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']))

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'])

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

xt = plt.xticks(rotation=45)

g = sns.FacetGrid(train, col='YrSold', col_wrap=3)
g.map(sns.boxplot, 'MoSold', 'SalePrice', palette='Set2', order=range(1, 13)).set(ylim=(0, 500000))
plt.tight_layout()

fig, ax = plt.subplots(2, 1, figsize=(10, 6))
sns.boxplot(x='SaleType', y='SalePrice', data=train, ax=ax[0])
sns.boxplot(x='SaleCondition', y='SalePrice', data=train, ax=ax[1])
plt.tight_layout()