

In [4]:

    
import pandas as pd

X_train = "~/Documents/data_science/house_prices/data/X_train.csv"



In [5]:

    
X_train = pd.read_csv(X_train)



In [8]:

    
X_train[X_train.columns.values[1:]].head()









    Out[8]:






  
    
      
      MSSubClass
      LotArea
      OverallQual
      OverallCond
      YearBuilt
      YearRemodAdd
      MasVnrArea
      BsmtFinSF1
      BsmtFinSF2
      BsmtUnfSF
      ...
      SaleType_4
      SaleType_5
      SaleType_6
      SaleType_7
      SaleType_8
      SaleCondition_0
      SaleCondition_1
      SaleCondition_2
      SaleCondition_3
      SaleCondition_4
    
  
  
    
      0
      60
      8450
      7
      5
      2003
      2003
      0.0
      0.023500
      0.0
      0.015672
      ...
      0
      0
      0
      0
      0
      0
      0
      0
      0
      1
    
    
      1
      20
      9600
      6
      8
      1976
      1976
      0.0
      0.024668
      0.0
      0.017668
      ...
      0
      0
      0
      0
      0
      0
      0
      0
      0
      1
    
    
      2
      60
      11250
      7
      5
      2001
      2002
      0.0
      0.022162
      0.0
      0.018995
      ...
      0
      0
      0
      0
      0
      0
      0
      0
      0
      1
    
    
      3
      70
      9550
      7
      5
      1915
      1970
      0.0
      0.019257
      0.0
      0.019678
      ...
      0
      0
      0
      0
      0
      1
      0
      0
      0
      0
    
    
      4
      60
      14260
      8
      5
      2000
      2000
      0.0
      0.023232
      0.0
      0.019374
      ...
      0
      0
      0
      0
      0
      0
      0
      0
      0
      1
    
  

5 rows × 205 columns

	MSSubClass	LotArea	OverallQual	OverallCond	YearBuilt	YearRemodAdd	BsmtFinSF1	BsmtUnfSF	...	SaleCondition_0	SaleCondition_4
0	60	8450	7	5	2003	2003	0.023500	0.015672	...	0	1
1	20	9600	6	8	1976	1976	0.024668	0.017668	...	0	1
2	60	11250	7	5	2001	2002	0.022162	0.018995	...	0	1
3	70	9550	7	5	1915	1970	0.019257	0.019678	...	1	0
4	60	14260	8	5	2000	2000	0.023232	0.019374	...	0	1