01-Dealing-with-Missing-Data.ipynb

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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___\n",
    "\n",
    "<a href='http://www.pieriandata.com'><img src='../Pierian_Data_Logo.png'/></a>\n",
    "___\n",
    "<center><em>Copyright by Pierian Data Inc.</em></center>\n",
    "<center><em>For more information, visit us at <a href='http://www.pieriandata.com'>www.pieriandata.com</a></em></center>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dealing with Missing Data\n",
    "\n",
    "We already reviewed Pandas operations for missing data, now let's apply this to clean a real data file. Keep in mind, there is no 100% correct way of doing this, and this notebook just serves as an example of some reasonable approaches to take on this data.\n",
    "\n",
    "#### Note: Throughout this section we will be slowly cleaning and adding features to the Ames Housing Dataset for use in the next section. Make sure to always be loading the same file name as in the notebook.\n",
    "\n",
    "#### 2nd Note: Some of the methods shown here may not lead to optimal performance, but instead are shown to display examples of various methods available.\n",
    "-----\n",
    "\n",
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSSubClass: Identifies the type of dwelling involved in the sale.\t\n",
      "\n",
      "        20\t1-STORY 1946 & NEWER ALL STYLES\n",
      "        30\t1-STORY 1945 & OLDER\n",
      "        40\t1-STORY W/FINISHED ATTIC ALL AGES\n",
      "        45\t1-1/2 STORY - UNFINISHED ALL AGES\n",
      "        50\t1-1/2 STORY FINISHED ALL AGES\n",
      "        60\t2-STORY 1946 & NEWER\n",
      "        70\t2-STORY 1945 & OLDER\n",
      "        75\t2-1/2 STORY ALL AGES\n",
      "        80\tSPLIT OR MULTI-LEVEL\n",
      "        85\tSPLIT FOYER\n",
      "        90\tDUPLEX - ALL STYLES AND AGES\n",
      "       120\t1-STORY PUD (Planned Unit Development) - 1946 & NEWER\n",
      "       150\t1-1/2 STORY PUD - ALL AGES\n",
      "       160\t2-STORY PUD - 1946 & NEWER\n",
      "       180\tPUD - MULTILEVEL - INCL SPLIT LEV/FOYER\n",
      "       190\t2 FAMILY CONVERSION - ALL STYLES AND AGES\n",
      "\n",
      "MSZoning: Identifies the general zoning classification of the sale.\n",
      "\t\t\n",
      "       A\tAgriculture\n",
      "       C\tCommercial\n",
      "       FV\tFloating Village Residential\n",
      "       I\tIndustrial\n",
      "       RH\tResidential High Density\n",
      "       RL\tResidential Low Density\n",
      "       RP\tResidential Low Density Park \n",
      "       RM\tResidential Medium Density\n",
      "\t\n",
      "LotFrontage: Linear feet of street connected to property\n",
      "\n",
      "LotArea: Lot size in square feet\n",
      "\n",
      "Street: Type of road access to property\n",
      "\n",
      "       Grvl\tGravel\t\n",
      "       Pave\tPaved\n",
      "       \t\n",
      "Alley: Type of alley access to property\n",
      "\n",
      "       Grvl\tGravel\n",
      "       Pave\tPaved\n",
      "       NA \tNo alley access\n",
      "\t\t\n",
      "LotShape: General shape of property\n",
      "\n",
      "       Reg\tRegular\t\n",
      "       IR1\tSlightly irregular\n",
      "       IR2\tModerately Irregular\n",
      "       IR3\tIrregular\n",
      "       \n",
      "LandContour: Flatness of the property\n",
      "\n",
      "       Lvl\tNear Flat/Level\t\n",
      "       Bnk\tBanked - Quick and significant rise from street grade to building\n",
      "       HLS\tHillside - Significant slope from side to side\n",
      "       Low\tDepression\n",
      "\t\t\n",
      "Utilities: Type of utilities available\n",
      "\t\t\n",
      "       AllPub\tAll public Utilities (E,G,W,& S)\t\n",
      "       NoSewr\tElectricity, Gas, and Water (Septic Tank)\n",
      "       NoSeWa\tElectricity and Gas Only\n",
      "       ELO\tElectricity only\t\n",
      "\t\n",
      "LotConfig: Lot configuration\n",
      "\n",
      "       Inside\tInside lot\n",
      "       Corner\tCorner lot\n",
      "       CulDSac\tCul-de-sac\n",
      "       FR2\tFrontage on 2 sides of property\n",
      "       FR3\tFrontage on 3 sides of property\n",
      "\t\n",
      "LandSlope: Slope of property\n",
      "\t\t\n",
      "       Gtl\tGentle slope\n",
      "       Mod\tModerate Slope\t\n",
      "       Sev\tSevere Slope\n",
      "\t\n",
      "Neighborhood: Physical locations within Ames city limits\n",
      "\n",
      "       Blmngtn\tBloomington Heights\n",
      "       Blueste\tBluestem\n",
      "       BrDale\tBriardale\n",
      "       BrkSide\tBrookside\n",
      "       ClearCr\tClear Creek\n",
      "       CollgCr\tCollege Creek\n",
      "       Crawfor\tCrawford\n",
      "       Edwards\tEdwards\n",
      "       Gilbert\tGilbert\n",
      "       IDOTRR\tIowa DOT and Rail Road\n",
      "       MeadowV\tMeadow Village\n",
      "       Mitchel\tMitchell\n",
      "       Names\tNorth Ames\n",
      "       NoRidge\tNorthridge\n",
      "       NPkVill\tNorthpark Villa\n",
      "       NridgHt\tNorthridge Heights\n",
      "       NWAmes\tNorthwest Ames\n",
      "       OldTown\tOld Town\n",
      "       SWISU\tSouth & West of Iowa State University\n",
      "       Sawyer\tSawyer\n",
      "       SawyerW\tSawyer West\n",
      "       Somerst\tSomerset\n",
      "       StoneBr\tStone Brook\n",
      "       Timber\tTimberland\n",
      "       Veenker\tVeenker\n",
      "\t\t\t\n",
      "Condition1: Proximity to various conditions\n",
      "\t\n",
      "       Artery\tAdjacent to arterial street\n",
      "       Feedr\tAdjacent to feeder street\t\n",
      "       Norm\tNormal\t\n",
      "       RRNn\tWithin 200' of North-South Railroad\n",
      "       RRAn\tAdjacent to North-South Railroad\n",
      "       PosN\tNear positive off-site feature--park, greenbelt, etc.\n",
      "       PosA\tAdjacent to postive off-site feature\n",
      "       RRNe\tWithin 200' of East-West Railroad\n",
      "       RRAe\tAdjacent to East-West Railroad\n",
      "\t\n",
      "Condition2: Proximity to various conditions (if more than one is present)\n",
      "\t\t\n",
      "       Artery\tAdjacent to arterial street\n",
      "       Feedr\tAdjacent to feeder street\t\n",
      "       Norm\tNormal\t\n",
      "       RRNn\tWithin 200' of North-South Railroad\n",
      "       RRAn\tAdjacent to North-South Railroad\n",
      "       PosN\tNear positive off-site feature--park, greenbelt, etc.\n",
      "       PosA\tAdjacent to postive off-site feature\n",
      "       RRNe\tWithin 200' of East-West Railroad\n",
      "       RRAe\tAdjacent to East-West Railroad\n",
      "\t\n",
      "BldgType: Type of dwelling\n",
      "\t\t\n",
      "       1Fam\tSingle-family Detached\t\n",
      "       2FmCon\tTwo-family Conversion; originally built as one-family dwelling\n",
      "       Duplx\tDuplex\n",
      "       TwnhsE\tTownhouse End Unit\n",
      "       TwnhsI\tTownhouse Inside Unit\n",
      "\t\n",
      "HouseStyle: Style of dwelling\n",
      "\t\n",
      "       1Story\tOne story\n",
      "       1.5Fin\tOne and one-half story: 2nd level finished\n",
      "       1.5Unf\tOne and one-half story: 2nd level unfinished\n",
      "       2Story\tTwo story\n",
      "       2.5Fin\tTwo and one-half story: 2nd level finished\n",
      "       2.5Unf\tTwo and one-half story: 2nd level unfinished\n",
      "       SFoyer\tSplit Foyer\n",
      "       SLvl\tSplit Level\n",
      "\t\n",
      "OverallQual: Rates the overall material and finish of the house\n",
      "\n",
      "       10\tVery Excellent\n",
      "       9\tExcellent\n",
      "       8\tVery Good\n",
      "       7\tGood\n",
      "       6\tAbove Average\n",
      "       5\tAverage\n",
      "       4\tBelow Average\n",
      "       3\tFair\n",
      "       2\tPoor\n",
      "       1\tVery Poor\n",
      "\t\n",
      "OverallCond: Rates the overall condition of the house\n",
      "\n",
      "       10\tVery Excellent\n",
      "       9\tExcellent\n",
      "       8\tVery Good\n",
      "       7\tGood\n",
      "       6\tAbove Average\t\n",
      "       5\tAverage\n",
      "       4\tBelow Average\t\n",
      "       3\tFair\n",
      "       2\tPoor\n",
      "       1\tVery Poor\n",
      "\t\t\n",
      "YearBuilt: Original construction date\n",
      "\n",
      "YearRemodAdd: Remodel date (same as construction date if no remodeling or additions)\n",
      "\n",
      "RoofStyle: Type of roof\n",
      "\n",
      "       Flat\tFlat\n",
      "       Gable\tGable\n",
      "       Gambrel\tGabrel (Barn)\n",
      "       Hip\tHip\n",
      "       Mansard\tMansard\n",
      "       Shed\tShed\n",
      "\t\t\n",
      "RoofMatl: Roof material\n",
      "\n",
      "       ClyTile\tClay or Tile\n",
      "       CompShg\tStandard (Composite) Shingle\n",
      "       Membran\tMembrane\n",
      "       Metal\tMetal\n",
      "       Roll\tRoll\n",
      "       Tar&Grv\tGravel & Tar\n",
      "       WdShake\tWood Shakes\n",
      "       WdShngl\tWood Shingles\n",
      "\t\t\n",
      "Exterior1st: Exterior covering on house\n",
      "\n",
      "       AsbShng\tAsbestos Shingles\n",
      "       AsphShn\tAsphalt Shingles\n",
      "       BrkComm\tBrick Common\n",
      "       BrkFace\tBrick Face\n",
      "       CBlock\tCinder Block\n",
      "       CemntBd\tCement Board\n",
      "       HdBoard\tHard Board\n",
      "       ImStucc\tImitation Stucco\n",
      "       MetalSd\tMetal Siding\n",
      "       Other\tOther\n",
      "       Plywood\tPlywood\n",
      "       PreCast\tPreCast\t\n",
      "       Stone\tStone\n",
      "       Stucco\tStucco\n",
      "       VinylSd\tVinyl Siding\n",
      "       Wd Sdng\tWood Siding\n",
      "       WdShing\tWood Shingles\n",
      "\t\n",
      "Exterior2nd: Exterior covering on house (if more than one material)\n",
      "\n",
      "       AsbShng\tAsbestos Shingles\n",
      "       AsphShn\tAsphalt Shingles\n",
      "       BrkComm\tBrick Common\n",
      "       BrkFace\tBrick Face\n",
      "       CBlock\tCinder Block\n",
      "       CemntBd\tCement Board\n",
      "       HdBoard\tHard Board\n",
      "       ImStucc\tImitation Stucco\n",
      "       MetalSd\tMetal Siding\n",
      "       Other\tOther\n",
      "       Plywood\tPlywood\n",
      "       PreCast\tPreCast\n",
      "       Stone\tStone\n",
      "       Stucco\tStucco\n",
      "       VinylSd\tVinyl Siding\n",
      "       Wd Sdng\tWood Siding\n",
      "       WdShing\tWood Shingles\n",
      "\t\n",
      "MasVnrType: Masonry veneer type\n",
      "\n",
      "       BrkCmn\tBrick Common\n",
      "       BrkFace\tBrick Face\n",
      "       CBlock\tCinder Block\n",
      "       None\tNone\n",
      "       Stone\tStone\n",
      "\t\n",
      "MasVnrArea: Masonry veneer area in square feet\n",
      "\n",
      "ExterQual: Evaluates the quality of the material on the exterior \n",
      "\t\t\n",
      "       Ex\tExcellent\n",
      "       Gd\tGood\n",
      "       TA\tAverage/Typical\n",
      "       Fa\tFair\n",
      "       Po\tPoor\n",
      "\t\t\n",
      "ExterCond: Evaluates the present condition of the material on the exterior\n",
      "\t\t\n",
      "       Ex\tExcellent\n",
      "       Gd\tGood\n",
      "       TA\tAverage/Typical\n",
      "       Fa\tFair\n",
      "       Po\tPoor\n",
      "\t\t\n",
      "Foundation: Type of foundation\n",
      "\t\t\n",
      "       BrkTil\tBrick & Tile\n",
      "       CBlock\tCinder Block\n",
      "       PConc\tPoured Contrete\t\n",
      "       Slab\tSlab\n",
      "       Stone\tStone\n",
      "       Wood\tWood\n",
      "\t\t\n",
      "BsmtQual: Evaluates the height of the basement\n",
      "\n",
      "       Ex\tExcellent (100+ inches)\t\n",
      "       Gd\tGood (90-99 inches)\n",
      "       TA\tTypical (80-89 inches)\n",
      "       Fa\tFair (70-79 inches)\n",
      "       Po\tPoor (<70 inches\n",
      "       NA\tNo Basement\n",
      "\t\t\n",
      "BsmtCond: Evaluates the general condition of the basement\n",
      "\n",
      "       Ex\tExcellent\n",
      "       Gd\tGood\n",
      "       TA\tTypical - slight dampness allowed\n",
      "       Fa\tFair - dampness or some cracking or settling\n",
      "       Po\tPoor - Severe cracking, settling, or wetness\n",
      "       NA\tNo Basement\n",
      "\t\n",
      "BsmtExposure: Refers to walkout or garden level walls\n",
      "\n",
      "       Gd\tGood Exposure\n",
      "       Av\tAverage Exposure (split levels or foyers typically score average or above)\t\n",
      "       Mn\tMimimum Exposure\n",
      "       No\tNo Exposure\n",
      "       NA\tNo Basement\n",
      "\t\n",
      "BsmtFinType1: Rating of basement finished area\n",
      "\n",
      "       GLQ\tGood Living Quarters\n",
      "       ALQ\tAverage Living Quarters\n",
      "       BLQ\tBelow Average Living Quarters\t\n",
      "       Rec\tAverage Rec Room\n",
      "       LwQ\tLow Quality\n",
      "       Unf\tUnfinshed\n",
      "       NA\tNo Basement\n",
      "\t\t\n",
      "BsmtFinSF1: Type 1 finished square feet\n",
      "\n",
      "BsmtFinType2: Rating of basement finished area (if multiple types)\n",
      "\n",
      "       GLQ\tGood Living Quarters\n",
      "       ALQ\tAverage Living Quarters\n",
      "       BLQ\tBelow Average Living Quarters\t\n",
      "       Rec\tAverage Rec Room\n",
      "       LwQ\tLow Quality\n",
      "       Unf\tUnfinshed\n",
      "       NA\tNo Basement\n",
      "\n",
      "BsmtFinSF2: Type 2 finished square feet\n",
      "\n",
      "BsmtUnfSF: Unfinished square feet of basement area\n",
      "\n",
      "TotalBsmtSF: Total square feet of basement area\n",
      "\n",
      "Heating: Type of heating\n",
      "\t\t\n",
      "       Floor\tFloor Furnace\n",
      "       GasA\tGas forced warm air furnace\n",
      "       GasW\tGas hot water or steam heat\n",
      "       Grav\tGravity furnace\t\n",
      "       OthW\tHot water or steam heat other than gas\n",
      "       Wall\tWall furnace\n",
      "\t\t\n",
      "HeatingQC: Heating quality and condition\n",
      "\n",
      "       Ex\tExcellent\n",
      "       Gd\tGood\n",
      "       TA\tAverage/Typical\n",
      "       Fa\tFair\n",
      "       Po\tPoor\n",
      "\t\t\n",
      "CentralAir: Central air conditioning\n",
      "\n",
      "       N\tNo\n",
      "       Y\tYes\n",
      "\t\t\n",
      "Electrical: Electrical system\n",
      "\n",
      "       SBrkr\tStandard Circuit Breakers & Romex\n",
      "       FuseA\tFuse Box over 60 AMP and all Romex wiring (Average)\t\n",
      "       FuseF\t60 AMP Fuse Box and mostly Romex wiring (Fair)\n",
      "       FuseP\t60 AMP Fuse Box and mostly knob & tube wiring (poor)\n",
      "       Mix\tMixed\n",
      "\t\t\n",
      "1stFlrSF: First Floor square feet\n",
      " \n",
      "2ndFlrSF: Second floor square feet\n",
      "\n",
      "LowQualFinSF: Low quality finished square feet (all floors)\n",
      "\n",
      "GrLivArea: Above grade (ground) living area square feet\n",
      "\n",
      "BsmtFullBath: Basement full bathrooms\n",
      "\n",
      "BsmtHalfBath: Basement half bathrooms\n",
      "\n",
      "FullBath: Full bathrooms above grade\n",
      "\n",
      "HalfBath: Half baths above grade\n",
      "\n",
      "Bedroom: Bedrooms above grade (does NOT include basement bedrooms)\n",
      "\n",
      "Kitchen: Kitchens above grade\n",
      "\n",
      "KitchenQual: Kitchen quality\n",
      "\n",
      "       Ex\tExcellent\n",
      "       Gd\tGood\n",
      "       TA\tTypical/Average\n",
      "       Fa\tFair\n",
      "       Po\tPoor\n",
      "       \t\n",
      "TotRmsAbvGrd: Total rooms above grade (does not include bathrooms)\n",
      "\n",
      "Functional: Home functionality (Assume typical unless deductions are warranted)\n",
      "\n",
      "       Typ\tTypical Functionality\n",
      "       Min1\tMinor Deductions 1\n",
      "       Min2\tMinor Deductions 2\n",
      "       Mod\tModerate Deductions\n",
      "       Maj1\tMajor Deductions 1\n",
      "       Maj2\tMajor Deductions 2\n",
      "       Sev\tSeverely Damaged\n",
      "       Sal\tSalvage only\n",
      "\t\t\n",
      "Fireplaces: Number of fireplaces\n",
      "\n",
      "FireplaceQu: Fireplace quality\n",
      "\n",
      "       Ex\tExcellent - Exceptional Masonry Fireplace\n",
      "       Gd\tGood - Masonry Fireplace in main level\n",
      "       TA\tAverage - Prefabricated Fireplace in main living area or Masonry Fireplace in basement\n",
      "       Fa\tFair - Prefabricated Fireplace in basement\n",
      "       Po\tPoor - Ben Franklin Stove\n",
      "       NA\tNo Fireplace\n",
      "\t\t\n",
      "GarageType: Garage location\n",
      "\t\t\n",
      "       2Types\tMore than one type of garage\n",
      "       Attchd\tAttached to home\n",
      "       Basment\tBasement Garage\n",
      "       BuiltIn\tBuilt-In (Garage part of house - typically has room above garage)\n",
      "       CarPort\tCar Port\n",
      "       Detchd\tDetached from home\n",
      "       NA\tNo Garage\n",
      "\t\t\n",
      "GarageYrBlt: Year garage was built\n",
      "\t\t\n",
      "GarageFinish: Interior finish of the garage\n",
      "\n",
      "       Fin\tFinished\n",
      "       RFn\tRough Finished\t\n",
      "       Unf\tUnfinished\n",
      "       NA\tNo Garage\n",
      "\t\t\n",
      "GarageCars: Size of garage in car capacity\n",
      "\n",
      "GarageArea: Size of garage in square feet\n",
      "\n",
      "GarageQual: Garage quality\n",
      "\n",
      "       Ex\tExcellent\n",
      "       Gd\tGood\n",
      "       TA\tTypical/Average\n",
      "       Fa\tFair\n",
      "       Po\tPoor\n",
      "       NA\tNo Garage\n",
      "\t\t\n",
      "GarageCond: Garage condition\n",
      "\n",
      "       Ex\tExcellent\n",
      "       Gd\tGood\n",
      "       TA\tTypical/Average\n",
      "       Fa\tFair\n",
      "       Po\tPoor\n",
      "       NA\tNo Garage\n",
      "\t\t\n",
      "PavedDrive: Paved driveway\n",
      "\n",
      "       Y\tPaved \n",
      "       P\tPartial Pavement\n",
      "       N\tDirt/Gravel\n",
      "\t\t\n",
      "WoodDeckSF: Wood deck area in square feet\n",
      "\n",
      "OpenPorchSF: Open porch area in square feet\n",
      "\n",
      "EnclosedPorch: Enclosed porch area in square feet\n",
      "\n",
      "3SsnPorch: Three season porch area in square feet\n",
      "\n",
      "ScreenPorch: Screen porch area in square feet\n",
      "\n",
      "PoolArea: Pool area in square feet\n",
      "\n",
      "PoolQC: Pool quality\n",
      "\t\t\n",
      "       Ex\tExcellent\n",
      "       Gd\tGood\n",
      "       TA\tAverage/Typical\n",
      "       Fa\tFair\n",
      "       NA\tNo Pool\n",
      "\t\t\n",
      "Fence: Fence quality\n",
      "\t\t\n",
      "       GdPrv\tGood Privacy\n",
      "       MnPrv\tMinimum Privacy\n",
      "       GdWo\tGood Wood\n",
      "       MnWw\tMinimum Wood/Wire\n",
      "       NA\tNo Fence\n",
      "\t\n",
      "MiscFeature: Miscellaneous feature not covered in other categories\n",
      "\t\t\n",
      "       Elev\tElevator\n",
      "       Gar2\t2nd Garage (if not described in garage section)\n",
      "       Othr\tOther\n",
      "       Shed\tShed (over 100 SF)\n",
      "       TenC\tTennis Court\n",
      "       NA\tNone\n",
      "\t\t\n",
      "MiscVal: $Value of miscellaneous feature\n",
      "\n",
      "MoSold: Month Sold (MM)\n",
      "\n",
      "YrSold: Year Sold (YYYY)\n",
      "\n",
      "SaleType: Type of sale\n",
      "\t\t\n",
      "       WD \tWarranty Deed - Conventional\n",
      "       CWD\tWarranty Deed - Cash\n",
      "       VWD\tWarranty Deed - VA Loan\n",
      "       New\tHome just constructed and sold\n",
      "       COD\tCourt Officer Deed/Estate\n",
      "       Con\tContract 15% Down payment regular terms\n",
      "       ConLw\tContract Low Down payment and low interest\n",
      "       ConLI\tContract Low Interest\n",
      "       ConLD\tContract Low Down\n",
      "       Oth\tOther\n",
      "\t\t\n",
      "SaleCondition: Condition of sale\n",
      "\n",
      "       Normal\tNormal Sale\n",
      "       Abnorml\tAbnormal Sale -  trade, foreclosure, short sale\n",
      "       AdjLand\tAdjoining Land Purchase\n",
      "       Alloca\tAllocation - two linked properties with separate deeds, typically condo with a garage unit\t\n",
      "       Family\tSale between family members\n",
      "       Partial\tHome was not completed when last assessed (associated with New Homes)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "with open('../DATA/Ames_Housing_Feature_Description.txt','r') as f: \n",
    "    print(f.read())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"../DATA/Ames_outliers_removed.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\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>PID</th>\n",
       "      <th>MS SubClass</th>\n",
       "      <th>MS Zoning</th>\n",
       "      <th>Lot Frontage</th>\n",
       "      <th>Lot Area</th>\n",
       "      <th>Street</th>\n",
       "      <th>Alley</th>\n",
       "      <th>Lot Shape</th>\n",
       "      <th>Land Contour</th>\n",
       "      <th>Utilities</th>\n",
       "      <th>...</th>\n",
       "      <th>Pool Area</th>\n",
       "      <th>Pool QC</th>\n",
       "      <th>Fence</th>\n",
       "      <th>Misc Feature</th>\n",
       "      <th>Misc Val</th>\n",
       "      <th>Mo Sold</th>\n",
       "      <th>Yr Sold</th>\n",
       "      <th>Sale Type</th>\n",
       "      <th>Sale Condition</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>526301100</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>141.0</td>\n",
       "      <td>31770</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>5</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>215000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>526350040</td>\n",
       "      <td>20</td>\n",
       "      <td>RH</td>\n",
       "      <td>80.0</td>\n",
       "      <td>11622</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>MnPrv</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>105000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>526351010</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>81.0</td>\n",
       "      <td>14267</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>Gar2</td>\n",
       "      <td>12500</td>\n",
       "      <td>6</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>172000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>526353030</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>93.0</td>\n",
       "      <td>11160</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>4</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>244000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>527105010</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>74.0</td>\n",
       "      <td>13830</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>MnPrv</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>189900</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 81 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         PID  MS SubClass MS Zoning  Lot Frontage  Lot Area Street Alley  \\\n",
       "0  526301100           20        RL         141.0     31770   Pave   NaN   \n",
       "1  526350040           20        RH          80.0     11622   Pave   NaN   \n",
       "2  526351010           20        RL          81.0     14267   Pave   NaN   \n",
       "3  526353030           20        RL          93.0     11160   Pave   NaN   \n",
       "4  527105010           60        RL          74.0     13830   Pave   NaN   \n",
       "\n",
       "  Lot Shape Land Contour Utilities  ... Pool Area Pool QC  Fence Misc Feature  \\\n",
       "0       IR1          Lvl    AllPub  ...         0     NaN    NaN          NaN   \n",
       "1       Reg          Lvl    AllPub  ...         0     NaN  MnPrv          NaN   \n",
       "2       IR1          Lvl    AllPub  ...         0     NaN    NaN         Gar2   \n",
       "3       Reg          Lvl    AllPub  ...         0     NaN    NaN          NaN   \n",
       "4       IR1          Lvl    AllPub  ...         0     NaN  MnPrv          NaN   \n",
       "\n",
       "  Misc Val Mo Sold Yr Sold  Sale Type  Sale Condition  SalePrice  \n",
       "0        0       5    2010        WD           Normal     215000  \n",
       "1        0       6    2010        WD           Normal     105000  \n",
       "2    12500       6    2010        WD           Normal     172000  \n",
       "3        0       4    2010        WD           Normal     244000  \n",
       "4        0       3    2010        WD           Normal     189900  \n",
       "\n",
       "[5 rows x 81 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "81"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2927 entries, 0 to 2926\n",
      "Data columns (total 81 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   PID              2927 non-null   int64  \n",
      " 1   MS SubClass      2927 non-null   int64  \n",
      " 2   MS Zoning        2927 non-null   object \n",
      " 3   Lot Frontage     2437 non-null   float64\n",
      " 4   Lot Area         2927 non-null   int64  \n",
      " 5   Street           2927 non-null   object \n",
      " 6   Alley            198 non-null    object \n",
      " 7   Lot Shape        2927 non-null   object \n",
      " 8   Land Contour     2927 non-null   object \n",
      " 9   Utilities        2927 non-null   object \n",
      " 10  Lot Config       2927 non-null   object \n",
      " 11  Land Slope       2927 non-null   object \n",
      " 12  Neighborhood     2927 non-null   object \n",
      " 13  Condition 1      2927 non-null   object \n",
      " 14  Condition 2      2927 non-null   object \n",
      " 15  Bldg Type        2927 non-null   object \n",
      " 16  House Style      2927 non-null   object \n",
      " 17  Overall Qual     2927 non-null   int64  \n",
      " 18  Overall Cond     2927 non-null   int64  \n",
      " 19  Year Built       2927 non-null   int64  \n",
      " 20  Year Remod/Add   2927 non-null   int64  \n",
      " 21  Roof Style       2927 non-null   object \n",
      " 22  Roof Matl        2927 non-null   object \n",
      " 23  Exterior 1st     2927 non-null   object \n",
      " 24  Exterior 2nd     2927 non-null   object \n",
      " 25  Mas Vnr Type     2904 non-null   object \n",
      " 26  Mas Vnr Area     2904 non-null   float64\n",
      " 27  Exter Qual       2927 non-null   object \n",
      " 28  Exter Cond       2927 non-null   object \n",
      " 29  Foundation       2927 non-null   object \n",
      " 30  Bsmt Qual        2847 non-null   object \n",
      " 31  Bsmt Cond        2847 non-null   object \n",
      " 32  Bsmt Exposure    2844 non-null   object \n",
      " 33  BsmtFin Type 1   2847 non-null   object \n",
      " 34  BsmtFin SF 1     2926 non-null   float64\n",
      " 35  BsmtFin Type 2   2846 non-null   object \n",
      " 36  BsmtFin SF 2     2926 non-null   float64\n",
      " 37  Bsmt Unf SF      2926 non-null   float64\n",
      " 38  Total Bsmt SF    2926 non-null   float64\n",
      " 39  Heating          2927 non-null   object \n",
      " 40  Heating QC       2927 non-null   object \n",
      " 41  Central Air      2927 non-null   object \n",
      " 42  Electrical       2926 non-null   object \n",
      " 43  1st Flr SF       2927 non-null   int64  \n",
      " 44  2nd Flr SF       2927 non-null   int64  \n",
      " 45  Low Qual Fin SF  2927 non-null   int64  \n",
      " 46  Gr Liv Area      2927 non-null   int64  \n",
      " 47  Bsmt Full Bath   2925 non-null   float64\n",
      " 48  Bsmt Half Bath   2925 non-null   float64\n",
      " 49  Full Bath        2927 non-null   int64  \n",
      " 50  Half Bath        2927 non-null   int64  \n",
      " 51  Bedroom AbvGr    2927 non-null   int64  \n",
      " 52  Kitchen AbvGr    2927 non-null   int64  \n",
      " 53  Kitchen Qual     2927 non-null   object \n",
      " 54  TotRms AbvGrd    2927 non-null   int64  \n",
      " 55  Functional       2927 non-null   object \n",
      " 56  Fireplaces       2927 non-null   int64  \n",
      " 57  Fireplace Qu     1505 non-null   object \n",
      " 58  Garage Type      2770 non-null   object \n",
      " 59  Garage Yr Blt    2768 non-null   float64\n",
      " 60  Garage Finish    2768 non-null   object \n",
      " 61  Garage Cars      2926 non-null   float64\n",
      " 62  Garage Area      2926 non-null   float64\n",
      " 63  Garage Qual      2768 non-null   object \n",
      " 64  Garage Cond      2768 non-null   object \n",
      " 65  Paved Drive      2927 non-null   object \n",
      " 66  Wood Deck SF     2927 non-null   int64  \n",
      " 67  Open Porch SF    2927 non-null   int64  \n",
      " 68  Enclosed Porch   2927 non-null   int64  \n",
      " 69  3Ssn Porch       2927 non-null   int64  \n",
      " 70  Screen Porch     2927 non-null   int64  \n",
      " 71  Pool Area        2927 non-null   int64  \n",
      " 72  Pool QC          12 non-null     object \n",
      " 73  Fence            572 non-null    object \n",
      " 74  Misc Feature     105 non-null    object \n",
      " 75  Misc Val         2927 non-null   int64  \n",
      " 76  Mo Sold          2927 non-null   int64  \n",
      " 77  Yr Sold          2927 non-null   int64  \n",
      " 78  Sale Type        2927 non-null   object \n",
      " 79  Sale Condition   2927 non-null   object \n",
      " 80  SalePrice        2927 non-null   int64  \n",
      "dtypes: float64(11), int64(27), object(43)\n",
      "memory usage: 1.8+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Removing the PID\n",
    "\n",
    "We already have an index, so we don't need the PID unique identifier for the regression we will perform later on."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop('PID',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "80"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Observing NaN Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2922</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2923</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2924</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2925</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2926</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2927 rows × 80 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      MS SubClass  MS Zoning  Lot Frontage  Lot Area  Street  Alley  \\\n",
       "0           False      False         False     False   False   True   \n",
       "1           False      False         False     False   False   True   \n",
       "2           False      False         False     False   False   True   \n",
       "3           False      False         False     False   False   True   \n",
       "4           False      False         False     False   False   True   \n",
       "...           ...        ...           ...       ...     ...    ...   \n",
       "2922        False      False         False     False   False   True   \n",
       "2923        False      False          True     False   False   True   \n",
       "2924        False      False         False     False   False   True   \n",
       "2925        False      False         False     False   False   True   \n",
       "2926        False      False         False     False   False   True   \n",
       "\n",
       "      Lot Shape  Land Contour  Utilities  Lot Config  ...  Pool Area  Pool QC  \\\n",
       "0         False         False      False       False  ...      False     True   \n",
       "1         False         False      False       False  ...      False     True   \n",
       "2         False         False      False       False  ...      False     True   \n",
       "3         False         False      False       False  ...      False     True   \n",
       "4         False         False      False       False  ...      False     True   \n",
       "...         ...           ...        ...         ...  ...        ...      ...   \n",
       "2922      False         False      False       False  ...      False     True   \n",
       "2923      False         False      False       False  ...      False     True   \n",
       "2924      False         False      False       False  ...      False     True   \n",
       "2925      False         False      False       False  ...      False     True   \n",
       "2926      False         False      False       False  ...      False     True   \n",
       "\n",
       "      Fence  Misc Feature  Misc Val  Mo Sold  Yr Sold  Sale Type  \\\n",
       "0      True          True     False    False    False      False   \n",
       "1     False          True     False    False    False      False   \n",
       "2      True         False     False    False    False      False   \n",
       "3      True          True     False    False    False      False   \n",
       "4     False          True     False    False    False      False   \n",
       "...     ...           ...       ...      ...      ...        ...   \n",
       "2922  False          True     False    False    False      False   \n",
       "2923  False          True     False    False    False      False   \n",
       "2924  False         False     False    False    False      False   \n",
       "2925   True          True     False    False    False      False   \n",
       "2926   True          True     False    False    False      False   \n",
       "\n",
       "      Sale Condition  SalePrice  \n",
       "0              False      False  \n",
       "1              False      False  \n",
       "2              False      False  \n",
       "3              False      False  \n",
       "4              False      False  \n",
       "...              ...        ...  \n",
       "2922           False      False  \n",
       "2923           False      False  \n",
       "2924           False      False  \n",
       "2925           False      False  \n",
       "2926           False      False  \n",
       "\n",
       "[2927 rows x 80 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MS SubClass         0\n",
       "MS Zoning           0\n",
       "Lot Frontage      490\n",
       "Lot Area            0\n",
       "Street              0\n",
       "                 ... \n",
       "Mo Sold             0\n",
       "Yr Sold             0\n",
       "Sale Type           0\n",
       "Sale Condition      0\n",
       "SalePrice           0\n",
       "Length: 80, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MS SubClass        0.00000\n",
       "MS Zoning          0.00000\n",
       "Lot Frontage      16.74069\n",
       "Lot Area           0.00000\n",
       "Street             0.00000\n",
       "                    ...   \n",
       "Mo Sold            0.00000\n",
       "Yr Sold            0.00000\n",
       "Sale Type          0.00000\n",
       "Sale Condition     0.00000\n",
       "SalePrice          0.00000\n",
       "Length: 80, dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "100* df.isnull().sum() / len(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def percent_missing(df):\n",
    "    percent_nan = 100* df.isnull().sum() / len(df)\n",
    "    percent_nan = percent_nan[percent_nan>0].sort_values()\n",
    "    return percent_nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "percent_nan = percent_missing(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=percent_nan.index,y=percent_nan)\n",
    "plt.xticks(rotation=90);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Removing Features or Removing Rows\n",
    "\n",
    "If only a few rows relative to the size of your dataset are missing some values, then it might just be a good idea to drop those rows. What does this cost you in terms of performace? It essentialy removes potential training/testing data, but if its only a few rows, its unlikely to change performance.\n",
    "\n",
    "Sometimes it is a good idea to remove a feature entirely if it has too many null values. However, you should carefully consider why it has so many null values, in certain situations null could just be used as a separate category. \n",
    "\n",
    "Take for example a feature column for the number of cars that can fit into a garage. Perhaps if there is no garage then there is a null value, instead of a zero. It probably makes more sense to quickly fill the null values in this case with a zero instead of a null. Only you can decide based off your domain expertise and knowledge of the data set!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Working based on Rows Missing Data\n",
    "\n",
    "## Filling in Data or Dropping Data?\n",
    "\n",
    "Let's explore how to choose to remove or fill in missing data for rows that are missing some data. Let's choose some threshold where we decide it is ok to drop a row if its missing some data (instead of attempting to fill in that missing data point). We will choose 1% as our threshold. This means if less than 1% of the rows are missing this feature, we will consider just dropping that row, instead of dealing with the feature itself. There is no right answer here, just use common sense and your domain knowledge of the dataset, obviously you don't want to drop a very high threshold like 50% , you should also explore correlation to the dataset, maybe it makes sense to drop the feature instead.\n",
    "\n",
    "Based on the text description of the features, you will see that most of this missing data is actually NaN on purpose as a placeholder for 0 or \"none\"."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example of Filling in Data : Basement Columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=percent_nan.index,y=percent_nan)\n",
    "plt.xticks(rotation=90);\n",
    "\n",
    "# Set 1% Threshold\n",
    "plt.ylim(0,1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's drop or fill the rows based on this data. You could either manually fill in the data (especially the Basement data based on the description text file) OR you could simply drop the row and not consider it. Watch the video for a full explanation of this, in reality it probably makes more sense to fill in the Missing Basement data since its well described in the text description."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Electrical        0.034165\n",
       "Garage Area       0.034165\n",
       "Total Bsmt SF     0.034165\n",
       "Bsmt Unf SF       0.034165\n",
       "BsmtFin SF 1      0.034165\n",
       "BsmtFin SF 2      0.034165\n",
       "Garage Cars       0.034165\n",
       "Bsmt Full Bath    0.068329\n",
       "Bsmt Half Bath    0.068329\n",
       "Mas Vnr Area      0.785787\n",
       "Mas Vnr Type      0.785787\n",
       "dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Could also imply we should ex\n",
    "percent_nan[percent_nan < 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0341646737273659"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "100/len(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>MS SubClass</th>\n",
       "      <th>MS Zoning</th>\n",
       "      <th>Lot Frontage</th>\n",
       "      <th>Lot Area</th>\n",
       "      <th>Street</th>\n",
       "      <th>Alley</th>\n",
       "      <th>Lot Shape</th>\n",
       "      <th>Land Contour</th>\n",
       "      <th>Utilities</th>\n",
       "      <th>Lot Config</th>\n",
       "      <th>...</th>\n",
       "      <th>Pool Area</th>\n",
       "      <th>Pool QC</th>\n",
       "      <th>Fence</th>\n",
       "      <th>Misc Feature</th>\n",
       "      <th>Misc Val</th>\n",
       "      <th>Mo Sold</th>\n",
       "      <th>Yr Sold</th>\n",
       "      <th>Sale Type</th>\n",
       "      <th>Sale Condition</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1341</th>\n",
       "      <td>20</td>\n",
       "      <td>RM</td>\n",
       "      <td>99.0</td>\n",
       "      <td>5940</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>FR3</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MnPrv</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2008</td>\n",
       "      <td>ConLD</td>\n",
       "      <td>Abnorml</td>\n",
       "      <td>79000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 80 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      MS SubClass MS Zoning  Lot Frontage  Lot Area Street Alley Lot Shape  \\\n",
       "1341           20        RM          99.0      5940   Pave   NaN       IR1   \n",
       "\n",
       "     Land Contour Utilities Lot Config  ... Pool Area Pool QC  Fence  \\\n",
       "1341          Lvl    AllPub        FR3  ...         0     NaN  MnPrv   \n",
       "\n",
       "     Misc Feature Misc Val Mo Sold  Yr Sold  Sale Type  Sale Condition  \\\n",
       "1341          NaN        0       4     2008      ConLD         Abnorml   \n",
       "\n",
       "      SalePrice  \n",
       "1341      79000  \n",
       "\n",
       "[1 rows x 80 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['Total Bsmt SF'].isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MS SubClass</th>\n",
       "      <th>MS Zoning</th>\n",
       "      <th>Lot Frontage</th>\n",
       "      <th>Lot Area</th>\n",
       "      <th>Street</th>\n",
       "      <th>Alley</th>\n",
       "      <th>Lot Shape</th>\n",
       "      <th>Land Contour</th>\n",
       "      <th>Utilities</th>\n",
       "      <th>Lot Config</th>\n",
       "      <th>...</th>\n",
       "      <th>Pool Area</th>\n",
       "      <th>Pool QC</th>\n",
       "      <th>Fence</th>\n",
       "      <th>Misc Feature</th>\n",
       "      <th>Misc Val</th>\n",
       "      <th>Mo Sold</th>\n",
       "      <th>Yr Sold</th>\n",
       "      <th>Sale Type</th>\n",
       "      <th>Sale Condition</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1341</th>\n",
       "      <td>20</td>\n",
       "      <td>RM</td>\n",
       "      <td>99.0</td>\n",
       "      <td>5940</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>FR3</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MnPrv</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2008</td>\n",
       "      <td>ConLD</td>\n",
       "      <td>Abnorml</td>\n",
       "      <td>79000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1497</th>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>123.0</td>\n",
       "      <td>47007</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>284700</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 80 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      MS SubClass MS Zoning  Lot Frontage  Lot Area Street Alley Lot Shape  \\\n",
       "1341           20        RM          99.0      5940   Pave   NaN       IR1   \n",
       "1497           20        RL         123.0     47007   Pave   NaN       IR1   \n",
       "\n",
       "     Land Contour Utilities Lot Config  ... Pool Area Pool QC  Fence  \\\n",
       "1341          Lvl    AllPub        FR3  ...         0     NaN  MnPrv   \n",
       "1497          Lvl    AllPub     Inside  ...         0     NaN    NaN   \n",
       "\n",
       "     Misc Feature Misc Val Mo Sold  Yr Sold  Sale Type  Sale Condition  \\\n",
       "1341          NaN        0       4     2008      ConLD         Abnorml   \n",
       "1497          NaN        0       7     2008        WD           Normal   \n",
       "\n",
       "      SalePrice  \n",
       "1341      79000  \n",
       "1497     284700  \n",
       "\n",
       "[2 rows x 80 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['Bsmt Half Bath'].isnull()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Filling in data based on column names. There are 2 types of basement features, numerical and string descriptives.**\n",
    "\n",
    "The numerical basement columns:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "bsmt_num_cols = ['BsmtFin SF 1', 'BsmtFin SF 2', 'Bsmt Unf SF','Total Bsmt SF', 'Bsmt Full Bath', 'Bsmt Half Bath']\n",
    "df[bsmt_num_cols] = df[bsmt_num_cols].fillna(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The string basement columns:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "bsmt_str_cols =  ['Bsmt Qual', 'Bsmt Cond', 'Bsmt Exposure', 'BsmtFin Type 1', 'BsmtFin Type 2']\n",
    "df[bsmt_str_cols] = df[bsmt_str_cols].fillna('None')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "percent_nan = percent_missing(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=percent_nan.index,y=percent_nan)\n",
    "plt.xticks(rotation=90);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dropping Rows\n",
    "\n",
    "A few of these features appear that it is just one or two rows missing the data. Based on our description .txt file of the dataset, we could also fill in these data points easily, and that is the more correct approach, but here we show how to drop in case you find yourself in a situation where it makes more sense to drop a row, based on missing column features."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    df.dropna() ---\n",
    "        subset : array-like, optional\n",
    "                Labels along other axis to consider, e.g. if you are dropping rows\n",
    "                these would be a list of columns to include."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.dropna(axis=0,subset= ['Electrical','Garage Cars'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "percent_nan = percent_missing(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=percent_nan.index,y=percent_nan)\n",
    "plt.xticks(rotation=90);\n",
    "plt.ylim(0,1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Mas Vnr Feature "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on the Description Text File, Mas Vnr Type and Mas Vnr Area being missing (NaN) is likely to mean the house simply just doesn't have a masonry veneer, in which case, we will fill in this data as we did before."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Mas Vnr Type\"] = df[\"Mas Vnr Type\"].fillna(\"None\")\n",
    "df[\"Mas Vnr Area\"] = df[\"Mas Vnr Area\"].fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "percent_nan = percent_missing(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=percent_nan.index,y=percent_nan)\n",
    "plt.xticks(rotation=90);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Filling In Missing Column Data\n",
    "\n",
    "Our previous approaches were based more on rows missing data, now we will take an approach based on the column features themselves, since larger percentages of the data appears to be missing."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Garage Columns\n",
    "\n",
    "Based on the data description, these NaN seem to indicate no garage, so we will substitute with \"None\" or 0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>Garage Type</th>\n",
       "      <th>Garage Finish</th>\n",
       "      <th>Garage Qual</th>\n",
       "      <th>Garage Cond</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Attchd</td>\n",
       "      <td>Fin</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Attchd</td>\n",
       "      <td>Unf</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Attchd</td>\n",
       "      <td>Unf</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Attchd</td>\n",
       "      <td>Fin</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Attchd</td>\n",
       "      <td>Fin</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2922</th>\n",
       "      <td>Detchd</td>\n",
       "      <td>Unf</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2923</th>\n",
       "      <td>Attchd</td>\n",
       "      <td>Unf</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2924</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2925</th>\n",
       "      <td>Attchd</td>\n",
       "      <td>RFn</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2926</th>\n",
       "      <td>Attchd</td>\n",
       "      <td>Fin</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2925 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Garage Type Garage Finish Garage Qual Garage Cond\n",
       "0         Attchd           Fin          TA          TA\n",
       "1         Attchd           Unf          TA          TA\n",
       "2         Attchd           Unf          TA          TA\n",
       "3         Attchd           Fin          TA          TA\n",
       "4         Attchd           Fin          TA          TA\n",
       "...          ...           ...         ...         ...\n",
       "2922      Detchd           Unf          TA          TA\n",
       "2923      Attchd           Unf          TA          TA\n",
       "2924         NaN           NaN         NaN         NaN\n",
       "2925      Attchd           RFn          TA          TA\n",
       "2926      Attchd           Fin          TA          TA\n",
       "\n",
       "[2925 rows x 4 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['Garage Type', 'Garage Finish', 'Garage Qual', 'Garage Cond']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "gar_str_cols = ['Garage Type', 'Garage Finish', 'Garage Qual', 'Garage Cond']\n",
    "df[gar_str_cols] = df[gar_str_cols].fillna('None')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Garage Yr Blt'] = df['Garage Yr Blt'].fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "percent_nan = percent_missing(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAEtCAYAAADz1SBvAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy86wFpkAAAACXBIWXMAAAsTAAALEwEAmpwYAAAZO0lEQVR4nO3de7ScdX3v8feHBAXFCEigUS5Bm6pcBDRYFA5YI/VWhYooKDRalVJRRG3l1gp4isul1dOKBz0pFOIRQQoicM4pQqMJohSbAAoYEMpNNMK2IuANuXzOH8+zybCzc9l7ZvZvz28+r7WyZp5n9mS+s5L9mWd+V9kmIiLqslHpAiIiovcS7hERFUq4R0RUKOEeEVGhhHtERIVmli4AYKuttvLcuXNLlxERMVBWrFjxM9uzx3tsWoT73LlzWb58eekyIiIGiqS71vZYmmUiIiqUcI+IqFDCPSKiQgn3iIgKJdwjIiq03nCX9M+S7pN0Y8e5LSVdIenW9naLjseOl3SbpFskvbpfhUdExNptyJX72cBrxpw7Dlhiex6wpD1G0k7AIcDO7XNOlzSjZ9VGRMQGWW+4274S+PmY0wcAi9v7i4EDO86fZ/th23cAtwEv7U2pERGxoSbb5r6N7VUA7e3W7fnnAD/q+Ll72nNrkHSEpOWSlo+MjEyyjIiIGE+vZ6hqnHPj7gZiexGwCGD+/PnZMSQipsyph725dAkTduKXLpjQz0/2yv1eSXMA2tv72vP3ANt1/Ny2wE8m+RoRETFJkw33S4CF7f2FwMUd5w+R9FRJOwLzgO92V2JEREzUeptlJJ0LvALYStI9wEnAJ4DzJb0LuBs4GMD2TZLOB34APAocZfuxPtUeERFrsd5wt33oWh5asJafPxU4tZuiIiKiO5mhGhFRoWmxnntETC+f+/ClpUuYsPd9+g2lS5hWcuUeEVGhhHtERIUS7hERFUq4R0RUKOEeEVGhhHtERIUyFDJiEpbtu1/pEiZsvyuXlS4hplCu3CMiKpRwj4ioUMI9IqJCCfeIiAol3CMiKpRwj4ioUMI9IqJCCfeIiAol3CMiKpRwj4ioUMI9IqJCCfeIiAol3CMiKpRwj4ioUMI9IqJCCfeIiAol3CMiKpRwj4ioUMI9IqJCCfeIiAol3CMiKpRwj4ioUMI9IqJCXYW7pA9KuknSjZLOlbSJpC0lXSHp1vZ2i14VGxERG2bS4S7pOcDRwHzbuwAzgEOA44AltucBS9rjiIiYQjN78PxNJT0CPA34CXA88Ir28cXAUuDYLl8nBtDep+1duoQJ+fb7v126hIiemfSVu+0fA38P3A2sAh6wfTmwje1V7c+sArYe7/mSjpC0XNLykZGRyZYRERHj6KZZZgvgAGBH4NnA0yUdtqHPt73I9nzb82fPnj3ZMiIiYhzddKi+CrjD9ojtR4CvAi8H7pU0B6C9va/7MiMiYiK6Cfe7gb0kPU2SgAXASuASYGH7MwuBi7srMSIiJmrSHaq2r5F0AXAt8ChwHbAI2Aw4X9K7aD4ADu5FoRERseG6Gi1j+yTgpDGnH6a5io+IiEIyQzUiokIJ94iICiXcIyIqlHCPiKhQwj0iokIJ94iICiXcIyIqlHCPiKhQwj0iokIJ94iICiXcIyIqlHCPiKhQwj0iokIJ94iICiXcIyIqlHCPiKhQwj0iokIJ94iICiXcIyIqlHCPiKhQwj0iokIJ94iICiXcIyIqlHCPiKhQwj0iokIJ94iICiXcIyIqlHCPiKhQwj0iokIJ94iICiXcIyIqlHCPiKhQwj0iokJdhbukzSVdIOlmSSslvUzSlpKukHRre7tFr4qNiIgN0+2V+z8Cl9l+AbAbsBI4Dlhiex6wpD2OiIgpNOlwlzQL2Bc4E8D272z/AjgAWNz+2GLgwO5KjIiIiermyv25wAhwlqTrJJ0h6enANrZXAbS3W4/3ZElHSFouafnIyEgXZURExFjdhPtM4MXA523vAfyKCTTB2F5ke77t+bNnz+6ijIiIGKubcL8HuMf2Ne3xBTRhf6+kOQDt7X3dlRgRERM16XC3/VPgR5Ke355aAPwAuARY2J5bCFzcVYURETFhM7t8/vuBcyQ9BbgdeCfNB8b5kt4F3A0c3OVrRETEBHUV7ravB+aP89CCbv7eiIjoTmaoRkRUKOEeEVGhhHtERIUS7hERFUq4R0RUKOEeEVGhhHtERIUS7hERFUq4R0RUKOEeEVGhhHtERIUS7hERFUq4R0RUKOEeEVGhhHtERIUS7hERFUq4R0RUKOEeEVGhbvdQjS7c/bFdS5cwIdt/9IbSJUTEBsqVe0REhRLuEREVSrhHRFQo4R4RUaGEe0REhRLuEREVSrhHRFQo4R4RUaGEe0REhRLuEREVSrhHRFQo4R4RUaGEe0REhboOd0kzJF0n6f+0x1tKukLSre3tFt2XGRERE9GLK/cPACs7jo8DltieByxpjyMiYgp1Fe6StgVeD5zRcfoAYHF7fzFwYDevERERE9ftlfs/AB8BHu84t43tVQDt7dbjPVHSEZKWS1o+MjLSZRkREdFp0uEu6U+A+2yvmMzzbS+yPd/2/NmzZ0+2jIiIGEc32+ztDbxR0uuATYBZkr4E3Ctpju1VkuYA9/Wi0IiI2HCTvnK3fbztbW3PBQ4BvmH7MOASYGH7YwuBi7uuMiIiJqQf49w/Aewv6VZg//Y4IiKmUDfNMk+wvRRY2t7/L2BBL/7eiIiYnMxQjYioUMI9IqJCCfeIiAol3CMiKpRwj4ioUMI9IqJCCfeIiAol3CMiKpRwj4ioUMI9IqJCCfeIiAol3CMiKpRwj4ioUMI9IqJCCfeIiAol3CMiKpRwj4ioUMI9IqJCCfeIiAol3CMiKpRwj4ioUMI9IqJCCfeIiAol3CMiKpRwj4ioUMI9IqJCCfeIiAol3CMiKpRwj4ioUMI9IqJCCfeIiAol3CMiKpRwj4io0KTDXdJ2kr4paaWkmyR9oD2/paQrJN3a3m7Ru3IjImJDdHPl/ijwYdsvBPYCjpK0E3AcsMT2PGBJexwREVNo0uFue5Xta9v7DwErgecABwCL2x9bDBzYZY0RETFBPWlzlzQX2AO4BtjG9ipoPgCArdfynCMkLZe0fGRkpBdlREREq+twl7QZcCFwjO0HN/R5thfZnm97/uzZs7stIyIiOnQV7pI2pgn2c2x/tT19r6Q57eNzgPu6KzEiIiaqm9EyAs4EVtr+TMdDlwAL2/sLgYsnX15EREzGzC6euzdwOHCDpOvbcycAnwDOl/Qu4G7g4K4qjIiICZt0uNu+CtBaHl4w2b83IiK6lxmqEREV6qZZpu9e8tdfLF3ChK341J+VLiEiIlfuERE1SrhHRFQo4R4RUaGEe0REhRLuEREVSrhHRFQo4R4RUaGEe0REhRLuEREVSrhHRFQo4R4RUaGEe0REhRLuEREVSrhHRFQo4R4RUaGEe0REhRLuEREVSrhHRFQo4R4RUaGEe0REhRLuEREVSrhHRFQo4R4RUaGEe0REhRLuEREVSrhHRFQo4R4RUaGEe0REhRLuEREVSrhHRFQo4R4RUaG+hbuk10i6RdJtko7r1+tERMSa+hLukmYA/xN4LbATcKiknfrxWhERsaZ+Xbm/FLjN9u22fwecBxzQp9eKiIgxZLv3f6n0ZuA1tt/dHh8O/KHt93X8zBHAEe3h84Fbel7I2m0F/GwKX2+q5f0NtprfX83vDab+/e1ge/Z4D8zs0wtqnHNP+hSxvQhY1KfXXydJy23PL/HaUyHvb7DV/P5qfm8wvd5fv5pl7gG26zjeFvhJn14rIiLG6Fe4/wcwT9KOkp4CHAJc0qfXioiIMfrSLGP7UUnvA74OzAD+2fZN/XitSSrSHDSF8v4GW83vr+b3BtPo/fWlQzUiIsrKDNWIiAol3CMiKpRwj4io0FCEuxqHSfpoe7y9pJeWrqtX2vezxp/SdcWGkbRL6Rr6pdbfvfY9HT7O+fdIeluJmsYaig5VSZ8HHgdeafuFkrYALre9Z+HSekLSDTSTxARsAuwI3GJ756KF9ZCkbYCPA8+2/dp2raKX2T6zcGldk3QV8BTgbODLtn9RtKAeqvV3T9J1wL62Hxpz/hnAUtsvKVPZakNx5U6z9MFRwG8BbN9P88tUBdu72n5RezuPZm2fq0rX1WNn0wytfXZ7/EPgmFLF9JLtfYC300z8Wy7py5L2L1xWr9T6uzdjbLADtOc2LlDPGoYl3B9pV6o0gKTZNFcTVbJ9LTDQV0bj2Mr2+bT/brYfBR4rW1Lv2L4V+BvgWGA/4LOSbpb0prKVda3W372NJT197Mn2yn1afHj1a22Z6eazwEXA1pJOBd5M84tUBUkf6jjcCHgxMFKonH75laRnsTok9gIeKFtSb0h6EfBO4PXAFcAbbF8r6dnA1cBXS9bXpVp/984ELpD0l7bvBJA0l2ap82nRVDgUbe4Akl4ALKBpl15ie2XhknpG0kkdh48CdwIX2v5tmYp6T9KLgdOAXYAbgdnAm21/v2hhPSDpSuCfgAts/2bMY4fb/t9lKuuOpI2AvYCfU+HvnqQjgeOBzdpTvwQ+Yfvz5apabSjCXdKW45x+yPYjU15Mn0iaBWD7wdK19IukmTTLQ4umw7imf79Nge1tT+XS130n6WrbLytdRz9J2owmS9dogy9pWNrcr6VppvghcGt7/w5J10oq3qvdDUnHSPoxcAdwp6QfSjqkfWy7dT97cEg6CtjM9k22bwQ2k/Te0nX1gqQ3ANcDl7XHu0uqZaG9yyUdJGm8ZcAHmqQ9JH0JuBJYJmmRpN9vHyve5D0s4X4Z8DrbW9l+Fs32f+cD7wVOL1pZFySdDPwx8N9sP8v2lsBrgLdLOhb4Zsn6euw9nUME21EX7ylXTk+dTDPC6RcAtq8H5harprc+BPwL8LCkByU9JGngv11KOojmfX0DeAdNn8m/07TDv4xmZFdRxT9dpsh820eOHti+XNLHbX9I0lNLFtaltwO7drat275d0ltovp1Mi8kUPbKRJLltR2xHYEyLUQk98KjtByq8uMX2M0rX0CcnAa8a7UxtfU/SN4Cbgc8UqarDsIT7z9sr2fPa47cC97cBMcjDsh4fr9PU9m8k/dh2LV/tobkSOl/SF2hGzBxJ24xRgRvbWY0zJM0Djga+U7imnpC073jnbV851bX02MwxwQ6A7Tsl3WX7hAI1PcmwhPvbaD5pv0bTGXdVe24G8JZyZXXtHkkLbC/pPCnplcCPC9XUL8cCfwH8Jc2/4eXAGUUr6p33AycCDwPn0nyQ/feiFfXOX3fc34Sm+WkF8Moy5fTMI5K2t31350lJO9D8OxY3FKNlaiVpZ+Bimg+rFTRXtHsCewNvtP2DguVFrKHt5P+k7UNL19INSQcCn6RZEqPzd+844FjbXytWXGsowr2dFfcRYGeaqwcAbA/61QOSNqH5FrIzzRXtTcA5NY1xB5C0N03H4w403zgF2PZzS9bVDUmXMmbj+E623ziF5UyJdtTM923vWrqWbknaDfgwq3/3bgQ+bft7RQtrDUu4Xw58BfgrmrbahcCI7WOLFhYbTNLNwAdprpKeWHbA9n8VK6pLkvZb1+O2l01VLf0i6TRWf4BtBOwO3Gn7sGJFDYlhCfcVtl8i6fu2X9SeW2Z7nb9cMX1Iusb2H5auIyZG0sKOw0dpgv3bpeoZJsPSoTo6k3GVpNcDPwG2LVhPTNw3JX2KZp2VJzqs2kXSBlLHUs1rPEQzEmq3KS6pHza3/Y+dJyR9YOy56L1huXL/E+BbNEuqngbMAk62fWnRwnqo1unroySNNyHLg9xv0o6sWOM0zYXHCbZfN8Ul9Zyka22/eMy562zvUaqmYTEsV+73236AZhXBP4InOuiq0E5f/3uaST07Stod+FhNHXK2/6h0Db1m+67R++2/2dtohubeAVxYqKyekHQozfvZccxSCs8ABrafZNSYvoQ12D56CssZ17CE+2k0y+Cu79ygOplm/PBSaKavt8uPVkMV7sQk6Q+AQ4BDaQLvKzTfpmv4IPsOsArYCvh0x/mHgIFfyRNYXrqA9ak63Ns1Hl4OzB6z5vksmglMtah2+nqHs4GzaCb7QLMI3FeYJmtnT9LNNM2Fb7B9G4CkD5YtqTfabyV3AVWuCGl7cedxu0mHbf+yUElrqH3hsKfQrLU8k+br4OifB2k2DajFk6avt18Zq5i+3qHGnZgOAn5K01n8T5JG1zyvhqS9JP2HpF9K+p2kx2pYOGyUpF3U7Kd6I/ADSSvayYXFVX3l3o4TXibp7M72zQp1Tl//Ms309b8rWlHvVbcTk+2LgIvUbNd2IM04/m3UbCp9ke3LS9bXI5+jaXr6F2A+8GfA7xetqLcWAR+y/U0ASa+g2Xjl5QVrAoZntMwf0ExgmkvHB9ogj7QYNqp4J6ZOajaWORh4aw3/PyUttz1/zByT79guHn69IOl7Y4esjneuhGEJ9+8BX2DN2Y0rihXVQ5KuAA4eXe9c0hbAebZfXbSwHuhcnEkV78RUKzVbCL6KZpG3n9J0sr5jOoRfL0i6iGYzoNGtEA+jWWL8wGJFtYYl3FfYHugdl9ZlvHHDtYwl7hwnLelC2weVrik2XDuW/16a/q8PAs8ETh/tQB507YXUKcA+NBcdV9LMobm/aGFU3ube4VI1W7JdxJNnN/68XEk99fiYK9wdWMcY3AHT2cE4sIuEDSvbd7UT7ObYPqV0Pb3WhvjRavYwfnw6jZYZlnAfXd+ic21pU09YnAhcJWl0oal9gSMK1tNLXsv9GAC1T7CTtCvwRWDL9vhnwMJ2n9+ihqJZZhhI2grYi+ZK92rbPytcUk9Iegz4Fc372hT49ehDNOOKZ5WqLdZP0ujGHEtHmwk7O1cHnaTvACeOGS3z8enQYTwUV+6SNqbZwWd0y6+lwP+qrEPuMeA+mvXqd5JUw1Zm2K5pstkwqn2C3dNHgx3A9tJ2aGtxQxHuwOeBjYHT2+PD23PvLlZRD0l6N/ABmgWnrqe5gr+awd/KLAZftfvDtm6X9Lc8ebTMHQXrecJQNMtM57GovdAuHbsn8O+2d5f0AuAU228tXFoMOUlPo+kT+uP21NeBv6tlp7Axo2WgGS1zSkbLTJ3HJD3P9n8CSHougz91vdNvbf9WEpKeavtmSc8vXVQML0nvs/0527+W9GXbJ67/WYOj3d7ySJrZtjcAH55uzbzDEu5/RbN+x+00HXE7AO8sW1JP3SNpc+BrwBWS7qfZkCSilD+nWXoAmiaLWlZgHbWYZhOgbwGvBV4IHFOyoLGqD3dJM4DdgHmsnt14s+2H1/nEAWL7T9u7J7ebWjwTuKxgSRGdauxN3Wl0k29JZwLfLVzPGqoPd9uPSXqj7f9BHetIP6Fdh2SsG9rbzYBaJmnF4Nlc0p/SrDw7S9KbOh+0/dUyZfXME00wth+djqOBhqVD9VSaq9mv0IyZBgZ7/00ASXfQTOwZ73+WbdcySSsGjKSz1vGwbf/5lBXTBx3zL+DJczCmzfyLYQn36vbfjIhYl6rDfbTHvr2/s+2bStfUL+3X3n1oruS/ZftrZSuKiJJqD/fOFQXX2IW9FpJOpxmSdW576q3Af9o+qlxVEVFS9R2qHaZfj0fv7Afs4vaTWtJiVnesRsQQqj3ca++xH3ULsD3NhsQA21HZyKAYTJKOAs4Zs5HMobZPX+cTo2u1N8tU3WM/ql3qd09Wj7Xdk2ZtmV8D1LK8agweSdfb3n3MuSo2kpnuqr5yt13TLNR1+WjpAiLWYiNJ6mgynEGztnv0WdXhPixsL2t3X5pn+9/anW9m2n6odG0x9L4OnC/pCzQjuY4ks6enRNXNMsNC0ntodl7a0vbz2qVVv2B7QeHSYshJ2ojm/+araAY1XA6cYbumhfumpaEI93alxIfXd25QSboeeClwTcduNzeMrn0RMR20y2Vsazud/VNgo9IFTJGrN/DcoHrY9u9GDyTNJPuNxjQgaamkWW2wXw+cJekzhcsaClW3uUv6PeA5wKaS9mD1WPdZwNOKFdZ7yySdQPM+9wfeC1xauKYIgGfafrDdLews2ydJypX7FKg63IFXA++g2X6u82rhIeCEEgX1ybE0WwbeAPwF8P+AM4pWFNGYKWkO8BaaHZliigxLm/tBti8sXUc/tB1W37e9S+laIsaSdDDwt8BVtt/b7oL2KdsHFS6tesMS7pvTjAXftz21DPiY7QeKFdVDks4Bjrd9d+laImJ6qL1ZZtSZwI00Xw0BDgfOAt601mcMljnATZK+y5PXq8/M1ChC0kdsf1LSaYzTuW/76AJlDZVhCffnjfkaeEo7fLAWp5QuIGKMle3t8qJVDLFhCfffSNrH9lUAkvYGflO4pp6xvax0DRGdbF/a3i4uXcuwGpY2992AL9JstQdwP7Bw0CdTSLrK9j6SHuLJX32nzVZfMZwkXbKux9Nk2H9DEe6jJM0CaMfdHmP7HwqX1BVJO9i+a/0/GTG1JI0AP6LZQOYaxuynkG+b/TdU4d5J0t22ty9dRzfG7DR1YYaXxXTRrv64P3Ao8CLg/wLn1rzV5XQzLMsPjKeGnZk638Nzi1URMYbtx2xfZnshsBdwG7BU0vsLlzY0hqVDdTw1fGXxWu5HFCfpqcDraa7e5wKfBWrZ/Wzaq7pZZpyOxiceAja1PdAfbpIeoxnXLmBT2p2XSIdqFNbu47sL8K/AebZvLFzS0Kk63COiDEmPs3pCXUZyFZBwj4io0DB3qEZEVCvhHhFRoYR7RESFEu4RERVKuEdEVOj/A2ROxYyjodMIAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=percent_nan.index,y=percent_nan)\n",
    "plt.xticks(rotation=90);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dropping Feature Columns\n",
    "\n",
    "Sometimes you may want to take the approach that above a certain missing percentage threshold, you will simply remove the feature from all the data. For example if 99% of rows are missing a feature, it will not be predictive, since almost all the data does not have any value for it. In our particular data set, many of these high percentage NaN features are actually plasceholders for \"none\" or 0. But for the sake of showing variations on dealing with missing data, we will remove these features, instead of filling them in with the appropriate value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Lot Frontage', 'Fireplace Qu', 'Fence', 'Alley', 'Misc Feature',\n",
       "       'Pool QC'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "percent_nan.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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>Lot Frontage</th>\n",
       "      <th>Fireplace Qu</th>\n",
       "      <th>Fence</th>\n",
       "      <th>Alley</th>\n",
       "      <th>Misc Feature</th>\n",
       "      <th>Pool QC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>141.0</td>\n",
       "      <td>Gd</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>80.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MnPrv</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>81.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Gar2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>93.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>74.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>MnPrv</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2922</th>\n",
       "      <td>37.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>GdPrv</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2923</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MnPrv</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2924</th>\n",
       "      <td>62.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MnPrv</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Shed</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2925</th>\n",
       "      <td>77.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2926</th>\n",
       "      <td>74.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2925 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Lot Frontage Fireplace Qu  Fence Alley Misc Feature Pool QC\n",
       "0            141.0           Gd    NaN   NaN          NaN     NaN\n",
       "1             80.0          NaN  MnPrv   NaN          NaN     NaN\n",
       "2             81.0          NaN    NaN   NaN         Gar2     NaN\n",
       "3             93.0           TA    NaN   NaN          NaN     NaN\n",
       "4             74.0           TA  MnPrv   NaN          NaN     NaN\n",
       "...            ...          ...    ...   ...          ...     ...\n",
       "2922          37.0          NaN  GdPrv   NaN          NaN     NaN\n",
       "2923           NaN          NaN  MnPrv   NaN          NaN     NaN\n",
       "2924          62.0          NaN  MnPrv   NaN         Shed     NaN\n",
       "2925          77.0           TA    NaN   NaN          NaN     NaN\n",
       "2926          74.0           TA    NaN   NaN          NaN     NaN\n",
       "\n",
       "[2925 rows x 6 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['Lot Frontage', 'Fireplace Qu', 'Fence', 'Alley', 'Misc Feature','Pool QC']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop(['Pool QC','Misc Feature','Alley','Fence'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "percent_nan = percent_missing(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=percent_nan.index,y=percent_nan)\n",
    "plt.xticks(rotation=90);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Filling in Fireplace Quality based on Description Text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Fireplace Qu'] = df['Fireplace Qu'].fillna(\"None\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "percent_nan = percent_missing(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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B7+HBp9JfBfxJVd29sKGkKfmXmOqrC1jZNvmN0T/3ABvdpSkddDwDVy8lub6qnjluTTqYeQauvvp+khfte5HkZOD7C5xHmppn4OqlJM8APsHKY9UA7gLOrqovL24qaToGXL2W5EiAqronyTlV9eEFjyRNzIBLI0luraonLHoOaVLugUsP8gk9aooBlx7k/46qKd5Kr15Jci/rhzrA4Zs8jjQX98AlqVFuoUhSowy4JDXKgEtSowy4JDXKgEtSo/4fM+F0DSqS3hEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=percent_nan.index,y=percent_nan)\n",
    "plt.xticks(rotation=90);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# [Imputation](https://en.wikipedia.org/wiki/Imputation_(statistics)) of Missing Data\n",
    "\n",
    "To impute missing data, we need to decide what other filled in (no NaN values) feature most probably relates and is correlated with the missing feature data. In this particular case we will use:\n",
    "\n",
    "Neighborhood: Physical locations within Ames city limits\n",
    "\n",
    "LotFrontage: Linear feet of street connected to property\n",
    "\n",
    "We will operate under the assumption that the Lot Frontage is related to what neighborhood a house is in."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['NAmes', 'Gilbert', 'StoneBr', 'NWAmes', 'Somerst', 'BrDale',\n",
       "       'NPkVill', 'NridgHt', 'Blmngtn', 'NoRidge', 'SawyerW', 'Sawyer',\n",
       "       'Greens', 'BrkSide', 'OldTown', 'IDOTRR', 'ClearCr', 'SWISU',\n",
       "       'Edwards', 'CollgCr', 'Crawfor', 'Blueste', 'Mitchel', 'Timber',\n",
       "       'MeadowV', 'Veenker', 'GrnHill', 'Landmrk'], dtype=object)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Neighborhood'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Lot Frontage', ylabel='Neighborhood'>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,12))\n",
    "sns.boxplot(x='Lot Frontage',y='Neighborhood',data=df,orient='h')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Impute Missing Data based on other Features\n",
    "\n",
    "There are more complex methods, but usually the simpler the better, it avoids building models on top of other models.\n",
    "\n",
    "More Info on Options: https://scikit-learn.org/stable/modules/impute.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas.core.groupby.generic.SeriesGroupBy object at 0x00000211F18E23C8>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('Neighborhood')['Lot Frontage']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Neighborhood\n",
       "Blmngtn    46.900000\n",
       "Blueste    27.300000\n",
       "BrDale     21.500000\n",
       "BrkSide    55.789474\n",
       "ClearCr    88.150000\n",
       "CollgCr    71.336364\n",
       "Crawfor    69.951807\n",
       "Edwards    64.794286\n",
       "Gilbert    74.207207\n",
       "Greens     41.000000\n",
       "GrnHill          NaN\n",
       "IDOTRR     62.383721\n",
       "Landmrk          NaN\n",
       "MeadowV    25.606061\n",
       "Mitchel    75.144444\n",
       "NAmes      75.210667\n",
       "NPkVill    28.142857\n",
       "NWAmes     81.517647\n",
       "NoRidge    91.629630\n",
       "NridgHt    84.184049\n",
       "OldTown    61.777293\n",
       "SWISU      59.068182\n",
       "Sawyer     74.551020\n",
       "SawyerW    70.669811\n",
       "Somerst    64.549383\n",
       "StoneBr    62.173913\n",
       "Timber     81.303571\n",
       "Veenker    72.000000\n",
       "Name: Lot Frontage, dtype: float64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('Neighborhood')['Lot Frontage'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Transform Column\n",
    "\n",
    "https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.transform.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    141.0\n",
       "1     80.0\n",
       "2     81.0\n",
       "3     93.0\n",
       "4     74.0\n",
       "Name: Lot Frontage, dtype: float64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()['Lot Frontage']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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>MS SubClass</th>\n",
       "      <th>MS Zoning</th>\n",
       "      <th>Lot Frontage</th>\n",
       "      <th>Lot Area</th>\n",
       "      <th>Street</th>\n",
       "      <th>Lot Shape</th>\n",
       "      <th>Land Contour</th>\n",
       "      <th>Utilities</th>\n",
       "      <th>Lot Config</th>\n",
       "      <th>Land Slope</th>\n",
       "      <th>...</th>\n",
       "      <th>Enclosed Porch</th>\n",
       "      <th>3Ssn Porch</th>\n",
       "      <th>Screen Porch</th>\n",
       "      <th>Pool Area</th>\n",
       "      <th>Misc Val</th>\n",
       "      <th>Mo Sold</th>\n",
       "      <th>Yr Sold</th>\n",
       "      <th>Sale Type</th>\n",
       "      <th>Sale Condition</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7980</td>\n",
       "      <td>Pave</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>500</td>\n",
       "      <td>3</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>185000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>120</td>\n",
       "      <td>RL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6820</td>\n",
       "      <td>Pave</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Corner</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>140</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>212000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>60</td>\n",
       "      <td>FV</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7500</td>\n",
       "      <td>Pave</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>216000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11241</td>\n",
       "      <td>Pave</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>CulDSac</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>700</td>\n",
       "      <td>3</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>149000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12537</td>\n",
       "      <td>Pave</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>CulDSac</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>149900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2891</th>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16669</td>\n",
       "      <td>Pave</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Corner</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>228000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2894</th>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11170</td>\n",
       "      <td>Pave</td>\n",
       "      <td>IR2</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Corner</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2895</th>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8098</td>\n",
       "      <td>Pave</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>202000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2909</th>\n",
       "      <td>90</td>\n",
       "      <td>RL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11836</td>\n",
       "      <td>Pave</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Corner</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>146500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2923</th>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8885</td>\n",
       "      <td>Pave</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Low</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>Mod</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>131000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>490 rows × 76 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      MS SubClass MS Zoning  Lot Frontage  Lot Area Street Lot Shape  \\\n",
       "11             20        RL           NaN      7980   Pave       IR1   \n",
       "14            120        RL           NaN      6820   Pave       IR1   \n",
       "22             60        FV           NaN      7500   Pave       Reg   \n",
       "23             20        RL           NaN     11241   Pave       IR1   \n",
       "24             20        RL           NaN     12537   Pave       IR1   \n",
       "...           ...       ...           ...       ...    ...       ...   \n",
       "2891           20        RL           NaN     16669   Pave       IR1   \n",
       "2894           60        RL           NaN     11170   Pave       IR2   \n",
       "2895           20        RL           NaN      8098   Pave       IR1   \n",
       "2909           90        RL           NaN     11836   Pave       IR1   \n",
       "2923           20        RL           NaN      8885   Pave       IR1   \n",
       "\n",
       "     Land Contour Utilities Lot Config Land Slope  ... Enclosed Porch  \\\n",
       "11            Lvl    AllPub     Inside        Gtl  ...              0   \n",
       "14            Lvl    AllPub     Corner        Gtl  ...              0   \n",
       "22            Lvl    AllPub     Inside        Gtl  ...              0   \n",
       "23            Lvl    AllPub    CulDSac        Gtl  ...              0   \n",
       "24            Lvl    AllPub    CulDSac        Gtl  ...              0   \n",
       "...           ...       ...        ...        ...  ...            ...   \n",
       "2891          Lvl    AllPub     Corner        Gtl  ...              0   \n",
       "2894          Lvl    AllPub     Corner        Gtl  ...              0   \n",
       "2895          Lvl    AllPub     Inside        Gtl  ...              0   \n",
       "2909          Lvl    AllPub     Corner        Gtl  ...              0   \n",
       "2923          Low    AllPub     Inside        Mod  ...              0   \n",
       "\n",
       "     3Ssn Porch Screen Porch Pool Area Misc Val  Mo Sold  Yr Sold  Sale Type  \\\n",
       "11            0            0         0      500        3     2010        WD    \n",
       "14            0          140         0        0        6     2010        WD    \n",
       "22            0            0         0        0        1     2010        WD    \n",
       "23            0            0         0      700        3     2010        WD    \n",
       "24            0            0         0        0        4     2010        WD    \n",
       "...         ...          ...       ...      ...      ...      ...        ...   \n",
       "2891          0            0         0        0        1     2006        WD    \n",
       "2894          0            0         0        0        4     2006        WD    \n",
       "2895          0            0         0        0       10     2006        WD    \n",
       "2909          0            0         0        0        3     2006        WD    \n",
       "2923          0            0         0        0        6     2006        WD    \n",
       "\n",
       "      Sale Condition SalePrice  \n",
       "11            Normal    185000  \n",
       "14            Normal    212000  \n",
       "22            Normal    216000  \n",
       "23            Normal    149000  \n",
       "24            Normal    149900  \n",
       "...              ...       ...  \n",
       "2891          Normal    228000  \n",
       "2894          Normal    250000  \n",
       "2895          Normal    202000  \n",
       "2909          Normal    146500  \n",
       "2923          Normal    131000  \n",
       "\n",
       "[490 rows x 76 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['Lot Frontage'].isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "21    85.0\n",
       "22     NaN\n",
       "23     NaN\n",
       "24     NaN\n",
       "25    65.0\n",
       "Name: Lot Frontage, dtype: float64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[21:26]['Lot Frontage']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       141.000000\n",
       "1        80.000000\n",
       "2        81.000000\n",
       "3        93.000000\n",
       "4        74.000000\n",
       "           ...    \n",
       "2922     37.000000\n",
       "2923     75.144444\n",
       "2924     62.000000\n",
       "2925     77.000000\n",
       "2926     74.000000\n",
       "Name: Lot Frontage, Length: 2925, dtype: float64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('Neighborhood')['Lot Frontage'].transform(lambda val: val.fillna(val.mean()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "21    85.000000\n",
       "22    64.549383\n",
       "23    75.210667\n",
       "24    75.210667\n",
       "25    65.000000\n",
       "Name: Lot Frontage, dtype: float64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('Neighborhood')['Lot Frontage'].transform(lambda val: val.fillna(val.mean())).iloc[21:26]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Lot Frontage'] = df.groupby('Neighborhood')['Lot Frontage'].transform(lambda val: val.fillna(val.mean()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "percent_nan = percent_missing(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=percent_nan.index,y=percent_nan)\n",
    "plt.xticks(rotation=90);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Lot Frontage'] = df['Lot Frontage'].fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "percent_nan = percent_missing(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Series([], dtype: float64)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "percent_nan"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Great! We no longer have any missing data in our entire data set! Keep in mind, we should eventually turn all these transformations into an easy to use function. For now, lets' save this dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv(\"../DATA/Ames_NO_Missing_Data.csv\",index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}