爬取中国大学排名并作可视化分析（应粉丝要求）——python作业
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导读
肥友们，最近有位粉丝找到我直言出价让我代做作业，我一听当场就急眼了。我肥学是这样的人吗？
直接就问他给多少钱，嘻嘻嘻！！！当然了多少钱不也不会干的，既然是粉丝我肯定尽量帮啊，于是我就开始了今天的博客。
爬取中国大学排名
链接：中国大学排名
其实还是挺简单的，这位粉丝肯定没有好好看我以前的文章，这种爬取说过很多次了。所以我们直接整起来。
我们直接找到要获得的这些信息的id或者class
res=requests.get(url=url,headers=header).content.decode('utf-8')
soup=BeautifulSoup(res,'lxml')
names=soup.findAll(name="a",attrs={"class":"name-cn"})
xinxi=soup.findAll("td")
with open("中国大学.csv", 'a', encoding="utf-8", newline="") as f:
for i in range(0,len(xinxi),6):
w=csv.writer(f)
w.writerow([xinxi[i].text.strip(),xinxi[i+1].text.strip(),xinxi[i+2].text.strip(),xinxi[i+3].text.strip(),xinxi[i+4].text.strip(),xinxi[i+5].text.strip()])
然后顺利拿到信息
可视化分析
词云
上面我们以经拿到了信息，我们先对这些大学集中的省市和类别做一个词云分析
可以看出来综合和北京的居多
import jieba
from wordcloud import WordCloud
from matplotlib import colors
import csv
import pandas as pd
info=pd.read_csv("中国大学.csv",usecols=[2,3])
text=info
cut_text = "".join(jieba.cut(str(text)))
color_list=['#FF0000','#9955FF','#66FFFF']#建立颜色数组
colormap=colors.ListedColormap(color_list)#调用
#color_mask = cv2.imread("11.jpg")
word_cloud=WordCloud(
font_path="msyh.ttc",
background_color='black',
mode="RGBA",
prefer_horizontal=1,
#mask=color_mask,
height=200,
width=200,
scale=1,
colormap=colormap,#设置颜色
margin=5
)
word_cloud1=word_cloud.generate(cut_text)
word_cloud1.to_file('2.png')
print("图片保存成功")
条形统计图
这里运用了Echarts做得有兴趣的大佬也可以用cufflinks做也可以达到同样的效果
//这里只把js可变部分贴了出来
option = {
title: {
text: '中国大学数据'
},
tooltip: {
trigger: 'axis'
},
legend: {
data: ['总分', '办学层次']
},
grid: {
left: '3%',
right: '4%',
bottom: '3%',
containLabel: true
},
toolbox: {
feature: {
saveAsImage: {}
}
},
xAxis: {
type: 'category',
boundaryGap: false,
data: [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30]
},
yAxis: {
type: 'value'
},
series: [
{
name: '总分',
type: 'line',
stack: 'Total',
data: [969.2,855.3,768.7,723.4,654.8,649.7,577.0,574.3,567.9,537.9,522.6,519.3,518.3,516.6,513.8,508.3,488.1,487.8,474.0,465.3,447.0,444.3,442.2,435.7,430.5,427.8,419.8,418.2,401.8,400.4]
},
{
name: '办学层次',
type: 'line',
stack: 'Total',
data: [37.9,36.1,34.3,35.5,35.1,36.6,40.0,32.1,31.8,34.5,32.7,30.9,34.8,30.7,32.8,33.2,34.3,34.5,32.3,31.5,28.8,32.7,30.8,30.4,32.4,32.7,30.5,30.2,35.2,31.8]
}
]
};
获取全国211以上大学的动态地理坐标
plt.rcParams['font.family'] = 'sans-serif'
plt.rcParams['font.sans-serif'] = ['SimHei']
plt.rcParams['axes.unicode_minus'] = False
plt.rcParams['animation.writer'] = 'html'
plt.rcParams['animation.embed_limit'] = 100
def rgba_to_rgb(img_rgba):
img_rgb = Image.new("RGB", img_rgba.size, (255, 255, 255))
img_rgb.paste(img_rgba, mask=img_rgba.split()[3])
return img_rgb
def html_to_gif(html_file, gif_file, duration=0.5):
path = html_file.replace(".html", "_frames")
images = [os.path.join(path, x) for x in sorted(os.listdir(path))]
frames = [imageio.imread(x) for x in images]
if frames[0].shape[-1] == 4:
frames = [np.array(rgba_to_rgb(Image.fromarray(x))) for x in frames]
imageio.mimsave(gif_file, frames, 'gif', duration=duration)
return gif_file
cmap = ['#2E91E5','#1CA71C','#DA16FF','#B68100','#EB663B','#00A08B','#FC0080','#6C7C32','#862A16','#620042','#DA60CA','#0D2A63'] * 100
def getCoords(geom):
if isinstance(geom, geo.MultiPolygon):
return [np.array(g.exterior) for g in geom.geoms]
elif isinstance(geom, geo.Polygon):
return [np.array(geom.exterior)]
elif isinstance(geom, geo.LineString):
return [np.array(geom)]
elif isinstance(geom, geo.MultiLineString):
return [np.array(x) for x in list(geom.geoms)]
else:
raise Exception("geom must be one of [polygon,MultiPolygon,LineString,MultiLineString]!")
# 底图数据
dfprovince = gpd.read_file("dfprovince.geojson").set_crs("epsg:4326").to_crs("epsg:2343")
dfnanhai = gpd.read_file("dfnanhai.geojson").set_crs("epsg:4326").to_crs("epsg:2343")
dfline9 = dfnanhai[(dfnanhai["LENGTH"] > 1.0) & (dfnanhai["LENGTH"] < 2.0)]
# 散点数据
df985 = gpd.read_file("中国985大学.geojson").set_crs("epsg:4326").to_crs("epsg:2343")
df211 = gpd.read_file("中国211大学.geojson").set_crs("epsg:4326").to_crs("epsg:2343")
dfpoints = pd.concat([df985, df211], axis=0)
df = pd.DataFrame({"x": [pt.x for pt in dfpoints["geometry"]],
"y": [pt.y for pt in dfpoints["geometry"]]})
df["z"] = 1.0
df.index = dfpoints["name"].values
def bubble_map_dance(df, title="中国116所211高校位置分布",
filename=None,
figsize=(8, 6), dpi=144,
duration=0.5,
anotate_points=["北京邮电大学", "南昌大学", "华中农业大学", "东华大学", "云南大学",
"陕西师范大学", "内蒙古大学", "西藏大学", "新疆大学", "青海大学", "哈尔滨工程大学"]):
fig, ax_base = plt.subplots(figsize=figsize, dpi=dpi)
ax_child = fig.add_axes([0.800, 0.125, 0.10, 0.20])
def plot_frame(i):
ax_base.clear()
ax_child.clear()
# 绘制省边界
polygons = [getCoords(x) for x in dfprovince["geometry"]]
for j, coords in enumerate(polygons):
for x in coords:
poly = plt.Polygon(x, fill=True, ec="gray", fc="white", alpha=0.5, linewidth=.8)
poly_child = plt.Polygon(x, fill=True, ec="gray", fc="white", alpha=0.5, linewidth=.8)
ax_base.add_patch(poly)
ax_child.add_patch(poly_child)
# 绘制九段线
coords = [getCoords(x) for x in dfline9["geometry"]]
lines = [y for x in coords for y in x]
for ln in lines:
x, y = np.transpose(ln)
line = plt.Line2D(x, y, color="gray", linestyle="-.", linewidth=1.5)
line_child = plt.Line2D(x, y, color="gray", linestyle="-.", linewidth=1.5)
ax_base.add_artist(line)
ax_child.add_artist(line_child)
# 设置spine格式
for spine in ['top', 'left', "bottom", "right"]:
ax_base.spines[spine].set_color("none")
ax_child.spines[spine].set_alpha(0.5)
ax_base.axis("off")
# 设置绘图范围
bounds = dfprovince.total_bounds
ax_base.set_xlim(bounds[0] - (bounds[2] - bounds[0]) / 10, bounds[2] + (bounds[2] - bounds[0]) / 10)
ax_base.set_ylim(bounds[1] + (bounds[3] - bounds[1]) / 3.5, bounds[3] + (bounds[3] - bounds[1]) / 100)
ax_child.set_xlim(bounds[2] - (bounds[2] - bounds[0]) / 2.5, bounds[2] - (bounds[2] - bounds[0]) / 20)
ax_child.set_ylim(bounds[1] - (bounds[3] - bounds[1]) / 20, bounds[1] + (bounds[3] - bounds[1]) / 2)
# 移除坐标轴刻度
ax_child.set_xticks([]);
ax_child.set_yticks([]);
k = i // 3 + 1
m = i % 3
text = "NO." + str(k)
dfdata = df.iloc[:k, :].copy()
dftmp = df.iloc[:k - 1, :].copy()
# 绘制散点图像
if len(dftmp) > 0:
ax_base.scatter(dftmp["x"], dftmp["y"], s=100 * dftmp["z"] / df["z"].mean(),
c=(cmap * 100)[0:len(dftmp)], alpha=0.3, zorder=3)
ax_child.scatter(dftmp["x"], dftmp["y"], s=100 * dftmp["z"] / df["z"].mean(),
c=(cmap * 100)[0:len(dftmp)], alpha=0.3, zorder=3)
# 添加注释文字
for i, p in enumerate(dftmp.index):
px, py, pz = dftmp.loc[p, ["x", "y", "z"]].tolist()
if p in anotate_points:
ax_base.annotate(p, xy=(px, py), xycoords="data", xytext=(-15, 爬取城市GDP排名