5_preparing_numeric_data_using_sklearn.ipynb

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    {
      "cell_type": "markdown",
      "source": [
        "# Python for Data 16: Preparing Numeric Data\n",
        "[back to index](https://www.kaggle.com/hamelg/python-for-data-analysis-index)"
      ],
      "metadata": {
        "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
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        "trusted": true,
        "id": "fNrB9iPbiX51"
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    {
      "cell_type": "markdown",
      "source": [
        "Numeric data tends to be better-behaved than text data. There's only so many symbols that appear in numbers and they have well-defined values. Despite its relative cleanliness, there are variety of preprocessing tasks you should consider before using numeric data. In this lesson, we'll learn some common operations used to prepare numeric data for use in analysis and predictive models."
      ],
      "metadata": {
        "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
        "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a",
        "id": "ee8Rzeb0iX55"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Centering and Scaling"
      ],
      "metadata": {
        "id": "q7eDQCLIiX57"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Numeric variables are often on different scales and cover different ranges, so they can't be easily compared. What's more, variables with large values can dominate those with smaller values when using certain modeling techniques. Centering and scaling is a common preprocessing task that puts numeric variables on a common scale so no single variable will dominate the others.\n",
        "\n",
        "The simplest way to center data is to subtract the mean value from each data point. Subtracting the mean centers the data around zero and sets the new mean to zero. Let's try zero-centering the mtcars dataset, a small set of car-related data."
      ],
      "metadata": {
        "id": "5btuQj8wiX57"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%matplotlib inline\n",
        "\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd"
      ],
      "metadata": {
        "execution": {
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      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "When working on local Jupyter Notebook, the best method to read a file is using the following code."
      ],
      "metadata": {
        "id": "4y75XVwalfmf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Use the following code when running Jupyter Notebook locally.\n",
        "mtcars = pd.read_csv(\"../input/mtcars/mtcars.csv\")\n",
        "\n",
        "#print (mtcars.head() )\n",
        "\n",
        "mtcars.index = mtcars.model       # Set row index to car model\n",
        "del mtcars[\"model\"]               # Drop car name column\n",
        "print (mtcars.head() )\n",
        "\n",
        "\n",
        "colmeans = mtcars.sum()/mtcars.shape[0]  # Get column means\n",
        "\n",
        "colmeans"
      ],
      "metadata": {
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      "execution_count": null,
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      "cell_type": "markdown",
      "source": [
        "## Reading data files from Google Colab\n",
        "\n",
        "Howeve, I am using Google Colab at the time of working on this notebook, and the best method to read a file is to upload the file to Google Drive. Follw the next code snippet"
      ],
      "metadata": {
        "id": "NQaHuKYcmjpb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import files\n",
        "import io\n",
        "\n",
        "uploaded = files.upload()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "pYiqd8cFm1VF",
        "outputId": "48c3b989-49ff-4354-ee46-02fb356f1089"
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      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
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              "\n",
              "     <input type=\"file\" id=\"files-1b99bf7b-ca92-4d05-b83c-063fb3100d6f\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-1b99bf7b-ca92-4d05-b83c-063fb3100d6f\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script>// Copyright 2017 Google LLC\n",
              "//\n",
              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
              "// you may not use this file except in compliance with the License.\n",
              "// You may obtain a copy of the License at\n",
              "//\n",
              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
              "//\n",
              "// Unless required by applicable law or agreed to in writing, software\n",
              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
              "// See the License for the specific language governing permissions and\n",
              "// limitations under the License.\n",
              "\n",
              "/**\n",
              " * @fileoverview Helpers for google.colab Python module.\n",
              " */\n",
              "(function(scope) {\n",
              "function span(text, styleAttributes = {}) {\n",
              "  const element = document.createElement('span');\n",
              "  element.textContent = text;\n",
              "  for (const key of Object.keys(styleAttributes)) {\n",
              "    element.style[key] = styleAttributes[key];\n",
              "  }\n",
              "  return element;\n",
              "}\n",
              "\n",
              "// Max number of bytes which will be uploaded at a time.\n",
              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
              "\n",
              "function _uploadFiles(inputId, outputId) {\n",
              "  const steps = uploadFilesStep(inputId, outputId);\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  // Cache steps on the outputElement to make it available for the next call\n",
              "  // to uploadFilesContinue from Python.\n",
              "  outputElement.steps = steps;\n",
              "\n",
              "  return _uploadFilesContinue(outputId);\n",
              "}\n",
              "\n",
              "// This is roughly an async generator (not supported in the browser yet),\n",
              "// where there are multiple asynchronous steps and the Python side is going\n",
              "// to poll for completion of each step.\n",
              "// This uses a Promise to block the python side on completion of each step,\n",
              "// then passes the result of the previous step as the input to the next step.\n",
              "function _uploadFilesContinue(outputId) {\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  const steps = outputElement.steps;\n",
              "\n",
              "  const next = steps.next(outputElement.lastPromiseValue);\n",
              "  return Promise.resolve(next.value.promise).then((value) => {\n",
              "    // Cache the last promise value to make it available to the next\n",
              "    // step of the generator.\n",
              "    outputElement.lastPromiseValue = value;\n",
              "    return next.value.response;\n",
              "  });\n",
              "}\n",
              "\n",
              "/**\n",
              " * Generator function which is called between each async step of the upload\n",
              " * process.\n",
              " * @param {string} inputId Element ID of the input file picker element.\n",
              " * @param {string} outputId Element ID of the output display.\n",
              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
              " */\n",
              "function* uploadFilesStep(inputId, outputId) {\n",
              "  const inputElement = document.getElementById(inputId);\n",
              "  inputElement.disabled = false;\n",
              "\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  outputElement.innerHTML = '';\n",
              "\n",
              "  const pickedPromise = new Promise((resolve) => {\n",
              "    inputElement.addEventListener('change', (e) => {\n",
              "      resolve(e.target.files);\n",
              "    });\n",
              "  });\n",
              "\n",
              "  const cancel = document.createElement('button');\n",
              "  inputElement.parentElement.appendChild(cancel);\n",
              "  cancel.textContent = 'Cancel upload';\n",
              "  const cancelPromise = new Promise((resolve) => {\n",
              "    cancel.onclick = () => {\n",
              "      resolve(null);\n",
              "    };\n",
              "  });\n",
              "\n",
              "  // Wait for the user to pick the files.\n",
              "  const files = yield {\n",
              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
              "    response: {\n",
              "      action: 'starting',\n",
              "    }\n",
              "  };\n",
              "\n",
              "  cancel.remove();\n",
              "\n",
              "  // Disable the input element since further picks are not allowed.\n",
              "  inputElement.disabled = true;\n",
              "\n",
              "  if (!files) {\n",
              "    return {\n",
              "      response: {\n",
              "        action: 'complete',\n",
              "      }\n",
              "    };\n",
              "  }\n",
              "\n",
              "  for (const file of files) {\n",
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              "    li.append(span(\n",
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              "\n",
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              "\n",
              "scope.google = scope.google || {};\n",
              "scope.google.colab = scope.google.colab || {};\n",
              "scope.google.colab._files = {\n",
              "  _uploadFiles,\n",
              "  _uploadFilesContinue,\n",
              "};\n",
              "})(self);\n",
              "</script> "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving mtcars.csv to mtcars.csv\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Use the following code when running Jupyter Notebook on Google Colab.\n",
        "\n",
        "mtcars = pd.read_csv(io.StringIO(uploaded['mtcars.csv'].decode('utf-8')))\n",
        "\n",
        "#print (mtcars.head() )\n",
        "\n",
        "mtcars.index = mtcars.model       # Set row index to car model\n",
        "del mtcars[\"model\"]               # Drop car name column\n",
        "print (mtcars.head() )\n",
        "\n",
        "colmeans = mtcars.sum()/mtcars.shape[0]  # Get column means\n",
        "\n",
        "colmeans"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 697
        },
        "id": "H7uY_uWcm9Gf",
        "outputId": "a8f247c2-cd01-40c0-d58d-4683eaf538ce"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                    mpg  cyl   disp   hp  drat     wt   qsec  vs  am  gear  \\\n",
            "model                                                                        \n",
            "Mazda RX4          21.0    6  160.0  110  3.90  2.620  16.46   0   1     4   \n",
            "Mazda RX4 Wag      21.0    6  160.0  110  3.90  2.875  17.02   0   1     4   \n",
            "Datsun 710         22.8    4  108.0   93  3.85  2.320  18.61   1   1     4   \n",
            "Hornet 4 Drive     21.4    6  258.0  110  3.08  3.215  19.44   1   0     3   \n",
            "Hornet Sportabout  18.7    8  360.0  175  3.15  3.440  17.02   0   0     3   \n",
            "\n",
            "                   carb  \n",
            "model                    \n",
            "Mazda RX4             4  \n",
            "Mazda RX4 Wag         4  \n",
            "Datsun 710            1  \n",
            "Hornet 4 Drive        1  \n",
            "Hornet Sportabout     2  \n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "mpg      20.090625\n",
              "cyl       6.187500\n",
              "disp    230.721875\n",
              "hp      146.687500\n",
              "drat      3.596563\n",
              "wt        3.217250\n",
              "qsec     17.848750\n",
              "vs        0.437500\n",
              "am        0.406250\n",
              "gear      3.687500\n",
              "carb      2.812500\n",
              "dtype: float64"
            ],
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              "      <th>mpg</th>\n",
              "      <td>20.090625</td>\n",
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              "      <th>cyl</th>\n",
              "      <td>6.187500</td>\n",
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              "      <th>disp</th>\n",
              "      <td>230.721875</td>\n",
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              "      <th>hp</th>\n",
              "      <td>146.687500</td>\n",
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              "      <th>drat</th>\n",
              "      <td>3.596563</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>wt</th>\n",
              "      <td>3.217250</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>qsec</th>\n",
              "      <td>17.848750</td>\n",
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              "      <th>vs</th>\n",
              "      <td>0.437500</td>\n",
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              "      <th>am</th>\n",
              "      <td>0.406250</td>\n",
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              "      <th>gear</th>\n",
              "      <td>3.687500</td>\n",
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          "metadata": {},
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      "cell_type": "markdown",
      "source": [
        "With the column means in hand, we just need to subtract the column means from each row in an element-wise fashion to zero center the data. Pandas performs math operations involving DataFrames and columns on an element-wise row-by-row basis by default, so we can simply subtract our column means series from the data set to center it:"
      ],
      "metadata": {
        "id": "rH-OaptSiX6A"
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    },
    {
      "cell_type": "code",
      "source": [
        "centered = mtcars-colmeans\n",
        "\n",
        "centered.describe()"
      ],
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      "execution_count": 18,
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            "text/plain": [
              "                mpg        cyl          disp          hp          drat  \\\n",
              "count  3.200000e+01  32.000000  3.200000e+01   32.000000  3.200000e+01   \n",
              "mean  -3.108624e-15   0.000000  2.131628e-14    0.000000 -1.526557e-16   \n",
              "std    6.026948e+00   1.785922  1.239387e+02   68.562868  5.346787e-01   \n",
              "min   -9.690625e+00  -2.187500 -1.596219e+02  -94.687500 -8.365625e-01   \n",
              "25%   -4.665625e+00  -2.187500 -1.098969e+02  -50.187500 -5.165625e-01   \n",
              "50%   -8.906250e-01  -0.187500 -3.442187e+01  -23.687500  9.843750e-02   \n",
              "75%    2.709375e+00   1.812500  9.527813e+01   33.312500  3.234375e-01   \n",
              "max    1.380937e+01   1.812500  2.412781e+02  188.312500  1.333437e+00   \n",
              "\n",
              "                 wt          qsec         vs         am       gear     carb  \n",
              "count  3.200000e+01  3.200000e+01  32.000000  32.000000  32.000000  32.0000  \n",
              "mean  -4.440892e-16 -2.609024e-15   0.000000   0.000000   0.000000   0.0000  \n",
              "std    9.784574e-01  1.786943e+00   0.504016   0.498991   0.737804   1.6152  \n",
              "min   -1.704250e+00 -3.348750e+00  -0.437500  -0.406250  -0.687500  -1.8125  \n",
              "25%   -6.360000e-01 -9.562500e-01  -0.437500  -0.406250  -0.687500  -0.8125  \n",
              "50%    1.077500e-01 -1.387500e-01  -0.437500  -0.406250   0.312500  -0.8125  \n",
              "75%    3.927500e-01  1.051250e+00   0.562500   0.593750   0.312500   1.1875  \n",
              "max    2.206750e+00  5.051250e+00   0.562500   0.593750   1.312500   5.1875  "
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              "    }\n",
              "\n",
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              "    }\n",
              "\n",
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              "        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>mpg</th>\n",
              "      <th>cyl</th>\n",
              "      <th>disp</th>\n",
              "      <th>hp</th>\n",
              "      <th>drat</th>\n",
              "      <th>wt</th>\n",
              "      <th>qsec</th>\n",
              "      <th>vs</th>\n",
              "      <th>am</th>\n",
              "      <th>gear</th>\n",
              "      <th>carb</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>3.200000e+01</td>\n",
              "      <td>32.000000</td>\n",
              "      <td>3.200000e+01</td>\n",
              "      <td>32.000000</td>\n",
              "      <td>3.200000e+01</td>\n",
              "      <td>3.200000e+01</td>\n",
              "      <td>3.200000e+01</td>\n",
              "      <td>32.000000</td>\n",
              "      <td>32.000000</td>\n",
              "      <td>32.000000</td>\n",
              "      <td>32.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>-3.108624e-15</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.131628e-14</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-1.526557e-16</td>\n",
              "      <td>-4.440892e-16</td>\n",
              "      <td>-2.609024e-15</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>6.026948e+00</td>\n",
              "      <td>1.785922</td>\n",
              "      <td>1.239387e+02</td>\n",
              "      <td>68.562868</td>\n",
              "      <td>5.346787e-01</td>\n",
              "      <td>9.784574e-01</td>\n",
              "      <td>1.786943e+00</td>\n",
              "      <td>0.504016</td>\n",
              "      <td>0.498991</td>\n",
              "      <td>0.737804</td>\n",
              "      <td>1.6152</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>-9.690625e+00</td>\n",
              "      <td>-2.187500</td>\n",
              "      <td>-1.596219e+02</td>\n",
              "      <td>-94.687500</td>\n",
              "      <td>-8.365625e-01</td>\n",
              "      <td>-1.704250e+00</td>\n",
              "      <td>-3.348750e+00</td>\n",
              "      <td>-0.437500</td>\n",
              "      <td>-0.406250</td>\n",
              "      <td>-0.687500</td>\n",
              "      <td>-1.8125</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>-4.665625e+00</td>\n",
              "      <td>-2.187500</td>\n",
              "      <td>-1.098969e+02</td>\n",
              "      <td>-50.187500</td>\n",
              "      <td>-5.165625e-01</td>\n",
              "      <td>-6.360000e-01</td>\n",
              "      <td>-9.562500e-01</td>\n",
              "      <td>-0.437500</td>\n",
              "      <td>-0.406250</td>\n",
              "      <td>-0.687500</td>\n",
              "      <td>-0.8125</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>-8.906250e-01</td>\n",
              "      <td>-0.187500</td>\n",
              "      <td>-3.442187e+01</td>\n",
              "      <td>-23.687500</td>\n",
              "      <td>9.843750e-02</td>\n",
              "      <td>1.077500e-01</td>\n",
              "      <td>-1.387500e-01</td>\n",
              "      <td>-0.437500</td>\n",
              "      <td>-0.406250</td>\n",
              "      <td>0.312500</td>\n",
              "      <td>-0.8125</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>2.709375e+00</td>\n",
              "      <td>1.812500</td>\n",
              "      <td>9.527813e+01</td>\n",
              "      <td>33.312500</td>\n",
              "      <td>3.234375e-01</td>\n",
              "      <td>3.927500e-01</td>\n",
              "      <td>1.051250e+00</td>\n",
              "      <td>0.562500</td>\n",
              "      <td>0.593750</td>\n",
              "      <td>0.312500</td>\n",
              "      <td>1.1875</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1.380937e+01</td>\n",
              "      <td>1.812500</td>\n",
              "      <td>2.412781e+02</td>\n",
              "      <td>188.312500</td>\n",
              "      <td>1.333437e+00</td>\n",
              "      <td>2.206750e+00</td>\n",
              "      <td>5.051250e+00</td>\n",
              "      <td>0.562500</td>\n",
              "      <td>0.593750</td>\n",
              "      <td>1.312500</td>\n",
              "      <td>5.1875</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "          + ' to learn more about interactive tables.';\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"centered\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"mpg\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12.993313936492653,\n        \"min\": -9.690625000000002,\n        \"max\": 32.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          -3.1086244689504383e-15,\n          -0.8906250000000036,\n          32.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"cyl\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11.392277752884588,\n        \"min\": -2.1875,\n        \"max\": 32.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          32.0,\n          0.0,\n          1.8125\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"disp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 129.82135788058437,\n        \"min\": -159.621875,\n        \"max\": 241.27812500000002,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          2.1316282072803006e-14,\n          -34.421874999999986,\n          32.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 85.68355275272155,\n        \"min\": -94.6875,\n        \"max\": 188.3125,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.0,\n          -23.6875,\n          32.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"drat\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11.285517062818887,\n        \"min\": -0.8365625000000003,\n        \"max\": 32.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          -1.5265566588595902e-16,\n          0.09843749999999996,\n          32.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"wt\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11.303046426943379,\n        \"min\": -1.7042500000000005,\n        \"max\": 32.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          -4.440892098500626e-16,\n          0.10774999999999957,\n          32.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"qsec\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11.394933493933028,\n        \"min\": -3.3487500000000026,\n        \"max\": 32.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          -2.609024107869118e-15,\n          -0.1387500000000017,\n          32.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"vs\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11.306865244938576,\n        \"min\": -0.4375,\n        \"max\": 32.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.0,\n          0.5625,\n          0.5040161287741853\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"am\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11.299051238390327,\n        \"min\": -0.40625,\n        \"max\": 32.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.0,\n          0.59375,\n          0.49899091723584604\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gear\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11.268120991375174,\n        \"min\": -0.6875,\n        \"max\": 32.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          32.0,\n          0.0,\n          1.3125\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"carb\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11.291730019044255,\n        \"min\": -1.8125,\n        \"max\": 32.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          32.0,\n          0.0,\n          1.1875\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "8Oqst4TKw6Rd"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "With zero-centered data, negative values are below average and positive values are above average.\n",
        "\n",
        "Now that the data is centered, we'd like to put it all on a common scale. One way to put data on a common scale is to divide by the standard deviation. Standard deviation is a statistic that describes the spread of numeric data. The higher the standard deviation, the further the data points tend to be spread away from the mean value. You can get standard deviations with df.std():"
      ],
      "metadata": {
        "id": "D67J0idMiX6C"
      }
    },
    {
      "cell_type": "code",
      "source": [
        " # Get column standard deviations\n",
        "column_deviations = mtcars.std(axis=0)\n",
        "\n",
        "centered_and_scaled = centered/column_deviations\n",
        "\n",
        "centered_and_scaled.describe()"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-08-23T12:32:51.771080Z",
          "iopub.execute_input": "2024-08-23T12:32:51.771550Z",
          "iopub.status.idle": "2024-08-23T12:32:51.846776Z",
          "shell.execute_reply.started": "2024-08-23T12:32:51.771479Z",
          "shell.execute_reply": "2024-08-23T12:32:51.845940Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "04NZNAZuiX6D",
        "outputId": "6ecfd107-bd4d-4b3b-bd47-d07d34996696"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                mpg           cyl          disp            hp          drat  \\\n",
              "count  3.200000e+01  3.200000e+01  3.200000e+01  3.200000e+01  3.200000e+01   \n",
              "mean  -4.996004e-16 -2.775558e-17  1.110223e-16  2.775558e-17 -3.053113e-16   \n",
              "std    1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00   \n",
              "min   -1.607883e+00 -1.224858e+00 -1.287910e+00 -1.381032e+00 -1.564608e+00   \n",
              "25%   -7.741273e-01 -1.224858e+00 -8.867035e-01 -7.319924e-01 -9.661175e-01   \n",
              "50%   -1.477738e-01 -1.049878e-01 -2.777331e-01 -3.454858e-01  1.841059e-01   \n",
              "75%    4.495434e-01  1.014882e+00  7.687521e-01  4.858679e-01  6.049193e-01   \n",
              "max    2.291272e+00  1.014882e+00  1.946754e+00  2.746567e+00  2.493904e+00   \n",
              "\n",
              "                 wt          qsec         vs            am          gear  \\\n",
              "count  3.200000e+01  3.200000e+01  32.000000  3.200000e+01  3.200000e+01   \n",
              "mean  -4.163336e-16 -1.443290e-15   0.000000  5.551115e-17 -2.775558e-17   \n",
              "std    1.000000e+00  1.000000e+00   1.000000  1.000000e+00  1.000000e+00   \n",
              "min   -1.741772e+00 -1.874010e+00  -0.868028 -8.141431e-01 -9.318192e-01   \n",
              "25%   -6.500027e-01 -5.351317e-01  -0.868028 -8.141431e-01 -9.318192e-01   \n",
              "50%    1.101223e-01 -7.764656e-02  -0.868028 -8.141431e-01  4.235542e-01   \n",
              "75%    4.013971e-01  5.882951e-01   1.116036  1.189901e+00  4.235542e-01   \n",
              "max    2.255336e+00  2.826755e+00   1.116036  1.189901e+00  1.778928e+00   \n",
              "\n",
              "               carb  \n",
              "count  3.200000e+01  \n",
              "mean   2.775558e-17  \n",
              "std    1.000000e+00  \n",
              "min   -1.122152e+00  \n",
              "25%   -5.030337e-01  \n",
              "50%   -5.030337e-01  \n",
              "75%    7.352031e-01  \n",
              "max    3.211677e+00  "
            ],
            "text/html": [
              "\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>mpg</th>\n",
              "      <th>cyl</th>\n",
              "      <th>disp</th>\n",
              "      <th>hp</th>\n",
              "      <th>drat</th>\n",
              "      <th>wt</th>\n",
              "      <th>qsec</th>\n",
              "      <th>vs</th>\n",
              "      <th>am</th>\n",
              "      <th>gear</th>\n",
              "      <th>carb</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>3.200000e+01</td>\n",
              "      <td>3.200000e+01</td>\n",
              "      <td>3.200000e+01</td>\n",
              "      <td>3.200000e+01</td>\n",
              "      <td>3.200000e+01</td>\n",
              "      <td>3.200000e+01</td>\n",
              "      <td>3.200000e+01</td>\n",
              "      <td>32.000000</td>\n",
              "      <td>3.200000e+01</td>\n",
              "      <td>3.200000e+01</td>\n",
              "      <td>3.200000e+01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>-4.996004e-16</td>\n",
              "      <td>-2.775558e-17</td>\n",
              "      <td>1.110223e-16</td>\n",
              "      <td>2.775558e-17</td>\n",
              "      <td>-3.053113e-16</td>\n",
              "      <td>-4.163336e-16</td>\n",
              "      <td>-1.443290e-15</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>5.551115e-17</td>\n",
              "      <td>-2.775558e-17</td>\n",
              "      <td>2.775558e-17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>-1.607883e+00</td>\n",
              "      <td>-1.224858e+00</td>\n",
              "      <td>-1.287910e+00</td>\n",
              "      <td>-1.381032e+00</td>\n",
              "      <td>-1.564608e+00</td>\n",
              "      <td>-1.741772e+00</td>\n",
              "      <td>-1.874010e+00</td>\n",
              "      <td>-0.868028</td>\n",
              "      <td>-8.141431e-01</td>\n",
              "      <td>-9.318192e-01</td>\n",
              "      <td>-1.122152e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>-7.741273e-01</td>\n",
              "      <td>-1.224858e+00</td>\n",
              "      <td>-8.867035e-01</td>\n",
              "      <td>-7.319924e-01</td>\n",
              "      <td>-9.661175e-01</td>\n",
              "      <td>-6.500027e-01</td>\n",
              "      <td>-5.351317e-01</td>\n",
              "      <td>-0.868028</td>\n",
              "      <td>-8.141431e-01</td>\n",
              "      <td>-9.318192e-01</td>\n",
              "      <td>-5.030337e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>-1.477738e-01</td>\n",
              "      <td>-1.049878e-01</td>\n",
              "      <td>-2.777331e-01</td>\n",
              "      <td>-3.454858e-01</td>\n",
              "      <td>1.841059e-01</td>\n",
              "      <td>1.101223e-01</td>\n",
              "      <td>-7.764656e-02</td>\n",
              "      <td>-0.868028</td>\n",
              "      <td>-8.141431e-01</td>\n",
              "      <td>4.235542e-01</td>\n",
              "      <td>-5.030337e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>4.495434e-01</td>\n",
              "      <td>1.014882e+00</td>\n",
              "      <td>7.687521e-01</td>\n",
              "      <td>4.858679e-01</td>\n",
              "      <td>6.049193e-01</td>\n",
              "      <td>4.013971e-01</td>\n",
              "      <td>5.882951e-01</td>\n",
              "      <td>1.116036</td>\n",
              "      <td>1.189901e+00</td>\n",
              "      <td>4.235542e-01</td>\n",
              "      <td>7.352031e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>2.291272e+00</td>\n",
              "      <td>1.014882e+00</td>\n",
              "      <td>1.946754e+00</td>\n",
              "      <td>2.746567e+00</td>\n",
              "      <td>2.493904e+00</td>\n",
              "      <td>2.255336e+00</td>\n",
              "      <td>2.826755e+00</td>\n",
              "      <td>1.116036</td>\n",
              "      <td>1.189901e+00</td>\n",
              "      <td>1.778928e+00</td>\n",
              "      <td>3.211677e+00</td>\n",
              "    </tr>\n",
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              "    0% {\n",
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              "    20% {\n",
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              "      border-color: transparent;\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "\n",
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              "summary": "{\n  \"name\": \"centered_and_scaled\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"mpg\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11.312362618963045,\n        \"min\": -1.6078826159188429,\n        \"max\": 32.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          -4.996003610813204e-16,\n          -0.14777379733533438,\n          32.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"cyl\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11.32785977182662,\n        \"min\": -1.2248577667111258,\n        \"max\": 32.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          32.0,\n          -2.7755575615628914e-17,\n          1.014882149560647\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"disp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11.298534669462498,\n        \"min\": -1.2879099340611486,\n        \"max\": 32.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          1.1102230246251565e-16,\n          -0.2777330786366911,\n          32.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11.29321775030716,\n        \"min\": -1.3810317754536277,\n        \"max\": 32.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          2.7755575615628914e-17,\n          -0.34548583689566,\n          32.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"drat\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11.292218249856344,\n        \"min\": -1.5646077608161282,\n        \"max\": 32.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          -3.0531133177191805e-16,\n          0.1841058814557639,\n          32.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"wt\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11.304102052743875,\n        \"min\": -1.7417722275101,\n        \"max\": 32.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          -4.163336342344337e-16,\n          0.11012231627649258,\n          32.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"qsec\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11.296333659379703,\n        \"min\": -1.8740102832334835,\n        \"max\": 32.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          -1.4432899320127035e-15,\n          -0.07764656268716649,\n          32.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"vs\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11.317990023477853,\n        \"min\": -0.868027777333319,\n        \"max\": 32.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.0,\n          1.1160357137142674,\n          0.9999999999999999\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"am\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11.30241058405698,\n        \"min\": -0.8141430754900646,\n        \"max\": 32.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          5.551115123125783e-17,\n          1.1899014180239407,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gear\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11.261644879840016,\n        \"min\": -0.9318192083430331,\n        \"max\": 32.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          32.0,\n          -2.7755575615628914e-17,\n          1.7789275795639723\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"carb\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11.250894018996508,\n        \"min\": -1.1221520710131645,\n        \"max\": 32.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          32.0,\n          2.7755575615628914e-17,\n          0.7352030810086251\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Notice that after dividing by the standard deviation, every variable now has a standard deviation of 1. At this point, all the columns have roughly the same mean and scale of spread about the mean.\n",
        "\n",
        "## Do all the above using scikit-learn\n",
        "\n",
        "Manually centering and scaling as we've done is a good exercise, but it is often possible to perform common data preprocessing automatically using functions built into Python libraries. The Python library scikit-learn, a popular package for predictive modeling and data analysis, has preprocessing tools including a scale() function for centering and scaling data:"
      ],
      "metadata": {
        "id": "8JZWDpe_iX6D"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import preprocessing"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-08-23T12:51:27.349693Z",
          "iopub.execute_input": "2024-08-23T12:51:27.350068Z",
          "iopub.status.idle": "2024-08-23T12:51:28.310860Z",
          "shell.execute_reply.started": "2024-08-23T12:51:27.349998Z",
          "shell.execute_reply": "2024-08-23T12:51:28.309570Z"
        },
        "trusted": true,
        "id": "AwT1CEKqiX6D"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "scaled_data = preprocessing.scale(mtcars)  # Scale the data*\n",
        "print(scaled_data.shape)\n",
        "\n",
        "scaled_cars = pd.DataFrame(scaled_data,    # preprocessing.scale() returns ndarrays so we convert it back into a DataFrame.*\n",
        "                           index=mtcars.index,\n",
        "                           columns=mtcars.columns)\n",
        "\n",
        "scaled_cars.describe()"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-08-23T12:55:24.492134Z",
          "iopub.execute_input": "2024-08-23T12:55:24.492527Z",
          "iopub.status.idle": "2024-08-23T12:55:24.574729Z",
          "shell.execute_reply.started": "2024-08-23T12:55:24.492466Z",
          "shell.execute_reply": "2024-08-23T12:55:24.573488Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 318
        },
        "id": "Na3CAUxFiX6E",
        "outputId": "8213be7c-0f38-4ec8-e1ef-b21303121368"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(32, 11)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                mpg           cyl          disp         hp          drat  \\\n",
              "count  3.200000e+01  3.200000e+01  3.200000e+01  32.000000  3.200000e+01   \n",
              "mean  -4.996004e-16  2.775558e-17  1.665335e-16   0.000000 -3.053113e-16   \n",
              "std    1.016001e+00  1.016001e+00  1.016001e+00   1.016001  1.016001e+00   \n",
              "min   -1.633610e+00 -1.244457e+00 -1.308518e+00  -1.403130 -1.589643e+00   \n",
              "25%   -7.865141e-01 -1.244457e+00 -9.008917e-01  -0.743705 -9.815764e-01   \n",
              "50%   -1.501383e-01 -1.066677e-01 -2.821771e-01  -0.351014  1.870518e-01   \n",
              "75%    4.567366e-01  1.031121e+00  7.810529e-01   0.493642  6.145986e-01   \n",
              "max    2.327934e+00  1.031121e+00  1.977904e+00   2.790515  2.533809e+00   \n",
              "\n",
              "                 wt          qsec         vs         am          gear  \\\n",
              "count  3.200000e+01  3.200000e+01  32.000000  32.000000  3.200000e+01   \n",
              "mean  -4.718448e-16 -1.471046e-15   0.000000   0.000000 -2.775558e-17   \n",
              "std    1.016001e+00  1.016001e+00   1.016001   1.016001  1.016001e+00   \n",
              "min   -1.769642e+00 -1.903996e+00  -0.881917  -0.827170 -9.467293e-01   \n",
              "25%   -6.604034e-01 -5.436944e-01  -0.881917  -0.827170 -9.467293e-01   \n",
              "50%    1.118844e-01 -7.888899e-02  -0.881917  -0.827170  4.303315e-01   \n",
              "75%    4.078199e-01  5.977084e-01   1.133893   1.208941  4.303315e-01   \n",
              "max    2.291423e+00  2.871986e+00   1.133893   1.208941  1.807392e+00   \n",
              "\n",
              "               carb  \n",
              "count  3.200000e+01  \n",
              "mean  -2.775558e-17  \n",
              "std    1.016001e+00  \n",
              "min   -1.140108e+00  \n",
              "25%   -5.110827e-01  \n",
              "50%   -5.110827e-01  \n",
              "75%    7.469671e-01  \n",
              "max    3.263067e+00  "
            ],
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              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>mpg</th>\n",
              "      <th>cyl</th>\n",
              "      <th>disp</th>\n",
              "      <th>hp</th>\n",
              "      <th>drat</th>\n",
              "      <th>wt</th>\n",
              "      <th>qsec</th>\n",
              "      <th>vs</th>\n",
              "      <th>am</th>\n",
              "      <th>gear</th>\n",
              "      <th>carb</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>3.200000e+01</td>\n",
              "      <td>3.200000e+01</td>\n",
              "      <td>3.200000e+01</td>\n",
              "      <td>32.000000</td>\n",
              "      <td>3.200000e+01</td>\n",
              "      <td>3.200000e+01</td>\n",
              "      <td>3.200000e+01</td>\n",
              "      <td>32.000000</td>\n",
              "      <td>32.000000</td>\n",
              "      <td>3.200000e+01</td>\n",
              "      <td>3.200000e+01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>-4.996004e-16</td>\n",
              "      <td>2.775558e-17</td>\n",
              "      <td>1.665335e-16</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-3.053113e-16</td>\n",
              "      <td>-4.718448e-16</td>\n",
              "      <td>-1.471046e-15</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-2.775558e-17</td>\n",
              "      <td>-2.775558e-17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.016001e+00</td>\n",
              "      <td>1.016001e+00</td>\n",
              "      <td>1.016001e+00</td>\n",
              "      <td>1.016001</td>\n",
              "      <td>1.016001e+00</td>\n",
              "      <td>1.016001e+00</td>\n",
              "      <td>1.016001e+00</td>\n",
              "      <td>1.016001</td>\n",
              "      <td>1.016001</td>\n",
              "      <td>1.016001e+00</td>\n",
              "      <td>1.016001e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>-1.633610e+00</td>\n",
              "      <td>-1.244457e+00</td>\n",
              "      <td>-1.308518e+00</td>\n",
              "      <td>-1.403130</td>\n",
              "      <td>-1.589643e+00</td>\n",
              "      <td>-1.769642e+00</td>\n",
              "      <td>-1.903996e+00</td>\n",
              "      <td>-0.881917</td>\n",
              "      <td>-0.827170</td>\n",
              "      <td>-9.467293e-01</td>\n",
              "      <td>-1.140108e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>-7.865141e-01</td>\n",
              "      <td>-1.244457e+00</td>\n",
              "      <td>-9.008917e-01</td>\n",
              "      <td>-0.743705</td>\n",
              "      <td>-9.815764e-01</td>\n",
              "      <td>-6.604034e-01</td>\n",
              "      <td>-5.436944e-01</td>\n",
              "      <td>-0.881917</td>\n",
              "      <td>-0.827170</td>\n",
              "      <td>-9.467293e-01</td>\n",
              "      <td>-5.110827e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>-1.501383e-01</td>\n",
              "      <td>-1.066677e-01</td>\n",
              "      <td>-2.821771e-01</td>\n",
              "      <td>-0.351014</td>\n",
              "      <td>1.870518e-01</td>\n",
              "      <td>1.118844e-01</td>\n",
              "      <td>-7.888899e-02</td>\n",
              "      <td>-0.881917</td>\n",
              "      <td>-0.827170</td>\n",
              "      <td>4.303315e-01</td>\n",
              "      <td>-5.110827e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>4.567366e-01</td>\n",
              "      <td>1.031121e+00</td>\n",
              "      <td>7.810529e-01</td>\n",
              "      <td>0.493642</td>\n",
              "      <td>6.145986e-01</td>\n",
              "      <td>4.078199e-01</td>\n",
              "      <td>5.977084e-01</td>\n",
              "      <td>1.133893</td>\n",
              "      <td>1.208941</td>\n",
              "      <td>4.303315e-01</td>\n",
              "      <td>7.469671e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>2.327934e+00</td>\n",
              "      <td>1.031121e+00</td>\n",
              "      <td>1.977904e+00</td>\n",
              "      <td>2.790515</td>\n",
              "      <td>2.533809e+00</td>\n",
              "      <td>2.291423e+00</td>\n",
              "      <td>2.871986e+00</td>\n",
              "      <td>1.133893</td>\n",
              "      <td>1.208941</td>\n",
              "      <td>1.807392e+00</td>\n",
              "      <td>3.263067e+00</td>\n",
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              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-29b31ad2-24c7-4b85-b081-93ba16caa187 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"scaled_cars\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"mpg\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11.313313663057867,\n        \"min\": -1.6336103713847328,\n        \"max\": 32.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          -4.996003610813204e-16,\n          -0.15013832823110304,\n          32.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"cyl\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11.328705535392519,\n        \"min\": -1.2444567354358613,\n        \"max\": 32.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          32.0,\n          2.7755575615628914e-17,\n          1.031121295075428\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"disp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11.299083428396633,\n        \"min\": -1.3085178015245826,\n        \"max\": 32.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          1.6653345369377348e-16,\n          -0.28217709007210934,\n          32.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11.294015351821868,\n        \"min\": -1.4031296869912744,\n        \"max\": 32.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.0,\n          -0.35101396130012735,\n          32.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"drat\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11.292965813092932,\n        \"min\": -1.5896430746330559,\n        \"max\": 32.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          -3.0531133177191805e-16,\n          0.18705176261091228,\n          32.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"wt\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11.304922050627637,\n        \"min\": -1.769642352793499,\n        \"max\": 32.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          -4.718447854656915e-16,\n          0.11188438522135762,\n          32.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"qsec\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11.297358443213971,\n        \"min\": -1.9039963517625231,\n        \"max\": 32.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          -1.4710455076283324e-15,\n          -0.07888898657918719,\n          32.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"vs\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11.318643541246232,\n        \"min\": -0.8819171036881969,\n        \"max\": 32.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.0,\n          1.1338934190276817,\n          1.016001016001524\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"am\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11.302816397234244,\n        \"min\": -0.827170191868511,\n        \"max\": 32.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.0,\n          1.2089410496539776,\n          1.0160010160015238\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gear\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11.261415327209745,\n        \"min\": -0.9467292624062575,\n        \"max\": 32.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          32.0,\n          -2.7755575615628914e-17,\n          1.807392228230128\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"carb\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11.251189422572503,\n        \"min\": -1.1401076442575897,\n        \"max\": 32.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          32.0,\n          -2.7755575615628914e-17,\n          0.7469670772722139\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "*Note: preprocessing.scale() returns ndarrays so we convert it back into a DataFrame.*\n",
        "\n",
        "Notice that the values are almost the same as those we calculated manually but not exactly the same. These small differences are likely due to rounding and details of the scikit-learn implementation of centering and scaling."
      ],
      "metadata": {
        "id": "GpWHUfFviX6E"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Dealing With Skewed Data"
      ],
      "metadata": {
        "id": "UHakT4d6iX6E"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The distribution of data--its overall shape and how it is spread out--can have a significant impact on analysis and modeling. Data that is roughly evenly spread around the mean value--known as normally distributed data--tends to be well-behaved. On the other hand, some data sets exhibit significant skewness or asymmetry. To illustrate, let's generate a few distributions"
      ],
      "metadata": {
        "id": "2v3kRRsCiX6E"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Generate normal data*\n",
        "normally_distributed = np.random.normal(size=10000)\n",
        "\n",
        "normally_distributed = pd.DataFrame(normally_distributed) # Convert to DF\n",
        "\n",
        "normally_distributed.hist(figsize=(8,8),            # Plot histogram\n",
        "                          bins=30);"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-08-23T12:57:17.823180Z",
          "iopub.execute_input": "2024-08-23T12:57:17.823529Z",
          "iopub.status.idle": "2024-08-23T12:57:18.365688Z",
          "shell.execute_reply.started": "2024-08-23T12:57:17.823468Z",
          "shell.execute_reply": "2024-08-23T12:57:18.363673Z"
        },
        "trusted": true,
        "id": "8UWOABM9iX6F",
        "outputId": "e77ceb25-401b-4bf2-f826-9b638c819d94"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 576x576 with 1 Axes>",
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "*Note: We will cover probability distributions and generating random data in a future lesson.*\n",
        "\n",
        "Notice how the normally distributed data looks roughly symmetric with a bell-shaped curve. Now let's generate some skewed data:"
      ],
      "metadata": {
        "id": "9btbZFz9iX6F"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "skewed = np.random.exponential(scale=2,      # Generate skewed data\n",
        "                               size= 10000)\n",
        "\n",
        "skewed = pd.DataFrame(skewed)                # Convert to DF\n",
        "\n",
        "skewed.hist(figsize=(8,8),                   # Plot histogram\n",
        "            bins=50);\n",
        "\n",
        "#### Test with another random set of skewed data\n",
        "skewed2 = np.random.exponential(scale=50,      # Generate skewed data\n",
        "                               size= 10000)\n",
        "\n",
        "skewed2 = pd.DataFrame(skewed2)                # Convert to DF\n",
        "\n",
        "skewed2.hist(figsize=(8,8),                   # Plot histogram\n",
        "            bins=50);"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-08-23T12:59:22.360630Z",
          "iopub.execute_input": "2024-08-23T12:59:22.361003Z",
          "iopub.status.idle": "2024-08-23T12:59:23.607928Z",
          "shell.execute_reply.started": "2024-08-23T12:59:22.360935Z",
          "shell.execute_reply": "2024-08-23T12:59:23.607004Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "OYNbHo3HiX6F",
        "outputId": "85993f00-b75f-4797-d965-925ff020d646"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Data with a long tail that goes off to the right is called positively skewed or right skewed. When you have a skewed distribution like the one above, the extreme values in the long tail can have a disproportionately large influence on whatever test you perform or models you build. Reducing skew may improve your results. **Taking the square root of each data point or taking the natural logarithm of each data point are two simple transformations that can reduce skew**. Let's see their effects on the skewed data:"
      ],
      "metadata": {
        "id": "A8ogKCTliX6G"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Get the square root of data points*\n",
        "sqrt_transformed = skewed.apply(np.sqrt)\n",
        "\n",
        "sqrt_transformed.hist(figsize=(8,8),     # Plot histogram\n",
        "                 bins=50);"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-08-23T13:01:22.706918Z",
          "iopub.execute_input": "2024-08-23T13:01:22.707258Z",
          "iopub.status.idle": "2024-08-23T13:01:23.406572Z",
          "shell.execute_reply.started": "2024-08-23T13:01:22.707205Z",
          "shell.execute_reply": "2024-08-23T13:01:23.405524Z"
        },
        "trusted": true,
        "id": "AaRaFjNUiX6G",
        "outputId": "73578442-71ce-47ae-ff4f-4e40fa22781b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 576x576 with 1 Axes>",
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAeoAAAHiCAYAAAAnPo9XAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAF7VJREFUeJzt3X+M5Hd93/HXOxwklkkwBLqybKuLFCcR5YRDT8QpVbVgpTJcFPsPQklpsJGjk1oSEeWk5NJ/qkj94/IHIVAhqhNOY1IaxyJBtjgrrWW8iioVgh3ADjgJV3Su72Rwww/DQZPokk//2K+Vi3O+nb3dnXnvzuMhrW7mO9+Z+eyHZZ/+fGf2OzXGCADQ03ctegAAwPMTagBoTKgBoDGhBoDGhBoAGhNqAGhMqAGgMaGGJVRVL6uqj1XVt6vqiar614seE3BxBxY9AGAhPpDkr5OsJLkhycmq+twY4/OLHRbwXOXMZLBcqurKJF9P8uoxxp9P2347ydkxxrGFDg74Bxz6huXzg0nOPxvpyeeS/JMFjQe4BKGG5fPiJN98zrZnknzvAsYCbEKoYfmcS/J9z9n2fUm+tYCxAJsQalg+f57kQFVdf8G21yTxRjJoyJvJYAlV1d1JRpKfzca7vu9P8s+86xv6saKG5fTvklyR5Okkv5Pk34o09GRFDQCNWVEDQGNCDQCNCTUANCbUANCYUANAYy0+PevlL3/5WF1d3fbjfPvb386VV165/QFxSeZ5fsz1fJjn+THXGx555JG/GGO8YpZ9W4R6dXU1Dz/88LYfZ319PWtra9sfEJdknufHXM+HeZ4fc72hqp6YdV+HvgGgMaEGgMaEGgAaE2oAaEyoAaAxoQaAxoQaABoTagBobKZQV9VVVfXRqvrTqnq8qn6sql5WVQ9U1Renf1867VtV9f6qOlVVj1bVa3f3WwCA/WvWFfX7kvzBGOOHk7wmyeNJjiV5cIxxfZIHp+tJ8qYk109fR5J8cEdHDABLZNNQV9VLkvyLJHcmyRjjr8cY30hyS5K7pt3uSnLrdPmWJB8eGz6Z5KqqunrHRw4AS2CWFfUrk/zfJP+lqj5TVR+qqiuTrIwxnpr2+XKSlenyNUmevOD+Z6ZtAMAWzfKhHAeSvDbJz48xPlVV78vfHeZOkowxRlWNrTxxVR3JxqHxrKysZH19fSt3v6hz587tyONwaeZ5fsz1fJjn+THXWzdLqM8kOTPG+NR0/aPZCPVXqurqMcZT06Htp6fbzya57oL7Xztt+3vGGCeSnEiSQ4cOjZ34NBWfyjIf5nl+zPV8mOf5Mddbt+mh7zHGl5M8WVU/NG26KckXktyX5LZp221J7p0u35fkHdO7v29M8swFh8gBgC2Y9fOofz7JR6rqRUm+lOSd2Yj8PVV1R5Inkrx12vf+JG9OcirJd6Z9AYDLMFOoxxifTXLoIjfddJF9R5J3bXNcAECcmQwAWhNqAGhs1teoWUKrx05edPvRg+dz+7GTOX388JxHBLB8rKgBoDGhBoDGhBoAGhNqAGhMqAGgMaEGgMaEGgAaE2oAaEyoAaAxoQaAxoQaABoTagBoTKgBoDGhBoDGhBoAGhNqAGhMqAGgMaEGgMaEGgAaE2oAaEyoAaAxoQaAxoQaABoTagBoTKgBoLEDix4Ai7F67OSihwDADKyoAaAxoQaAxoQaABoTagBoTKgBoDGhBoDGhBoAGhNqAGjMCU/YNZudVOX08cNzGgnA3mVFDQCNCTUANCbUANCYUANAY0INAI0JNQA0JtQA0JhQA0BjQg0AjQk1ADQm1ADQmFADQGNCDQCN+fQsLttmn44FwPZZUQNAY0INAI0JNQA0JtQA0Jg3k7Ewm70Z7fTxw3MaCUBfVtQA0JhQA0BjQg0AjXmNmra8hg0w44q6qk5X1WNV9dmqenja9rKqeqCqvjj9+9Jpe1XV+6vqVFU9WlWv3c1vAAD2s60c+n7DGOOGMcah6fqxJA+OMa5P8uB0PUnelOT66etIkg/u1GABYNls5zXqW5LcNV2+K8mtF2z/8NjwySRXVdXV23geAFhas4Z6JPkfVfVIVR2Ztq2MMZ6aLn85ycp0+ZokT15w3zPTNgBgi2Z9M9k/H2Ocrap/lOSBqvrTC28cY4yqGlt54in4R5JkZWUl6+vrW7n7RZ07d25HHmcZHD14/rLvu3LF9u6/U5bhf2s/0/NhnufHXG/dTKEeY5yd/n26qj6W5HVJvlJVV48xnpoObT897X42yXUX3P3aadtzH/NEkhNJcujQobG2tnbZ38Sz1tfXsxOPswxu38ZHVB49eD7veWzxfzBw+u1rix7CrvMzPR/meX7M9dZteui7qq6squ999nKSf5nkT5Lcl+S2abfbktw7Xb4vyTumd3/fmOSZCw6RAwBbMMuyaCXJx6rq2f3/2xjjD6rq00nuqao7kjyR5K3T/vcneXOSU0m+k+SdOz5qAFgSm4Z6jPGlJK+5yPavJrnpIttHknftyOgAYMk5hSgANCbUANCYUANAY0INAI0JNQA0tvizVrArNvuISAD2BitqAGhMqAGgMaEGgMaEGgAaE2oAaEyoAaAxoQaAxoQaABoTagBoTKgBoDGhBoDGhBoAGhNqAGhMqAGgMaEGgMaEGgAaE2oAaEyoAaCxA4seABe3euzkoocAQANW1ADQmFADQGNCDQCNCTUANCbUANCYUANAY0INAI0JNQA0JtQA0JhQA0BjTiHKvrXZaVhPHz88p5EAXD4ragBoTKgBoDGhBoDGhBoAGhNqAGhMqAGgMaEGgMb8HfWCbPY3vmzOHALLwIoaABoTagBoTKgBoDGhBoDGhBoAGhNqAGhMqAGgMaEGgMaEGgAaE2oAaEyoAaAxoQaAxoQaABoTagBoTKgBoDGhBoDGhBoAGps51FX1gqr6TFV9fLr+yqr6VFWdqqrfraoXTdu/e7p+arp9dXeGDgD734Et7PvuJI8n+b7p+q8lee8Y4+6q+s9J7kjywenfr48xfqCq3jbt9692cMywI1aPnbzk7aePH57TSACe30wr6qq6NsnhJB+arleSNyb56LTLXUlunS7fMl3PdPtN0/4AwBbNeuj7N5L8UpK/na5/f5JvjDHOT9fPJLlmunxNkieTZLr9mWl/AGCLNj30XVU/keTpMcYjVbW2U09cVUeSHEmSlZWVrK+vb/sxz507tyOPMw9HD57ffKemVq7Y2+OfVYefpb30M72Xmef5MddbN8tr1K9P8pNV9eYk35ON16jfl+SqqjowrZqvTXJ22v9skuuSnKmqA0lekuSrz33QMcaJJCeS5NChQ2NtbW2b38rGL9adeJx5uH2T10c7O3rwfN7z2Fbe3rA3nX772qKHsKd+pvcy8zw/5nrrNj30Pcb4lTHGtWOM1SRvS/KJMcbbkzyU5C3TbrcluXe6fN90PdPtnxhjjB0dNQAsie38HfUvJ/nFqjqVjdeg75y235nk+6ftv5jk2PaGCADLa0vHL8cY60nWp8tfSvK6i+zzl0l+agfGBgBLz5nJAKAxoQaAxoQaABoTagBoTKgBoDGhBoDGhBoAGhNqAGhMqAGgMaEGgMaEGgAaE2oAaEyoAaAxoQaAxoQaABoTagBo7MCiBwBdrR47uek+p48fnsNIgGVmRQ0AjQk1ADQm1ADQmFADQGNCDQCNCTUANCbUANCYUANAY0INAI05M9kumeWsVgCwGStqAGhMqAGgMaEGgMaEGgAaE2oAaEyoAaAxoQaAxoQaABoTagBoTKgBoDGhBoDGhBoAGhNqAGhMqAGgMaEGgMaEGgAaE2oAaEyoAaAxoQaAxoQaABoTagBo7MCiBwB72eqxk5e8/fTxw3MaCbBfWVEDQGNCDQCNCTUANCbUANCYUANAY0INAI0JNQA0JtQA0JhQA0BjQg0AjQk1ADQm1ADQ2Kahrqrvqao/qqrPVdXnq+pXp+2vrKpPVdWpqvrdqnrRtP27p+unpttXd/dbAID9a5YV9V8leeMY4zVJbkhyc1XdmOTXkrx3jPEDSb6e5I5p/zuSfH3a/t5pPwDgMmwa6rHh3HT1hdPXSPLGJB+dtt+V5Nbp8i3T9Uy331RVtWMjBoAlMtPnUVfVC5I8kuQHknwgyf9O8o0xxvlplzNJrpkuX5PkySQZY5yvqmeSfH+Sv9jBcS/cZp9DDAA7YaZQjzH+JskNVXVVko8l+eHtPnFVHUlyJElWVlayvr6+3YfMuXPnduRxZnH04PnNd9qnVq5Y7u9/K7b78zjPn+llZp7nx1xv3UyhftYY4xtV9VCSH0tyVVUdmFbV1yY5O+12Nsl1Sc5U1YEkL0ny1Ys81okkJ5Lk0KFDY21t7bK/iWetr69nJx5nFrcv8Yr66MHzec9jW/rRWVqn3762rfvP82d6mZnn+THXWzfLu75fMa2kU1VXJPnxJI8neSjJW6bdbkty73T5vul6pts/McYYOzloAFgWsyyLrk5y1/Q69XcluWeM8fGq+kKSu6vqPyb5TJI7p/3vTPLbVXUqydeSvG0Xxg0AS2HTUI8xHk3yIxfZ/qUkr7vI9r9M8lM7MjoAWHLOTAYAjQk1ADQm1ADQmFADQGNCDQCNCTUANCbUANCYUANAY0INAI0JNQA0JtQA0JhQA0BjQg0Ajc3yMZfAZVo9dvKSt58+fnhOIwH2KitqAGhMqAGgMaEGgMaEGgAaE2oAaEyoAaAxoQaAxoQaABoTagBoTKgBoDGhBoDGhBoAGhNqAGhMqAGgMaEGgMaEGgAaE2oAaEyoAaCxA4seACyz1WMnL3n7b9185ZxGAnRlRQ0AjQk1ADQm1ADQmFADQGNCDQCNCTUANCbUANCYUANAY0INAI0JNQA0JtQA0JhQA0BjQg0AjQk1ADQm1ADQmFADQGNCDQCNCTUANCbUANCYUANAY0INAI0JNQA0dmDRA+hq9djJRQ8BAKyoAaAzK2po7LGzz+T2SxzdOX388BxHAyyCFTUANCbUANCYUANAY16jhj1ss79O8Bo27H2brqir6rqqeqiqvlBVn6+qd0/bX1ZVD1TVF6d/Xzptr6p6f1WdqqpHq+q1u/1NAMB+Ncuh7/NJjo4xXpXkxiTvqqpXJTmW5MExxvVJHpyuJ8mbklw/fR1J8sEdHzUALIlNQz3GeGqM8cfT5W8leTzJNUluSXLXtNtdSW6dLt+S5MNjwyeTXFVVV+/4yAFgCdQYY/adq1aT/GGSVyf5P2OMq6btleTrY4yrqurjSY6PMf7ndNuDSX55jPHwcx7rSDZW3FlZWfmnd99997a/mXPnzuXFL37xth8n2fj7VS5u5YrkK/9v0aNYDtud64PXvGTnBrOP7eTvDi7NXG94wxve8MgY49As+878ZrKqenGS30vyC2OMb260ecMYY1TV7MXfuM+JJCeS5NChQ2NtbW0rd7+o9fX17MTjJLnkSSaW3dGD5/Oex7wPcR62O9en3762c4PZx3bydweXZq63bqY/z6qqF2Yj0h8ZY/z+tPkrzx7Snv59etp+Nsl1F9z92mkbALBFs7zru5LcmeTxMcavX3DTfUlumy7fluTeC7a/Y3r3941JnhljPLWDYwaApTHLMbXXJ/mZJI9V1Wenbf8+yfEk91TVHUmeSPLW6bb7k7w5yakk30nyzh0dMQAskU1DPb0prJ7n5psusv9I8q5tjgsAiFOIAkBrQg0AjQk1ADQm1ADQmFADQGNCDQCNCTUANOaEzbCPrW5yzvrTxw/PaSTA5bKiBoDGhBoAGhNqAGhMqAGgMaEGgMaEGgAaE2oAaEyoAaAxoQaAxoQaABoTagBoTKgBoDGhBoDGhBoAGhNqAGhMqAGgMaEGgMaEGgAaE2oAaEyoAaAxoQaAxoQaABoTagBoTKgBoDGhBoDGhBoAGhNqAGhMqAGgsQOLHsAirB47ueghAMBMrKgBoLGlXFEDGzY7unT6+OE5jQR4PlbUANCYUANAY0INAI0JNQA0JtQA0JhQA0BjQg0AjQk1ADQm1ADQmFADQGNCDQCNCTUANCbUANCYUANAY0INAI0JNQA0dmDRAwD6Wj12ctN9Th8/PIeRwPKyogaAxoQaABoTagBoTKgBoDGhBoDGNg11Vf1mVT1dVX9ywbaXVdUDVfXF6d+XTturqt5fVaeq6tGqeu1uDh4A9rtZVtS/leTm52w7luTBMcb1SR6crifJm5JcP30dSfLBnRkmACynTUM9xvjDJF97zuZbktw1Xb4rya0XbP/w2PDJJFdV1dU7NVgAWDaX+xr1yhjjqenyl5OsTJevSfLkBfudmbYBAJdh22cmG2OMqhpbvV9VHcnG4fGsrKxkfX19u0PJuXPnZnqcowfPb/u5ltnKFeZwXvbCXP+nj9x7ydsPXvOSOY3k8s36u4PtM9dbd7mh/kpVXT3GeGo6tP30tP1skusu2O/aads/MMY4keREkhw6dGisra1d5lD+zvr6emZ5nNtnOC0iz+/owfN5z2POPjsP+2GuT799bdFD2NSsvzvYPnO9dZd76Pu+JLdNl29Lcu8F298xvfv7xiTPXHCIHADYok3/U72qfifJWpKXV9WZJP8hyfEk91TVHUmeSPLWaff7k7w5yakk30nyzl0YMwAsjU1DPcb46ee56aaL7DuSvGu7gwIANjgzGQA0JtQA0JhQA0BjQg0AjQk1ADQm1ADQmFADQGNCDQCNCTUANCbUANCYUANAY0INAI0JNQA0trc/kR5ob/XYyUvefvr44TmNBPYmoQYWSsjh0hz6BoDGhBoAGhNqAGhMqAGgMaEGgMaEGgAa8+dZQGv+fItlty9Dvdn/sQFgr3DoGwAaE2oAaEyoAaAxoQaAxoQaABrbl+/6BpaHP99iv7OiBoDGhBoAGhNqAGhMqAGgMaEGgMaEGgAaE2oAaEyoAaAxoQaAxoQaABoTagBoTKgBoDGhBoDGhBoAGhNqAGjM51ED+5rPq2avs6IGgMaEGgAac+gbWGqrx07m6MHzuf15DpE7NM6iCTXANmz2Gngi9myPQ98A0JgVNcAlzLJiht1kRQ0AjQk1ADQm1ADQmNeoAXaZs6OxHVbUANCYUANAY0INAI15jRqgue3+LbfXwPc2oQZYMCdV4VIc+gaAxnYl1FV1c1X9WVWdqqpju/EcALAMdvzQd1W9IMkHkvx4kjNJPl1V940xvrDTzwXA5hZ9aN1r5NuzG69Rvy7JqTHGl5Kkqu5OcksSoQZYQhf+h8Lzffa3mD+/3Qj1NUmevOD6mSQ/ugvPA8A+sdtnb9vL75yvMcbOPmDVW5LcPMb42en6zyT50THGzz1nvyNJjkxXfyjJn+3A0788yV/swONwaeZ5fsz1fJjn+THXG/7xGOMVs+y4Gyvqs0muu+D6tdO2v2eMcSLJiZ184qp6eIxxaCcfk3/IPM+PuZ4P8zw/5nrrduNd359Ocn1VvbKqXpTkbUnu24XnAYB9b8dX1GOM81X1c0n+e5IXJPnNMcbnd/p5AGAZ7MqZycYY9ye5fzceexM7eiid52We58dcz4d5nh9zvUU7/mYyAGDnOIUoADS2L0LtlKXzUVW/WVVPV9WfLHos+1lVXVdVD1XVF6rq81X17kWPab+qqu+pqj+qqs9Nc/2rix7TflZVL6iqz1TVxxc9lr1kz4f6glOWvinJq5L8dFW9arGj2rd+K8nNix7EEjif5OgY41VJbkzyLj/Tu+avkrxxjPGaJDckubmqblzwmPazdyd5fNGD2Gv2fKhzwSlLxxh/neTZU5ayw8YYf5jka4sex343xnhqjPHH0+VvZeMX2zWLHdX+NDacm66+cPryxp1dUFXXJjmc5EOLHstesx9CfbFTlvqlxr5QVatJfiTJpxY7kv1rOhz72SRPJ3lgjGGud8dvJPmlJH+76IHsNfsh1LAvVdWLk/xekl8YY3xz0ePZr8YYfzPGuCEbZ1F8XVW9etFj2m+q6ieSPD3GeGTRY9mL9kOoZzplKewlVfXCbET6I2OM31/0eJbBGOMbSR6K92Hshtcn+cmqOp2NlyffWFX/dbFD2jv2Q6idspR9paoqyZ1JHh9j/Pqix7OfVdUrquqq6fIVSX48yZ8udlT7zxjjV8YY144xVrPxO/oTY4x/s+Bh7Rl7PtRjjPNJnj1l6eNJ7nHK0t1RVb+T5H8l+aGqOlNVdyx6TPvU65P8TDZWHZ+dvt686EHtU1cneaiqHs3Gf/Q/MMbwp0O04sxkANDYnl9RA8B+JtQA0JhQA0BjQg0AjQk1ADQm1ADQmFADQGNCDQCN/X9xP1oNQqrxEgAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "*Note: The df.apply() function applies a given function to each row or column of the DataFrame. In this case we pass in np.sqrt to get the square root of each value.*\n",
        "\n",
        "Now let's look at a log transformation:"
      ],
      "metadata": {
        "id": "a1Prf2YpiX6G"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "log_transformed = (skewed+1).apply(np.log)   # Get the log of the data\n",
        "\n",
        "log_transformed.hist(figsize = (8,8),          # Plot histogram\n",
        "                 bins=50);"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-08-23T13:02:49.615596Z",
          "iopub.execute_input": "2024-08-23T13:02:49.615969Z",
          "iopub.status.idle": "2024-08-23T13:02:50.229487Z",
          "shell.execute_reply.started": "2024-08-23T13:02:49.615904Z",
          "shell.execute_reply": "2024-08-23T13:02:50.228175Z"
        },
        "trusted": true,
        "id": "T44Z2vKHiX6H",
        "outputId": "bb1e54e1-ff08-446a-856e-8135fa2c4fc3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 576x576 with 1 Axes>",
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "*Note: Adding 1 before taking the log ensures we don't end up with negative values.*\n",
        "\n",
        "Both the sqrt() and log() transforms reduced the skew of the data. It's still not quite normally distributed, but the amount of extreme data in the tails has been reduced to the point where we might not be so worried about it having a large influence on our results."
      ],
      "metadata": {
        "id": "F_Y4QNVyiX6H"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Highly Correlated Variables"
      ],
      "metadata": {
        "id": "n6meKVwViX6H"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "In predictive modeling, each variable you use to construct a model would ideally represent some unique feature of the data. In other words, you want each variable to tell you something different. In reality, variables often exhibit collinearity--a strong correlation or tendency to move together, typically due to some underlying similarity or common influencing factor. Variables with strong correlations can interfere with one another when performing modeling and muddy results.\n",
        "\n",
        "You can check the pairwise correlations between numeric variables using the df.corr() function:"
      ],
      "metadata": {
        "id": "9nMnFoVhiX6H"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "mtcars.iloc[:,0:6].corr()   # Check the pairwise correlations of 6 variables\n",
        "# OR just get the co-relations of all vraibles as below\n",
        "#mtcars.corr()"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-08-23T13:04:16.674260Z",
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      "execution_count": 34,
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          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           mpg       cyl      disp        hp      drat        wt\n",
              "mpg   1.000000 -0.852162 -0.847551 -0.776168  0.681172 -0.867659\n",
              "cyl  -0.852162  1.000000  0.902033  0.832447 -0.699938  0.782496\n",
              "disp -0.847551  0.902033  1.000000  0.790949 -0.710214  0.887980\n",
              "hp   -0.776168  0.832447  0.790949  1.000000 -0.448759  0.658748\n",
              "drat  0.681172 -0.699938 -0.710214 -0.448759  1.000000 -0.712441\n",
              "wt   -0.867659  0.782496  0.887980  0.658748 -0.712441  1.000000"
            ],
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              "      <th>mpg</th>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"#mtcars\",\n  \"rows\": 6,\n  \"fields\": [\n    {\n      \"column\": \"mpg\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.8721479347729922,\n        \"min\": -0.8676593765172276,\n        \"max\": 1.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          1.0,\n          -0.8521619594266132,\n          -0.8676593765172276\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"cyl\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.8592518592208531,\n        \"min\": -0.8521619594266132,\n        \"max\": 1.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          -0.8521619594266132,\n          1.0,\n          0.7824957944632407\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"disp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.8681357130221891,\n        \"min\": -0.8475513792624786,\n        \"max\": 1.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          -0.8475513792624786,\n          0.9020328721469987,\n          0.8879799220581379\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.755113596048875,\n        \"min\": -0.7761683718265863,\n        \"max\": 1.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          -0.7761683718265863,\n          0.8324474527218193,\n          0.658747887344759\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"drat\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.7791275546571967,\n        \"min\": -0.7124406466973717,\n        \"max\": 1.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          0.6811719078067492,\n          -0.6999381138287701,\n          -0.7124406466973717\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"wt\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.8467887231265945,\n        \"min\": -0.8676593765172276,\n        \"max\": 1.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          -0.8676593765172276,\n          0.7824957944632407,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "A positive correlation implies that when one variable goes up the other tends to go up as well. Negative correlations indicate an inverse relationship: when one variable goes up the other tends to go down. A correlation near zero indicates low correlation while a correlation near -1 or 1 indicates a large negative or positive correlation.\n",
        "\n",
        "Inspecting the data table, we see that the number of cylinders a car has (cyl) and its weight (wt) have fairly strong negative correlations to gas mileage (mpg.). This indicates that heavier cars and cars with more cylinders tend to get lower gas mileage.\n",
        "\n",
        "A scatter plot matrix can be a helpful visual aide for inspecting collinearity. We can create one with the pandas scatter_matrix() function located in the tools.plotting pandas folder:"
      ],
      "metadata": {
        "id": "pZeVYbqoiX6J"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#from pandas.tools.plotting import scatter_matrix\n",
        "from pandas.plotting import scatter_matrix\n",
        "\n",
        "scatter_matrix(mtcars.iloc[:,0:6], # Make a scatter matrix of 6 columns\n",
        "               figsize=(10, 10),   # Set plot size\n",
        "               diagonal='kde');    # Show distribution estimates on diagonal"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-08-23T13:06:48.900421Z",
          "iopub.execute_input": "2024-08-23T13:06:48.900777Z",
          "iopub.status.idle": "2024-08-23T13:06:51.946677Z",
          "shell.execute_reply.started": "2024-08-23T13:06:48.900726Z",
          "shell.execute_reply": "2024-08-23T13:06:51.945548Z"
        },
        "trusted": true,
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          "height": 857
        },
        "id": "OiKr1XKviX6K",
        "outputId": "d952335f-c353-4cee-9425-844b9b447f6c"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 36 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "A scatter plot matrix creates pairwise scatter plots that let you visually inspect the relationships between pairs of variables. It can also help identify oddities in the data, such as variables like cyl that only take on values in a small discrete set.\n",
        "\n",
        "If you find highly correlated variables, there are a few things you can do including:\n",
        "\n",
        "1. Leave them be\n",
        "2. Remove one or more variables\n",
        "3. Combine them in some way\n",
        "\n",
        "Reducing the number of variables under consideration, either by removing some or by combining them some way is known as \"dimensionality reduction.\" How you choose to handle correlated variables is ultimately a subjective decision that should be informed by your goal."
      ],
      "metadata": {
        "id": "gBfXw1Q9iX6K"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Imputing with Sklearn"
      ],
      "metadata": {
        "id": "vS_-4B4ZiX6K"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "In the lesson on initial data exploration, we explored Titanic survivor data and found that several passengers had missing listed for age. Missing values in numeric data are troublesome because you can't simply treat them as a category: you have to either remove them or fill them in.\n",
        "\n",
        "Imputation describes filling in missing data with estimates based on the rest of the data set. When working with the titanic data set, we set all the missing Age values to the median age for the data set. Other simple imputation methods include setting missing values to the mean or most common value (mode.). The scikit-learn offers an Imupter tool that can automatically carry out these imputations for us. Let's start by loading the Imputer and introducing some missing values into the mpg data:"
      ],
      "metadata": {
        "id": "OO7ALUAziX6L"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#from sklearn.preprocessing import Imputer #deprecated from sicikit-learn v0.20.4\n",
        "from sklearn.impute import SimpleImputer\n",
        "\n",
        "# The following line sets a few mpg values to None\n",
        "mtcars[\"mpg\"] = np.where(mtcars[\"mpg\"]>22, np.nan, mtcars[\"mpg\"])\n",
        "\n",
        "mtcars[\"mpg\"]       # Confirm that missing values were added"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-08-23T13:10:47.357278Z",
          "iopub.execute_input": "2024-08-23T13:10:47.357762Z",
          "iopub.status.idle": "2024-08-23T13:10:47.370970Z",
          "shell.execute_reply.started": "2024-08-23T13:10:47.357684Z",
          "shell.execute_reply": "2024-08-23T13:10:47.369323Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "VHgexdkriX6L",
        "outputId": "8a2a650d-7d29-4476-8e6d-a779a81ca0ac"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "model\n",
              "Mazda RX4              21.0\n",
              "Mazda RX4 Wag          21.0\n",
              "Datsun 710             None\n",
              "Hornet 4 Drive         21.4\n",
              "Hornet Sportabout      18.7\n",
              "Valiant                18.1\n",
              "Duster 360             14.3\n",
              "Merc 240D              None\n",
              "Merc 230               None\n",
              "Merc 280               19.2\n",
              "Merc 280C              17.8\n",
              "Merc 450SE             16.4\n",
              "Merc 450SL             17.3\n",
              "Merc 450SLC            15.2\n",
              "Cadillac Fleetwood     10.4\n",
              "Lincoln Continental    10.4\n",
              "Chrysler Imperial      14.7\n",
              "Fiat 128               None\n",
              "Honda Civic            None\n",
              "Toyota Corolla         None\n",
              "Toyota Corona          21.5\n",
              "Dodge Challenger       15.5\n",
              "AMC Javelin            15.2\n",
              "Camaro Z28             13.3\n",
              "Pontiac Firebird       19.2\n",
              "Fiat X1-9              None\n",
              "Porsche 914-2          None\n",
              "Lotus Europa           None\n",
              "Ford Pantera L         15.8\n",
              "Ferrari Dino           19.7\n",
              "Maserati Bora          15.0\n",
              "Volvo 142E             21.4\n",
              "Name: mpg, dtype: object"
            ],
            "text/html": [
              "<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>mpg</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>model</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Mazda RX4</th>\n",
              "      <td>21.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mazda RX4 Wag</th>\n",
              "      <td>21.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Datsun 710</th>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Hornet 4 Drive</th>\n",
              "      <td>21.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Hornet Sportabout</th>\n",
              "      <td>18.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Valiant</th>\n",
              "      <td>18.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Duster 360</th>\n",
              "      <td>14.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Merc 240D</th>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Merc 230</th>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Merc 280</th>\n",
              "      <td>19.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Merc 280C</th>\n",
              "      <td>17.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Merc 450SE</th>\n",
              "      <td>16.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Merc 450SL</th>\n",
              "      <td>17.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Merc 450SLC</th>\n",
              "      <td>15.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Cadillac Fleetwood</th>\n",
              "      <td>10.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Lincoln Continental</th>\n",
              "      <td>10.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Chrysler Imperial</th>\n",
              "      <td>14.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Fiat 128</th>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Honda Civic</th>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Toyota Corolla</th>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Toyota Corona</th>\n",
              "      <td>21.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Dodge Challenger</th>\n",
              "      <td>15.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>AMC Javelin</th>\n",
              "      <td>15.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Camaro Z28</th>\n",
              "      <td>13.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Pontiac Firebird</th>\n",
              "      <td>19.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Fiat X1-9</th>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Porsche 914-2</th>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Lotus Europa</th>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Ford Pantera L</th>\n",
              "      <td>15.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Ferrari Dino</th>\n",
              "      <td>19.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Maserati Bora</th>\n",
              "      <td>15.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Volvo 142E</th>\n",
              "      <td>21.4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> object</label>"
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now let's use the Imputer fill in missing values based on the mean:"
      ],
      "metadata": {
        "id": "DIBIu97ciX6L"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# imp = Imputer(missing_values='NaN',  # Create imputation model\n",
        "#               strategy='mean',       # Use mean imputation\n",
        "#               axis=0)                # Impute by column\n",
        "\n",
        "######## IMP ########\n",
        "#Mehod mentioned above has been deprecared in the latest sicit-learn version\n",
        "#Use the SimpleImputer method below\n",
        "######## IMP ########\n",
        "\n",
        "imp = SimpleImputer(missing_values=np.nan, strategy='mean')\n",
        "\n",
        "imputed_cars = imp.fit_transform(mtcars)   # Use imputation model to get values\n",
        "\n",
        "imputed_cars = pd.DataFrame(imputed_cars,  # Remake DataFrame with new values\n",
        "                           index=mtcars.index,\n",
        "                           columns = mtcars.columns)\n",
        "\n",
        "imputed_cars.head(15)"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-08-23T13:12:00.795970Z",
          "iopub.execute_input": "2024-08-23T13:12:00.796390Z",
          "iopub.status.idle": "2024-08-23T13:12:00.846565Z",
          "shell.execute_reply.started": "2024-08-23T13:12:00.796317Z",
          "shell.execute_reply": "2024-08-23T13:12:00.845054Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 551
        },
        "id": "gCrhJCMPiX6L",
        "outputId": "bea0b96a-7f3f-478b-cf4d-9ce4465044bb"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                          mpg  cyl   disp     hp  drat     wt   qsec   vs  \\\n",
              "model                                                                       \n",
              "Mazda RX4           21.000000  6.0  160.0  110.0  3.90  2.620  16.46  0.0   \n",
              "Mazda RX4 Wag       21.000000  6.0  160.0  110.0  3.90  2.875  17.02  0.0   \n",
              "Datsun 710          17.065217  4.0  108.0   93.0  3.85  2.320  18.61  1.0   \n",
              "Hornet 4 Drive      21.400000  6.0  258.0  110.0  3.08  3.215  19.44  1.0   \n",
              "Hornet Sportabout   18.700000  8.0  360.0  175.0  3.15  3.440  17.02  0.0   \n",
              "Valiant             18.100000  6.0  225.0  105.0  2.76  3.460  20.22  1.0   \n",
              "Duster 360          14.300000  8.0  360.0  245.0  3.21  3.570  15.84  0.0   \n",
              "Merc 240D           17.065217  4.0  146.7   62.0  3.69  3.190  20.00  1.0   \n",
              "Merc 230            17.065217  4.0  140.8   95.0  3.92  3.150  22.90  1.0   \n",
              "Merc 280            19.200000  6.0  167.6  123.0  3.92  3.440  18.30  1.0   \n",
              "Merc 280C           17.800000  6.0  167.6  123.0  3.92  3.440  18.90  1.0   \n",
              "Merc 450SE          16.400000  8.0  275.8  180.0  3.07  4.070  17.40  0.0   \n",
              "Merc 450SL          17.300000  8.0  275.8  180.0  3.07  3.730  17.60  0.0   \n",
              "Merc 450SLC         15.200000  8.0  275.8  180.0  3.07  3.780  18.00  0.0   \n",
              "Cadillac Fleetwood  10.400000  8.0  472.0  205.0  2.93  5.250  17.98  0.0   \n",
              "\n",
              "                     am  gear  carb  \n",
              "model                                \n",
              "Mazda RX4           1.0   4.0   4.0  \n",
              "Mazda RX4 Wag       1.0   4.0   4.0  \n",
              "Datsun 710          1.0   4.0   1.0  \n",
              "Hornet 4 Drive      0.0   3.0   1.0  \n",
              "Hornet Sportabout   0.0   3.0   2.0  \n",
              "Valiant             0.0   3.0   1.0  \n",
              "Duster 360          0.0   3.0   4.0  \n",
              "Merc 240D           0.0   4.0   2.0  \n",
              "Merc 230            0.0   4.0   2.0  \n",
              "Merc 280            0.0   4.0   4.0  \n",
              "Merc 280C           0.0   4.0   4.0  \n",
              "Merc 450SE          0.0   3.0   3.0  \n",
              "Merc 450SL          0.0   3.0   3.0  \n",
              "Merc 450SLC         0.0   3.0   3.0  \n",
              "Cadillac Fleetwood  0.0   3.0   4.0  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-e9e0305b-4436-4291-9e37-fe929c087725\" class=\"colab-df-container\">\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>mpg</th>\n",
              "      <th>cyl</th>\n",
              "      <th>disp</th>\n",
              "      <th>hp</th>\n",
              "      <th>drat</th>\n",
              "      <th>wt</th>\n",
              "      <th>qsec</th>\n",
              "      <th>vs</th>\n",
              "      <th>am</th>\n",
              "      <th>gear</th>\n",
              "      <th>carb</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>model</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>Mazda RX4</th>\n",
              "      <td>21.000000</td>\n",
              "      <td>6.0</td>\n",
              "      <td>160.0</td>\n",
              "      <td>110.0</td>\n",
              "      <td>3.90</td>\n",
              "      <td>2.620</td>\n",
              "      <td>16.46</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mazda RX4 Wag</th>\n",
              "      <td>21.000000</td>\n",
              "      <td>6.0</td>\n",
              "      <td>160.0</td>\n",
              "      <td>110.0</td>\n",
              "      <td>3.90</td>\n",
              "      <td>2.875</td>\n",
              "      <td>17.02</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Datsun 710</th>\n",
              "      <td>17.065217</td>\n",
              "      <td>4.0</td>\n",
              "      <td>108.0</td>\n",
              "      <td>93.0</td>\n",
              "      <td>3.85</td>\n",
              "      <td>2.320</td>\n",
              "      <td>18.61</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Hornet 4 Drive</th>\n",
              "      <td>21.400000</td>\n",
              "      <td>6.0</td>\n",
              "      <td>258.0</td>\n",
              "      <td>110.0</td>\n",
              "      <td>3.08</td>\n",
              "      <td>3.215</td>\n",
              "      <td>19.44</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Hornet Sportabout</th>\n",
              "      <td>18.700000</td>\n",
              "      <td>8.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>175.0</td>\n",
              "      <td>3.15</td>\n",
              "      <td>3.440</td>\n",
              "      <td>17.02</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Valiant</th>\n",
              "      <td>18.100000</td>\n",
              "      <td>6.0</td>\n",
              "      <td>225.0</td>\n",
              "      <td>105.0</td>\n",
              "      <td>2.76</td>\n",
              "      <td>3.460</td>\n",
              "      <td>20.22</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Duster 360</th>\n",
              "      <td>14.300000</td>\n",
              "      <td>8.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>245.0</td>\n",
              "      <td>3.21</td>\n",
              "      <td>3.570</td>\n",
              "      <td>15.84</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Merc 240D</th>\n",
              "      <td>17.065217</td>\n",
              "      <td>4.0</td>\n",
              "      <td>146.7</td>\n",
              "      <td>62.0</td>\n",
              "      <td>3.69</td>\n",
              "      <td>3.190</td>\n",
              "      <td>20.00</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Merc 230</th>\n",
              "      <td>17.065217</td>\n",
              "      <td>4.0</td>\n",
              "      <td>140.8</td>\n",
              "      <td>95.0</td>\n",
              "      <td>3.92</td>\n",
              "      <td>3.150</td>\n",
              "      <td>22.90</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Merc 280</th>\n",
              "      <td>19.200000</td>\n",
              "      <td>6.0</td>\n",
              "      <td>167.6</td>\n",
              "      <td>123.0</td>\n",
              "      <td>3.92</td>\n",
              "      <td>3.440</td>\n",
              "      <td>18.30</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Merc 280C</th>\n",
              "      <td>17.800000</td>\n",
              "      <td>6.0</td>\n",
              "      <td>167.6</td>\n",
              "      <td>123.0</td>\n",
              "      <td>3.92</td>\n",
              "      <td>3.440</td>\n",
              "      <td>18.90</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Merc 450SE</th>\n",
              "      <td>16.400000</td>\n",
              "      <td>8.0</td>\n",
              "      <td>275.8</td>\n",
              "      <td>180.0</td>\n",
              "      <td>3.07</td>\n",
              "      <td>4.070</td>\n",
              "      <td>17.40</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Merc 450SL</th>\n",
              "      <td>17.300000</td>\n",
              "      <td>8.0</td>\n",
              "      <td>275.8</td>\n",
              "      <td>180.0</td>\n",
              "      <td>3.07</td>\n",
              "      <td>3.730</td>\n",
              "      <td>17.60</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Merc 450SLC</th>\n",
              "      <td>15.200000</td>\n",
              "      <td>8.0</td>\n",
              "      <td>275.8</td>\n",
              "      <td>180.0</td>\n",
              "      <td>3.07</td>\n",
              "      <td>3.780</td>\n",
              "      <td>18.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Cadillac Fleetwood</th>\n",
              "      <td>10.400000</td>\n",
              "      <td>8.0</td>\n",
              "      <td>472.0</td>\n",
              "      <td>205.0</td>\n",
              "      <td>2.93</td>\n",
              "      <td>5.250</td>\n",
              "      <td>17.98</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.0</td>\n",
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              "\n",
              "    <script>\n",
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              "        document.querySelector('#df-e9e0305b-4436-4291-9e37-fe929c087725 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-e9e0305b-4436-4291-9e37-fe929c087725');\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",
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              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "      --disabled-fill-color: #666;\n",
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              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
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              "    border-radius: 50%;\n",
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              "    display: none;\n",
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              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
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              "  .colab-df-quickchart-complete:disabled,\n",
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              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
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              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "      border-top-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
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              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-d0e8189c-dcf6-4ef9-9436-d03e397658f6 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "\n",
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              "type": "dataframe",
              "variable_name": "imputed_cars",
              "summary": "{\n  \"name\": \"imputed_cars\",\n  \"rows\": 32,\n  \"fields\": [\n    {\n      \"column\": \"model\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 32,\n        \"samples\": [\n          \"Ferrari Dino\",\n          \"Lincoln Continental\",\n          \"Pontiac Firebird\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mpg\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.785863935687221,\n        \"min\": 10.4,\n        \"max\": 21.5,\n        \"num_unique_values\": 19,\n        \"samples\": [\n          21.0,\n          14.3,\n          10.4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"cyl\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.7859216469465444,\n        \"min\": 4.0,\n        \"max\": 8.0,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          6.0,\n          4.0,\n          8.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"disp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 123.93869383138194,\n        \"min\": 71.1,\n        \"max\": 472.0,\n        \"num_unique_values\": 27,\n        \"samples\": [\n          275.8,\n          75.7,\n          472.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 68.56286848932059,\n        \"min\": 52.0,\n        \"max\": 335.0,\n        \"num_unique_values\": 22,\n        \"samples\": [\n          110.0,\n          52.0,\n          180.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"drat\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.5346787360709715,\n        \"min\": 2.76,\n        \"max\": 4.93,\n        \"num_unique_values\": 22,\n        \"samples\": [\n          3.9,\n          4.93,\n          3.07\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"wt\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.9784574429896966,\n        \"min\": 1.513,\n        \"max\": 5.424,\n        \"num_unique_values\": 29,\n        \"samples\": [\n          2.77,\n          1.615,\n          5.25\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"qsec\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.7869432360968431,\n        \"min\": 14.5,\n        \"max\": 22.9,\n        \"num_unique_values\": 30,\n        \"samples\": [\n          15.5,\n          17.42,\n          17.05\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"vs\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.5040161287741853,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1.0,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"am\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.49899091723584604,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.0,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gear\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.7378040652569471,\n        \"min\": 3.0,\n        \"max\": 5.0,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          4.0,\n          3.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"carb\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.6151999776318522,\n        \"min\": 1.0,\n        \"max\": 8.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          4.0,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Imputer supports the \"mean\", \"median\" and \"most_frequent\" (mode) as strategies for imputation.\n",
        "\n",
        "Other imputation methods include filling in values based on \"similar\" or \"neighboring\" records (K-nearest-neighbors imputation) and filling in values based on regression models. Using predictive models to fill in missing values adds an extra layer of complexity to an analysis and can significantly increase processing time, although it may result in better predictive performance. We'll revisit predictive modeling in a future lesson. In the example above, we created missing values by setting the largest mpg values to \"NaN\". In this case, it would be difficult to find a good estimate for the missing mpg values, since all the missing values were greater than any of the mpg values we had in hand."
      ],
      "metadata": {
        "id": "fCShKhSJiX6M"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Wrap Up"
      ],
      "metadata": {
        "id": "72rrNmLUiX6M"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "In the past two lessons, we covered a variety of methods for preparing text data and numeric data. The majority of data you encounter will likely fall in one of these two categories, but there is one other type of data that appears with enough frequency that you will have to deal with it sooner or later: dates."
      ],
      "metadata": {
        "id": "clgd3TqjiX6M"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Next Lesson: [Python for Data 17: Dealing With Dates](https://www.kaggle.com/hamelg/python-for-data-17-dealing-with-dates)\n",
        "[back to index](https://www.kaggle.com/hamelg/python-for-data-analysis-index)"
      ],
      "metadata": {
        "id": "gFW6GaZwiX6M"
      }
    }
  ]
}