【python】Visualization of field data（NBA）

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【python】Visualization of field data（NBA）

2024-06-25 19:34| 来源: 网络整理| 查看: 265

文章目录 1 画篮球场2 爬数据3 可视化投篮结果4 Kobe Bryant5 Others

代码来源：

用Python把NBA球员投篮数据可视化

虎扑热帖|数据分析|用python展示NBA球员出手喜好

1 画篮球场 from matplotlib import pyplot as plt from matplotlib.patches import Arc, Circle, Rectangle %matplotlib inline def draw_ball_field(color='#20458C', lw=2): """ 绘制篮球场 """ # 新建一个大小为(6,6)的绘图窗口 plt.figure(figsize=(9, 9)) # 获得当前的Axes对象ax,进行绘图 ax = plt.gca() # 对篮球场进行底色填充 lines_outer_rec = Rectangle(xy=(-250, -47.5), width=500, height=470, linewidth=lw, color='#F0F0F0', fill=True) # 设置篮球场填充图层为最底层 lines_outer_rec.set_zorder(0) # 将rec添加进ax ax.add_patch(lines_outer_rec) # 绘制篮筐,半径为7.5 circle_ball = Circle(xy=(0, 0), radius=7.5, linewidth=lw, color=color, fill=False) # 将circle添加进ax ax.add_patch(circle_ball) # 绘制篮板,尺寸为(60,1) plate = Rectangle(xy=(-30, -7.5), width=60, height=-1, linewidth=lw, color=color, fill=False) # 将rec添加进ax ax.add_patch(plate) # 绘制2分区的外框线,尺寸为(160,190) outer_rec = Rectangle(xy=(-80, -47.5), width=160, height=190, linewidth=lw, color=color, fill=False) # 将rec添加进ax ax.add_patch(outer_rec) # 绘制2分区的内框线,尺寸为(120,190) inner_rec = Rectangle(xy=(-60, -47.5), width=120, height=190, linewidth=lw, color=color, fill=False) # 将rec添加进ax ax.add_patch(inner_rec) # 绘制罚球区域圆圈,半径为60 circle_punish = Circle(xy=(0, 142.5), radius=60, linewidth=lw, color=color, fill=False) # 将circle添加进ax ax.add_patch(circle_punish) # 绘制三分线的左边线 three_left_rec = Rectangle(xy=(-220, -47.5), width=0, height=140, linewidth=lw, color=color, fill=False) # 将rec添加进ax ax.add_patch(three_left_rec) # 绘制三分线的右边线 three_right_rec = Rectangle(xy=(220, -47.5), width=0, height=140, linewidth=lw, color=color, fill=False) # 将rec添加进ax ax.add_patch(three_right_rec) # 绘制三分线的圆弧,圆心为(0,0),半径为238.66,起始角度为22.8,结束角度为157.2 three_arc = Arc(xy=(0, 0), width=477.32, height=477.32, theta1=22.8, theta2=157.2, linewidth=lw, color=color, fill=False) # 将arc添加进ax ax.add_patch(three_arc) # 绘制中场处的外半圆,半径为60 center_outer_arc = Arc(xy=(0, 422.5), width=120, height=120, theta1=180, theta2=0, linewidth=lw, color=color, fill=False) # 将arc添加进ax ax.add_patch(center_outer_arc) # 绘制中场处的内半圆,半径为20 center_inner_arc = Arc(xy=(0, 422.5), width=40, height=40, theta1=180, theta2=0, linewidth=lw, color=color, fill=False) # 将arc添加进ax ax.add_patch(center_inner_arc) # 绘制篮球场外框线,尺寸为(500,470) lines_outer_rec = Rectangle(xy=(-250, -47.5), width=500, height=470, linewidth=lw, color=color, fill=False) # 将rec添加进ax ax.add_patch(lines_outer_rec) return ax

调用一下

axs = draw_ball_field(color='#20458C', lw=2) # 设置坐标轴范围 axs.set_xlim(-250, 250) axs.set_ylim(422.5, -47.5) # 消除坐标轴刻度 axs.set_xticks([]) axs.set_yticks([]) # 添加备注信息 plt.annotate('By xiao F', xy=(100, 160), xytext=(178, 418)) plt.show()

在这里插入图片描述

2 爬数据 import requests import json headers = { 'User-Agent': 'Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/63.0.3239.132 Safari/537.36' } def shot_data(years,player_id): # years 球员职业生涯时间, list # player_id 球员ID, str for i in range(years[0], years[1]): # 赛季 season = str(i) + '-' + str(i + 1)[-2:] # 请求网址 url = 'https://stats.nba.com/stats/shotchartdetail?AheadBehind=&CFID=33&CFPARAMS=' + season + '&ClutchTime=&Conference=&ContextFilter=&ContextMeasure=FGA&DateFrom=&DateTo=&Division=&EndPeriod=10&EndRange=28800&GROUP_ID=&GameEventID=&GameID=&GameSegment=&GroupID=&GroupMode=&GroupQuantity=5&LastNGames=0&LeagueID=00&Location=&Month=0&OnOff=&OpponentTeamID=0&Outcome=&PORound=0&Period=0&PlayerID=' + player_id + '&PlayerID1=&PlayerID2=&PlayerID3=&PlayerID4=&PlayerID5=&PlayerPosition=&PointDiff=&Position=&RangeType=0&RookieYear=&Season=' + season + '&SeasonSegment=&SeasonType=Regular+Season&ShotClockRange=&StartPeriod=1&StartRange=0&StarterBench=&TeamID=0&VsConference=&VsDivision=&VsPlayerID1=&VsPlayerID2=&VsPlayerID3=&VsPlayerID4=&VsPlayerID5=&VsTeamID=' # 请求结果 response = requests.get(url=url, headers=headers) result = json.loads(response.text) # 获取数据 for item in result['resultSets'][0]['rowSet']: flag = item[10] # 是否进球得分 loc_x = str(item[17]) # 横坐标 loc_y = str(item[18]) # 纵坐标 with open('curry.csv', 'a+') as f: f.write(loc_x + ',' + loc_y + ',' + flag + '\n')

调用一下

years = [2017, 2019]# 球员职业生涯时间 player_id = '201939'# 球员ID shot_data(years,player_id)

201939 是 Stephen Curry 在这里插入图片描述生成如下所示：

3 可视化投篮结果 import pandas as pd import urllib.request from matplotlib.offsetbox import OffsetImage def draw_shot(player_id): # 读取数据 df = pd.read_csv('curry.csv', header=None, names=['width', 'height', 'type']) # 分类数据 df1 = df[df['type'] == 'Made Shot'] df2 = df[df['type'] == 'Missed Shot'] # 画篮球场 axs = draw_ball_field(color='#20458C', lw=2) # 绘制散点图 axs.scatter(x=df2['width'], y=df2['height'], s=30, marker='x', color='#A82B2B') axs.scatter(x=df1['width'], y=df1['height'], s=30, marker='o', edgecolors='#3A7711', color="#F0F0F0", linewidths=2) # 设置坐标轴范围 axs.set_xlim(-250, 250) axs.set_ylim(422.5, -47.5) # 消除坐标轴刻度 axs.set_xticks([]) axs.set_yticks([]) # 添加备注信息 plt.annotate('By Bryant', xy=(90, 160), xytext=(190, 418)) # 画头像 pic = urllib.request.urlretrieve("http://stats.nba.com/media/players/230x185/" + player_id + ".png") head_pic = plt.imread(pic[0]) # urlretrieve 返回元组，image 在第一个 img = OffsetImage(head_pic, zoom=0.6) # 将球员图片放置于左下角，并设置缩小等级以配合整个图片 img.set_offset((45,45)) # (x, y) 控制将球员放在你想要放的位置 axs.add_artist(img) # 添加球员图片 plt.show()

调用一下（只用了17，19年的数据）

draw_shot('201939')

在这里插入图片描述哈哈，只画了半场，不能完全展现出萌神的 logo shot