This article mainly introduces a Beijing second-hand house data analysis project, the purpose is to familiarize with some common methods of python data analysis and visualization.

data collection

Obtain Beijing second-hand housing data set from second-hand housing transaction data website by writing python script (reptile)

Data interpretation

Direction：方向
District：区域
Elevator：电梯
Floor：楼层
Garden；花园
Id：编号
Layout：布局
Price：价格
Region：地区
Renovation：翻修，革新
Size：大小
Year：年限
## python源代码

# 1.数据初探
# 1.1首先导入要使用的科学计算包numpy，pandas，可视化matplotlib，seaborn,以及机器学习包sklearn。
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']})
# % matplotlib inline

# 检查Python版本
from sys import version_info

if version_info.major != 3:
    raise Exception('请使用Python 3 来完成此项目')

# 1.2然后导入数据，并进行初步的观察，这些观察包括了解数据特征的缺失值，异常值，以及大概的描述性统计。
# 导入链家二手房数据
lianjia_df = pd.read_csv('lianjia.csv')
display(lianjia_df.head(n=2))

# 检查缺失值情况
lianjia_df.info()

# 1.3初步观察到一共有11个特征变量，Price 在这里是我们的目标变量，然后我们继续深入观察一下。
# 检查缺失值情况
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))

# 2 数据可视化分析
# 2.1 Region特征分析

# 对于区域特征，我们可以分析不同区域房价和数量的对比。
# 对二手房区域分组对比二手房数量和每平米房价
# 使用了pandas的网络透视功能 groupby 分组排序。
# 区域特征可视化直接采用 seaborn 完成，颜色使用调色板 palette 参数，颜色渐变，越浅说明越少，反之越多
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=(20, 15))
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=12)
ax1.set_xlabel('区域', fontsize=12)
ax1.set_ylabel('每平米单价', fontsize=12)

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=12)
ax2.set_xlabel('区域', fontsize=12)
ax2.set_ylabel('数量', fontsize=12)

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=12)
ax3.set_xlabel('区域', fontsize=12)
ax3.set_ylabel('房屋总价', fontsize=12)

# plt.show()
plt.savefig("Region.png")

# 2.2 Size特征分析
f, [ax1, ax2] = plt.subplots(1, 2, figsize=(15, 10))

# 二手房面积的分布情况
sns.distplot(df['Size'], bins=20, ax=ax1, color='r')
sns.kdeplot(df['Size'], shade=True, ax=ax1)
ax1.set_title('二手房面积的分布情况', fontsize=12)
ax1.set_xlabel('面积/平米', fontsize=12)
ax1.set_ylabel('所占比例', fontsize=12)

# 建房时间和出售价格的关系
sns.regplot(x='Size', y='Price', data=df, ax=ax2)
ax2.set_title('建房时间和出售价格的关系', fontsize=12)
ax2.set_xlabel('面积/平米', fontsize=12)
ax2.set_ylabel('价格', fontsize=12)
# plt.show()
plt.savefig('Size.png')

print("房屋面积小于10平米：")
print(df.loc[df['Size'] < 10])

print("房屋面积大于1000平米：")
print(df.loc[df['Size'] > 1000])

# 2.3 Layout特征分析
f, ax1 = plt.subplots(figsize=(20, 20))
sns.countplot(y='Layout', data=df, ax=ax1)
ax1.set_title('房屋户型与数量关系', fontsize=12)
ax1.set_xlabel('数量', fontsize=12)
ax1.set_ylabel('户型', fontsize=12)
# plt.show()
plt.savefig('Layout.png')

# 2.4Renovation特征分析
print(df['Renovation'].value_counts())
# 去掉数据中装修特征“南北”
df['Renovation'] = df.loc[(df['Renovation'] != '南北'), 'Renovation']
# 画幅设置
f, [ax1, ax2, ax3] = plt.subplots(1, 3, figsize=(20, 10))
sns.countplot(df['Renovation'], ax=ax1)
ax1.set_title('装修特征与数量关系', fontsize=12)
ax1.set_xlabel('装修特征', fontsize=12)
ax1.set_ylabel('数量', fontsize=12)

sns.barplot(x='Renovation', y='Price', data=df, ax=ax2)
ax2.set_title('装修特征与价格关系', fontsize=12)
ax2.set_xlabel('装修特征', fontsize=12)
ax2.set_ylabel('价格', fontsize=12)

sns.boxplot(x='Renovation', y='Price', data=df, ax=ax3)
ax3.set_title('装修特征与数量关系箱型图', fontsize=12)
ax3.set_xlabel('装修特征', fontsize=12)
ax3.set_ylabel('数量', fontsize=12)
# plt.show()
plt.savefig('Renovation.png')

# 2.5Elevator特征分析
# 初探数据时，Elevator有大量的缺失值
misn = len(df.loc[(df['Elevator'].isnull()), 'Elevator'])
print("电梯的缺失值数量：", 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'

f, [ax1, ax2] = plt.subplots(1, 2, figsize=(20, 10))
sns.countplot(df['Elevator'], ax=ax1)
ax1.set_title('有无电梯数量对比', fontsize=15)
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=15)
ax2.set_xlabel('是否有电梯')
ax2.set_ylabel('总价')
# plt.show()
plt.savefig("Elevator.png")

#2.6 Year特征分析
grid = sns.FacetGrid(df, row='Elevator', col='Renovation', palette='seismic',size=4)
grid.map(plt.scatter, 'Year', 'Price')
grid.add_legend()
grid.savefig("Year.png")

Analysis results

The average price of second-hand housing: the most expensive average price in Xicheng District is about 110,000 per square meter, because Xicheng is within the Second Ring Road and is the gathering place for popular school district housing. Secondly, Dongcheng is about 100,000 / ping, then Haidian is about 85,000 / ping, and others are below 80,000 / ping.

Number of second-hand housing: From a statistical point of view, there are currently hot areas in the second-hand housing market. Haidian District and Chaoyang District have the largest number of second-hand houses, almost all of which are close to 3,000 units. After all, large districts have large demand. Then there is the Fengtai District, which is undergoing renovation and construction in recent years and has the tendency to catch up.

The total price of second-hand houses: As seen from the box diagram, the median of the total house prices in all major areas is below 10 million, and the discrete value of the total house price is relatively high. Is too distributed.