Untitled.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data = pd.read_csv(\"C:/Machine Learning/Hackathons/Kaggle/House Price/train_Copy.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1460, 81)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.shape"
   ]
  },
  {
   "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>Id</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>...</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",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\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>...</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",
       "      <td>208500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\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>...</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",
       "      <td>181500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\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>...</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",
       "      <td>223500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\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>...</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",
       "      <td>140000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\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>...</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",
       "      <td>250000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 81 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id  MSSubClass MSZoning  LotFrontage  LotArea Street Alley LotShape  \\\n",
       "0   1          60       RL         65.0     8450   Pave   NaN      Reg   \n",
       "1   2          20       RL         80.0     9600   Pave   NaN      Reg   \n",
       "2   3          60       RL         68.0    11250   Pave   NaN      IR1   \n",
       "3   4          70       RL         60.0     9550   Pave   NaN      IR1   \n",
       "4   5          60       RL         84.0    14260   Pave   NaN      IR1   \n",
       "\n",
       "  LandContour Utilities  ... PoolArea PoolQC Fence MiscFeature MiscVal MoSold  \\\n",
       "0         Lvl    AllPub  ...        0    NaN   NaN         NaN       0      2   \n",
       "1         Lvl    AllPub  ...        0    NaN   NaN         NaN       0      5   \n",
       "2         Lvl    AllPub  ...        0    NaN   NaN         NaN       0      9   \n",
       "3         Lvl    AllPub  ...        0    NaN   NaN         NaN       0      2   \n",
       "4         Lvl    AllPub  ...        0    NaN   NaN         NaN       0     12   \n",
       "\n",
       "  YrSold  SaleType  SaleCondition  SalePrice  \n",
       "0   2008        WD         Normal     208500  \n",
       "1   2007        WD         Normal     181500  \n",
       "2   2008        WD         Normal     223500  \n",
       "3   2006        WD        Abnorml     140000  \n",
       "4   2008        WD         Normal     250000  \n",
       "\n",
       "[5 rows x 81 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1460 entries, 0 to 1459\n",
      "Data columns (total 81 columns):\n",
      "Id               1460 non-null int64\n",
      "MSSubClass       1460 non-null int64\n",
      "MSZoning         1460 non-null object\n",
      "LotFrontage      1201 non-null float64\n",
      "LotArea          1460 non-null int64\n",
      "Street           1460 non-null object\n",
      "Alley            91 non-null object\n",
      "LotShape         1460 non-null object\n",
      "LandContour      1460 non-null object\n",
      "Utilities        1460 non-null object\n",
      "LotConfig        1460 non-null object\n",
      "LandSlope        1460 non-null object\n",
      "Neighborhood     1460 non-null object\n",
      "Condition1       1460 non-null object\n",
      "Condition2       1460 non-null object\n",
      "BldgType         1460 non-null object\n",
      "HouseStyle       1460 non-null object\n",
      "OverallQual      1460 non-null int64\n",
      "OverallCond      1460 non-null int64\n",
      "YearBuilt        1460 non-null int64\n",
      "YearRemodAdd     1460 non-null int64\n",
      "RoofStyle        1460 non-null object\n",
      "RoofMatl         1460 non-null object\n",
      "Exterior1st      1460 non-null object\n",
      "Exterior2nd      1460 non-null object\n",
      "MasVnrType       1452 non-null object\n",
      "MasVnrArea       1452 non-null float64\n",
      "ExterQual        1460 non-null object\n",
      "ExterCond        1460 non-null object\n",
      "Foundation       1460 non-null object\n",
      "BsmtQual         1423 non-null object\n",
      "BsmtCond         1423 non-null object\n",
      "BsmtExposure     1422 non-null object\n",
      "BsmtFinType1     1423 non-null object\n",
      "BsmtFinSF1       1460 non-null int64\n",
      "BsmtFinType2     1422 non-null object\n",
      "BsmtFinSF2       1460 non-null int64\n",
      "BsmtUnfSF        1460 non-null int64\n",
      "TotalBsmtSF      1460 non-null int64\n",
      "Heating          1460 non-null object\n",
      "HeatingQC        1460 non-null object\n",
      "CentralAir       1460 non-null object\n",
      "Electrical       1459 non-null object\n",
      "1stFlrSF         1460 non-null int64\n",
      "2ndFlrSF         1460 non-null int64\n",
      "LowQualFinSF     1460 non-null int64\n",
      "GrLivArea        1460 non-null int64\n",
      "BsmtFullBath     1460 non-null int64\n",
      "BsmtHalfBath     1460 non-null int64\n",
      "FullBath         1460 non-null int64\n",
      "HalfBath         1460 non-null int64\n",
      "BedroomAbvGr     1460 non-null int64\n",
      "KitchenAbvGr     1460 non-null int64\n",
      "KitchenQual      1460 non-null object\n",
      "TotRmsAbvGrd     1460 non-null int64\n",
      "Functional       1460 non-null object\n",
      "Fireplaces       1460 non-null int64\n",
      "FireplaceQu      770 non-null object\n",
      "GarageType       1379 non-null object\n",
      "GarageYrBlt      1379 non-null float64\n",
      "GarageFinish     1379 non-null object\n",
      "GarageCars       1460 non-null int64\n",
      "GarageArea       1460 non-null int64\n",
      "GarageQual       1379 non-null object\n",
      "GarageCond       1379 non-null object\n",
      "PavedDrive       1460 non-null object\n",
      "WoodDeckSF       1460 non-null int64\n",
      "OpenPorchSF      1460 non-null int64\n",
      "EnclosedPorch    1460 non-null int64\n",
      "3SsnPorch        1460 non-null int64\n",
      "ScreenPorch      1460 non-null int64\n",
      "PoolArea         1460 non-null int64\n",
      "PoolQC           7 non-null object\n",
      "Fence            281 non-null object\n",
      "MiscFeature      54 non-null object\n",
      "MiscVal          1460 non-null int64\n",
      "MoSold           1460 non-null int64\n",
      "YrSold           1460 non-null int64\n",
      "SaleType         1460 non-null object\n",
      "SaleCondition    1460 non-null object\n",
      "SalePrice        1460 non-null int64\n",
      "dtypes: float64(3), int64(35), object(43)\n",
      "memory usage: 924.0+ KB\n"
     ]
    }
   ],
   "source": [
    "train_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</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>...</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",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1201.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1452.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>730.500000</td>\n",
       "      <td>56.897260</td>\n",
       "      <td>70.049958</td>\n",
       "      <td>10516.828082</td>\n",
       "      <td>6.099315</td>\n",
       "      <td>5.575342</td>\n",
       "      <td>1971.267808</td>\n",
       "      <td>1984.865753</td>\n",
       "      <td>103.685262</td>\n",
       "      <td>443.639726</td>\n",
       "      <td>...</td>\n",
       "      <td>94.244521</td>\n",
       "      <td>46.660274</td>\n",
       "      <td>21.954110</td>\n",
       "      <td>3.409589</td>\n",
       "      <td>15.060959</td>\n",
       "      <td>2.758904</td>\n",
       "      <td>43.489041</td>\n",
       "      <td>6.321918</td>\n",
       "      <td>2007.815753</td>\n",
       "      <td>180921.195890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>421.610009</td>\n",
       "      <td>42.300571</td>\n",
       "      <td>24.284752</td>\n",
       "      <td>9981.264932</td>\n",
       "      <td>1.382997</td>\n",
       "      <td>1.112799</td>\n",
       "      <td>30.202904</td>\n",
       "      <td>20.645407</td>\n",
       "      <td>181.066207</td>\n",
       "      <td>456.098091</td>\n",
       "      <td>...</td>\n",
       "      <td>125.338794</td>\n",
       "      <td>66.256028</td>\n",
       "      <td>61.119149</td>\n",
       "      <td>29.317331</td>\n",
       "      <td>55.757415</td>\n",
       "      <td>40.177307</td>\n",
       "      <td>496.123024</td>\n",
       "      <td>2.703626</td>\n",
       "      <td>1.328095</td>\n",
       "      <td>79442.502883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>1300.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1872.000000</td>\n",
       "      <td>1950.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</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",
       "      <td>1.000000</td>\n",
       "      <td>2006.000000</td>\n",
       "      <td>34900.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>365.750000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>7553.500000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1954.000000</td>\n",
       "      <td>1967.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</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",
       "      <td>5.000000</td>\n",
       "      <td>2007.000000</td>\n",
       "      <td>129975.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>730.500000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>69.000000</td>\n",
       "      <td>9478.500000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1973.000000</td>\n",
       "      <td>1994.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>383.500000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>25.000000</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>6.000000</td>\n",
       "      <td>2008.000000</td>\n",
       "      <td>163000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1095.250000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>11601.500000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2004.000000</td>\n",
       "      <td>166.000000</td>\n",
       "      <td>712.250000</td>\n",
       "      <td>...</td>\n",
       "      <td>168.000000</td>\n",
       "      <td>68.000000</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>8.000000</td>\n",
       "      <td>2009.000000</td>\n",
       "      <td>214000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1460.000000</td>\n",
       "      <td>190.000000</td>\n",
       "      <td>313.000000</td>\n",
       "      <td>215245.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>2010.000000</td>\n",
       "      <td>2010.000000</td>\n",
       "      <td>1600.000000</td>\n",
       "      <td>5644.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>857.000000</td>\n",
       "      <td>547.000000</td>\n",
       "      <td>552.000000</td>\n",
       "      <td>508.000000</td>\n",
       "      <td>480.000000</td>\n",
       "      <td>738.000000</td>\n",
       "      <td>15500.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>2010.000000</td>\n",
       "      <td>755000.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 38 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                Id   MSSubClass  LotFrontage        LotArea  OverallQual  \\\n",
       "count  1460.000000  1460.000000  1201.000000    1460.000000  1460.000000   \n",
       "mean    730.500000    56.897260    70.049958   10516.828082     6.099315   \n",
       "std     421.610009    42.300571    24.284752    9981.264932     1.382997   \n",
       "min       1.000000    20.000000    21.000000    1300.000000     1.000000   \n",
       "25%     365.750000    20.000000    59.000000    7553.500000     5.000000   \n",
       "50%     730.500000    50.000000    69.000000    9478.500000     6.000000   \n",
       "75%    1095.250000    70.000000    80.000000   11601.500000     7.000000   \n",
       "max    1460.000000   190.000000   313.000000  215245.000000    10.000000   \n",
       "\n",
       "       OverallCond    YearBuilt  YearRemodAdd   MasVnrArea   BsmtFinSF1  ...  \\\n",
       "count  1460.000000  1460.000000   1460.000000  1452.000000  1460.000000  ...   \n",
       "mean      5.575342  1971.267808   1984.865753   103.685262   443.639726  ...   \n",
       "std       1.112799    30.202904     20.645407   181.066207   456.098091  ...   \n",
       "min       1.000000  1872.000000   1950.000000     0.000000     0.000000  ...   \n",
       "25%       5.000000  1954.000000   1967.000000     0.000000     0.000000  ...   \n",
       "50%       5.000000  1973.000000   1994.000000     0.000000   383.500000  ...   \n",
       "75%       6.000000  2000.000000   2004.000000   166.000000   712.250000  ...   \n",
       "max       9.000000  2010.000000   2010.000000  1600.000000  5644.000000  ...   \n",
       "\n",
       "        WoodDeckSF  OpenPorchSF  EnclosedPorch    3SsnPorch  ScreenPorch  \\\n",
       "count  1460.000000  1460.000000    1460.000000  1460.000000  1460.000000   \n",
       "mean     94.244521    46.660274      21.954110     3.409589    15.060959   \n",
       "std     125.338794    66.256028      61.119149    29.317331    55.757415   \n",
       "min       0.000000     0.000000       0.000000     0.000000     0.000000   \n",
       "25%       0.000000     0.000000       0.000000     0.000000     0.000000   \n",
       "50%       0.000000    25.000000       0.000000     0.000000     0.000000   \n",
       "75%     168.000000    68.000000       0.000000     0.000000     0.000000   \n",
       "max     857.000000   547.000000     552.000000   508.000000   480.000000   \n",
       "\n",
       "          PoolArea       MiscVal       MoSold       YrSold      SalePrice  \n",
       "count  1460.000000   1460.000000  1460.000000  1460.000000    1460.000000  \n",
       "mean      2.758904     43.489041     6.321918  2007.815753  180921.195890  \n",
       "std      40.177307    496.123024     2.703626     1.328095   79442.502883  \n",
       "min       0.000000      0.000000     1.000000  2006.000000   34900.000000  \n",
       "25%       0.000000      0.000000     5.000000  2007.000000  129975.000000  \n",
       "50%       0.000000      0.000000     6.000000  2008.000000  163000.000000  \n",
       "75%       0.000000      0.000000     8.000000  2009.000000  214000.000000  \n",
       "max     738.000000  15500.000000    12.000000  2010.000000  755000.000000  \n",
       "\n",
       "[8 rows x 38 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option(\"display.max_rows\",85)\n",
    "pd.set_option(\"display.max_columns\",89)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": 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>index</th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>PoolQC</td>\n",
       "      <td>99.520548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>MiscFeature</td>\n",
       "      <td>96.301370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Alley</td>\n",
       "      <td>93.767123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>Fence</td>\n",
       "      <td>80.753425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>FireplaceQu</td>\n",
       "      <td>47.260274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LotFrontage</td>\n",
       "      <td>17.739726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>GarageYrBlt</td>\n",
       "      <td>5.547945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>GarageCond</td>\n",
       "      <td>5.547945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>GarageType</td>\n",
       "      <td>5.547945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>GarageFinish</td>\n",
       "      <td>5.547945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>GarageQual</td>\n",
       "      <td>5.547945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>BsmtFinType2</td>\n",
       "      <td>2.602740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>BsmtExposure</td>\n",
       "      <td>2.602740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>BsmtQual</td>\n",
       "      <td>2.534247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>BsmtCond</td>\n",
       "      <td>2.534247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>BsmtFinType1</td>\n",
       "      <td>2.534247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>MasVnrArea</td>\n",
       "      <td>0.547945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>MasVnrType</td>\n",
       "      <td>0.547945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>Electrical</td>\n",
       "      <td>0.068493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Id</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>Functional</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>Fireplaces</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>KitchenQual</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>KitchenAbvGr</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>BedroomAbvGr</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>HalfBath</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>FullBath</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>BsmtHalfBath</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>TotRmsAbvGrd</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>GarageCars</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>GrLivArea</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>GarageArea</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>PavedDrive</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>WoodDeckSF</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>OpenPorchSF</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>EnclosedPorch</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>3SsnPorch</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>ScreenPorch</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>PoolArea</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>MiscVal</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>MoSold</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>YrSold</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>SaleType</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>SaleCondition</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>BsmtFullBath</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>HeatingQC</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>LowQualFinSF</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>LandSlope</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>OverallQual</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>HouseStyle</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>BldgType</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Condition2</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Condition1</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Neighborhood</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>LotConfig</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>YearBuilt</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Utilities</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>LandContour</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>LotShape</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Street</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LotArea</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>MSZoning</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>OverallCond</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>YearRemodAdd</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>2ndFlrSF</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>BsmtFinSF2</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>1stFlrSF</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>CentralAir</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>MSSubClass</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>Heating</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>TotalBsmtSF</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>BsmtUnfSF</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>BsmtFinSF1</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>RoofStyle</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Foundation</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>ExterCond</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>ExterQual</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Exterior2nd</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Exterior1st</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>RoofMatl</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>SalePrice</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            index          0\n",
       "72         PoolQC  99.520548\n",
       "74    MiscFeature  96.301370\n",
       "6           Alley  93.767123\n",
       "73          Fence  80.753425\n",
       "57    FireplaceQu  47.260274\n",
       "3     LotFrontage  17.739726\n",
       "59    GarageYrBlt   5.547945\n",
       "64     GarageCond   5.547945\n",
       "58     GarageType   5.547945\n",
       "60   GarageFinish   5.547945\n",
       "63     GarageQual   5.547945\n",
       "35   BsmtFinType2   2.602740\n",
       "32   BsmtExposure   2.602740\n",
       "30       BsmtQual   2.534247\n",
       "31       BsmtCond   2.534247\n",
       "33   BsmtFinType1   2.534247\n",
       "26     MasVnrArea   0.547945\n",
       "25     MasVnrType   0.547945\n",
       "42     Electrical   0.068493\n",
       "0              Id   0.000000\n",
       "55     Functional   0.000000\n",
       "56     Fireplaces   0.000000\n",
       "53    KitchenQual   0.000000\n",
       "52   KitchenAbvGr   0.000000\n",
       "51   BedroomAbvGr   0.000000\n",
       "50       HalfBath   0.000000\n",
       "49       FullBath   0.000000\n",
       "48   BsmtHalfBath   0.000000\n",
       "54   TotRmsAbvGrd   0.000000\n",
       "61     GarageCars   0.000000\n",
       "46      GrLivArea   0.000000\n",
       "62     GarageArea   0.000000\n",
       "65     PavedDrive   0.000000\n",
       "66     WoodDeckSF   0.000000\n",
       "67    OpenPorchSF   0.000000\n",
       "68  EnclosedPorch   0.000000\n",
       "69      3SsnPorch   0.000000\n",
       "70    ScreenPorch   0.000000\n",
       "71       PoolArea   0.000000\n",
       "75        MiscVal   0.000000\n",
       "76         MoSold   0.000000\n",
       "77         YrSold   0.000000\n",
       "78       SaleType   0.000000\n",
       "79  SaleCondition   0.000000\n",
       "47   BsmtFullBath   0.000000\n",
       "40      HeatingQC   0.000000\n",
       "45   LowQualFinSF   0.000000\n",
       "11      LandSlope   0.000000\n",
       "17    OverallQual   0.000000\n",
       "16     HouseStyle   0.000000\n",
       "15       BldgType   0.000000\n",
       "14     Condition2   0.000000\n",
       "13     Condition1   0.000000\n",
       "12   Neighborhood   0.000000\n",
       "10      LotConfig   0.000000\n",
       "19      YearBuilt   0.000000\n",
       "9       Utilities   0.000000\n",
       "8     LandContour   0.000000\n",
       "7        LotShape   0.000000\n",
       "5          Street   0.000000\n",
       "4         LotArea   0.000000\n",
       "2        MSZoning   0.000000\n",
       "18    OverallCond   0.000000\n",
       "20   YearRemodAdd   0.000000\n",
       "44       2ndFlrSF   0.000000\n",
       "36     BsmtFinSF2   0.000000\n",
       "43       1stFlrSF   0.000000\n",
       "41     CentralAir   0.000000\n",
       "1      MSSubClass   0.000000\n",
       "39        Heating   0.000000\n",
       "38    TotalBsmtSF   0.000000\n",
       "37      BsmtUnfSF   0.000000\n",
       "34     BsmtFinSF1   0.000000\n",
       "21      RoofStyle   0.000000\n",
       "29     Foundation   0.000000\n",
       "28      ExterCond   0.000000\n",
       "27      ExterQual   0.000000\n",
       "24    Exterior2nd   0.000000\n",
       "23    Exterior1st   0.000000\n",
       "22       RoofMatl   0.000000\n",
       "80      SalePrice   0.000000"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "((train_data.isnull().sum()/len(train_data))*100).reset_index().sort_values(ascending = False,\n",
    "                                                                         by = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "col = ((train_data.isnull().sum()/len(train_data))*100).reset_index().sort_values(ascending = False,\n",
    "                                                                         by = 0).iloc[0:4,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data.drop(columns = col, inplace= True, axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1460, 77)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "object     39\n",
       "int64      35\n",
       "float64     3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.dtypes.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "just to run it quickly,i used the mean and mode, but in actual we have to see the distribution of each categorical value and then fill it accordingly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data.fillna(train_data.mean(), inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "the reason to do the iloc is because the mode returns the dataframe and mean return the series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data.fillna(train_data.mode().iloc[0],inplace= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data.std().sort_values(ascending= False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data.skew().sort_values(ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data[\"SalePrice\"].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "corrmat = train_data.corr()\n",
    "#f, ax = plt.subplots(figsize=(12, 9))\n",
    "#sns.heatmap(corrmat, vmax=.8, square=True);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x12b09f70fd0>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x648 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(figsize=(12,9))\n",
    "sns.heatmap(train_data.corr(), linewidth= .2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Id              -0.021917\n",
       "MSSubClass      -0.084284\n",
       "LotFrontage      0.334901\n",
       "LotArea          0.263843\n",
       "OverallQual      0.790982\n",
       "OverallCond     -0.077856\n",
       "YearBuilt        0.522897\n",
       "YearRemodAdd     0.507101\n",
       "MasVnrArea       0.475241\n",
       "BsmtFinSF1       0.386420\n",
       "BsmtFinSF2      -0.011378\n",
       "BsmtUnfSF        0.214479\n",
       "TotalBsmtSF      0.613581\n",
       "1stFlrSF         0.605852\n",
       "2ndFlrSF         0.319334\n",
       "LowQualFinSF    -0.025606\n",
       "GrLivArea        0.708624\n",
       "BsmtFullBath     0.227122\n",
       "BsmtHalfBath    -0.016844\n",
       "FullBath         0.560664\n",
       "HalfBath         0.284108\n",
       "BedroomAbvGr     0.168213\n",
       "KitchenAbvGr    -0.135907\n",
       "TotRmsAbvGrd     0.533723\n",
       "Fireplaces       0.466929\n",
       "GarageYrBlt      0.470177\n",
       "GarageCars       0.640409\n",
       "GarageArea       0.623431\n",
       "WoodDeckSF       0.324413\n",
       "OpenPorchSF      0.315856\n",
       "EnclosedPorch   -0.128578\n",
       "3SsnPorch        0.044584\n",
       "ScreenPorch      0.111447\n",
       "PoolArea         0.092404\n",
       "MiscVal         -0.021190\n",
       "MoSold           0.046432\n",
       "YrSold          -0.028923\n",
       "SalePrice        1.000000\n",
       "Name: SalePrice, dtype: float64"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corrmat.loc[:,\"SalePrice\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SalePrice        1.000000\n",
       "OverallQual      0.790982\n",
       "GrLivArea        0.708624\n",
       "GarageCars       0.640409\n",
       "GarageArea       0.623431\n",
       "TotalBsmtSF      0.613581\n",
       "1stFlrSF         0.605852\n",
       "FullBath         0.560664\n",
       "TotRmsAbvGrd     0.533723\n",
       "YearBuilt        0.522897\n",
       "YearRemodAdd     0.507101\n",
       "MasVnrArea       0.475241\n",
       "GarageYrBlt      0.470177\n",
       "Fireplaces       0.466929\n",
       "BsmtFinSF1       0.386420\n",
       "LotFrontage      0.334901\n",
       "WoodDeckSF       0.324413\n",
       "2ndFlrSF         0.319334\n",
       "OpenPorchSF      0.315856\n",
       "HalfBath         0.284108\n",
       "LotArea          0.263843\n",
       "BsmtFullBath     0.227122\n",
       "BsmtUnfSF        0.214479\n",
       "BedroomAbvGr     0.168213\n",
       "ScreenPorch      0.111447\n",
       "PoolArea         0.092404\n",
       "MoSold           0.046432\n",
       "3SsnPorch        0.044584\n",
       "BsmtFinSF2      -0.011378\n",
       "BsmtHalfBath    -0.016844\n",
       "MiscVal         -0.021190\n",
       "Id              -0.021917\n",
       "LowQualFinSF    -0.025606\n",
       "YrSold          -0.028923\n",
       "OverallCond     -0.077856\n",
       "MSSubClass      -0.084284\n",
       "EnclosedPorch   -0.128578\n",
       "KitchenAbvGr    -0.135907\n",
       "dtype: float64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(train_data.corrwith(train_data[\"SalePrice\"], axis=0).sort_values(ascending=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-46-4a360a0ba46e>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0msns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpairplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtrain_data\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\axisgrid.py\u001b[0m in \u001b[0;36mpairplot\u001b[1;34m(data, hue, hue_order, palette, vars, x_vars, y_vars, kind, diag_kind, markers, height, aspect, dropna, plot_kws, diag_kws, grid_kws, size)\u001b[0m\n\u001b[0;32m   2119\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mkind\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m\"scatter\"\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2120\u001b[0m         \u001b[1;32mfrom\u001b[0m \u001b[1;33m.\u001b[0m\u001b[0mrelational\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mscatterplot\u001b[0m  \u001b[1;31m# Avoid circular import\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2121\u001b[1;33m         \u001b[0mplotter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mscatterplot\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mplot_kws\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2122\u001b[0m     \u001b[1;32melif\u001b[0m \u001b[0mkind\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m\"reg\"\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2123\u001b[0m         \u001b[1;32mfrom\u001b[0m \u001b[1;33m.\u001b[0m\u001b[0mregression\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mregplot\u001b[0m  \u001b[1;31m# Avoid circular import\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\axisgrid.py\u001b[0m in \u001b[0;36mmap_offdiag\u001b[1;34m(self, func, **kwargs)\u001b[0m\n\u001b[0;32m   1511\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1512\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmap_lower\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1513\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmap_upper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1514\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1515\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\axisgrid.py\u001b[0m in \u001b[0;36mmap_upper\u001b[1;34m(self, func, **kwargs)\u001b[0m\n\u001b[0;32m   1488\u001b[0m                 \u001b[0mcolor\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpalette\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mkw_color\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m \u001b[1;32melse\u001b[0m \u001b[0mkw_color\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1489\u001b[0m                 func(data_k[x_var], data_k[y_var], label=label_k,\n\u001b[1;32m-> 1490\u001b[1;33m                      color=color, **kwargs)\n\u001b[0m\u001b[0;32m   1491\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1492\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_clean_axis\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\relational.py\u001b[0m in \u001b[0;36mscatterplot\u001b[1;34m(x, y, hue, style, size, data, palette, hue_order, hue_norm, sizes, size_order, size_norm, markers, style_order, x_bins, y_bins, units, estimator, ci, n_boot, alpha, x_jitter, y_jitter, legend, ax, **kwargs)\u001b[0m\n\u001b[0;32m   1333\u001b[0m         \u001b[0mx_bins\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mx_bins\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_bins\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0my_bins\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1334\u001b[0m         \u001b[0mestimator\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mestimator\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mci\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mci\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mn_boot\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mn_boot\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1335\u001b[1;33m         \u001b[0malpha\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0malpha\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx_jitter\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mx_jitter\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_jitter\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0my_jitter\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlegend\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlegend\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1336\u001b[0m     )\n\u001b[0;32m   1337\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\relational.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, x, y, hue, size, style, data, palette, hue_order, hue_norm, sizes, size_order, size_norm, dashes, markers, style_order, x_bins, y_bins, units, estimator, ci, n_boot, alpha, x_jitter, y_jitter, legend)\u001b[0m\n\u001b[0;32m    850\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    851\u001b[0m         plot_data = self.establish_variables(\n\u001b[1;32m--> 852\u001b[1;33m             \u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mhue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msize\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstyle\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0munits\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    853\u001b[0m         )\n\u001b[0;32m    854\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\relational.py\u001b[0m in \u001b[0;36mestablish_variables\u001b[1;34m(self, x, y, hue, size, style, units, data)\u001b[0m\n\u001b[0;32m    155\u001b[0m                 \u001b[0munits\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0munits\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    156\u001b[0m             )\n\u001b[1;32m--> 157\u001b[1;33m             \u001b[0mplot_data\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mplot_data\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    158\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    159\u001b[0m         \u001b[1;31m# Option 3:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, data, index, columns, dtype, copy)\u001b[0m\n\u001b[0;32m    408\u001b[0m             )\n\u001b[0;32m    409\u001b[0m         \u001b[1;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdict\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 410\u001b[1;33m             \u001b[0mmgr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0minit_dict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    411\u001b[0m         \u001b[1;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mma\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mMaskedArray\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    412\u001b[0m             \u001b[1;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mma\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmrecords\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mmrecords\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\internals\\construction.py\u001b[0m in \u001b[0;36minit_dict\u001b[1;34m(data, index, columns, dtype)\u001b[0m\n\u001b[0;32m    255\u001b[0m             \u001b[0marr\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mis_datetime64tz_dtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marr\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32melse\u001b[0m \u001b[0marr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0marr\u001b[0m \u001b[1;32min\u001b[0m \u001b[0marrays\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    256\u001b[0m         ]\n\u001b[1;32m--> 257\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0marrays_to_mgr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marrays\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata_names\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    258\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    259\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\internals\\construction.py\u001b[0m in \u001b[0;36marrays_to_mgr\u001b[1;34m(arrays, arr_names, index, columns, dtype)\u001b[0m\n\u001b[0;32m     85\u001b[0m     \u001b[0maxes\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mensure_index\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     86\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 87\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0mcreate_block_manager_from_arrays\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marrays\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0marr_names\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxes\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     88\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     89\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\internals\\managers.py\u001b[0m in \u001b[0;36mcreate_block_manager_from_arrays\u001b[1;34m(arrays, names, axes)\u001b[0m\n\u001b[0;32m   1691\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1692\u001b[0m     \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1693\u001b[1;33m         \u001b[0mblocks\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mform_blocks\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marrays\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnames\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxes\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1694\u001b[0m         \u001b[0mmgr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mBlockManager\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mblocks\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxes\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1695\u001b[0m         \u001b[0mmgr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_consolidate_inplace\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\internals\\managers.py\u001b[0m in \u001b[0;36mform_blocks\u001b[1;34m(arrays, names, axes)\u001b[0m\n\u001b[0;32m   1780\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1781\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitems_dict\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"ObjectBlock\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1782\u001b[1;33m         \u001b[0mobject_blocks\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_simple_blockify\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitems_dict\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"ObjectBlock\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mobject_\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1783\u001b[0m         \u001b[0mblocks\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobject_blocks\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1784\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\internals\\managers.py\u001b[0m in \u001b[0;36m_simple_blockify\u001b[1;34m(tuples, dtype)\u001b[0m\n\u001b[0;32m   1824\u001b[0m     \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcoerce\u001b[0m \u001b[0mto\u001b[0m \u001b[0mthis\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1825\u001b[0m     \"\"\"\n\u001b[1;32m-> 1826\u001b[1;33m     \u001b[0mvalues\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mplacement\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_stack_arrays\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtuples\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1827\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1828\u001b[0m     \u001b[1;31m# CHECK DTYPE?\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\internals\\managers.py\u001b[0m in \u001b[0;36m_stack_arrays\u001b[1;34m(tuples, dtype)\u001b[0m\n\u001b[0;32m   1871\u001b[0m     \u001b[0mshape\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marrays\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0m_shape_compat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfirst\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1872\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1873\u001b[1;33m     \u001b[0mstacked\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mempty\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1874\u001b[0m     \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0marr\u001b[0m \u001b[1;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marrays\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1875\u001b[0m         \u001b[0mstacked\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_asarray_compat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Error in callback <function install_repl_displayhook.<locals>.post_execute at 0x0000012B7FA5F2F0> (for post_execute):\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\pyplot.py\u001b[0m in \u001b[0;36mpost_execute\u001b[1;34m()\u001b[0m\n\u001b[0;32m    107\u001b[0m             \u001b[1;32mdef\u001b[0m \u001b[0mpost_execute\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    108\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mis_interactive\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 109\u001b[1;33m                     \u001b[0mdraw_all\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    110\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    111\u001b[0m             \u001b[1;31m# IPython >= 2\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\_pylab_helpers.py\u001b[0m in \u001b[0;36mdraw_all\u001b[1;34m(cls, force)\u001b[0m\n\u001b[0;32m    126\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mf_mgr\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mcls\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_all_fig_managers\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    127\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mforce\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mf_mgr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstale\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 128\u001b[1;33m                 \u001b[0mf_mgr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdraw_idle\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    129\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    130\u001b[0m \u001b[0matexit\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mregister\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mGcf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdestroy_all\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backend_bases.py\u001b[0m in \u001b[0;36mdraw_idle\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1905\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_is_idle_drawing\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1906\u001b[0m             \u001b[1;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_idle_draw_cntx\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1907\u001b[1;33m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1908\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1909\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mdraw_cursor\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mevent\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py\u001b[0m in \u001b[0;36mdraw\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    386\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrenderer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_renderer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcleared\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    387\u001b[0m         \u001b[1;32mwith\u001b[0m \u001b[0mRendererAgg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlock\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 388\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    389\u001b[0m             \u001b[1;31m# A GUI class may be need to update a window using this draw, so\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    390\u001b[0m             \u001b[1;31m# don't forget to call the superclass.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[1;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[0;32m     36\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     37\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 38\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0martist\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     39\u001b[0m         \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     40\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\figure.py\u001b[0m in \u001b[0;36mdraw\u001b[1;34m(self, renderer)\u001b[0m\n\u001b[0;32m   1707\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpatch\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1708\u001b[0m             mimage._draw_list_compositing_images(\n\u001b[1;32m-> 1709\u001b[1;33m                 renderer, self, artists, self.suppressComposite)\n\u001b[0m\u001b[0;32m   1710\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1711\u001b[0m             \u001b[0mrenderer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclose_group\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'figure'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\image.py\u001b[0m in \u001b[0;36m_draw_list_compositing_images\u001b[1;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[0;32m    133\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mnot_composite\u001b[0m \u001b[1;32mor\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mhas_images\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    134\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[1;32min\u001b[0m \u001b[0martists\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 135\u001b[1;33m             \u001b[0ma\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    136\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    137\u001b[0m         \u001b[1;31m# Composite any adjacent images together\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[1;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[0;32m     36\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     37\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 38\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0martist\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     39\u001b[0m         \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     40\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_base.py\u001b[0m in \u001b[0;36mdraw\u001b[1;34m(self, renderer, inframe)\u001b[0m\n\u001b[0;32m   2643\u001b[0m             \u001b[0mrenderer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstop_rasterizing\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2644\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2645\u001b[1;33m         \u001b[0mmimage\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_draw_list_compositing_images\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0martists\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2646\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2647\u001b[0m         \u001b[0mrenderer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclose_group\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'axes'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\image.py\u001b[0m in \u001b[0;36m_draw_list_compositing_images\u001b[1;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[0;32m    133\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mnot_composite\u001b[0m \u001b[1;32mor\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mhas_images\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    134\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[1;32min\u001b[0m \u001b[0martists\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 135\u001b[1;33m             \u001b[0ma\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    136\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    137\u001b[0m         \u001b[1;31m# Composite any adjacent images together\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[1;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[0;32m     36\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     37\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 38\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0martist\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     39\u001b[0m         \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     40\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\axis.py\u001b[0m in \u001b[0;36mdraw\u001b[1;34m(self, renderer, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1202\u001b[0m         \u001b[0mrenderer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mopen_group\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0m__name__\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1203\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1204\u001b[1;33m         \u001b[0mticks_to_draw\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_update_ticks\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1205\u001b[0m         ticklabelBoxes, ticklabelBoxes2 = self._get_tick_bboxes(ticks_to_draw,\n\u001b[0;32m   1206\u001b[0m                                                                 renderer)\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\axis.py\u001b[0m in \u001b[0;36m_update_ticks\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m   1080\u001b[0m         \u001b[0mmajor_locs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_majorticklocs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1081\u001b[0m         \u001b[0mmajor_labels\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmajor\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformatter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat_ticks\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmajor_locs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1082\u001b[1;33m         \u001b[0mmajor_ticks\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_major_ticks\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmajor_locs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1083\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmajor\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformatter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_locs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmajor_locs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1084\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mtick\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mloc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabel\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmajor_ticks\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmajor_locs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmajor_labels\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\axis.py\u001b[0m in \u001b[0;36mget_major_ticks\u001b[1;34m(self, numticks)\u001b[0m\n\u001b[0;32m   1410\u001b[0m         \u001b[1;32mwhile\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmajorTicks\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m<\u001b[0m \u001b[0mnumticks\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1411\u001b[0m             \u001b[1;31m# Update the new tick label properties from the old.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1412\u001b[1;33m             \u001b[0mtick\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_tick\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmajor\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1413\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmajorTicks\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtick\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1414\u001b[0m             \u001b[0mtick\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgridline\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_visible\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_gridOnMajor\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\axis.py\u001b[0m in \u001b[0;36m_get_tick\u001b[1;34m(self, major)\u001b[0m\n\u001b[0;32m   2213\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2214\u001b[0m             \u001b[0mtick_kw\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_minor_tick_kw\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2215\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mYTick\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0maxes\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m''\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmajor\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmajor\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mtick_kw\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2216\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2217\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_get_label\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\axis.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, axes, loc, label, size, width, color, tickdir, pad, labelsize, labelcolor, zorder, gridOn, tick1On, tick2On, label1On, label2On, major, labelrotation, grid_color, grid_linestyle, grid_linewidth, grid_alpha, **kw)\u001b[0m\n\u001b[0;32m    157\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    158\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtick1line\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_tick1line\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 159\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtick2line\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_tick2line\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    160\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgridline\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_gridline\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    161\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlabel1\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_text1\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\axis.py\u001b[0m in \u001b[0;36m_get_tick2line\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    595\u001b[0m                           \u001b[0mmarkersize\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_size\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    596\u001b[0m                           \u001b[0mmarkeredgewidth\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_width\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 597\u001b[1;33m                           zorder=self._zorder)\n\u001b[0m\u001b[0;32m    598\u001b[0m         \u001b[0ml\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_transform\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0maxes\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_yaxis_transform\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mwhich\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'tick2'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    599\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_set_artist_props\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ml\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\lines.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, xdata, ydata, linewidth, linestyle, color, marker, markersize, markeredgewidth, markeredgecolor, markerfacecolor, markerfacecoloralt, fillstyle, antialiased, dash_capstyle, solid_capstyle, dash_joinstyle, solid_joinstyle, pickradius, drawstyle, markevery, **kwargs)\u001b[0m\n\u001b[0;32m    380\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_color\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    381\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_color\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcolor\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 382\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_marker\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mMarkerStyle\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmarker\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfillstyle\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    383\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    384\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_markevery\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\markers.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, marker, fillstyle)\u001b[0m\n\u001b[0;32m    241\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_marker_function\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    242\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_fillstyle\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfillstyle\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 243\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_marker\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmarker\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    244\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    245\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_recache\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\markers.py\u001b[0m in \u001b[0;36mset_marker\u001b[1;34m(self, marker)\u001b[0m\n\u001b[0;32m    314\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    315\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_marker\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmarker\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 316\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_recache\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    317\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    318\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mget_path\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\markers.py\u001b[0m in \u001b[0;36m_recache\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    254\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_capstyle\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'butt'\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    255\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_filled\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 256\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_marker_function\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    257\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    258\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__bool__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\markers.py\u001b[0m in \u001b[0;36m_set_tickright\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    753\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    754\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_set_tickright\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 755\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_transform\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mAffine2D\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mscale\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1.0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1.0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    756\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_snap_threshold\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m1.0\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    757\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_filled\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\transforms.py\u001b[0m in \u001b[0;36mscale\u001b[1;34m(self, sx, sy)\u001b[0m\n\u001b[0;32m   1989\u001b[0m         scale_mtx = np.array(\n\u001b[0;32m   1990\u001b[0m             [[sx, 0.0, 0.0], [0.0, sy, 0.0], [0.0, 0.0, 1.0]], float)\n\u001b[1;32m-> 1991\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_mtx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mscale_mtx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_mtx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1992\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minvalidate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1993\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Error in callback <function flush_figures at 0x0000012B7FB2D2F0> (for post_execute):\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel\\pylab\\backend_inline.py\u001b[0m in \u001b[0;36mflush_figures\u001b[1;34m()\u001b[0m\n\u001b[0;32m    115\u001b[0m         \u001b[1;31m# ignore the tracking, just draw and close all figures\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    116\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 117\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    118\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    119\u001b[0m             \u001b[1;31m# safely show traceback if in IPython, else raise\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel\\pylab\\backend_inline.py\u001b[0m in \u001b[0;36mshow\u001b[1;34m(close, block)\u001b[0m\n\u001b[0;32m     37\u001b[0m             display(\n\u001b[0;32m     38\u001b[0m                 \u001b[0mfigure_manager\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 39\u001b[1;33m                 \u001b[0mmetadata\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0m_fetch_figure_metadata\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfigure_manager\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     40\u001b[0m             )\n\u001b[0;32m     41\u001b[0m     \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel\\pylab\\backend_inline.py\u001b[0m in \u001b[0;36m_fetch_figure_metadata\u001b[1;34m(fig)\u001b[0m\n\u001b[0;32m    175\u001b[0m         \u001b[1;31m# the background is transparent\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    176\u001b[0m         ticksLight = _is_light([label.get_color()\n\u001b[1;32m--> 177\u001b[1;33m                                 \u001b[1;32mfor\u001b[0m \u001b[0maxes\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mfig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0maxes\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    178\u001b[0m                                 \u001b[1;32mfor\u001b[0m \u001b[0maxis\u001b[0m \u001b[1;32min\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0maxes\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mxaxis\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxes\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0myaxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    179\u001b[0m                                 for label in axis.get_ticklabels()])\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel\\pylab\\backend_inline.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m    177\u001b[0m                                 \u001b[1;32mfor\u001b[0m \u001b[0maxes\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mfig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0maxes\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    178\u001b[0m                                 \u001b[1;32mfor\u001b[0m \u001b[0maxis\u001b[0m \u001b[1;32min\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0maxes\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mxaxis\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxes\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0myaxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 179\u001b[1;33m                                 for label in axis.get_ticklabels()])\n\u001b[0m\u001b[0;32m    180\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mticksLight\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msize\u001b[0m \u001b[1;32mand\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mticksLight\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mticksLight\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mall\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    181\u001b[0m             \u001b[1;31m# there are one or more tick labels, all with the same lightness\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\axis.py\u001b[0m in \u001b[0;36mget_ticklabels\u001b[1;34m(self, minor, which)\u001b[0m\n\u001b[0;32m   1295\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mminor\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1296\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_minorticklabels\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1297\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_majorticklabels\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1298\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1299\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mget_majorticklines\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\axis.py\u001b[0m in \u001b[0;36mget_majorticklabels\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m   1251\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mget_majorticklabels\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1252\u001b[0m         \u001b[1;34m'Return a list of Text instances for the major ticklabels.'\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1253\u001b[1;33m         \u001b[0mticks\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_major_ticks\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1254\u001b[0m         \u001b[0mlabels1\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mtick\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlabel1\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mtick\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mticks\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mtick\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlabel1\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_visible\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1255\u001b[0m         \u001b[0mlabels2\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mtick\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlabel2\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mtick\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mticks\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mtick\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlabel2\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_visible\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\axis.py\u001b[0m in \u001b[0;36mget_major_ticks\u001b[1;34m(self, numticks)\u001b[0m\n\u001b[0;32m   1406\u001b[0m         \u001b[1;34m'Get the tick instances; grow as necessary.'\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1407\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mnumticks\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1408\u001b[1;33m             \u001b[0mnumticks\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_majorticklocs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1409\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1410\u001b[0m         \u001b[1;32mwhile\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmajorTicks\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m<\u001b[0m \u001b[0mnumticks\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\axis.py\u001b[0m in \u001b[0;36mget_majorticklocs\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m   1323\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mget_majorticklocs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1324\u001b[0m         \u001b[1;34m\"\"\"Get the array of major tick locations in data coordinates.\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1325\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmajor\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlocator\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1326\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1327\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mget_minorticklocs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\ticker.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m   2076\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2077\u001b[0m         \u001b[0mvmin\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvmax\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_view_interval\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2078\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtick_values\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvmin\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvmax\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2079\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2080\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mtick_values\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvmin\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvmax\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\ticker.py\u001b[0m in \u001b[0;36mtick_values\u001b[1;34m(self, vmin, vmax)\u001b[0m\n\u001b[0;32m   2084\u001b[0m         vmin, vmax = mtransforms.nonsingular(\n\u001b[0;32m   2085\u001b[0m             vmin, vmax, expander=1e-13, tiny=1e-14)\n\u001b[1;32m-> 2086\u001b[1;33m         \u001b[0mlocs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_raw_ticks\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvmin\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvmax\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2087\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2088\u001b[0m         \u001b[0mprune\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_prune\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\ticker.py\u001b[0m in \u001b[0;36m_raw_ticks\u001b[1;34m(self, vmin, vmax)\u001b[0m\n\u001b[0;32m   2023\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_nbins\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'auto'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2024\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0maxis\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2025\u001b[1;33m                 nbins = np.clip(self.axis.get_tick_space(),\n\u001b[0m\u001b[0;32m   2026\u001b[0m                                 max(1, self._min_n_ticks - 1), 9)\n\u001b[0;32m   2027\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\axis.py\u001b[0m in \u001b[0;36mget_tick_space\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m   2173\u001b[0m         \u001b[0mends\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0maxes\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtransAxes\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtransform\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2174\u001b[0m         \u001b[0mlength\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mends\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mends\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m/\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0maxes\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdpi\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m*\u001b[0m \u001b[1;36m72\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2175\u001b[1;33m         \u001b[0mtick\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_tick\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2176\u001b[0m         \u001b[1;31m# There is a heuristic here that the aspect ratio of tick text\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2177\u001b[0m         \u001b[1;31m# is no more than 3:1\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\axis.py\u001b[0m in \u001b[0;36m_get_tick\u001b[1;34m(self, major)\u001b[0m\n\u001b[0;32m   1904\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1905\u001b[0m             \u001b[0mtick_kw\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_minor_tick_kw\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1906\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mXTick\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0maxes\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m''\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmajor\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmajor\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mtick_kw\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1907\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1908\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_get_label\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\axis.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, axes, loc, label, size, width, color, tickdir, pad, labelsize, labelcolor, zorder, gridOn, tick1On, tick2On, label1On, label2On, major, labelrotation, grid_color, grid_linestyle, grid_linewidth, grid_alpha, **kw)\u001b[0m\n\u001b[0;32m    157\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    158\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtick1line\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_tick1line\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 159\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtick2line\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_tick2line\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    160\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgridline\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_gridline\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    161\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlabel1\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_text1\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\axis.py\u001b[0m in \u001b[0;36m_get_tick2line\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    476\u001b[0m                           \u001b[0mmarkersize\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_size\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    477\u001b[0m                           \u001b[0mmarkeredgewidth\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_width\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 478\u001b[1;33m                           zorder=self._zorder)\n\u001b[0m\u001b[0;32m    479\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    480\u001b[0m         \u001b[0ml\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_transform\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0maxes\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_xaxis_transform\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mwhich\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'tick2'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\lines.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, xdata, ydata, linewidth, linestyle, color, marker, markersize, markeredgewidth, markeredgecolor, markerfacecolor, markerfacecoloralt, fillstyle, antialiased, dash_capstyle, solid_capstyle, dash_joinstyle, solid_joinstyle, pickradius, drawstyle, markevery, **kwargs)\u001b[0m\n\u001b[0;32m    380\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_color\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    381\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_color\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcolor\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 382\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_marker\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mMarkerStyle\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmarker\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfillstyle\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    383\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    384\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_markevery\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\markers.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, marker, fillstyle)\u001b[0m\n\u001b[0;32m    241\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_marker_function\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    242\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_fillstyle\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfillstyle\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 243\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_marker\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmarker\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    244\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    245\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_recache\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\markers.py\u001b[0m in \u001b[0;36mset_marker\u001b[1;34m(self, marker)\u001b[0m\n\u001b[0;32m    314\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    315\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_marker\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmarker\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 316\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_recache\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    317\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    318\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mget_path\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\markers.py\u001b[0m in \u001b[0;36m_recache\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    254\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_capstyle\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'butt'\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    255\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_filled\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 256\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_marker_function\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    257\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    258\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__bool__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\markers.py\u001b[0m in \u001b[0;36m_set_tickup\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    761\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    762\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_set_tickup\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 763\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_transform\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mAffine2D\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mscale\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1.0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1.0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    764\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_snap_threshold\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m1.0\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    765\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_filled\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\transforms.py\u001b[0m in \u001b[0;36mscale\u001b[1;34m(self, sx, sy)\u001b[0m\n\u001b[0;32m   1989\u001b[0m         scale_mtx = np.array(\n\u001b[0;32m   1990\u001b[0m             [[sx, 0.0, 0.0], [0.0, sy, 0.0], [0.0, 0.0, 1.0]], float)\n\u001b[1;32m-> 1991\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_mtx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mscale_mtx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_mtx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1992\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minvalidate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1993\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "sns.pairplot(train_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>a</td>\n",
       "      <td>1.0</td>\n",
       "      <td>aa</td>\n",
       "    </tr>\n",
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       "      <th>1</th>\n",
       "      <td>a</td>\n",
       "      <td>2.0</td>\n",
       "      <td>aa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>c</td>\n",
       "      <td>NaN</td>\n",
       "      <td>cc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
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      ],
      "text/plain": [
       "     a    b    c\n",
       "0    a  1.0   aa\n",
       "1    a  2.0   aa\n",
       "2    c  NaN   cc\n",
       "3  NaN  4.0  NaN"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = pd.DataFrame({\"a\":[\"a\",\"a\",\"c\",np.NaN],\"b\":[1,2,None,4], \"c\":[\"aa\",\"aa\",\"cc\",np.NaN]})\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "a.fillna(a.mean(), inplace= True)\n",
    "a.fillna(a.mode().iloc[0],inplace= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "a[\"a\"].sort_values(ascending= False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "a.select_dtypes(include=\"float64\").fillna(a.select_dtypes(include=\"float64\").mean(), inplace= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10\n",
      "9\n",
      "8\n",
      "7\n",
      "6\n",
      "5\n",
      "4\n",
      "3\n",
      "2\n"
     ]
    }
   ],
   "source": [
    "for i in range(10,1,-1):\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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