from pyecharts.charts import Bar
from pyecharts import options as opts
from snapshot_phantomjs import snapshot
# 导入输出图片工具
from pyecharts.render import make_snapshot
# 使用snapshot-selenium 渲染图片
from snapshot_selenium import snapshot
# 创建一个柱状图Bar实例
bar = (
Bar()
# 添加X轴数据
.add_xaxis(["衬衫", "羊毛衫", "雪纺衫", "裤子", "高跟鞋", "袜子"])
# 添加Y轴数据,系列的名称
.add_yaxis("商家A", [5, 20, 36, 10, 75, 90])
.add_yaxis("商家B", [8, 15, 60, 20, 25, 30])
# 添加标题
.set_global_opts(title_opts=opts.TitleOpts(title="主标题: 双十一销量", subtitle="副标题:服饰类"))
)
# 输出保存为图片
make_snapshot(snapshot, bar.render("./图片.html"), "./003_Options配置项_自定义样式_保存图片.jpeg")
# make_snapshot(snapshot, bar.render(), "./图片.png")import requests
import json
from lxml import etree
import pandas as pd
import time
from pyecharts import options as opts
from pyecharts.charts import Bar
def sleeptime(hour, min, sec): #时间转换
return hour * 3600 + min * 60 + sec
def Get_HTML(url):
headers={'User-Agent':'Mozilla/5.0'}
try:
r=requests.get(url,headers=headers)
r.raise_for_status()
r.encoding=r.apparent_encoding
return r.text
except:
print("error!")
def Parse_page():
C_name,Ljqz,Cured,Died,Xcqz,Xzqz=[],[],[],[],[],[]
url='https://m.look.360.cn/events/feiyan?sv=&version=&market=&device=2&net=4&stype=&scene=&sub_scene=&refer_scene=&refer_subscene=&f=jsonp&location=true&sort=2&_=1649252949072&callback=jsonp2'
re=Get_HTML(url)
re=re[7:-2]
response=json.loads(re)
for i in range(34):
c_name=response['data'][i]['data']['provinceName'] #城市名称
ljqz=response['data'][i]['diagnosed'] #累计确诊
cured=response['data'][i]['cured'] #治愈
died=response['data'][i]['died'] #死亡
xcqz=response['data'][i]['currentConfirmed'] #现存确诊
xzqz=response['data'][i]['diffDiagnosed'] #新增确诊
C_name.append(c_name)
Ljqz.append(ljqz)
Cured.append(cured)
Died.append(died)
Xcqz.append(xcqz)
Xzqz.append(xzqz)
# columns=['新增确诊','现存确诊','累计确诊','治愈人数','死亡人数']
# index=C_name
data={
"地区":C_name,
"新增确诊":Xzqz,
"现存确诊":Xcqz,
"累计确诊":Ljqz,
"治愈人数":Cured,
"死亡人数":Died
}
# print(data)
return data
def View():
data=Parse_page()
c = (
Bar()
.add_xaxis(data['地区'])
.add_yaxis("新增确诊",data['新增确诊'], stack="stack1")
.add_yaxis("现存确诊",data['现存确诊'] , stack="stack1")
.add_yaxis("累计确诊",data['累计确诊'] , stack="stack1")
.add_yaxis("治愈人数",data['治愈人数'] , stack="stack1")
.add_yaxis("死亡人数",data['死亡人数'] , stack="stack1")
.set_global_opts(
title_opts=opts.TitleOpts(title="国内疫情数据数据（全部）"),
datazoom_opts=opts.DataZoomOpts(type_="inside"),
)
.render("国内疫情数据数据.html")
)
def main():
data=Parse_page()
data=pd.DataFrame(data)
second = sleeptime(24,0,0)
while True:
# print(data)
data.to_csv('./国内疫情数据.csv')
View()
time.sleep(second)
# main()
data=Parse_page()
data=pd.DataFrame(data)
dataimport requests
def BaiDu_fanyi(url):
kw = input('请输入待翻译的英文/中文单词：')
data={'kw':kw}
headers = {
'User_Agent':'Mozilla/5.0',
'content-length': str(len(data)),
'content-type': 'application/x-www-form-urlencoded; charset=UTF-8',
'referer': 'https://fanyi.baidu.com/',
'x-requested-with': 'XMLHttpRequest'
}
response=requests.post(url,headers=headers,data=data)
# print(response.json())
# 请输入待翻译的英文/中文单词：word
# {'errno': 0, 'data': [{'k': 'word', 'v': 'n. 单词; 话语; 诺言; 消息 vt. 措辞，用词; 用言语表达 vi. 讲话'},
# {'k': 'Word', 'v': '微软公司生产的文字处理软件。; [人名] 沃德'}, {'k': 'words', 'v': 'n. 字( word的名词复数 );
# （说的）话; 诺言; 口令'}, {'k': 'Words', 'v': '[医]言语'}, {'k': 'WORDS', 'v': 'abbr. Western Operational Research Discussion Soci'}]}
result=''
for i in response.json()['data']:
result+=i['v']+'\n'
print("{}的翻译结果为：".format(kw))
print(result)
url='https://fanyi.baidu.com/sug'
BaiDu_fanyi(url)打开摄像头
import cv2
import numpy as np
def video_demo():
capture = cv2.VideoCapture(0)#0为电脑内置摄像头
while(True):
ret, frame = capture.read()#摄像头读取,ret为是否成功打开摄像头,true,false。 frame为视频的每一帧图像
frame = cv2.flip(frame, 1)#摄像头是和人对立的，将图像左右调换回来正常显示。
cv2.imshow("video", frame)
c = cv2.waitKey(50)
if c == 27:
break
video_demo()
cv2.destroyAllWindows()from pyecharts import options as opts
from pyecharts.charts import Bar, Grid, Line, Liquid, Page, Pie
from pyecharts.faker import Faker
from pyecharts.globals import SymbolType
#饼状图
def pie():
c = (
Pie()
.add("", [['跳水', 12], ['射击', 11], ['举重', 8], ['竞技体操', 8], ['乒乓球', 7], ['游泳', 6], ['羽毛球', 6], ['田径', 5], ['静水皮划艇', 3], ['蹦床体操', 3], ['自由式摔跤', 3], ['赛艇', 3], ['空手道', 2], ['拳击', 2], ['帆船', 2], ['花样游泳', 2], ['跆拳道', 1], ['场地自行车赛', 1], ['古典式摔跤', 1], ['击剑', 1], ['三人篮球', 1]],center=["50%", "60%"],)
.set_series_opts(label_opts=opts.LabelOpts(formatter="{b}: {c}"))
# .render("饼图.html")
)
return c
# return c.render_notebook()
#水滴图
def wb():
c = (
Liquid()
.add("", [0.3, 0.7], is_outline_show=False, shape=SymbolType.DIAMOND)
.set_global_opts(title_opts=opts.TitleOpts(title="Liquid-Shape-Diamond"))
# .render("liquid_shape_diamond.html")
)
return c
# return c.render_notebook()
#柱状图
def bar():
c = (
Bar()
.add_xaxis(Faker.choose())
.add_yaxis("商家A", Faker.values(), stack="stack1")
.add_yaxis("商家B", Faker.values(), stack="stack1")
.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
.set_global_opts(title_opts=opts.TitleOpts(title="Bar-堆叠数据（全部）"))
# .render("bar_stack0.html")
)
return c
# return c.render_notebook()
page = Page(layout=Page.DraggablePageLayout, page_title="2020东京奥运会奖牌榜")
# 在页面中添加图表
page.add(
pie(),
wb(),
bar())
page.render('test.html')CPU 架构
import platform
print("操作系统名称及版本号:",platform.platform()) #获取操作系统名称及版本号
print("操作系统版本号:",platform.version()) #获取操作系统版本号
print("操作系统的位数:",platform.architecture()) #获取操作系统的位数
print("计算机类型:",platform.machine()) # 计算机类型
print("计算机网络名称：",platform.node()) #计算机网络名称
print("计算机处理器信息:",platform.processor()) #计算机处理器信息
print("上面所有的信息汇总:",platform.uname()) #上面所有的信息汇总CPU 核数
# 方法一：
# 如果当前Python版本大于2.6，可以简单的通过下面模块来获取当前cpu数量，
import multiprocessing
print("CPU 核数:",multiprocessing.cpu_count())
# 如果是比较老的Python版本，可以用下面的模块来获取当前cpu数量：
import psutil
print("CPU 核数:",psutil.cpu_count())
# 方法二：
# Python可以读系统文件/proc/cpuinfo，过滤“processor”字段统计核心数。
def get_cpu_info():
processor_cnt = 0
cpu_model = ""
f_cpu_info = open("/proc/cpuinfo")
try:
for line in f_cpu_info:
# print(line)
if (line.find("processor") == 0):
processor_cnt += 1
print("cpu counts:{}" .format(processor_cnt))
finally:
f_cpu_info.close()
get_cpu_info()内存信息
# 读取内存信息可以直接读系统文件/proc/meminfo。
def get_mem_info():
mem_info = ""
f_mem_info = open("/proc/meminfo")
try:
for line in f_mem_info:
# print(line.strip())
if (line.find("MemTotal") == 0):
mem_info += line.strip()+ ", "
elif (line.find("SwapTotal") == 0):
mem_info += line.strip()
break;
print("mem_info---- {:s}".format(mem_info))
finally:
f_mem_info.close()
get_mem_info()磁盘信息
import os
import subprocess
def get_disc_info():
# 方法一：
disc_info=os.popen("df -h").read()
disc_info=subprocess.Popen("df -h",shell=True).communicate()[0]
print(disc_info)
# 方法二：
# pipe=subprocess.Popen("df -h",stdout=subprocess.PIPE,shell=True)
# disc_info=pipe.stdout.read()
# print(disc_info)
get_disc_info()网络信息
import netifaces
routingGateway = netifaces.gateways()['default'][netifaces.AF_INET][0] #网关
routingNicName = netifaces.gateways()['default'][netifaces.AF_INET][1] #网络适配器信息
for interface in netifaces.interfaces():
if interface == routingNicName:
#print (netifaces.ifaddresses(interface))
try:
routingIPAddr = netifaces.ifaddresses(interface)[netifaces.AF_INET][0]['addr'] #获取IP
except KeyError:
pass
#print ("Routing IP Address:%s"% routingIPAddr)
return routingIPAddr
if __name__ == "__main__":
try:
print ("Routing IP Address:",GetNetworkIP())
except:
print ("Unable to get the address, there may not be installed netifaces module! command:pip install netifaces")