Python 3.x 获取错误“；ElementNotInteractitableException:消息：element not Interactitable“；在python selenium中使用send_key（）函数时

我尝试了CSS选择器，在搜索框中使用send_key（）函数发送值，然后提交，这样我就可以得到特定年份的医生列表，但下面出现了错误 “ElementNotInteractitableException:消息：元素不可交互”

下面是我编写的代码：

from selenium import webdriver
from bs4 import BeautifulSoup as bs
from selenium.webdriver.common.action_chains import ActionChains
from selenium.webdriver.common.keys import Keys
import time

url = "https://www.mciindia.org/CMS/information-desk/indian-medical-register"
driver = webdriver.Chrome(r"C:\chromedriver.exe")
driver.get(url)

driver.find_element_by_xpath("//a[contains(text(),'Year of Registration')]").click()
driver.find_elements_by_css_selector("input[type='text']")[-1].send_keys("2015")
driver.find_element_by_css_selector("input[value='Submit']").click()

next_page = True
while next_page == True:
    soup = bs(driver.page_source, 'html.parser')
    table1 = soup.find('table',{'id':'doct_info2'})
    try:
        rows = table1.find_all('tr')

        for row in rows:
            if len(row.find_all('td')) == 7:
                data = row.find_all('td')

                link = data[6].click()

                soup2 = bs(link, 'html.parser')
                table2 = soup2.find('table',{'id':'doctorBiodata'})
                rows = table2.find_all('tr')
                print(rows)
    except:
         pass

    time.sleep(5)
    try:
        driver.find_element_by_xpath("//a[contains(text(),'Next')]").click()
    except:
        print ('No more pages')
        next_page=False

driver.close()

---

要在文本框中输入值，您需要导入

WebDriverWait

（），等待

元素可点击
（）并使用
发送键
（）

要获取表格，您需要导入
WebDriverWait
（），并等待表格可见
visibility\u（位于
（）

代码：

from selenium.webdriver.common.by import By
from selenium.webdriver.support.ui import WebDriverWait
from selenium.webdriver.support import expected_conditions as EC
from selenium import webdriver
from bs4 import BeautifulSoup as bs
import time

url = "https://www.mciindia.org/CMS/information-desk/indian-medical-register"
driver = webdriver.Chrome(r"C:\chromedriver.exe")
driver.get(url)
WebDriverWait(driver,10).until(EC.element_to_be_clickable((By.XPATH,"//a[text()='Year of Registration']"))).click()
WebDriverWait(driver,10).until(EC.element_to_be_clickable((By.XPATH,"//input[@id='doctor_year']"))).send_keys("2015")
WebDriverWait(driver,10).until(EC.element_to_be_clickable((By.XPATH,"//button[@id='doctor_year_details']"))).click()
WebDriverWait(driver,20).until(EC.visibility_of_element_located((By.CSS_SELECTOR,"table.table.table-bordered.dataTable.no-footer")))

next_page = True
while next_page == True:
    soup = bs(driver.page_source, 'html.parser')
    table1 = soup.find('table',{'id':'doct_info2'})
    try:
        rows = table1.find_all('tr')

        for row in rows:
            if len(row.find_all('td')) == 7:
                data = row.find_all('td')

                link = data[6].click()

                soup2 = bs(link, 'html.parser')
                table2 = soup2.find('table',{'id':'doctorBiodata'})
                rows = table2.find_all('tr')
                print(rows)
    except:
         pass

    time.sleep(5)
    try:
        driver.find_element_by_xpath("//a[contains(text(),'Next')]").click()
    except:
        print ('No more pages')
        next_page=False

driver.close()

你可以通过简单的请求更快地完成整个事情。更改draw参数以获得不同的页面。您可以动态添加或删除时间戳参数“\u1”。将“年份”更改为不同的年份。初始json响应提供了一个记录计数，因此很容易计算500个批次中所有结果的循环结束计数。使用
Session
对象提高循环中多个请求的tcp连接重用效率

import requests
import pandas as pd

params = (
    ('service', 'getPaginatedDoctor'),
    ('draw', '1'),
    ('columns[0][data]', '0'),
    ('columns[0][name]', ''),
    ('columns[0][searchable]', 'true'),
    ('columns[0][orderable]', 'true'),
    ('columns[0][search][value]', ''),
    ('columns[0][search][regex]', 'false'),
    ('columns[1][data]', '1'),
    ('columns[1][name]', ''),
    ('columns[1][searchable]', 'true'),
    ('columns[1][orderable]', 'true'),
    ('columns[1][search][value]', ''),
    ('columns[1][search][regex]', 'false'),
    ('columns[2][data]', '2'),
    ('columns[2][name]', ''),
    ('columns[2][searchable]', 'true'),
    ('columns[2][orderable]', 'true'),
    ('columns[2][search][value]', ''),
    ('columns[2][search][regex]', 'false'),
    ('columns[3][data]', '3'),
    ('columns[3][name]', ''),
    ('columns[3][searchable]', 'true'),
    ('columns[3][orderable]', 'true'),
    ('columns[3][search][value]', ''),
    ('columns[3][search][regex]', 'false'),
    ('columns[4][data]', '4'),
    ('columns[4][name]', ''),
    ('columns[4][searchable]', 'true'),
    ('columns[4][orderable]', 'true'),
    ('columns[4][search][value]', ''),
    ('columns[4][search][regex]', 'false'),
    ('columns[5][data]', '5'),
    ('columns[5][name]', ''),
    ('columns[5][searchable]', 'true'),
    ('columns[5][orderable]', 'true'),
    ('columns[5][search][value]', ''),
    ('columns[5][search][regex]', 'false'),
    ('columns[6][data]', '6'),
    ('columns[6][name]', ''),
    ('columns[6][searchable]', 'true'),
    ('columns[6][orderable]', 'true'),
    ('columns[6][search][value]', ''),
    ('columns[6][search][regex]', 'false'),
    ('order[0][column]', '0'),
    ('order[0][dir]', 'asc'),
    ('start', '0'),
    ('length', '500'),
    ('search[value]', ''),
    ('search[regex]', 'false'),
    ('name', ''),
    ('registrationNo', ''),
    ('smcId', ''),
    ('year', '2015'),
    ('_', '1577634512046'),
)

table_headers = ['Sl. No.','Year of Info','Registration Number','State Medical Councils','Name','Father Name','Action']
r = requests.get('https://mciindia.org/MCIRest/open/getPaginatedData',  params=params)
df = pd.DataFrame(r.json()['data'], columns = table_headers)
print(df)

以下代码用于第一个
10
结果

您可以将
长度
从
10
更改为
42354
，这是
2015年的最大值
，您将在同一秒内获得
out.csv
文件

第二个文件是
data.csv
，它将包含内部医生详细信息

页面是通过
JavaScript
呈现的，因此我从浏览器
Developer Tools
在
Network Table
下找到了
XHR
请求。这是
JS

import pandas as pd
import csv
import re
import requests


def Table():
    table = pd.read_json("https://mciindia.org/MCIRest/open/getPaginatedData?service=getPaginatedDoctor&draw=1&columns[0][data]=0&columns[0][name]=&columns[0][searchable]=true&columns[0][orderable]=true&columns[0][search][value]=&columns[0][search][regex]=false&columns[1][data]=1&columns[1][name]=&columns[1][searchable]=true&columns[1][orderable]=true&columns[1][search][value]=&columns[1][search][regex]=false&columns[2][data]=2&columns[2][name]=&columns[2][searchable]=true&columns[2][orderable]=true&columns[2][search][value]=&columns[2][search][regex]=false&columns[3][data]=3&columns[3][name]=&columns[3][searchable]=true&columns[3][orderable]=true&columns[3][search][value]=&columns[3][search][regex]=false&columns[4][data]=4&columns[4][name]=&columns[4][searchable]=true&columns[4][orderable]=true&columns[4][search][value]=&columns[4][search][regex]=false&columns[5][data]=5&columns[5][name]=&columns[5][searchable]=true&columns[5][orderable]=true&columns[5][search][value]=&columns[5][search][regex]=false&columns[6][data]=6&columns[6][name]=&columns[6][searchable]=true&columns[6][orderable]=true&columns[6][search][value]=&columns[6][search][regex]=false&order[0][column]=0&order[0][dir]=asc&start=0&length=10&search[value]=&search[regex]=false&year=2015&_=1577626804003")['data']
    with open('out.csv', 'w', newline="") as f:
        writer = csv.writer(f)
        writer.writerow(
            ['Year Of The Info', 'Registration#', 'State Medical Councils', 'Name', 'FatherName'])
        data = []
        for item in table:
            writer.writerow(item[1:6])
            required = item[6]
            match = re.search(
                r"openDoctorDetailsnew\('([^']*)', '([^']*)'", required)
            data.append(match.group().split("'")[1:4:2])
        print("Data Saved Into out.csv")
    return data


def Details():
    names = []
    items = []
    for doc, val in Table():
        print(f"Extracting DoctorID# {doc}, RegValue# {val}")
        json = {'doctorId': doc, 'regdNoValue': val}
        r = requests.post(
            "https://mciindia.org/MCIRest/open/getDataFromService?service=getDoctorDetailsByIdImr", json=json).json()
        if r.keys() not in names:
            names.append(r.keys())
        items.append(r.values())
    print("Done")
    return names, items


def Save():
    with open('data.csv', 'w', newline="") as d:
        writer = csv.writer(d)
        n, i = Details()
        writer.writerows(n)
        writer.writerows(i)


Save()

检查输出样本和

注意：如果你需要大量的内部数据，你必须查看一下concurrent.futures

是否要查看
2015
的整个搜索结果？以及每个
医生的全部数据
？检查我的回答我大约在2小时前完成了代码，但正在检查
TCP
转储，以便一次性访问内部医生详细信息。实际上，
OP
正在对内部医生详细信息进行整轮操作，您可以通过单击
view
按钮查看这些详细信息。tbh我没有费心玩params，但看起来您可能可以删除相当多的值空值可以关闭。但是对于内部表。不一样@NiteshRao:）欢迎你。对于mecan来说，这是一项非常有趣的任务，请在上面的代码中添加注释，这样我就可以理解您的代码，因为我不知道json在python中是如何工作的，也不知道从哪里获取“table=pd.read_json（在链接上方）”和post link获取此错误“解码“string”时UTF-8序列中的无效八位字节”如何解决此问题。