application_vnd.jupyter.stderr_output_1.png
from subprocess import check_output

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

import seaborn as sns

import warnings

import numpy as np
import pandas as pd
pd.set_option('display.float_format', lambda x: '{:.3f}'.format(x))
import matplotlib.pyplot as plt
import seaborn as sns
color = sns.color_palette()
sns.set_style('darkgrid')
import warnings
def ignore_warn(*args, **kwargs):
    pass
warnings.warn = ignore_warn
from scipy import stats
from scipy.stats import norm, skew
from subprocess import check_output

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

print('The TRAIN data size before dropping ID feature is: ', train.shape)
print('The TEST data size before dropping ID feature is: ', test.shape)
train_ID = train['Id']
test_ID = test['Id']
train.drop('Id', axis=1, inplace=True)
test.drop('Id', axis=1, inplace=True)
print('The TRAIN data size before dropping ID feature is: ', train.shape)
print('The TEST data size before dropping ID feature is: ', test.shape)