Import packages and the dataset
In [2]:
import pandas as pd
import seaborn as sns
In [3]:
train = pd.read_csv('train.csv')
test = pd.read_csv('test.csv')
In [4]:
df = train.append(test)
C:\User_Files\Lucy_Wan\Programming\Anaconda2\lib\site-packages\pandas\core\frame.py:6201: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version of pandas will change to not sort by default. To accept the future behavior, pass 'sort=True'. To retain the current behavior and silence the warning, pass sort=False sort=sort)
Analyze the dataset
In [4]:
df.info()
<class 'pandas.core.frame.DataFrame'> Int64Index: 2919 entries, 0 to 1458 Data columns (total 81 columns): 1stFlrSF 2919 non-null int64 2ndFlrSF 2919 non-null int64 3SsnPorch 2919 non-null int64 Alley 198 non-null object BedroomAbvGr 2919 non-null int64 BldgType 2919 non-null object BsmtCond 2837 non-null object BsmtExposure 2837 non-null object BsmtFinSF1 2918 non-null float64 BsmtFinSF2 2918 non-null float64 BsmtFinType1 2840 non-null object BsmtFinType2 2839 non-null object BsmtFullBath 2917 non-null float64 BsmtHalfBath 2917 non-null float64 BsmtQual 2838 non-null object BsmtUnfSF 2918 non-null float64 CentralAir 2919 non-null object Condition1 2919 non-null object Condition2 2919 non-null object Electrical 2918 non-null object EnclosedPorch 2919 non-null int64 ExterCond 2919 non-null object ExterQual 2919 non-null object Exterior1st 2918 non-null object Exterior2nd 2918 non-null object Fence 571 non-null object FireplaceQu 1499 non-null object Fireplaces 2919 non-null int64 Foundation 2919 non-null object FullBath 2919 non-null int64 Functional 2917 non-null object GarageArea 2918 non-null float64 GarageCars 2918 non-null float64 GarageCond 2760 non-null object GarageFinish 2760 non-null object GarageQual 2760 non-null object GarageType 2762 non-null object GarageYrBlt 2760 non-null float64 GrLivArea 2919 non-null int64 HalfBath 2919 non-null int64 Heating 2919 non-null object HeatingQC 2919 non-null object HouseStyle 2919 non-null object Id 2919 non-null int64 KitchenAbvGr 2919 non-null int64 KitchenQual 2918 non-null object LandContour 2919 non-null object LandSlope 2919 non-null object LotArea 2919 non-null int64 LotConfig 2919 non-null object LotFrontage 2433 non-null float64 LotShape 2919 non-null object LowQualFinSF 2919 non-null int64 MSSubClass 2919 non-null int64 MSZoning 2915 non-null object MasVnrArea 2896 non-null float64 MasVnrType 2895 non-null object MiscFeature 105 non-null object MiscVal 2919 non-null int64 MoSold 2919 non-null int64 Neighborhood 2919 non-null object OpenPorchSF 2919 non-null int64 OverallCond 2919 non-null int64 OverallQual 2919 non-null int64 PavedDrive 2919 non-null object PoolArea 2919 non-null int64 PoolQC 10 non-null object RoofMatl 2919 non-null object RoofStyle 2919 non-null object SaleCondition 2919 non-null object SalePrice 1460 non-null float64 SaleType 2918 non-null object ScreenPorch 2919 non-null int64 Street 2919 non-null object TotRmsAbvGrd 2919 non-null int64 TotalBsmtSF 2918 non-null float64 Utilities 2917 non-null object WoodDeckSF 2919 non-null int64 YearBuilt 2919 non-null int64 YearRemodAdd 2919 non-null int64 YrSold 2919 non-null int64 dtypes: float64(12), int64(26), object(43) memory usage: 1.8+ MB
In [5]:
df.isnull().sum()
Out[5]:
1stFlrSF 0
2ndFlrSF 0
3SsnPorch 0
Alley 2721
BedroomAbvGr 0
BldgType 0
BsmtCond 82
BsmtExposure 82
BsmtFinSF1 1
BsmtFinSF2 1
BsmtFinType1 79
BsmtFinType2 80
BsmtFullBath 2
BsmtHalfBath 2
BsmtQual 81
BsmtUnfSF 1
CentralAir 0
Condition1 0
Condition2 0
Electrical 1
EnclosedPorch 0
ExterCond 0
ExterQual 0
Exterior1st 1
Exterior2nd 1
Fence 2348
FireplaceQu 1420
Fireplaces 0
Foundation 0
FullBath 0
...
LotShape 0
LowQualFinSF 0
MSSubClass 0
MSZoning 4
MasVnrArea 23
MasVnrType 24
MiscFeature 2814
MiscVal 0
MoSold 0
Neighborhood 0
OpenPorchSF 0
OverallCond 0
OverallQual 0
PavedDrive 0
PoolArea 0
PoolQC 2909
RoofMatl 0
RoofStyle 0
SaleCondition 0
SalePrice 1459
SaleType 1
ScreenPorch 0
Street 0
TotRmsAbvGrd 0
TotalBsmtSF 1
Utilities 2
WoodDeckSF 0
YearBuilt 0
YearRemodAdd 0
YrSold 0
Length: 81, dtype: int64
Analyze the unique values in each column of the dataset
In [6]:
for item in df.columns:
print(item)
print(df[item].dtypes)
print(df[item].unique())
1stFlrSF
int64
[ 856 1262 920 ... 1778 1650 1960]
2ndFlrSF
int64
[ 854 0 866 756 1053 566 983 752 1142 1218 668 1320 631 716
676 860 1519 530 808 977 1330 833 765 462 213 548 960 670
1116 876 612 1031 881 790 755 592 939 520 639 656 1414 884
729 1523 728 351 688 941 1032 848 836 475 739 1151 448 896
524 1194 956 1070 1096 467 547 551 880 703 901 720 316 1518
704 1178 754 601 1360 929 445 564 882 920 518 817 1257 741
672 1306 504 1304 1100 730 689 591 888 1020 828 700 842 1286
864 829 1092 709 844 1106 596 807 625 649 698 840 780 568
795 648 975 702 1242 1818 1121 371 804 325 809 1200 871 1274
1347 1332 1177 1080 695 167 915 576 605 862 495 403 838 517
1427 784 711 468 1081 886 793 665 858 874 526 590 406 1157
299 936 438 1098 766 1101 1028 1017 1254 378 1160 682 110 600
678 834 384 512 930 868 224 1103 560 811 878 574 910 620
687 546 902 1000 846 1067 914 660 1538 1015 1237 611 707 527
1288 832 806 1182 1040 439 717 511 1129 1370 636 533 745 584
812 684 595 988 800 677 573 1066 778 661 1440 872 788 843
713 567 651 762 482 738 586 679 644 900 887 1872 1281 472
1312 319 978 1093 473 664 1540 1276 441 348 1060 714 744 1203
783 1097 734 767 1589 742 686 1128 1111 1174 787 1072 1088 1063
545 966 623 432 581 540 769 1051 761 779 514 455 1426 785
521 252 813 1120 1037 1169 1001 1215 928 1140 1243 571 1196 1038
561 979 701 332 368 883 1336 1141 634 912 798 985 826 831
750 456 602 855 336 408 980 998 1168 1208 797 850 898 1054
895 954 772 1230 727 454 370 628 304 582 1122 1134 885 640
580 1112 653 220 240 1362 534 539 650 918 933 712 1796 971
1175 743 523 1216 2065 272 685 776 630 984 875 913 464 1039
1259 940 892 725 924 764 925 1479 192 589 992 903 430 748
587 994 950 1323 732 1357 557 1296 390 1185 873 1611 457 796
908 550 989 932 358 1392 349 691 1349 768 208 622 857 556
1044 708 626 904 510 1104 830 981 870 694 1152 563 823 604
715 532 537 505 424 606 185 498 492 608 1074 662 499 180
942 558 614 328 1788 1075 380 615 645 663 1275 816 839 1325
1012 1295 683 1126 1089 1221 967 841 1209 897 786 1629 782 1369
972 1315 726 322 760 629 496 690 646 917 624 320 588 425
747 1114 1619 718 815 926 444 436 1240 516 1420 1158 1162 1139
1285 1061 1250 919 861 794 825 893 1319 959 792 1345 453 412
182 501 375 680 658 552 396 308 973 363 594 554 428 536
486 1721 1099 735 899 1198 343 673 442 890 943 330 420 770
1342 1377 845 1402 1036 570 1238 923 757 1048 1131 1407 1171 1277
995 528 863 1232 976 1008 1309 228 500 544 1778 616 494 642
659 671 144 525 423 1164 356 245 1042 477 1005 1087 638 400
376 916 927 869 753 450 1133 674 125 531 585 775 851 957
1340 955 990 1384 1862 1371 1405 1358 465 466 1335 814 488 1321
1029 1368 1567 1189 1234 1248 821 1007 476 502 867 297 810 434
583 341 1836 541 1246 1124 1045 827 1150 312 218 493 736 818
610 549 697 360 1004]
3SsnPorch
int64
[ 0 320 407 130 180 168 140 508 238 245 196 144 182 162 23 216 96 153
290 304 224 255 225 360 150 174 120 219 176 86 323]
Alley
object
[nan 'Grvl' 'Pave']
BedroomAbvGr
int64
[3 4 1 2 0 5 6 8]
BldgType
object
['1Fam' '2fmCon' 'Duplex' 'TwnhsE' 'Twnhs']
BsmtCond
object
['TA' 'Gd' nan 'Fa' 'Po']
BsmtExposure
object
['No' 'Gd' 'Mn' 'Av' nan]
BsmtFinSF1
float64
[7.060e+02 9.780e+02 4.860e+02 2.160e+02 6.550e+02 7.320e+02 1.369e+03
8.590e+02 0.000e+00 8.510e+02 9.060e+02 9.980e+02 7.370e+02 7.330e+02
5.780e+02 6.460e+02 5.040e+02 8.400e+02 1.880e+02 2.340e+02 1.218e+03
1.277e+03 1.018e+03 1.153e+03 1.213e+03 7.310e+02 6.430e+02 9.670e+02
7.470e+02 2.800e+02 1.790e+02 4.560e+02 1.351e+03 2.400e+01 7.630e+02
1.820e+02 1.040e+02 1.810e+03 3.840e+02 4.900e+02 6.490e+02 6.320e+02
9.410e+02 7.390e+02 9.120e+02 1.013e+03 6.030e+02 1.880e+03 5.650e+02
3.200e+02 4.620e+02 2.280e+02 3.360e+02 4.480e+02 1.201e+03 3.300e+01
5.880e+02 6.000e+02 7.130e+02 1.046e+03 6.480e+02 3.100e+02 1.162e+03
5.200e+02 1.080e+02 5.690e+02 1.200e+03 2.240e+02 7.050e+02 4.440e+02
2.500e+02 9.840e+02 3.500e+01 7.740e+02 4.190e+02 1.700e+02 1.470e+03
9.380e+02 5.700e+02 3.000e+02 1.200e+02 1.160e+02 5.120e+02 5.670e+02
4.450e+02 6.950e+02 4.050e+02 1.005e+03 6.680e+02 8.210e+02 4.320e+02
1.300e+03 5.070e+02 6.790e+02 1.332e+03 2.090e+02 6.800e+02 7.160e+02
1.400e+03 4.160e+02 4.290e+02 2.220e+02 5.700e+01 6.600e+02 1.016e+03
3.700e+02 3.510e+02 3.790e+02 1.288e+03 3.600e+02 6.390e+02 4.950e+02
2.880e+02 1.398e+03 4.770e+02 8.310e+02 1.904e+03 4.360e+02 3.520e+02
6.110e+02 1.086e+03 2.970e+02 6.260e+02 5.600e+02 3.900e+02 5.660e+02
1.126e+03 1.036e+03 1.088e+03 6.410e+02 6.170e+02 6.620e+02 3.120e+02
1.065e+03 7.870e+02 4.680e+02 3.600e+01 8.220e+02 3.780e+02 9.460e+02
3.410e+02 1.600e+01 5.500e+02 5.240e+02 5.600e+01 3.210e+02 8.420e+02
6.890e+02 6.250e+02 3.580e+02 4.020e+02 9.400e+01 1.078e+03 3.290e+02
9.290e+02 6.970e+02 1.573e+03 2.700e+02 9.220e+02 5.030e+02 1.334e+03
3.610e+02 6.720e+02 5.060e+02 7.140e+02 4.030e+02 7.510e+02 2.260e+02
6.200e+02 5.460e+02 3.920e+02 4.210e+02 9.050e+02 9.040e+02 4.300e+02
6.140e+02 4.500e+02 2.100e+02 2.920e+02 7.950e+02 1.285e+03 8.190e+02
4.200e+02 8.410e+02 2.810e+02 8.940e+02 1.464e+03 7.000e+02 2.620e+02
1.274e+03 5.180e+02 1.236e+03 4.250e+02 6.920e+02 9.870e+02 9.700e+02
2.800e+01 2.560e+02 1.619e+03 4.000e+01 8.460e+02 1.124e+03 7.200e+02
8.280e+02 1.249e+03 8.100e+02 2.130e+02 5.850e+02 1.290e+02 4.980e+02
1.270e+03 5.730e+02 1.410e+03 1.082e+03 2.360e+02 3.880e+02 3.340e+02
8.740e+02 9.560e+02 7.730e+02 3.990e+02 1.620e+02 7.120e+02 6.090e+02
3.710e+02 5.400e+02 7.200e+01 6.230e+02 4.280e+02 3.500e+02 2.980e+02
1.445e+03 2.180e+02 9.850e+02 6.310e+02 1.280e+03 2.410e+02 6.900e+02
2.660e+02 7.770e+02 8.120e+02 7.860e+02 1.116e+03 7.890e+02 1.056e+03
5.000e+01 1.128e+03 7.750e+02 1.309e+03 1.246e+03 9.860e+02 6.160e+02
1.518e+03 6.640e+02 3.870e+02 4.710e+02 3.850e+02 3.650e+02 1.767e+03
1.330e+02 6.420e+02 2.470e+02 3.310e+02 7.420e+02 1.606e+03 9.160e+02
1.850e+02 5.440e+02 5.530e+02 3.260e+02 7.780e+02 3.860e+02 4.260e+02
3.680e+02 4.590e+02 1.350e+03 1.196e+03 6.300e+02 9.940e+02 1.680e+02
1.261e+03 1.567e+03 2.990e+02 8.970e+02 6.070e+02 8.360e+02 5.150e+02
3.740e+02 1.231e+03 1.110e+02 3.560e+02 4.000e+02 6.980e+02 1.247e+03
2.570e+02 3.800e+02 2.700e+01 1.410e+02 9.910e+02 6.500e+02 5.210e+02
1.436e+03 2.260e+03 7.190e+02 3.770e+02 1.330e+03 3.480e+02 1.219e+03
7.830e+02 9.690e+02 6.730e+02 1.358e+03 1.260e+03 1.440e+02 5.840e+02
5.540e+02 1.002e+03 6.190e+02 1.800e+02 5.590e+02 3.080e+02 8.660e+02
8.950e+02 6.370e+02 6.040e+02 1.302e+03 1.071e+03 2.900e+02 7.280e+02
2.000e+00 1.441e+03 9.430e+02 2.310e+02 4.140e+02 3.490e+02 4.420e+02
3.280e+02 5.940e+02 8.160e+02 1.460e+03 1.324e+03 1.338e+03 6.850e+02
1.422e+03 1.283e+03 8.100e+01 4.540e+02 9.030e+02 6.050e+02 9.900e+02
2.060e+02 1.500e+02 4.570e+02 4.800e+01 8.710e+02 4.100e+01 6.740e+02
6.240e+02 4.800e+02 1.154e+03 7.380e+02 4.930e+02 1.121e+03 2.820e+02
5.000e+02 1.310e+02 1.696e+03 8.060e+02 1.361e+03 9.200e+02 1.721e+03
1.870e+02 1.138e+03 9.880e+02 1.930e+02 5.510e+02 7.670e+02 1.186e+03
8.920e+02 3.110e+02 8.270e+02 5.430e+02 1.003e+03 1.059e+03 2.390e+02
9.450e+02 2.000e+01 1.455e+03 9.650e+02 9.800e+02 8.630e+02 5.330e+02
1.084e+03 1.173e+03 5.230e+02 1.148e+03 1.910e+02 1.234e+03 3.750e+02
8.080e+02 7.240e+02 1.520e+02 1.180e+03 2.520e+02 8.320e+02 5.750e+02
9.190e+02 4.390e+02 3.810e+02 4.380e+02 5.490e+02 6.120e+02 1.163e+03
4.370e+02 3.940e+02 1.416e+03 4.220e+02 7.620e+02 9.750e+02 1.097e+03
2.510e+02 6.860e+02 6.560e+02 5.680e+02 5.390e+02 8.620e+02 1.970e+02
5.160e+02 6.630e+02 6.080e+02 1.636e+03 7.840e+02 2.490e+02 1.040e+03
4.830e+02 1.960e+02 5.720e+02 3.380e+02 3.300e+02 1.560e+02 1.390e+03
5.130e+02 4.600e+02 6.590e+02 3.640e+02 5.640e+02 3.060e+02 5.050e+02
9.320e+02 7.500e+02 6.400e+01 6.330e+02 1.170e+03 8.990e+02 9.020e+02
1.238e+03 5.280e+02 1.024e+03 1.064e+03 2.850e+02 2.188e+03 4.650e+02
3.220e+02 8.600e+02 5.990e+02 3.540e+02 6.300e+01 2.230e+02 3.010e+02
4.430e+02 4.890e+02 2.840e+02 2.940e+02 8.140e+02 1.650e+02 5.520e+02
8.330e+02 4.640e+02 9.360e+02 7.720e+02 1.440e+03 7.480e+02 9.820e+02
3.980e+02 5.620e+02 4.840e+02 4.170e+02 6.990e+02 6.960e+02 8.960e+02
5.560e+02 1.106e+03 6.510e+02 8.670e+02 8.540e+02 1.646e+03 1.074e+03
5.360e+02 1.172e+03 9.150e+02 5.950e+02 1.237e+03 2.730e+02 6.840e+02
3.240e+02 1.165e+03 1.380e+02 1.513e+03 3.170e+02 1.012e+03 1.022e+03
5.090e+02 9.000e+02 1.085e+03 1.104e+03 2.400e+02 3.830e+02 6.440e+02
3.970e+02 7.400e+02 8.370e+02 2.200e+02 5.860e+02 5.350e+02 4.100e+02
7.500e+01 8.240e+02 5.920e+02 1.039e+03 5.100e+02 4.230e+02 6.610e+02
2.480e+02 7.040e+02 4.120e+02 1.032e+03 2.190e+02 7.080e+02 4.150e+02
1.004e+03 3.530e+02 7.020e+02 3.690e+02 6.220e+02 2.120e+02 6.450e+02
8.520e+02 1.150e+03 1.258e+03 2.750e+02 1.760e+02 2.960e+02 5.380e+02
1.157e+03 4.920e+02 1.198e+03 1.387e+03 5.220e+02 6.580e+02 1.216e+03
1.480e+03 2.096e+03 1.159e+03 4.400e+02 1.456e+03 8.830e+02 5.470e+02
7.880e+02 4.850e+02 3.400e+02 1.220e+03 4.270e+02 3.440e+02 7.560e+02
1.540e+03 6.660e+02 8.030e+02 1.000e+03 8.850e+02 1.386e+03 3.190e+02
5.340e+02 1.250e+02 1.314e+03 6.020e+02 1.920e+02 5.930e+02 8.040e+02
1.053e+03 5.320e+02 1.158e+03 1.014e+03 1.940e+02 1.670e+02 7.760e+02
5.644e+03 6.940e+02 1.572e+03 7.460e+02 1.406e+03 9.250e+02 4.820e+02
1.890e+02 7.650e+02 8.000e+01 1.443e+03 2.590e+02 7.350e+02 7.340e+02
1.447e+03 5.480e+02 3.150e+02 1.282e+03 4.080e+02 3.090e+02 2.030e+02
8.650e+02 2.040e+02 7.900e+02 1.320e+03 7.690e+02 1.070e+03 2.640e+02
7.590e+02 1.373e+03 9.760e+02 7.810e+02 2.500e+01 1.110e+03 4.040e+02
5.800e+02 6.780e+02 9.580e+02 1.336e+03 1.079e+03 4.900e+01 8.300e+02
9.230e+02 7.910e+02 2.630e+02 9.350e+02 1.051e+03 5.140e+02 1.100e+02
1.414e+03 1.260e+02 1.129e+03 1.298e+03 3.760e+02 4.660e+02 2.440e+02
1.137e+03 6.870e+02 1.010e+03 1.500e+03 6.700e+02 9.440e+02 1.188e+03
8.560e+02 3.390e+02 4.810e+02 7.170e+02 5.790e+02 2.740e+02 7.800e+02
2.830e+02 4.740e+02 4.520e+02 2.760e+02 9.600e+02 7.660e+02 1.026e+03
7.300e+01 7.360e+02 1.319e+03 2.670e+02 1.092e+03 9.640e+02 9.540e+02
1.346e+03 1.433e+03 8.700e+02 1.980e+02 1.682e+03 2.380e+02 3.430e+02
7.600e+01 6.150e+02 7.800e+01 4.200e+01 4.690e+02 2.070e+02 4.580e+02
4.760e+02 1.341e+03 8.440e+02 8.470e+02 8.500e+02 1.965e+03 7.410e+02
3.630e+02 2.250e+02 1.333e+03 8.880e+02 6.360e+02 7.260e+02 2.540e+02
4.350e+02 3.890e+02 2.790e+02 1.360e+03 1.232e+03 2.288e+03 1.531e+03
1.230e+03 1.015e+03 1.037e+03 1.142e+03 1.262e+03 1.972e+03 8.810e+02
8.760e+02 2.146e+03 1.557e+03 8.000e+02 6.520e+02 4.940e+02 6.830e+02
9.130e+02 1.294e+03 2.158e+03 6.820e+02 1.430e+03 7.710e+02 5.400e+01
5.200e+01 6.800e+01 8.640e+02 1.400e+02 1.733e+03 6.010e+02 9.620e+02
1.252e+03 1.210e+02 9.550e+02 1.000e+02 1.312e+03 1.720e+02 1.550e+02
9.310e+02 8.720e+02 7.450e+02 6.210e+02 4.330e+02 8.260e+02 1.340e+02
1.690e+02 7.490e+02 1.152e+03 5.270e+02 3.420e+02 1.730e+02 7.000e+01
1.094e+03 8.200e+02 1.021e+03 1.359e+03 7.550e+02 9.500e+02 6.060e+02
1.259e+03 7.100e+02 1.111e+03 1.478e+03 3.320e+02 7.930e+02 2.460e+02
1.540e+02 6.500e+01 1.476e+03 5.500e+01 1.758e+03 1.115e+03 1.640e+03
1.140e+02 7.180e+02 4.960e+02 1.337e+03 1.034e+03 9.830e+02 1.206e+03
8.900e+02 1.023e+03 1.190e+02 2.860e+02 1.728e+03 1.375e+03 1.420e+03
2.257e+03 1.149e+03 1.075e+03 3.720e+02 1.204e+03 1.073e+03 1.087e+03
1.660e+03 1.096e+03 7.290e+02 3.620e+02 5.370e+02 4.720e+02 5.300e+01
7.640e+02 1.900e+02 1.027e+03 1.141e+03 6.810e+02 8.130e+02 1.280e+02
1.044e+03 2.600e+02 5.830e+02 3.200e+01 5.310e+02 1.480e+02 7.440e+02
9.600e+01 5.900e+02 2.000e+02 4.060e+02 1.750e+02 2.010e+02 nan
7.580e+02 2.210e+02 6.340e+02 1.035e+03 7.790e+02 1.271e+03 3.550e+02
2.085e+03 7.700e+02 7.220e+02 1.308e+03 6.880e+02 8.800e+01 1.194e+03
1.538e+03 1.593e+03 1.033e+03 3.660e+02 1.474e+03 1.383e+03 8.930e+02
1.029e+03 1.223e+03 1.011e+03 1.571e+03 3.180e+02 5.010e+02 7.850e+02
6.380e+02 6.470e+02 8.380e+02 1.860e+02 9.260e+02 1.101e+03 1.047e+03
7.970e+02 1.558e+03 1.328e+03 3.140e+02 9.300e+02 7.250e+02 1.151e+03
1.304e+03 1.812e+03 1.684e+03 6.690e+02 1.178e+03 1.030e+03 8.480e+02
9.180e+02 5.740e+02 1.181e+03 1.048e+03 3.350e+02 1.225e+03 7.270e+02
9.680e+02 6.000e+01 9.370e+02 9.010e+02 1.732e+03 1.632e+03 9.730e+02
9.100e+02 3.460e+02 7.920e+02 6.540e+02 1.300e+02 8.730e+02 9.080e+02
4.410e+02 8.500e+01 2.420e+02 9.520e+02 1.098e+03 7.820e+02 1.220e+02
3.160e+02 2.580e+02 5.870e+02 4.910e+02 4.530e+02 5.570e+02 1.080e+03
4.970e+02 5.100e+01 5.020e+02 6.710e+02 1.412e+03 7.090e+02 1.320e+02
4.010e+03 4.670e+02 7.700e+01 1.130e+02 5.770e+02 4.340e+02 1.001e+03
1.392e+03 1.239e+03 9.240e+02 9.490e+02 2.150e+02 1.329e+03 1.112e+03
7.960e+02 8.110e+02 1.090e+03 5.960e+02 1.127e+03 2.050e+02 1.191e+03
9.510e+02 3.820e+02 3.730e+02 1.505e+03 1.290e+03 8.800e+02 1.038e+03
1.182e+03 1.562e+03 1.836e+03 2.780e+02 1.810e+02 1.118e+03 7.600e+02
7.990e+02 9.960e+02 9.390e+02 9.140e+02 2.710e+02 4.880e+02 7.010e+02
4.550e+02 8.090e+02 9.530e+02 2.080e+02 1.430e+02 5.760e+02 3.470e+02
7.940e+02 2.300e+02 2.610e+02 3.930e+02 1.576e+03 1.122e+03 8.530e+02
4.750e+02 6.910e+02 4.240e+02 3.050e+02 5.260e+02 1.564e+03 9.090e+02
1.136e+03 1.243e+03 1.490e+02 1.224e+03 3.370e+02]
BsmtFinSF2
float64
[ 0. 32. 668. 486. 93. 491. 506. 712. 362. 41. 169. 869.
150. 670. 28. 1080. 181. 768. 215. 374. 208. 441. 184. 279.
306. 180. 580. 690. 692. 228. 125. 1063. 620. 175. 820. 1474.
264. 479. 147. 232. 380. 544. 294. 258. 121. 391. 531. 344.
539. 713. 210. 311. 1120. 165. 532. 96. 495. 174. 1127. 139.
202. 645. 123. 551. 219. 606. 612. 480. 182. 132. 336. 468.
287. 35. 499. 723. 119. 40. 117. 239. 80. 472. 64. 1057.
127. 630. 128. 377. 764. 345. 1085. 435. 823. 500. 290. 324.
634. 411. 841. 1061. 466. 396. 354. 149. 193. 273. 465. 400.
682. 557. 230. 106. 791. 240. 547. 469. 177. 108. 600. 492.
211. 168. 1031. 438. 375. 144. 81. 906. 608. 276. 661. 68.
173. 972. 105. 420. 546. 334. 352. 872. 110. 627. 163. 1029.
78. 859. 981. 42. 46. 162. 350. 263. 1073. 12. 159. 474.
453. 684. 387. 688. 252. 590. 284. 622. 113. 1526. 360. 774.
364. 596. 884. 92. 216. 136. 201. 512. 247. 483. 750. 60.
102. 95. 63. 262. 393. 286. 450. 72. 243. 694. 875. 507.
419. 250. 116. 624. 76. 270. 288. 186. 449. 48. 613. 852.
555. 799. 811. 842. 382. 456. 308. 52. 196. 488. 319. nan
956. 120. 679. 604. 153. 619. 6. 351. 1037. 829. 38. 206.
167. 543. 259. 404. 138. 955. 691. 66. 154. 442. 448. 227.
398. 722. 761. 529. 522. 873. 891. 755. 321. 915. 417. 432.
831. 278. 1020. 530. 904. 156. 1393. 1039. 497. 402. 748. 281.
912. 373. 982. 826. 850. 1164. 1083. 337. 297.]
BsmtFinType1
object
['GLQ' 'ALQ' 'Unf' 'Rec' 'BLQ' nan 'LwQ']
BsmtFinType2
object
['Unf' 'BLQ' nan 'ALQ' 'Rec' 'LwQ' 'GLQ']
BsmtFullBath
float64
[ 1. 0. 2. 3. nan]
BsmtHalfBath
float64
[ 0. 1. 2. nan]
BsmtQual
object
['Gd' 'TA' 'Ex' nan 'Fa']
BsmtUnfSF
float64
[ 150. 284. 434. ... 129. 45. 1503.]
CentralAir
object
['Y' 'N']
Condition1
object
['Norm' 'Feedr' 'PosN' 'Artery' 'RRAe' 'RRNn' 'RRAn' 'PosA' 'RRNe']
Condition2
object
['Norm' 'Artery' 'RRNn' 'Feedr' 'PosN' 'PosA' 'RRAn' 'RRAe']
Electrical
object
['SBrkr' 'FuseF' 'FuseA' 'FuseP' 'Mix' nan]
EnclosedPorch
int64
[ 0 272 228 205 176 87 172 102 37 144 64 114 202 128
156 44 77 192 140 180 183 39 184 40 552 30 126 96
60 150 120 112 252 52 224 234 244 268 137 24 108 294
177 218 242 91 160 130 169 105 34 248 236 32 80 115
291 116 158 210 36 200 84 148 136 240 54 100 189 293
164 216 239 67 90 56 129 98 143 70 386 154 185 134
196 264 275 230 254 68 194 318 48 94 138 226 174 19
170 220 214 280 190 330 208 145 259 81 42 123 162 286
168 20 301 198 221 212 50 99 186 113 135 334 246 18
41 35 364 45 86 265 222 209 260 203 432 25 238 51
213 288 211 55 57 78 72 368 165 92 16 66 109 139
219 101 117 204 122 231 121 207 249 290 175 26 88 1012
43 584 133 324 161 75 167 28 104 296 256 225 429 132
23]
ExterCond
object
['TA' 'Gd' 'Fa' 'Po' 'Ex']
ExterQual
object
['Gd' 'TA' 'Ex' 'Fa']
Exterior1st
object
['VinylSd' 'MetalSd' 'Wd Sdng' 'HdBoard' 'BrkFace' 'WdShing' 'CemntBd'
'Plywood' 'AsbShng' 'Stucco' 'BrkComm' 'AsphShn' 'Stone' 'ImStucc'
'CBlock' nan]
Exterior2nd
object
['VinylSd' 'MetalSd' 'Wd Shng' 'HdBoard' 'Plywood' 'Wd Sdng' 'CmentBd'
'BrkFace' 'Stucco' 'AsbShng' 'Brk Cmn' 'ImStucc' 'AsphShn' 'Stone'
'Other' 'CBlock' nan]
Fence
object
[nan 'MnPrv' 'GdWo' 'GdPrv' 'MnWw']
FireplaceQu
object
[nan 'TA' 'Gd' 'Fa' 'Ex' 'Po']
Fireplaces
int64
[0 1 2 3 4]
Foundation
object
['PConc' 'CBlock' 'BrkTil' 'Wood' 'Slab' 'Stone']
FullBath
int64
[2 1 3 0 4]
Functional
object
['Typ' 'Min1' 'Maj1' 'Min2' 'Mod' 'Maj2' 'Sev' nan]
GarageArea
float64
[ 548. 460. 608. 642. 836. 480. 636. 484. 468. 205. 384. 736.
352. 840. 576. 516. 294. 853. 280. 534. 572. 270. 890. 772.
319. 240. 250. 271. 447. 556. 691. 672. 498. 246. 0. 440.
308. 504. 300. 670. 826. 386. 388. 528. 894. 565. 641. 288.
645. 852. 558. 220. 667. 360. 427. 490. 379. 297. 283. 509.
405. 758. 461. 400. 462. 420. 432. 506. 684. 472. 366. 476.
410. 740. 648. 273. 546. 325. 792. 450. 180. 430. 594. 390.
540. 264. 530. 435. 453. 750. 487. 624. 471. 318. 766. 660.
470. 720. 577. 380. 434. 866. 495. 564. 312. 625. 680. 678.
726. 532. 216. 303. 789. 511. 616. 521. 451. 1166. 252. 497.
682. 666. 786. 795. 856. 473. 398. 500. 349. 454. 644. 299.
210. 431. 438. 675. 968. 721. 336. 810. 494. 457. 818. 463.
604. 389. 538. 520. 309. 429. 673. 884. 868. 492. 413. 924.
1053. 439. 671. 338. 573. 732. 505. 575. 626. 898. 529. 685.
281. 539. 418. 588. 282. 375. 683. 843. 552. 870. 888. 746.
708. 513. 1025. 656. 872. 292. 441. 189. 880. 676. 301. 474.
706. 617. 445. 200. 592. 566. 514. 296. 244. 610. 834. 639.
501. 846. 560. 596. 600. 373. 947. 350. 396. 864. 304. 784.
696. 569. 628. 550. 493. 578. 198. 422. 228. 526. 525. 908.
499. 508. 694. 874. 164. 402. 515. 286. 603. 900. 583. 889.
858. 502. 392. 403. 527. 765. 367. 426. 615. 871. 570. 406.
590. 612. 650. 1390. 275. 452. 842. 816. 621. 544. 486. 230.
261. 531. 393. 774. 749. 364. 627. 260. 256. 478. 442. 562.
512. 839. 330. 711. 1134. 416. 779. 702. 567. 832. 326. 551.
606. 739. 408. 475. 704. 983. 768. 632. 541. 320. 800. 831.
554. 878. 752. 614. 481. 496. 423. 841. 895. 412. 865. 630.
605. 602. 618. 444. 397. 455. 409. 820. 1020. 598. 857. 595.
433. 776. 1220. 458. 613. 456. 436. 812. 686. 611. 425. 343.
479. 619. 902. 574. 523. 414. 738. 354. 483. 327. 756. 690.
284. 833. 601. 533. 522. 788. 555. 689. 796. 808. 510. 255.
424. 305. 368. 824. 328. 160. 437. 665. 290. 912. 905. 542.
716. 586. 467. 582. 1248. 1043. 254. 712. 719. 862. 928. 782.
466. 714. 1052. 225. 234. 324. 306. 830. 807. 358. 186. 693.
482. 813. 995. 757. 1356. 459. 701. 322. 315. 668. 404. 543.
954. 850. 477. 276. 518. 1014. 753. 1418. 213. 844. 860. 748.
248. 287. 825. 647. 342. 770. 663. 377. 804. 936. 722. 208.
662. 754. 622. 620. 370. 1069. 372. 923. 192. 730. 751. 958.
962. 762. 713. 535. 517. 263. 780. 363. 365. 231. 591. 209.
1017. 580. 399. 741. 253. 581. 345. 896. 932. 640. 927. 700.
886. 949. 649. 394. 658. 815. 623. 972. 984. 692. 845. 559.
465. 524. 561. 549. 907. 162. 357. 207. 1184. 316. 226. 340.
266. 1138. 904. 1231. 195. 313. 215. 307. 295. 351. 885. 920.
698. 557. 489. 1314. 787. 1150. 1003. 944. 428. 687. 938. 783.
851. 545. 469. 464. 267. 1488. 401. 311. 828. 869. 355. 249.
1348. 811. 725. 715. 814. 369. 599. 344. 356. 185. 892. 257.
729. 1110. 724. 585. 488. 1040. 1174. 728. 916. 876. 631. 925.
806. 933. 1092. 859. 744. 1105. 310. 293. 371. 1200. 184. 374.
331. 224. 217. 323. 638. 332. 674. 747. 242. 597. 579. 1154.
nan 100. 571. 1041. 963. 443. 773. 485. 1085. 899. 959. 803.
760. 584. 449. 688. 568. 353. 791. 1008. 378. 258. 848. 317.
646. 265. 609. 272.]
GarageCars
float64
[ 2. 3. 1. 0. 4. 5. nan]
GarageCond
object
['TA' 'Fa' nan 'Gd' 'Po' 'Ex']
GarageFinish
object
['RFn' 'Unf' 'Fin' nan]
GarageQual
object
['TA' 'Fa' 'Gd' nan 'Ex' 'Po']
GarageType
object
['Attchd' 'Detchd' 'BuiltIn' 'CarPort' nan 'Basment' '2Types']
GarageYrBlt
float64
[2003. 1976. 2001. 1998. 2000. 1993. 2004. 1973. 1931. 1939. 1965. 2005.
1962. 2006. 1960. 1991. 1970. 1967. 1958. 1930. 2002. 1968. 2007. 2008.
1957. 1920. 1966. 1959. 1995. 1954. 1953. nan 1983. 1977. 1997. 1985.
1963. 1981. 1964. 1999. 1935. 1990. 1945. 1987. 1989. 1915. 1956. 1948.
1974. 2009. 1950. 1961. 1921. 1900. 1979. 1951. 1969. 1936. 1975. 1971.
1923. 1984. 1926. 1955. 1986. 1988. 1916. 1932. 1972. 1918. 1980. 1924.
1996. 1940. 1949. 1994. 1910. 1978. 1982. 1992. 1925. 1941. 2010. 1927.
1947. 1937. 1942. 1938. 1952. 1928. 1922. 1934. 1906. 1914. 1946. 1908.
1929. 1933. 1917. 1896. 1895. 2207. 1943. 1919.]
GrLivArea
int64
[1710 1262 1786 ... 2315 641 1778]
HalfBath
int64
[1 0 2]
Heating
object
['GasA' 'GasW' 'Grav' 'Wall' 'OthW' 'Floor']
HeatingQC
object
['Ex' 'Gd' 'TA' 'Fa' 'Po']
HouseStyle
object
['2Story' '1Story' '1.5Fin' '1.5Unf' 'SFoyer' 'SLvl' '2.5Unf' '2.5Fin']
Id
int64
[ 1 2 3 ... 2917 2918 2919]
KitchenAbvGr
int64
[1 2 3 0]
KitchenQual
object
['Gd' 'TA' 'Ex' 'Fa' nan]
LandContour
object
['Lvl' 'Bnk' 'Low' 'HLS']
LandSlope
object
['Gtl' 'Mod' 'Sev']
LotArea
int64
[ 8450 9600 11250 ... 1894 20000 10441]
LotConfig
object
['Inside' 'FR2' 'Corner' 'CulDSac' 'FR3']
LotFrontage
float64
[ 65. 80. 68. 60. 84. 85. 75. nan 51. 50. 70. 91. 72. 66.
101. 57. 44. 110. 98. 47. 108. 112. 74. 115. 61. 48. 33. 52.
100. 24. 89. 63. 76. 81. 95. 69. 21. 32. 78. 121. 122. 40.
105. 73. 77. 64. 94. 34. 90. 55. 88. 82. 71. 120. 107. 92.
134. 62. 86. 141. 97. 54. 41. 79. 174. 99. 67. 83. 43. 103.
93. 30. 129. 140. 35. 37. 118. 87. 116. 150. 111. 49. 96. 59.
36. 56. 102. 58. 38. 109. 130. 53. 137. 45. 106. 104. 42. 39.
144. 114. 128. 149. 313. 168. 182. 138. 160. 152. 124. 153. 46. 26.
25. 119. 31. 28. 117. 113. 125. 135. 136. 22. 123. 195. 155. 126.
200. 131. 133.]
LotShape
object
['Reg' 'IR1' 'IR2' 'IR3']
LowQualFinSF
int64
[ 0 360 513 234 528 572 144 392 371 390 420 473 156 515
80 53 232 481 120 514 397 479 205 384 362 1064 431 436
259 312 108 697 512 114 140 450]
MSSubClass
int64
[ 60 20 70 50 190 45 90 120 30 85 80 160 75 180 40 150]
MSZoning
object
['RL' 'RM' 'C (all)' 'FV' 'RH' nan]
MasVnrArea
float64
[1.960e+02 0.000e+00 1.620e+02 3.500e+02 1.860e+02 2.400e+02 2.860e+02
3.060e+02 2.120e+02 1.800e+02 3.800e+02 2.810e+02 6.400e+02 2.000e+02
2.460e+02 1.320e+02 6.500e+02 1.010e+02 4.120e+02 2.720e+02 4.560e+02
1.031e+03 1.780e+02 5.730e+02 3.440e+02 2.870e+02 1.670e+02 1.115e+03
4.000e+01 1.040e+02 5.760e+02 4.430e+02 4.680e+02 6.600e+01 2.200e+01
2.840e+02 7.600e+01 2.030e+02 6.800e+01 1.830e+02 4.800e+01 2.800e+01
3.360e+02 6.000e+02 7.680e+02 4.800e+02 2.200e+02 1.840e+02 1.129e+03
1.160e+02 1.350e+02 2.660e+02 8.500e+01 3.090e+02 1.360e+02 2.880e+02
7.000e+01 3.200e+02 5.000e+01 1.200e+02 4.360e+02 2.520e+02 8.400e+01
6.640e+02 2.260e+02 3.000e+02 6.530e+02 1.120e+02 4.910e+02 2.680e+02
7.480e+02 9.800e+01 2.750e+02 1.380e+02 2.050e+02 2.620e+02 1.280e+02
2.600e+02 1.530e+02 6.400e+01 3.120e+02 1.600e+01 9.220e+02 1.420e+02
2.900e+02 1.270e+02 5.060e+02 2.970e+02 nan 6.040e+02 2.540e+02
3.600e+01 1.020e+02 4.720e+02 4.810e+02 1.080e+02 3.020e+02 1.720e+02
3.990e+02 2.700e+02 4.600e+01 2.100e+02 1.740e+02 3.480e+02 3.150e+02
2.990e+02 3.400e+02 1.660e+02 7.200e+01 3.100e+01 3.400e+01 2.380e+02
1.600e+03 3.650e+02 5.600e+01 1.500e+02 2.780e+02 2.560e+02 2.250e+02
3.700e+02 3.880e+02 1.750e+02 2.960e+02 1.460e+02 1.130e+02 1.760e+02
6.160e+02 3.000e+01 1.060e+02 8.700e+02 3.620e+02 5.300e+02 5.000e+02
5.100e+02 2.470e+02 3.050e+02 2.550e+02 1.250e+02 1.000e+02 4.320e+02
1.260e+02 4.730e+02 7.400e+01 1.450e+02 2.320e+02 3.760e+02 4.200e+01
1.610e+02 1.100e+02 1.800e+01 2.240e+02 2.480e+02 8.000e+01 3.040e+02
2.150e+02 7.720e+02 4.350e+02 3.780e+02 5.620e+02 1.680e+02 8.900e+01
2.850e+02 3.600e+02 9.400e+01 3.330e+02 9.210e+02 7.620e+02 5.940e+02
2.190e+02 1.880e+02 4.790e+02 5.840e+02 1.820e+02 2.500e+02 2.920e+02
2.450e+02 2.070e+02 8.200e+01 9.700e+01 3.350e+02 2.080e+02 4.200e+02
1.700e+02 4.590e+02 2.800e+02 9.900e+01 1.920e+02 2.040e+02 2.330e+02
1.560e+02 4.520e+02 5.130e+02 2.610e+02 1.640e+02 2.590e+02 2.090e+02
2.630e+02 2.160e+02 3.510e+02 6.600e+02 3.810e+02 5.400e+01 5.280e+02
2.580e+02 4.640e+02 5.700e+01 1.470e+02 1.170e+03 2.930e+02 6.300e+02
4.660e+02 1.090e+02 4.100e+01 1.600e+02 2.890e+02 6.510e+02 1.690e+02
9.500e+01 4.420e+02 2.020e+02 3.380e+02 8.940e+02 3.280e+02 6.730e+02
6.030e+02 1.000e+00 3.750e+02 9.000e+01 3.800e+01 1.570e+02 1.100e+01
1.400e+02 1.300e+02 1.480e+02 8.600e+02 4.240e+02 1.047e+03 2.430e+02
8.160e+02 3.870e+02 2.230e+02 1.580e+02 1.370e+02 1.150e+02 1.890e+02
2.740e+02 1.170e+02 6.000e+01 1.220e+02 9.200e+01 4.150e+02 7.600e+02
2.700e+01 7.500e+01 3.610e+02 1.050e+02 3.420e+02 2.980e+02 5.410e+02
2.360e+02 1.440e+02 4.230e+02 4.400e+01 1.510e+02 9.750e+02 4.500e+02
2.300e+02 5.710e+02 2.400e+01 5.300e+01 2.060e+02 1.400e+01 3.240e+02
2.950e+02 3.960e+02 6.700e+01 1.540e+02 4.250e+02 4.500e+01 1.378e+03
3.370e+02 1.490e+02 1.430e+02 5.100e+01 1.710e+02 2.340e+02 6.300e+01
7.660e+02 3.200e+01 8.100e+01 1.630e+02 5.540e+02 2.180e+02 6.320e+02
1.140e+02 5.670e+02 3.590e+02 4.510e+02 6.210e+02 7.880e+02 8.600e+01
7.960e+02 3.910e+02 2.280e+02 8.800e+01 1.650e+02 4.280e+02 4.100e+02
5.640e+02 3.680e+02 3.180e+02 5.790e+02 6.500e+01 7.050e+02 4.080e+02
2.440e+02 1.230e+02 3.660e+02 7.310e+02 4.480e+02 2.940e+02 3.100e+02
2.370e+02 4.260e+02 9.600e+01 4.380e+02 1.940e+02 1.190e+02 2.000e+01
5.040e+02 4.920e+02 6.150e+02 1.095e+03 1.159e+03 2.650e+02 9.100e+01
7.710e+02 4.700e+01 1.770e+02 3.710e+02 4.300e+02 4.400e+02 2.290e+02
7.260e+02 4.180e+02 7.240e+02 3.830e+02 7.300e+02 4.700e+02 3.080e+02
6.340e+02 3.720e+02 1.980e+02 1.210e+02 2.640e+02 1.410e+02 2.830e+02
5.090e+02 2.170e+02 3.000e+00 6.570e+02 1.240e+02 4.440e+02 2.300e+01
2.420e+02 3.640e+02 3.520e+02 4.060e+02 4.020e+02 4.220e+02 3.560e+02
6.800e+02 1.110e+03 2.210e+02 7.140e+02 6.470e+02 1.290e+03 4.950e+02
5.680e+02 1.790e+02 1.050e+03 1.870e+02 5.200e+01 2.760e+02 3.900e+01
1.900e+02 2.510e+02 2.270e+02 1.340e+02 2.220e+02 5.800e+01 6.680e+02
6.740e+02 1.970e+02 7.100e+02 9.450e+02 5.490e+02 2.530e+02 4.000e+02
9.700e+02 5.020e+02 3.940e+02 2.350e+02 5.150e+02 5.260e+02 7.540e+02
3.530e+02 5.250e+02 8.700e+01 2.910e+02 6.900e+01 2.790e+02 3.230e+02
2.140e+02 5.190e+02 1.224e+03 6.520e+02 8.860e+02 9.020e+02 4.340e+02
6.620e+02 7.340e+02 5.500e+02 5.140e+02 3.850e+02 5.180e+02 5.720e+02
3.220e+02 8.770e+02 3.970e+02 7.380e+02 5.010e+02 1.180e+02 6.920e+02
3.320e+02 5.220e+02 3.790e+02 5.320e+02 6.200e+01 1.990e+02 3.550e+02
4.050e+02 3.270e+02 2.570e+02 3.820e+02]
MasVnrType
object
['BrkFace' 'None' 'Stone' 'BrkCmn' nan]
MiscFeature
object
[nan 'Shed' 'Gar2' 'Othr' 'TenC']
MiscVal
int64
[ 0 700 350 500 400 480 450 15500 1200 800 2000 600
3500 1300 54 620 560 1400 8300 1150 2500 12500 1500 300
80 490 650 900 750 6500 1000 4500 3000 17000 1512 455
460 420]
MoSold
int64
[ 2 5 9 12 10 8 11 4 1 7 3 6]
Neighborhood
object
['CollgCr' 'Veenker' 'Crawfor' 'NoRidge' 'Mitchel' 'Somerst' 'NWAmes'
'OldTown' 'BrkSide' 'Sawyer' 'NridgHt' 'NAmes' 'SawyerW' 'IDOTRR'
'MeadowV' 'Edwards' 'Timber' 'Gilbert' 'StoneBr' 'ClearCr' 'NPkVill'
'Blmngtn' 'BrDale' 'SWISU' 'Blueste']
OpenPorchSF
int64
[ 61 0 42 35 84 30 57 204 4 21 33 213 112 102 154 159 110 90
56 32 50 258 54 65 38 47 64 52 138 104 82 43 146 75 72 70
49 11 36 151 29 94 101 199 99 234 162 63 68 46 45 122 184 120
20 24 130 205 108 80 66 48 25 96 111 106 40 114 8 136 132 62
228 60 238 260 27 74 16 198 26 83 34 55 22 98 172 119 208 105
140 168 28 39 148 12 51 150 117 250 10 81 44 144 175 195 128 76
17 59 214 121 53 231 134 192 123 78 187 85 133 176 113 137 125 523
100 285 88 406 155 73 182 502 274 158 142 243 235 312 124 267 265 87
288 23 152 341 116 160 174 247 291 18 170 156 166 129 418 240 77 364
188 207 67 69 131 191 41 118 252 189 282 135 95 224 169 319 58 93
244 185 200 92 180 263 304 229 103 211 287 292 241 547 91 86 262 210
141 15 126 236 278 197 273 190 183 165 226 178 177 254 215 222 193 201
173 153 251 230 299 365 139 216 89 372 217 276 164 368 203 127 256 194
324 171 570 484 742 444 266 97 37 246 31 382 6 115 253 245 107 225]
OverallCond
int64
[5 8 6 7 4 2 3 9 1]
OverallQual
int64
[ 7 6 8 5 9 4 10 3 1 2]
PavedDrive
object
['Y' 'N' 'P']
PoolArea
int64
[ 0 512 648 576 555 480 519 738 144 368 444 228 561 800]
PoolQC
object
[nan 'Ex' 'Fa' 'Gd']
RoofMatl
object
['CompShg' 'WdShngl' 'Metal' 'WdShake' 'Membran' 'Tar&Grv' 'Roll'
'ClyTile']
RoofStyle
object
['Gable' 'Hip' 'Gambrel' 'Mansard' 'Flat' 'Shed']
SaleCondition
object
['Normal' 'Abnorml' 'Partial' 'AdjLand' 'Alloca' 'Family']
SalePrice
float64
[208500. 181500. 223500. 140000. 250000. 143000. 307000. 200000. 129900.
118000. 129500. 345000. 144000. 279500. 157000. 132000. 149000. 90000.
159000. 139000. 325300. 139400. 230000. 154000. 256300. 134800. 306000.
207500. 68500. 40000. 149350. 179900. 165500. 277500. 309000. 145000.
153000. 109000. 82000. 160000. 170000. 130250. 141000. 319900. 239686.
249700. 113000. 127000. 177000. 114500. 110000. 385000. 130000. 180500.
172500. 196500. 438780. 124900. 158000. 101000. 202500. 219500. 317000.
180000. 226000. 80000. 225000. 244000. 185000. 144900. 107400. 91000.
135750. 136500. 193500. 153500. 245000. 126500. 168500. 260000. 174000.
164500. 85000. 123600. 109900. 98600. 163500. 133900. 204750. 214000.
94750. 83000. 128950. 205000. 178000. 118964. 198900. 169500. 100000.
115000. 190000. 136900. 383970. 217000. 259500. 176000. 155000. 320000.
163990. 136000. 153900. 181000. 84500. 128000. 87000. 150000. 150750.
220000. 171000. 231500. 166000. 204000. 125000. 105000. 222500. 122000.
372402. 235000. 79000. 109500. 269500. 254900. 162500. 412500. 103200.
152000. 127500. 325624. 183500. 228000. 128500. 215000. 239000. 163000.
184000. 243000. 211000. 501837. 200100. 120000. 475000. 173000. 135000.
153337. 286000. 315000. 192000. 148500. 311872. 104000. 274900. 171500.
112000. 143900. 277000. 98000. 186000. 252678. 156000. 161750. 134450.
210000. 107000. 311500. 167240. 204900. 97000. 386250. 290000. 106000.
192500. 148000. 403000. 94500. 128200. 216500. 89500. 185500. 194500.
318000. 262500. 110500. 241500. 137000. 76500. 276000. 151000. 73000.
175500. 179500. 120500. 266000. 124500. 201000. 415298. 228500. 244600.
179200. 164700. 88000. 153575. 233230. 135900. 131000. 167000. 142500.
175000. 158500. 267000. 149900. 295000. 305900. 82500. 360000. 165600.
119900. 375000. 188500. 270000. 187500. 342643. 354000. 301000. 126175.
242000. 324000. 145250. 214500. 78000. 119000. 284000. 207000. 228950.
377426. 202900. 87500. 140200. 151500. 157500. 437154. 318061. 95000.
105900. 177500. 134000. 280000. 198500. 147000. 165000. 162000. 172400.
134432. 123000. 61000. 340000. 394432. 179000. 187750. 213500. 76000.
240000. 81000. 191000. 426000. 106500. 129000. 67000. 241000. 245500.
164990. 108000. 258000. 168000. 339750. 60000. 222000. 181134. 149500.
126000. 142000. 206300. 275000. 109008. 195400. 85400. 79900. 122500.
212000. 116000. 90350. 555000. 162900. 199900. 119500. 188000. 256000.
161000. 263435. 62383. 188700. 124000. 178740. 146500. 187000. 440000.
251000. 132500. 208900. 380000. 297000. 89471. 326000. 374000. 164000.
86000. 133000. 172785. 91300. 34900. 430000. 226700. 289000. 208300.
164900. 202665. 96500. 402861. 265000. 234000. 106250. 184750. 315750.
446261. 200624. 107500. 39300. 111250. 272000. 248000. 213250. 179665.
229000. 263000. 112500. 255500. 121500. 268000. 325000. 316600. 135960.
142600. 224500. 118500. 146000. 131500. 181900. 253293. 369900. 79500.
185900. 451950. 138000. 319000. 114504. 194201. 217500. 221000. 359100.
313000. 261500. 75500. 137500. 183200. 105500. 314813. 305000. 165150.
139900. 209500. 93000. 264561. 274000. 370878. 143250. 98300. 205950.
350000. 145500. 97500. 197900. 402000. 423000. 230500. 173500. 103600.
257500. 372500. 159434. 285000. 227875. 148800. 392000. 194700. 755000.
335000. 108480. 141500. 89000. 123500. 138500. 196000. 312500. 361919.
213000. 55000. 302000. 254000. 179540. 52000. 102776. 189000. 130500.
159500. 341000. 103000. 236500. 131400. 93500. 239900. 299800. 236000.
265979. 260400. 275500. 158900. 179400. 215200. 337000. 264132. 216837.
538000. 134900. 102000. 395000. 221500. 175900. 187100. 161500. 233000.
107900. 160200. 146800. 269790. 143500. 485000. 582933. 227680. 135500.
159950. 144500. 55993. 157900. 224900. 271000. 224000. 183000. 139500.
232600. 147400. 237000. 139950. 174900. 133500. 189950. 250580. 248900.
169000. 200500. 66500. 303477. 132250. 328900. 122900. 154500. 118858.
142953. 611657. 125500. 255000. 154300. 173733. 75000. 35311. 238000.
176500. 145900. 169990. 193000. 117500. 184900. 253000. 239799. 244400.
150900. 197500. 172000. 116500. 214900. 178900. 37900. 99500. 182000.
167500. 85500. 178400. 336000. 159895. 255900. 117000. 395192. 195000.
197000. 348000. 173900. 337500. 121600. 206000. 232000. 136905. 119200.
227000. 203000. 213490. 194000. 287000. 293077. 310000. 119750. 84000.
315500. 262280. 278000. 139600. 556581. 84900. 176485. 200141. 185850.
328000. 167900. 151400. 91500. 138800. 155900. 83500. 252000. 92900.
176432. 274725. 134500. 184100. 133700. 118400. 212900. 163900. 259000.
239500. 94000. 424870. 174500. 116900. 201800. 218000. 235128. 108959.
233170. 245350. 625000. 171900. 154900. 392500. 745000. 186700. 104900.
262000. 219210. 116050. 271900. 229456. 80500. 137900. 367294. 101800.
138887. 265900. 248328. 465000. 186500. 169900. 171750. 294000. 165400.
301500. 99900. 128900. 183900. 378500. 381000. 185750. 68400. 150500.
281000. 333168. 206900. 295493. 111000. 156500. 72500. 52500. 155835.
108500. 283463. 410000. 156932. 144152. 216000. 274300. 466500. 58500.
237500. 377500. 246578. 281213. 137450. 193879. 282922. 257000. 223000.
274970. 182900. 192140. 143750. 64500. 394617. 149700. 149300. 121000.
179600. 92000. 287090. 266500. 142125. 147500. nan]
SaleType
object
['WD' 'New' 'COD' 'ConLD' 'ConLI' 'CWD' 'ConLw' 'Con' 'Oth' nan]
ScreenPorch
int64
[ 0 176 198 291 252 99 184 168 130 142 192 410 224 266 170 154 153 144
128 259 160 271 234 374 185 182 90 396 140 276 180 161 145 200 122 95
120 60 126 189 260 147 385 287 156 100 216 210 197 204 225 152 175 312
222 265 322 190 233 63 53 143 273 288 263 80 163 116 480 178 440 155
220 119 165 40 256 240 148 166 108 490 196 121 92 342 255 111 112 231
110 117 195 115 141 208 94 164 64 576 227 221 171 135 174 217 201 109
150 84 228 138 88 280 123 264 270 162 348 113 104]
Street
object
['Pave' 'Grvl']
TotRmsAbvGrd
int64
[ 8 6 7 9 5 11 4 10 12 3 2 14 13 15]
TotalBsmtSF
float64
[ 856. 1262. 920. ... 498. 432. 1381.]
Utilities
object
['AllPub' 'NoSeWa' nan]
WoodDeckSF
int64
[ 0 298 192 40 255 235 90 147 140 160 48 240 171 100
406 222 288 49 203 113 392 145 196 168 112 106 857 115
120 12 576 301 144 300 74 127 232 158 352 182 180 166
224 80 367 53 188 105 24 98 276 200 409 239 400 476
178 574 237 210 441 116 280 104 87 132 238 149 355 60
139 108 351 209 216 248 143 365 370 58 197 263 123 138
333 250 292 95 262 81 289 124 172 110 208 468 256 302
190 340 233 184 201 142 122 155 670 135 495 536 306 64
364 353 66 159 146 296 125 44 215 264 88 89 96 414
519 206 141 260 324 156 220 38 261 126 85 466 270 78
169 320 268 72 349 42 35 326 382 161 179 103 253 148
335 176 390 328 312 185 269 195 57 236 517 304 198 426
28 316 322 307 257 219 416 344 380 68 114 327 165 187
181 92 228 245 503 315 241 303 133 403 36 52 265 207
150 290 486 278 70 418 234 26 342 97 272 121 243 511
154 164 173 384 202 56 321 86 194 421 305 117 550 509
153 394 371 63 252 136 186 170 474 214 199 728 436 55
431 448 361 362 162 229 439 379 356 84 635 325 33 212
314 242 294 30 128 45 177 227 218 309 404 500 668 402
283 183 175 586 295 32 366 736 393 360 157 483 275 23
277 657 51 54 221 226 496 336 450 71 331 375 174 22
287 129 225 319 99 230 231 297 205 462 502 501 266 244
189 131 73 329 279 467 119 308 152 16 411 358 385 20
25 490 76 204 311 102 50 424 339 211 259 134 213 318
428 282 167 407 130 460 286 193 455 284 285 14 521 646
386 405 546 118 291 274 1424 690 330 246 444 354 247 870
432 4 641 94 191 75 631 345 520 27 77 684 453 413
530]
YearBuilt
int64
[2003 1976 2001 1915 2000 1993 2004 1973 1931 1939 1965 2005 1962 2006
1960 1929 1970 1967 1958 1930 2002 1968 2007 1951 1957 1927 1920 1966
1959 1994 1954 1953 1955 1983 1975 1997 1934 1963 1981 1964 1999 1972
1921 1945 1982 1998 1956 1948 1910 1995 1991 2009 1950 1961 1977 1985
1979 1885 1919 1990 1969 1935 1988 1971 1952 1936 1923 1924 1984 1926
1940 1941 1987 1986 2008 1908 1892 1916 1932 1918 1912 1947 1925 1900
1980 1989 1992 1949 1880 1928 1978 1922 1996 2010 1946 1913 1937 1942
1938 1974 1893 1914 1906 1890 1898 1904 1882 1875 1911 1917 1872 1905
1907 1896 1902 1895 1879 1901]
YearRemodAdd
int64
[2003 1976 2002 1970 2000 1995 2005 1973 1950 1965 2006 1962 2007 1960
2001 1967 2004 2008 1997 1959 1990 1955 1983 1980 1966 1963 1987 1964
1972 1996 1998 1989 1953 1956 1968 1981 1992 2009 1982 1961 1993 1999
1985 1979 1977 1969 1958 1991 1971 1952 1975 2010 1984 1986 1994 1988
1954 1957 1951 1978 1974]
YrSold
int64
[2008 2007 2006 2009 2010]
The dictionary below contains the attributes generated from an automated feature engineering algorithm. Analyze the scatter plots between these attributes and the sales price as well as some of the existing attributes and the sales price. See if new attributes can be created and analyze their graphs.
In [11]:
addpear = {'Fireplaces + OverallQual': 0.8006100291440367, 'FullBath + OverallQual': 0.8025579999588428, 'GarageCars + OverallQual': 0.8161012518357501}
multpear = {'GarageCars * GrLivArea': 0.8090051541538753, 'GarageCars * OverallQual': 0.8098591718925291, 'GrLivArea * OverallQual': 0.8320574514356219}
In [6]:
import matplotlib.pyplot as plt
In [16]:
plt.scatter(filledPrice['3SsnPorch'], filledPrice['SalePrice'])
plt.show()
In [36]:
filledAlley = filledPrice[filledPrice['Alley'].notnull()]
plt.bar(filledAlley['Alley'], filledAlley['SalePrice'])
plt.show()
In [40]:
dumAlley = pd.get_dummies(filledAlley['Alley'], drop_first = True)
alleyGraph = pd.concat([dumAlley, filledAlley['SalePrice']], axis=1, sort=False)
In [42]:
plt.scatter(alleyGraph['Pave'], alleyGraph['SalePrice'])
plt.show()
In [52]:
alleyGraph['Pave'].corr(alleyGraph['SalePrice'], method = 'spearman')
Out[52]:
0.6012063893691018
In [44]:
#abvGr = # of ___ above ground
df['BedroomAbvGr'].head()
Out[44]:
0 3 1 3 2 3 3 3 4 4 Name: BedroomAbvGr, dtype: int64
In [48]:
plt.scatter(filledPrice['BedroomAbvGr'], filledPrice['SalePrice'])
plt.show()
In [53]:
plt.scatter(filledPrice['BldgType'], filledPrice['SalePrice'])
plt.show()
In [157]:
#turn total quality SF into column
df['SalePrice'].corr(df['TotalBsmtSF']+df['OpenPorchSF']+df['1stFlrSF']+df['2ndFlrSF']+df['MasVnrArea']+df['WoodDeckSF']+df['GarageArea']+df['3SsnPorch']+df['PoolArea']+df['ScreenPorch']-df['LowQualFinSF'])
Out[157]:
0.8224933270965377
In [98]:
df['SalePrice'].corr(df['TotalBsmtSF']-df['BsmtUnfSF'])
Out[98]:
0.36632769193495623
In [109]:
df['SalePrice'].corr(df['OpenPorchSF']+df['EnclosedPorch'])
Out[109]:
0.15221453739347915
In [110]:
#indicator variable, for enclsed porch
plt.scatter(filledPrice['EnclosedPorch'], filledPrice['SalePrice'])
plt.show()
In [112]:
#indicator varialbe for has open porch
plt.scatter(filledPrice['OpenPorchSF'], filledPrice['SalePrice'])
plt.show()
#use boolean columns if many 0s
In [124]:
df['SalePrice'].corr(df['OpenPorchSF']+df['EnclosedPorch']+df['3SsnPorch']+df['ScreenPorch']+df['PoolArea'])
Out[124]:
0.2093168585738864
In [143]:
plt.scatter(filledPrice['GrLivArea'], filledPrice['SalePrice'])
plt.show()
In [129]:
df['BsmtFullBath'].head()
Out[129]:
0 1.0 1 0.0 2 1.0 3 1.0 4 1.0 Name: BsmtFullBath, dtype: float64
In [139]:
#total number of bathrooms
df['SalePrice'].corr(df['BsmtFullBath']+df['BsmtHalfBath']+df['FullBath']+df['HalfBath'], method = 'spearman')
Out[139]:
0.6911604449033122
In [137]:
#2018-GarageYrBlt
Out[137]:
0.31870159743361987
In [151]:
df['MoSold'].head()
Out[151]:
0 2 1 5 2 9 3 2 4 12 Name: MoSold, dtype: int64
In [173]:
df['SalePrice'].corr(df['FullBath']+df['Fireplaces']+df['GarageCars']+df['OverallQual'])
Out[173]:
0.8351937351464955
In [174]:
df.OverallQual.head()
Out[174]:
0 7 1 6 2 7 3 7 4 8 Name: OverallQual, dtype: int64