Python爬取全国大学排名 用pyecharts进行大屏可视化
2024-03-21 17:24:59
爬取全国大学排名 用pyecharts进行可视化
院校网址：http://college.gaokao.com/schlist/p
F12 先找到对应的全部list
需要先安装requests,lxml
可直接用 pip install requests pip install lxml 命令安装
导入需要的相关包
import requests
from lxml import etree
import time
import random
import csv#避免网页反爬虫
headers = {'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/75.0.3770.100 Safari/537.36'}
url = 'http://college.gaokao.com/schlist/p'
response = requests.get(url,headers=headers)
time.sleep(random.randint(0,2)) #同样用于反爬虫
再调用 lxml 获取到整页的学校名称
selector = etree.HTML(response.text)
all_list = selector.xpath('//*[starts-with(@class,"scores_List")]/dl') #页面中全部学校 全部dl列
调用 for 循环获取dl中所有需要的数据
for sel in all_list:name = sel.xpath('dt/strong/a/text()')[0] #学校名称place = sel.xpath('dd/ul/li[1]/text()')[0][6:] #高校所在地type = sel.xpath('dd/ul/li[3]/text()')[0][5:] #高校类型nature = sel.xpath('dd/ul/li[5]/text()')[0][5:] #高校性质try: #获取的数据院校特色有地方空缺为避免出现空缺无法爬取数据tese = sel.xpath('string(dd/ul/li[2])')[5:] #院校特色except:tese='' #遇到空缺值让院校特色等于nulllishu = sel.xpath('dd/ul/li[4]/text()')[0][5:] #高校隶属最后将爬取的数据保存（保存成CSV文件格式）
with open('school.csv','a',encoding='gbk',newline='')as file:writer = csv.writer(file)try:writer.writerow(item)except Exception as e:print(e)爬取的部分内容如下
最后用函数将全部代码串接
附上完整代码
import requests
from lxml import etree
import time
import random #用于反爬 产生随机数
import csvdef csv_writer(item):with open('school.csv','a',encoding='gbk',newline='')as file: #newline='' 保证写入到CSV中不空行writer = csv.writer(file)try:writer.writerow(item)except Exception as e:print(e)
def spider(url_):time.sleep(random.randint(0,2)) # 同样用于反爬虫res = requests.get(url_,headers=headers)return etree.HTML(res.text)
def parse(list_url):selector = spider(list_url)all_list = selector.xpath('//*[starts-with(@class,"scores_List")]/dl') #页面中全部学校 全部dl列for sel in all_list:try:name = sel.xpath('dt/strong/a/text()')[0] #学校名称except:name = ''place = sel.xpath('dd/ul/li[1]/text()')[0][6:] #高校所在地type = sel.xpath('dd/ul/li[3]/text()')[0][5:] #高校类型nature = sel.xpath('dd/ul/li[5]/text()')[0][5:] #高校性质try: #获取的数据院校特色有地方空缺为避免出现空缺无法爬取数据tese = sel.xpath('string(dd/ul/li[2])')[5:] #院校特色except:tese='' #遇到空缺值让院校特色等于nulllishu = sel.xpath('dd/ul/li[4]/text()')[0][5:] #高校隶属# print(name,place,type,nature,tese,lishu)csv_writer([name,place,type,nature,tese,lishu])#避免网页反爬虫
headers = {'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/75.0.3770.100 Safari/537.36'}
url_ = 'http://college.gaokao.com/schlist/p'
all_url = [url_ + str(i) for i in range(1,107)] #提取到所有学校的全部网址
for url in all_url:parse(url)
*为了方便观看爬取到情况将爬取的文件进行整合并进行可视化
**整合内容如下：
1、柱状图
from pyecharts.charts import Bar,Line
from pyecharts import options as opts
from pyecharts.globals import ThemeType #渲染主题
import pandas as pd
datafile = r'D:/Demo/school.xlsx'
data = pd.read_excel(datafile)x1 = data['城市'].tolist() #变成列表形式
y1 = data['本科'].tolist()
y2 = data['专科'].tolist()# 更新后有两种调用方法 不习惯链式调用依旧可以单独调用方法
# 链式调用 V1版本要求>=1.0
bar = (Bar(init_opts=opts.InitOpts(theme=ThemeType.CHALK)) #主题.add_xaxis(x1).add_yaxis("本科",y1,markpoint_opts=opts.MarkPointOpts(data=[opts.MarkPointItem(type_="max",name="最大值"),opts.MarkPointItem(type_="min",name="最小值")]),markline_opts=opts.MarkLineOpts(data=[opts.MarkLineItem(type_="max",name="最大值"),opts.MarkLineItem(type_="min",name="最小值")])).add_yaxis("专科",y2,markpoint_opts=opts.MarkPointOpts(data=[opts.MarkPointItem(type_="max",name="最大值"),opts.MarkPointItem(type_="min",name="最小值")]),markline_opts=opts.MarkLineOpts(data=[opts.MarkLineItem(type_="max",name="最大值"),opts.MarkLineItem(type_="min",name="最小值")]))# .set_series_opts(# label_opts=opts.LabelOpts(is_show=False), #标记点数字隐藏# markline_opts=opts.MarkLineOpts(data=[opts.MarkLineItem(type_="max",name="最大值")])# )# .reversal_axis()#柱状左侧# .set_series_opts(label_opts=opts.LabelOpts(position="right")).set_global_opts(title_opts=opts.TitleOpts(title="全国高校",subtitle="情况"),datazoom_opts=[opts.DataZoomOpts(type_="slider")],#inside区域缩放 slider左右缩放xaxis_opts=opts.AxisOpts(name='城市',axislabel_opts=opts.LabelOpts(rotate=30)),#标签顺时针旋转30度yaxis_opts=opts.AxisOpts(name='数量')).render(path='bar.html')
)# 柱折混用
# line = (
# Line(init_opts=opts.InitOpts(theme=ThemeType.CHALK))
# .add_xaxis(x1)
# .add_yaxis("本科",y1)
# .add_yaxis("专科",y2)
# .set_global_opts(
# title_opts=opts.TitleOpts(title="全国高校", subtitle="情况"),
# datazoom_opts=[opts.DataZoomOpts(type_="slider")],
# xaxis_opts=opts.AxisOpts(name='城市', axislabel_opts=opts.LabelOpts(rotate=30)),
# yaxis_opts=opts.AxisOpts(name='数量'),
# toolbox_opts=opts.ToolboxOpts(is_show=True)
# )
# )
# bar.overlap(line).render("bar.html")# 单独调用# bar = Bar()
# bar.add_xaxis(x1)
# bar.add_yaxis("本科",y1)
# bar.add_yaxis("专科",y2)
# bar.set_global_opts(title_opts=opts.TitleOpts(title="大学",subtitle="情况"))
# bar.render(path='bar.html')
2、折线图
#-*- coding:utf-8 -*-
from pyecharts.charts import Line
import pandas as pd
from pyecharts import options as opts
from pyecharts.globals import ThemeType #渲染主题
datafile = r'D:/Demo/school.xlsx'
data = pd.read_excel(datafile)x1 = data['城市'].tolist() #变成列表形式
y1 = data['本科'].tolist()
y2 = data['专科'].tolist()# 单独调用
# line = Line()
# line.add_xaxis(x1)
# line.add_yaxis("本科",y1)
# line.add_yaxis("专科",y2)
# line.set_global_opts(title_opts=opts.TitleOpts(title=""))
# line.render(path='line.html')# 链式调用
line = (Line(init_opts=opts.InitOpts(theme=ThemeType.CHALK)).add_xaxis(x1).add_yaxis("本科",y1,markpoint_opts=opts.MarkPointOpts(data=[opts.MarkPointItem(type_="max",name="最大值")]),markline_opts=opts.MarkLineOpts(data=[opts.MarkLineItem(type_="max",name="最大值")])).add_yaxis("专科",y2,markpoint_opts=opts.MarkPointOpts(data=[opts.MarkPointItem(type_="max",name="最大值")]),markline_opts=opts.MarkLineOpts(data=[opts.MarkLineItem(type_="max",name="最大值")])).set_global_opts(title_opts=opts.TitleOpts(title="全国高校", subtitle="情况"),datazoom_opts=[opts.DataZoomOpts(type_="slider")],xaxis_opts=opts.AxisOpts(name='城市', axislabel_opts=opts.LabelOpts(rotate=30)),yaxis_opts=opts.AxisOpts(name='数量'),toolbox_opts=opts.ToolboxOpts(is_show=True) # 显示工具箱).render(path='line.html')
)
3、环形图
#-*- coding:utf-8 -*-
from pyecharts.charts import Pie
import pandas as pd
from pyecharts import options as opts
from pyecharts.globals import ThemeType #渲染主题
datafile = r'D:/Demo/school.xlsx'
data = pd.read_excel(datafile)
# 单独调用
# pie = Pie()
# pie.add("", [list(z) for z in zip(data['城市'].values.tolist(), data['专科'].values.tolist())],
# radius=["30%", "75%"],
# center=["40%", "50%"],
# rosetype="radius")
# pie.set_global_opts(
# title_opts=opts.TitleOpts(title=""),
# legend_opts=opts.LegendOpts(
# type_="scroll", pos_left="80%", orient="vertical"
# )
# )
# pie.render('pie.html')# 链式调用
pie =(Pie(init_opts=opts.InitOpts(theme=ThemeType.CHALK)).add("本科", [list(z) for z in zip(data['城市'].values.tolist(), data['本科'].values.tolist())],radius=["32%", "38%"],center=["60%", "50%"],rosetype="radius").add("专科", [list(z) for z in zip(data['城市'].values.tolist(), data['专科'].values.tolist())],radius=["32%", "38%"],center=["20%", "50%"],rosetype="radius").set_global_opts(title_opts=opts.TitleOpts(title="全国高校"),legend_opts=opts.LegendOpts(type_="scroll", pos_left="80%", orient="vertical")).render(path='pie.html')
)
4、散点图
#-*- coding:utf-8 -*-
import pyecharts.options as opts
from pyecharts.charts import Scatter
from pyecharts.globals import ThemeType #渲染主题
import pandas as pd
datafile = r'D:/Demo/school.xlsx'
data = pd.read_excel(datafile)
x1 = data['城市'].tolist()
y1 = data['本科'].tolist()
y2 = data['专科'].tolist()#单独调用方法
# scatter = Scatter()
# scatter.add_xaxis(x1)
# scatter.add_yaxis('本科',y1)
# scatter.add_yaxis('专科',y2)
# scatter.set_global_opts(title_opts=opts.TitleOpts(title="高校"))
# scatter.render(path='scatter.html')# 链式调用 效果和单独调用一样 但更方便观看
scatter = (Scatter(init_opts=opts.InitOpts(theme=ThemeType.CHALK)).add_xaxis(x1).add_yaxis("本科", y1, markpoint_opts=opts.MarkPointOpts(data=[opts.MarkPointItem(type_="max", name="最大值")]),markline_opts=opts.MarkLineOpts(data=[opts.MarkLineItem(type_="max", name="最大值")])).add_yaxis("专科", y2, markpoint_opts=opts.MarkPointOpts(data=[opts.MarkPointItem(type_="max", name="最大值")]),markline_opts=opts.MarkLineOpts(data=[opts.MarkLineItem(type_="max", name="最大值")])).set_global_opts(title_opts=opts.TitleOpts(title=""),datazoom_opts=[opts.DataZoomOpts(type_="slider")],xaxis_opts=opts.AxisOpts(name='城市',axislabel_opts=opts.LabelOpts(rotate=30)),yaxis_opts=opts.AxisOpts(name='数量')).render(path='scatter.html')
)
5、Geo
#-*- coding:utf-8 -*-
from pyecharts.charts import Geo
import pandas as pd
from pyecharts import options as opts
from pyecharts.globals import ThemeType #渲染主题
datafile = r'D:/Demo/school.xlsx'
data = pd.read_excel(datafile)# 单独调用
# geo = Geo()
# geo.add_schema(maptype="china")
# geo.add("高校分布图",[list(z) for z in zip(data['城市'].values.tolist(), data['本科'].values.tolist())])
# geo.set_global_opts(visualmap_opts=opts.VisualMapOpts(is_piecewise=True,max_=150),
# title_opts=opts.TitleOpts(title="各地区高校数量"))
# geo.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
# geo.render(path='geo.html')# 链式调用
geo = (Geo(init_opts=opts.InitOpts(theme=ThemeType.CHALK)).add_schema(maptype="china").add("本科",[list(z) for z in zip(data['城市'].values.tolist(), data['本科'].values.tolist())]).add("专科",[list(z) for z in zip(data['城市'].values.tolist(), data['专科'].values.tolist())]).set_global_opts(visualmap_opts=opts.VisualMapOpts(is_piecewise=True,max_=150),title_opts=opts.TitleOpts(title="各地区高校数量")).set_series_opts(label_opts=opts.LabelOpts(is_show=False)).render(path='geo.html')
)
6、Map
#-*- coding:utf-8 -*-
from pyecharts.charts import Map
import pandas as pd
from pyecharts import options as opts
from pyecharts.globals import ThemeType #渲染主题
datafile = r'D:/Demo/school.xlsx'
data = pd.read_excel(datafile)# 单独调用
# map = Map()
# map.add("高校分布图",[list(z) for z in zip(data['城市'].values.tolist(), data['本科'].values.tolist())])
# map.set_global_opts(visualmap_opts=opts.VisualMapOpts(max_=150),
# title_opts=opts.TitleOpts(title="各地区高校数量"))
# map.render(path='map.html')# 链式调用
map = (Map(init_opts=opts.InitOpts(theme=ThemeType.CHALK)).add("本科",[list(z) for z in zip(data['城市'].values.tolist(), data['本科'].values.tolist())]).add("专科",[list(z) for z in zip(data['城市'].values.tolist(), data['专科'].values.tolist())]).set_global_opts(visualmap_opts=opts.VisualMapOpts(max_=150),title_opts=opts.TitleOpts(title="各地区高校数量")).render(path='map.html')
)
7、词云图
#-*- coding:utf-8 -*-
from pyecharts.charts import WordCloud
from pyecharts import options as opts
import pandas as pd
from pyecharts.globals import ThemeType #渲染主题datafile = r'D:/Demo/school.xlsx'
data = pd.read_excel(datafile)
num = [10000, 5181, 4386, 4055, 3000,2467, 2244, 1898,1600, 1484, 1112,1000,965, 847, 582, 555, 550, 500,462, 366, 360,300, 282, 273, 265,245] #词大小size
words = list(data['城市']) #调取转换为列表
# print(type(words))
# print(type(num)) #查看类型
word = [(a,b)for a,b in zip(words,num)] #将num，words两个列表整合合并
# print(word)# 链式调用
wordcloud = (WordCloud(init_opts=opts.InitOpts(theme=ThemeType.CHALK)).add('',word,word_size_range=[20,100],shape='circle') # 词云图轮廓，有 'circle', 'cardioid', 'diamond', 'triangle-forward', 'triangle', 'pentagon', 'star' 可选.set_global_opts(title_opts=opts.TitleOpts(title="")).render(path="wordcloud.html")
)# 单独调用
# wordcloud = WordCloud()
# wordcloud.add('',word,word_size_range=[20,100])
# wordcloud.set_global_opts(title_opts=opts.TitleOpts(title="wordcloud"))
# wordcloud.render(path="wordcloud.html")
代码整合将图片放置同一页面中
#-*- coding:utf-8 -*-
from pyecharts.charts import Bar,Line,Pie,Map,WordCloud,Scatter,Page
import pandas as pd
from pyecharts import options as opts
from pyecharts.globals import ThemeType #渲染主题
datafile = r'D:/Demo/school.xlsx'
data = pd.read_excel(datafile)
# coding='utf-8'
x1 = data['城市'].tolist() #变成列表形式
y1 = data['本科'].tolist()
y2 = data['专科'].tolist()
num = [10000, 5181, 4386, 4055, 3000,2467, 2244, 1898,1600, 1484, 1112,1000,965, 847, 582, 555, 550, 500,462, 366, 360,300, 282, 273, 265,245] #词大小size
words = list(data['城市']) #调取前十个转换为列表
# print(type(words))
# print(type(num)) #查看类型
word = [(a,b)for a,b in zip(words,num)] #将num，words两个列表整合合并
# 链式调用
def bar_slider() -> Bar:c = (Bar(init_opts=opts.InitOpts(theme=ThemeType.CHALK)) # 主题.add_xaxis(x1).add_yaxis("本科", y1, markpoint_opts=opts.MarkPointOpts(data=[opts.MarkPointItem(type_="max", name="最大值"), opts.MarkPointItem(type_="min", name="最小值")]), #标记点markline_opts=opts.MarkLineOpts(data=[opts.MarkLineItem(type_="max", name="最大值"), # 标记线opts.MarkLineItem(type_="min", name="最小值")])).add_yaxis("专科", y2, markpoint_opts=opts.MarkPointOpts(data=[opts.MarkPointItem(type_="max", name="最大值"), opts.MarkPointItem(type_="min", name="最小值")]),markline_opts=opts.MarkLineOpts(data=[opts.MarkLineItem(type_="max", name="最大值"),opts.MarkLineItem(type_="min", name="最小值")]))# .set_series_opts(# label_opts=opts.LabelOpts(is_show=False),# markline_opts=opts.MarkLineOpts(data=[opts.MarkLineItem(type_="max",name="最大值")])# ).set_global_opts(title_opts=opts.TitleOpts(title="", subtitle=""),datazoom_opts=[opts.DataZoomOpts(type_="slider")],#inside区域缩放 slider左右缩放xaxis_opts=opts.AxisOpts(name='城市', axislabel_opts=opts.LabelOpts(rotate=30)), #标签顺时针旋转30度yaxis_opts=opts.AxisOpts(name='数量')))return cdef line_slider() -> Line:c = (Line(init_opts=opts.InitOpts(theme=ThemeType.CHALK)).add_xaxis(x1).add_yaxis("本科", y1, markpoint_opts=opts.MarkPointOpts(data=[opts.MarkPointItem(type_="max", name="最大值")]),markline_opts=opts.MarkLineOpts(data=[opts.MarkLineItem(type_="max", name="最大值")])).add_yaxis("专科", y2, markpoint_opts=opts.MarkPointOpts(data=[opts.MarkPointItem(type_="max", name="最大值")]),markline_opts=opts.MarkLineOpts(data=[opts.MarkLineItem(type_="max", name="最大值")])).set_global_opts(title_opts=opts.TitleOpts(title="", subtitle=""),datazoom_opts=[opts.DataZoomOpts(type_="slider")],xaxis_opts=opts.AxisOpts(name='城市', axislabel_opts=opts.LabelOpts(rotate=30)),yaxis_opts=opts.AxisOpts(name='数量'),toolbox_opts=opts.ToolboxOpts(is_show=True) # 显示工具箱))return c
def pie_slider() -> Pie:c = (Pie(init_opts=opts.InitOpts(theme=ThemeType.CHALK)).add("本科", [list(z) for z in zip(data['城市'].values.tolist(), data['本科'].values.tolist())],radius=["32%", "38%"],center=["60%", "50%"],rosetype="radius").add("专科", [list(z) for z in zip(data['城市'].values.tolist(), data['专科'].values.tolist())],radius=["32%", "38%"],center=["20%", "50%"],rosetype="radius").set_global_opts(title_opts=opts.TitleOpts(title=""),legend_opts=opts.LegendOpts(type_="scroll", pos_left="80%", orient="vertical")))return c
def scatter_slider() -> Scatter:c = (Scatter(init_opts=opts.InitOpts(theme=ThemeType.CHALK)).add_xaxis(x1).add_yaxis("本科", y1, markpoint_opts=opts.MarkPointOpts(data=[opts.MarkPointItem(type_="max", name="最大值")]),markline_opts=opts.MarkLineOpts(data=[opts.MarkLineItem(type_="max", name="最大值")])).add_yaxis("专科", y2, markpoint_opts=opts.MarkPointOpts(data=[opts.MarkPointItem(type_="max", name="最大值")]),markline_opts=opts.MarkLineOpts(data=[opts.MarkLineItem(type_="max", name="最大值")])).set_global_opts(title_opts=opts.TitleOpts(title=""),datazoom_opts=[opts.DataZoomOpts(type_="slider")],xaxis_opts=opts.AxisOpts(name='城市', axislabel_opts=opts.LabelOpts(rotate=30)),yaxis_opts=opts.AxisOpts(name='数量')))return c
def map_slider() -> Map:c = (Map(init_opts=opts.InitOpts(theme=ThemeType.CHALK)).add("本科", [list(z) for z in zip(data['城市'].values.tolist(), data['本科'].values.tolist())]).add("专科", [list(z) for z in zip(data['城市'].values.tolist(), data['专科'].values.tolist())]).set_global_opts(visualmap_opts=opts.VisualMapOpts(max_=150),title_opts=opts.TitleOpts(title="")))return c
def word_slider() -> WordCloud:c = (WordCloud(init_opts=opts.InitOpts(theme=ThemeType.CHALK)).add('', word, word_size_range=[20, 100],shape='circle') # 词云图轮廓，有 'circle', 'cardioid', 'diamond', 'triangle-forward', 'triangle', 'pentagon', 'star' 可选.set_global_opts(title_opts=opts.TitleOpts(title="")))return c
def page_draggable_layout():page = Page(layout=Page.DraggablePageLayout)page.add(bar_slider(),line_slider(),pie_slider(),scatter_slider(),map_slider(),word_slider())page.render("page.html")if __name__ == "__main__":page_draggable_layout()运行代码生成page.html打开如下可随机拖拽的页面，随意调整大小拼接成自己想要的就行随后左上角Save Config保存
Save Config保存后会自动生成一个chart_config.json文件将该文件移至该项目目录中，再重新创建一个py文件运行以下代码即可生成一个新的网页resize_render.html
from pyecharts.charts import Page
Page.save_resize_html('./page.html',cfg_file='chart_config.json')
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