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数据分析-二手房频数地理图.py
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数据分析-二手房频数地理图.py
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import os
import pandas as pd
import requests
import json
from pyecharts.charts import Geo
from pyecharts import options as opts
from pyecharts import options
from pyecharts.globals import GeoType
info_table = pd.DataFrame(columns=['位置', '次数'])
row = 0
ddata = {}
def formatdata(filenames):
global row
for filename in filenames:
f = open(os.path.join('./爬取结果/', filename), encoding='gb18030')
data = pd.read_csv(f, encoding='gb18030', header=None)
data.columns = ['名称', '小区名', '位置', '房间类型', '面积', '朝向', '装修', '楼层', '年份', '楼况', '关注人数', '发布时间', '总价', '单价', '标签']
for i in range(0, len(data)):
if data['小区名'].iloc[i] not in ddata:
ddata[data['小区名'].iloc[i]] = 1
else:
ddata[data['小区名'].iloc[i]] += 1
print(filename, 'finished')
for i in range(0, len(list(ddata.values()))):
alist = []
alist.append(list(ddata.keys())[i])
alist.append(list(ddata.values())[i])
info_table.loc[row] = alist
row += 1
info_table.to_csv('./临时数据文件/频数统计.csv', encoding='gb18030')
print('频数统计完成！')
def gain_location(address):
#这个api_url网址里的city=填你想填的城市名（非必需参数，即可删除city=xxx），ak=填入你自己的密匙，output=json意思是输出json格式
api_url = f'http://api.map.baidu.com/geocoding/v3/?city=北京市&address={address}&ak=cU38ZCTGT8Psgkcmlh2bD8THnKP2kH3H&output=json&callback=showLocation'
r = requests.get(api_url)
r = r.text
'''经历以下两次去除，使得最终结果为json格式的数据
原来的数据格式：showLocation&&showLocation(' showLocation&&showLocation('showLocation&&showLocation({"status":0,"result":{"location":{"lng":108.94646555063274,"lat":34.34726881662395},"precise":0,"confidence":12,"comprehension":63,"level":"城市"}}）
去除后的数据格式为将json字符串转换为字典类型：showLocation&&showLocation({"status":0,"result":{"location":{"lng":108.94646555063274,"lat":34.34726881662395},"precise":0,"confidence":12,"comprehension":63,"level":"城市"}}
'''
r = r.strip('showLocation&&showLocation(')
r = r.strip(')')
jsonData = json.loads(r) # 将json字符串转换为字典类型转为字典格式类型
return jsonData
def Bmap(filename):
g = Geo(init_opts=opts.InitOpts(
width='1800px',
height='900px',
)).add_schema(maptype="广州")
stationid_list = []
Longitude_list = []
Latitude_list = []
point_list = []
file = pd.read_csv(filename, encoding='gb18030')
for i in range(0, len(file)):
stationid_list.append(file['地点'].iloc[i])
Longitude_list.append(file['经度'].iloc[i])
Latitude_list.append(file['纬度'].iloc[i])
point_list.append(file['频数'].iloc[i])
# 给所有点附上标签 'StationID'
for i in range(0, len(stationid_list)):
g.add_coordinate(stationid_list[i], Longitude_list[i], Latitude_list[i])
# 给每个点赋值
# for i in range(0, len(stationid_list)):
for i in range(0, len(file)):
data_pair = []
data_pair.append((stationid_list[i], int(point_list[i])))
if int(point_list[i]) <= 3:
g.add('', data_pair, type_=GeoType.EFFECT_SCATTER, symbol_size=3)
else:
g.add('', data_pair, type_=GeoType.EFFECT_SCATTER, symbol_size=int(point_list[i]))
# 画图
# g.add('', data_pair, type_=GeoType.EFFECT_SCATTER, symbol_size=8)
g.set_series_opts(label_opts=options.LabelOpts(is_show=False))
g.set_global_opts(title_opts=options.TitleOpts(title="全地点热力图展示"))
# 保存结果到 html
result = g.render('./分析图表/二手房频数地理热力图.html')
print('地理热力图生成完毕！')
def lalo_change(data):
row1 = 0
for i in range(0, len(data)):
address = data['位置'].iloc[i]
location = gain_location(address)
lng = location['result']['location']['lng']
lat = location['result']['location']['lat']
alist = []
alist.append(data['位置'].iloc[i])
alist.append(data['次数'].iloc[i])
alist.append(lng)
alist.append(lat)
info_table2.loc[row1] = alist
row1 += 1
info_table2.to_csv('./临时数据文件/经纬度转换.csv', encoding='gb18030')
print('经纬度转换完毕！')
if __name__ == '__main__':
f = []
filenames = os.listdir('./爬取结果/') # 设定调用文件的相对路径
for i in filenames:
if '.csv' in str(i):
f.append(i)
filenames = f
formatdata(filenames)
info_table2 = pd.DataFrame(columns=['地点', '频数', '经度', '纬度'])
data = pd.read_csv('./临时数据文件/频数统计.csv', encoding='gb18030')
lalo_change(data)
Bmap('./临时数据文件/经纬度转换.csv')