python/examples/basic/Merging_Profiles.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">### 🚩 *Create a free WhyLabs account to get more value out of whylogs!*<br> \n",
    ">*Did you know you can store, visualize, and monitor whylogs profiles with the [WhyLabs Observability Platform](https://whylabs.ai/whylogs-free-signup?utm_source=whylogs-Github&utm_medium=whylogs-example&utm_campaign=Merging_Profiles)? Sign up for a [free WhyLabs account](https://whylabs.ai/whylogs-free-signup?utm_source=whylogs-Github&utm_medium=whylogs-example&utm_campaign=Merging_Profiles) to leverage the power of whylogs and WhyLabs together!*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "IJ2tqS2oh8wp"
   },
   "source": [
    "# Merging Profiles"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/whylabs/whylogs/blob/mainline/python/examples/basic/Merging_Profiles.ipynb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "TTP91R40h8wr"
   },
   "source": [
    "Sometimes we may want to profile a dataset in chunks. For example, we may have our dataset distributed across multiple files or nodes, or perhaps our dataset is too large to fit in memory. Maybe we already profiled our dataset for several different date ranges and we want to see a holistic view of our data across the entire range.\n",
    "\n",
    "In any case, merging profiles is a solution!\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rZoJE6nYh8wr"
   },
   "source": [
    "## Installing whylogs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ubeZjbMzh8ws"
   },
   "source": [
    "whylogs is made available as a Python package. You can get the latest version from PyPI with `pip install whylogs`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "LgAFe39bh8ws"
   },
   "outputs": [],
   "source": [
    "# Note: you may need to restart the kernel to use updated packages.\n",
    "%pip install whylogs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "4NnapN6Mh8wt"
   },
   "source": [
    "## Loading a Pandas DataFrame"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "zAW_RioVh8wt"
   },
   "source": [
    "Before profiling data, lets create a Pandas DataFrame from a public dataset. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 381
    },
    "id": "bI4RnpBoh8wt",
    "outputId": "db4e9122-434f-4ef2-f6d2-25650333d135"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "row count: 945\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "  <div id=\"df-5ec231cd-3765-4aaa-857c-0434535980f2\">\n",
       "    <div class=\"colab-df-container\">\n",
       "      <div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Transaction ID</th>\n",
       "      <th>Customer ID</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>Item Price</th>\n",
       "      <th>Total Tax</th>\n",
       "      <th>Total Amount</th>\n",
       "      <th>Store Type</th>\n",
       "      <th>Product Category</th>\n",
       "      <th>Product Subcategory</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Transaction Type</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>682</th>\n",
       "      <td>T74278458640</td>\n",
       "      <td>C267835</td>\n",
       "      <td>2</td>\n",
       "      <td>63.2</td>\n",
       "      <td>13.2720</td>\n",
       "      <td>139.6720</td>\n",
       "      <td>TeleShop</td>\n",
       "      <td>Books</td>\n",
       "      <td>DIY</td>\n",
       "      <td>M</td>\n",
       "      <td>Purchase</td>\n",
       "      <td>33.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>256</th>\n",
       "      <td>T54377774372</td>\n",
       "      <td>C270496</td>\n",
       "      <td>5</td>\n",
       "      <td>75.5</td>\n",
       "      <td>39.6375</td>\n",
       "      <td>417.1375</td>\n",
       "      <td>MBR</td>\n",
       "      <td>Electronics</td>\n",
       "      <td>Audio and video</td>\n",
       "      <td>M</td>\n",
       "      <td>Purchase</td>\n",
       "      <td>23.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>T64030190529</td>\n",
       "      <td>C269524</td>\n",
       "      <td>5</td>\n",
       "      <td>52.5</td>\n",
       "      <td>27.5625</td>\n",
       "      <td>290.0625</td>\n",
       "      <td>e-Shop</td>\n",
       "      <td>Bags</td>\n",
       "      <td>Mens</td>\n",
       "      <td>F</td>\n",
       "      <td>Purchase</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>762</th>\n",
       "      <td>T18970114223</td>\n",
       "      <td>C272730</td>\n",
       "      <td>2</td>\n",
       "      <td>80.6</td>\n",
       "      <td>16.9260</td>\n",
       "      <td>178.1260</td>\n",
       "      <td>e-Shop</td>\n",
       "      <td>Home and kitchen</td>\n",
       "      <td>Kitchen</td>\n",
       "      <td>M</td>\n",
       "      <td>Purchase</td>\n",
       "      <td>39.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>T94404065446</td>\n",
       "      <td>C271648</td>\n",
       "      <td>5</td>\n",
       "      <td>48.0</td>\n",
       "      <td>25.2000</td>\n",
       "      <td>265.2000</td>\n",
       "      <td>e-Shop</td>\n",
       "      <td>Clothing</td>\n",
       "      <td>Kids</td>\n",
       "      <td>F</td>\n",
       "      <td>Purchase</td>\n",
       "      <td>34.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>T30540748600</td>\n",
       "      <td>C269603</td>\n",
       "      <td>1</td>\n",
       "      <td>127.4</td>\n",
       "      <td>13.3770</td>\n",
       "      <td>140.7770</td>\n",
       "      <td>TeleShop</td>\n",
       "      <td>Footwear</td>\n",
       "      <td>Women</td>\n",
       "      <td>M</td>\n",
       "      <td>Purchase</td>\n",
       "      <td>41.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>161</th>\n",
       "      <td>T78998671169</td>\n",
       "      <td>C270907</td>\n",
       "      <td>1</td>\n",
       "      <td>104.9</td>\n",
       "      <td>11.0145</td>\n",
       "      <td>115.9145</td>\n",
       "      <td>MBR</td>\n",
       "      <td>Books</td>\n",
       "      <td>DIY</td>\n",
       "      <td>F</td>\n",
       "      <td>Purchase</td>\n",
       "      <td>41.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>574</th>\n",
       "      <td>T19424023275</td>\n",
       "      <td>C270462</td>\n",
       "      <td>2</td>\n",
       "      <td>127.5</td>\n",
       "      <td>26.7750</td>\n",
       "      <td>281.7750</td>\n",
       "      <td>TeleShop</td>\n",
       "      <td>Electronics</td>\n",
       "      <td>Cameras</td>\n",
       "      <td>F</td>\n",
       "      <td>Purchase</td>\n",
       "      <td>38.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>583</th>\n",
       "      <td>T7986658313</td>\n",
       "      <td>C269047</td>\n",
       "      <td>3</td>\n",
       "      <td>25.9</td>\n",
       "      <td>8.1585</td>\n",
       "      <td>85.8585</td>\n",
       "      <td>e-Shop</td>\n",
       "      <td>Clothing</td>\n",
       "      <td>Women</td>\n",
       "      <td>M</td>\n",
       "      <td>Purchase</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>805</th>\n",
       "      <td>T36786634925</td>\n",
       "      <td>C267437</td>\n",
       "      <td>3</td>\n",
       "      <td>50.0</td>\n",
       "      <td>15.7500</td>\n",
       "      <td>165.7500</td>\n",
       "      <td>TeleShop</td>\n",
       "      <td>Books</td>\n",
       "      <td>Children</td>\n",
       "      <td>M</td>\n",
       "      <td>Purchase</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5ec231cd-3765-4aaa-857c-0434535980f2')\"\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 0 24 24\"\n",
       "       width=\"24px\">\n",
       "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
       "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
       "  </svg>\n",
       "      </button>\n",
       "      \n",
       "  <style>\n",
       "    .colab-df-container {\n",
       "      display:flex;\n",
       "      flex-wrap:wrap;\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",
       "    [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-5ec231cd-3765-4aaa-857c-0434535980f2 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-5ec231cd-3765-4aaa-857c-0434535980f2');\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",
       "  </div>\n",
       "  "
      ],
      "text/plain": [
       "    Transaction ID Customer ID  Quantity  Item Price  Total Tax  Total Amount  \\\n",
       "682   T74278458640     C267835         2        63.2    13.2720      139.6720   \n",
       "256   T54377774372     C270496         5        75.5    39.6375      417.1375   \n",
       "67    T64030190529     C269524         5        52.5    27.5625      290.0625   \n",
       "762   T18970114223     C272730         2        80.6    16.9260      178.1260   \n",
       "94    T94404065446     C271648         5        48.0    25.2000      265.2000   \n",
       "197   T30540748600     C269603         1       127.4    13.3770      140.7770   \n",
       "161   T78998671169     C270907         1       104.9    11.0145      115.9145   \n",
       "574   T19424023275     C270462         2       127.5    26.7750      281.7750   \n",
       "583    T7986658313     C269047         3        25.9     8.1585       85.8585   \n",
       "805   T36786634925     C267437         3        50.0    15.7500      165.7500   \n",
       "\n",
       "    Store Type  Product Category Product Subcategory Gender Transaction Type  \\\n",
       "682   TeleShop             Books                 DIY      M         Purchase   \n",
       "256        MBR       Electronics     Audio and video      M         Purchase   \n",
       "67      e-Shop              Bags                Mens      F         Purchase   \n",
       "762     e-Shop  Home and kitchen             Kitchen      M         Purchase   \n",
       "94      e-Shop          Clothing                Kids      F         Purchase   \n",
       "197   TeleShop          Footwear               Women      M         Purchase   \n",
       "161        MBR             Books                 DIY      F         Purchase   \n",
       "574   TeleShop       Electronics             Cameras      F         Purchase   \n",
       "583     e-Shop          Clothing               Women      M         Purchase   \n",
       "805   TeleShop             Books            Children      M         Purchase   \n",
       "\n",
       "      Age  \n",
       "682  33.0  \n",
       "256  23.0  \n",
       "67   24.0  \n",
       "762  39.0  \n",
       "94   34.0  \n",
       "197  41.0  \n",
       "161  41.0  \n",
       "574  38.0  \n",
       "583  25.0  \n",
       "805  35.0  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df_full= pd.read_csv(\"https://whylabs-public.s3.us-west-2.amazonaws.com/datasets/tour/current.csv\")\n",
    "\n",
    "print('row count: {}'.format(df_full.shape[0]))\n",
    "df_full.sample(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6l8P81Q4oDcG"
   },
   "source": [
    "This dataset contains 945 rows and contains a mix of numeric and categorical features. Lets split this DataFrame into 3 chunks of different sizes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vCvnKAIbomkC"
   },
   "source": [
    "## Splitting the DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "tYIlfMjmokvx",
    "outputId": "758d2734-b4b2-47de-b541-cea34e6be96c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Row Counts:\n",
      "Subset 1: 100\n",
      "Subset 2: 300\n",
      "Subset 3: 545\n"
     ]
    }
   ],
   "source": [
    "df_subset1= df_full[0:100]\n",
    "df_subset2= df_full[100:400]\n",
    "df_subset3= df_full[400:]\n",
    "\n",
    "print('Row Counts:')\n",
    "print('Subset 1: {}'.format(df_subset1.shape[0]))\n",
    "print('Subset 2: {}'.format(df_subset2.shape[0]))\n",
    "print('Subset 3: {}'.format(df_subset3.shape[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "S7g4GV4OpLPz"
   },
   "source": [
    "## Profiling a Single Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "X9AZ9QwYpN9R"
   },
   "source": [
    "Lets profile the first subset. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "-LPBoK0vpTIj"
   },
   "outputs": [],
   "source": [
    "import whylogs as why\n",
    "\n",
    "results = why.log(df_subset1)\n",
    "profile = results.profile()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "iO97sPGmqBIR"
   },
   "source": [
    "The code above generates a *ProfileResultSet* instance and assigns it to the **results** variable. We then call the **profile** method on this object to generate a *DatasetProfile* instance which we assign to the **profile** variable. \n",
    "\n",
    "We can inspect our profile by generating a pandas DataFrame from it. Lets view the first few rows. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 383
    },
    "id": "kphCdAQmqHAY",
    "outputId": "04f837a2-39f8-423d-cdcc-741066553e6d"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "  <div id=\"df-17744163-ef87-4587-955f-862f70699167\">\n",
       "    <div class=\"colab-df-container\">\n",
       "      <div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>counts/n</th>\n",
       "      <th>counts/null</th>\n",
       "      <th>types/integral</th>\n",
       "      <th>types/fractional</th>\n",
       "      <th>types/boolean</th>\n",
       "      <th>types/string</th>\n",
       "      <th>types/object</th>\n",
       "      <th>cardinality/est</th>\n",
       "      <th>cardinality/upper_1</th>\n",
       "      <th>cardinality/lower_1</th>\n",
       "      <th>...</th>\n",
       "      <th>distribution/q_05</th>\n",
       "      <th>distribution/q_10</th>\n",
       "      <th>distribution/q_25</th>\n",
       "      <th>distribution/median</th>\n",
       "      <th>distribution/q_75</th>\n",
       "      <th>distribution/q_90</th>\n",
       "      <th>distribution/q_95</th>\n",
       "      <th>distribution/q_99</th>\n",
       "      <th>ints/max</th>\n",
       "      <th>ints/min</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>column</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Gender</th>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000100</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total Amount</th>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>99.000024</td>\n",
       "      <td>99.004967</td>\n",
       "      <td>99.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-153.816</td>\n",
       "      <td>8.619</td>\n",
       "      <td>66.521</td>\n",
       "      <td>216.359</td>\n",
       "      <td>321.555</td>\n",
       "      <td>580.788</td>\n",
       "      <td>642.5575</td>\n",
       "      <td>795.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Customer ID</th>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
       "      <td>98.000024</td>\n",
       "      <td>98.004917</td>\n",
       "      <td>98.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Item Price</th>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>97.000023</td>\n",
       "      <td>97.004866</td>\n",
       "      <td>97.0</td>\n",
       "      <td>...</td>\n",
       "      <td>10.000</td>\n",
       "      <td>25.700</td>\n",
       "      <td>40.700</td>\n",
       "      <td>76.800</td>\n",
       "      <td>111.100</td>\n",
       "      <td>135.200</td>\n",
       "      <td>139.8000</td>\n",
       "      <td>148.9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Transaction ID</th>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
       "      <td>99.000024</td>\n",
       "      <td>99.004967</td>\n",
       "      <td>99.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 28 columns</p>\n",
       "</div>\n",
       "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-17744163-ef87-4587-955f-862f70699167')\"\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 0 24 24\"\n",
       "       width=\"24px\">\n",
       "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
       "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
       "  </svg>\n",
       "      </button>\n",
       "      \n",
       "  <style>\n",
       "    .colab-df-container {\n",
       "      display:flex;\n",
       "      flex-wrap:wrap;\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",
       "    [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-17744163-ef87-4587-955f-862f70699167 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-17744163-ef87-4587-955f-862f70699167');\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",
       "  </div>\n",
       "  "
      ],
      "text/plain": [
       "                counts/n  counts/null  types/integral  types/fractional  \\\n",
       "column                                                                    \n",
       "Gender               100            0               0                 0   \n",
       "Total Amount         100            0               0               100   \n",
       "Customer ID          100            0               0                 0   \n",
       "Item Price           100            0               0               100   \n",
       "Transaction ID       100            0               0                 0   \n",
       "\n",
       "                types/boolean  types/string  types/object  cardinality/est  \\\n",
       "column                                                                       \n",
       "Gender                      0           100             0         2.000000   \n",
       "Total Amount                0             0             0        99.000024   \n",
       "Customer ID                 0           100             0        98.000024   \n",
       "Item Price                  0             0             0        97.000023   \n",
       "Transaction ID              0           100             0        99.000024   \n",
       "\n",
       "                cardinality/upper_1  cardinality/lower_1  ...  \\\n",
       "column                                                    ...   \n",
       "Gender                     2.000100                  2.0  ...   \n",
       "Total Amount              99.004967                 99.0  ...   \n",
       "Customer ID               98.004917                 98.0  ...   \n",
       "Item Price                97.004866                 97.0  ...   \n",
       "Transaction ID            99.004967                 99.0  ...   \n",
       "\n",
       "               distribution/q_05 distribution/q_10  distribution/q_25  \\\n",
       "column                                                                  \n",
       "Gender                       NaN               NaN                NaN   \n",
       "Total Amount            -153.816             8.619             66.521   \n",
       "Customer ID                  NaN               NaN                NaN   \n",
       "Item Price                10.000            25.700             40.700   \n",
       "Transaction ID               NaN               NaN                NaN   \n",
       "\n",
       "                distribution/median  distribution/q_75  distribution/q_90  \\\n",
       "column                                                                      \n",
       "Gender                          NaN                NaN                NaN   \n",
       "Total Amount                216.359            321.555            580.788   \n",
       "Customer ID                     NaN                NaN                NaN   \n",
       "Item Price                   76.800            111.100            135.200   \n",
       "Transaction ID                  NaN                NaN                NaN   \n",
       "\n",
       "                distribution/q_95  distribution/q_99  ints/max  ints/min  \n",
       "column                                                                    \n",
       "Gender                        NaN                NaN       NaN       NaN  \n",
       "Total Amount             642.5575              795.6       NaN       NaN  \n",
       "Customer ID                   NaN                NaN       NaN       NaN  \n",
       "Item Price               139.8000              148.9       NaN       NaN  \n",
       "Transaction ID                NaN                NaN       NaN       NaN  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subset1_profile_df = profile.view().to_pandas()\n",
    "subset1_profile_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "FTBfgUxYqYJ0"
   },
   "source": [
    "From the **counts/n** column, we can see that our subset of data contained 100 rows, as expected. Before we start merging new profiles, lets grab the mean of the \"Item Price\" column for another point of reference. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "id": "XQUZYX9rq39p",
    "outputId": "decf807d-1644-415e-b2ca-7e0ea09a09c4"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "type": "string"
      },
      "text/plain": [
       "'Mean Item Price for Subset 1: 74.189'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"Mean Item Price for Subset 1: {}\".format(subset1_profile_df['distribution/mean'].loc['Item Price'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "IaKTy7I4rZBH"
   },
   "source": [
    "## Merging Profiles"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "LnZxOBYotJAT"
   },
   "source": [
    "We can call the track method on our profile to profile a new dataset and merge this with our existing profile in one step. This can be done successively for multiple subsets of data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "o-S-Eum9sM3l"
   },
   "outputs": [],
   "source": [
    "profile.track(df_subset2)\n",
    "profile.track(df_subset3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "QHjBSPhBtlnY"
   },
   "source": [
    "Lets now inspect the merged profile as a Pandas DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 383
    },
    "id": "dyFkZ8vdstZ_",
    "outputId": "e7bc0992-7129-47a7-c01b-6e4a6a3a58df"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "  <div id=\"df-07259287-e766-47b0-8dfa-e0f75d746d32\">\n",
       "    <div class=\"colab-df-container\">\n",
       "      <div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>counts/n</th>\n",
       "      <th>counts/null</th>\n",
       "      <th>types/integral</th>\n",
       "      <th>types/fractional</th>\n",
       "      <th>types/boolean</th>\n",
       "      <th>types/string</th>\n",
       "      <th>types/object</th>\n",
       "      <th>cardinality/est</th>\n",
       "      <th>cardinality/upper_1</th>\n",
       "      <th>cardinality/lower_1</th>\n",
       "      <th>...</th>\n",
       "      <th>distribution/q_05</th>\n",
       "      <th>distribution/q_10</th>\n",
       "      <th>distribution/q_25</th>\n",
       "      <th>distribution/median</th>\n",
       "      <th>distribution/q_75</th>\n",
       "      <th>distribution/q_90</th>\n",
       "      <th>distribution/q_95</th>\n",
       "      <th>distribution/q_99</th>\n",
       "      <th>ints/max</th>\n",
       "      <th>ints/min</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>column</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Gender</th>\n",
       "      <td>945</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>945</td>\n",
       "      <td>0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000100</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total Amount</th>\n",
       "      <td>945</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>945</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>844.069184</td>\n",
       "      <td>855.117588</td>\n",
       "      <td>833.289540</td>\n",
       "      <td>...</td>\n",
       "      <td>-233.376</td>\n",
       "      <td>14.365</td>\n",
       "      <td>79.0075</td>\n",
       "      <td>179.452</td>\n",
       "      <td>356.915</td>\n",
       "      <td>580.788</td>\n",
       "      <td>654.16</td>\n",
       "      <td>804.44</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Customer ID</th>\n",
       "      <td>945</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>945</td>\n",
       "      <td>0</td>\n",
       "      <td>869.683985</td>\n",
       "      <td>881.067672</td>\n",
       "      <td>858.577213</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Item Price</th>\n",
       "      <td>945</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>945</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>705.028228</td>\n",
       "      <td>714.256661</td>\n",
       "      <td>696.024282</td>\n",
       "      <td>...</td>\n",
       "      <td>13.800</td>\n",
       "      <td>22.300</td>\n",
       "      <td>45.0000</td>\n",
       "      <td>80.600</td>\n",
       "      <td>116.600</td>\n",
       "      <td>138.200</td>\n",
       "      <td>145.10</td>\n",
       "      <td>149.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Transaction ID</th>\n",
       "      <td>945</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>945</td>\n",
       "      <td>0</td>\n",
       "      <td>935.275741</td>\n",
       "      <td>947.517988</td>\n",
       "      <td>923.331294</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 28 columns</p>\n",
       "</div>\n",
       "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-07259287-e766-47b0-8dfa-e0f75d746d32')\"\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 0 24 24\"\n",
       "       width=\"24px\">\n",
       "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
       "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
       "  </svg>\n",
       "      </button>\n",
       "      \n",
       "  <style>\n",
       "    .colab-df-container {\n",
       "      display:flex;\n",
       "      flex-wrap:wrap;\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",
       "    [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-07259287-e766-47b0-8dfa-e0f75d746d32 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-07259287-e766-47b0-8dfa-e0f75d746d32');\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",
       "  </div>\n",
       "  "
      ],
      "text/plain": [
       "                counts/n  counts/null  types/integral  types/fractional  \\\n",
       "column                                                                    \n",
       "Gender               945            0               0                 0   \n",
       "Total Amount         945            0               0               945   \n",
       "Customer ID          945            0               0                 0   \n",
       "Item Price           945            0               0               945   \n",
       "Transaction ID       945            0               0                 0   \n",
       "\n",
       "                types/boolean  types/string  types/object  cardinality/est  \\\n",
       "column                                                                       \n",
       "Gender                      0           945             0         2.000000   \n",
       "Total Amount                0             0             0       844.069184   \n",
       "Customer ID                 0           945             0       869.683985   \n",
       "Item Price                  0             0             0       705.028228   \n",
       "Transaction ID              0           945             0       935.275741   \n",
       "\n",
       "                cardinality/upper_1  cardinality/lower_1  ...  \\\n",
       "column                                                    ...   \n",
       "Gender                     2.000100             2.000000  ...   \n",
       "Total Amount             855.117588           833.289540  ...   \n",
       "Customer ID              881.067672           858.577213  ...   \n",
       "Item Price               714.256661           696.024282  ...   \n",
       "Transaction ID           947.517988           923.331294  ...   \n",
       "\n",
       "               distribution/q_05 distribution/q_10  distribution/q_25  \\\n",
       "column                                                                  \n",
       "Gender                       NaN               NaN                NaN   \n",
       "Total Amount            -233.376            14.365            79.0075   \n",
       "Customer ID                  NaN               NaN                NaN   \n",
       "Item Price                13.800            22.300            45.0000   \n",
       "Transaction ID               NaN               NaN                NaN   \n",
       "\n",
       "                distribution/median  distribution/q_75  distribution/q_90  \\\n",
       "column                                                                      \n",
       "Gender                          NaN                NaN                NaN   \n",
       "Total Amount                179.452            356.915            580.788   \n",
       "Customer ID                     NaN                NaN                NaN   \n",
       "Item Price                   80.600            116.600            138.200   \n",
       "Transaction ID                  NaN                NaN                NaN   \n",
       "\n",
       "                distribution/q_95  distribution/q_99  ints/max  ints/min  \n",
       "column                                                                    \n",
       "Gender                        NaN                NaN       NaN       NaN  \n",
       "Total Amount               654.16             804.44       NaN       NaN  \n",
       "Customer ID                   NaN                NaN       NaN       NaN  \n",
       "Item Price                 145.10             149.00       NaN       NaN  \n",
       "Transaction ID                NaN                NaN       NaN       NaN  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "full_profile_df = profile.view().to_pandas()\n",
    "full_profile_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rrOONmeztuuv"
   },
   "source": [
    "We now see that each column has a count of 945 which we expect. Lets revisit the mean of the Items Price column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "id": "7B_0YW4iuBX0",
    "outputId": "081d136c-a4e5-41fe-af44-d7c87fc8beae"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "type": "string"
      },
      "text/plain": [
       "'Mean Item Price from merged profile: 79.84814814814818'"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"Mean Item Price from merged profile: {}\".format(full_profile_df['distribution/mean'].loc['Item Price'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "4VgGQiyGuNtU"
   },
   "source": [
    "Lets compare this with the mean we get using the the **mean** method from Pandas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "3UWpqHP-uP98",
    "outputId": "5b2b6bee-9026-4029-97c7-d01b10e041f3"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "79.84814814814808"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_full['Item Price'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "amhK30yeuv_6"
   },
   "source": [
    "Its nearly an exact match! Note that in this example, we profiled 3 datasets of unequal sizes independently and merged together 3 profiles. This merged profile captured telemetry describing our entire dataset. \n",
    "\n",
    "This property of **mergeability** makes whylogs particularly powerful. It allows us to profile datasets which live in distributed pipeline even if our data is never together in one place at any time. \n",
    "\n",
    "Mergeability also makes it a trivial matter to roll up from hourly to daily, weekly, or monthly level views of your data. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gFMVcxf4h8wu"
   },
   "source": [
    "## Merging Profile Views"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "tvqfp4WEv-Vk"
   },
   "source": [
    "Another option is to merge Profile *Views*. \n",
    "\n",
    "A ProfileView object can be generated from a DatasetProfile object which allows for inspection of individiaul profiles, as well as the ability to visualize profiles using the our visualization module. \n",
    "\n",
    "This is a good option if users wish to inspect profiles of their entire dataset while maintaining the ability to inspect individual profiles of the subsets of data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "xnf-GXRpw039"
   },
   "outputs": [],
   "source": [
    "results = why.log(df_subset1)\n",
    "profile_view1 = results.profile().view()\n",
    "\n",
    "results = why.log(df_subset2)\n",
    "profile_view2 = results.profile().view()\n",
    "\n",
    "results = why.log(df_subset3)\n",
    "profile_view3 = results.profile().view()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "zy_gZdTbx2PV"
   },
   "source": [
    "Similar to the previous example, we find that the first profile view counted 100 rows in the subset of data it profiled."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 383
    },
    "id": "7XaAUFVHyEB3",
    "outputId": "1ecbbfc0-6c24-42b9-df7d-6f5df4c6c105"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "  <div id=\"df-d1ae8943-5e48-4026-a120-d800856f157e\">\n",
       "    <div class=\"colab-df-container\">\n",
       "      <div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>counts/n</th>\n",
       "      <th>counts/null</th>\n",
       "      <th>types/integral</th>\n",
       "      <th>types/fractional</th>\n",
       "      <th>types/boolean</th>\n",
       "      <th>types/string</th>\n",
       "      <th>types/object</th>\n",
       "      <th>cardinality/est</th>\n",
       "      <th>cardinality/upper_1</th>\n",
       "      <th>cardinality/lower_1</th>\n",
       "      <th>...</th>\n",
       "      <th>distribution/q_05</th>\n",
       "      <th>distribution/q_10</th>\n",
       "      <th>distribution/q_25</th>\n",
       "      <th>distribution/median</th>\n",
       "      <th>distribution/q_75</th>\n",
       "      <th>distribution/q_90</th>\n",
       "      <th>distribution/q_95</th>\n",
       "      <th>distribution/q_99</th>\n",
       "      <th>ints/max</th>\n",
       "      <th>ints/min</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>column</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Gender</th>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000100</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total Amount</th>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>99.000024</td>\n",
       "      <td>99.004967</td>\n",
       "      <td>99.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-153.816</td>\n",
       "      <td>8.619</td>\n",
       "      <td>66.521</td>\n",
       "      <td>216.359</td>\n",
       "      <td>321.555</td>\n",
       "      <td>580.788</td>\n",
       "      <td>642.5575</td>\n",
       "      <td>795.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Customer ID</th>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
       "      <td>98.000024</td>\n",
       "      <td>98.004917</td>\n",
       "      <td>98.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Item Price</th>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>97.000023</td>\n",
       "      <td>97.004866</td>\n",
       "      <td>97.0</td>\n",
       "      <td>...</td>\n",
       "      <td>10.000</td>\n",
       "      <td>25.700</td>\n",
       "      <td>40.700</td>\n",
       "      <td>76.800</td>\n",
       "      <td>111.100</td>\n",
       "      <td>135.200</td>\n",
       "      <td>139.8000</td>\n",
       "      <td>148.9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Transaction ID</th>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
       "      <td>99.000024</td>\n",
       "      <td>99.004967</td>\n",
       "      <td>99.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 28 columns</p>\n",
       "</div>\n",
       "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d1ae8943-5e48-4026-a120-d800856f157e')\"\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 0 24 24\"\n",
       "       width=\"24px\">\n",
       "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
       "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
       "  </svg>\n",
       "      </button>\n",
       "      \n",
       "  <style>\n",
       "    .colab-df-container {\n",
       "      display:flex;\n",
       "      flex-wrap:wrap;\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",
       "    [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-d1ae8943-5e48-4026-a120-d800856f157e 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-d1ae8943-5e48-4026-a120-d800856f157e');\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",
       "  </div>\n",
       "  "
      ],
      "text/plain": [
       "                counts/n  counts/null  types/integral  types/fractional  \\\n",
       "column                                                                    \n",
       "Gender               100            0               0                 0   \n",
       "Total Amount         100            0               0               100   \n",
       "Customer ID          100            0               0                 0   \n",
       "Item Price           100            0               0               100   \n",
       "Transaction ID       100            0               0                 0   \n",
       "\n",
       "                types/boolean  types/string  types/object  cardinality/est  \\\n",
       "column                                                                       \n",
       "Gender                      0           100             0         2.000000   \n",
       "Total Amount                0             0             0        99.000024   \n",
       "Customer ID                 0           100             0        98.000024   \n",
       "Item Price                  0             0             0        97.000023   \n",
       "Transaction ID              0           100             0        99.000024   \n",
       "\n",
       "                cardinality/upper_1  cardinality/lower_1  ...  \\\n",
       "column                                                    ...   \n",
       "Gender                     2.000100                  2.0  ...   \n",
       "Total Amount              99.004967                 99.0  ...   \n",
       "Customer ID               98.004917                 98.0  ...   \n",
       "Item Price                97.004866                 97.0  ...   \n",
       "Transaction ID            99.004967                 99.0  ...   \n",
       "\n",
       "               distribution/q_05 distribution/q_10  distribution/q_25  \\\n",
       "column                                                                  \n",
       "Gender                       NaN               NaN                NaN   \n",
       "Total Amount            -153.816             8.619             66.521   \n",
       "Customer ID                  NaN               NaN                NaN   \n",
       "Item Price                10.000            25.700             40.700   \n",
       "Transaction ID               NaN               NaN                NaN   \n",
       "\n",
       "                distribution/median  distribution/q_75  distribution/q_90  \\\n",
       "column                                                                      \n",
       "Gender                          NaN                NaN                NaN   \n",
       "Total Amount                216.359            321.555            580.788   \n",
       "Customer ID                     NaN                NaN                NaN   \n",
       "Item Price                   76.800            111.100            135.200   \n",
       "Transaction ID                  NaN                NaN                NaN   \n",
       "\n",
       "                distribution/q_95  distribution/q_99  ints/max  ints/min  \n",
       "column                                                                    \n",
       "Gender                        NaN                NaN       NaN       NaN  \n",
       "Total Amount             642.5575              795.6       NaN       NaN  \n",
       "Customer ID                   NaN                NaN       NaN       NaN  \n",
       "Item Price               139.8000              148.9       NaN       NaN  \n",
       "Transaction ID                NaN                NaN       NaN       NaN  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "profile_view1.to_pandas().head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "JZ_J3i1QyI4Q"
   },
   "source": [
    "We can merge these ProfileView objects using the **merge** method. We assign the result to a new variable and view a few rows of the profile's DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 400
    },
    "id": "aFcvdlKJyM4z",
    "outputId": "762016fa-5079-47dd-f5c7-a93ef42358bd"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "  <div id=\"df-b02d2f5e-2db6-46f3-ad7f-8ee84fcc1901\">\n",
       "    <div class=\"colab-df-container\">\n",
       "      <div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>counts/n</th>\n",
       "      <th>counts/null</th>\n",
       "      <th>types/integral</th>\n",
       "      <th>types/fractional</th>\n",
       "      <th>types/boolean</th>\n",
       "      <th>types/string</th>\n",
       "      <th>types/object</th>\n",
       "      <th>frequent_items/frequent_strings</th>\n",
       "      <th>cardinality/est</th>\n",
       "      <th>cardinality/upper_1</th>\n",
       "      <th>...</th>\n",
       "      <th>distribution/q_05</th>\n",
       "      <th>distribution/q_10</th>\n",
       "      <th>distribution/q_25</th>\n",
       "      <th>distribution/median</th>\n",
       "      <th>distribution/q_75</th>\n",
       "      <th>distribution/q_90</th>\n",
       "      <th>distribution/q_95</th>\n",
       "      <th>distribution/q_99</th>\n",
       "      <th>ints/max</th>\n",
       "      <th>ints/min</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>column</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Gender</th>\n",
       "      <td>945</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>945</td>\n",
       "      <td>0</td>\n",
       "      <td>[FrequentItem(value='M', est=489, upper=489, l...</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000100</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total Amount</th>\n",
       "      <td>945</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>945</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>849.185065</td>\n",
       "      <td>860.300432</td>\n",
       "      <td>...</td>\n",
       "      <td>-233.376</td>\n",
       "      <td>14.365</td>\n",
       "      <td>78.676</td>\n",
       "      <td>178.126</td>\n",
       "      <td>357.0255</td>\n",
       "      <td>580.346</td>\n",
       "      <td>657.475</td>\n",
       "      <td>804.44</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Customer ID</th>\n",
       "      <td>945</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>945</td>\n",
       "      <td>0</td>\n",
       "      <td>[FrequentItem(value='C273096', est=3, upper=2,...</td>\n",
       "      <td>858.998625</td>\n",
       "      <td>873.131713</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Item Price</th>\n",
       "      <td>945</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>945</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>701.002487</td>\n",
       "      <td>710.178225</td>\n",
       "      <td>...</td>\n",
       "      <td>15.000</td>\n",
       "      <td>22.700</td>\n",
       "      <td>45.200</td>\n",
       "      <td>81.200</td>\n",
       "      <td>116.7000</td>\n",
       "      <td>138.200</td>\n",
       "      <td>145.600</td>\n",
       "      <td>149.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Transaction ID</th>\n",
       "      <td>945</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>945</td>\n",
       "      <td>0</td>\n",
       "      <td>[FrequentItem(value='T79960195196', est=3, upp...</td>\n",
       "      <td>942.466233</td>\n",
       "      <td>957.972612</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 28 columns</p>\n",
       "</div>\n",
       "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b02d2f5e-2db6-46f3-ad7f-8ee84fcc1901')\"\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 0 24 24\"\n",
       "       width=\"24px\">\n",
       "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
       "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
       "  </svg>\n",
       "      </button>\n",
       "      \n",
       "  <style>\n",
       "    .colab-df-container {\n",
       "      display:flex;\n",
       "      flex-wrap:wrap;\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",
       "    [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-b02d2f5e-2db6-46f3-ad7f-8ee84fcc1901 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-b02d2f5e-2db6-46f3-ad7f-8ee84fcc1901');\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",
       "  </div>\n",
       "  "
      ],
      "text/plain": [
       "                counts/n  counts/null  types/integral  types/fractional  \\\n",
       "column                                                                    \n",
       "Gender               945            0               0                 0   \n",
       "Total Amount         945            0               0               945   \n",
       "Customer ID          945            0               0                 0   \n",
       "Item Price           945            0               0               945   \n",
       "Transaction ID       945            0               0                 0   \n",
       "\n",
       "                types/boolean  types/string  types/object  \\\n",
       "column                                                      \n",
       "Gender                      0           945             0   \n",
       "Total Amount                0             0             0   \n",
       "Customer ID                 0           945             0   \n",
       "Item Price                  0             0             0   \n",
       "Transaction ID              0           945             0   \n",
       "\n",
       "                                  frequent_items/frequent_strings  \\\n",
       "column                                                              \n",
       "Gender          [FrequentItem(value='M', est=489, upper=489, l...   \n",
       "Total Amount                                                  NaN   \n",
       "Customer ID     [FrequentItem(value='C273096', est=3, upper=2,...   \n",
       "Item Price                                                    NaN   \n",
       "Transaction ID  [FrequentItem(value='T79960195196', est=3, upp...   \n",
       "\n",
       "                cardinality/est  cardinality/upper_1  ...  distribution/q_05  \\\n",
       "column                                                ...                      \n",
       "Gender                 2.000000             2.000100  ...                NaN   \n",
       "Total Amount         849.185065           860.300432  ...           -233.376   \n",
       "Customer ID          858.998625           873.131713  ...                NaN   \n",
       "Item Price           701.002487           710.178225  ...             15.000   \n",
       "Transaction ID       942.466233           957.972612  ...                NaN   \n",
       "\n",
       "               distribution/q_10  distribution/q_25  distribution/median  \\\n",
       "column                                                                     \n",
       "Gender                       NaN                NaN                  NaN   \n",
       "Total Amount              14.365             78.676              178.126   \n",
       "Customer ID                  NaN                NaN                  NaN   \n",
       "Item Price                22.700             45.200               81.200   \n",
       "Transaction ID               NaN                NaN                  NaN   \n",
       "\n",
       "                distribution/q_75  distribution/q_90  distribution/q_95  \\\n",
       "column                                                                    \n",
       "Gender                        NaN                NaN                NaN   \n",
       "Total Amount             357.0255            580.346            657.475   \n",
       "Customer ID                   NaN                NaN                NaN   \n",
       "Item Price               116.7000            138.200            145.600   \n",
       "Transaction ID                NaN                NaN                NaN   \n",
       "\n",
       "                distribution/q_99  ints/max  ints/min  \n",
       "column                                                 \n",
       "Gender                        NaN       NaN       NaN  \n",
       "Total Amount               804.44       NaN       NaN  \n",
       "Customer ID                   NaN       NaN       NaN  \n",
       "Item Price                 149.00       NaN       NaN  \n",
       "Transaction ID                NaN       NaN       NaN  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_profile_view = profile_view1.merge(profile_view2).merge(profile_view3)\n",
    "\n",
    "merged_profile_view.to_pandas().head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8XRzAjSSy4U2"
   },
   "source": [
    "As expected, we see 945 rows. Unlike the **track** method, the merge method doesn't update the original objects directly. In other words, we can still inspect the individual profiles views from our subsets of data. \n",
    "\n",
    "Keep in mind that the **track** method only works on *DatasetProfile* objects, while the **merge** method only operates on *DatasetProfileView* objects. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 383
    },
    "id": "RghlXHuozMpY",
    "outputId": "c787e26d-b46d-43ec-9181-5dd271357208"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "  <div id=\"df-60447814-1fb5-4dcf-93da-2dd880673131\">\n",
       "    <div class=\"colab-df-container\">\n",
       "      <div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>counts/n</th>\n",
       "      <th>counts/null</th>\n",
       "      <th>types/integral</th>\n",
       "      <th>types/fractional</th>\n",
       "      <th>types/boolean</th>\n",
       "      <th>types/string</th>\n",
       "      <th>types/object</th>\n",
       "      <th>cardinality/est</th>\n",
       "      <th>cardinality/upper_1</th>\n",
       "      <th>cardinality/lower_1</th>\n",
       "      <th>...</th>\n",
       "      <th>distribution/q_05</th>\n",
       "      <th>distribution/q_10</th>\n",
       "      <th>distribution/q_25</th>\n",
       "      <th>distribution/median</th>\n",
       "      <th>distribution/q_75</th>\n",
       "      <th>distribution/q_90</th>\n",
       "      <th>distribution/q_95</th>\n",
       "      <th>distribution/q_99</th>\n",
       "      <th>ints/max</th>\n",
       "      <th>ints/min</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>column</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Gender</th>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000100</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total Amount</th>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>99.000024</td>\n",
       "      <td>99.004967</td>\n",
       "      <td>99.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-153.816</td>\n",
       "      <td>8.619</td>\n",
       "      <td>66.521</td>\n",
       "      <td>216.359</td>\n",
       "      <td>321.555</td>\n",
       "      <td>580.788</td>\n",
       "      <td>642.5575</td>\n",
       "      <td>795.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Customer ID</th>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
       "      <td>98.000024</td>\n",
       "      <td>98.004917</td>\n",
       "      <td>98.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Item Price</th>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>97.000023</td>\n",
       "      <td>97.004866</td>\n",
       "      <td>97.0</td>\n",
       "      <td>...</td>\n",
       "      <td>10.000</td>\n",
       "      <td>25.700</td>\n",
       "      <td>40.700</td>\n",
       "      <td>76.800</td>\n",
       "      <td>111.100</td>\n",
       "      <td>135.200</td>\n",
       "      <td>139.8000</td>\n",
       "      <td>148.9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Transaction ID</th>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
       "      <td>99.000024</td>\n",
       "      <td>99.004967</td>\n",
       "      <td>99.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 28 columns</p>\n",
       "</div>\n",
       "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-60447814-1fb5-4dcf-93da-2dd880673131')\"\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 0 24 24\"\n",
       "       width=\"24px\">\n",
       "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
       "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
       "  </svg>\n",
       "      </button>\n",
       "      \n",
       "  <style>\n",
       "    .colab-df-container {\n",
       "      display:flex;\n",
       "      flex-wrap:wrap;\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",
       "    [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-60447814-1fb5-4dcf-93da-2dd880673131 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-60447814-1fb5-4dcf-93da-2dd880673131');\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",
       "  </div>\n",
       "  "
      ],
      "text/plain": [
       "                counts/n  counts/null  types/integral  types/fractional  \\\n",
       "column                                                                    \n",
       "Gender               100            0               0                 0   \n",
       "Total Amount         100            0               0               100   \n",
       "Customer ID          100            0               0                 0   \n",
       "Item Price           100            0               0               100   \n",
       "Transaction ID       100            0               0                 0   \n",
       "\n",
       "                types/boolean  types/string  types/object  cardinality/est  \\\n",
       "column                                                                       \n",
       "Gender                      0           100             0         2.000000   \n",
       "Total Amount                0             0             0        99.000024   \n",
       "Customer ID                 0           100             0        98.000024   \n",
       "Item Price                  0             0             0        97.000023   \n",
       "Transaction ID              0           100             0        99.000024   \n",
       "\n",
       "                cardinality/upper_1  cardinality/lower_1  ...  \\\n",
       "column                                                    ...   \n",
       "Gender                     2.000100                  2.0  ...   \n",
       "Total Amount              99.004967                 99.0  ...   \n",
       "Customer ID               98.004917                 98.0  ...   \n",
       "Item Price                97.004866                 97.0  ...   \n",
       "Transaction ID            99.004967                 99.0  ...   \n",
       "\n",
       "               distribution/q_05 distribution/q_10  distribution/q_25  \\\n",
       "column                                                                  \n",
       "Gender                       NaN               NaN                NaN   \n",
       "Total Amount            -153.816             8.619             66.521   \n",
       "Customer ID                  NaN               NaN                NaN   \n",
       "Item Price                10.000            25.700             40.700   \n",
       "Transaction ID               NaN               NaN                NaN   \n",
       "\n",
       "                distribution/median  distribution/q_75  distribution/q_90  \\\n",
       "column                                                                      \n",
       "Gender                          NaN                NaN                NaN   \n",
       "Total Amount                216.359            321.555            580.788   \n",
       "Customer ID                     NaN                NaN                NaN   \n",
       "Item Price                   76.800            111.100            135.200   \n",
       "Transaction ID                  NaN                NaN                NaN   \n",
       "\n",
       "                distribution/q_95  distribution/q_99  ints/max  ints/min  \n",
       "column                                                                    \n",
       "Gender                        NaN                NaN       NaN       NaN  \n",
       "Total Amount             642.5575              795.6       NaN       NaN  \n",
       "Customer ID                   NaN                NaN       NaN       NaN  \n",
       "Item Price               139.8000              148.9       NaN       NaN  \n",
       "Transaction ID                NaN                NaN       NaN       NaN  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "profile_view1.to_pandas().head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1rXsivqCzv4E"
   },
   "source": [
    "## Mergeability in WhyLabs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3P5Giv-YzzeK"
   },
   "source": [
    "In WhyLabs, profile merging is done automatically. If you have a WhyLabs dataset with a daily batch frequency of 1 day, then any profiles uploaded during that day will automatically merged for a day-level view of your data. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rNlxz2TLh8wv"
   },
   "source": [
    "## What's Next?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hbUXVXyYh8wv"
   },
   "source": [
    "There's a lot you can do with the profiles you just created. You can take a look at our other examples at https://whylogs.readthedocs.io/en/latest/examples !"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "name": "Getting_Started.ipynb",
   "provenance": []
  },
  "interpreter": {
   "hash": "f76ec28949fecf16b926a3fc5a03c1aa6468ee82fa5da4ce6fd607df021af5b5"
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}