NBA球员投篮数据可视化。

from matplotlib import pyplot as plt
from matplotlib.patches import Arc, Circle, Rectangle


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=(6, 6))
    # 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


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()

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'
    }

# 球员职业生涯时间
years = [2018, 2019]
for i in range(years[0], years[1]):
    # 赛季
    season = str(i) + '-' + str(i + 1)[-2:]
    # 球员ID
    player_id = '201939'
    # 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']:
        # 是否进球得分
        flag = item[10]
        # 横坐标
        loc_x = str(item[17])
        # 纵坐标
        loc_y = str(item[18])
        with open('curry.csv', 'a+') as f:
            f.write(loc_x + ',' + loc_y + ',' + flag + '\n')

import pandas as pd

# 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']
# 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)

import seaborn as sns
import matplotlib as mpl

# 读取数据
df = pd.read_csv('curry.csv', header=None, names=['width', 'height', 'type'])


def colormap():
    """
    颜色转换
    """
    return mpl.colors.LinearSegmentedColormap.from_list('cmap', ['#C5C5C5', '#9F9F9F', '#706A7C', '#675678', '#713A71','#9D3E5E', '#BC5245',  '#C86138', '#C96239', '#D37636', '#D67F39', '#DA8C3E', '#E1A352'], 256)


# 绘制球员投篮热力图
shot_heatmap = sns.jointplot(df['width'], df['height'], stat_func=None, kind='kde', space=0, color='w', cmap=colormap())
# 设置图像大小
shot_heatmap.fig.set_size_inches(6, 6)
# 图像反向
ax = shot_heatmap.ax_joint
# 绘制投篮散点图
ax.scatter(x=df['width'], y=df['height'], s=0.1, marker='o', color="w", alpha=1)
# 添加篮球场
draw_ball_field(color='w', lw=2)
# 将坐标轴颜色更改为白色
lines = plt.gca()
lines.spines['top'].set_color('none')
lines.spines['left'].set_color('none')
# 去除坐标轴标签
ax.axis('off')