python的一些常用操作

判断文件或者文件夹是否存在

if(os.path.exists(rootdir) == False)

创建文件夹

os.mkdir(rootdir)

调用系统命令

os.system(cmd)

字典循环

for key,value in dict.items()

打开文件并读取内容进行处理

fd = open('xxxx.txt', encoding='utf-8')

for line in fd:

print line

fd.close()

创建文件并写入内容

fd = open('xxxx.txt', 'a+', encoding='utf-8')

fd.write('aaaaa' + '\n')

fd.close()

使用xlrd读取EXCEL

导入

import xlrd

打开excel

data = xlrd.open_workbook('demo.xls') #注意这里的workbook首字母是小写

查看文件中包含sheet的名称

data.sheet_names()

得到第一个工作表，或者通过索引顺序或工作表名称

table = data.sheets()[0]

table = data.sheet_by_index(0)

table = data.sheet_by_name(u'Sheet1')

获取行数和列数

nrows = table.nrows

ncols = table.ncols

获取整行和整列的值（数组）

table.row_values(i)

table.col_values(i)

循环行,得到索引的列表

for rownum in range(table.nrows):

print table.row_values(rownum)

单元格

cell_A1 = table.cell(0,0).value

cell_C4 = table.cell(2,3).value

分别使用行列索引

cell_A1 = table.row(0)[0].value

cell_A2 = table.col(1)[0].value

简单的写入

row = 0

col = 0

ctype = 1 # 类型 0 empty,1 string, 2 number, 3 date, 4 boolean, 5 error

value = 'lixiaoluo'

xf = 0 # 扩展的格式化 (默认是0)

table.put_cell(row, col, ctype, value, xf)

table.cell(0,0) # 文本:u'lixiaoluo'

table.cell(0,0).value # 'lixiaoluo'

使用xlwt写入EXCEL

导入xlwt

import xlwt

新建一个excel文件

file = xlwt.Workbook() #注意这里的Workbook首字母是大写，无语吧

新建一个sheet

table = file.add_sheet('sheet name')

写入数据table.write(行,列,value)

table.write(0,0,'test')

如果对一个单元格重复操作，会引发

returns error:

# Exception: Attempt to overwrite cell:

# sheetname=u'sheet 1' rowx=0 colx=0

所以在打开时加cell_overwrite_ok=True解决

table = file.add_sheet('sheet name',cell_overwrite_ok=True)

保存文件

file.save('demo.xls')

另外，使用style

style = xlwt.XFStyle() #初始化样式

font = xlwt.Font() #为样式创建字体

font.name = 'Times New Roman'

font.bold = True

style.font = font #为样式设置字体

table.write(0, 0, 'some bold Times text', style) # 使用样式

命令行getopt

try:

options,args = getopt.getopt(sys.argv[1:],"hp:i:",["help","ip=","port="])

except getopt.GetoptError:

sys.exit()

for name,value in options:

if name in ("-h","--help"):

usage()

if name in ("-i","--ip"):

print(value)

if name in ("-p","--port"):

print(value)

简单爬虫

import requests

AGENT = 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_10_3) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/52.0.2743.116 Safari/537.36'

HEADERS = {

'User-Agent': AGENT,

'Content-Type':'application/x-www-form-urlencoded; charset=UTF-8',

'X-Requested-With':'XMLHttpRequest',

'Accept':'*/*'

session = requests.session()

#模拟登录

postdata = {

'defaults':'xxx',

'fromLogin':'xxx',

'userName':'xxx',

'password':'xxxx'

}

url = 'xxxxxxxx'

login_info = session.post(url, headers = HEADERS, data = postdata,verify = False)

if(login_info.status_code == requests.codes.ok):

print('login success')

return True

else:

print('login err')

return False

}

#下载html页面

def downloadUrl(rootdir, url, orgid, page):

html = session.get(url, headers=global_config.HEADERS, verify=False)

if(html.text[1:7] == 'script'):

print(html.text)

return "err"

if(len(html.text) < 60):

return "err"

sample = open(rootdir + "/" + str(orgid) + '_' + str(page) + ".html", "w", encoding='utf-8')

sample.write(html.text)

sample.close()

return 'ok'

解析JOSN文件内容

def scrapy_by_file(json_file_name):

#读取JSON文件的内容

text = open(json_file_name, encoding='utf-8').read()

#特殊处理,去除从WINDOWS系统带过来的BOM特殊字符

if text.startswith(u'\ufeff'):

text = text.encode('utf8')[3:].decode('utf8')

#将文本内容的JSON数据转换成自定义的JSON对象

try:

json_data = json.loads(text)

except:

print(json_file_name)

return

for row in json_data['rows']:

def scrapy_by_row(row):

try:

orgid = row['organization']['id']

familyid = row['censusRegisterFamily']['id']

except:

print('errrr')

return

scrapy_by_row(row)

遍历文件夹

#遍历目录(rootdir) 遍历到的每个文件都执行dirFunc

def waklThroughDir(rootdir, dirFunc):

for parent, dirnames, filenames in os.walk(rootdir):

for filename in filenames:

print(filename)

#获取后缀为txt的文件

if(filename.split('.')[-1] == 'html'):

dirFunc(os.path.join(parent, filename))

采集温州房产网基本信息

# -*- coding: utf-8 -*-

import re

import requests

import time

#-----------------------------用于解析的正则表达式常量------------------------------------------------------------------

#解析页数

PAGE_NUM = '共找到 (.*?) 符合条件的记录'

#解析小区名称

NAME = 'texttext_title"><ahref(.*?)</a></div><divclass="texttext_moreinfo">'

#解析小区价格

PRICE = 'class="hot_price">(.*?)</span>'

#解析小区地址

ADDRESS = 'text_moreinfo">(.*?)</div><divclass="texttext_moreinfo"><span>'

#文件生成路径

ROOTDIR = 'F:\\test\\'

#-----------------------------模拟请求的头部信息，否则将被识别出是程序抓包而被拦截--------------------------------------

HEADERS = {

'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,*/*;q=0.8',

'Accept-Encoding': 'gzip, deflate, sdch',

'User-Agent': 'Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/51.0.2704.106 Safari/537.36',

'Host': 'www.0577home.net',

'Upgrade-Insecure-Requests': '1'

}

#-----------------------------抓取某一页的房产信息，pageNo为页号--------------------------------------------------------

def getHouseListByPageno(pageNo):

#建立一个连接用于后续发起请求

session = requests.session()

url = 'http://www.0577home.net/xiaoqu/list_0_0_0_0_0_0_0_' + str(pageNo) + '.html'

houseList = session.get(url, headers = HEADERS, verify = False)

#以写入模式打开文件

fh = open(ROOTDIR + "houseList_pageNo" + str(pageNo) + ".txt", 'w' ,encoding='utf-8')

#将movieList写入文件

fh.write(houseList.text)

#关闭文件

fh.close()

#-------------------------------获取需要抓取的页面总数------------------------------------------------------------------

def getPageNum():

#打开已经下载好的第一页房产内容

f = open(ROOTDIR + 'houseList_pageNo1.txt', encoding='utf-8')

#获取文件内容

rawContent = f.read()

#用正则表达式解析页面内容

pageNum = re.findall(PAGE_NUM, rawContent)

#返回页面号

return int(pageNum[0]) / 20 + 1

def parseHouseListToFile(srcFile, dstFile):

#打开待解析的文件

f = open(srcFile, encoding='utf-8')

#读取文件内容以备解析

rawContent = f.read()

p = re.compile('\s+')

content = re.sub(p, '', rawContent)

dnames = re.findall(NAME, content)

names = []

for dname in dnames:

idx = dname.rfind('>')

names.append(dname[idx + 1:])

prices = re.findall(PRICE, content)

daddress = re.findall(ADDRESS, content)

address = []

for daddr in daddress:

id = daddr.rfind('>')

address.append(daddr[id + 1:])

i = 0

for x in names:

#写入时用'$'做分割，结尾加上回车符

dstFile.write(names[i] + '$' + prices[i] + '$' + address[i] + '\n')

i = i + 1

#-------------------------------主函数，下载并解析房产信息--------------------------------------------------------------

if __name__ == '__main__':

#---------------------抓取页面-----------------------------

#抓取第一页房产信息

getHouseListByPageno(1)

#通过第一页房产信息获取总共要抓取的页面数量

pageNum = getPageNum()

#抓取剩余的页面

for i in range(2, int(pageNum) + 1):

getHouseListByPageno(str(i))

#---------------------解析页面-----------------------------

#获取当前年月日

localtime = time.strftime('%Y%m%d', time.localtime(time.time()))

#创建一个文件，文件名前面带上年月日

f = open(ROOTDIR + localtime + '_houseList.txt', 'a+', encoding='utf-8')

#解析所有的页面

#for k in range(1, int(pageNum) + 1):

for k in range(1, 115):

parseHouseListToFile(ROOTDIR + "houseList_pageNo" + str(k) + ".txt", f)

#关闭文件

f.close()

采集温州房产网详细信息

# -*- coding: utf-8 -*-

import re

import requests

import time

import os

#-----------------------------用于解析的正则表达式常量------------------------------------------------------------------

#解析页数

PAGE_NUM = '共找到 (.*?) 符合条件的记录'

#解析小区名称

NAME = 'texttext_title"><ahref(.*?)</a></div><divclass="texttext_moreinfo">'

#解析小区价格

PRICE = 'class="hot_price">(.*?)</span>'

#解析小区地址

ADDRESS = 'text_moreinfo">(.*?)</div><divclass="texttext_moreinfo"><span>'

#解析小区编号

ID = 'class="picdiv_left"><ahref="http://www.0577home.net/xiaoqu/(.*?).html'

#解析小区所属区域

LOCATION = '<div><a>所属区域：</a><span>(.*?)</span></div>'

#解析小区占地面积

AREA = '<div><a>占地面积：</a><span>(.*?)</span></div>'

#解析小区绿化率

GREENINGRATE = '<div><a>绿化率：</a><span>(.*?)</span></div>'

#解析小区楼总数

LAYER = '<div><a>楼总数：</a><span>(.*?)</span></div>'

#解析小区物业类型

TYPE = '<div><a>物业类型：</a><span>(.*?)</span></div>'

#解析小区所属小学

PRIMARYSCHOOL = '<div><a>所属小学：</a><span>(.*?)</span></div>'

#解析小区总建筑面积

BUILDINGAREA = '<div><a>总建筑面积：</a><span>(.*?)</span></div>'

#解析小区容积率

PLOTRATIO = '<div><a>容积率：</a><span>(.*?)</span></div>'

#解析小区开发商

DEVEPLOPER = '<div><a>开发商：</a><span>(.*?)</span></div>'

#文件生成路径

ROOTDIR = 'F:\\test\\'

#-----------------------------模拟请求的头部信息，否则将被识别出是程序抓包而被拦截--------------------------------------

HEADERS = {

'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,*/*;q=0.8',

'Accept-Encoding': 'gzip, deflate, sdch',

'User-Agent': 'Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/51.0.2704.106 Safari/537.36',

'Host': 'www.0577home.net',

'Upgrade-Insecure-Requests': '1'

}

#-----------------------------抓取某一页的房产信息，pageNo为页号--------------------------------------------------------

def getHouseListByPageno(pageNo):

#建立一个连接用于后续发起请求

session = requests.session()

url = 'http://www.0577home.net/xiaoqu/list_0_0_0_0_0_0_0_' + str(pageNo) + '.html'

houseList = session.get(url, headers = HEADERS, verify = False)

#以写入模式打开文件

fh = open(ROOTDIR + "houseList_pageNo" + str(pageNo) + ".txt", 'w' ,encoding='utf-8')

#将movieList写入文件

fh.write(houseList.text)

#关闭文件

fh.close()

def getHouseInfoByPageno(pageNo, k):

if(os.path.exists(ROOTDIR + "houseInfo_pageNo" + str(pageNo) + ".html")):

return

print('downloading !, count %s, page %s' % (str(k), str(pageNo)))

#建立一个连接用于后续发起请求

session = requests.session()

url = 'http://www.0577home.net/xiaoqu/detail_' + str(pageNo) + '.html'

houseList = session.get(url, headers = HEADERS, verify = False)

#以写入模式打开文件

fh = open(ROOTDIR + "houseInfo_pageNo" + str(pageNo) + ".html", 'w' ,encoding='utf-8')

#将movieList写入文件

fh.write(houseList.text)

#关闭文件

fh.close()

#-------------------------------获取需要抓取的页面总数------------------------------------------------------------------

def getPageNum():

#打开已经下载好的第一页房产内容

f = open(ROOTDIR + 'houseList_pageNo1.txt', encoding='utf-8')

#获取文件内容

rawContent = f.read()

#用正则表达式解析页面内容

pageNum = re.findall(PAGE_NUM, rawContent)

#返回页面号

return int(pageNum[0]) / 20 + 1

def parseHouseInfo(srcFile):

#打开待解析的文件

f = open(srcFile, encoding='utf-8')

#读取文件内容以备解析

content = f.read()

# p = re.compile('\s+')

# content = re.sub(p, '', rawContent)

location = re.findall(LOCATION, content)[0]

location = location.split(' ')

category1 = location[0]

category2 = location[1]

area = re.findall(AREA, content)[0]

greeningrate = re.findall(GREENINGRATE, content)[0]

layer = re.findall(LAYER, content)[0]

type = re.findall(TYPE, content)[0]

primaryschool = re.findall(PRIMARYSCHOOL, content)[0]

buildingarea = re.findall(BUILDINGAREA, content)[0]

plotratio = re.findall(PLOTRATIO, content)[0]

developer = re.findall(DEVEPLOPER, content)[0]

f.close()

return (category1, category2, area, greeningrate, layer, type, primaryschool, buildingarea, plotratio, developer)

def parseHouseListToFile(srcFile, dstFile):

#打开待解析的文件

f = open(srcFile, encoding='utf-8')

#读取文件内容以备解析

rawContent = f.read()

p = re.compile('\s+')

content = re.sub(p, '', rawContent)

dnames = re.findall(NAME, content)

names = []

for dname in dnames:

idx = dname.rfind('>')

names.append(dname[idx + 1:])

prices = re.findall(PRICE, content)

daddress = re.findall(ADDRESS, content)

ids = re.findall(ID, content)

address = []

for daddr in daddress:

id = daddr.rfind('>')

address.append(daddr[id + 1:])

i = 0

f.close()

for x in names:

#写入时用'$'做分割，结尾加上回车符

dstFile.write(names[i] + '$' + prices[i] + '$' + address[i] + '$' + ids[i] + '\n')

i = i + 1

#-------------------------------主函数，下载并解析房产信息--------------------------------------------------------------

if __name__ == '__main__':

#---------------------抓取页面-----------------------------

#抓取第一页房产信息

# getHouseListByPageno(1)

# #通过第一页房产信息获取总共要抓取的页面数量

# pageNum = getPageNum()

# #抓取剩余的页面

# for i in range(2, int(pageNum) + 1):

# getHouseListByPageno(str(i))

#---------------------解析页面-----------------------------

#获取当前年月日

localtime = time.strftime('%Y%m%d', time.localtime(time.time()))

#创建一个文件，文件名前面带上年月日

f = open(ROOTDIR + localtime + '_houseList.txt', 'a+', encoding='utf-8')

#解析所有的页面

#for k in range(1, int(pageNum) + 1):

for k in range(1, 115):

parseHouseListToFile(ROOTDIR + "houseList_pageNo" + str(k) + ".txt", f)

#关闭文件

f.close()

f = open(ROOTDIR + localtime + '_houseList.txt', encoding='utf-8')

fd = open(ROOTDIR + localtime + '_houseInfo.txt', 'w', encoding='utf-8')

k = 0

for line in f:

data = line.strip('\n')

data = data.split('$')

idx = data[3]

getHouseInfoByPageno(idx, k)

houseInfo = parseHouseInfo(ROOTDIR + "houseInfo_pageNo" + str(idx) + ".html")

print(str(k) + "$".join(data) + '$' + "$".join(houseInfo))

fd.write("$".join(data) + '$' + "$".join(houseInfo) + '\n')

k += 1

f.close()

fd.close()

读取csv文件

with open('job.csv', 'r') as f:

reader = csv.reader(f)

for row in reader:

print(row)

写入csv文件

#创建CSV文件并写入第一行

def createCsv(file):

if not os.path.exists(file):

csvfile = open(file, 'a+', encoding='utf-8', newline='')

writer = csv.writer(csvfile)

writer.writerow(paramname)

else:

csvfile = open(file, 'a+', newline='')

writer = csv.writer(csvfile)

return writer

python调用JAVA

import sys

import jpype

name = sys.argv[1]

jarpath = '/home/dsadm/why/python'

jpype.startJVM(jpype.getDefaultJVMPath(), "-Djava.ext.dirs=%s" % jarpath)

DECRYPT = jpype.JClass('why.fmrt.decrypt.DECRYPT')

upperName =DECRYPT.decrypt(name)

print(upperName)

jpype.shutdownJVM()

简单验证码破解

from urllib.request import urlretrieve

from urllib.request import urlopen

from bs4 import BeautifulSoup

import subprocess

import requests

from PIL import Image

from PIL import ImageOps

def cleanImage(imagePath):

image = Image.open(imagePath)

image = image.point(lambda x: 0 if x<143 else 255)

borderImage = ImageOps.expand(image,border=20,fill='white')

borderImage.save(imagePath)

html = urlopen("http://www.pythonscraping.com/humans-only")

bsObj = BeautifulSoup(html, "html.parser")

#Gather prepopulated form values

imageLocation = bsObj.find("img", {"title": "Image CAPTCHA"})["src"]

formBuildId = bsObj.find("input", {"name":"form_build_id"})["value"]

captchaSid = bsObj.find("input", {"name":"captcha_sid"})["value"]

captchaToken = bsObj.find("input", {"name":"captcha_token"})["value"]

captchaUrl = "http://pythonscraping.com"+imageLocation

urlretrieve(captchaUrl, "captcha.jpg")

cleanImage("captcha.jpg")

p = subprocess.Popen(["tesseract", "captcha.jpg", "captcha"], stdout=

subprocess.PIPE,stderr=subprocess.PIPE)

p.wait()

f = open("captcha.txt", "r")

#Clean any whitespace characters

captchaResponse = f.read().replace(" ", "").replace("\n", "")

print("Captcha solution attempt: "+captchaResponse)

if len(captchaResponse) == 5:

params = {"captcha_token":captchaToken, "captcha_sid":captchaSid,

"form_id":"comment_node_page_form", "form_build_id": formBuildId,

"captcha_response":captchaResponse, "name":"Ryan Mitchell",

"subject": "I come to seek the Grail",

"comment_body[und][0][value]":

"...and I am definitely not a bot"}

r = requests.post("http://www.pythonscraping.com/comment/reply/10",

data=params)

responseObj = BeautifulSoup(r.text)

if responseObj.find("div", {"class":"messages"}) is not None:

print(responseObj.find("div", {"class":"messages"}).get_text())

else:

print("There was a problem reading the CAPTCHA correctly!")

滑块验证码破解

from selenium import webdriver

from selenium.webdriver.support.ui import WebDriverWait

from selenium.webdriver.common.action_chains import ActionChains

import PIL.Image as image

import time,re, random

import requests

try:

from StringIO import StringIO

except ImportError:

from io import StringIO

#爬虫模拟的浏览器头部信息

agent = 'Mozilla/5.0 (Windows NT 5.1; rv:33.0) Gecko/20100101 Firefox/33.0'

headers = {

'User-Agent': agent

}

# 根据位置对图片进行合并还原

# filename:图片

# location_list:图片位置

#内部两个图片处理函数的介绍

#crop函数带的参数为(起始点的横坐标，起始点的纵坐标，宽度，高度）

#paste函数的参数为(需要修改的图片，粘贴的起始点的横坐标，粘贴的起始点的纵坐标）

def get_merge_image(filename,location_list):

#打开图片文件

im = image.open(filename)

#创建新的图片,大小为260*116

new_im = image.new('RGB', (260,116))

im_list_upper=[]

im_list_down=[]

# 拷贝图片

for location in location_list:

#上面的图片

if location['y']==-58:

im_list_upper.append(im.crop((abs(location['x']),58,abs(location['x'])+10,166)))

#下面的图片

if location['y']==0:

im_list_down.append(im.crop((abs(location['x']),0,abs(location['x'])+10,58)))

new_im = image.new('RGB', (260,116))

x_offset = 0

#黏贴图片

for im in im_list_upper:

new_im.paste(im, (x_offset,0))

x_offset += im.size[0]

x_offset = 0

for im in im_list_down:

new_im.paste(im, (x_offset,58))

x_offset += im.size[0]

return new_im

#下载并还原图片

# driver:webdriver

# div:图片的div

def get_image(driver,div):

#找到图片所在的div

background_images=driver.find_elements_by_xpath(div)

location_list=[]

imageurl=''

#图片是被CSS按照位移的方式打乱的,我们需要找出这些位移,为后续还原做好准备

for background_image in background_images:

location={}

#在html里面解析出小图片的url地址，还有长高的数值

location['x']=int(re.findall("background-image: url$\"(.*)\"$; background-position: (.*)px (.*)px;",background_image.get_attribute('style'))[0][1])

location['y']=int(re.findall("background-image: url$\"(.*)\"$; background-position: (.*)px (.*)px;",background_image.get_attribute('style'))[0][2])

imageurl=re.findall("background-image: url$\"(.*)\"$; background-position: (.*)px (.*)px;",background_image.get_attribute('style'))[0][0]

location_list.append(location)

#替换图片的后缀,获得图片的URL

imageurl=imageurl.replace("webp","jpg")

#获得图片的名字

imageName = imageurl.split('/')[-1]

#获得图片

session = requests.session()

r = session.get(imageurl, headers = headers, verify = False)

#下载图片

with open(imageName, 'wb') as f:

f.write(r.content)

f.close()

#重新合并还原图片

image=get_merge_image(imageName, location_list)

return image

#对比RGB值

def is_similar(image1,image2,x,y):

pass

#获取指定位置的RGB值

pixel1=image1.getpixel((x,y))

pixel2=image2.getpixel((x,y))

for i in range(0,3):

# 如果相差超过50则就认为找到了缺口的位置

if abs(pixel1[i]-pixel2[i])>=50:

return False

return True

#计算缺口的位置

def get_diff_location(image1,image2):

i=0

# 两张原始图的大小都是相同的260*116

# 那就通过两个for循环依次对比每个像素点的RGB值

# 如果相差超过50则就认为找到了缺口的位置

for i in range(0,260):

for j in range(0,116):

if is_similar(image1,image2,i,j)==False:

return i

#根据缺口的位置模拟x轴移动的轨迹

def get_track(length):

pass

list=[]

#间隔通过随机范围函数来获得,每次移动一步或者两步

x=random.randint(1,3)

#生成轨迹并保存到list内

while length-x>=5:

list.append(x)

length=length-x

x=random.randint(1,3)

#最后五步都是一步步移动

for i in range(length):

list.append(1)

return list

#滑动验证码破解程序

def main():

#打开火狐浏览器

driver = webdriver.Firefox()

#用火狐浏览器打开网页

driver.get("http://www.geetest.com/exp_embed")

#等待页面的上元素刷新出来

WebDriverWait(driver, 30).until(lambda the_driver: the_driver.find_element_by_xpath("//div[@class='gt_slider_knob gt_show']").is_displayed())

WebDriverWait(driver, 30).until(lambda the_driver: the_driver.find_element_by_xpath("//div[@class='gt_cut_bg gt_show']").is_displayed())

WebDriverWait(driver, 30).until(lambda the_driver: the_driver.find_element_by_xpath("//div[@class='gt_cut_fullbg gt_show']").is_displayed())

#下载图片

image1=get_image(driver, "//div[@class='gt_cut_bg gt_show']/div")

image2=get_image(driver, "//div[@class='gt_cut_fullbg gt_show']/div")

#计算缺口位置

loc=get_diff_location(image1, image2)

#生成x的移动轨迹点

track_list=get_track(loc)

#找到滑动的圆球

element=driver.find_element_by_xpath("//div[@class='gt_slider_knob gt_show']")

location=element.location

#获得滑动圆球的高度

y=location['y']

#鼠标点击元素并按住不放

print ("第一步,点击元素")

ActionChains(driver).click_and_hold(on_element=element).perform()

time.sleep(0.15)

print ("第二步，拖动元素")

track_string = ""

for track in track_list:

#不能移动太快,否则会被认为是程序执行

track_string = track_string + "{%d,%d}," % (track, y - 445)

#xoffset=track+22:这里的移动位置的值是相对于滑动圆球左上角的相对值，而轨迹变量里的是圆球的中心点，所以要加上圆球长度的一半。

#yoffset=y-445:这里也是一样的。不过要注意的是不同的浏览器渲染出来的结果是不一样的，要保证最终的计算后的值是22，也就是圆球高度的一半

ActionChains(driver).move_to_element_with_offset(to_element=element, xoffset=track+22, yoffset=y-445).perform()

#间隔时间也通过随机函数来获得,间隔不能太快,否则会被认为是程序执行

time.sleep(random.randint(10,50)/100)

print (track_string)

#xoffset=21，本质就是向后退一格。这里退了5格是因为圆球的位置和滑动条的左边缘有5格的距离