diff --git a/notebooks/intro_data_science/Introduction_to_Clustering.ipynb b/notebooks/intro_data_science/Introduction_to_Clustering.ipynb
index 44a3793..eefce3b 100644
--- a/notebooks/intro_data_science/Introduction_to_Clustering.ipynb
+++ b/notebooks/intro_data_science/Introduction_to_Clustering.ipynb
@@ -1,42 +1,14 @@
 {
-  "nbformat": 4,
-  "nbformat_minor": 0,
-  "metadata": {
-    "colab": {
-      "name": "Introduction to Clustering.ipynb",
-      "provenance": [],
-      "collapsed_sections": [],
-      "toc_visible": true,
-      "include_colab_link": true
-    },
-    "kernelspec": {
-      "name": "python3",
-      "display_name": "Python 3"
-    },
-    "language_info": {
-      "name": "python"
-    }
-  },
   "cells": [
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "view-in-github",
-        "colab_type": "text"
+        "id": "9uqlvgsuICzJ"
       },
-      "source": [
-        "<a href=\"https://colab.research.google.com/github/datacommonsorg/api-python/blob/master/notebooks/intro_data_science/Introduction_to_Clustering.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
-      ]
-    },
-    {
-      "cell_type": "markdown",
       "source": [
         "Copyright 2022 Google LLC.\n",
         "SPDX-License-Identifier: Apache-2.0"
-      ],
-      "metadata": {
-        "id": "9uqlvgsuICzJ"
-      }
+      ]
     },
     {
       "cell_type": "markdown",
@@ -78,35 +50,37 @@
     },
     {
       "cell_type": "code",
+      "execution_count": 1,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
         "id": "UKw3L8d2CrUb",
-        "outputId": "90e696ea-b544-4e9c-b930-3c55a5c44a7d"
+        "outputId": "a76d7d9e-d496-42ad-fb4a-445a2982a9aa"
       },
-      "source": [
-        "!pip install datacommons --upgrade --quiet\n",
-        "!pip install datacommons_pandas --upgrade --quiet"
-      ],
-      "execution_count": null,
       "outputs": [
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
           "text": [
-            "\u001b[K     |████████████████████████████████| 46 kB 1.1 MB/s \n",
-            "\u001b[K     |████████████████████████████████| 45 kB 878 kB/s \n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m46.5/46.5 kB\u001b[0m \u001b[31m473.9 kB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m45.8/45.8 kB\u001b[0m \u001b[31m621.3 kB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
             "\u001b[?25h"
           ]
         }
+      ],
+      "source": [
+        "!pip install datacommons --upgrade --quiet\n",
+        "!pip install datacommons_pandas --upgrade --quiet"
       ]
     },
     {
       "cell_type": "code",
+      "execution_count": 2,
       "metadata": {
         "id": "OeYiyM0VDa0h"
       },
+      "outputs": [],
       "source": [
         "import datacommons\n",
         "import datacommons_pandas\n",
@@ -119,9 +93,7 @@
         "\n",
         "# for clustering\n",
         "from sklearn.cluster import KMeans"
-      ],
-      "execution_count": null,
-      "outputs": []
+      ]
     },
     {
       "cell_type": "markdown",
@@ -140,14 +112,42 @@
     },
     {
       "cell_type": "code",
+      "execution_count": 3,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
-          "height": 483
+          "height": 809
         },
         "id": "-SnJqDECF0Il",
-        "outputId": "dcbd165f-dfe1-4d91-cbf9-7a6adbb8eeb7"
+        "outputId": "2007d684-bcda-4f48-9e72-9a2962c6bc0c"
       },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-labels-idx1-ubyte.gz\n",
+            "29515/29515 [==============================] - 0s 0us/step\n",
+            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-images-idx3-ubyte.gz\n",
+            "26421880/26421880 [==============================] - 0s 0us/step\n",
+            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-labels-idx1-ubyte.gz\n",
+            "5148/5148 [==============================] - 0s 0us/step\n",
+            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-images-idx3-ubyte.gz\n",
+            "4422102/4422102 [==============================] - 0s 0us/step\n",
+            "Loaded 2000 images. The first 25 are:\n"
+          ]
+        },
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 1100x800 with 25 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
       "source": [
         "# load dataset\n",
         "from keras.datasets import fashion_mnist\n",
@@ -166,26 +166,6 @@
         "  plt.imshow(fashion_mnist[i], cmap=plt.get_cmap('gray'))\n",
         "  plt.axis(\"off\")\n",
         "plt.show()"
-      ],
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Loaded 2000 images. The first 25 are:\n"
-          ]
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "image/png": 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\n",
-            "text/plain": [
-              "<Figure size 792x576 with 25 Axes>"
-            ]
-          },
-          "metadata": {}
-        }
       ]
     },
     {
@@ -201,15 +181,15 @@
     },
     {
       "cell_type": "code",
+      "execution_count": 4,
       "metadata": {
         "id": "9jSk-CgsGNTM"
       },
+      "outputs": [],
       "source": [
         "# squash images into 1D\n",
         "fashion_data = fashion_mnist.reshape((-1, 28*28))"
-      ],
-      "execution_count": null,
-      "outputs": []
+      ]
     },
     {
       "cell_type": "markdown",
@@ -222,77 +202,85 @@
     },
     {
       "cell_type": "code",
+      "execution_count": 5,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
-          "height": 513
+          "height": 762
         },
         "id": "rSTIeeK8GRqh",
-        "outputId": "2c362d1e-90b9-43ca-e51e-8e5278f345be"
+        "outputId": "a255e072-be31-495e-bc84-cb2c33815cca"
       },
-      "source": [
-        "# set number of clusters\n",
-        "k = 2   #Change me!\n",
-        "\n",
-        "# cluster the images\n",
-        "kmeans = KMeans(n_clusters=k).fit(fashion_data)\n",
-        "\n",
-        "# create a dictionary of clusters to images\n",
-        "clusters = {n:[] for n in range(k)}\n",
-        "for i in range(fashion_data.shape[0]):\n",
-        "  key = kmeans.labels_[i]\n",
-        "  value = fashion_data[i,:].reshape(1, 28, 28).squeeze()\n",
-        "  clusters[key].append(value)\n",
-        "\n",
-        "# display images from each cluster\n",
-        "sns.set(rc={'figure.figsize':(6,4)})\n",
-        "for cluster_num, images in clusters.items():\n",
-        "  print(f'Cluster {cluster_num} contains {len(images)} images. The first 25 are:')\n",
-        "  for i in range(min(25, len(images))):\n",
-        "    # define subplot\n",
-        "    plt.subplot(5, 5, i+1)\n",
-        "    # plot raw pixel data\n",
-        "    plt.imshow(images[i], cmap=plt.get_cmap('gray'))\n",
-        "    plt.axis(\"off\")\n",
-        "  # show the figure\n",
-        "  plt.show()"
-      ],
-      "execution_count": null,
       "outputs": [
         {
+          "name": "stderr",
           "output_type": "stream",
+          "text": [
+            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+            "  warnings.warn(\n"
+          ]
+        },
+        {
           "name": "stdout",
+          "output_type": "stream",
           "text": [
             "Cluster 0 contains 1068 images. The first 25 are:\n"
           ]
         },
         {
-          "output_type": "display_data",
           "data": {
-            "image/png": 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\n",
+            "image/png": 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",
             "text/plain": [
-              "<Figure size 432x288 with 25 Axes>"
+              "<Figure size 600x400 with 25 Axes>"
             ]
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
           "text": [
             "Cluster 1 contains 932 images. The first 25 are:\n"
           ]
         },
         {
-          "output_type": "display_data",
           "data": {
-            "image/png": 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\n",
+            "image/png": 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",
             "text/plain": [
-              "<Figure size 432x288 with 25 Axes>"
+              "<Figure size 600x400 with 25 Axes>"
             ]
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         }
+      ],
+      "source": [
+        "# set number of clusters\n",
+        "k = 2   #Change me!\n",
+        "\n",
+        "# cluster the images\n",
+        "kmeans = KMeans(n_clusters=k).fit(fashion_data)\n",
+        "\n",
+        "# create a dictionary of clusters to images\n",
+        "clusters = {n:[] for n in range(k)}\n",
+        "for i in range(fashion_data.shape[0]):\n",
+        "  key = kmeans.labels_[i]\n",
+        "  value = fashion_data[i,:].reshape(1, 28, 28).squeeze()\n",
+        "  clusters[key].append(value)\n",
+        "\n",
+        "# display images from each cluster\n",
+        "sns.set(rc={'figure.figsize':(6,4)})\n",
+        "for cluster_num, images in clusters.items():\n",
+        "  print(f'Cluster {cluster_num} contains {len(images)} images. The first 25 are:')\n",
+        "  for i in range(min(25, len(images))):\n",
+        "    # define subplot\n",
+        "    plt.subplot(5, 5, i+1)\n",
+        "    # plot raw pixel data\n",
+        "    plt.imshow(images[i], cmap=plt.get_cmap('gray'))\n",
+        "    plt.axis(\"off\")\n",
+        "  # show the figure\n",
+        "  plt.show()"
       ]
     },
     {
@@ -328,7 +316,7 @@
         "\n",
         "**Q4: Now compare `k=10` with `k=8`. Which items or traits group together in `k=8` compared to `k=10`?**\n",
         "\n",
-        "A4: Students should notice that items that look similar (e.g. bag with similar area and shading to shirts) will group first with those items that are most similar. "
+        "A4: Students should notice that items that look similar (e.g. bag with similar area and shading to shirts) will group first with those items that are most similar."
       ]
     },
     {
@@ -341,7 +329,7 @@
         "\n",
         "**Q5 (Extension): Imagine you have a set of 1 million unlabeled images that look just like these fashion MNIST images, and you want to generate categorical labels for each of the images. However, labeling each of those 1 million images is too time intensive and costly.**\n",
         "\n",
-        "**Devise a strategy for how you could use clustering to help you label each of the images.** \n",
+        "**Devise a strategy for how you could use clustering to help you label each of the images.**\n",
         "\n",
         "*A5: Cluster the 1 million images. Label each of the clusters, and assign every image the label corresponding to its cluster*\n",
         "\n",
@@ -370,73 +358,32 @@
         "* [Fertility rate](https://data.oecd.org/pop/fertility-rates.htm)\n",
         "* Number of deaths per year, normalized by population\n",
         "\n",
-        "Run the following code boxes to load and cluster the data associated with each country. "
+        "Run the following code boxes to load and cluster the data associated with each country."
       ]
     },
     {
       "cell_type": "code",
+      "execution_count": 6,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
-          "height": 487
+          "height": 492
         },
         "id": "6LwJykBsuUPg",
-        "outputId": "082878f3-94c7-413a-a7c8-e4643f59808d"
+        "outputId": "1c55a74a-892a-4153-fd2c-7b4ce398f3a1"
       },
-      "source": [
-        "dcids = [\n",
-        "  'country/AGO', 'country/ALB', 'country/ARG', 'country/ARM', 'country/AUS',\n",
-        "  'country/AZE', 'country/BDI', 'country/BGD', 'country/BGR', 'country/BIH',\n",
-        "  'country/BLZ', 'country/BOL', 'country/BRA', 'country/BTN', 'country/BWA',\n",
-        "  'country/CAN', 'country/CHL', 'country/CHN', 'country/CMR', 'country/COD',\n",
-        "  'country/COG', 'country/COL', 'country/CRI', 'country/CZE', 'country/DOM',\n",
-        "  'country/DZA', 'country/ECU', 'country/EGY', 'country/ETH', 'country/FJI',\n",
-        "  'country/GAB', 'country/GEO', 'country/GHA', 'country/GTM', 'country/GUY',\n",
-        "  'country/HND', 'country/IDN', 'country/IND', 'country/IRN', 'country/JAM',\n",
-        "  'country/JOR', 'country/JPN', 'country/KAZ', 'country/KEN', 'country/KGZ',\n",
-        "  'country/KIR', 'country/KOR', 'country/LAO', 'country/LBN', 'country/LCA',\n",
-        "  'country/LSO', 'country/MAR', 'country/MDA', 'country/MDG', 'country/MEX',\n",
-        "  'country/MKD', 'country/MLI', 'country/MMR', 'country/MNE', 'country/MNG',\n",
-        "  'country/MOZ', 'country/MRT', 'country/MWI', 'country/MYS', 'country/NAM',\n",
-        "  'country/NER', 'country/NGA', 'country/NIC', 'country/NPL', 'country/PAK',\n",
-        "  'country/PAN', 'country/PER', 'country/PHL', 'country/PNG', 'country/PRY',\n",
-        "  'country/ROU', 'country/RWA', 'country/SDN', 'country/SLV', 'country/SRB',\n",
-        "  'country/SWZ', 'country/SYR', 'country/THA', 'country/TJK', 'country/TKM',\n",
-        "  'country/TLS', 'country/TON', 'country/TTO', 'country/TUN', 'country/TUR',\n",
-        "  'country/TZA', 'country/UGA', 'country/UKR', 'country/USA', 'country/UZB',\n",
-        "  'country/VNM', 'country/VUT', 'country/WSM', 'country/YEM', 'country/ZAF',\n",
-        "  'country/ZMB', 'country/ZWE'\n",
-        "]\n",
-        "\n",
-        "\n",
-        "stat_vars_to_query = [\n",
-        "  \"Amount_Emissions_CarbonDioxide_PerCapita\",\n",
-        "  \"LifeExpectancy_Person\",\n",
-        "  \"Count_Person_IsInternetUser_PerCapita\",\n",
-        "  \"GrowthRate_Count_Person\",\n",
-        "  \"Count_Person_Upto4Years_Overweight_AsFractionOf_Count_Person_Upto4Years\",\n",
-        "  \"GiniIndex_EconomicActivity\",\n",
-        "  \"Count_Product_MobileCellularSubscription_AsFractionOf_Count_Person\",\n",
-        "  \"Amount_EconomicActivity_GrossDomesticProduction_Nominal_PerCapita\",\n",
-        "  \"FertilityRate_Person_Female\",\n",
-        "  \"Count_Death_AsAFractionOfCount_Person\"\n",
-        "]\n",
-        "\n",
-        "df = datacommons_pandas.build_multivariate_dataframe(dcids,stat_vars_to_query)\n",
-        "\n",
-        "# swap index for names\n",
-        "df.insert(0, 'name', df.index.map(datacommons.get_property_values(df.index, 'name')).str[0])\n",
-        "df.set_index('name', drop=True, inplace=True)\n",
-        "\n",
-        "display(df)"
-      ],
-      "execution_count": null,
       "outputs": [
         {
-          "output_type": "display_data",
           "data": {
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 102,\n  \"fields\": [\n    {\n      \"column\": \"name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 102,\n        \"samples\": [\n          \"Gabon\",\n          \"Nicaragua\",\n          \"Malawi\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"LifeExpectancy_Person\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6.318317726536181,\n        \"min\": 52.676,\n        \"max\": 84.4456097560976,\n        \"num_unique_values\": 102,\n        \"samples\": [\n          65.821,\n          73.837,\n          62.904\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"GiniIndex_EconomicActivity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8.025478985639545,\n        \"min\": 25.6,\n        \"max\": 63.0,\n        \"num_unique_values\": 82,\n        \"samples\": [\n          34.2,\n          51.3,\n          26.2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Amount_EconomicActivity_GrossDomesticProduction_Nominal_PerCapita\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11920.878206024423,\n        \"min\": 259.025031363515,\n        \"max\": 76329.5822652029,\n        \"num_unique_values\": 102,\n        \"samples\": [\n          8820.34733682316,\n          2255.42599496242,\n          645.158692067185\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Count_Death_AsAFractionOfCount_Person\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3.3894928836979576,\n        \"min\": 3.868,\n        \"max\": 21.7,\n        \"num_unique_values\": 102,\n        \"samples\": [\n          7.258,\n          4.859,\n          6.983\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Amount_Emissions_CarbonDioxide_PerCapita\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3.224825238113954,\n        \"min\": 0.0325847808481787,\n        \"max\": 14.7761369066259,\n        \"num_unique_values\": 102,\n        \"samples\": [\n          2.33327357514897,\n          0.678252104273379,\n          0.0846361581872504\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"GrowthRate_Count_Person\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.0743081848143072,\n        \"min\": -14.2570374896829,\n        \"max\": 3.71298781842953,\n        \"num_unique_values\": 102,\n        \"samples\": [\n          2.02168710210066,\n          1.41827846169398,\n          2.5591382705715\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Count_Person_Upto4Years_Overweight_AsFractionOf_Count_Person_Upto4Years\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.047975529459375395,\n        \"min\": 0.008,\n        \"max\": 0.265,\n        \"num_unique_values\": 77,\n        \"samples\": [\n          0.22,\n          0.08,\n          0.073\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Count_Product_MobileCellularSubscription_AsFractionOf_Count_Person\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3334648603664825,\n        \"min\": 0.4207158261,\n        \"max\": 2.031681662,\n        \"num_unique_values\": 102,\n        \"samples\": [\n          1.25387067,\n          0.9710898703,\n          0.6012725997\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"FertilityRate_Person_Female\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.2204395068521257,\n        \"min\": 0.808,\n        \"max\": 6.82,\n        \"num_unique_values\": 100,\n        \"samples\": [\n          2.667,\n          3.851,\n          2.325\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Count_Person_IsInternetUser_PerCapita\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 24.737037965462047,\n        \"min\": 5.804921577,\n        \"max\": 97.3986404,\n        \"num_unique_values\": 102,\n        \"samples\": [\n          71.74940641,\n          57.14684935,\n          24.40560063\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
+              "type": "dataframe",
+              "variable_name": "df"
+            },
             "text/html": [
-              "<div>\n",
+              "\n",
+              "  <div id=\"df-6807b101-83ba-4217-be46-88df56192e5c\" class=\"colab-df-container\">\n",
+              "    <div>\n",
               "<style scoped>\n",
               "    .dataframe tbody tr th:only-of-type {\n",
               "        vertical-align: middle;\n",
@@ -454,16 +401,16 @@
               "  <thead>\n",
               "    <tr style=\"text-align: right;\">\n",
               "      <th></th>\n",
-              "      <th>Count_Death_AsAFractionOfCount_Person</th>\n",
-              "      <th>Amount_Emissions_CarbonDioxide_PerCapita</th>\n",
               "      <th>LifeExpectancy_Person</th>\n",
               "      <th>GiniIndex_EconomicActivity</th>\n",
-              "      <th>Count_Product_MobileCellularSubscription_AsFractionOf_Count_Person</th>\n",
               "      <th>Amount_EconomicActivity_GrossDomesticProduction_Nominal_PerCapita</th>\n",
-              "      <th>FertilityRate_Person_Female</th>\n",
-              "      <th>Count_Person_IsInternetUser_PerCapita</th>\n",
+              "      <th>Count_Death_AsAFractionOfCount_Person</th>\n",
+              "      <th>Amount_Emissions_CarbonDioxide_PerCapita</th>\n",
               "      <th>GrowthRate_Count_Person</th>\n",
               "      <th>Count_Person_Upto4Years_Overweight_AsFractionOf_Count_Person_Upto4Years</th>\n",
+              "      <th>Count_Product_MobileCellularSubscription_AsFractionOf_Count_Person</th>\n",
+              "      <th>FertilityRate_Person_Female</th>\n",
+              "      <th>Count_Person_IsInternetUser_PerCapita</th>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>name</th>\n",
@@ -482,68 +429,68 @@
               "  <tbody>\n",
               "    <tr>\n",
               "      <th>Angola</th>\n",
-              "      <td>0.008190</td>\n",
-              "      <td>0.887380</td>\n",
-              "      <td>61.147</td>\n",
+              "      <td>61.643</td>\n",
               "      <td>51.3</td>\n",
-              "      <td>0.465986</td>\n",
-              "      <td>1895.770869</td>\n",
-              "      <td>5.442</td>\n",
-              "      <td>36.000000</td>\n",
-              "      <td>3.218530</td>\n",
+              "      <td>3000.444231</td>\n",
+              "      <td>8.009</td>\n",
+              "      <td>0.592743</td>\n",
+              "      <td>3.096753</td>\n",
               "      <td>0.034</td>\n",
+              "      <td>0.673735</td>\n",
+              "      <td>5.304</td>\n",
+              "      <td>32.602302</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>Albania</th>\n",
-              "      <td>0.007898</td>\n",
-              "      <td>1.939732</td>\n",
-              "      <td>78.573</td>\n",
-              "      <td>33.2</td>\n",
-              "      <td>0.912930</td>\n",
-              "      <td>5215.276752</td>\n",
-              "      <td>1.597</td>\n",
-              "      <td>72.237677</td>\n",
-              "      <td>-0.577942</td>\n",
+              "      <td>76.463</td>\n",
+              "      <td>29.4</td>\n",
+              "      <td>6810.114041</td>\n",
+              "      <td>11.325</td>\n",
+              "      <td>1.544550</td>\n",
+              "      <td>-1.215790</td>\n",
               "      <td>0.164</td>\n",
+              "      <td>0.978857</td>\n",
+              "      <td>1.390</td>\n",
+              "      <td>82.613686</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>Argentina</th>\n",
-              "      <td>0.007609</td>\n",
-              "      <td>3.987234</td>\n",
-              "      <td>76.667</td>\n",
-              "      <td>41.4</td>\n",
-              "      <td>1.308744</td>\n",
-              "      <td>8441.919439</td>\n",
-              "      <td>2.247</td>\n",
-              "      <td>74.294907</td>\n",
-              "      <td>0.970054</td>\n",
-              "      <td>0.100</td>\n",
+              "      <td>75.390</td>\n",
+              "      <td>42.0</td>\n",
+              "      <td>13650.604629</td>\n",
+              "      <td>8.955</td>\n",
+              "      <td>3.405618</td>\n",
+              "      <td>0.925835</td>\n",
+              "      <td>0.124</td>\n",
+              "      <td>1.323578</td>\n",
+              "      <td>1.885</td>\n",
+              "      <td>88.375357</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>Armenia</th>\n",
-              "      <td>0.009857</td>\n",
-              "      <td>1.880246</td>\n",
-              "      <td>75.087</td>\n",
-              "      <td>34.4</td>\n",
-              "      <td>1.223488</td>\n",
-              "      <td>4267.452119</td>\n",
-              "      <td>1.758</td>\n",
-              "      <td>66.543950</td>\n",
-              "      <td>0.185983</td>\n",
+              "      <td>72.043</td>\n",
+              "      <td>27.9</td>\n",
+              "      <td>7018.051504</td>\n",
+              "      <td>13.007</td>\n",
+              "      <td>2.404684</td>\n",
+              "      <td>-0.377102</td>\n",
               "      <td>0.137</td>\n",
+              "      <td>1.352540</td>\n",
+              "      <td>1.575</td>\n",
+              "      <td>78.612258</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>Australia</th>\n",
-              "      <td>0.006300</td>\n",
-              "      <td>15.475516</td>\n",
-              "      <td>82.900</td>\n",
-              "      <td>34.4</td>\n",
-              "      <td>1.106209</td>\n",
-              "      <td>51812.154037</td>\n",
-              "      <td>1.657</td>\n",
-              "      <td>86.545049</td>\n",
-              "      <td>1.258698</td>\n",
+              "      <td>83.300</td>\n",
+              "      <td>34.3</td>\n",
+              "      <td>65099.845912</td>\n",
+              "      <td>6.700</td>\n",
+              "      <td>14.776137</td>\n",
+              "      <td>1.238639</td>\n",
               "      <td>0.220</td>\n",
+              "      <td>1.070312</td>\n",
+              "      <td>1.700</td>\n",
+              "      <td>96.240000</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>...</th>\n",
@@ -560,134 +507,557 @@
               "    </tr>\n",
               "    <tr>\n",
               "      <th>Samoa</th>\n",
-              "      <td>0.005239</td>\n",
-              "      <td>1.631588</td>\n",
-              "      <td>73.321</td>\n",
+              "      <td>72.767</td>\n",
               "      <td>38.7</td>\n",
-              "      <td>0.635832</td>\n",
-              "      <td>4067.472630</td>\n",
-              "      <td>3.830</td>\n",
-              "      <td>33.610939</td>\n",
-              "      <td>0.665990</td>\n",
-              "      <td>0.053</td>\n",
+              "      <td>3745.560367</td>\n",
+              "      <td>5.343</td>\n",
+              "      <td>0.960782</td>\n",
+              "      <td>1.640310</td>\n",
+              "      <td>0.087</td>\n",
+              "      <td>0.604006</td>\n",
+              "      <td>3.930</td>\n",
+              "      <td>78.245732</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>Yemen</th>\n",
-              "      <td>0.005983</td>\n",
-              "      <td>0.326682</td>\n",
-              "      <td>66.125</td>\n",
+              "      <td>63.753</td>\n",
               "      <td>36.7</td>\n",
-              "      <td>0.536789</td>\n",
-              "      <td>824.117718</td>\n",
-              "      <td>3.700</td>\n",
-              "      <td>26.718355</td>\n",
-              "      <td>2.251561</td>\n",
+              "      <td>650.272218</td>\n",
+              "      <td>6.845</td>\n",
+              "      <td>0.308515</td>\n",
+              "      <td>2.144628</td>\n",
               "      <td>0.025</td>\n",
+              "      <td>0.460195</td>\n",
+              "      <td>3.795</td>\n",
+              "      <td>26.718355</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>South Africa</th>\n",
-              "      <td>0.009435</td>\n",
-              "      <td>7.496645</td>\n",
-              "      <td>64.131</td>\n",
+              "      <td>62.341</td>\n",
               "      <td>63.0</td>\n",
-              "      <td>1.655999</td>\n",
-              "      <td>5090.715025</td>\n",
-              "      <td>2.381</td>\n",
-              "      <td>68.200000</td>\n",
-              "      <td>1.273356</td>\n",
-              "      <td>0.133</td>\n",
+              "      <td>6766.481254</td>\n",
+              "      <td>11.432</td>\n",
+              "      <td>6.687563</td>\n",
+              "      <td>0.841058</td>\n",
+              "      <td>0.116</td>\n",
+              "      <td>1.673959</td>\n",
+              "      <td>2.374</td>\n",
+              "      <td>72.310492</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>Zambia</th>\n",
-              "      <td>0.006456</td>\n",
-              "      <td>0.446065</td>\n",
-              "      <td>63.886</td>\n",
-              "      <td>57.1</td>\n",
-              "      <td>0.964144</td>\n",
-              "      <td>1050.919283</td>\n",
-              "      <td>4.559</td>\n",
-              "      <td>19.000000</td>\n",
-              "      <td>2.885686</td>\n",
+              "      <td>61.223</td>\n",
+              "      <td>55.9</td>\n",
+              "      <td>1456.901570</td>\n",
+              "      <td>6.973</td>\n",
+              "      <td>0.401903</td>\n",
+              "      <td>2.758032</td>\n",
               "      <td>0.052</td>\n",
+              "      <td>0.991024</td>\n",
+              "      <td>4.308</td>\n",
+              "      <td>21.231531</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>Zimbabwe</th>\n",
-              "      <td>0.007883</td>\n",
-              "      <td>0.849793</td>\n",
-              "      <td>61.490</td>\n",
-              "      <td>44.3</td>\n",
-              "      <td>0.901023</td>\n",
-              "      <td>1128.210711</td>\n",
-              "      <td>3.531</td>\n",
-              "      <td>25.100000</td>\n",
-              "      <td>1.473871</td>\n",
+              "      <td>59.253</td>\n",
+              "      <td>50.3</td>\n",
+              "      <td>1676.821489</td>\n",
+              "      <td>9.057</td>\n",
+              "      <td>0.530484</td>\n",
+              "      <td>2.024036</td>\n",
               "      <td>0.025</td>\n",
+              "      <td>0.876245</td>\n",
+              "      <td>3.491</td>\n",
+              "      <td>34.813971</td>\n",
               "    </tr>\n",
               "  </tbody>\n",
               "</table>\n",
               "<p>102 rows × 10 columns</p>\n",
-              "</div>"
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6807b101-83ba-4217-be46-88df56192e5c')\"\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-6807b101-83ba-4217-be46-88df56192e5c 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-6807b101-83ba-4217-be46-88df56192e5c');\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=\"df-dfa198ac-6549-4503-9b2a-d00baf3b0110\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-dfa198ac-6549-4503-9b2a-d00baf3b0110')\"\n",
+              "            title=\"Suggest charts\"\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",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-dfa198ac-6549-4503-9b2a-d00baf3b0110 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "  <div id=\"id_46b6b0cb-c604-4399-b7c1-740f255cbf2b\">\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_46b6b0cb-c604-4399-b7c1-740f255cbf2b 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"
             ],
             "text/plain": [
-              "              Count_Death_AsAFractionOfCount_Person  ...  Count_Person_Upto4Years_Overweight_AsFractionOf_Count_Person_Upto4Years\n",
-              "name                                                 ...                                                                         \n",
-              "Angola                                     0.008190  ...                                              0.034                      \n",
-              "Albania                                    0.007898  ...                                              0.164                      \n",
-              "Argentina                                  0.007609  ...                                              0.100                      \n",
-              "Armenia                                    0.009857  ...                                              0.137                      \n",
-              "Australia                                  0.006300  ...                                              0.220                      \n",
-              "...                                             ...  ...                                                ...                      \n",
-              "Samoa                                      0.005239  ...                                              0.053                      \n",
-              "Yemen                                      0.005983  ...                                              0.025                      \n",
-              "South Africa                               0.009435  ...                                              0.133                      \n",
-              "Zambia                                     0.006456  ...                                              0.052                      \n",
-              "Zimbabwe                                   0.007883  ...                                              0.025                      \n",
+              "              LifeExpectancy_Person  GiniIndex_EconomicActivity  \\\n",
+              "name                                                              \n",
+              "Angola                       61.643                        51.3   \n",
+              "Albania                      76.463                        29.4   \n",
+              "Argentina                    75.390                        42.0   \n",
+              "Armenia                      72.043                        27.9   \n",
+              "Australia                    83.300                        34.3   \n",
+              "...                             ...                         ...   \n",
+              "Samoa                        72.767                        38.7   \n",
+              "Yemen                        63.753                        36.7   \n",
+              "South Africa                 62.341                        63.0   \n",
+              "Zambia                       61.223                        55.9   \n",
+              "Zimbabwe                     59.253                        50.3   \n",
+              "\n",
+              "              Amount_EconomicActivity_GrossDomesticProduction_Nominal_PerCapita  \\\n",
+              "name                                                                              \n",
+              "Angola                                              3000.444231                   \n",
+              "Albania                                             6810.114041                   \n",
+              "Argentina                                          13650.604629                   \n",
+              "Armenia                                             7018.051504                   \n",
+              "Australia                                          65099.845912                   \n",
+              "...                                                         ...                   \n",
+              "Samoa                                               3745.560367                   \n",
+              "Yemen                                                650.272218                   \n",
+              "South Africa                                        6766.481254                   \n",
+              "Zambia                                              1456.901570                   \n",
+              "Zimbabwe                                            1676.821489                   \n",
+              "\n",
+              "              Count_Death_AsAFractionOfCount_Person  \\\n",
+              "name                                                  \n",
+              "Angola                                        8.009   \n",
+              "Albania                                      11.325   \n",
+              "Argentina                                     8.955   \n",
+              "Armenia                                      13.007   \n",
+              "Australia                                     6.700   \n",
+              "...                                             ...   \n",
+              "Samoa                                         5.343   \n",
+              "Yemen                                         6.845   \n",
+              "South Africa                                 11.432   \n",
+              "Zambia                                        6.973   \n",
+              "Zimbabwe                                      9.057   \n",
+              "\n",
+              "              Amount_Emissions_CarbonDioxide_PerCapita  \\\n",
+              "name                                                     \n",
+              "Angola                                        0.592743   \n",
+              "Albania                                       1.544550   \n",
+              "Argentina                                     3.405618   \n",
+              "Armenia                                       2.404684   \n",
+              "Australia                                    14.776137   \n",
+              "...                                                ...   \n",
+              "Samoa                                         0.960782   \n",
+              "Yemen                                         0.308515   \n",
+              "South Africa                                  6.687563   \n",
+              "Zambia                                        0.401903   \n",
+              "Zimbabwe                                      0.530484   \n",
+              "\n",
+              "              GrowthRate_Count_Person  \\\n",
+              "name                                    \n",
+              "Angola                       3.096753   \n",
+              "Albania                     -1.215790   \n",
+              "Argentina                    0.925835   \n",
+              "Armenia                     -0.377102   \n",
+              "Australia                    1.238639   \n",
+              "...                               ...   \n",
+              "Samoa                        1.640310   \n",
+              "Yemen                        2.144628   \n",
+              "South Africa                 0.841058   \n",
+              "Zambia                       2.758032   \n",
+              "Zimbabwe                     2.024036   \n",
+              "\n",
+              "              Count_Person_Upto4Years_Overweight_AsFractionOf_Count_Person_Upto4Years  \\\n",
+              "name                                                                                    \n",
+              "Angola                                                    0.034                         \n",
+              "Albania                                                   0.164                         \n",
+              "Argentina                                                 0.124                         \n",
+              "Armenia                                                   0.137                         \n",
+              "Australia                                                 0.220                         \n",
+              "...                                                         ...                         \n",
+              "Samoa                                                     0.087                         \n",
+              "Yemen                                                     0.025                         \n",
+              "South Africa                                              0.116                         \n",
+              "Zambia                                                    0.052                         \n",
+              "Zimbabwe                                                  0.025                         \n",
+              "\n",
+              "              Count_Product_MobileCellularSubscription_AsFractionOf_Count_Person  \\\n",
+              "name                                                                               \n",
+              "Angola                                                 0.673735                    \n",
+              "Albania                                                0.978857                    \n",
+              "Argentina                                              1.323578                    \n",
+              "Armenia                                                1.352540                    \n",
+              "Australia                                              1.070312                    \n",
+              "...                                                         ...                    \n",
+              "Samoa                                                  0.604006                    \n",
+              "Yemen                                                  0.460195                    \n",
+              "South Africa                                           1.673959                    \n",
+              "Zambia                                                 0.991024                    \n",
+              "Zimbabwe                                               0.876245                    \n",
+              "\n",
+              "              FertilityRate_Person_Female  \\\n",
+              "name                                        \n",
+              "Angola                              5.304   \n",
+              "Albania                             1.390   \n",
+              "Argentina                           1.885   \n",
+              "Armenia                             1.575   \n",
+              "Australia                           1.700   \n",
+              "...                                   ...   \n",
+              "Samoa                               3.930   \n",
+              "Yemen                               3.795   \n",
+              "South Africa                        2.374   \n",
+              "Zambia                              4.308   \n",
+              "Zimbabwe                            3.491   \n",
+              "\n",
+              "              Count_Person_IsInternetUser_PerCapita  \n",
+              "name                                                 \n",
+              "Angola                                    32.602302  \n",
+              "Albania                                   82.613686  \n",
+              "Argentina                                 88.375357  \n",
+              "Armenia                                   78.612258  \n",
+              "Australia                                 96.240000  \n",
+              "...                                             ...  \n",
+              "Samoa                                     78.245732  \n",
+              "Yemen                                     26.718355  \n",
+              "South Africa                              72.310492  \n",
+              "Zambia                                    21.231531  \n",
+              "Zimbabwe                                  34.813971  \n",
               "\n",
               "[102 rows x 10 columns]"
             ]
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "z1eNSt7fvnjY"
-      },
+      ],
       "source": [
-        "### 2.1) The Importance of Normalization\n",
-        "Scroll through the dataframe generated by the codebox above, and take note of the scales of each of the features. They can vary drastically in magnitude! Let's normalize our data before clustering.\n",
-        "\n",
-        "**Q1: What do you think would happen if we didn't normalize our data before clustering?**\n",
-        "\n",
-        "*A1:Generally the features with higher magnitudes will end up dominating the clustering.*"
-      ]
-    },
-    {
-      "cell_type": "code",
+        "dcids = [\n",
+        "  'country/AGO', 'country/ALB', 'country/ARG', 'country/ARM', 'country/AUS',\n",
+        "  'country/AZE', 'country/BDI', 'country/BGD', 'country/BGR', 'country/BIH',\n",
+        "  'country/BLZ', 'country/BOL', 'country/BRA', 'country/BTN', 'country/BWA',\n",
+        "  'country/CAN', 'country/CHL', 'country/CHN', 'country/CMR', 'country/COD',\n",
+        "  'country/COG', 'country/COL', 'country/CRI', 'country/CZE', 'country/DOM',\n",
+        "  'country/DZA', 'country/ECU', 'country/EGY', 'country/ETH', 'country/FJI',\n",
+        "  'country/GAB', 'country/GEO', 'country/GHA', 'country/GTM', 'country/GUY',\n",
+        "  'country/HND', 'country/IDN', 'country/IND', 'country/IRN', 'country/JAM',\n",
+        "  'country/JOR', 'country/JPN', 'country/KAZ', 'country/KEN', 'country/KGZ',\n",
+        "  'country/KIR', 'country/KOR', 'country/LAO', 'country/LBN', 'country/LCA',\n",
+        "  'country/LSO', 'country/MAR', 'country/MDA', 'country/MDG', 'country/MEX',\n",
+        "  'country/MKD', 'country/MLI', 'country/MMR', 'country/MNE', 'country/MNG',\n",
+        "  'country/MOZ', 'country/MRT', 'country/MWI', 'country/MYS', 'country/NAM',\n",
+        "  'country/NER', 'country/NGA', 'country/NIC', 'country/NPL', 'country/PAK',\n",
+        "  'country/PAN', 'country/PER', 'country/PHL', 'country/PNG', 'country/PRY',\n",
+        "  'country/ROU', 'country/RWA', 'country/SDN', 'country/SLV', 'country/SRB',\n",
+        "  'country/SWZ', 'country/SYR', 'country/THA', 'country/TJK', 'country/TKM',\n",
+        "  'country/TLS', 'country/TON', 'country/TTO', 'country/TUN', 'country/TUR',\n",
+        "  'country/TZA', 'country/UGA', 'country/UKR', 'country/USA', 'country/UZB',\n",
+        "  'country/VNM', 'country/VUT', 'country/WSM', 'country/YEM', 'country/ZAF',\n",
+        "  'country/ZMB', 'country/ZWE'\n",
+        "]\n",
+        "\n",
+        "\n",
+        "stat_vars_to_query = [\n",
+        "  \"Amount_Emissions_CarbonDioxide_PerCapita\",\n",
+        "  \"LifeExpectancy_Person\",\n",
+        "  \"Count_Person_IsInternetUser_PerCapita\",\n",
+        "  \"GrowthRate_Count_Person\",\n",
+        "  \"Count_Person_Upto4Years_Overweight_AsFractionOf_Count_Person_Upto4Years\",\n",
+        "  \"GiniIndex_EconomicActivity\",\n",
+        "  \"Count_Product_MobileCellularSubscription_AsFractionOf_Count_Person\",\n",
+        "  \"Amount_EconomicActivity_GrossDomesticProduction_Nominal_PerCapita\",\n",
+        "  \"FertilityRate_Person_Female\",\n",
+        "  \"Count_Death_AsAFractionOfCount_Person\"\n",
+        "]\n",
+        "\n",
+        "df = datacommons_pandas.build_multivariate_dataframe(dcids,stat_vars_to_query)\n",
+        "\n",
+        "# swap index for names\n",
+        "df.insert(0, 'name', df.index.map(datacommons.get_property_values(df.index, 'name')).str[0])\n",
+        "df.set_index('name', drop=True, inplace=True)\n",
+        "\n",
+        "display(df)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "z1eNSt7fvnjY"
+      },
+      "source": [
+        "### 2.1) The Importance of Normalization\n",
+        "Scroll through the dataframe generated by the codebox above, and take note of the scales of each of the features. They can vary drastically in magnitude! Let's normalize our data before clustering.\n",
+        "\n",
+        "**Q1: What do you think would happen if we didn't normalize our data before clustering?**\n",
+        "\n",
+        "*A1:Generally the features with higher magnitudes will end up dominating the clustering.*"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
-          "height": 487
+          "height": 492
         },
         "id": "QhOQzx81v1I2",
-        "outputId": "efb6c14a-98de-472c-968e-2ba56fb29250"
+        "outputId": "fad4db4d-7830-4b8b-8183-5d9984465c9b"
       },
-      "source": [
-        "# normalize the values\n",
-        "mean_df = df.mean()\n",
-        "std_df = df.std()\n",
-        "normalized_df = ((df-mean_df)/std_df)\n",
-        "display(normalized_df)"
-      ],
-      "execution_count": null,
       "outputs": [
         {
-          "output_type": "display_data",
           "data": {
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "summary": "{\n  \"name\": \"normalized_df\",\n  \"rows\": 102,\n  \"fields\": [\n    {\n      \"column\": \"name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 102,\n        \"samples\": [\n          \"Gabon\",\n          \"Nicaragua\",\n          \"Malawi\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"LifeExpectancy_Person\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0,\n        \"min\": -2.6571494151115074,\n        \"max\": 2.371025983961734,\n        \"num_unique_values\": 102,\n        \"samples\": [\n          -0.5766905700628382,\n          0.6920015639736888,\n          -1.038364092391249\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"GiniIndex_EconomicActivity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0,\n        \"min\": -1.6735913922462622,\n        \"max\": 2.986566595447929,\n        \"num_unique_values\": 82,\n        \"samples\": [\n          -0.6020042613861004,\n          1.528709684859104,\n          -1.5988294993955536\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Amount_EconomicActivity_GrossDomesticProduction_Nominal_PerCapita\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0,\n        \"min\": -0.6420653587051781,\n        \"max\": 5.739222657088474,\n        \"num_unique_values\": 102,\n        \"samples\": [\n          0.07611346650360826,\n          -0.4745943948134416,\n          -0.6096739816580969\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Count_Death_AsAFractionOfCount_Person\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0,\n        \"min\": -1.410758206621121,\n        \"max\": 3.8502057817573343,\n        \"num_unique_values\": 102,\n        \"samples\": [\n          -0.41060859241054665,\n          -1.1183840863607437,\n          -0.4917416732093397\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Amount_Emissions_CarbonDioxide_PerCapita\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.9999999999999999,\n        \"min\": -0.8663609354858812,\n        \"max\": 3.7055308841156425,\n        \"num_unique_values\": 102,\n        \"samples\": [\n          -0.15293040067452682,\n          -0.6661431637461573,\n          -0.8502200988526644\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"GrowthRate_Count_Person\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.9999999999999998,\n        \"min\": -7.364255386089317,\n        \"max\": 1.2988861083463874,\n        \"num_unique_values\": 102,\n        \"samples\": [\n          0.48352958190994744,\n          0.19263325087093303,\n          0.7426285780983816\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Count_Person_Upto4Years_Overweight_AsFractionOf_Count_Person_Upto4Years\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0,\n        \"min\": -1.3370787054647872,\n        \"max\": 4.019818923307019,\n        \"num_unique_values\": 77,\n        \"samples\": [\n          3.0818407392808274,\n          0.1636863889771195,\n          0.01777867146193398\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Count_Product_MobileCellularSubscription_AsFractionOf_Count_Person\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.9999999999999998,\n        \"min\": -1.9593438501670521,\n        \"max\": 2.8716474396783793,\n        \"num_unique_values\": 102,\n        \"samples\": [\n          0.5391348290687488,\n          -0.3088729621846383,\n          -1.4178871779360842\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"FertilityRate_Person_Female\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.9999999999999998,\n        \"min\": -1.5628490673967872,\n        \"max\": 3.3632451521411615,\n        \"num_unique_values\": 100,\n        \"samples\": [\n          -0.039627318540991475,\n          0.9305149894984183,\n          -0.3198542352212936\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Count_Person_IsInternetUser_PerCapita\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.9999999999999998,\n        \"min\": -2.2847628154464386,\n        \"max\": 1.4179326710092772,\n        \"num_unique_values\": 102,\n        \"samples\": [\n          0.3810569534794921,\n          -0.2092545090484047,\n          -1.5328264243971004\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
+              "type": "dataframe",
+              "variable_name": "normalized_df"
+            },
             "text/html": [
-              "<div>\n",
+              "\n",
+              "  <div id=\"df-4c6c90f5-a522-4181-967d-b6639420b4ff\" class=\"colab-df-container\">\n",
+              "    <div>\n",
               "<style scoped>\n",
               "    .dataframe tbody tr th:only-of-type {\n",
               "        vertical-align: middle;\n",
@@ -705,16 +1075,16 @@
               "  <thead>\n",
               "    <tr style=\"text-align: right;\">\n",
               "      <th></th>\n",
-              "      <th>Count_Death_AsAFractionOfCount_Person</th>\n",
-              "      <th>Amount_Emissions_CarbonDioxide_PerCapita</th>\n",
               "      <th>LifeExpectancy_Person</th>\n",
               "      <th>GiniIndex_EconomicActivity</th>\n",
-              "      <th>Count_Product_MobileCellularSubscription_AsFractionOf_Count_Person</th>\n",
               "      <th>Amount_EconomicActivity_GrossDomesticProduction_Nominal_PerCapita</th>\n",
-              "      <th>FertilityRate_Person_Female</th>\n",
-              "      <th>Count_Person_IsInternetUser_PerCapita</th>\n",
+              "      <th>Count_Death_AsAFractionOfCount_Person</th>\n",
+              "      <th>Amount_Emissions_CarbonDioxide_PerCapita</th>\n",
               "      <th>GrowthRate_Count_Person</th>\n",
               "      <th>Count_Person_Upto4Years_Overweight_AsFractionOf_Count_Person_Upto4Years</th>\n",
+              "      <th>Count_Product_MobileCellularSubscription_AsFractionOf_Count_Person</th>\n",
+              "      <th>FertilityRate_Person_Female</th>\n",
+              "      <th>Count_Person_IsInternetUser_PerCapita</th>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>name</th>\n",
@@ -733,68 +1103,68 @@
               "  <tbody>\n",
               "    <tr>\n",
               "      <th>Angola</th>\n",
-              "      <td>0.360543</td>\n",
-              "      <td>-0.618384</td>\n",
-              "      <td>-1.609796</td>\n",
-              "      <td>1.478088</td>\n",
-              "      <td>-1.723268</td>\n",
-              "      <td>-0.456024</td>\n",
-              "      <td>2.168842</td>\n",
-              "      <td>-0.581863</td>\n",
-              "      <td>1.783722</td>\n",
-              "      <td>-0.837218</td>\n",
+              "      <td>-1.237943</td>\n",
+              "      <td>1.528710</td>\n",
+              "      <td>-0.412097</td>\n",
+              "      <td>-0.189042</td>\n",
+              "      <td>-0.692659</td>\n",
+              "      <td>1.001806</td>\n",
+              "      <td>-0.795136</td>\n",
+              "      <td>-1.200586</td>\n",
+              "      <td>2.121070</td>\n",
+              "      <td>-1.201473</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>Albania</th>\n",
-              "      <td>0.240552</td>\n",
-              "      <td>-0.324421</td>\n",
-              "      <td>1.149030</td>\n",
-              "      <td>-0.857253</td>\n",
-              "      <td>-0.399413</td>\n",
-              "      <td>-0.126101</td>\n",
-              "      <td>-1.009484</td>\n",
-              "      <td>0.815159</td>\n",
-              "      <td>-1.809708</td>\n",
-              "      <td>1.615071</td>\n",
+              "      <td>1.107619</td>\n",
+              "      <td>-1.200099</td>\n",
+              "      <td>-0.092518</td>\n",
+              "      <td>0.789276</td>\n",
+              "      <td>-0.397509</td>\n",
+              "      <td>-1.077221</td>\n",
+              "      <td>1.914579</td>\n",
+              "      <td>-0.285581</td>\n",
+              "      <td>-1.085972</td>\n",
+              "      <td>0.820248</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>Argentina</th>\n",
-              "      <td>0.121795</td>\n",
-              "      <td>0.247525</td>\n",
-              "      <td>0.847278</td>\n",
-              "      <td>0.200747</td>\n",
-              "      <td>0.772994</td>\n",
-              "      <td>0.194593</td>\n",
-              "      <td>-0.472185</td>\n",
-              "      <td>0.894469</td>\n",
-              "      <td>-0.344501</td>\n",
-              "      <td>0.407790</td>\n",
+              "      <td>0.937795</td>\n",
+              "      <td>0.369900</td>\n",
+              "      <td>0.481307</td>\n",
+              "      <td>0.090056</td>\n",
+              "      <td>0.179597</td>\n",
+              "      <td>-0.044768</td>\n",
+              "      <td>1.080821</td>\n",
+              "      <td>0.748175</td>\n",
+              "      <td>-0.680380</td>\n",
+              "      <td>1.053165</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>Armenia</th>\n",
-              "      <td>1.045555</td>\n",
-              "      <td>-0.341038</td>\n",
-              "      <td>0.597138</td>\n",
-              "      <td>-0.702424</td>\n",
-              "      <td>0.520463</td>\n",
-              "      <td>-0.220304</td>\n",
-              "      <td>-0.876399</td>\n",
-              "      <td>0.595657</td>\n",
-              "      <td>-1.086638</td>\n",
-              "      <td>1.105750</td>\n",
+              "      <td>0.408065</td>\n",
+              "      <td>-1.387004</td>\n",
+              "      <td>-0.075075</td>\n",
+              "      <td>1.285515</td>\n",
+              "      <td>-0.130787</td>\n",
+              "      <td>-0.672899</td>\n",
+              "      <td>1.351792</td>\n",
+              "      <td>0.835025</td>\n",
+              "      <td>-0.934387</td>\n",
+              "      <td>0.658489</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>Australia</th>\n",
-              "      <td>-0.416106</td>\n",
-              "      <td>3.456646</td>\n",
-              "      <td>1.834066</td>\n",
-              "      <td>-0.702424</td>\n",
-              "      <td>0.173081</td>\n",
-              "      <td>4.505133</td>\n",
-              "      <td>-0.959887</td>\n",
-              "      <td>1.366732</td>\n",
-              "      <td>-0.071294</td>\n",
-              "      <td>2.671442</td>\n",
+              "      <td>2.189710</td>\n",
+              "      <td>-0.589544</td>\n",
+              "      <td>4.797200</td>\n",
+              "      <td>-0.575235</td>\n",
+              "      <td>3.705531</td>\n",
+              "      <td>0.106031</td>\n",
+              "      <td>3.081841</td>\n",
+              "      <td>-0.011324</td>\n",
+              "      <td>-0.831965</td>\n",
+              "      <td>1.371094</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>...</th>\n",
@@ -811,94 +1181,478 @@
               "    </tr>\n",
               "    <tr>\n",
               "      <th>Samoa</th>\n",
-              "      <td>-0.852098</td>\n",
-              "      <td>-0.410498</td>\n",
-              "      <td>0.317551</td>\n",
-              "      <td>-0.147619</td>\n",
-              "      <td>-1.220183</td>\n",
-              "      <td>-0.240180</td>\n",
-              "      <td>0.836342</td>\n",
-              "      <td>-0.673965</td>\n",
-              "      <td>-0.632304</td>\n",
-              "      <td>-0.478807</td>\n",
+              "      <td>0.522653</td>\n",
+              "      <td>-0.041290</td>\n",
+              "      <td>-0.349592</td>\n",
+              "      <td>-0.975590</td>\n",
+              "      <td>-0.578532</td>\n",
+              "      <td>0.299672</td>\n",
+              "      <td>0.309594</td>\n",
+              "      <td>-1.409691</td>\n",
+              "      <td>0.995246</td>\n",
+              "      <td>0.643672</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>Yemen</th>\n",
-              "      <td>-0.546370</td>\n",
-              "      <td>-0.775008</td>\n",
-              "      <td>-0.821696</td>\n",
-              "      <td>-0.405668</td>\n",
-              "      <td>-1.513548</td>\n",
-              "      <td>-0.562535</td>\n",
-              "      <td>0.728883</td>\n",
-              "      <td>-0.939686</td>\n",
-              "      <td>0.868468</td>\n",
-              "      <td>-1.006992</td>\n",
+              "      <td>-0.903993</td>\n",
+              "      <td>-0.290496</td>\n",
+              "      <td>-0.609245</td>\n",
+              "      <td>-0.532456</td>\n",
+              "      <td>-0.780797</td>\n",
+              "      <td>0.542798</td>\n",
+              "      <td>-0.982731</td>\n",
+              "      <td>-1.840952</td>\n",
+              "      <td>0.884630</td>\n",
+              "      <td>-1.439333</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>South Africa</th>\n",
-              "      <td>0.872145</td>\n",
-              "      <td>1.227839</td>\n",
-              "      <td>-1.137379</td>\n",
-              "      <td>2.987674</td>\n",
-              "      <td>1.801570</td>\n",
-              "      <td>-0.138481</td>\n",
-              "      <td>-0.361419</td>\n",
-              "      <td>0.659500</td>\n",
-              "      <td>-0.057420</td>\n",
-              "      <td>1.030295</td>\n",
+              "      <td>-1.127470</td>\n",
+              "      <td>2.986567</td>\n",
+              "      <td>-0.096178</td>\n",
+              "      <td>0.820844</td>\n",
+              "      <td>1.197310</td>\n",
+              "      <td>-0.085638</td>\n",
+              "      <td>0.914069</td>\n",
+              "      <td>1.798902</td>\n",
+              "      <td>-0.279705</td>\n",
+              "      <td>0.403739</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>Zambia</th>\n",
-              "      <td>-0.352002</td>\n",
-              "      <td>-0.741660</td>\n",
-              "      <td>-1.176167</td>\n",
-              "      <td>2.226430</td>\n",
-              "      <td>-0.247717</td>\n",
-              "      <td>-0.539994</td>\n",
-              "      <td>1.438943</td>\n",
-              "      <td>-1.237241</td>\n",
-              "      <td>1.468679</td>\n",
-              "      <td>-0.497671</td>\n",
+              "      <td>-1.304416</td>\n",
+              "      <td>2.101884</td>\n",
+              "      <td>-0.541580</td>\n",
+              "      <td>-0.494692</td>\n",
+              "      <td>-0.751838</td>\n",
+              "      <td>0.838513</td>\n",
+              "      <td>-0.419944</td>\n",
+              "      <td>-0.249094</td>\n",
+              "      <td>1.304970</td>\n",
+              "      <td>-1.661139</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>Zimbabwe</th>\n",
-              "      <td>0.234389</td>\n",
-              "      <td>-0.628883</td>\n",
-              "      <td>-1.555493</td>\n",
-              "      <td>0.574918</td>\n",
-              "      <td>-0.434683</td>\n",
-              "      <td>-0.532312</td>\n",
-              "      <td>0.589185</td>\n",
-              "      <td>-1.002076</td>\n",
-              "      <td>0.132371</td>\n",
-              "      <td>-1.006992</td>\n",
+              "      <td>-1.616208</td>\n",
+              "      <td>1.404107</td>\n",
+              "      <td>-0.523131</td>\n",
+              "      <td>0.120149</td>\n",
+              "      <td>-0.711965</td>\n",
+              "      <td>0.484662</td>\n",
+              "      <td>-0.982731</td>\n",
+              "      <td>-0.593295</td>\n",
+              "      <td>0.635539</td>\n",
+              "      <td>-1.112066</td>\n",
               "    </tr>\n",
               "  </tbody>\n",
               "</table>\n",
               "<p>102 rows × 10 columns</p>\n",
-              "</div>"
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4c6c90f5-a522-4181-967d-b6639420b4ff')\"\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-4c6c90f5-a522-4181-967d-b6639420b4ff 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-4c6c90f5-a522-4181-967d-b6639420b4ff');\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=\"df-9617c752-664b-4be5-bb8a-ef91e16edc72\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-9617c752-664b-4be5-bb8a-ef91e16edc72')\"\n",
+              "            title=\"Suggest charts\"\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",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-9617c752-664b-4be5-bb8a-ef91e16edc72 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "  <div id=\"id_34af5ba3-092c-4588-a07c-180c0a4b358f\">\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('normalized_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_34af5ba3-092c-4588-a07c-180c0a4b358f button.colab-df-generate');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      buttonEl.onclick = () => {\n",
+              "        google.colab.notebook.generateWithVariable('normalized_df');\n",
+              "      }\n",
+              "      })();\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
             ],
             "text/plain": [
-              "              Count_Death_AsAFractionOfCount_Person  ...  Count_Person_Upto4Years_Overweight_AsFractionOf_Count_Person_Upto4Years\n",
-              "name                                                 ...                                                                         \n",
-              "Angola                                     0.360543  ...                                          -0.837218                      \n",
-              "Albania                                    0.240552  ...                                           1.615071                      \n",
-              "Argentina                                  0.121795  ...                                           0.407790                      \n",
-              "Armenia                                    1.045555  ...                                           1.105750                      \n",
-              "Australia                                 -0.416106  ...                                           2.671442                      \n",
-              "...                                             ...  ...                                                ...                      \n",
-              "Samoa                                     -0.852098  ...                                          -0.478807                      \n",
-              "Yemen                                     -0.546370  ...                                          -1.006992                      \n",
-              "South Africa                               0.872145  ...                                           1.030295                      \n",
-              "Zambia                                    -0.352002  ...                                          -0.497671                      \n",
-              "Zimbabwe                                   0.234389  ...                                          -1.006992                      \n",
+              "              LifeExpectancy_Person  GiniIndex_EconomicActivity  \\\n",
+              "name                                                              \n",
+              "Angola                    -1.237943                    1.528710   \n",
+              "Albania                    1.107619                   -1.200099   \n",
+              "Argentina                  0.937795                    0.369900   \n",
+              "Armenia                    0.408065                   -1.387004   \n",
+              "Australia                  2.189710                   -0.589544   \n",
+              "...                             ...                         ...   \n",
+              "Samoa                      0.522653                   -0.041290   \n",
+              "Yemen                     -0.903993                   -0.290496   \n",
+              "South Africa              -1.127470                    2.986567   \n",
+              "Zambia                    -1.304416                    2.101884   \n",
+              "Zimbabwe                  -1.616208                    1.404107   \n",
+              "\n",
+              "              Amount_EconomicActivity_GrossDomesticProduction_Nominal_PerCapita  \\\n",
+              "name                                                                              \n",
+              "Angola                                                -0.412097                   \n",
+              "Albania                                               -0.092518                   \n",
+              "Argentina                                              0.481307                   \n",
+              "Armenia                                               -0.075075                   \n",
+              "Australia                                              4.797200                   \n",
+              "...                                                         ...                   \n",
+              "Samoa                                                 -0.349592                   \n",
+              "Yemen                                                 -0.609245                   \n",
+              "South Africa                                          -0.096178                   \n",
+              "Zambia                                                -0.541580                   \n",
+              "Zimbabwe                                              -0.523131                   \n",
+              "\n",
+              "              Count_Death_AsAFractionOfCount_Person  \\\n",
+              "name                                                  \n",
+              "Angola                                    -0.189042   \n",
+              "Albania                                    0.789276   \n",
+              "Argentina                                  0.090056   \n",
+              "Armenia                                    1.285515   \n",
+              "Australia                                 -0.575235   \n",
+              "...                                             ...   \n",
+              "Samoa                                     -0.975590   \n",
+              "Yemen                                     -0.532456   \n",
+              "South Africa                               0.820844   \n",
+              "Zambia                                    -0.494692   \n",
+              "Zimbabwe                                   0.120149   \n",
+              "\n",
+              "              Amount_Emissions_CarbonDioxide_PerCapita  \\\n",
+              "name                                                     \n",
+              "Angola                                       -0.692659   \n",
+              "Albania                                      -0.397509   \n",
+              "Argentina                                     0.179597   \n",
+              "Armenia                                      -0.130787   \n",
+              "Australia                                     3.705531   \n",
+              "...                                                ...   \n",
+              "Samoa                                        -0.578532   \n",
+              "Yemen                                        -0.780797   \n",
+              "South Africa                                  1.197310   \n",
+              "Zambia                                       -0.751838   \n",
+              "Zimbabwe                                     -0.711965   \n",
+              "\n",
+              "              GrowthRate_Count_Person  \\\n",
+              "name                                    \n",
+              "Angola                       1.001806   \n",
+              "Albania                     -1.077221   \n",
+              "Argentina                   -0.044768   \n",
+              "Armenia                     -0.672899   \n",
+              "Australia                    0.106031   \n",
+              "...                               ...   \n",
+              "Samoa                        0.299672   \n",
+              "Yemen                        0.542798   \n",
+              "South Africa                -0.085638   \n",
+              "Zambia                       0.838513   \n",
+              "Zimbabwe                     0.484662   \n",
+              "\n",
+              "              Count_Person_Upto4Years_Overweight_AsFractionOf_Count_Person_Upto4Years  \\\n",
+              "name                                                                                    \n",
+              "Angola                                                -0.795136                         \n",
+              "Albania                                                1.914579                         \n",
+              "Argentina                                              1.080821                         \n",
+              "Armenia                                                1.351792                         \n",
+              "Australia                                              3.081841                         \n",
+              "...                                                         ...                         \n",
+              "Samoa                                                  0.309594                         \n",
+              "Yemen                                                 -0.982731                         \n",
+              "South Africa                                           0.914069                         \n",
+              "Zambia                                                -0.419944                         \n",
+              "Zimbabwe                                              -0.982731                         \n",
+              "\n",
+              "              Count_Product_MobileCellularSubscription_AsFractionOf_Count_Person  \\\n",
+              "name                                                                               \n",
+              "Angola                                                -1.200586                    \n",
+              "Albania                                               -0.285581                    \n",
+              "Argentina                                              0.748175                    \n",
+              "Armenia                                                0.835025                    \n",
+              "Australia                                             -0.011324                    \n",
+              "...                                                         ...                    \n",
+              "Samoa                                                 -1.409691                    \n",
+              "Yemen                                                 -1.840952                    \n",
+              "South Africa                                           1.798902                    \n",
+              "Zambia                                                -0.249094                    \n",
+              "Zimbabwe                                              -0.593295                    \n",
+              "\n",
+              "              FertilityRate_Person_Female  \\\n",
+              "name                                        \n",
+              "Angola                           2.121070   \n",
+              "Albania                         -1.085972   \n",
+              "Argentina                       -0.680380   \n",
+              "Armenia                         -0.934387   \n",
+              "Australia                       -0.831965   \n",
+              "...                                   ...   \n",
+              "Samoa                            0.995246   \n",
+              "Yemen                            0.884630   \n",
+              "South Africa                    -0.279705   \n",
+              "Zambia                           1.304970   \n",
+              "Zimbabwe                         0.635539   \n",
+              "\n",
+              "              Count_Person_IsInternetUser_PerCapita  \n",
+              "name                                                 \n",
+              "Angola                                    -1.201473  \n",
+              "Albania                                    0.820248  \n",
+              "Argentina                                  1.053165  \n",
+              "Armenia                                    0.658489  \n",
+              "Australia                                  1.371094  \n",
+              "...                                             ...  \n",
+              "Samoa                                      0.643672  \n",
+              "Yemen                                     -1.439333  \n",
+              "South Africa                               0.403739  \n",
+              "Zambia                                    -1.661139  \n",
+              "Zimbabwe                                  -1.112066  \n",
               "\n",
               "[102 rows x 10 columns]"
             ]
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         }
+      ],
+      "source": [
+        "# normalize the values\n",
+        "mean_df = df.mean()\n",
+        "std_df = df.std()\n",
+        "normalized_df = ((df-mean_df)/std_df)\n",
+        "display(normalized_df)"
       ]
     },
     {
@@ -908,18 +1662,44 @@
       },
       "source": [
         "### 2.2) Interpreting Clusters\n",
-        "Now let's cluster our data! Once again, play around with $k$ to see how the results change. "
+        "Now let's cluster our data! Once again, play around with $k$ to see how the results change."
       ]
     },
     {
       "cell_type": "code",
+      "execution_count": 8,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
         "id": "0IrIOjDlwJdn",
-        "outputId": "4a9c92e1-81e1-4b99-f694-f742fd8246c9"
+        "outputId": "cd0be54b-1cee-4452-e7f1-0a25e4b3d5e3"
       },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Countries in Cluster 0:\n",
+            "['Angola', 'Burundi', 'Cameroon', 'Congo [DRC]', 'Congo [Republic]', 'Ethiopia', 'Ghana', 'Kenya', 'Kiribati', 'Lesotho', 'Madagascar', 'Mali', 'Myanmar', 'Mozambique', 'Mauritania', 'Malawi', 'Namibia', 'Niger', 'Nigeria', 'Pakistan', 'Papua New Guinea', 'Rwanda', 'Sudan', 'Eswatini', 'Tajikistan', 'East Timor', 'Tanzania', 'Uganda', 'Yemen', 'Zambia', 'Zimbabwe']\n",
+            "31\n",
+            "Countries in Cluster 1:\n",
+            "['Albania', 'Armenia', 'Australia', 'Bulgaria', 'Bosnia and Herzegovina', 'Canada', 'China', 'Czech Republic', 'Georgia', 'Japan', 'Kazakhstan', 'South Korea', 'Moldova', 'Macedonia [FYROM]', 'Montenegro', 'Romania', 'Serbia', 'Trinidad and Tobago', 'Ukraine', 'United States']\n",
+            "20\n",
+            "Countries in Cluster 2:\n",
+            "['Argentina', 'Azerbaijan', 'Bangladesh', 'Belize', 'Bolivia', 'Brazil', 'Bhutan', 'Botswana', 'Chile', 'Colombia', 'Costa Rica', 'Dominican Republic', 'Algeria', 'Ecuador', 'Egypt', 'Fiji', 'Gabon', 'Guatemala', 'Guyana', 'Honduras', 'Indonesia', 'India', 'Iran', 'Jamaica', 'Jordan', 'Kyrgyzstan', 'Laos', 'Lebanon', 'Saint Lucia', 'Morocco', 'Mexico', 'Mongolia', 'Malaysia', 'Nicaragua', 'Nepal', 'Panama', 'Peru', 'Philippines', 'Paraguay', 'El Salvador', 'Syria', 'Thailand', 'Turkmenistan', 'Tonga', 'Tunisia', 'Turkey', 'Uzbekistan', 'Vietnam', 'Vanuatu', 'Samoa', 'South Africa']\n",
+            "51\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+            "  warnings.warn(\n"
+          ]
+        }
+      ],
       "source": [
         "# Clustering using K-means\n",
         "n_clusters = 3\n",
@@ -931,24 +1711,6 @@
         "  print(f'Countries in Cluster {i}:')\n",
         "  print(labels_df.index[labels_df['cluster']==i].tolist())\n",
         "  print(len(labels_df.index[labels_df['cluster']==i].tolist()))"
-      ],
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Countries in Cluster 0:\n",
-            "['Albania', 'Armenia', 'Australia', 'Bulgaria', 'Bosnia and Herzegovina', 'Canada', 'China', 'Czech Republic', 'Georgia', 'Japan', 'Kazakhstan', 'South Korea', 'Moldova', 'Macedonia [FYROM]', 'Montenegro', 'Romania', 'Serbia', 'Thailand', 'Trinidad and Tobago', 'Ukraine', 'United States']\n",
-            "21\n",
-            "Countries in Cluster 1:\n",
-            "['Angola', 'Burundi', 'Cameroon', 'Congo [DRC]', 'Congo [Republic]', 'Ethiopia', 'Ghana', 'Kenya', 'Kiribati', 'Laos', 'Lesotho', 'Madagascar', 'Mali', 'Mozambique', 'Mauritania', 'Malawi', 'Namibia', 'Niger', 'Nigeria', 'Pakistan', 'Papua New Guinea', 'Rwanda', 'Sudan', 'Eswatini', 'Tajikistan', 'East Timor', 'Tanzania', 'Uganda', 'Vanuatu', 'Yemen', 'Zambia', 'Zimbabwe']\n",
-            "32\n",
-            "Countries in Cluster 2:\n",
-            "['Argentina', 'Azerbaijan', 'Bangladesh', 'Belize', 'Bolivia', 'Brazil', 'Bhutan', 'Botswana', 'Chile', 'Colombia', 'Costa Rica', 'Dominican Republic', 'Algeria', 'Ecuador', 'Egypt', 'Fiji', 'Gabon', 'Guatemala', 'Guyana', 'Honduras', 'Indonesia', 'India', 'Iran', 'Jamaica', 'Jordan', 'Kyrgyzstan', 'Lebanon', 'Saint Lucia', 'Morocco', 'Mexico', 'Myanmar', 'Mongolia', 'Malaysia', 'Nicaragua', 'Nepal', 'Panama', 'Peru', 'Philippines', 'Paraguay', 'El Salvador', 'Syria', 'Turkmenistan', 'Tonga', 'Tunisia', 'Turkey', 'Uzbekistan', 'Vietnam', 'Samoa', 'South Africa']\n",
-            "49\n"
-          ]
-        }
       ]
     },
     {
@@ -968,105 +1730,105 @@
     },
     {
       "cell_type": "code",
+      "execution_count": 9,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
-          "height": 680
+          "height": 694
         },
         "id": "LV-U4hpZw7ei",
-        "outputId": "96ef5678-0263-4fbe-d565-a3a57e9f1de3"
+        "outputId": "fbc060f2-3c36-4637-838e-b88a94e71a6c"
       },
-      "source": [
-        "# Get centroids of each cluster.\n",
-        "for i in range(n_clusters):\n",
-        "  print(f'\\nCluster {i}:')\n",
-        "  # display non-normalized mean values\n",
-        "  mean_to_display = df[labels_df['cluster']==i].mean()\n",
-        "  display(mean_to_display)"
-      ],
-      "execution_count": null,
       "outputs": [
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
           "text": [
             "\n",
             "Cluster 0:\n"
           ]
         },
         {
-          "output_type": "display_data",
           "data": {
             "text/plain": [
-              "Count_Death_AsAFractionOfCount_Person                                          0.010107\n",
-              "Amount_Emissions_CarbonDioxide_PerCapita                                       7.478873\n",
-              "LifeExpectancy_Person                                                         77.071137\n",
-              "GiniIndex_EconomicActivity                                                    34.109524\n",
-              "Count_Product_MobileCellularSubscription_AsFractionOf_Count_Person             1.249365\n",
-              "Amount_EconomicActivity_GrossDomesticProduction_Nominal_PerCapita          17489.255561\n",
-              "FertilityRate_Person_Female                                                    1.614162\n",
-              "Count_Person_IsInternetUser_PerCapita                                         79.778089\n",
-              "GrowthRate_Count_Person                                                       -0.012263\n",
-              "Count_Person_Upto4Years_Overweight_AsFractionOf_Count_Person_Upto4Years        0.121714\n",
+              "LifeExpectancy_Person                                                        62.534161\n",
+              "GiniIndex_EconomicActivity                                                   41.051613\n",
+              "Amount_EconomicActivity_GrossDomesticProduction_Nominal_PerCapita          1607.089393\n",
+              "Count_Death_AsAFractionOfCount_Person                                         8.066000\n",
+              "Amount_Emissions_CarbonDioxide_PerCapita                                      0.486050\n",
+              "GrowthRate_Count_Person                                                       2.281569\n",
+              "Count_Person_Upto4Years_Overweight_AsFractionOf_Count_Person_Upto4Years       0.037613\n",
+              "Count_Product_MobileCellularSubscription_AsFractionOf_Count_Person            0.831259\n",
+              "FertilityRate_Person_Female                                                   4.126935\n",
+              "Count_Person_IsInternetUser_PerCapita                                        32.817589\n",
               "dtype: float64"
             ]
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
           "text": [
             "\n",
             "Cluster 1:\n"
           ]
         },
         {
-          "output_type": "display_data",
           "data": {
             "text/plain": [
-              "Count_Death_AsAFractionOfCount_Person                                         0.007492\n",
-              "Amount_Emissions_CarbonDioxide_PerCapita                                      0.592647\n",
-              "LifeExpectancy_Person                                                        64.067656\n",
-              "GiniIndex_EconomicActivity                                                   41.812500\n",
-              "Count_Product_MobileCellularSubscription_AsFractionOf_Count_Person            0.774612\n",
-              "Amount_EconomicActivity_GrossDomesticProduction_Nominal_PerCapita          1437.420128\n",
-              "FertilityRate_Person_Female                                                   4.243062\n",
-              "Count_Person_IsInternetUser_PerCapita                                        22.828761\n",
-              "GrowthRate_Count_Person                                                       2.393820\n",
-              "Count_Person_Upto4Years_Overweight_AsFractionOf_Count_Person_Upto4Years       0.044219\n",
+              "LifeExpectancy_Person                                                         75.427243\n",
+              "GiniIndex_EconomicActivity                                                    32.885000\n",
+              "Amount_EconomicActivity_GrossDomesticProduction_Nominal_PerCapita          21398.108282\n",
+              "Count_Death_AsAFractionOfCount_Person                                         13.079550\n",
+              "Amount_Emissions_CarbonDioxide_PerCapita                                       7.026586\n",
+              "GrowthRate_Count_Person                                                       -1.049861\n",
+              "Count_Person_Upto4Years_Overweight_AsFractionOf_Count_Person_Upto4Years        0.106300\n",
+              "Count_Product_MobileCellularSubscription_AsFractionOf_Count_Person             1.284317\n",
+              "FertilityRate_Person_Female                                                    1.620700\n",
+              "Count_Person_IsInternetUser_PerCapita                                         83.552764\n",
               "dtype: float64"
             ]
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
           "text": [
             "\n",
             "Cluster 2:\n"
           ]
         },
         {
-          "output_type": "display_data",
           "data": {
             "text/plain": [
-              "Count_Death_AsAFractionOfCount_Person                                         0.005998\n",
-              "Amount_Emissions_CarbonDioxide_PerCapita                                      2.863133\n",
-              "LifeExpectancy_Person                                                        73.581469\n",
-              "GiniIndex_EconomicActivity                                                   41.016327\n",
-              "Count_Product_MobileCellularSubscription_AsFractionOf_Count_Person            1.139772\n",
-              "Amount_EconomicActivity_GrossDomesticProduction_Nominal_PerCapita          5063.254570\n",
-              "FertilityRate_Person_Female                                                   2.403755\n",
-              "Count_Person_IsInternetUser_PerCapita                                        57.257828\n",
-              "GrowthRate_Count_Person                                                       1.218886\n",
-              "Count_Person_Upto4Years_Overweight_AsFractionOf_Count_Person_Upto4Years       0.082122\n",
+              "LifeExpectancy_Person                                                        71.339157\n",
+              "GiniIndex_EconomicActivity                                                   40.213725\n",
+              "Amount_EconomicActivity_GrossDomesticProduction_Nominal_PerCapita          6457.742674\n",
+              "Count_Death_AsAFractionOfCount_Person                                         7.267412\n",
+              "Amount_Emissions_CarbonDioxide_PerCapita                                      2.601929\n",
+              "GrowthRate_Count_Person                                                       1.062270\n",
+              "Count_Person_Upto4Years_Overweight_AsFractionOf_Count_Person_Upto4Years       0.079745\n",
+              "Count_Product_MobileCellularSubscription_AsFractionOf_Count_Person            1.139248\n",
+              "FertilityRate_Person_Female                                                   2.286627\n",
+              "Count_Person_IsInternetUser_PerCapita                                        71.932636\n",
               "dtype: float64"
             ]
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         }
+      ],
+      "source": [
+        "# Get centroids of each cluster.\n",
+        "for i in range(n_clusters):\n",
+        "  print(f'\\nCluster {i}:')\n",
+        "  # display non-normalized mean values\n",
+        "  mean_to_display = df[labels_df['cluster']==i].mean()\n",
+        "  display(mean_to_display)"
       ]
     },
     {
@@ -1081,14 +1843,27 @@
     },
     {
       "cell_type": "code",
+      "execution_count": 10,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 1000
         },
         "id": "D_BDDdvtyxlz",
-        "outputId": "16784bc6-a6dd-4a82-f829-20254eb9a66d"
+        "outputId": "b17f9d70-e194-4cc7-e715-ab4e8c58b170"
       },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 1170x827 with 2 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
       "source": [
         "# get normalized values\n",
         "normalized_means = []\n",
@@ -1116,19 +1891,6 @@
         "ax = sns.heatmap(normalized_means_df.to_numpy(), linewidth=0.5, xticklabels=normalized_means_df.columns, center=0)\n",
         "ax.set_ylabel(\"Cluster\")\n",
         "plt.show()"
-      ],
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "image/png": 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\n",
-            "text/plain": [
-              "<Figure size 842.4x595.44 with 2 Axes>"
-            ]
-          },
-          "metadata": {}
-        }
       ]
     },
     {
@@ -1154,38 +1916,27 @@
     },
     {
       "cell_type": "code",
+      "execution_count": 11,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
-          "height": 193
+          "height": 195
         },
         "id": "VWzVMbV6uY3s",
-        "outputId": "77a71113-79e4-40e7-9d39-dcc1ba3d6df0"
+        "outputId": "5d59bd99-33dc-41a5-ca0a-63ec25ef9d31"
       },
-      "source": [
-        "dcids_holdout = ['country/HTI','country/DEU', 'country/IRQ']\n",
-        "\n",
-        "# get values for each holdout\n",
-        "df_holdout = datacommons_pandas.build_multivariate_dataframe(dcids_holdout,stat_vars_to_query)\n",
-        "df_holdout = df_holdout.dropna()\n",
-        "\n",
-        "# swap index for names\n",
-        "df_holdout.insert(0, 'name', df_holdout.index.map(datacommons.get_property_values(df_holdout.index, 'name')).str[0])\n",
-        "df_holdout.set_index('name', drop=True, inplace=True)\n",
-        "\n",
-        "# show values\n",
-        "display(df_holdout)\n",
-        "\n",
-        "# normalized version for clustering later\n",
-        "normalized_df_holdout = ((df_holdout-mean_df)/std_df)"
-      ],
-      "execution_count": null,
       "outputs": [
         {
-          "output_type": "display_data",
           "data": {
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "summary": "{\n  \"name\": \"df_holdout\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Germany\",\n          \"Haiti\",\n          \"Iraq\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"LifeExpectancy_Person\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8.906733677677602,\n        \"min\": 63.192,\n        \"max\": 80.9009756097561,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          80.9009756097561,\n          63.192,\n          70.378\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"GiniIndex_EconomicActivity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6.161168720299746,\n        \"min\": 29.5,\n        \"max\": 41.1,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          31.7,\n          41.1,\n          29.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Amount_EconomicActivity_GrossDomesticProduction_Nominal_PerCapita\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 25993.267063019415,\n        \"min\": 1748.25713229469,\n        \"max\": 48717.9911402128,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          48717.9911402128,\n          1748.25713229469,\n          5937.19546600271\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Count_Death_AsAFractionOfCount_Person\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3.7744870291648027,\n        \"min\": 4.753,\n        \"max\": 12.3,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          12.3,\n          8.676,\n          4.753\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Amount_Emissions_CarbonDioxide_PerCapita\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3.4859554261447103,\n        \"min\": 0.283815024249564,\n        \"max\": 7.2552210281684,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          7.2552210281684,\n          0.283815024249564,\n          3.84217781974399\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"GrowthRate_Count_Person\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.7482973392282838,\n        \"min\": 0.720875455989295,\n        \"max\": 2.18691693437269,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.720875455989295,\n          1.19334195623455,\n          2.18691693437269\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Count_Person_Upto4Years_Overweight_AsFractionOf_Count_Person_Upto4Years\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.017214335111567138,\n        \"min\": 0.029,\n        \"max\": 0.061,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.029,\n          0.034,\n          0.061\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Count_Product_MobileCellularSubscription_AsFractionOf_Count_Person\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.30715649810963835,\n        \"min\": 0.639349717,\n        \"max\": 1.252251369,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1.252251369,\n          0.639349717,\n          0.9818424176\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"FertilityRate_Person_Female\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.9711621903678087,\n        \"min\": 1.58,\n        \"max\": 3.496,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1.58,\n          2.814,\n          3.496\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Count_Person_IsInternetUser_PerCapita\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 27.459571208798042,\n        \"min\": 38.94501834,\n        \"max\": 91.62984383,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          91.62984383,\n          38.94501834,\n          78.71561135\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
+              "type": "dataframe",
+              "variable_name": "df_holdout"
+            },
             "text/html": [
-              "<div>\n",
+              "\n",
+              "  <div id=\"df-756b1663-67d1-4e49-9531-b32695184023\" class=\"colab-df-container\">\n",
+              "    <div>\n",
               "<style scoped>\n",
               "    .dataframe tbody tr th:only-of-type {\n",
               "        vertical-align: middle;\n",
@@ -1204,15 +1955,15 @@
               "    <tr style=\"text-align: right;\">\n",
               "      <th></th>\n",
               "      <th>LifeExpectancy_Person</th>\n",
-              "      <th>Count_Person_IsInternetUser_PerCapita</th>\n",
               "      <th>GiniIndex_EconomicActivity</th>\n",
               "      <th>Amount_EconomicActivity_GrossDomesticProduction_Nominal_PerCapita</th>\n",
-              "      <th>FertilityRate_Person_Female</th>\n",
               "      <th>Count_Death_AsAFractionOfCount_Person</th>\n",
               "      <th>Amount_Emissions_CarbonDioxide_PerCapita</th>\n",
               "      <th>GrowthRate_Count_Person</th>\n",
               "      <th>Count_Person_Upto4Years_Overweight_AsFractionOf_Count_Person_Upto4Years</th>\n",
               "      <th>Count_Product_MobileCellularSubscription_AsFractionOf_Count_Person</th>\n",
+              "      <th>FertilityRate_Person_Female</th>\n",
+              "      <th>Count_Person_IsInternetUser_PerCapita</th>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>name</th>\n",
@@ -1231,59 +1982,380 @@
               "  <tbody>\n",
               "    <tr>\n",
               "      <th>Germany</th>\n",
-              "      <td>80.941463</td>\n",
-              "      <td>89.812941</td>\n",
-              "      <td>31.9</td>\n",
-              "      <td>45723.644104</td>\n",
-              "      <td>1.540</td>\n",
-              "      <td>0.011500</td>\n",
-              "      <td>8.558390</td>\n",
-              "      <td>0.177430</td>\n",
-              "      <td>0.032</td>\n",
-              "      <td>1.283570</td>\n",
+              "      <td>80.900976</td>\n",
+              "      <td>31.7</td>\n",
+              "      <td>48717.991140</td>\n",
+              "      <td>12.300</td>\n",
+              "      <td>7.255221</td>\n",
+              "      <td>0.720875</td>\n",
+              "      <td>0.029</td>\n",
+              "      <td>1.252251</td>\n",
+              "      <td>1.580</td>\n",
+              "      <td>91.629844</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>Haiti</th>\n",
-              "      <td>64.001000</td>\n",
-              "      <td>32.500000</td>\n",
+              "      <td>63.192000</td>\n",
               "      <td>41.1</td>\n",
-              "      <td>1176.755820</td>\n",
-              "      <td>2.887</td>\n",
-              "      <td>0.008534</td>\n",
-              "      <td>0.299375</td>\n",
-              "      <td>1.230549</td>\n",
+              "      <td>1748.257132</td>\n",
+              "      <td>8.676</td>\n",
+              "      <td>0.283815</td>\n",
+              "      <td>1.193342</td>\n",
               "      <td>0.034</td>\n",
-              "      <td>0.575289</td>\n",
+              "      <td>0.639350</td>\n",
+              "      <td>2.814</td>\n",
+              "      <td>38.945018</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>Iraq</th>\n",
-              "      <td>70.604000</td>\n",
-              "      <td>49.359999</td>\n",
+              "      <td>70.378000</td>\n",
               "      <td>29.5</td>\n",
-              "      <td>4157.484495</td>\n",
-              "      <td>3.597</td>\n",
-              "      <td>0.004776</td>\n",
-              "      <td>4.895195</td>\n",
-              "      <td>2.295304</td>\n",
+              "      <td>5937.195466</td>\n",
+              "      <td>4.753</td>\n",
+              "      <td>3.842178</td>\n",
+              "      <td>2.186917</td>\n",
               "      <td>0.061</td>\n",
-              "      <td>0.950402</td>\n",
+              "      <td>0.981842</td>\n",
+              "      <td>3.496</td>\n",
+              "      <td>78.715611</td>\n",
               "    </tr>\n",
               "  </tbody>\n",
               "</table>\n",
-              "</div>"
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-756b1663-67d1-4e49-9531-b32695184023')\"\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-756b1663-67d1-4e49-9531-b32695184023 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-756b1663-67d1-4e49-9531-b32695184023');\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=\"df-1e4097ca-644a-4773-8902-02fa870b13e9\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-1e4097ca-644a-4773-8902-02fa870b13e9')\"\n",
+              "            title=\"Suggest charts\"\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",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-1e4097ca-644a-4773-8902-02fa870b13e9 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "  <div id=\"id_596bcd70-9cb1-4afb-aa95-20b5993d2482\">\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_holdout')\"\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_596bcd70-9cb1-4afb-aa95-20b5993d2482 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_holdout');\n",
+              "      }\n",
+              "      })();\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
             ],
             "text/plain": [
-              "         LifeExpectancy_Person  ...  Count_Product_MobileCellularSubscription_AsFractionOf_Count_Person\n",
-              "name                            ...                                                                    \n",
-              "Germany              80.941463  ...                                           1.283570                 \n",
-              "Haiti                64.001000  ...                                           0.575289                 \n",
-              "Iraq                 70.604000  ...                                           0.950402                 \n",
+              "         LifeExpectancy_Person  GiniIndex_EconomicActivity  \\\n",
+              "name                                                         \n",
+              "Germany              80.900976                        31.7   \n",
+              "Haiti                63.192000                        41.1   \n",
+              "Iraq                 70.378000                        29.5   \n",
+              "\n",
+              "         Amount_EconomicActivity_GrossDomesticProduction_Nominal_PerCapita  \\\n",
+              "name                                                                         \n",
+              "Germany                                       48717.991140                   \n",
+              "Haiti                                          1748.257132                   \n",
+              "Iraq                                           5937.195466                   \n",
+              "\n",
+              "         Count_Death_AsAFractionOfCount_Person  \\\n",
+              "name                                             \n",
+              "Germany                                 12.300   \n",
+              "Haiti                                    8.676   \n",
+              "Iraq                                     4.753   \n",
+              "\n",
+              "         Amount_Emissions_CarbonDioxide_PerCapita  GrowthRate_Count_Person  \\\n",
+              "name                                                                         \n",
+              "Germany                                  7.255221                 0.720875   \n",
+              "Haiti                                    0.283815                 1.193342   \n",
+              "Iraq                                     3.842178                 2.186917   \n",
               "\n",
-              "[3 rows x 10 columns]"
+              "         Count_Person_Upto4Years_Overweight_AsFractionOf_Count_Person_Upto4Years  \\\n",
+              "name                                                                               \n",
+              "Germany                                              0.029                         \n",
+              "Haiti                                                0.034                         \n",
+              "Iraq                                                 0.061                         \n",
+              "\n",
+              "         Count_Product_MobileCellularSubscription_AsFractionOf_Count_Person  \\\n",
+              "name                                                                          \n",
+              "Germany                                           1.252251                    \n",
+              "Haiti                                             0.639350                    \n",
+              "Iraq                                              0.981842                    \n",
+              "\n",
+              "         FertilityRate_Person_Female  Count_Person_IsInternetUser_PerCapita  \n",
+              "name                                                                         \n",
+              "Germany                        1.580                              91.629844  \n",
+              "Haiti                          2.814                              38.945018  \n",
+              "Iraq                           3.496                              78.715611  "
             ]
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         }
+      ],
+      "source": [
+        "dcids_holdout = ['country/HTI','country/DEU', 'country/IRQ']\n",
+        "\n",
+        "# get values for each holdout\n",
+        "df_holdout = datacommons_pandas.build_multivariate_dataframe(dcids_holdout,stat_vars_to_query)\n",
+        "\n",
+        "# ensure columns are in exactly the same order as those used previously for clustering\n",
+        "df_holdout = df_holdout[normalized_df.columns]\n",
+        "\n",
+        "# drop rows with missing values\n",
+        "df_holdout = df_holdout.dropna()\n",
+        "\n",
+        "# swap index for names\n",
+        "df_holdout.insert(0, 'name', df_holdout.index.map(datacommons.get_property_values(df_holdout.index, 'name')).str[0])\n",
+        "df_holdout.set_index('name', drop=True, inplace=True)\n",
+        "\n",
+        "# show values\n",
+        "display(df_holdout)\n",
+        "\n",
+        "# normalized version for clustering later\n",
+        "normalized_df_holdout = ((df_holdout-mean_df)/std_df)"
       ]
     },
     {
@@ -1297,13 +2369,25 @@
     },
     {
       "cell_type": "code",
+      "execution_count": 12,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
         "id": "s2pTEsnO2_ky",
-        "outputId": "8437b4b5-168e-4ef1-9f1e-67a61abe2d45"
+        "outputId": "d498d762-6add-4817-aa43-d20f7626ad3d"
       },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Germany belongs to cluster 1\n",
+            "Haiti belongs to cluster 0\n",
+            "Iraq belongs to cluster 2\n"
+          ]
+        }
+      ],
       "source": [
         "# find which cluster centroid is closest\n",
         "for country in df_holdout.index:\n",
@@ -1313,18 +2397,6 @@
         "  distance = np.linalg.norm(difference,axis=1)\n",
         "  cluster_membership = np.argmin(distance, axis=0)\n",
         "  print(f\"{country} belongs to cluster {cluster_membership}\")"
-      ],
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Germany belongs to cluster 0\n",
-            "Haiti belongs to cluster 2\n",
-            "Iraq belongs to cluster 2\n"
-          ]
-        }
       ]
     },
     {
@@ -1338,5 +2410,20 @@
         "**Q5: Now run this section again for a larger value of K. Are you able to find a setting with more nuance? What traits do the clusters highlight?**"
       ]
     }
-  ]
-}
\ No newline at end of file
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "toc_visible": true
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}