Python入门学习，用Python爬取新楼盘近况数据，分析数据_爬取住房政策评论数据

前言

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以下文章来源于数据STUDIO ，作者 云朵君

十三届全国人大四次会议5日上午9时在人民大会堂开幕，其中住房政策：“房住不炒”，解决好大城市住房突出问题。

本文通过爬取全国各城市在售新房，进行可视化分析。

数据获取

通过爬取全国城市在售预售新盘，下面以获取单个城市为例，介绍爬取数据部门主要代码。

定义函数

定义好获取每个项目信息的函数。

def get_house_status(soup):
  """
  获取房屋状态信息
  """
    house_status = []
    status = soup.find_all(attrs={'class': 'fangyuan'})
    for state in status:
        _status = state.span.text
        house_status.append(_status)
    return house_status
 
def get_house_price(soup):
  """
  获取房屋价格信息
  """
    house_price = []
    regex = re.compile('\s(\S+)\s')
    prices = soup.find_all(attrs={'class': 'nhouse_price'})
    for price in prices:
        _prices = regex.findall(price.text)
        _price = ''
        if _prices[0] == '价格待定':
            pass
        else:
            p = _prices[0].split('元')[0]
            if '万' in p:
                _price = p + '元/套'
            else:
                _price = p + '元/m2'
        house_price.append(_price)
    return house_price
 
def get_house_address(soup, c_city):
  """
  获取房屋地址信息
  """
    house_address = []
    region = []
    regex = re.compile('\s(\S+)\s')
    addresses = soup.find_all(attrs={'class': 'address'})
    for address in addresses:
        _address = regex.findall(address.text)
        if len(_address) > 1:
            region.append(_address[0].split('[')[1].split(']')[0])
        else:
            region.append(c_city)
        house_address.append(address.a['title'])
    return region, house_address
 
def get_house_type(soup):
  """
  获取房屋类型信息
  """
    house_type = []
    regex = re.compile('\s(\S+)\s')
    house_types = soup.find_all(attrs={'class': 'house_type clearfix'})
    for _house_type in house_types:
        type_list = regex.findall(_house_type.text)
        type_str = ''
        for i in type_list:
            type_str += i
        house_type.append(type_str)
    return house_type
 
def get_house_name(soup):
  """
  获取项目名称信息
  """
    house_name = []
    regex = re.compile('\s(\S+)\s')
    nlcd_names = soup.find_all(attrs={'class': 'nlcd_name'})
    for nlcd_name in nlcd_names:
        name = ''
        names = regex.findall(nlcd_name.text)
 
        if len(names) > 1:
            for n in names:
                name += n
            house_name.append(name)
        else:
            house_name.extend(names)
    return house_name
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获取数据的主函数

def get_data(c_city, city, start_page, cache):
  """
  获取数据
  """
    requests_cache.install_cache()
    requests_cache.clear()
    session = requests_cache.CachedSession()  # 创建缓存会话
    session.hooks = {'response': make_throttle_hook(np.random.randint(8, 12))}  # 配置钩子函数
    print(f'现在爬取{c_city}'.center(50, '*'))
    last_page = get_last_page(city)
    print(f'{c_city}共有{last_page}页')
    time.sleep(np.random.randint(15, 20))
    df_city = pd.DataFrame()
    user_agent = UserAgent().random
    for page in range(start_page, last_page):
        try:
            cache['start_page'] = page
            print(cache)
            cache_json = json.dumps(cache, ensure_ascii=False)
            with open('cache.txt', 'w', encoding='utf-8') as fout:
                fout.write(cache_json)
            print(f'现在爬取{c_city}的第{page + 1}页.')
            if page == 0:
                df_city = pd.DataFrame()
            else:
                df_city = pd.read_csv(f'df_{c_city}.csv', encoding='utf-8')
            url = html_url(city, page + 1)
            if page % 2 == 0:
                user_agent = UserAgent().random  
                # 创建随机请求头
            header = {"User-Agent": user_agent}
            res = session.post(url, headers=header)
            if res.status_code == 200:
                res.encoding = 'gb18030'
                soup = BeautifulSoup(res.text, features='lxml')  
                # 对html进行解析，完成初始化
                region, house_address = get_house_address(soup, c_city)
                house_name = get_house_name(soup)
                house_type = get_house_type(soup)
                house_price = get_house_price(soup)
                house_status = get_house_status(soup)
                df_page = to_df(c_city, 
                                region, 
                                house_name, 
                                house_address, 
                                house_type, 
                                house_price, 
                                house_status)
                df_city = pd.concat([df_city, df_page])
                df_city.head(2)
                time.sleep(np.random.randint(5, 10))
                df_city.to_csv(f'df_{c_city}.csv', 
                               encoding='utf-8', 
                               index=False)
        except:
           # 若报错则保存数据、以便继续
            df_city.to_csv(f'df_{c_city}.csv', encoding='utf-8', index=False)
            cache_json = json.dumps(cache, ensure_ascii=False)
            with open('cache.txt', 'w', encoding='utf-8') as fout:
                fout.write(cache_json)
    return df_city
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爬取过程中，将每个城市单独保存为一个csv文件：

合并数据

import os
import pandas as pd
df_total = pd.DataFrame()
for root, dirs, files in os.path.walk('./全国房价数据集'):
   for file in files:
       split_file = os.path.splitext(file)
        file_ext = split_file[1]
        if file_ext == '.csv':
           path = root + os.sep + file
           df_city = pd.read_csv(path, encoding='utf-8')
           df_total = pd.concat([df_total, df_city])
    df_total.to_csv(root+os.sep+'全国新房202102.csv', encoding='utf-8', index=False)
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数据清洗

导入需要用的模块

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import missingno as msno
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读取数据

raw_data = pd.read_csv('全国新房202102.csv', encoding='utf-8')
raw_data.sample(5)
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查看下数据基本情况

>>> raw_data.shape
(54733, 7)
 
>>> len(raw_data.city.drop_duplicates())
581
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爬取了全国581个城市，共计54733个在售、预售房产项目。

由于获取到的数据存在缺失值、异常值以及不能直接使用的数据，因此在分析前需要先处理缺失值、异常值等，以便后续分析。

缺失值分析

msno.matrix(raw_data)
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整体来看，处理house_price存在缺失值，这是因为这部分楼盘是预售状态，暂未公布售价。

house_type

再仔细分析，house_price有两种形式

除了预售缺失值外，有单价和总价两种，为方便统计，需将总价除以面积，将价格统一为单均价。因此需要对户型house_type进行处理，如下：

def deal_house_type(data):
    res = []
    if data is np.nan:
        return [np.nan, np.nan, np.nan]
    else:
        if '－'in data:
            types = data.split('－')[0]
            areas = data.split('－')[1]
            area = areas.split('~')
            if len(area) == 1:        
                min_area = areas.split('~')[0][0:-2]
                max_area = areas.split('~')[0][0:-2]
            else:
                min_area = areas.split('~')[0]
                max_area = areas.split('~')[1][0:-2]
 
            res = [types, int(min_area), int(max_area)]
            return res
        else:
            return [np.nan, np.nan, np.nan]
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series_type = raw_data.house_type.map(lambda x: deal_house_type(x))
df_type = pd.DataFrame(series_type.to_dict(), index=['house_type', 'min_area', 'max_area']).T
data_type = pd.concat([data_copy.drop(labels='house_type',axis=1), df_type], axis=1)
data_type.head()
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得到下表

house_price

得到户型面积后，接下来处理房屋价格。

def deal_house_price(data):
    try:
        if data.house_price is np.nan:
            return np.nan
        else:
            if "价格待定" in data.house_price:
                return np.nan
 
            elif "万" not in data.house_price:
                price = int(data.house_price.split('元')[0])
            else:
                price_total = int(float(data.house_price.split('万')[0])* 10000)
                if data.min_area is np.nan and data.max_area is np.nan:
                    return np.nan
                elif data.min_area is np.nan:
                    price = price_total/ data.max_area
                elif data.max_area is np.nan:
                    price = price_total / data.min_area
                else:
                    price = price_total / (data.min_area + data.max_area)
        return int(price)
    except:
        return np.nan
 
series_price = data_type.apply(lambda x:deal_house_price(x), axis=1 )
data_type['house_price'] = series_price
data_type.head()
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得到结果

data = data_type.copy()
# 房价缺失值用0填充
data['house_price'] = data_type.house_price.fillna(0)
data['house_type'] = data_type.house_type.fillna('未知')
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异常值分析

data.describe([.1, .25, .5, .75, .99]).T
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很明显有个缺失值，查看原网页，此数值因较特殊，清洗过程中多乘100000，因此直接将此值更改过来即可。

还可以通过可视化（箱图）的方式查看异常值。

from pyecharts import options as opts
from pyecharts.charts import Boxplot
 
v = [int(i) for i in data.house_price]
c = Boxplot()
c.add_xaxis(["house_price"])
c.add_yaxis("house_price", v)
c.set_global_opts(title_opts=opts.TitleOpts(title="house_price"))
c.render_notebook()
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可视化分析

全国城市在售新房均价TOP15

全国城市新房均价分析，房价是我们最关心的一个特征之一，因此看下全国均价最高的是哪几个城市。

# 将空值筛选掉
data1 =  data.query('house_price != 0')
data_pivot = data1.pivot_table(values='house_price',
                               index='city').sort_values(by='house_price', 
                                                         ascending=False)
data_pivot
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全国城市在售新房均价条形图

from pyecharts.charts import Bar
from pyecharts.globals import ThemeType
 
x_axis = [i for i in data_pivot.index[0:15]]
y_axis = [round(float(i), 1) for i in data_pivot.house_price.values[0:15]]
 
c = (
    Bar({"theme": ThemeType.DARK})
    .add_xaxis(x_axis)
    .add_yaxis("house_price_avg", y_axis)
    .set_global_opts(
        title_opts=opts.TitleOpts(title="全国城市在售新房均价TOP15", subtitle="数据: STUDIO"),
        brush_opts=opts.BrushOpts(),
    )
)
c.render_notebook()
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结果如下，排名前面的一直都是深圳、北京、上海等一线城市。

全国房价地理位置图

import pandas as pd
from pyecharts.globals import ThemeType, CurrentConfig, GeoType
from pyecharts import options as opts
from pyecharts.charts import Geo
 
#自定义各城市的经纬度
# geo_cities_coords = {df.iloc[i]['城市']:[df.iloc[i]['经度'],df.iloc[i]['纬度']] for i in range(len(df))}
 
datas = [(i, int(j)) for i, j in zip(data_pivot.index, data_pivot.values)]
# print(datas)
 
geo = (Geo(init_opts=opts.InitOpts(width='1000px', 
                                   height='600px', 
                                   theme=ThemeType.PURPLE_PASSION),
           is_ignore_nonexistent_coord = True)
       .add_schema(maptype='china', 
                   label_opts=opts.LabelOpts(is_show=True))   # 显示label  省名
       .add('均价', 
            data_pair=datas, 
            type_=GeoType.EFFECT_SCATTER, 
            symbol_size=8,
            # geo_cities_coords=geo_cities_coords
           )
       .set_series_opts(label_opts=opts.LabelOpts(is_show=False))
       .set_global_opts(
           title_opts=opts.TitleOpts(title='全国城市在售新房均价', subtitle="制图: 数据STUDIO"),
           visualmap_opts=opts.VisualMapOpts(max_=550,
                                             is_piecewise=True,
                                             pieces=[
                     {"max": 5000, "min": 1000, "label": "1000-5000", "color": "#708090"},
                     {"max": 10000, "min": 5001, "label": "5001-10000", "color": "#00FFFF"},
                     {"max": 20000, "min": 10001, "label": "10001-20000", "color": "#FF69B4"},
                     {"max": 30000, "min": 20001, "label": "20001-30000", "color": "#FFD700"},
                     {"max": 40000, "min": 30001, "label": "30001-40000", "color": "#FF0000"},
                     {"max": 100000, "min": 40001, "label": "40000-100000", "color": "#228B22"},])
       )
      )
 
geo.render('全国城市在售新房均价.html')
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近年来，火热的楼市价格一路飙升，为了稳定房价，各地政府相继出台各项调控政策。据统计，今年内全国各地累计出台楼市调控政策次数已高达97次（近100次），其中，1月份单月全国各地楼市调控政策次数高达42次，2月份比1月份多3次，共计45次。

全国新房项目总数排行榜

接下来看看全国在售\预售新房项目总数排行TOP20，排在前五的分别是四川成都–1000个，重庆–938个，湖北武汉–859个，陕西西安–840个，河南郑州–822个，均是新一线城市（成都、杭州、重庆、武汉、苏州、西安、天津、南京、郑州、长沙、沈阳、青岛、宁波、东莞和无锡）。

现在的新一线城市经济发展速度较快，未来发展前景广阔，可以说是仅次于北上广深。人口都在持续流入，人口流入将会增加对于房产的需求，房产需求增长将会让房产价格稳步攀升。也是很值得投资的。

from pyecharts import options as opts
from pyecharts.charts import Bar
 
city_counts = data.city.value_counts()[0:20]
x_values = city_counts.index.to_list()
y_values = [int(i) for i in city_counts.values]
 
bar = (
    Bar()
    .add_xaxis(x_values)
    .add_yaxis("",y_values,itemstyle_opts=opts.ItemStyleOpts(color="#749f83"))
    .set_global_opts(title_opts=opts.TitleOpts(title="全国新房项目总数TOP20"),
                     toolbox_opts=opts.ToolboxOpts(),
                     legend_opts=opts.LegendOpts(is_show=False),
                    datazoom_opts=opts.DataZoomOpts(),)
)
bar.render_notebook()
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结果

城市各行政区在售新房均价

以在售/预售房产项目最多的成都为例，看城市各行政区在售新房均价。

在"住房不炒"的大环境下，各大城市限购政策越来越严格。近日成都更是实行购房资格预审，热点楼盘优先向无房居民家庭销售，是我们这些刚需的一大福音。接下来一起看看吧。

成都各行政区在售新房均价

data2 = data.query('house_price != 0 and city=="成都"')
data_pivot_cd = data2.pivot_table(values='house_price',
                               index='region').sort_values(by='house_price')
 
x_axis2 = [i for i in data_pivot_cd.index[10:]]
y_axis2 = [round(float(i), 1) for i in data_pivot_cd.house_price.values[10:]]
 
c = (
    Bar({"theme": ThemeType.DARK})
    .add_xaxis(x_axis2)
    .add_yaxis("house_price_avg", y_axis2)
    .reversal_axis()
    .set_global_opts(
        title_opts=opts.TitleOpts(title="成都各行政区在售新房均价TOP10", subtitle="制图: 数据STUDIO"),
        brush_opts=opts.BrushOpts()
    )
    .set_series_opts(label_opts=opts.LabelOpts(position="right"))
)
c.render_notebook()
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结果如下

成都各行政区新房分布

from pyecharts.charts import Pie
 
data_cq = data[data.city=='成都']
df_cq = data_cq.region.value_counts()[0:15]
regions = df_cq.index.to_list()
values = df_cq.to_list()
c = (
   Pie(init_opts=opts.InitOpts(theme=ThemeType.DARK))
  .add("", list(zip(regions,values)))
  .set_global_opts(
    legend_opts = opts.LegendOpts(type_="scroll",
    pos_right="90%",
    pos_top='20%',
    orient="vertical"),
    title_opts=opts.TitleOpts(title="区域新房源数分布",
    subtitle="制图: 数据STUDIO",
    pos_top="0.5%",pos_left = 'left'))
  .set_series_opts(label_opts=opts.LabelOpts(formatter="{b}占比:{d}%",font_size=14))
)
c.render_notebook()
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结果如下：

由饼图发现，除了成都周边（综合）外，天府新区房源总数占比最高，成都直管区天府新区也是成都近年来发展势头很强劲的行政区。

2014年，四川天府新区正式获批成为中国第11个国家级新区。2020年，四川天府新区引进重大产业项目86个、协议总投资2285亿元，实现地区生产总值3561亿元、增长6.7%，居国家级新区第5位。

from pyecharts.charts import Pie
 
data_cq_notna = data[data.city=='成都']
data_cq_notna.dropna(subset=['min_area', 'max_area'], inplace=True)
data_cq_notna['min_area'] = data_cq_notna['min_area'].astype('int') 
data_cq_notna['max_area'] = data_cq_notna['max_area'].astype('int') 
data_cq_notna_pivot = data_cq_notna.pivot_table(values=['min_area', 'max_area'], index='region')
data_cq_notna_pivot.sort_values(by='max_area', ascending=False,inplace=True)
regions = data_cq_notna_pivot.index.to_list()
avg_area1 = data_cq_notna_pivot.min_area.to_list()
avg_area2 = data_cq_notna_pivot.max_area.to_list()
 
c = (
     Bar(init_opts=opts.InitOpts(theme=ThemeType.LIGHT))
    .add_xaxis(regions)
    .add_yaxis("平均最大铭面积", avg_area2, stack="stack1")
    .add_yaxis("平均最小铭面积", avg_area1, stack="stack1")
    .set_series_opts(label_opts=opts.LabelOpts(is_show=False))
    .set_global_opts(xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(font_size=13)),
                     #更改横坐标字体大小
                     yaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(font_size=13)), 
                     #更改纵坐标字体大小
                     title_opts=opts.TitleOpts(title="区域新房源户型面积",
                                               subtitle="制图: 数据STUDIO",
                                               pos_top="0.5%",pos_left = 'left'),
                     datazoom_opts=opts.DataZoomOpts())
    )
c.render_notebook()
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输出结果