我的dcd爬虫-Python

我自己写的dcd爬虫，这个网站比较简单。看了看别人的程序，觉得用起来挺别扭，就自己捣鼓了一天。弄出来了。

这个网站没有反爬，有一些是动态网页，有一些是静态。

首先，获取销量排行榜前300的车型。

import os
import json
import requests
from parsel import Selector
 
 
# ---------------------------------------------------------#
# ----           * 获得车辆销售排行榜前300、100的车         *        ----#
# ---------------------------------------------------------#
 
 
url = "https://www.dongchedi.com/motor/pc/car/rank_data"
headers = {
       "user-agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/101.0.4951.64 Safari/537.36",
    }
 
 
def get_param(page):
    params = {
        "aid": "1839",
        "app_name": "auto_web_pc",
        "city_name": "烟台",
        "count": "10",
        "offset": page,
        "month": "",
        "new_energy_type": "",
        "rank_data_type": "11",
        "brand_id": "",
        "price": "",
        "manufacturer": "",
        "outter_detail_type": "",
        "nation": "0"
    }
    return params
 
 
def get_response(pageNum):
    params = get_param(str(pageNum * 10))
 
    with requests.get(url=url, headers=headers, params=params, verify=False) as resp:
        resp.raise_for_status()
        print(resp.status_code)
    return resp
 
 
data_list = []
for i in range(30):
    print(f"销量前{i * 10} 的车")
    response = get_response(i)
    data_list.append(response.json())

获取之后，就能访问该车型，一般一个车型有好多款式，我的目的是向比较一些车型的尺寸，所以一个车型就选第一种款式，访问进入该车型第一种款式的参数配置，这样把参数下载下来，放到一个文件里，就可以比较现在卖的车的尺寸情况。

第二部分，我尝试了一下动态请求车型的价格。不过这一部分后面数据分析没有用到。

 
len(data_list)
import jsonpath
data_list[0]['data']['list'][0]['series_name']
 
name_list = jsonpath.jsonpath(data_list, "$..series_name")
id_list = jsonpath.jsonpath(data_list, "$..series_id")
id_list
first_list = jsonpath.jsonpath(data_list, "$..online_car_ids")
 
first_list[0][0]
 
car_id_list = []
for ls in first_list:
    if ls:
        first_id = ls[0]
    else:
        first_id = None
    car_id_list.append(first_id)
len(car_id_list)
 
import pandas as pd
df = pd.DataFrame({
    "name": name_list,
    "series": id_list,
    "first_id":car_id_list
})
 
df
 
df[df['first_id'] == None]
 
df2 = df.dropna()
df.shape
df2.shape
 
df2.to_csv("Pythn-Anlys-138/dcd/top300cars.csv")
df = pd.read_csv("Pythn-Anlys-138/dcd/top300cars.csv")
df.keys()
df.columns
df.columns = ['rank', 'name', 'series', 'first_id']
df.to_csv("Pythn-Anlys-138/dcd/top300cars.csv")
 
# ---------------------------------------------------------#
# ----           * 价格         *        ----#
# ---------------------------------------------------------#
 
first_id
 
def get_price(car_id):
    import json
    import os
    wk_dir = "Pythn-Anlys-138/dcd"
    # fpath = wk_dir + "/" + car_id + ".csv"
    fname = car_id + ".json"
    url = "https://www.dongchedi.com/motor/pc/car/series/car_dealer_price"
    headers = {
                  。。。
                      }
    params = {
        "aid": "1839",
        "app_name": "auto_web_pc",
        "car_ids": car_id,
        "city_name": "烟台"
    }
 
    with requests.get(url=url, headers=headers, params=params, verify=False) as resp:
        resp.raise_for_status()
        # print(resp.json())
        rj = resp.json()
    with open(os.path.join(wk_dir, fname), 'w',  encoding="utf-8") as f:
        f.write(json.dumps(rj, ensure_ascii=False))
        print(f"保存文件成功 {car_id} !!!")
 
first_id = str(first_id)
 
get_price(first_id)

这一部分呢后期没什么用，代码也很乱。

第三部分，获取某一车型的第一种款式的参数。

 
# ---------------------------------------------------------#
# ----           * 参数配置        *        ----#
# ---------------------------------------------------------#
 
from parsel import Selector
 
def get_detail_page(id):
    url = "https://www.dongchedi.com/auto/params-carIds-" + id
    headers = {
。。。
}
 
    with requests.get(url=url, headers=headers, verify=False) as resp:
        resp.raise_for_status()
        # print(resp.text)
    return resp.text
 
html = get_detail_page(id)
 
html
 
selector = Selector(html)
 
selector.css('div[data-row-anchor]')
len(selector.css('div[data-row-anchor]'))
 
all_rows = selector.css('div[data-row-anchor]')
 
dct_list = []
for row in all_rows:
    dct_item = {}
    label = row.css('div:nth-child(1) label::text').get()
    value = row.css('div:nth-child(2) div::text').get()
    dct_item[label] = value
    dct_list.append(dct_item)
 
dct_list
 
first_row = all_rows[0]
 
def parse_detail(id):
    html = get_detail_page(id)
    selector = Selector(html)
    all_rows = selector.css('div[data-row-anchor]')
    dct_list = []
    for row in all_rows:
        dct_item = {}
        label = row.css('div:nth-child(1) label::text').get()
        value = row.css('div:nth-child(2) div::text').get()
        dct_item[label] = value
        dct_list.append(dct_item)
 
    dct_detail = {
        "id":id,
        "detail":dct_list
    }
    return dct_detail
 
dct_detail = parse_detail(id)
dct_detail
 
 
first_id_list
 
def save_detail(id, dct_detail):
    fname = id + "_dcd_detail.json"
    with open(os.path.join("Pythn-Anlys-138/dcd", fname), 'w', encoding='utf8') as f:
        f.write(json.dumps(dct_detail, ensure_ascii=False))
        print(f"Detail file {id}  saved!!!")
 
for fid in first_id_list:
    dct_detail = parse_detail(fid)
    save_detail(fid, dct_detail)
 
 

最后，下载了一些json文件。后期做了一些数据整理。做成了数据表是这样的。

 结果还不错。