tutorials/scale_numerical_data.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "6477b6a3",
   "metadata": {},
   "source": [
    "# How to Scale Numerical Data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be643cf1",
   "metadata": {},
   "source": [
    "In this short tutorial, we will focus on:\n",
    "\n",
    "1. Why scaling is important\n",
    "2. Numerical Data Scaling Methods\n",
    "3. Scaling the Diabetes Dataset\n",
    "4. Performing `MinMaxScaler` Transform\n",
    "5. Performing `StandardScaler` Transform"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d658aa1",
   "metadata": {},
   "source": [
    "## Why is scaling important"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9333a8b8",
   "metadata": {},
   "source": [
    "Many machine learning algorithms -- like SVM, Neural Networks, and K-nearest neighbours -- perform better when numerical input variables are scaled to a standard range.\n",
    "\n",
    "Feature scaling is extremely essential to these models, especially when the range of the features is very different. Otherwise, features with a large range will have a large influence in computing the distance.\n",
    "\n",
    "The two most popular techniques for scaling numerical data prior to modeling are **normalization** and **standardization**. \n",
    "\n",
    "- **Normalization** scales each input variable separately to the range 0-1, which is the range for floating-point values where we have the most precision. \n",
    "\n",
    "- **Standardization (or Z-score Normalization)** scales each input variable separately by subtracting the mean (called centering) and dividing by the standard deviation to shift the distribution to have a mean of 0 and a standard deviation of 1."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a0c1e66",
   "metadata": {},
   "source": [
    "## Numerical Data Scaling Methods\n",
    "\n",
    "Both normalization and standardization can be achieved using the scikit-learn library."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ebc3504a",
   "metadata": {},
   "source": [
    "### Data Normalization\n",
    "\n",
    "Normalization is a rescaling of the data from the original range so that all values are within the new range of 0 and 1:\n",
    "\n",
    "\n",
    "$x_{i_{norm}} = \\frac{x_i - min(x)}{max(x) - min(x)}$\n",
    "\n",
    "- Normalization requires that you know or are able to accurately estimate the minimum and maximum observable values. \n",
    "- You may be able to estimate these values from your available data.\n",
    "\n",
    "You can normalize your dataset using the scikit-learn object `MinMaxScale`. Good practice usage is as follows:\n",
    "\n",
    "- **Fit the scaler using available training data**. For normalization, this means the training data will be used to estimate the minimum and maximum observable values. This is done by calling the `fit()` function.\n",
    "\n",
    "\n",
    "- **Apply the scale to training data**. This means you can use the normalized data to train your model. This is done by calling the`transform()` function.\n",
    "\n",
    "- **Apply the scale to data going forward**. This means you can prepare new data in the future on which you want to make predictions.\n",
    "\n",
    "If needed, the transform can be inverted. This is useful for converting predictions back into their original scale for reporting or plotting. This can be done by calling the `inverse_transform()` function. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e7dc773c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1.0e+02 1.0e-03]\n",
      " [8.0e+00 5.0e-02]\n",
      " [5.0e+01 5.0e-03]\n",
      " [8.8e+01 7.0e-02]\n",
      " [4.0e+00 1.0e-01]]\n"
     ]
    }
   ],
   "source": [
    "# Normalization example\n",
    "\n",
    "from numpy import asarray\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "# Define Data\n",
    "data = asarray([[100, 0.001],\n",
    "       [8, 0.05],\n",
    "       [50, 0.005],\n",
    "       [88, 0.07],\n",
    "       [4, 0.1]])\n",
    "print(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "34791f48",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1.         0.        ]\n",
      " [0.04166667 0.49494949]\n",
      " [0.47916667 0.04040404]\n",
      " [0.875      0.6969697 ]\n",
      " [0.         1.        ]]\n"
     ]
    }
   ],
   "source": [
    "# Define Min Max scaler\n",
    "scaler = MinMaxScaler()\n",
    "\n",
    "# Transform\n",
    "scaled = scaler.fit_transform(data)\n",
    "print(scaled)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48e61bb5",
   "metadata": {},
   "source": [
    "Running the example first reports the raw dataset, showing 2 columns with 4 rows. Next, the scaler is defined, fit on the whole dataset and then used to create a transformed version of the dataset with each column normalized independently.\n",
    "\n",
    "**We can see that the largest raw value for each column now has the value 1.0 and the smallest value for each column now has the value 0.0.**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "386ad362",
   "metadata": {},
   "source": [
    "## Data Standardization\n",
    "\n",
    "Standardizing a dataset involves rescaling the distribution of values so that the mean of observed values is 0 and the standard deviation is 1.\n",
    "\n",
    "$x_{i_{stand}} = \\frac{x_i - mean(x)}{std(x)}$\n",
    "\n",
    "- Standardization assumes that your observations fit a Gaussian distribution (bell curve) with a well-behaved mean and standard deviation. \n",
    "- You can still standardize your data if this expectation is not met, but you may not get reliable results.\n",
    "- Standardization requires that you know or are able to accurately estimate the mean and standard deviation of observable values. You may be able to estimate these values from your training data, not the entire dataset.\n",
    "\n",
    "You can standardize your dataset using the scikit-learn object `StandardScaler`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1d7c4611",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1.0e+02 1.0e-03]\n",
      " [8.0e+00 5.0e-02]\n",
      " [5.0e+01 5.0e-03]\n",
      " [8.8e+01 7.0e-02]\n",
      " [4.0e+00 1.0e-01]]\n"
     ]
    }
   ],
   "source": [
    "# Standardization Example\n",
    "\n",
    "from numpy import asarray\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# Define data\n",
    "data = asarray([[100, 0.001],\n",
    "       [8, 0.05],\n",
    "       [50, 0.005],\n",
    "       [88, 0.07],\n",
    "       [4, 0.1]])\n",
    "\n",
    "print(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "fb624cd1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1.26398112 -1.16389967]\n",
      " [-1.06174414  0.12639634]\n",
      " [ 0.         -1.05856939]\n",
      " [ 0.96062565  0.65304778]\n",
      " [-1.16286263  1.44302493]]\n"
     ]
    }
   ],
   "source": [
    "# Define Standard Scaler\n",
    "scaler = StandardScaler()\n",
    "\n",
    "# Transform data\n",
    "scaled = scaler.fit_transform(data)\n",
    "print(scaled)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2113d88f",
   "metadata": {},
   "source": [
    "Running the example first reports the raw dataset, showing 2 columns with 4 rows as before. Next, the scaler is defined, fit on the whole dataset and then used to create a transformed version of the dataset with each column standardized independently. \n",
    "\n",
    "**We can see that the mean value in each column is assigned a value of 0.0 if present and the values are centered around 0.0 with values both positive and negative.**\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77b89880",
   "metadata": {},
   "source": [
    "Now, we can introduce a real dataset that provides the basis for applying normalization and standardization transforms as a part of modeling: [Diabetes Dataset](https://www.kaggle.com/datasets/uciml/pima-indians-diabetes-database)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a527e188",
   "metadata": {},
   "source": [
    "## The Diabetes Dataset\n",
    "\n",
    "This dataset classifies patients data as either an onset of diabetes within five years, and we have used it before:\n",
    "\n",
    "- [Understand Your Data With Descriptive Statistics](https://github.com/Data-Centric-AI-Community/awesome-python-for-data-science/blob/main/tutorials/data_descriptive_statistics.ipynb)\n",
    "- [Understand Your Data With Visualization](https://github.com/Data-Centric-AI-Community/awesome-python-for-data-science/blob/main/tutorials/data_basic_visualization.ipynb)\n",
    "- [Introduction to Missing Data](https://github.com/Data-Centric-AI-Community/awesome-python-for-data-science/blob/main/tutorials/data_preparation/missing_data/missing_data_introduction.ipynb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "08e75a92",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(768, 9)\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Load the dataset\n",
    "dataset = pd.read_csv('diabetes.csv')\n",
    "print(dataset.shape)  # summarize the shape of the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "adc58ff8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       Pregnancies     Glucose  BloodPressure  SkinThickness     Insulin  \\\n",
      "count   768.000000  768.000000     768.000000     768.000000  768.000000   \n",
      "mean      3.845052  120.894531      69.105469      20.536458   79.799479   \n",
      "std       3.369578   31.972618      19.355807      15.952218  115.244002   \n",
      "min       0.000000    0.000000       0.000000       0.000000    0.000000   \n",
      "25%       1.000000   99.000000      62.000000       0.000000    0.000000   \n",
      "50%       3.000000  117.000000      72.000000      23.000000   30.500000   \n",
      "75%       6.000000  140.250000      80.000000      32.000000  127.250000   \n",
      "max      17.000000  199.000000     122.000000      99.000000  846.000000   \n",
      "\n",
      "              BMI  DiabetesPedigreeFunction         Age     Outcome  \n",
      "count  768.000000                768.000000  768.000000  768.000000  \n",
      "mean    31.992578                  0.471876   33.240885    0.348958  \n",
      "std      7.884160                  0.331329   11.760232    0.476951  \n",
      "min      0.000000                  0.078000   21.000000    0.000000  \n",
      "25%     27.300000                  0.243750   24.000000    0.000000  \n",
      "50%     32.000000                  0.372500   29.000000    0.000000  \n",
      "75%     36.600000                  0.626250   41.000000    1.000000  \n",
      "max     67.100000                  2.420000   81.000000    1.000000  \n"
     ]
    }
   ],
   "source": [
    "# summarize each variable\n",
    "print(dataset.describe())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae97b5b4",
   "metadata": {},
   "source": [
    "This confirms the 7 input variables, one output variable, and 768 rows of data. \n",
    "\n",
    "**A statistical summary of the input variables is provided show that each variable has a very different scale. This makes it a good dataset for exploring data scaling methods.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "378658c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# histograms of the variables\n",
    "fig = dataset.hist(xlabelsize=4, ylabelsize=4)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e9e9a58c",
   "metadata": {},
   "source": [
    "**The plots confirm the differing scale for each input variable and show that the variables have differing scales.**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5a31582",
   "metadata": {},
   "source": [
    "## MinMaxScaler Transform\n",
    "\n",
    "We can apply the MinMaxScaler to the diabetes dataset directly to normalize the input variables. We will use the default configuration and scale values to the range 0 and 1. First, a `MinMaxScaler` instance is defined with default hyperparameters. Once defined, we can call the `fit_transform()` function and pass it to our dataset to create a transformed version of our dataset:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "deed7822",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "# Load the data\n",
    "dataset = pd.read_csv('diabetes.csv') \n",
    "\n",
    "# retrieve just the numeric input values\n",
    "data = dataset.iloc[:,:-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "2e3f6ac7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>763</th>\n",
       "      <td>10</td>\n",
       "      <td>101</td>\n",
       "      <td>76</td>\n",
       "      <td>48</td>\n",
       "      <td>180</td>\n",
       "      <td>32.9</td>\n",
       "      <td>0.171</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>764</th>\n",
       "      <td>2</td>\n",
       "      <td>122</td>\n",
       "      <td>70</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>36.8</td>\n",
       "      <td>0.340</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>765</th>\n",
       "      <td>5</td>\n",
       "      <td>121</td>\n",
       "      <td>72</td>\n",
       "      <td>23</td>\n",
       "      <td>112</td>\n",
       "      <td>26.2</td>\n",
       "      <td>0.245</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>766</th>\n",
       "      <td>1</td>\n",
       "      <td>126</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.1</td>\n",
       "      <td>0.349</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>767</th>\n",
       "      <td>1</td>\n",
       "      <td>93</td>\n",
       "      <td>70</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>30.4</td>\n",
       "      <td>0.315</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>768 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0              6      148             72             35        0  33.6   \n",
       "1              1       85             66             29        0  26.6   \n",
       "2              8      183             64              0        0  23.3   \n",
       "3              1       89             66             23       94  28.1   \n",
       "4              0      137             40             35      168  43.1   \n",
       "..           ...      ...            ...            ...      ...   ...   \n",
       "763           10      101             76             48      180  32.9   \n",
       "764            2      122             70             27        0  36.8   \n",
       "765            5      121             72             23      112  26.2   \n",
       "766            1      126             60              0        0  30.1   \n",
       "767            1       93             70             31        0  30.4   \n",
       "\n",
       "     DiabetesPedigreeFunction  Age  \n",
       "0                       0.627   50  \n",
       "1                       0.351   31  \n",
       "2                       0.672   32  \n",
       "3                       0.167   21  \n",
       "4                       2.288   33  \n",
       "..                        ...  ...  \n",
       "763                     0.171   63  \n",
       "764                     0.340   27  \n",
       "765                     0.245   30  \n",
       "766                     0.349   47  \n",
       "767                     0.315   23  \n",
       "\n",
       "[768 rows x 8 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "1cae6db9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# perform a robust scaler transform of the dataset\n",
    "scaler = MinMaxScaler()\n",
    "\n",
    "scaled_data = scaler.fit_transform(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "21bf76bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.35294118, 0.74371859, 0.59016393, ..., 0.50074516, 0.23441503,\n",
       "        0.48333333],\n",
       "       [0.05882353, 0.42713568, 0.54098361, ..., 0.39642325, 0.11656704,\n",
       "        0.16666667],\n",
       "       [0.47058824, 0.91959799, 0.52459016, ..., 0.34724292, 0.25362938,\n",
       "        0.18333333],\n",
       "       ...,\n",
       "       [0.29411765, 0.6080402 , 0.59016393, ..., 0.390462  , 0.07130658,\n",
       "        0.15      ],\n",
       "       [0.05882353, 0.63316583, 0.49180328, ..., 0.4485842 , 0.11571307,\n",
       "        0.43333333],\n",
       "       [0.05882353, 0.46733668, 0.57377049, ..., 0.45305514, 0.10119556,\n",
       "        0.03333333]])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaled_data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a9772f3",
   "metadata": {},
   "source": [
    "The `fit_transform` method returns a **NumPy array**. If you want to convert it back to a DataFrame and preserve the column names, you can do so like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "a56ffc3f",
   "metadata": {},
   "outputs": [],
   "source": [
    "scaled_df = pd.DataFrame(scaled_data, columns=dataset.columns[:-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "7dfb8b11",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.352941</td>\n",
       "      <td>0.743719</td>\n",
       "      <td>0.590164</td>\n",
       "      <td>0.353535</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.500745</td>\n",
       "      <td>0.234415</td>\n",
       "      <td>0.483333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.058824</td>\n",
       "      <td>0.427136</td>\n",
       "      <td>0.540984</td>\n",
       "      <td>0.292929</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.396423</td>\n",
       "      <td>0.116567</td>\n",
       "      <td>0.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.470588</td>\n",
       "      <td>0.919598</td>\n",
       "      <td>0.524590</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.347243</td>\n",
       "      <td>0.253629</td>\n",
       "      <td>0.183333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.058824</td>\n",
       "      <td>0.447236</td>\n",
       "      <td>0.540984</td>\n",
       "      <td>0.232323</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.418778</td>\n",
       "      <td>0.038002</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.688442</td>\n",
       "      <td>0.327869</td>\n",
       "      <td>0.353535</td>\n",
       "      <td>0.198582</td>\n",
       "      <td>0.642325</td>\n",
       "      <td>0.943638</td>\n",
       "      <td>0.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>763</th>\n",
       "      <td>0.588235</td>\n",
       "      <td>0.507538</td>\n",
       "      <td>0.622951</td>\n",
       "      <td>0.484848</td>\n",
       "      <td>0.212766</td>\n",
       "      <td>0.490313</td>\n",
       "      <td>0.039710</td>\n",
       "      <td>0.700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>764</th>\n",
       "      <td>0.117647</td>\n",
       "      <td>0.613065</td>\n",
       "      <td>0.573770</td>\n",
       "      <td>0.272727</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.548435</td>\n",
       "      <td>0.111870</td>\n",
       "      <td>0.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>765</th>\n",
       "      <td>0.294118</td>\n",
       "      <td>0.608040</td>\n",
       "      <td>0.590164</td>\n",
       "      <td>0.232323</td>\n",
       "      <td>0.132388</td>\n",
       "      <td>0.390462</td>\n",
       "      <td>0.071307</td>\n",
       "      <td>0.150000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>766</th>\n",
       "      <td>0.058824</td>\n",
       "      <td>0.633166</td>\n",
       "      <td>0.491803</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.448584</td>\n",
       "      <td>0.115713</td>\n",
       "      <td>0.433333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>767</th>\n",
       "      <td>0.058824</td>\n",
       "      <td>0.467337</td>\n",
       "      <td>0.573770</td>\n",
       "      <td>0.313131</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.453055</td>\n",
       "      <td>0.101196</td>\n",
       "      <td>0.033333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>768 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Pregnancies   Glucose  BloodPressure  SkinThickness   Insulin       BMI  \\\n",
       "0       0.352941  0.743719       0.590164       0.353535  0.000000  0.500745   \n",
       "1       0.058824  0.427136       0.540984       0.292929  0.000000  0.396423   \n",
       "2       0.470588  0.919598       0.524590       0.000000  0.000000  0.347243   \n",
       "3       0.058824  0.447236       0.540984       0.232323  0.111111  0.418778   \n",
       "4       0.000000  0.688442       0.327869       0.353535  0.198582  0.642325   \n",
       "..           ...       ...            ...            ...       ...       ...   \n",
       "763     0.588235  0.507538       0.622951       0.484848  0.212766  0.490313   \n",
       "764     0.117647  0.613065       0.573770       0.272727  0.000000  0.548435   \n",
       "765     0.294118  0.608040       0.590164       0.232323  0.132388  0.390462   \n",
       "766     0.058824  0.633166       0.491803       0.000000  0.000000  0.448584   \n",
       "767     0.058824  0.467337       0.573770       0.313131  0.000000  0.453055   \n",
       "\n",
       "     DiabetesPedigreeFunction       Age  \n",
       "0                    0.234415  0.483333  \n",
       "1                    0.116567  0.166667  \n",
       "2                    0.253629  0.183333  \n",
       "3                    0.038002  0.000000  \n",
       "4                    0.943638  0.200000  \n",
       "..                        ...       ...  \n",
       "763                  0.039710  0.700000  \n",
       "764                  0.111870  0.100000  \n",
       "765                  0.071307  0.150000  \n",
       "766                  0.115713  0.433333  \n",
       "767                  0.101196  0.033333  \n",
       "\n",
       "[768 rows x 8 columns]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaled_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "1062899c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.226180</td>\n",
       "      <td>0.607510</td>\n",
       "      <td>0.566438</td>\n",
       "      <td>0.207439</td>\n",
       "      <td>0.094326</td>\n",
       "      <td>0.476790</td>\n",
       "      <td>0.168179</td>\n",
       "      <td>0.204015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.198210</td>\n",
       "      <td>0.160666</td>\n",
       "      <td>0.158654</td>\n",
       "      <td>0.161134</td>\n",
       "      <td>0.136222</td>\n",
       "      <td>0.117499</td>\n",
       "      <td>0.141473</td>\n",
       "      <td>0.196004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.058824</td>\n",
       "      <td>0.497487</td>\n",
       "      <td>0.508197</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.406855</td>\n",
       "      <td>0.070773</td>\n",
       "      <td>0.050000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.176471</td>\n",
       "      <td>0.587940</td>\n",
       "      <td>0.590164</td>\n",
       "      <td>0.232323</td>\n",
       "      <td>0.036052</td>\n",
       "      <td>0.476900</td>\n",
       "      <td>0.125747</td>\n",
       "      <td>0.133333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.352941</td>\n",
       "      <td>0.704774</td>\n",
       "      <td>0.655738</td>\n",
       "      <td>0.323232</td>\n",
       "      <td>0.150414</td>\n",
       "      <td>0.545455</td>\n",
       "      <td>0.234095</td>\n",
       "      <td>0.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Pregnancies     Glucose  BloodPressure  SkinThickness     Insulin  \\\n",
       "count   768.000000  768.000000     768.000000     768.000000  768.000000   \n",
       "mean      0.226180    0.607510       0.566438       0.207439    0.094326   \n",
       "std       0.198210    0.160666       0.158654       0.161134    0.136222   \n",
       "min       0.000000    0.000000       0.000000       0.000000    0.000000   \n",
       "25%       0.058824    0.497487       0.508197       0.000000    0.000000   \n",
       "50%       0.176471    0.587940       0.590164       0.232323    0.036052   \n",
       "75%       0.352941    0.704774       0.655738       0.323232    0.150414   \n",
       "max       1.000000    1.000000       1.000000       1.000000    1.000000   \n",
       "\n",
       "              BMI  DiabetesPedigreeFunction         Age  \n",
       "count  768.000000                768.000000  768.000000  \n",
       "mean     0.476790                  0.168179    0.204015  \n",
       "std      0.117499                  0.141473    0.196004  \n",
       "min      0.000000                  0.000000    0.000000  \n",
       "25%      0.406855                  0.070773    0.050000  \n",
       "50%      0.476900                  0.125747    0.133333  \n",
       "75%      0.545455                  0.234095    0.333333  \n",
       "max      1.000000                  1.000000    1.000000  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaled_df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a1d492e",
   "metadata": {},
   "source": [
    "We can see that the distributions have been adjusted and that the **minimum and maximum values for each variable are now 0.0 and 1.0 respectively.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "8f03a63b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = scaled_df.hist(xlabelsize=4, ylabelsize=4)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5eb26791",
   "metadata": {},
   "source": [
    "Histogram plots of the variables are created, although the distributions don’t look much different from their original distributions seen in the previous section. **We can confirm that the minimum and maximum values are now zero and one respectively, as we expected.**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95cd8278",
   "metadata": {},
   "source": [
    "## StandardScaler Transform\n",
    "\n",
    "We can apply the StandardScaler to the diabetes dataset directly to standardize the input variables. We will use the default configuration and scale values to subtract the mean to center them on 0.0 and divide by the standard deviation to give the standard deviation of 1.0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "e29a8c72",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# Load the data\n",
    "dataset = pd.read_csv('diabetes.csv') \n",
    "\n",
    "# retrieve just the numeric input values\n",
    "data = dataset.iloc[:,:-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "df24618d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>763</th>\n",
       "      <td>10</td>\n",
       "      <td>101</td>\n",
       "      <td>76</td>\n",
       "      <td>48</td>\n",
       "      <td>180</td>\n",
       "      <td>32.9</td>\n",
       "      <td>0.171</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>764</th>\n",
       "      <td>2</td>\n",
       "      <td>122</td>\n",
       "      <td>70</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>36.8</td>\n",
       "      <td>0.340</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>765</th>\n",
       "      <td>5</td>\n",
       "      <td>121</td>\n",
       "      <td>72</td>\n",
       "      <td>23</td>\n",
       "      <td>112</td>\n",
       "      <td>26.2</td>\n",
       "      <td>0.245</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>766</th>\n",
       "      <td>1</td>\n",
       "      <td>126</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.1</td>\n",
       "      <td>0.349</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>767</th>\n",
       "      <td>1</td>\n",
       "      <td>93</td>\n",
       "      <td>70</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>30.4</td>\n",
       "      <td>0.315</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>768 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0              6      148             72             35        0  33.6   \n",
       "1              1       85             66             29        0  26.6   \n",
       "2              8      183             64              0        0  23.3   \n",
       "3              1       89             66             23       94  28.1   \n",
       "4              0      137             40             35      168  43.1   \n",
       "..           ...      ...            ...            ...      ...   ...   \n",
       "763           10      101             76             48      180  32.9   \n",
       "764            2      122             70             27        0  36.8   \n",
       "765            5      121             72             23      112  26.2   \n",
       "766            1      126             60              0        0  30.1   \n",
       "767            1       93             70             31        0  30.4   \n",
       "\n",
       "     DiabetesPedigreeFunction  Age  \n",
       "0                       0.627   50  \n",
       "1                       0.351   31  \n",
       "2                       0.672   32  \n",
       "3                       0.167   21  \n",
       "4                       2.288   33  \n",
       "..                        ...  ...  \n",
       "763                     0.171   63  \n",
       "764                     0.340   27  \n",
       "765                     0.245   30  \n",
       "766                     0.349   47  \n",
       "767                     0.315   23  \n",
       "\n",
       "[768 rows x 8 columns]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "6beac6e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# perform a robust scaler transform of the dataset\n",
    "scaler = StandardScaler()\n",
    "\n",
    "scaled_data = scaler.fit_transform(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "428e8fc6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.63994726,  0.84832379,  0.14964075, ...,  0.20401277,\n",
       "         0.46849198,  1.4259954 ],\n",
       "       [-0.84488505, -1.12339636, -0.16054575, ..., -0.68442195,\n",
       "        -0.36506078, -0.19067191],\n",
       "       [ 1.23388019,  1.94372388, -0.26394125, ..., -1.10325546,\n",
       "         0.60439732, -0.10558415],\n",
       "       ...,\n",
       "       [ 0.3429808 ,  0.00330087,  0.14964075, ..., -0.73518964,\n",
       "        -0.68519336, -0.27575966],\n",
       "       [-0.84488505,  0.1597866 , -0.47073225, ..., -0.24020459,\n",
       "        -0.37110101,  1.17073215],\n",
       "       [-0.84488505, -0.8730192 ,  0.04624525, ..., -0.20212881,\n",
       "        -0.47378505, -0.87137393]])"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaled_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "4414b27f",
   "metadata": {},
   "outputs": [],
   "source": [
    "scaled_df = pd.DataFrame(scaled_data, columns=dataset.columns[:-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "6a8f9484",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.639947</td>\n",
       "      <td>0.848324</td>\n",
       "      <td>0.149641</td>\n",
       "      <td>0.907270</td>\n",
       "      <td>-0.692891</td>\n",
       "      <td>0.204013</td>\n",
       "      <td>0.468492</td>\n",
       "      <td>1.425995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.844885</td>\n",
       "      <td>-1.123396</td>\n",
       "      <td>-0.160546</td>\n",
       "      <td>0.530902</td>\n",
       "      <td>-0.692891</td>\n",
       "      <td>-0.684422</td>\n",
       "      <td>-0.365061</td>\n",
       "      <td>-0.190672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.233880</td>\n",
       "      <td>1.943724</td>\n",
       "      <td>-0.263941</td>\n",
       "      <td>-1.288212</td>\n",
       "      <td>-0.692891</td>\n",
       "      <td>-1.103255</td>\n",
       "      <td>0.604397</td>\n",
       "      <td>-0.105584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.844885</td>\n",
       "      <td>-0.998208</td>\n",
       "      <td>-0.160546</td>\n",
       "      <td>0.154533</td>\n",
       "      <td>0.123302</td>\n",
       "      <td>-0.494043</td>\n",
       "      <td>-0.920763</td>\n",
       "      <td>-1.041549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.141852</td>\n",
       "      <td>0.504055</td>\n",
       "      <td>-1.504687</td>\n",
       "      <td>0.907270</td>\n",
       "      <td>0.765836</td>\n",
       "      <td>1.409746</td>\n",
       "      <td>5.484909</td>\n",
       "      <td>-0.020496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>763</th>\n",
       "      <td>1.827813</td>\n",
       "      <td>-0.622642</td>\n",
       "      <td>0.356432</td>\n",
       "      <td>1.722735</td>\n",
       "      <td>0.870031</td>\n",
       "      <td>0.115169</td>\n",
       "      <td>-0.908682</td>\n",
       "      <td>2.532136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>764</th>\n",
       "      <td>-0.547919</td>\n",
       "      <td>0.034598</td>\n",
       "      <td>0.046245</td>\n",
       "      <td>0.405445</td>\n",
       "      <td>-0.692891</td>\n",
       "      <td>0.610154</td>\n",
       "      <td>-0.398282</td>\n",
       "      <td>-0.531023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>765</th>\n",
       "      <td>0.342981</td>\n",
       "      <td>0.003301</td>\n",
       "      <td>0.149641</td>\n",
       "      <td>0.154533</td>\n",
       "      <td>0.279594</td>\n",
       "      <td>-0.735190</td>\n",
       "      <td>-0.685193</td>\n",
       "      <td>-0.275760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>766</th>\n",
       "      <td>-0.844885</td>\n",
       "      <td>0.159787</td>\n",
       "      <td>-0.470732</td>\n",
       "      <td>-1.288212</td>\n",
       "      <td>-0.692891</td>\n",
       "      <td>-0.240205</td>\n",
       "      <td>-0.371101</td>\n",
       "      <td>1.170732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>767</th>\n",
       "      <td>-0.844885</td>\n",
       "      <td>-0.873019</td>\n",
       "      <td>0.046245</td>\n",
       "      <td>0.656358</td>\n",
       "      <td>-0.692891</td>\n",
       "      <td>-0.202129</td>\n",
       "      <td>-0.473785</td>\n",
       "      <td>-0.871374</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>768 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Pregnancies   Glucose  BloodPressure  SkinThickness   Insulin       BMI  \\\n",
       "0       0.639947  0.848324       0.149641       0.907270 -0.692891  0.204013   \n",
       "1      -0.844885 -1.123396      -0.160546       0.530902 -0.692891 -0.684422   \n",
       "2       1.233880  1.943724      -0.263941      -1.288212 -0.692891 -1.103255   \n",
       "3      -0.844885 -0.998208      -0.160546       0.154533  0.123302 -0.494043   \n",
       "4      -1.141852  0.504055      -1.504687       0.907270  0.765836  1.409746   \n",
       "..           ...       ...            ...            ...       ...       ...   \n",
       "763     1.827813 -0.622642       0.356432       1.722735  0.870031  0.115169   \n",
       "764    -0.547919  0.034598       0.046245       0.405445 -0.692891  0.610154   \n",
       "765     0.342981  0.003301       0.149641       0.154533  0.279594 -0.735190   \n",
       "766    -0.844885  0.159787      -0.470732      -1.288212 -0.692891 -0.240205   \n",
       "767    -0.844885 -0.873019       0.046245       0.656358 -0.692891 -0.202129   \n",
       "\n",
       "     DiabetesPedigreeFunction       Age  \n",
       "0                    0.468492  1.425995  \n",
       "1                   -0.365061 -0.190672  \n",
       "2                    0.604397 -0.105584  \n",
       "3                   -0.920763 -1.041549  \n",
       "4                    5.484909 -0.020496  \n",
       "..                        ...       ...  \n",
       "763                 -0.908682  2.532136  \n",
       "764                 -0.398282 -0.531023  \n",
       "765                 -0.685193 -0.275760  \n",
       "766                 -0.371101  1.170732  \n",
       "767                 -0.473785 -0.871374  \n",
       "\n",
       "[768 rows x 8 columns]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaled_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "0b69ef16",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>7.680000e+02</td>\n",
       "      <td>7.680000e+02</td>\n",
       "      <td>7.680000e+02</td>\n",
       "      <td>7.680000e+02</td>\n",
       "      <td>7.680000e+02</td>\n",
       "      <td>7.680000e+02</td>\n",
       "      <td>7.680000e+02</td>\n",
       "      <td>7.680000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-6.476301e-17</td>\n",
       "      <td>-9.251859e-18</td>\n",
       "      <td>1.503427e-17</td>\n",
       "      <td>1.006140e-16</td>\n",
       "      <td>-3.006854e-17</td>\n",
       "      <td>2.590520e-16</td>\n",
       "      <td>2.451743e-16</td>\n",
       "      <td>1.931325e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.000652e+00</td>\n",
       "      <td>1.000652e+00</td>\n",
       "      <td>1.000652e+00</td>\n",
       "      <td>1.000652e+00</td>\n",
       "      <td>1.000652e+00</td>\n",
       "      <td>1.000652e+00</td>\n",
       "      <td>1.000652e+00</td>\n",
       "      <td>1.000652e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-1.141852e+00</td>\n",
       "      <td>-3.783654e+00</td>\n",
       "      <td>-3.572597e+00</td>\n",
       "      <td>-1.288212e+00</td>\n",
       "      <td>-6.928906e-01</td>\n",
       "      <td>-4.060474e+00</td>\n",
       "      <td>-1.189553e+00</td>\n",
       "      <td>-1.041549e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-8.448851e-01</td>\n",
       "      <td>-6.852363e-01</td>\n",
       "      <td>-3.673367e-01</td>\n",
       "      <td>-1.288212e+00</td>\n",
       "      <td>-6.928906e-01</td>\n",
       "      <td>-5.955785e-01</td>\n",
       "      <td>-6.889685e-01</td>\n",
       "      <td>-7.862862e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-2.509521e-01</td>\n",
       "      <td>-1.218877e-01</td>\n",
       "      <td>1.496408e-01</td>\n",
       "      <td>1.545332e-01</td>\n",
       "      <td>-4.280622e-01</td>\n",
       "      <td>9.419788e-04</td>\n",
       "      <td>-3.001282e-01</td>\n",
       "      <td>-3.608474e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.399473e-01</td>\n",
       "      <td>6.057709e-01</td>\n",
       "      <td>5.632228e-01</td>\n",
       "      <td>7.190857e-01</td>\n",
       "      <td>4.120079e-01</td>\n",
       "      <td>5.847705e-01</td>\n",
       "      <td>4.662269e-01</td>\n",
       "      <td>6.602056e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.906578e+00</td>\n",
       "      <td>2.444478e+00</td>\n",
       "      <td>2.734528e+00</td>\n",
       "      <td>4.921866e+00</td>\n",
       "      <td>6.652839e+00</td>\n",
       "      <td>4.455807e+00</td>\n",
       "      <td>5.883565e+00</td>\n",
       "      <td>4.063716e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Pregnancies       Glucose  BloodPressure  SkinThickness       Insulin  \\\n",
       "count  7.680000e+02  7.680000e+02   7.680000e+02   7.680000e+02  7.680000e+02   \n",
       "mean  -6.476301e-17 -9.251859e-18   1.503427e-17   1.006140e-16 -3.006854e-17   \n",
       "std    1.000652e+00  1.000652e+00   1.000652e+00   1.000652e+00  1.000652e+00   \n",
       "min   -1.141852e+00 -3.783654e+00  -3.572597e+00  -1.288212e+00 -6.928906e-01   \n",
       "25%   -8.448851e-01 -6.852363e-01  -3.673367e-01  -1.288212e+00 -6.928906e-01   \n",
       "50%   -2.509521e-01 -1.218877e-01   1.496408e-01   1.545332e-01 -4.280622e-01   \n",
       "75%    6.399473e-01  6.057709e-01   5.632228e-01   7.190857e-01  4.120079e-01   \n",
       "max    3.906578e+00  2.444478e+00   2.734528e+00   4.921866e+00  6.652839e+00   \n",
       "\n",
       "                BMI  DiabetesPedigreeFunction           Age  \n",
       "count  7.680000e+02              7.680000e+02  7.680000e+02  \n",
       "mean   2.590520e-16              2.451743e-16  1.931325e-16  \n",
       "std    1.000652e+00              1.000652e+00  1.000652e+00  \n",
       "min   -4.060474e+00             -1.189553e+00 -1.041549e+00  \n",
       "25%   -5.955785e-01             -6.889685e-01 -7.862862e-01  \n",
       "50%    9.419788e-04             -3.001282e-01 -3.608474e-01  \n",
       "75%    5.847705e-01              4.662269e-01  6.602056e-01  \n",
       "max    4.455807e+00              5.883565e+00  4.063716e+00  "
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaled_df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "efd90171",
   "metadata": {},
   "source": [
    "We can see that the distributions have been adjusted and that the **mean is a very small number close to zero and the standard deviation is very close to 1.0** for each variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "c34c43f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = scaled_df.hist(xlabelsize=4, ylabelsize=4)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6a94c31",
   "metadata": {},
   "source": [
    "Histogram plots of the variables are created, although the distributions don’t look much different from their original distributions seen in the previous section other than their scale on the x-axis. We can see that the center of mass for each distribution is centered on zero, which is more obvious for some variables than others."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a914cb10",
   "metadata": {},
   "source": [
    "# What is next?\n",
    "\n",
    "In the next tutorials, we'll show you how to scale data with outliers and how to encode categorial data."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "87af4586",
   "metadata": {},
   "source": [
    "## 👾 Join our [Discord community](https://tiny.ydata.ai/dcai-community-github) and follow our Code-with-Me sessions to learn more about data science!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a78d0ef1",
   "metadata": {},
   "source": [
    "### 💻 Would you like to collaborate? Check some of the [topics we have open in GitHub issues](https://github.com/Data-Centric-AI-Community/awesome-python-for-data-science/issues), comment in one and let's get started! You can also drop us a line in the #contributors channel on Discord."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}