Python crawler introductory case of crawling the second-hand housing data

import requests # Data request module Third-party module pip install requests
import parsel # Data parsing module
import re
import csv

url = '/ershoufang/pg1/'
# Need to carry a request header: disguise python code as a browser and send a request to the server.
# User-Agent Basic information about the browser
headers = {
    'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/94.0.4606.61 Safari/537.36'
}
response = (url=url, headers=headers)

print()

selector_1 = ()
# Convert the retrieved data into a selector object.
href = selector_1.css(' li  a::attr(href)').getall()
for link in href:
    html_data = (url=link, headers=headers).text
    selector = (html_data)
    # css selector syntax
    # try:
    title = ('.title h1::text').get() # Title
    area = ('.areaName .info a:nth-child(1)::text').get()  # Region
    community_name = ('.communityName .info::text').get()  # The neighborhood #
    room = ('.room .mainInfo::text').get()  # Households
    room_type = ('.type .mainInfo::text').get()  # Orientation
    height = ('.room .subInfo::text').get().split('/')[-1]  # Floor
    # Middle floor / 5 floors in total split('/') do string split ['middle floor', '5 floors in total'] [-1]
    # ['Middle floor', 'Total 5 floors'][-1] List index position taken Fetch the last element in the list Total 5 floors
    # ('Total (\d+) floors', Total 5 floors) >>> [5][0] >>>> 5
    height = ('common(\d+)floor (of a building)', height)[0]
    sub_info = ('.type .subInfo::text').get().split('/')[-1]  # Renovation
    Elevator = ('.content li:nth-child(12)::text').get()  # Elevator
    # if Elevator == 'No Data Elevator' or Elevator == None.
    # Elevator = 'no elevator'
    house_area = ('.content li:nth-child(3)::text').get().replace('㎡', '')  # Area
    price = ('.price .total::text').get()  # Price ($ million)
    date = ('.area .subInfo::text').get().replace('Year built', '')  # Year
    dit = {
        'Title': title,
        'Downtown': area,
        'Neighborhoods': community_name,
        'Household type': room,
        'Orientation': room_type,
        'Floor': height,
        'Renovation status': sub_info,
        'Elevator': Elevator,
        'area (of a floor, piece of land etc)(㎡)': house_area,
        'prices(ten thousand dollars)': price,
        'particular year': date,
    }
    csv_writer.writerow(dit)
    print(title, area, community_name, room, room_type, height, sub_info, Elevator, house_area, price, date,
          sep='|')

f = open('Second-hand housing data.csv', mode='a', encoding='utf-8', newline='')
csv_writer = (f, fieldnames=[
    'Title',
    'Downtown',
    'Neighborhoods',
    'Household type',
    'Orientation',
    'Floor',
    'Renovation status',
    'Elevator',
    'area (of a floor, piece of land etc)(㎡)',
    'prices(ten thousand dollars)',
    'particular year',
])
csv_writer.writeheader()

import pandas as pd
from  import Map
from  import Bar
from  import Line
from  import Grid
from  import Pie
from  import Scatter
from pyecharts import options as opts

df = pd.read_csv('Chain.csv', encoding = 'utf-8')
()

new = [x + 'District' for x in region]
m = (
        Map()
        .add('', [list(z) for z in zip(new, count)], 'Beijing')
        .set_global_opts(
            title_opts=(title='Distribution of second-hand housing in Beijing by district'),
            visualmap_opts=(max_=3000),
        )
    )
m.render_notebook()

df_price.()
price = [round(x,2) for x in df_price.()]
bar = (
    Bar()
    .add_xaxis(region)
    .add_yaxis('Number', count,
              label_opts=(is_show=True))
    .extend_axis(
        yaxis=(
            name="Price (in millions of dollars)",
            type_="value",
            min_=200,
            max_=900,
            interval=100,
            axislabel_opts=(formatter="{value}"),
        )
    )
    .set_global_opts(
        title_opts=(title='Histogram of the number of second-hand homes - average price by urban area'),
        tooltip_opts=(
            is_show=True, trigger="axis", axis_pointer_type="cross"
        ),
        xaxis_opts=(
            type_="category",
            axispointer_opts=(is_show=True, type_="shadow"),
        ),
        yaxis_opts=(name='Number',
            axistick_opts=(is_show=True),
            splitline_opts=(is_show=False),)
    )
)

line2 = (
    Line()
    .add_xaxis(xaxis_data=region)
    .add_yaxis(
        
        series_name="Price.",
        yaxis_index=1,
        y_axis=price,
        label_opts=(is_show=True),
        z=10
        )
)

(line2)
grid = Grid()
(bar, (pos_left="5%", pos_right="20%"), is_control_axis_index=True)
grid.render_notebook()

area0 = top_price['Neighborhoods'].()
count = top_price['prices(ten thousand dollars)'].()

bar = (
    Bar()
    .add_xaxis(area0)
    .add_yaxis('Number', count,category_gap = '50%')
    .set_global_opts(
        yaxis_opts=(name='prices(ten thousand dollars)'),
        xaxis_opts=(name='Number'),
    )
)
bar.render_notebook()

s = (
    Scatter()
    .add_xaxis(df['area (of a floor, piece of land etc)(㎡)'].())
    .add_yaxis('',df['prices(ten thousand dollars)'].())
    .set_global_opts(xaxis_opts=(type_='value'))
)
s.render_notebook()

directions = df_direction.()
count = df_direction.()

c1 = (
    Pie(init_opts=(
            width='800px', height='600px',
            )
       )
        .add(
        '',
        [list(z) for z in zip(directions, count)],
        radius=['20%', '60%'],
        center=['40%', '50%'],
#         rosetype="radius",
        label_opts=(is_show=True),
        )    
        .set_global_opts(title_opts=(title='Percentage of house orientation',pos_left='33%',pos_top="5%"),
                        legend_opts=(type_="scroll", pos_left="80%",pos_top="25%",orient="vertical")
                        )
        .set_series_opts(label_opts=(formatter='{b}:{c} ({d}%)'),position="outside")
    )
c1.render_notebook()

fitment = df_fitment.()
count1 = df_fitment.()

directions = df_direction.()
count2 = df_direction.()

bar = (
    Bar()
    .add_xaxis(fitment)
    .add_yaxis('', count1, category_gap = '50%')
    .reversal_axis()
    .set_series_opts(label_opts=(position='right'))    
    .set_global_opts(
        xaxis_opts=(name='Number'),
        title_opts=(title='Condition of renovation/with or without elevator rose diagram (combination diagram)',pos_left='33%',pos_top="5%"),
        legend_opts=(type_="scroll", pos_left="90%",pos_top="58%",orient="vertical")
    )
)

c2 = (
    Pie(init_opts=(
            width='800px', height='600px',
            )
       )
        .add(
        '',
        [list(z) for z in zip(directions, count2)],
        radius=['10%', '30%'],
        center=['75%', '65%'],
        rosetype="radius",
        label_opts=(is_show=True),
        )    
        .set_global_opts(title_opts=(title='With/without elevator',pos_left='33%',pos_top="5%"),
                        legend_opts=(type_="scroll", pos_left="90%",pos_top="15%",orient="vertical")
                        )
        .set_series_opts(label_opts=(formatter='{b}:{c} \n ({d}%)'),position="outside")
    )

(c2)
bar.render_notebook()

floor = df_floor.()
count = df_floor.()
bar = (
    Bar()
    .add_xaxis(floor)
    .add_yaxis('Number', count)
    .set_global_opts(
        title_opts=(title='Columnar scaling diagram of floor distribution of second-hand houses'),
        yaxis_opts=(name='Number'),
        xaxis_opts=(name='Floor'),
        datazoom_opts=(type_='slider')
    )
)
bar.render_notebook()

area = df_area.()
count = df_area.()

bar = (
    Bar()
    .add_xaxis(area)
    .add_yaxis('Number', count)
    .reversal_axis()
    .set_series_opts(label_opts=(position="right"))
    .set_global_opts(
        title_opts=(title='Vertical bar chart of house size distribution'),
        yaxis_opts=(name='area (of a floor, piece of land etc)(㎡)'),
        xaxis_opts=(name='Number'),
    )
)
bar.render_notebook()