时间戳

首页 > 解决方案 > 使用函数在 df 中添加列

python - 使用函数在 df 中添加列

问题描述

      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'

标签： pythonpandasdatetimemerge

解决方案

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

推荐阅读