Chapter02/Activity2.02/Activity_2_02.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import missingno as msno\n",
    "from sklearn.impute import SimpleImputer\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('..\\Datasets\\house_prices.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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7duC4oc2PBe6e4MCXApcCrFixYo+3CiRJ2h9NNcjuRxO8AM5jcM98Shmcqn8AuL2q3jn01QZgVVteBVwx1L4yyUFJTgCWA9eN2A9JkjRk0jP4qnrHruUkhwAXAq8G1gHvmGy7IaczeArdLUluam2/B1wMrE9yHvAt4Nx2vM1J1gO3MbjXf0FVPbjXPZIkSVPfg0+yGHgt8KsMRryfUlXfH2XHVfVFJr6vDvD8SbZZA6wZZf+SJGlyU92D/2Pg5Qzud/9MVf1w1qqSJEnTMtU9+NcB/wJ4E3B3kvva6wdJ7pud8iRJ0r6Y6h78Xs9yJ0mS5gdDXJKkDhnwkiR1yICXJKlDBrwkSR0y4CVJ6pABL0lShwx4SZI6ZMBLktQhA16SpA4Z8JIkdciAlySpQwa8JEkdMuAlSeqQAS9JUocMeEmSOmTAS5LUIQNekqQOGfCSJHXIgJckqUMGvCRJHTLgJUnqkAEvSVKHDHhJkjpkwEuS1CEDXpKkDhnwkiR1yICXJKlDBrwkSR0y4CVJ6pABL0lShwx4SZI6ZMBLktQhA16SpA4Z8JIkdciAlySpQwa8JEkdMuAlSeqQAS9JUocMeEmSOmTAS5LUIQNekqQOGfCSJHXIgJckqUMGvCRJHTLgJUnqkAEvSVKHDHhJkjpkwEuS1KED5rqA/dmy1VfNdQnaC7P572vbxWfN2rEk9ckzeEmSOjS2gE/ywSQ7k9w61LY4yTVJ7mjvRwx9d1GSrUm2JHnRuOqSJGl/MM4z+A8DZ+7WthrYWFXLgY3tM0lOBFYCJ7VtLkmyaIy1SZLUtbEFfFX9NfC93ZrPAda25bXAy4ba11XV/VV1F7AVOHVctUmS1LvZvgd/dFXtAGjvR7X2Y4BvD623vbVJkqR9MF8G2WWCtppwxeT8JJuSbLr33nvHXJYkSQvTbAf8PUmWArT3na19O3Dc0HrHAndPtIOqurSqVlTViiVLloy1WEmSFqrZDvgNwKq2vAq4Yqh9ZZKDkpwALAeum+XaJEnqxtgmuknyceC5wJFJtgNvAS4G1ic5D/gWcC5AVW1Osh64DXgAuKCqHhxXbZIk9W5sAV9Vr5zkq+dPsv4aYM246pEkaX8yXwbZSZKkGWTAS5LUIQNekqQOGfCSJHXIgJckqUMGvCRJHTLgJUnqkAEvSVKHDHhJkjpkwEuS1CEDXpKkDhnwkiR1yICXJKlDBrwkSR0y4CVJ6pABL0lShwx4SZI6ZMBLktQhA16SpA4Z8JIkdeiAuS5A0k9atvqqWTvWtovPmrVjSZo9nsFLktQhA16SpA4Z8JIkdciAlySpQwa8JEkdMuAlSeqQAS9JUocMeEmSOmTAS5LUIQNekqQOGfCSJHXIgJckqUMGvCRJHTLgJUnqkAEvSVKHDHhJkjpkwEuS1CEDXpKkDh0w1wVImlvLVl81a8fadvFZs3YsaX/nGbwkSR0y4CVJ6pABL0lShwx4SZI6ZMBLktQhA16SpA4Z8JIkdciAlySpQ050I2nWOKmONHs8g5ckqUMGvCRJHTLgJUnqkAEvSVKH5l3AJzkzyZYkW5Osnut6JElaiObVKPoki4D3Ai8AtgPXJ9lQVbfNVg2zOcpX0vg4Yl/7u3kV8MCpwNaquhMgyTrgHGDWAl6S5jP/4qJRzbeAPwb49tDn7cAz56gWSRpJr1f+ev3LRK/92t18C/hM0FaPWCE5Hzi/ffxhki0zXMORwHdmeJ/zgf1aWOzXwmK/pilvn42j/NhC79cTRllpvgX8duC4oc/HAncPr1BVlwKXjquAJJuqasW49j9X7NfCYr8WFvu1sPTar93Nt1H01wPLk5yQ5NHASmDDHNckSdKCM6/O4KvqgST/AfgssAj4YFVtnuOyJElacOZVwANU1WeAz8xhCWO7/D/H7NfCYr8WFvu1sPTar0dIVe15LUmStKDMt3vwkiRpBhjwzXydIjfJB5PsTHLrUNviJNckuaO9HzH03UWtD1uSvGio/RlJbmnfvTtJWvtBST7R2r+SZNnQNqvaMe5IsmoG+3Rcks8luT3J5iQX9tCvtu+Dk1yX5ObWt7d11LdFSW5McmUvfWr739ZquinJpl76luTwJJcl+Xr7s3baQu9Xkqe0f0+7Xvclec1C79fYVNV+/2IwoO8bwBOBRwM3AyfOdV2ttmcDpwC3DrX9EbC6La8G3t6WT2y1HwSc0Pq0qH13HXAag7kG/gp4cWv/98Cft+WVwCfa8mLgzvZ+RFs+Yob6tBQ4pS0fAvxNq31B96vtP8Dj2/KBwFeAZ3XSt9cCHwOu7OG/w6F+bQOO3K1twfcNWAv827b8aODwHvo11L9FwN8x+E14N/2a0X9Gc13AfHi1f8mfHfp8EXDRXNc1VM8yHhnwW4ClbXkpsGWiuhn8GuG0ts7Xh9pfCbxveJ22fACDyR8yvE777n3AK8fUvysYPH+gt349Fvgqg9kYF3TfGMxJsRE4g4cDfkH3aWif2/jJgF/QfQMOBe6ijbPqpV+79eWFwP/urV8z+fIS/cBEU+QeM0e1jOLoqtoB0N6Pau2T9eOYtrx7+yO2qaoHgH8AfmqKfc2odvnr6QzOdLvoV7uUfROwE7imqnro27uA1wMPDbUt9D7tUsDVSW7IYKZMWPh9eyJwL/Chdlvl/Uke10G/hq0EPt6We+rXjDHgB/Y4Re4CMVk/purfvmwzI5I8HvgU8Jqqum+qVSepZV72q6oerKqTGZz1nprkqVOsPu/7luSlwM6qumHUTSapY970aTenV9UpwIuBC5I8e4p1F0rfDmBwa++/VtXTgR8xuHQ9mYXSr8GBBxOhnQ18ck+rTlLLvOzXTDPgB/Y4Re48c0+SpQDtfWdrn6wf29vy7u2P2CbJAcBhwPem2NeMSHIgg3D/aFX9RWte8P0aVlV/D1wLnMnC7tvpwNlJtgHrgDOSfGSB9+nHquru9r4TuJzBUy0Xet+2A9vb1SOAyxgE/kLv1y4vBr5aVfe0z730a2bN9T2C+fBi8LfdOxkMwtg1yO6kua5rqL5lPPIe/B/zyAElf9SWT+KRA0ru5OEBJdczGOy1a0DJS1r7BTxyQMn6tryYwT28I9rrLmDxDPUnwH8H3rVb+4LuV9v/EuDwtvwY4AvAS3voWzvGc3n4HvyC7xPwOOCQoeX/w+AvZD307QvAU9ryW1ufFny/2jHWAa/u6f8d43jNeQHz5QW8hMFo7m8Ab5zreobq+jiwA/hnBn+DPI/B/aCNwB3tffHQ+m9sfdhCGxXa2lcAt7bv3sPDkxwdzOAy11YGo0qfOLTNb7b2rcN/mGagT7/A4NLW14Cb2uslC71fbd8/C9zY+nYr8ObWvuD71vb/XB4O+AXfJwb3qm9ur820P/ud9O1kYFP7b/HTDEKph349FvgucNhQ24Lv1zhezmQnSVKHvAcvSVKHDHhJkjpkwEuS1CEDXpKkDhnwkiR1yICXOpXkjRk80e5r7clbz9zD+h9O8oq92P9bk/xt2/etSc6eZL2zM4+e0CjtLw6Y6wIkzbwkpzGYYOeUqro/yZEMJnGaaX9aVX+S5KeBLyQ5qqp+PF99kgOqagOwYQzHljQFz+ClPi0FvlNV9wNU1XeqTcma5M1Jrm9n3Zfueg72sPas7M+3B7B8dtc0oJOpqtuBB4Aj25WAdyb5HPD2JL+R5D1tv0cnuTzJze318639VUmua1cD3pdk0cz+45D2Pwa81KergeOS/E2SS5I8Z+i791TVz1XVUxlMp/vS4Q3bcwL+C/CKqnoG8EFgzVQHa5f/H2LwBDOAfwn8YlW9brdV3w18vqqexmBu9M3t7P9XGDz05WTgQeBX977LkoZ5iV7qUFX9MMkzgH8FPA/4RJLVVfVh4HlJXs9gys/FDKZo/cuhzZ8CPBW4pp3cL2IwXfJEfjfJq4AfAL9SVdW2+WRVPTjB+mcAv95qfBD4hyS/BjwDuL5t+xgefliIpH1kwEudagF6LXBtkluAVUnWAZcAK6rq20neymDu7WEBNlfVaSMc5k+r6k8maP/RXpQaYG1VXbQX20jaAy/RSx1K8pQky4eaTga+ycNh/p0kjwcmGjW/BVjSBuqR5MAkJ81QaRuB3277XZTk0Nb2iiRHtfbFSZ4wQ8eT9lsGvNSnxwNrk9yW5GvAicBba/CM+v8G3MLgCWPX775hVf0Tg+B/e5KbGTzt7+dnqK4LGdwiuAW4gcFjmW8D3gRc3Wq9hsEgQUnT4NPkJEnqkGfwkiR1yICXJKlDBrwkSR0y4CVJ6pABL0lShwx4SZI6ZMBLktQhA16SpA79f52BlwGP8eCkAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,6))\n",
    "\n",
    "plt.hist(data.SalePrice, bins=range(0,800000,50000))\n",
    "\n",
    "plt.ylabel('Number of Houses')\n",
    "plt.xlabel('Sale Price')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Street            2\n",
       "Alley             2\n",
       "CentralAir        2\n",
       "Utilities         2\n",
       "LandSlope         3\n",
       "PoolQC            3\n",
       "PavedDrive        3\n",
       "GarageFinish      3\n",
       "BsmtQual          4\n",
       "ExterQual         4\n",
       "MasVnrType        4\n",
       "KitchenQual       4\n",
       "BsmtCond          4\n",
       "BsmtExposure      4\n",
       "Fence             4\n",
       "MiscFeature       4\n",
       "LandContour       4\n",
       "LotShape          4\n",
       "FireplaceQu       5\n",
       "Electrical        5\n",
       "HeatingQC         5\n",
       "GarageQual        5\n",
       "GarageCond        5\n",
       "MSZoning          5\n",
       "LotConfig         5\n",
       "BldgType          5\n",
       "ExterCond         5\n",
       "BsmtFinType1      6\n",
       "RoofStyle         6\n",
       "GarageType        6\n",
       "Foundation        6\n",
       "Heating           6\n",
       "BsmtFinType2      6\n",
       "SaleCondition     6\n",
       "Functional        7\n",
       "RoofMatl          8\n",
       "HouseStyle        8\n",
       "Condition2        8\n",
       "SaleType          9\n",
       "Condition1        9\n",
       "Exterior1st      15\n",
       "Exterior2nd      16\n",
       "Neighborhood     25\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "object_variables = data.select_dtypes(include=[np.object])\n",
    "object_variables.nunique().sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>index</th>\n",
       "      <th>HouseStyle</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1.5Fin</td>\n",
       "      <td>154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>1.5Unf</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1Story</td>\n",
       "      <td>726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>2.5Fin</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>2.5Unf</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2Story</td>\n",
       "      <td>445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>SFoyer</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>SLvl</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    index  HouseStyle\n",
       "2  1.5Fin         154\n",
       "5  1.5Unf          14\n",
       "0  1Story         726\n",
       "7  2.5Fin           8\n",
       "6  2.5Unf          11\n",
       "1  2Story         445\n",
       "4  SFoyer          37\n",
       "3    SLvl          65"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counts = data.HouseStyle.value_counts(dropna=False)\n",
    "counts.reset_index().sort_values(by='index')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10,10))\n",
    "slices = ax.pie(counts, \n",
    "                labels = counts.index, \n",
    "                colors = ['white'], \n",
    "               wedgeprops = {'edgecolor': 'black'} )\n",
    "patches = slices[0]\n",
    "hatches =  ['/', '\\\\', '|', '-', '+', 'x', 'o', 'O', '\\.', '*']\n",
    "colors = ['white', 'white', 'lightgrey', 'white', \n",
    "          'lightgrey', 'white', 'lightgrey', 'white']\n",
    "for patch in range(len(patches)):\n",
    "    patches[patch].set_hatch(hatches[patch])\n",
    "    patches[patch].set_facecolor(colors[patch])\n",
    "plt.title('Pie chart showing counts for\\nvarious house styles')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Id               1460\n",
       "LotArea          1073\n",
       "GrLivArea         861\n",
       "BsmtUnfSF         780\n",
       "1stFlrSF          753\n",
       "TotalBsmtSF       721\n",
       "SalePrice         663\n",
       "BsmtFinSF1        637\n",
       "GarageArea        441\n",
       "2ndFlrSF          417\n",
       "MasVnrArea        327\n",
       "WoodDeckSF        274\n",
       "OpenPorchSF       202\n",
       "BsmtFinSF2        144\n",
       "EnclosedPorch     120\n",
       "YearBuilt         112\n",
       "LotFrontage       110\n",
       "GarageYrBlt        97\n",
       "ScreenPorch        76\n",
       "YearRemodAdd       61\n",
       "LowQualFinSF       24\n",
       "MiscVal            21\n",
       "3SsnPorch          20\n",
       "MSSubClass         15\n",
       "MoSold             12\n",
       "TotRmsAbvGrd       12\n",
       "OverallQual        10\n",
       "OverallCond         9\n",
       "PoolArea            8\n",
       "BedroomAbvGr        8\n",
       "YrSold              5\n",
       "GarageCars          5\n",
       "KitchenAbvGr        4\n",
       "Fireplaces          4\n",
       "BsmtFullBath        4\n",
       "FullBath            4\n",
       "HalfBath            3\n",
       "BsmtHalfBath        3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numeric_variables = data.select_dtypes(include=[np.number]) \n",
    "numeric_variables.nunique().sort_values(ascending=False) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,7))\n",
    "sns.distplot(data.LotArea.dropna(), bins=range(0,100000,1000))\n",
    "plt.xlim(0,100000)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GarageYrBlt      -0.649415\n",
       "YearBuilt        -0.613461\n",
       "YearRemodAdd     -0.503562\n",
       "GarageCars       -0.342549\n",
       "Id                0.000000\n",
       "FullBath          0.036562\n",
       "YrSold            0.096269\n",
       "GarageArea        0.179981\n",
       "BedroomAbvGr      0.211790\n",
       "MoSold            0.212053\n",
       "OverallQual       0.216944\n",
       "BsmtFullBath      0.596067\n",
       "Fireplaces        0.649565\n",
       "HalfBath          0.675897\n",
       "TotRmsAbvGrd      0.676341\n",
       "OverallCond       0.693067\n",
       "2ndFlrSF          0.813030\n",
       "BsmtUnfSF         0.920268\n",
       "GrLivArea         1.366560\n",
       "1stFlrSF          1.376757\n",
       "MSSubClass        1.407657\n",
       "TotalBsmtSF       1.524255\n",
       "WoodDeckSF        1.541376\n",
       "BsmtFinSF1        1.685503\n",
       "SalePrice         1.882876\n",
       "LotFrontage       2.163569\n",
       "OpenPorchSF       2.364342\n",
       "MasVnrArea        2.669084\n",
       "EnclosedPorch     3.089872\n",
       "BsmtHalfBath      4.103403\n",
       "ScreenPorch       4.122214\n",
       "BsmtFinSF2        4.255261\n",
       "KitchenAbvGr      4.488397\n",
       "LowQualFinSF      9.011341\n",
       "3SsnPorch        10.304342\n",
       "LotArea          12.207688\n",
       "PoolArea         14.828374\n",
       "MiscVal          24.476794\n",
       "dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.skew().sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Id                -1.200000\n",
       "MSSubClass         1.580188\n",
       "LotFrontage       17.452867\n",
       "LotArea          203.243271\n",
       "OverallQual        0.096293\n",
       "OverallCond        1.106413\n",
       "YearBuilt         -0.439552\n",
       "YearRemodAdd      -1.272245\n",
       "MasVnrArea        10.082417\n",
       "BsmtFinSF1        11.118236\n",
       "BsmtFinSF2        20.113338\n",
       "BsmtUnfSF          0.474994\n",
       "TotalBsmtSF       13.250483\n",
       "1stFlrSF           5.745841\n",
       "2ndFlrSF          -0.553464\n",
       "LowQualFinSF      83.234817\n",
       "GrLivArea          4.895121\n",
       "BsmtFullBath      -0.839098\n",
       "BsmtHalfBath      16.396642\n",
       "FullBath          -0.857043\n",
       "HalfBath          -1.076927\n",
       "BedroomAbvGr       2.230875\n",
       "KitchenAbvGr      21.532404\n",
       "TotRmsAbvGrd       0.880762\n",
       "Fireplaces        -0.217237\n",
       "GarageYrBlt       -0.418341\n",
       "GarageCars         0.220998\n",
       "GarageArea         0.917067\n",
       "WoodDeckSF         2.992951\n",
       "OpenPorchSF        8.490336\n",
       "EnclosedPorch     10.430766\n",
       "3SsnPorch        123.662379\n",
       "ScreenPorch       18.439068\n",
       "PoolArea         223.268499\n",
       "MiscVal          701.003342\n",
       "MoSold            -0.404109\n",
       "YrSold            -1.190601\n",
       "SalePrice          6.536282\n",
       "dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.kurt()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": 2
}