使用函数在 df 中添加列

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   

尝试添加到抓取的数据集以对 NBA 比赛上座率进行分析。我正在尝试添加一些列，例如竞技场播放量和容量。这是我为添加竞技场而编写的一段函数。有一个更好的方法吗？我有日期时间中的日期，所以我将如何正确提取年份以将正确的竞技场分配给在过去几年中建造新竞技场的球队（萨克拉门托国王队）。还有没有办法为此增加体育场容量并用一块石头杀死两只鸟而不是创建另一个功能？

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

这是你可以做的来简化你的逻辑：

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，您希望在 'Home' 和 'Date'> 2016 上合并，这可能需要您创建一个临时列，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列“季节”=“2015-16”、“2016-17”。随着您的数据库变得更大，您似乎需要它。 （对于游戏数据库，您可以从“Date”值中自动提取“Season”。对于“home_arenas_capacities”数据框，您需要手动编辑它）。

Answer 3

这里有一个使用numpy.select的方法，如果你不反对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，要使您的df['Date'] 条件起作用，您需要将df['Date'] 设置为日期时间系列（如果您还没有这样做，可以通过df['Date'] = pd.to_datetime(df['Date']) 执行此操作）