Explore_Prepare_Thompson.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Acquire"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import Acquire\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "import sklearn.preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = Acquire.get_mvp_home_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop('id',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.dropna(how='any',axis=0) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "calculatedfinishedsquarefeet    0\n",
       "bedroomcnt                      0\n",
       "bathroomcnt                     0\n",
       "taxvaluedollarcnt               0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>calculatedfinishedsquarefeet</th>\n",
       "      <th>bedroomcnt</th>\n",
       "      <th>bathroomcnt</th>\n",
       "      <th>taxvaluedollarcnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1819.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>478430.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>27400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1503.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>171687.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1604.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>498347.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2384.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>549917.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   calculatedfinishedsquarefeet  bedroomcnt  bathroomcnt  taxvaluedollarcnt\n",
       "0                        1819.0         3.0          2.0           478430.0\n",
       "1                        1000.0         0.0          0.0            27400.0\n",
       "2                        1503.0         2.0          2.0           171687.0\n",
       "3                        1604.0         4.0          2.0           498347.0\n",
       "4                        2384.0         3.0          3.0           549917.0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 18490 entries, 0 to 19148\n",
      "Data columns (total 4 columns):\n",
      " #   Column                        Non-Null Count  Dtype  \n",
      "---  ------                        --------------  -----  \n",
      " 0   calculatedfinishedsquarefeet  18490 non-null  float64\n",
      " 1   bedroomcnt                    18490 non-null  float64\n",
      " 2   bathroomcnt                   18490 non-null  float64\n",
      " 3   taxvaluedollarcnt             18490 non-null  float64\n",
      "dtypes: float64(4)\n",
      "memory usage: 722.3 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "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>calculatedfinishedsquarefeet</th>\n",
       "      <th>bedroomcnt</th>\n",
       "      <th>bathroomcnt</th>\n",
       "      <th>taxvaluedollarcnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>18490.00000</td>\n",
       "      <td>18490.000000</td>\n",
       "      <td>18490.000000</td>\n",
       "      <td>1.849000e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1794.93629</td>\n",
       "      <td>3.113413</td>\n",
       "      <td>2.284072</td>\n",
       "      <td>4.483963e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>968.72753</td>\n",
       "      <td>1.016633</td>\n",
       "      <td>0.965707</td>\n",
       "      <td>5.490209e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>60.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1210.00000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.979842e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1566.00000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.342260e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2114.00000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>5.308875e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>26345.00000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>19.500000</td>\n",
       "      <td>1.717000e+07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       calculatedfinishedsquarefeet    bedroomcnt   bathroomcnt  \\\n",
       "count                   18490.00000  18490.000000  18490.000000   \n",
       "mean                     1794.93629      3.113413      2.284072   \n",
       "std                       968.72753      1.016633      0.965707   \n",
       "min                        60.00000      0.000000      0.000000   \n",
       "25%                      1210.00000      2.000000      2.000000   \n",
       "50%                      1566.00000      3.000000      2.000000   \n",
       "75%                      2114.00000      4.000000      3.000000   \n",
       "max                     26345.00000     10.000000     19.500000   \n",
       "\n",
       "       taxvaluedollarcnt  \n",
       "count       1.849000e+04  \n",
       "mean        4.483963e+05  \n",
       "std         5.490209e+05  \n",
       "min         1.000000e+02  \n",
       "25%         1.979842e+05  \n",
       "50%         3.342260e+05  \n",
       "75%         5.308875e+05  \n",
       "max         1.717000e+07  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pre-Prepare Explore"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x7fb218f01250>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x720 with 14 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(df, corner=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb21b6f6310>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df.corr(),cmap=\"Blues\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.columns = ['square_feet','bed_count','bath_count','tax_value']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['square_feet', 'bed_count', 'bath_count', 'tax_value'], dtype='object')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Prepare"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_validate_test(df):\n",
    "    '''\n",
    "    Takes a dataframe and splits into train, validate, test \n",
    "    into 70%, 20%, 10% respectively.\n",
    "    '''\n",
    "\n",
    "    # Import to use split function, can only split two at a time\n",
    "    from sklearn.model_selection import train_test_split\n",
    "\n",
    "    # Frist, split into train + validate together and test by itself\n",
    "    # Test will be about %10 of the data, train + validate is %70 for now\n",
    "    # Set random_state so we can reproduce the same 'random' data\n",
    "    train_validate, test = train_test_split(df, test_size = .10, random_state = 123)\n",
    "\n",
    "    # Second, we plit train + validate into their seperate variables\n",
    "    # Train will be about %70 of the data, Validate will be about %20 of the data\n",
    "    train, validate = train_test_split(train_validate, test_size = .20, random_state = 123)\n",
    "\n",
    "    # These two print functions allow us to ensure the date is properly split\n",
    "    # Will print the shape of each variable when running the function\n",
    "    print(\"train shape: \", train.shape, \", validate shape: \", validate.shape, \", test shape: \", test.shape)\n",
    "\n",
    "    # Will print the shape of eachvariable as a percentage of the total data set\n",
    "    # Varialbe to hold the sum of all rows (total observations in the data)\n",
    "    total = df.count()[0]\n",
    "    print(\"\\ntrain percent: \", round(((train.shape[0])/total),2) * 100, \n",
    "            \", validate percent: \", round(((validate.shape[0])/total),2) * 100, \n",
    "            \", test percent: \", round(((test.shape[0])/total),2) * 100)\n",
    "\n",
    "    return train, validate, test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train shape:  (13312, 4) , validate shape:  (3329, 4) , test shape:  (1849, 4)\n",
      "\n",
      "train percent:  72.0 , validate percent:  18.0 , test percent:  10.0\n"
     ]
    }
   ],
   "source": [
    "train, validate, test = train_validate_test(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>square_feet</th>\n",
       "      <th>bed_count</th>\n",
       "      <th>bath_count</th>\n",
       "      <th>tax_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11548</th>\n",
       "      <td>1414.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>156164.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4070</th>\n",
       "      <td>1541.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>245710.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4206</th>\n",
       "      <td>1672.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>85486.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13273</th>\n",
       "      <td>2555.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>587732.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12135</th>\n",
       "      <td>1040.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>403858.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       square_feet  bed_count  bath_count  tax_value\n",
       "11548       1414.0        3.0         2.0   156164.0\n",
       "4070        1541.0        2.0         3.0   245710.0\n",
       "4206        1672.0        3.0         2.0    85486.0\n",
       "13273       2555.0        4.0         3.0   587732.0\n",
       "12135       1040.0        3.0         1.0   403858.0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Renaming Columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scaling data\n",
    "- taxvaluedollarcnt has a much higher range than the other features, especially bath/bedroom counts\n",
    "- we will fit the scaling object to the train df, then apply this to the validate and test\n",
    "    - *Only* fit to the *train* data, so the other data is still unseen"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1800x1080 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Construct a figure with subplots of histograms of features\n",
    "\n",
    "# sets size, can adjust to liking\n",
    "plt.subplots(figsize=(25, 15))\n",
    "\n",
    "plt.subplot(2,2,1);\n",
    "line1 = plt.hist(df.square_feet)\n",
    "plt.title('square_feet');\n",
    "\n",
    "plt.subplot(2,2,2);\n",
    "line2 = plt.hist(df.bed_count);\n",
    "plt.title('bed_count');\n",
    "\n",
    "plt.subplot(2,2,3);\n",
    "line3 = plt.hist(df.bath_count);\n",
    "plt.title('bath_count');\n",
    "\n",
    "plt.subplot(2,2,4);\n",
    "line3 = plt.hist(df.tax_value);\n",
    "plt.title('tax_value');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/anaconda3/lib/python3.7/site-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n",
      "/usr/local/anaconda3/lib/python3.7/site-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n",
      "/usr/local/anaconda3/lib/python3.7/site-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n",
      "/usr/local/anaconda3/lib/python3.7/site-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb21ccff410>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1800x1080 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Construct a figure with subplots of histograms of features\n",
    "\n",
    "# sets size, can adjust to liking\n",
    "plt.subplots(figsize=(25, 15))\n",
    "\n",
    "plt.subplot(221)\n",
    "sns.boxplot(df.square_feet)\n",
    "\n",
    "plt.subplot(222)\n",
    "sns.boxplot(df.bed_count)\n",
    "\n",
    "plt.subplot(223)\n",
    "sns.boxplot(df.bath_count)\n",
    "\n",
    "plt.subplot(224)\n",
    "sns.boxplot(df.tax_value)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Takeaways\n",
    "- Bedroom count is the only normally distributed feature\n",
    "- Tax value is represented in scientific notation due to the large range\n",
    "- Outliers are in square_feet, bath_count, and tax_value\n",
    "- Need to remove outliers before scaling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Remove Outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy import stats\n",
    "\n",
    "# Calculate z-score for each value in df\n",
    "z = np.abs(stats.zscore(train))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The Z-score is the signed number of standard deviations by which the value of an observation or data point is above the mean value of what is being observed or measured.\n",
    "\n",
    "#### In most of the cases a threshold of 3 or -3 is used i.e if the Z-score value is greater than or less than 3 or -3 respectively, that data point will be identified as outliers.\n",
    "\n",
    "- https://towardsdatascience.com/ways-to-detect-and-remove-the-outliers-404d16608dba"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "27.467826737211077\n"
     ]
    }
   ],
   "source": [
    "print(z.max())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# outliers will be any value that is more than 3 standdard deviations from the average\n",
    "threshold = 3\n",
    "# print(np.where(z > 3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "square_feet       897.0\n",
      "bed_count           2.0\n",
      "bath_count          1.0\n",
      "tax_value      334050.0\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "Q1 = train.quantile(0.25)\n",
    "Q3 = train.quantile(0.75)\n",
    "IQR = Q3 - Q1\n",
    "print(IQR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_o = train[(z < 3).all(axis=1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>square_feet</th>\n",
       "      <th>bed_count</th>\n",
       "      <th>bath_count</th>\n",
       "      <th>tax_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11548</th>\n",
       "      <td>1414.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>156164.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4070</th>\n",
       "      <td>1541.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>245710.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4206</th>\n",
       "      <td>1672.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>85486.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13273</th>\n",
       "      <td>2555.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>587732.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12135</th>\n",
       "      <td>1040.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>403858.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       square_feet  bed_count  bath_count  tax_value\n",
       "11548       1414.0        3.0         2.0   156164.0\n",
       "4070        1541.0        2.0         3.0   245710.0\n",
       "4206        1672.0        3.0         2.0    85486.0\n",
       "13273       2555.0        4.0         3.0   587732.0\n",
       "12135       1040.0        3.0         1.0   403858.0"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_o.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Scaling DF with Outliers Removed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. Create the Scaling Object\n",
    "scaler = sklearn.preprocessing.StandardScaler()\n",
    "\n",
    "# 2. Fit to the train data only\n",
    "scaler.fit(train.drop('tax_value', axis=1))\n",
    "\n",
    "# 3. use the object on the whole df\n",
    "# this returns an array, so we convert to df in the same line\n",
    "train_scaled = pd.DataFrame(scaler.transform(train.drop('tax_value', axis=1)))\n",
    "validate_scaled = pd.DataFrame(scaler.transform(validate.drop('tax_value', axis=1)))\n",
    "test_scaled = pd.DataFrame(scaler.transform(test.drop('tax_value', axis=1)))\n",
    "\n",
    "# the result of changing an array to a df resets the index and columns\n",
    "# for each train, validate, and test, we change the index and columns back to original values\n",
    "\n",
    "# Train\n",
    "train_scaled.index = train.index\n",
    "train_scaled.columns = ['bath_count_scaled','bed_count_scaled','square_feet_scaled']\n",
    "train = pd.concat((train, train_scaled), axis=1)\n",
    "\n",
    "# Validate\n",
    "validate_scaled.index = validate.index\n",
    "validate_scaled.columns = ['bath_count_scaled','bed_count_scaled','square_feet_scaled']\n",
    "validate = pd.concat((validate, validate_scaled), axis=1)\n",
    "\n",
    "# Test\n",
    "test_scaled.index = test.index\n",
    "test_scaled.columns = ['bath_count_scaled','bed_count_scaled','square_feet_scaled']\n",
    "test = pd.concat((test, test_scaled), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>square_feet</th>\n",
       "      <th>bed_count</th>\n",
       "      <th>bath_count</th>\n",
       "      <th>tax_value</th>\n",
       "      <th>bath_count_scaled</th>\n",
       "      <th>bed_count_scaled</th>\n",
       "      <th>square_feet_scaled</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11548</th>\n",
       "      <td>1414.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>156164.0</td>\n",
       "      <td>-0.393602</td>\n",
       "      <td>-0.113343</td>\n",
       "      <td>-0.295933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4070</th>\n",
       "      <td>1541.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>245710.0</td>\n",
       "      <td>-0.260492</td>\n",
       "      <td>-1.096286</td>\n",
       "      <td>0.753889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4206</th>\n",
       "      <td>1672.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>85486.0</td>\n",
       "      <td>-0.123189</td>\n",
       "      <td>-0.113343</td>\n",
       "      <td>-0.295933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13273</th>\n",
       "      <td>2555.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>587732.0</td>\n",
       "      <td>0.802292</td>\n",
       "      <td>0.869601</td>\n",
       "      <td>0.753889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12135</th>\n",
       "      <td>1040.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>403858.0</td>\n",
       "      <td>-0.785595</td>\n",
       "      <td>-0.113343</td>\n",
       "      <td>-1.345755</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2281</th>\n",
       "      <td>1470.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>956364.0</td>\n",
       "      <td>-0.334908</td>\n",
       "      <td>-1.096286</td>\n",
       "      <td>0.753889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10801</th>\n",
       "      <td>1288.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>402821.0</td>\n",
       "      <td>-0.525664</td>\n",
       "      <td>-1.096286</td>\n",
       "      <td>-1.345755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>324</th>\n",
       "      <td>2547.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2274016.0</td>\n",
       "      <td>0.793907</td>\n",
       "      <td>-0.113343</td>\n",
       "      <td>0.228978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17727</th>\n",
       "      <td>756.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>54834.0</td>\n",
       "      <td>-1.083258</td>\n",
       "      <td>-1.096286</td>\n",
       "      <td>-1.345755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17717</th>\n",
       "      <td>1451.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>681000.0</td>\n",
       "      <td>-0.354822</td>\n",
       "      <td>-0.113343</td>\n",
       "      <td>-1.345755</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>13312 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       square_feet  bed_count  bath_count  tax_value  bath_count_scaled  \\\n",
       "11548       1414.0        3.0         2.0   156164.0          -0.393602   \n",
       "4070        1541.0        2.0         3.0   245710.0          -0.260492   \n",
       "4206        1672.0        3.0         2.0    85486.0          -0.123189   \n",
       "13273       2555.0        4.0         3.0   587732.0           0.802292   \n",
       "12135       1040.0        3.0         1.0   403858.0          -0.785595   \n",
       "...            ...        ...         ...        ...                ...   \n",
       "2281        1470.0        2.0         3.0   956364.0          -0.334908   \n",
       "10801       1288.0        2.0         1.0   402821.0          -0.525664   \n",
       "324         2547.0        3.0         2.5  2274016.0           0.793907   \n",
       "17727        756.0        2.0         1.0    54834.0          -1.083258   \n",
       "17717       1451.0        3.0         1.0   681000.0          -0.354822   \n",
       "\n",
       "       bed_count_scaled  square_feet_scaled  \n",
       "11548         -0.113343           -0.295933  \n",
       "4070          -1.096286            0.753889  \n",
       "4206          -0.113343           -0.295933  \n",
       "13273          0.869601            0.753889  \n",
       "12135         -0.113343           -1.345755  \n",
       "...                 ...                 ...  \n",
       "2281          -1.096286            0.753889  \n",
       "10801         -1.096286           -1.345755  \n",
       "324           -0.113343            0.228978  \n",
       "17727         -1.096286           -1.345755  \n",
       "17717         -0.113343           -1.345755  \n",
       "\n",
       "[13312 rows x 7 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Standard Scaled Square Feet')"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x720 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 10))\n",
    "\n",
    "plt.subplot(321)\n",
    "plt.hist(train.square_feet, bins=25, ec='black')\n",
    "plt.title('Original Square Feet')\n",
    "plt.subplot(322)\n",
    "plt.hist(train.square_feet_scaled, bins=25, ec='black')\n",
    "plt.title('Standard Scaled Square Feet')\n",
    "\n",
    "plt.subplot(323)\n",
    "plt.hist(train.bed_count, bins=25, ec='black')\n",
    "plt.title('Original Bed Count')\n",
    "plt.subplot(324)\n",
    "plt.hist(train.bed_count_scaled, bins=25, ec='black')\n",
    "plt.title('Standard Scaled Bed Count')\n",
    "\n",
    "plt.subplot(325)\n",
    "plt.hist(train.bath_count, bins=25, ec='black')\n",
    "plt.title('Original Square Feet')\n",
    "plt.subplot(326)\n",
    "plt.hist(train.bath_count_scaled, bins=25, ec='black')\n",
    "plt.title('Standard Scaled Square Feet')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}