Question

      Date             Visitor  V_PTS                 Home  H_PTS  \
0 2012-10-30 19:00:00  Washington Wizards     84  Cleveland Cavaliers     94   
1 2012-10-30 19:30:00    Dallas Mavericks     99   Los Angeles Lakers     91   
2 2012-10-30 20:00:00      Boston Celtics    107           Miami Heat    120   
3 2012-10-31 19:00:00    Sacramento Kings     87        Chicago Bulls     93   
4 2012-10-31 19:30:00     Houston Rockets    105      Detroit Pistons     96

尝试添加一个已删除的数据集来对NBA游戏参与度进行分析。我试图添加一些列，如竞技场和容量。这是我写的一个添加竞技场的功能。有一个更好的方法吗？我在日期时间有日期，所以如何正确地提取年份，为过去几年建立新竞技场的球队（萨克拉门托国王队）分配正确的竞技场。无论如何还要增加体育场的能力，一石二鸟，而不是创造另一个功能？

def label_arena (hometeam):
    if hometeam == 'Toronto Raptors' :
        return 'Air Canada Centre'
    if hometeam == 'Miami Heat' :
        return 'American Airlines Arena'
    if hometeam == 'Dallas Mavericks' :
        return 'American Airlines Center'
    if hometeam == 'Orlando Magic' :
        return 'Amway Center'
    if hometeam == 'San Antonio Spurs' :
        return 'AT&T Center'
    if hometeam == 'Indiana Pacers' :
        return 'Bankers Life Fieldhouse'
    if hometeam == 'Brooklyn Nets' :
        return 'Barclays Center'
    if hometeam == 'Milwaukee Bucks' :
        return 'Bradley Center'
    if hometeam == 'Washington Wizards' :
        return 'Capital One Arena'
    if hometeam == 'Oklahoma City Thunder' :
        return 'Chesapeake Energy Arena'
    if hometeam == 'Memphis Grizzlies' :
        return 'FedExForum'
    if hometeam == 'Sacramento Kings' and df['Date'] < 2016:
        return 'Sleep Train Arena'
    if hometeam == 'Sacramento Kings' and df['Date'] > 2016:
        return 'Golden 1 Center'

Answer 1

您可以采取以下措施来简化逻辑：

import pandas as pd

df = pd.DataFrame({'Date': ['2012-10-30', '2012-10-30', '2012-10-30',
                            '2012-10-31', '2017-10-31'],
                   'Home': ['Toronto Raptors', 'Los Angeles Lakers', 'Miami Heat',
                            'Sacramento Kings', 'Sacramento Kings']})

df['Date'] = pd.to_datetime(df['Date'])

d = {'Toronto Raptors': 'Air Canada Centre',
     'Los Angeles Lakers': 'Staples Center',
     'Miami Heat': 'American Airlines Arena'}

# general criteria
df['Arena'] = df['Home'].map(d)

# custom criteria
df.loc[(df['Home'] == 'Sacramento Kings') &
       (df['Date'].dt.year < 2016), 'Arena'] = 'Sleep Train Arena'
df.loc[(df['Home'] == 'Sacramento Kings') &
       (df['Date'].dt.year >= 2016), 'Arena'] = 'Golden 1 Center'

print(df)

        Date                Home                    Arena
0 2012-10-30     Toronto Raptors        Air Canada Centre
1 2012-10-30  Los Angeles Lakers           Staples Center
2 2012-10-30          Miami Heat  American Airlines Arena
3 2012-10-31    Sacramento Kings        Sleep Train Arena
4 2017-10-31    Sacramento Kings          Golden 1 Center

Answer 2

import pandas as pd

home_arenas_capacities = pd.DataFrame([
     ['Toronto Raptors', 'Air Canada Centre', 20511],
     ['Miami Heat', 'American Airlines Arena', 19600],
     ...
    ]) 

df.merge(home_arenas_capacities, on='Home')

对于萨克拉门托国王队，你想在“主页”和“日期”＆gt;上合并。 2016年，可能要求您制作临时列，然后df.merge(..., on=['Home','Date_GE_2016'])并删除“Date_GE_2016”列。

但更简洁的方法是添加一个专栏'Season'='2015-16'，'2016-17'。随着您的数据库变得越来越大，您似乎需要它。（对于游戏数据库，您可以从“日期”值自动提取“季节”。对于'home_arenas_capacities'数据框，您需要手动编辑它。

Answer 3

以下是使用numpy.select的方法，如果您不反对numpy：

import numpy as np

conditions = [
    df['Home'] == 'Toronto Raptors',
    df['Home'] == 'Miami Heat',
    df['Home'] == 'Dallas Mavericks',
    ...
    (df['Home'] == 'Sacramento Kings') & (df['Date'].dt.year < 2016),
    (df['Home'] == 'Sacramento Kings') & (df['Date'].dt.year > 2016)]

choices = [
        'Air Canada Centre',
        'American Airlines Arena',
        'American Airlines Center',
        ...
        'Sleep Train Arena',
        'Golden 1 Center']

df['arena'] = np.select(conditions, choices)

请注意，要使df['Date']条件生效，您需要将df['Date']设置为日期时间系列（如果您尚未完成，可以通过df['Date'] = pd.to_datetime(df['Date'])执行此操作所以）

使用函数

3 个答案: