数据来源百度网盘，提取码：lnqc
二手房数据分析——文件名：lianjia.csv
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib as mpl
import matplotlib.pyplot as plt
from IPython.display import display
plt.style.use("fivethirtyeight")
sns.set_style({'font.sans-serif':['simhei','Arial']})
# 导入链家二手房数据
lianjia_df = pd.read_csv('lianjia.csv')
display(lianjia_df.head(n=3))
lianjia_df.info()
lianjia_df.describe()
#添加新特征房屋均价
df = lianjia_df.copy()
df['PerPrice'] = lianjia_df['Price']/lianjia_df['Size']
#重新摆放列位置
columns = ['Region', 'District', 'Garden', 'Layout', 'Floor', 'Year', 'Size', 'Elevator', 'Direction', 'Renovation', 'PerPrice', 'Price']
df = pd.DataFrame(df, columns = columns)
#重新审视数据集
display(df.head(n=2))
# 对二手房区域分组对比二手房数量和每平米房价
df_house_count = df.groupby('Region')['Price'].count().sort_values(ascending=False).to_frame().reset_index()
df_house_mean = df.groupby('Region')['PerPrice'].mean().sort_values(ascending=False).to_frame().reset_index()
f, [ax1,ax2,ax3] = plt.subplots(3,1,figsize=(30,50))
sns.barplot(x='Region', y='PerPrice', palette="Blues_d", data=df_house_mean, ax=ax1)
ax1.set_title('北京各大区二手房每平米单价对比',fontsize=15)
ax1.set_xlabel('区域')
ax1.set_ylabel('每平米单价')
sns.barplot(x='Region', y='Price', palette="Greens_d", data=df_house_count, ax=ax2)
ax2.set_title('北京各大区二手房数量对比',fontsize=15)
ax2.set_xlabel('区域')
ax2.set_ylabel('数量')
sns.boxplot(x='Region', y='Price', data=df, ax=ax3)
ax3.set_title('北京各大区二手房房屋总价',fontsize=15)
ax3.set_xlabel('区域')
ax3.set_ylabel('房屋总价')
f1, [ax1,ax2] = plt.subplots(1, 2, figsize=(15, 10))
# 建房时间的分布情况
sns.distplot(df['Size'], bins=20, ax=ax1, color='r')
sns.kdeplot(df['Size'], shade=True, ax=ax1)
# 建房时间和出售价格的关系
sns.regplot(x='Size', y='Price', data=df, ax=ax2)
plt.show()
df.loc[df['Size']<10]
df.loc[df['Size']>1000]
df = df[(df['Layout']!='叠拼别墅')&(df['Size']<1000)]
# 特征分析
f2, ax1= plt.subplots(figsize=(20,40))
sns.countplot(y='Layout', data=df, ax=ax1)
ax1.set_title('房屋户型',fontsize=15)
ax1.set_xlabel('数量')
ax1.set_ylabel('户型')
plt.show()
df['Renovation'].value_counts()
# 去掉错误数据“南北”，因为爬虫过程中一些信息位置为空，导致“Direction”的特征出现在这里，需要清除或替换
df['Renovation'] = df.loc[(df['Renovation'] != '南北'), 'Renovation']
# 画幅设置
f3, [ax1,ax2,ax3] = plt.subplots(1, 3, figsize=(10, 5))
sns.countplot(df['Renovation'], ax=ax1)
sns.barplot(x='Renovation', y='Price', data=df, ax=ax2)
sns.boxplot(x='Renovation', y='Price', data=df, ax=ax3)
plt.show()
misn = len(df.loc[(df['Elevator'].isnull()), 'Elevator'])
print('Elevator缺失值数量为：'+ str(misn))
# 由于存在个别类型错误，如简装和精装，特征值错位，故需要移除
df['Elevator'] = df.loc[(df['Elevator'] == '有电梯')|(df['Elevator'] == '无电梯'), 'Elevator']
# 填补Elevator缺失值
df.loc[(df['Floor']>6)&(df['Elevator'].isnull()), 'Elevator'] = '有电梯'
df.loc[(df['Floor']<=6)&(df['Elevator'].isnull()), 'Elevator'] = '无电梯'
f5, [ax1,ax2] = plt.subplots(1, 2, figsize=(5, 7))
sns.countplot(df['Elevator'], ax=ax1)
ax1.set_title('有无电梯数量对比',fontsize=3)
ax1.set_xlabel('是否有电梯')
ax1.set_ylabel('数量')
sns.barplot(x='Elevator', y='Price', data=df, ax=ax2)
ax2.set_title('有无电梯房价对比',fontsize=3)
ax2.set_xlabel('是否有电梯')
ax2.set_ylabel('总价')
plt.show()
grid = sns.FacetGrid(df, row='Elevator', col='Renovation', palette='seismic',size=4)
grid.map(plt.scatter, 'Year', 'Price')
grid.add_legend()
f6, ax1= plt.subplots(figsize=(20,5))
sns.countplot(x='Floor', data=df, ax=ax1)
ax1.set_title('房屋户型',fontsize=5)
ax1.set_xlabel('数量')
ax1.set_ylabel('户型')
plt.show()
