@@ -727,22 +439,22 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 8,
       "metadata": {
-        "id": "9RAydRsiKPR7",
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
+        "id": "9RAydRsiKPR7",
         "outputId": "18ec37ec-3a95-4ef9-c111-847ac8f6ecec"
       },
       "outputs": [
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
           "text": [
             "FEATURES: Index(['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)',\n",
             "       'petal width (cm)'],\n",
-            "      dtype='object')\n",
+            "      dtype='str')\n",
             "\n",
             "First feature: sepal length (cm)\n",
             "\n",
@@ -774,21 +486,21 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 9,
       "metadata": {
-        "id": "PWkPTOGnKPR8",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 1000
         },
+        "id": "PWkPTOGnKPR8",
         "outputId": "f6a46e8f-0169-4646-864f-1b3ed749f238"
       },
       "outputs": [
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
           "text": [
-            "<class 'pandas.core.frame.DataFrame'>\n",
+            "<class 'pandas.DataFrame'>\n",
             "RangeIndex: 150 entries, 0 to 149\n",
             "Data columns (total 4 columns):\n",
             " #   Column             Non-Null Count  Dtype  \n",
@@ -816,24 +528,24 @@
           ]
         },
         {
-          "output_type": "error",
           "ename": "KeyError",
           "evalue": "'irisname'",
+          "output_type": "error",
           "traceback": [
-            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
-            "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3804\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3805\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3806\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-            "\u001b[0;32mindex.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
-            "\u001b[0;32mindex.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
-            "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
-            "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
-            "\u001b[0;31mKeyError\u001b[0m: 'irisname'",
+            "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
+            "\u001b[31mKeyError\u001b[39m                                  Traceback (most recent call last)",
+            "\u001b[36mFile \u001b[39m\u001b[32m~/.local/lib/python3.14/site-packages/pandas/core/indexes/base.py:3641\u001b[39m, in \u001b[36mIndex.get_loc\u001b[39m\u001b[34m(self, key)\u001b[39m\n\u001b[32m   3640\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m-> \u001b[39m\u001b[32m3641\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_engine\u001b[49m\u001b[43m.\u001b[49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcasted_key\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m   3642\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n",
+            "\u001b[36mFile \u001b[39m\u001b[32mpandas/_libs/index.pyx:168\u001b[39m, in \u001b[36mpandas._libs.index.IndexEngine.get_loc\u001b[39m\u001b[34m()\u001b[39m\n\u001b[32m--> \u001b[39m\u001b[32m168\u001b[39m \u001b[33m'Could not get source, probably due dynamically evaluated source code.'\u001b[39m\n",
+            "\u001b[36mFile \u001b[39m\u001b[32mpandas/_libs/index.pyx:197\u001b[39m, in \u001b[36mpandas._libs.index.IndexEngine.get_loc\u001b[39m\u001b[34m()\u001b[39m\n\u001b[32m--> \u001b[39m\u001b[32m197\u001b[39m \u001b[33m'Could not get source, probably due dynamically evaluated source code.'\u001b[39m\n",
+            "\u001b[36mFile \u001b[39m\u001b[32mpandas/_libs/hashtable_class_helper.pxi:7668\u001b[39m, in \u001b[36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[39m\u001b[34m()\u001b[39m\n\u001b[32m-> \u001b[39m\u001b[32m7668\u001b[39m \u001b[33m'Could not get source, probably due dynamically evaluated source code.'\u001b[39m\n",
+            "\u001b[36mFile \u001b[39m\u001b[32mpandas/_libs/hashtable_class_helper.pxi:7676\u001b[39m, in \u001b[36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[39m\u001b[34m()\u001b[39m\n\u001b[32m-> \u001b[39m\u001b[32m7676\u001b[39m \u001b[33m'Could not get source, probably due dynamically evaluated source code.'\u001b[39m\n",
+            "\u001b[31mKeyError\u001b[39m: 'irisname'",
             "\nThe above exception was the direct cause of the following exception:\n",
-            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
-            "\u001b[0;32m/tmp/ipykernel_6512/325098609.py\u001b[0m in \u001b[0;36m<cell line: 0>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     16\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m \u001b[0;31m# or more to the point, grab that column and inspect:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 18\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"Irisname entries: {df_clean['irisname'].unique()}\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     19\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     20\u001b[0m \u001b[0;31m# also note how the last two rows in the df have problems, among others...\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-            "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   4100\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4101\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4102\u001b[0;31m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4103\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4104\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-            "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3810\u001b[0m             ):\n\u001b[1;32m   3811\u001b[0m                 \u001b[0;32mraise\u001b[0m \u001b[0mInvalidIndexError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3812\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3813\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3814\u001b[0m             \u001b[0;31m# If we have a listlike key, _check_indexing_error will raise\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-            "\u001b[0;31mKeyError\u001b[0m: 'irisname'"
+            "\u001b[31mKeyError\u001b[39m                                  Traceback (most recent call last)",
+            "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[9]\u001b[39m\u001b[32m, line 18\u001b[39m\n\u001b[32m     14\u001b[39m print(df_clean)\n\u001b[32m     15\u001b[39m print(\u001b[33m'='\u001b[39m * \u001b[32m70\u001b[39m)\n\u001b[32m     16\u001b[39m \n\u001b[32m     17\u001b[39m \u001b[38;5;66;03m# or more to the point, grab that column and inspect:\u001b[39;00m\n\u001b[32m---> \u001b[39m\u001b[32m18\u001b[39m print(f\"Irisname entries: {df_clean[\u001b[33m'irisname'\u001b[39m].unique()}\")\n\u001b[32m     19\u001b[39m \n\u001b[32m     20\u001b[39m \u001b[38;5;66;03m# also note how the last two rows in the df have problems, among others...\u001b[39;00m\n",
+            "\u001b[36mFile \u001b[39m\u001b[32m~/.local/lib/python3.14/site-packages/pandas/core/frame.py:4378\u001b[39m, in \u001b[36mDataFrame.__getitem__\u001b[39m\u001b[34m(self, key)\u001b[39m\n\u001b[32m   4374\u001b[39m \n\u001b[32m   4375\u001b[39m         \u001b[38;5;28;01mif\u001b[39;00m is_single_key:\n\u001b[32m   4376\u001b[39m             \u001b[38;5;28;01mif\u001b[39;00m self.columns.nlevels > \u001b[32m1\u001b[39m:\n\u001b[32m   4377\u001b[39m                 \u001b[38;5;28;01mreturn\u001b[39;00m self._getitem_multilevel(key)\n\u001b[32m-> \u001b[39m\u001b[32m4378\u001b[39m             indexer = self.columns.get_loc(key)\n\u001b[32m   4379\u001b[39m             \u001b[38;5;28;01mif\u001b[39;00m is_integer(indexer):\n\u001b[32m   4380\u001b[39m                 indexer = [indexer]\n\u001b[32m   4381\u001b[39m         \u001b[38;5;28;01melse\u001b[39;00m:\n",
+            "\u001b[36mFile \u001b[39m\u001b[32m~/.local/lib/python3.14/site-packages/pandas/core/indexes/base.py:3648\u001b[39m, in \u001b[36mIndex.get_loc\u001b[39m\u001b[34m(self, key)\u001b[39m\n\u001b[32m   3643\u001b[39m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(casted_key, \u001b[38;5;28mslice\u001b[39m) \u001b[38;5;129;01mor\u001b[39;00m (\n\u001b[32m   3644\u001b[39m         \u001b[38;5;28misinstance\u001b[39m(casted_key, abc.Iterable)\n\u001b[32m   3645\u001b[39m         \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28many\u001b[39m(\u001b[38;5;28misinstance\u001b[39m(x, \u001b[38;5;28mslice\u001b[39m) \u001b[38;5;28;01mfor\u001b[39;00m x \u001b[38;5;129;01min\u001b[39;00m casted_key)\n\u001b[32m   3646\u001b[39m     ):\n\u001b[32m   3647\u001b[39m         \u001b[38;5;28;01mraise\u001b[39;00m InvalidIndexError(key) \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01merr\u001b[39;00m\n\u001b[32m-> \u001b[39m\u001b[32m3648\u001b[39m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key) \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01merr\u001b[39;00m\n\u001b[32m   3649\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[32m   3650\u001b[39m     \u001b[38;5;66;03m# If we have a listlike key, _check_indexing_error will raise\u001b[39;00m\n\u001b[32m   3651\u001b[39m     \u001b[38;5;66;03m#  InvalidIndexError. Otherwise we fall through and re-raise\u001b[39;00m\n\u001b[32m   3652\u001b[39m     \u001b[38;5;66;03m#  the TypeError.\u001b[39;00m\n\u001b[32m   3653\u001b[39m     \u001b[38;5;28mself\u001b[39m._check_indexing_error(key)\n",
+            "\u001b[31mKeyError\u001b[39m: 'irisname'"
           ]
         }
       ],
@@ -862,9 +574,7 @@
     },
     {
       "cell_type": "code",
-      "source": [
-        "df.columns"
-      ],
+      "execution_count": null,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -872,10 +582,8 @@
         "id": "4B2An3iML1ay",
         "outputId": "5492db65-acd8-4c11-f669-742a79b6cb62"
       },
-      "execution_count": null,
       "outputs": [
         {
-          "output_type": "execute_result",
           "data": {
             "text/plain": [
               "Index(['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)',\n",
@@ -883,9 +591,13 @@
               "      dtype='object')"
             ]
           },
+          "execution_count": 19,
           "metadata": {},
-          "execution_count": 19
+          "output_type": "execute_result"
         }
+      ],
+      "source": [
+        "df.columns"
       ]
     },
     {
@@ -1763,9 +1475,9 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.12.2"
+      "version": "3.14.3"
     }
   },
   "nbformat": 4,
   "nbformat_minor": 0
-}
\ No newline at end of file
+}