foreword
QS World University Rankings (QS World University Rankings) is an annual world university ranking published by Quacquarelli Symonds (QS), an international education market consulting company in the United Kingdom.
Collect global university ranking data (the source code has been shared, please like it)
import requests # 发送请求
import re
import csv
with open('rank.csv', mode='a', encoding='utf-8', newline='') as f:
csv_writer = csv.writer(f)
csv_writer.writerow(['country', 'rank', 'region', 'score_1', 'score_2', 'score_3', 'score_4', 'score_5', 'score_6', 'total_score', 'stars', 'university', 'year'])
def replace(str_):
str_ = re.findall('<div class="td-wrap"><div class="td-wrap-in">(.*?)</div></div>', str_)[0]
return str_
url = 'https://www.qschina.cn/sites/default/files/qs-rankings-data/cn/2057712_indicators.txt'
# 1. 发送请求
response = requests.get(url)
# <Response [200]>: 请求成功
# 2. 获取数据
json_data = response.json() # Python 字典
# 3. 解析数据
# 字典
data_list = json_data['data']
for i in data_list:
country = i['location'] # 国家/地区
rank = i['overall_rank'] # 排名
region = i['region'] # 大洲
score_1 = replace(i['ind_76']) # 学术声誉
score_2 = replace(i['ind_77']) # 雇主声誉
score_3 = replace(i['ind_36']) # 师生比
score_4 = replace(i['ind_73']) # 教员引用率
score_5 = replace(i['ind_18']) # 国际教师
score_6 = replace(i['ind_14']) # 国际学生
total_score = replace(i['overall']) # 总分
stars = i['stars'] # 星级
uni = i['uni'] # 大学名称
university = re.findall('<div class="td-wrap"><div class="td-wrap-in"><a href=".*?" class="uni-link">(.*?)</a></div></div>', uni)[0]
year = "2021" # 年份
print(country, rank, region, score_1, score_2, score_3, score_4, score_5, score_6, total_score, stars, university, year)
with open('rank.csv', mode='a', encoding='utf-8', newline='') as f:
csv_writer = csv.writer(f)
csv_writer.writerow([country, rank, region, score_1, score_2, score_3, score_4, score_5, score_6, total_score, stars, university, year])
Visual display
import required modules
from pyecharts.charts import *
from pyecharts import options as opts
from pyecharts.commons.utils import JsCode
from pyecharts.components import Table
import re
import pandas as pd
Import Data
df = pd.read_csv('rank.csv')
# 香港，澳门与中国大陆地区等在榜单中是分开的记录的，这边都归为china
df['loc'] = df['country']
df['country'].replace(['China (Mainland)', 'Hong Kong SAR', 'Taiwan', 'Macau SAR'],'China',inplace=True)
2021 World University Rankings (QS) TOP 100
bar = (Bar()
.add_xaxis(university)
.add_yaxis('', score, category_gap='30%')
.set_global_opts(title_opts=opts.TitleOpts(title="2021年世界大学排名（QS） TOP 100",
pos_left="center",
title_textstyle_opts=opts.TextStyleOpts(font_size=20)),
datazoom_opts=opts.DataZoomOpts(range_start=70, range_end=100, orient='vertical'),
visualmap_opts=opts.VisualMapOpts(is_show=False, max_=100, min_=60, dimension=0,
range_color=['#00FFFF', '#FF7F50']),
legend_opts=opts.LegendOpts(is_show=False),
xaxis_opts=opts.AxisOpts(is_show=False, is_scale=True),
yaxis_opts=opts.AxisOpts(axistick_opts=opts.AxisTickOpts(is_show=False),
axisline_opts=opts.AxisLineOpts(is_show=False),
axislabel_opts=opts.LabelOpts(font_size=12)))
.set_series_opts(label_opts=opts.LabelOpts(is_show=True,
position='right',
font_style='italic'),
itemstyle_opts={
"normal": {
"barBorderRadius": [30, 30, 30, 30],
'shadowBlur': 10,
'shadowColor': 'rgba(120, 36, 50, 0.5)',
'shadowOffsetY': 5,
}
}
).reversal_axis())
grid = (
Grid(init_opts=opts.InitOpts(theme='purple-passion', width='1000px', height='1200px'))
.add(bar, grid_opts=opts.GridOpts(pos_right='10%', pos_left='20%'))
)
grid.render_notebook()
Chinese universities in the TOP 500
bar = (Bar()
.add_xaxis(university)
.add_yaxis('', score, category_gap='30%')
.set_global_opts(title_opts=opts.TitleOpts(title="TOP 500中的中国大学",
pos_left="center",
title_textstyle_opts=opts.TextStyleOpts(font_size=20)),
datazoom_opts=opts.DataZoomOpts(range_start=50, range_end=100, orient='vertical'),
visualmap_opts=opts.VisualMapOpts(is_show=False, max_=90, min_=20, dimension=0,
range_color=['#00FFFF', '#FF7F50']),
legend_opts=opts.LegendOpts(is_show=False),
xaxis_opts=opts.AxisOpts(is_show=False, is_scale=True),
yaxis_opts=opts.AxisOpts(axistick_opts=opts.AxisTickOpts(is_show=False),
axisline_opts=opts.AxisLineOpts(is_show=False),
axislabel_opts=opts.LabelOpts(font_size=12)))
.set_series_opts(label_opts=opts.LabelOpts(is_show=True,
position='right',
font_style='italic'),
itemstyle_opts={
"normal": {
"barBorderRadius": [30, 30, 30, 30],
'shadowBlur': 10,
'shadowColor': 'rgba(120, 36, 50, 0.5)',
'shadowOffsetY': 5,
}
}
).reversal_axis())
grid = (
Grid(init_opts=opts.InitOpts(theme='purple-passion', width='1000px', height='1200px'))
.add(bar, grid_opts=opts.GridOpts(pos_right='10%', pos_left='20%'))
)
grid.render_notebook()
TOP 1000 universities by continent
t_data = df[(df.year==2021) & (df['rank']<=1000)]
t_data = t_data.groupby(['region'])['university'].count().reset_index()
t_data.columns = ['region', 'num']
t_data = t_data.sort_values(by="num" , ascending=False)
bar = (Bar(init_opts=opts.InitOpts(theme='purple-passion', width='1000px', height='600px'))
.add_xaxis(t_data['region'].tolist())
.add_yaxis('出现次数', t_data['num'].tolist(), category_gap='50%')
.set_global_opts(title_opts=opts.TitleOpts(title="TOP 1000高校按大洲分布",
pos_left="center",
title_textstyle_opts=opts.TextStyleOpts(font_size=20)),
visualmap_opts=opts.VisualMapOpts(is_show=False, max_=300, min_=0, dimension=1,
range_color=['#00FFFF', '#FF7F50']),
legend_opts=opts.LegendOpts(is_show=False),
xaxis_opts=opts.AxisOpts(axistick_opts=opts.AxisTickOpts(is_show=False),
axisline_opts=opts.AxisLineOpts(is_show=False),
axislabel_opts=opts.LabelOpts(font_size=15)),
yaxis_opts=opts.AxisOpts(is_show=False))
.set_series_opts(label_opts=opts.LabelOpts(is_show=True,
position='top',
font_size=15,
font_style='italic'),
itemstyle_opts={
"normal": {
"barBorderRadius": [30, 30, 30, 30],
'shadowBlur': 10,
'shadowColor': 'rgba(120, 36, 50, 0.5)',
'shadowOffsetY': 5,
}
}
))
bar.render_notebook()
TOP 1000 universities by country
fmt_js = """function (params) {return params.name+': '+Number(params.value[2]);}"""
mp = Map()
mp.add(
"高校数量",
data_pair,
"world",
is_map_symbol_show=False,
is_roam=False)
mp.set_series_opts(label_opts=opts.LabelOpts(is_show=False),
itemstyle_opts={
'normal': {
'areaColor': '#191970',
'borderColor': '#1773c3',
'shadowColor': '#1773c3',
'shadowBlur': 20,
'opacity': 0.8
}
})
mp.set_global_opts(
title_opts=opts.TitleOpts(title="TOP 1000高校按国家分布", pos_left='center',
title_textstyle_opts=opts.TextStyleOpts(font_size=18)),
legend_opts=opts.LegendOpts(is_show=False),
visualmap_opts=opts.VisualMapOpts(is_show=False,
max_=100,
is_piecewise=False,
dimension=0,
range_color=['rgba(255,228,225,0.6)', 'rgba(255,0,0,0.9)', 'rgba(255,0,0,1)'])
)
data_pair = [[x, y] for x, y in data_pair if x in country_list]
geo = Geo()
# 需要先将几个国家的经纬度信息加入到geo中
for k, v in loc.items():
geo.add_coordinate(k, v[0], v[1])
# 这里将geo的地图透明度配置为0
geo.add_schema(maptype="world", is_roam=False, itemstyle_opts={
'normal': {
'opacity': 0}})
geo.add("", data_pair, symbol_size=1)
# 显示标签配置
geo.set_series_opts(
label_opts=opts.LabelOpts(
is_show=True,
position='right',
color='white',
font_size=12,
font_weight='bold',
formatter=JsCode(fmt_js)),
)
grid = (
Grid(init_opts=opts.InitOpts(theme='chalk', width='1000px', height='600px'))
.add(mp, grid_opts=opts.GridOpts(pos_top="12%"))
.add(geo, grid_opts=opts.GridOpts(pos_bottom="12%"))
)
grid.render_notebook()
Continent-Country Distribution
c = (Sunburst(
init_opts=opts.InitOpts(
theme='purple-passion',
width="1000px",
height="1000px"))
.add(
"",
data_pair=data_pair,
highlight_policy="ancestor",
radius=[0, "100%"],
sort_='null',
levels=[
{
},
{
"r0": "20%",
"r": "48%",
"itemStyle": {
"borderColor": 'rgb(220,220,220)', "borderWidth": 2}
},
{
"r0": "50%", "r": "80%", "label": {
"align": "right"},
"itemStyle": {
"borderColor": 'rgb(220,220,220)', "borderWidth": 1}}
],
)
.set_global_opts(
visualmap_opts=opts.VisualMapOpts(is_show=False, max_=300, min_=0, is_piecewise=False,
range_color=['#4285f4', '#34a853', '#fbbc05', '#ea4335', '#ea4335']),
title_opts=opts.TitleOpts(title="TOP 1000\n\n大学地理分布",
pos_left="center",
pos_top="center",
title_textstyle_opts=opts.TextStyleOpts(font_style='oblique', font_size=20),))
.set_series_opts(label_opts=opts.LabelOpts(font_size=14, formatter="{b}: {c}"))
)
c.render_notebook()
Python crawls global university rankings and adds visual analysis