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from sklearn.preprocessing import LabelEncoder

import pandas as pd

import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)

Housetrain = pd.read_csv('../input/train.csv')

Housetrain.isnull().sum(axis=0)

Housetrain1 = Housetrain.dropna(axis=1, how='any')

y = Housetrain2.SalePrice
X = Housetrain2.drop('SalePrice', axis=1)

le = LabelEncoder()

Housetrain2 = Housetrain1.apply(le.fit_transform)

Housetrain2.describe().transpose()