当前位置：首页 > 篮球资讯 > 正文内容

给大家科普一下顺盈娱乐(2023已更新(今日/知乎)

杏彩体育2年前 (2023-01-23)篮球资讯34

本期我们参考FiveThirtyEight发布的世界足球俱乐部排名，利用Matplotlib手把手教大家如何制作新闻级别可视化作品，希望对小伙伴们有所帮助，如有疑问或者需要改进的地方可以联系小编。

先看看效果：

1. 导入模块

import matplotlib.pyplot as plt import matplotlib.patches as patches import matplotlib.image as mpimg from matplotlib.offsetbox import OffsetImage, AnnotationBbox

主要用到：matplotlib — patches、image、offsetbox

2. 示例数据

dic_t = {Premier League:England,Bundesliga:Germany,Ligue 1:France, La Liga:Spain, Eredivisie:Netherlands,Serie A:Italy, Primeira Liga:Portugal,Bundesliga_1:Austria} team = [Man.City, Liverpool, Bayern Munich, Chelsea, PSG, Real Madrid, Ajax, Barcelona, Tottnham, Inter Milan, RB Leipzig, Porto, RB Salzburg, Dortmund, Atlético Madrid, Arsenal, Villarreal, Brighton, AC Milan, Sporting][::-1] league = [Premier League, Premier League, Bundesliga, Premier League, Ligue 1, La Liga, Eredivisie, La Liga, Premier League, Serie A, Bundesliga, Primeira Liga, Bundesliga_1, Bundesliga, La Liga, Premier League, La Liga, Premier League, Serie A, Primeira Liga][::-1] league_country = [dic_t[i] for i in league] rank = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20][::-1] OFF = [3.3,2.9,3.4,2.4,2.8,2.7,3.0,2.4,2.5,2.5, 2.5,2.3,2.5,2.6,2.0,2.2,2.3,2.1,2.1,2.2][::-1] DEF = [0.3,0.2,0.6,0.3,0.7,0.6,0.9,0.5,0.6,0.7, 0.3,0.2,0.6,0.3,0.7,0.6,0.9,0.5,0.6,0.7][::-1] SPI = [93.5,93.3,91.6,88.4,85.0,85.5,84.6,84.3,84.1,82.1, 81.8,81.6,81.5,81.1,80.4,80.2,80.1,80.0,80.0,79.8][::-1]

截取世界排名前20的足球俱乐部。

3. 画布设置

fig, ax = plt.subplots(1, 1, dpi=300) # 隐藏xy轴 ax.get_yaxis().set_visible(False) ax.get_xaxis().set_visible(False) # 将刻度小横线设置为白色，标签颜色为黑色 plt.tick_params(axis=x, color=w) # x,y轴刻度范围 ax.set_xlim(0, 2) ax.set_ylim(0, 4)

4. 画布区域主题分配

# 设置上下左右外框变为透明 for i in [top, right, left, bottom]: ax.spines[i].set_visible(False) title_size = 3.2 title_pos = 40 ax.axhline(y=title_pos, xmax=.8, c=black, lw=0.4) ax.axvline(x=.7, ymin=0.002, ymax=.909, c=black, lw=0.4) ax.axvline(x=3.95, ymin=0.002, ymax=.909, c=black, lw=0.4) ax.axhline(y=0.1, xmax=.8, c=black, lw=0.4)

5. 添加数据散点

x1 = [4.1] * len(rank) y1 = [1 + i * 2 for i in range(len(rank))] ax.scatter(x1, y1, s=40,alpha=0.8)

6. 添加表头

ax.text(0.08, 0.2 + title_pos, RANK, fontdict={color: #1C1C1C, size: title_size}, fontweight=bold) ax.text(0.39, 0.2 + title_pos, 1-WEEK\nCHANGE, fontdict={color: #1C1C1C, size: title_size}, fontweight=heavy) ax.text(0.75, 0.2 + title_pos, TEAM, fontdict={color: #1C1C1C, size: title_size}, fontweight=heavy)

7. 添加数据

for i in range(len(rank)): ax.axhline(y=0.1 + i * 2, xmax=.8, c=gray, lw=0.4, alpha=0.2) ax.text(0.13, 0.78 + i * 2, str(rank[i]), fontdict={color: #1C1C1C, size: 4}, fontweight=normal) ax.text(.75, 0.78 + i * 2, str(team[i]), fontdict={color: #1C1C1C, size: 4}, fontweight=heavy) ax.text(4.05, 0.75 + i * 2, str(OFF[i]), fontdict={color: #1C1C1C, size: 3.6}, fontweight=normal)

8. 设置colarbar

x1 = [4.1] * len(rank) y1 = [1 + i * 2 for i in range(len(rank))] ax.scatter(x1, y1, c=OFF, s=40, cmap=Greens, vmin=1, vmax=4, alpha=0.8, edgecolors=none)