text_html_output_1.png
import matplotlib

import matplotlib.pyplot as plt

import numpy as np

import pandas as pd

import seaborn as sns

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

all_data = pd.concat((train.loc[:, 'MSSubClass':'SaleCondition'], test.loc[:, 'MSSubClass':'SaleCondition']))

var = 'GrLivArea'
data = pd.concat([train['SalePrice'], train[var]], axis=1)

#box plot overallqual/saleprice
var = 'OverallQual'
data = pd.concat([train['SalePrice'], train[var]], axis=1)
f, ax = plt.subplots(figsize=(8, 6))
fig = sns.boxplot(x=var, y="SalePrice", data=data)
fig.axis(ymin=0, ymax=800000);

matplotlib.rcParams['figure.figsize'] = (12.0, 6.0)
prices = pd.DataFrame({'price': train['SalePrice'], 'log(price + 1)': np.log1p(train['SalePrice'])})

corr = train.select_dtypes(include=['float64', 'int64']).iloc[:, 1:].corr()

total = train.isnull().sum().sort_values(ascending=False)
percent = (train.isnull().sum() / train.isnull().count()).sort_values(ascending=False)
missing_data = pd.concat([total, percent], axis=1, keys=['Total', 'Percent'])
missing_data.head(20)