Day_010_HW.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# 作業 : (Kaggle)房價預測"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# [作業目標]\n",
    "- 試著模仿範例寫法, 在房價預測中, 觀察去除離群值的影響"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# [作業重點]\n",
    "- 觀察將極端值以上下限值取代, 對於分布與迴歸分數的影響 (In[5], Out[5])\n",
    "- 觀察將極端值資料直接刪除, 對於分布與迴歸分數的影響 (In[6], Out[6])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>MSSubClass</th>\n",
       "      <th>MSZoning</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>Street</th>\n",
       "      <th>Alley</th>\n",
       "      <th>LotShape</th>\n",
       "      <th>LandContour</th>\n",
       "      <th>Utilities</th>\n",
       "      <th>LotConfig</th>\n",
       "      <th>...</th>\n",
       "      <th>ScreenPorch</th>\n",
       "      <th>PoolArea</th>\n",
       "      <th>PoolQC</th>\n",
       "      <th>Fence</th>\n",
       "      <th>MiscFeature</th>\n",
       "      <th>MiscVal</th>\n",
       "      <th>MoSold</th>\n",
       "      <th>YrSold</th>\n",
       "      <th>SaleType</th>\n",
       "      <th>SaleCondition</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>65.0</td>\n",
       "      <td>8450</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>80.0</td>\n",
       "      <td>9600</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>FR2</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2007</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>68.0</td>\n",
       "      <td>11250</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>70</td>\n",
       "      <td>RL</td>\n",
       "      <td>60.0</td>\n",
       "      <td>9550</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Corner</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Abnorml</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>84.0</td>\n",
       "      <td>14260</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>FR2</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 79 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   MSSubClass MSZoning  LotFrontage  LotArea Street Alley LotShape  \\\n",
       "0          60       RL         65.0     8450   Pave   NaN      Reg   \n",
       "1          20       RL         80.0     9600   Pave   NaN      Reg   \n",
       "2          60       RL         68.0    11250   Pave   NaN      IR1   \n",
       "3          70       RL         60.0     9550   Pave   NaN      IR1   \n",
       "4          60       RL         84.0    14260   Pave   NaN      IR1   \n",
       "\n",
       "  LandContour Utilities LotConfig  ... ScreenPorch PoolArea PoolQC Fence  \\\n",
       "0         Lvl    AllPub    Inside  ...           0        0    NaN   NaN   \n",
       "1         Lvl    AllPub       FR2  ...           0        0    NaN   NaN   \n",
       "2         Lvl    AllPub    Inside  ...           0        0    NaN   NaN   \n",
       "3         Lvl    AllPub    Corner  ...           0        0    NaN   NaN   \n",
       "4         Lvl    AllPub       FR2  ...           0        0    NaN   NaN   \n",
       "\n",
       "  MiscFeature MiscVal  MoSold  YrSold  SaleType  SaleCondition  \n",
       "0         NaN       0       2    2008        WD         Normal  \n",
       "1         NaN       0       5    2007        WD         Normal  \n",
       "2         NaN       0       9    2008        WD         Normal  \n",
       "3         NaN       0       2    2006        WD        Abnorml  \n",
       "4         NaN       0      12    2008        WD         Normal  \n",
       "\n",
       "[5 rows x 79 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 做完特徵工程前的所有準備 (與前範例相同)\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "data_path = 'data/'\n",
    "df_train = pd.read_csv(data_path + 'house_train.csv.gz')\n",
    "\n",
    "train_Y = np.log1p(df_train['SalePrice'])\n",
    "df = df_train.drop(['Id', 'SalePrice'] , axis=1)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "36 Numeric Features : ['MSSubClass', 'LotFrontage', 'LotArea', 'OverallQual', 'OverallCond', 'YearBuilt', 'YearRemodAdd', 'MasVnrArea', 'BsmtFinSF1', 'BsmtFinSF2', 'BsmtUnfSF', 'TotalBsmtSF', '1stFlrSF', '2ndFlrSF', 'LowQualFinSF', 'GrLivArea', 'BsmtFullBath', 'BsmtHalfBath', 'FullBath', 'HalfBath', 'BedroomAbvGr', 'KitchenAbvGr', 'TotRmsAbvGrd', 'Fireplaces', 'GarageYrBlt', 'GarageCars', 'GarageArea', 'WoodDeckSF', 'OpenPorchSF', 'EnclosedPorch', '3SsnPorch', 'ScreenPorch', 'PoolArea', 'MiscVal', 'MoSold', 'YrSold']\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#只取 int64, float64 兩種數值型欄位, 存於 num_features 中\n",
    "num_features = []\n",
    "for dtype, feature in zip(df.dtypes, df.columns):\n",
    "    if dtype == 'float64' or dtype == 'int64':\n",
    "        num_features.append(feature)\n",
    "print(f'{len(num_features)} Numeric Features : {num_features}\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>MSSubClass</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>OverallQual</th>\n",
       "      <th>OverallCond</th>\n",
       "      <th>YearBuilt</th>\n",
       "      <th>YearRemodAdd</th>\n",
       "      <th>MasVnrArea</th>\n",
       "      <th>BsmtFinSF1</th>\n",
       "      <th>BsmtFinSF2</th>\n",
       "      <th>...</th>\n",
       "      <th>GarageArea</th>\n",
       "      <th>WoodDeckSF</th>\n",
       "      <th>OpenPorchSF</th>\n",
       "      <th>EnclosedPorch</th>\n",
       "      <th>3SsnPorch</th>\n",
       "      <th>ScreenPorch</th>\n",
       "      <th>PoolArea</th>\n",
       "      <th>MiscVal</th>\n",
       "      <th>MoSold</th>\n",
       "      <th>YrSold</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>60</td>\n",
       "      <td>65.0</td>\n",
       "      <td>8450</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>2003</td>\n",
       "      <td>2003</td>\n",
       "      <td>196.0</td>\n",
       "      <td>706</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>548</td>\n",
       "      <td>0</td>\n",
       "      <td>61</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20</td>\n",
       "      <td>80.0</td>\n",
       "      <td>9600</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>1976</td>\n",
       "      <td>1976</td>\n",
       "      <td>0.0</td>\n",
       "      <td>978</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>460</td>\n",
       "      <td>298</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>60</td>\n",
       "      <td>68.0</td>\n",
       "      <td>11250</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>2001</td>\n",
       "      <td>2002</td>\n",
       "      <td>162.0</td>\n",
       "      <td>486</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>608</td>\n",
       "      <td>0</td>\n",
       "      <td>42</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>70</td>\n",
       "      <td>60.0</td>\n",
       "      <td>9550</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>1915</td>\n",
       "      <td>1970</td>\n",
       "      <td>0.0</td>\n",
       "      <td>216</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>642</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>272</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>60</td>\n",
       "      <td>84.0</td>\n",
       "      <td>14260</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>2000</td>\n",
       "      <td>2000</td>\n",
       "      <td>350.0</td>\n",
       "      <td>655</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>836</td>\n",
       "      <td>192</td>\n",
       "      <td>84</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 36 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   MSSubClass  LotFrontage  LotArea  OverallQual  OverallCond  YearBuilt  \\\n",
       "0          60         65.0     8450            7            5       2003   \n",
       "1          20         80.0     9600            6            8       1976   \n",
       "2          60         68.0    11250            7            5       2001   \n",
       "3          70         60.0     9550            7            5       1915   \n",
       "4          60         84.0    14260            8            5       2000   \n",
       "\n",
       "   YearRemodAdd  MasVnrArea  BsmtFinSF1  BsmtFinSF2  ...  GarageArea  \\\n",
       "0          2003       196.0         706           0  ...         548   \n",
       "1          1976         0.0         978           0  ...         460   \n",
       "2          2002       162.0         486           0  ...         608   \n",
       "3          1970         0.0         216           0  ...         642   \n",
       "4          2000       350.0         655           0  ...         836   \n",
       "\n",
       "   WoodDeckSF  OpenPorchSF  EnclosedPorch  3SsnPorch  ScreenPorch  PoolArea  \\\n",
       "0           0           61              0          0            0         0   \n",
       "1         298            0              0          0            0         0   \n",
       "2           0           42              0          0            0         0   \n",
       "3           0           35            272          0            0         0   \n",
       "4         192           84              0          0            0         0   \n",
       "\n",
       "   MiscVal  MoSold  YrSold  \n",
       "0        0       2    2008  \n",
       "1        0       5    2007  \n",
       "2        0       9    2008  \n",
       "3        0       2    2006  \n",
       "4        0      12    2008  \n",
       "\n",
       "[5 rows x 36 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 削減文字型欄位, 只剩數值型欄位\n",
    "df = df[num_features]\n",
    "df = df.fillna(-1)\n",
    "MMEncoder = MinMaxScaler()\n",
    "train_num = train_Y.shape[0]\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# 作業1\n",
    "* 試著限制 '1樓地板面積(平方英尺)' (1stFlrSF) 欄位的上下限, 看看能否再進一步提高分數?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\henry\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\preprocessing\\data.py:323: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by MinMaxScaler.\n",
      "  return self.partial_fit(X, y)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.8466510874327978"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 顯示 1stFlrSF 與目標值的散佈圖\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "\n",
    "sns.regplot(x = df['1stFlrSF'][:train_num], y=train_Y)\n",
    "plt.show()\n",
    "\n",
    "# # 做線性迴歸, 觀察分數\n",
    "train_X = MMEncoder.fit_transform(df)\n",
    "estimator = LinearRegression()\n",
    "cross_val_score(estimator, train_X, train_Y, cv=5).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1460 1457\n"
     ]
    }
   ],
   "source": [
    "# 將 1stFlrSF 限制在你覺得適合的範圍內, 調整離群值\n",
    "\n",
    "def lim(x):\n",
    "    if x > 2000:\n",
    "        return 2000\n",
    "    if x < 500:\n",
    "        return 500\n",
    "    return x\n",
    "\n",
    "train_X = df['1stFlrSF'].map(lim)\n",
    "cols = list(df.columns)\n",
    "cols.remove('1stFlrSF')\n",
    "\n",
    "n_df = pd.concat([df[cols],pd.DataFrame(train_X,columns=['1stFlrSF'])],axis = 1)\n",
    "print(len(n_df),len(n_train_Y))\n",
    "\n",
    "# sns.regplot(x = n_df['1stFlrSF'][:train_num], y=train_Y)\n",
    "# plt.show()\n",
    "\n",
    "# 做線性迴歸, 觀察分數\n",
    "# train_X = MMEncoder.fit_transform(n_df)\n",
    "# estimator = LinearRegression()\n",
    "# cross_val_score(estimator, train_X, train_Y, cv=5).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# 作業2\n",
    "* 續前題, 去除離群值有兩類方式 :  捨棄離群值(刪除離群的資料) 以及調整離群值,  \n",
    "請試著用同樣的上下限, 改為 '捨棄離群值' 的方法, 看看結果會變好還是變差? 並試著解釋原因。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\henry\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\preprocessing\\data.py:323: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by MinMaxScaler.\n",
      "  return self.partial_fit(X, y)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.8554123916599952"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 將 1stFlrSF 限制在你覺得適合的範圍內, 捨棄離群值\n",
    "\n",
    "n_df = pd.concat([df,train_Y],axis = 1)\n",
    "n_df = n_df[n_df['1stFlrSF']<2000]\n",
    "n_df = n_df[n_df['1stFlrSF']>300]\n",
    "n_train_Y = n_df['SalePrice']\n",
    "# print(n_df.shape,n_train_Y.shape)\n",
    "\n",
    "n_df = n_df.drop(['SalePrice'],axis = 1)\n",
    "\n",
    "sns.regplot(x = n_df['1stFlrSF'][:n_train_Y.shape[0]], y=n_train_Y)\n",
    "plt.show()\n",
    "\n",
    "\n",
    "# 做線性迴歸, 觀察分數\n",
    "train_X = MMEncoder.fit_transform(n_df)\n",
    "estimator = LinearRegression()\n",
    "cross_val_score(estimator, train_X, n_train_Y, cv=5).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "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": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}