pandas分析房源信息

最新推荐文章于 2024-09-22 20:54:19 发布

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最新推荐文章于 2024-09-22 20:54:19 发布 · 375 阅读

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#python #panda #房源

本文通过Python的pandas库，对86行29列的房源信息数据进行了全面的分析，涵盖了数据预处理、统计分析和可视化等多个环节，揭示了房源市场的潜在规律。

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import pandas as pd
import os

root_path = r'D:\00data\desktop\上课\《数据分析工程资料》课件\数据分析工程实践'.encode('utf-8')
os.chdir(root_path)
os.getcwd()    #获取当前工作目录

'D:\\00data\\desktop\\上课\\《数据分析工程资料》课件\\数据分析工程实践'

data = pd.read_csv('lianjia.csv')

D:\00installer\Anaconda3\lib\site-packages\IPython\core\interactiveshell.py:2785: DtypeWarning: Columns (1,11,12,13,14,27) have mixed types. Specify dtype option on import or set low_memory=False.
  interactivity=interactivity, compiler=compiler, result=result)

data.shape

(318851, 29)

pd.set_option('display.max_columns', None)
pd.set_option('max_colwidth',100)
pd.set_option('max_info_columns',100)
data.head(2)

	URL	ID	Lng	Lat	CommunityID	TradeTime	DOM	Followers	Total price	Price	Square	Bed Room	Living room	Kitchen	Bathroom	楼	Building Type	Construction time	renovation condition	building structure	Ladder ratio(梯户比)	Elevator	Property rights for five years（房屋满五年）	Subway	District	Community average	Unnamed: 26	Unnamed: 27	Unnamed: 28
0	https://bj.lianjia.com/chengjiao/101084782030.html	1.01085e+11	116.475489	40.019520	1111027376244	2016/8/9	1464.0	106	415.0	31680	131.00	2	1	1	1	高 26	塔楼	2005	简装	钢混	0.217	1.0	0.0	1.0	朝阳	56021.0	NaN	NaN	NaN
1	https://bj.lianjia.com/chengjiao/101086012217.html	1.01086e+11	116.453917	39.881534	1111027381879	2016/7/28	903.0	126	575.0	43436	132.38	2	2	1	2	高 22	塔楼	2004	精装	钢混	0.667	1.0	1.0	0.0	朝阳	71539.0	NaN	NaN	NaN

data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 318851 entries, 0 to 318850
Data columns (total 29 columns):
URL                                      318851 non-null object
ID                                       318851 non-null object
Lng                                      318851 non-null float64
Lat                                      318851 non-null float64
CommunityID                              318851 non-null int64
TradeTime                                318851 non-null object
DOM                                      160874 non-null float64
Followers                                318851 non-null int64
Total price                              318851 non-null float64
Price                                    318851 non-null int64
Square                                   318851 non-null float64
Bed Room                                 318851 non-null object
Living room                              318851 non-null object
Kitchen                                  318851 non-null object
Bathroom                                 318851 non-null object
楼                                        318851 non-null object
Building Type                            316830 non-null object
Construction time                        318851 non-null object
renovation condition                     318851 non-null object
building structure                       318851 non-null object
Ladder ratio(梯户比)                        318851 non-null float64
Elevator                                 318819 non-null float64
Property rights for five years（房屋满五年）    318819 non-null float64
Subway                                   318819 non-null float64
District                                 318851 non-null object
Community average                        318388 non-null float64
Unnamed: 26                              0 non-null float64
Unnamed: 27                              184584 non-null object
Unnamed: 28                              184389 non-null float64
dtypes: float64(12), int64(3), object(14)
memory usage: 70.5+ MB

district_sum = data['District'].value_counts().sort_values(ascending=False)
district_sum

朝阳       107244
昌平        38634
海淀        38200
西城        31293
丰台        29338
东城        17086
大兴        15313
通州        13974
石景山       11371
顺义         9202
房山         2955
亦庄开发区      2537
门头沟        1704
Name: District, dtype: int64

data[['Lng','Lat']].describe()

	Lng	Lat
count	318851.000000	318851.000000
mean	116.418459	39.949591
std	0.112054	0.091983
min	116.072514	39.627030
25%	116.344985	39.893200
50%	116.416780	39.934527
75%	116.477581	40.003018
max	116.732378	40.252758

#map用于series上;applymap用于dataframe上，是元素级别的操作；
data.groupby('District')['Price'].mean().map(lambda x :

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