image_output_1.png
import matplotlib.pyplot as plt

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);

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

plt.figure(figsize=(12, 6))
sns.countplot(x='Neighborhood', data=train)
xt = plt.xticks(rotation=45)