application_vnd.jupyter.stderr_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'])})

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

train.sort_values(by='GrLivArea', ascending=False)[:2]
train = train.drop(train[train['Id'] == 1299].index)
train = train.drop(train[train['Id'] == 524].index)

var = 'TotalBsmtSF'
data = pd.concat([df_train['SalePrice'], df_train[var]], axis=1)
data.plot.scatter(x=var, y='SalePrice', ylim=(0, 800000))
var = 'TotalBsmtSF'
data = pd.concat([train['SalePrice'], train[var]], axis=1)
data.plot.scatter(x=var, y='SalePrice', ylim=(0, 800000))