2020赛季NBA各队胜负场展示——python数据处理与可视化_利用pandas与相关可视化库完成体育类数据的分析、处理与可视化任务

import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import warnings
warnings.filterwarnings("ignore")
plt.rcParams['axes.unicode_minus'] = False
sns.set_style('darkgrid', {'font.sans-serif':['SimHei', 'Arial']})
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df_player = pd.read_csv(r"C:\Users\imqqdong\Downloads\NBAdata\season_2021_detailed.csv")
df_team = pd.read_csv(r"C:\Users\imqqdong\Downloads\NBAdata\season_2021_basic.csv")
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df_team.head()
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df_team_sim = df_team.drop(columns=["weekday","attendance","overtime","remarks"])
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df_team_sim["winner"] = 0
df_team_sim["loser"] = 0
for i in range(len(df_team_sim)):
    if df_team_sim["home_score"][i] > df_team_sim["away_score"][i]:
        df_team_sim["winner"][i] = df_team_sim["home_team"][i]
        df_team_sim["loser"][i] = df_team_sim["away_team"][i]
    elif df_team_sim["home_score"][i] < df_team_sim["away_score"][i]:
        df_team_sim["winner"][i] = df_team_sim["away_team"][i]
        df_team_sim["loser"][i] = df_team_sim["home_team"][i]

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sns.countplot(x = df_team_sim["winner"],color = "orange")
plt.xticks(rotation = 90)
plt.show() 
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win_lose_times = pd.DataFrame(df_team_sim["winner"].value_counts())
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win_lose_times["lose_times"] = df_team_sim["loser"].value_counts()
#会不会自动对齐索引
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win_lose_times.head()
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win_lose_times.rename(columns={"winner":"win_times"},inplace = True)
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win_lose_times["wp"] = win_lose_times["win_times"]/(win_lose_times["win_times"]+win_lose_times["lose_times"])
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win_lose_times.sort_values(by = "wp",ascending=False)
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fig = plt.figure(figsize = (30,20),dpi =100)
x = np.arange(len(win_lose_times.index.values))
width = 0.4
plt.bar(x = x - width/2,height = win_lose_times["win_times"],label = "胜利场次",align="center",width = width)
plt.bar(x = x + width/2,height = win_lose_times["lose_times"],label = "失败场次",align="center",width = width)
plt.xticks(ticks = x,rotation = 90,fontsize =25,labels = win_lose_times.index.values)
plt.yticks(fontsize = 25)
plt.legend(fontsize = 25)
plt.show()
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