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+# coding=utf-8
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+'''
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+Created on 2017年9月12日
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+@vsersion:python 3.6
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+@author: liuyuqi
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+'''
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+
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+# 导入需要的第三方库
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+
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+import matplotlib.pyplot as plt
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+import numpy as np
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+import pandas as pd
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+
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+# 导入数据，预览数据
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+
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+team_season = pd.read_csv('../input/NBAdata/team_season.csv')
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+advanced_basic = pd.read_csv('../input/NBAdata/advanced_basic.csv')
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+advanced_shooting = pd.read_csv('../input/NBAdata/advanced_shooting.csv')
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+avg = pd.read_csv('../input/NBAdata/avg.csv')
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+coach_playoff = pd.read_csv('../input/NBAdata/coach_playoff.csv')
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+coach_season = pd.read_csv('../input/NBAdata/coach_season.csv')
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+player_playoff = pd.read_csv('../input/NBAdata/player_playoff.csv')
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+player_salary = pd.read_csv('../input/NBAdata/player_salary.csv')
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+player_season = pd.read_csv('../input/NBAdata/player_season.csv')
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+single = pd.read_csv('../input/NBAdata/single.csv')
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+team_playoff = pd.read_csv('../input/NBAdata/team_playoff.csv')
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+team_season = pd.read_csv('../input/NBAdata/team_season.csv')
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+tot = pd.read_csv('../input/NBAdata/tot.csv')
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+
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+team_season.head()
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+team_playoff.columns
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+
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+# 将比赛时间转换成所处赛季，按照季后赛所在年为标准
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+def convert_time_to_season(s):
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+ assert type(s) == str
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+ return int(s[:4])
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+
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+# 将失分单独列出
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+def get_loss_score(s):
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+ assert type(s) == str
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+ index_of_divider = s.index('-')
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+ loss_score = int(s[:index_of_divider][3:])
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+ return loss_score
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+
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+team_season['失分'] = team_season['比分'].map(get_loss_score)
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+team_season['赛季'] = team_season['时间'].map(convert_time_to_season)
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+team_season['回合'] = (team_season['出手'] + 0.44 * team_season['罚球出手'] - 0.96 * team_season['前场'] + team_season['失误']) / 2
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+team_season.head()
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+
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+team_playoff['失分'] = team_playoff['比分'].map(get_loss_score)
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+team_playoff['赛季'] = team_playoff['时间'].map(convert_time_to_season)
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+team_playoff['回合'] = (team_playoff['出手'] + 0.44 * team_playoff['罚球出手'] - 0.96 * team_playoff['前场'] + team_playoff['失误']) / 2
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+team_playoff.head()
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+
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+champions = {}
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+
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+for year in range(1986, 2017):
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+ current_playoff = team_playoff[team_playoff['赛季'] == year]
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+ current_win = 0
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+ single_playoff = {}
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+ for i in range(len(current_playoff)):
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+ if current_playoff.iloc[i]['结果'] == 'W':
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+ if current_playoff.iloc[i]['球队'] in single_playoff.keys():
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+ single_playoff[current_playoff.iloc[i]['球队']] += 1
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+ else:
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+ single_playoff[current_playoff.iloc[i]['球队']] = 1
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+ for team in single_playoff.keys():
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+ if single_playoff[team] > current_win:
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+ current_win = single_playoff[team]
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+ champions[year] = team
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+
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+champions
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+
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+# 生成Series对象
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+champion_series = pd.Series(champions)
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+
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+
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+# 查看哪些队伍、分别夺得几次冠军
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+champions_count = champion_series.value_counts()
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+champions_count.sort_values(ascending=False, inplace=True)
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+champions_count
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+
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+plt.bar(np.arange(10), champions_count.values, width=0.5)
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+plt.xticks(np.arange(10), list(champions_count.index))
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+plt.xlabel('Team Name')
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+plt.ylabel('Champion Number')
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+plt.grid(True)
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+plt.title('Champions Statistics From 1986 to 2016')
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+
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+score_loss_ratio = []
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+for i in range(31):
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+ score_loss_ratio.append(abs(champion_score[i] / champion_loss[i]))
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+
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+plt.scatter(np.arange(31), score_loss_ratio)
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+plt.hlines(np.array(score_loss_ratio).mean(), 0, 30, linestyles='dashed')
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+plt.xticks(np.arange(31), champion_teams, size='small', rotation=90)
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+plt.xlabel('Champion Team')
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+plt.ylabel('Score Loss Ratio')
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+plt.title('Score Loss Ratio of Champion Team in Playoff from 1986 to 2016')
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+plt.grid(True)
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+
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+
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+round_count = []
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+
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+for year in range(1986, 2017):
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+ champion_team = team_playoff[(team_playoff['赛季'] == year) & (team_playoff['球队'] == champions[year])]
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+ round_count.append(champion_team['回合'].mean())
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+
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+plt.bar(np.arange(31), round_count)
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+plt.xlabel('Champion Team')
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+plt.ylabel('Average Round in Playoff')
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+plt.xticks(np.arange(31), champion_teams, size='small', rotation=90)
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+plt.hlines(np.array(round_count).mean(), 0, 30, linestyles='dashed')
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+plt.title('Average Round of Champion from 1986 to 2016')
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+
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+
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+shoot = {}
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+
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+for year in range(1986, 2017):
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+ shoot[year] = {}
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+ champion_team = team_playoff[(team_playoff['赛季'] == year) & (team_playoff['球队'] == champions[year])]
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+ shoot[year]['三分出手'] = champion_team['三分出手'].sum()
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+ shoot[year]['三分命中'] = champion_team['三分命中'].sum()
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+ shoot[year]['场均三分出手'] = champion_team['三分出手'].mean()
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+ shoot[year]['场均三分命中'] = champion_team['三分命中'].mean()
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+ shoot[year]['场均两分出手'] = champion_team['出手'].mean() - shoot[year]['场均三分出手']
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+ shoot[year]['场均两分命中'] = champion_team['命中'].mean() - shoot[year]['场均三分命中']
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+ shoot[year]['出手'] = champion_team['出手'].sum()
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+ shoot[year]['命中'] = champion_team['命中'].sum()
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+ shoot[year]['场均出手'] = champion_team['出手'].mean()
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+ shoot[year]['场均命中'] = champion_team['命中'].mean()
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+ shoot[year]['两分出手'] = champion_team['出手'].sum() - champion_team['三分出手'].sum()
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+ shoot[year]['两分命中'] = champion_team['命中'].sum() - champion_team['三分命中'].sum()
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+ shoot[year]['罚球出手'] = champion_team['罚球出手'].sum()
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+ shoot[year]['罚球命中'] = champion_team['罚球命中'].sum()
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+ shoot[year]['罚球命中率'] = shoot[year]['罚球命中'] / shoot[year]['罚球出手']
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+ shoot[year]['两分命中率'] = shoot[year]['两分命中'] / shoot[year]['两分出手']
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+ shoot[year]['三分命中率'] = shoot[year]['三分命中'] / shoot[year]['三分出手']
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+ shoot[year]['得分'] = champion_team['得分'].sum()
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+ shoot[year]['场均得分'] = champion_team['得分'].mean()
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+ shoot[year]['真实命中率'] = shoot[year]['得分'] / (2 * (shoot[year]['出手'] + 0.44 * shoot[year]['罚球出手']))
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+
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+shoot_data = pd.DataFrame(shoot).T
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+shoot_data.head()
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+
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+
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+plt.scatter(shoot_data['场均得分'], shoot_data['真实命中率'])
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+plt.vlines(shoot_data['场均得分'].mean(), 0.48, 0.6, linestyles='dashed')
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+plt.hlines(shoot_data['真实命中率'].mean(), 85, 125, linestyles='dashed')
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+plt.xlabel('Average Score')
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+plt.ylabel('TS')
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+plt.title('TS-AverageScore of Champions of 1986-2016')
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+plt.grid(True)
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+
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+
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+print(shoot_data.sort_values(by='场均得分', ascending=False).iloc[0].name)
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+print(shoot_data.sort_values(by='场均得分', ascending=False).iloc[1].name)
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+print(shoot_data.sort_values(by='场均得分', ascending=True).iloc[0].name)
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+
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+
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+three_of_champions = shoot_data[['场均三分出手', '场均三分命中']]
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+three_of_champions = three_of_champions.rename(columns={'场均三分出手': '3PA', '场均三分命中': '3P'})
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+three_of_champions.plot(kind='bar')
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+plt.hlines(three_of_champions['3PA'].mean(), 0, 30, linestyles='dashed')
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+plt.hlines(three_of_champions['3P'].mean(), 0, 30, linestyles='dashed')
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+plt.xticks(np.arange(31), champion_teams, size='small', rotation=90)
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+plt.xlabel('Champion Team')
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+plt.ylabel('Three Point Statistics')
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+plt.title('Three Point Statistics of Champion Team From 1986 to 2016')
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+
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+
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+two_of_champions = shoot_data[['场均两分出手', '场均两分命中']]
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+two_of_champions = two_of_champions.rename(columns={'场均两分出手': '2PA', '场均两分命中': '2P'})
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+two_of_champions.plot(kind='bar')
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+plt.hlines(two_of_champions['2PA'].mean(), 0, 30, linestyles='dashed')
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+plt.hlines(two_of_champions['2P'].mean(), 0, 30, linestyles='dashed')
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+plt.xticks(np.arange(31), champion_teams, size='small', rotation=90)
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+plt.xlabel('Champion Team')
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+plt.ylabel('Three Point Statistics')
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+plt.title('Three Point Statistics of Champion Team From 1986 to 2016')
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+
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+
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+team_playoff['百回合得分'] = team_playoff['得分'] / (2 * team_playoff['回合']) * 100
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+team_playoff['百回合失分'] = team_playoff['失分'] / (2 * team_playoff['回合']) * 100
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+
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+team_playoff.head()
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+
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+team_season['百回合得分'] = team_season['得分'] / (2 * team_season['回合']) * 100
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+team_season['百回合失分'] = team_season['失分'] / (2 * team_season['回合']) * 100
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+
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+team_season.head()
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+
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+# 计算百回合得分、失分以及百回合得失分比
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+efficiency = {}
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+
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+for year in range(1986, 2017):
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+ efficiency[year] = {}
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+ champion_team = team_playoff[(team_playoff['赛季'] == year) & (team_playoff['球队'] == champions[year])]
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+ efficiency[year]['offensive'] = champion_team['百回合得分'].mean()
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+ efficiency[year]['defensive'] = champion_team['百回合失分'].mean()
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+ efficiency[year]['ratio'] = efficiency[year]['offensive'] / efficiency[year]['defensive']
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+
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+efficiency = pd.DataFrame(efficiency).T
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+efficiency
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+
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+plt.bar(np.arange(31), list(efficiency['offensive'].values))
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+plt.bar(np.arange(31), list(-1 * efficiency['defensive'].values))
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+plt.hlines(efficiency['offensive'].mean(), 0, 30, linestyles='dashed')
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+plt.hlines(-1 * efficiency['defensive'].mean(), 0, 30, linestyles='dashed')
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+plt.xticks(np.arange(31), champion_teams, size='small', rotation=90)
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+plt.xlabel('Champion Team')
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+plt.ylabel('Offensive & Defensive Efficiency')
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+plt.title('Offensive & Defensive Efficiency of Champion Team from 1986 to 2016')
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+
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+
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+plt.scatter(np.arange(31), list(efficiency['ratio'].values))
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+plt.hlines(efficiency['ratio'].mean(), 0, 30, linestyles='dashed')
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+plt.xticks(np.arange(31), champion_teams, size='small', rotation=90)
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+plt.xlabel('Champion Team')
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+plt.ylabel('Offensive & Defensive Efficiency Ratio')
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+plt.title('Offensive & Defensive Efficiency Ratio of Champion Team in Playoff from 1986 to 2016')
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+plt.grid(True)
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+
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+CHI1991 = team_playoff[(team_playoff['赛季'] == 1991) & (team_playoff['球队'] == 'CHI')]
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+CHI1996 = team_playoff[(team_playoff['赛季'] == 1996) & (team_playoff['球队'] == 'CHI')]
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+LAL2001 = team_playoff[(team_playoff['赛季'] == 2001) & (team_playoff['球队'] == 'LAL')]
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+
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+CHI1991
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+
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+team_playoff.columns
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+
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+
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+NYK_season_1991 = team_season[(team_season['球队'] == 'NYK') & (team_season['赛季'] == 1991)]
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+NYK_season_average_1991 = NYK_season_1991.mean()
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+
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+NYK_playoff_1991 = team_playoff[(team_playoff['球队'] == 'NYK') & (team_playoff['赛季'] == 1991)].tail(3)
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+NYK_playoff_average_1991 = NYK_playoff_1991.mean()
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+
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+PHI_season_1991 = team_season[(team_season['球队'] == 'PHI') & (team_season['赛季'] == 1991)]
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+PHI_season_average_1991 = PHI_season_1991.mean()
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+
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+PHI_playoff_1991 = team_playoff[(team_playoff['球队'] == 'PHI') & (team_playoff['赛季'] == 1991)].tail(5)
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+PHI_playoff_average_1991 = PHI_playoff_1991.mean()
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+
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+DET_season_1991 = team_season[(team_season['球队'] == 'DET') & (team_season['赛季'] == 1991)]
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+DET_season_average_1991 = DET_season_1991.mean()
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+
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+DET_playoff_1991 = team_playoff[(team_playoff['球队'] == 'DET') & (team_playoff['赛季'] == 1991)].tail(4)
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+DET_playoff_average_1991 = DET_playoff_1991.mean()
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+
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+LAL_season_1991 = team_season[(team_season['球队'] == 'LAL') & (team_season['赛季'] == 1991)]
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+LAL_season_average_1991 = LAL_season_1991.mean()
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+
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+LAL_playoff_1991 = team_playoff[(team_playoff['球队'] == 'LAL') & (team_playoff['赛季'] == 1991)].tail(5)
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+LAL_playoff_average_1991 = LAL_playoff_1991.mean()
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+
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+total_1991 = [NYK_season_average_1991, NYK_playoff_average_1991, PHI_season_average_1991, PHI_playoff_average_1991,
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+ DET_season_average_1991, DET_playoff_average_1991, LAL_season_average_1991, LAL_playoff_average_1991]
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+
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+
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+season_score_1991 = []
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+season_loss_1991 = []
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+playoff_score_1991 = []
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+playoff_loss_1991 = []
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+
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+for i in range(len(total_1991)):
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+ if i % 2 == 0:
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+ season_score_1991.append(total_1991[i]['百回合得分'])
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+ season_loss_1991.append(-total_1991[i]['百回合失分'])
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+ else:
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+ playoff_score_1991.append(total_1991[i]['百回合得分'])
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+ playoff_loss_1991.append(-total_1991[i]['百回合失分'])
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+
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+change1991 = pd.DataFrame({'season_score': season_score_1991, 'season_loss': season_loss_1991,
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+ 'playoff_score': playoff_score_1991, 'playoff_loss': playoff_loss_1991})
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+
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+
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+change1991[['season_score', 'playoff_score']].plot(kind='bar')
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+plt.xticks(np.arange(4), ['NYK', 'PHI', 'DET', 'LAL'])
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+plt.xlabel('Team')
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+plt.ylabel('Score per 100 Round')
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+change1991[['season_loss', 'playoff_loss']].plot(kind='bar')
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+plt.xticks(np.arange(4), ['NYK', 'PHI', 'DET', 'LAL'])
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+plt.xlabel('Team')
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+plt.ylabel('Loss per 100 Round')
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+
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+season1991 = team_season[team_season['赛季'] == 1991]
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+season1991_score = season1991['百回合得分'].groupby(season1991['球队']).mean()
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+season1991_loss = season1991['百回合失分'].groupby(season1991['球队']).mean()
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+season1991_average = pd.concat([season1991_score, season1991_loss], axis=1)
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+season1991_average['得失分比'] = season1991_average['百回合得分'] / season1991_average['百回合失分']
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+season1991_average.sort_values(by='得失分比', ascending=False)
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+
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+compare1991 = pd.DataFrame([season_score_1991, season_loss_1991, playoff_score_1991, playoff_loss_1991],
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+ index=['season score', 'season loss', 'playoff score', 'playoff loss'],
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+ columns=['NYK', 'PHI', 'DET', 'LAL']).T
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+compare1991['season ratio'] = compare1991['season score'] / (-1 * compare1991['season loss'])
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+compare1991['playoff ratio'] = compare1991['playoff score'] / (-1 * compare1991['playoff loss'])
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+
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+compare1991
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+
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+CHI1996
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+
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+MIA_season_1996 = team_season[(team_season['球队'] == 'MIA') & (team_season['赛季'] == 1996)]
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+MIA_season_average_1996 = MIA_season_1996.mean()
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+
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+MIA_playoff_1996 = team_playoff[(team_playoff['球队'] == 'MIA') & (team_playoff['赛季'] == 1996)].tail(3)
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+MIA_playoff_average_1996 = MIA_playoff_1996.mean()
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+
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+NYK_season_1996 = team_season[(team_season['球队'] == 'NYK') & (team_season['赛季'] == 1996)]
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+NYK_season_average_1996 = NYK_season_1996.mean()
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+
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+NYK_playoff_1996 = team_playoff[(team_playoff['球队'] == 'NYK') & (team_playoff['赛季'] == 1996)].tail(5)
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+NYK_playoff_average_1996 = NYK_playoff_1996.mean()
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+
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+ORL_season_1996 = team_season[(team_season['球队'] == 'ORL') & (team_season['赛季'] == 1996)]
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+ORL_season_average_1996 = ORL_season_1996.mean()
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+
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+ORL_playoff_1996 = team_playoff[(team_playoff['球队'] == 'ORL') & (team_playoff['赛季'] == 1996)].tail(4)
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+ORL_playoff_average_1996 = ORL_playoff_1996.mean()
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+
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+total_1996 = [MIA_season_average_1996, MIA_playoff_average_1996, NYK_season_average_1996, NYK_playoff_average_1996,
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+ ORL_season_average_1996, ORL_playoff_average_1996]
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+
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+CHI1996.tail(6)['百回合得分'].mean()
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+
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+CHI1996.tail(6)['百回合失分'].mean()
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+
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+season_score_1996 = []
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+season_loss_1996 = []
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+playoff_score_1996 = []
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+playoff_loss_1996 = []
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+
|
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+for i in range(len(total_1996)):
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+ if i % 2 == 0:
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+ season_score_1996.append(total_1996[i]['百回合得分'])
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|
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+ season_loss_1996.append(-total_1996[i]['百回合失分'])
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+ else:
|
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+ playoff_score_1996.append(total_1996[i]['百回合得分'])
|
|
|
+ playoff_loss_1996.append(-total_1996[i]['百回合失分'])
|
|
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+
|
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+season_score_1996.append(107.523563)
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|
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+season_loss_1996.append(-99.497880)
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|
|
+playoff_score_1996.append(100.551875)
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|
|
+playoff_loss_1996.append(-104.907100)
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+
|
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|
+change1996 = pd.DataFrame({'season_score': season_score_1996, 'season_loss': season_loss_1996,
|
|
|
+ 'playoff_score': playoff_score_1996, 'playoff_loss': playoff_loss_1996})
|
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+
|
|
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+
|
|
|
+change1996[['season_score', 'playoff_score']].plot(kind='bar')
|
|
|
+plt.xticks(np.arange(4), ['MIA', 'NYK', 'ORL', 'SEA'])
|
|
|
+plt.xlabel('Team')
|
|
|
+plt.ylabel('Score per 100 Round')
|
|
|
+change1996[['season_loss', 'playoff_loss']].plot(kind='bar')
|
|
|
+plt.xticks(np.arange(4), ['MIA', 'NYK', 'ORL', 'SEA'])
|
|
|
+plt.xlabel('Team')
|
|
|
+plt.ylabel('Loss per 100 Round')
|
|
|
+
|
|
|
+
|
|
|
+season1996 = team_season[team_season['赛季'] == 1996]
|
|
|
+season1996_score = season1996['百回合得分'].groupby(season1996['球队']).mean()
|
|
|
+season1996_loss = season1996['百回合失分'].groupby(season1996['球队']).mean()
|
|
|
+season1996_average = pd.concat([season1996_score, season1996_loss], axis=1).T
|
|
|
+season1996_average['SEA'] = [107.523563, 99.497880]
|
|
|
+season1996_average = season1996_average.T
|
|
|
+season1996_average['得失分比'] = season1996_average['百回合得分'] / season1996_average['百回合失分']
|
|
|
+season1996_average.sort_values(by='得失分比', ascending=False)
|
|
|
+
|
|
|
+
|
|
|
+compare1996 = pd.DataFrame([season_score_1996, season_loss_1996, playoff_score_1996, playoff_loss_1996],
|
|
|
+ index=['season score', 'season loss', 'playoff score', 'playoff loss'],
|
|
|
+ columns=['MIA', 'NYK', 'ORL', 'SEA']).T
|
|
|
+compare1996['season ratio'] = compare1996['season score'] / (-1 * compare1996['season loss'])
|
|
|
+compare1996['playoff ratio'] = compare1996['playoff score'] / (-1 * compare1996['playoff loss'])
|
|
|
+
|
|
|
+compare1996
|
|
|
+
|
|
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+
|
|
|
+LAL2001
|
|
|
+
|
|
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+
|
|
|
+POR_season_2001 = team_season[(team_season['球队'] == 'POR') & (team_season['赛季'] == 2001)]
|
|
|
+POR_season_average_2001 = POR_season_2001.mean()
|
|
|
+
|
|
|
+POR_playoff_2001 = team_playoff[(team_playoff['球队'] == 'POR') & (team_playoff['赛季'] == 2001)].tail(3)
|
|
|
+POR_playoff_average_2001 = POR_playoff_2001.mean()
|
|
|
+
|
|
|
+SAC_season_2001 = team_season[(team_season['球队'] == 'SAC') & (team_season['赛季'] == 2001)]
|
|
|
+SAC_season_average_2001 = SAC_season_2001.mean()
|
|
|
+
|
|
|
+SAC_playoff_2001 = team_playoff[(team_playoff['球队'] == 'SAC') & (team_playoff['赛季'] == 2001)].tail(4)
|
|
|
+SAC_playoff_average_2001 = SAC_playoff_2001.mean()
|
|
|
+
|
|
|
+SAS_season_2001 = team_season[(team_season['球队'] == 'SAS') & (team_season['赛季'] == 2001)]
|
|
|
+SAS_season_average_2001 = SAS_season_2001.mean()
|
|
|
+
|
|
|
+SAS_playoff_2001 = team_playoff[(team_playoff['球队'] == 'SAS') & (team_playoff['赛季'] == 2001)].tail(4)
|
|
|
+SAS_playoff_average_2001 = SAS_playoff_2001.mean()
|
|
|
+
|
|
|
+PHI_season_2001 = team_season[(team_season['球队'] == 'PHI') & (team_season['赛季'] == 2001)]
|
|
|
+PHI_season_average_2001 = PHI_season_2001.mean()
|
|
|
+
|
|
|
+PHI_playoff_2001 = team_playoff[(team_playoff['球队'] == 'PHI') & (team_playoff['赛季'] == 2001)].tail(5)
|
|
|
+PHI_playoff_average_2001 = PHI_playoff_2001.mean()
|
|
|
+
|
|
|
+total_2001 = [POR_season_average_2001, POR_playoff_average_2001, SAC_season_average_2001, SAC_playoff_average_2001,
|
|
|
+ SAS_season_average_2001, SAS_playoff_average_2001, PHI_season_average_2001, PHI_playoff_average_2001]
|
|
|
+
|
|
|
+
|
|
|
+
|
|
|
+season_score_2001 = []
|
|
|
+season_loss_2001 = []
|
|
|
+playoff_score_2001 = []
|
|
|
+playoff_loss_2001 = []
|
|
|
+
|
|
|
+for i in range(len(total_2001)):
|
|
|
+ if i % 2 == 0:
|
|
|
+ season_score_2001.append(total_2001[i]['百回合得分'])
|
|
|
+ season_loss_2001.append(-total_2001[i]['百回合失分'])
|
|
|
+ else:
|
|
|
+ playoff_score_2001.append(total_2001[i]['百回合得分'])
|
|
|
+ playoff_loss_2001.append(-total_2001[i]['百回合失分'])
|
|
|
+
|
|
|
+change2001 = pd.DataFrame({'season_score': season_score_2001, 'season_loss': season_loss_2001,
|
|
|
+ 'playoff_score': playoff_score_2001, 'playoff_loss': playoff_loss_2001})
|
|
|
+
|
|
|
+
|
|
|
+change2001[['season_score', 'playoff_score']].plot(kind='bar')
|
|
|
+plt.xticks(np.arange(4), ['POR', 'SAC', 'SAS', 'PHI'])
|
|
|
+plt.xlabel('Team')
|
|
|
+plt.ylabel('Score per 100 Round')
|
|
|
+change2001[['season_loss', 'playoff_loss']].plot(kind='bar')
|
|
|
+plt.xticks(np.arange(4), ['POR', 'SAC', 'SAS', 'PHI'])
|
|
|
+plt.xlabel('Team')
|
|
|
+plt.ylabel('Loss per 100 Round')
|
|
|
+
|
|
|
+
|
|
|
+season2001 = team_season[team_season['赛季'] == 2001]
|
|
|
+season2001_score = season2001['百回合得分'].groupby(season2001['球队']).mean()
|
|
|
+season2001_loss = season2001['百回合失分'].groupby(season2001['球队']).mean()
|
|
|
+season2001_average = pd.concat([season2001_score, season2001_loss], axis=1)
|
|
|
+season2001_average['得失分比'] = season2001_average['百回合得分'] / season2001_average['百回合失分']
|
|
|
+season2001_average.sort_values(by='得失分比', ascending=False)
|
|
|
+
|
|
|
+
|
|
|
+compare2001 = pd.DataFrame([season_score_2001, season_loss_2001, playoff_score_2001, playoff_loss_2001],
|
|
|
+ index=['season score', 'season loss', 'playoff score', 'playoff loss'],
|
|
|
+ columns=['POR', 'SAC', 'SAS', 'PHI']).T
|
|
|
+compare2001['season ratio'] = compare2001['season score'] / (-1 * compare2001['season loss'])
|
|
|
+compare2001['playoff ratio'] = compare2001['playoff score'] / (-1 * compare2001['playoff loss'])
|
|
|
+
|
|
|
+compare2001
|
|
|
+
|
|
|
+
|
|
|
+
|
|
|
+
|
|
|
+
|
|
|
+
|
