我有一个这样的数据集,其中每一行代表gameID
指定的特定匹配中的一个。
gameID Won/Lost Home Away metric2 metric3 metric4 team1 team2 team3 team4
2017020001 1 1 0 10 10 10 1 0 0 0
2017020001 0 0 1 10 10 10 0 1 0 0
我要做的是创建一个函数,该函数将具有相同gameID
的行合并起来。正如您在下面的数据示例中看到的那样,这两行代表一个比赛,该比赛分为一个主队(row_1)和一个客队(row_2)。我希望这两行只能坐在一行上。
Won/Lost h_metric2 h_metric3 h_metric4 a_metric2 a_metric3 a_metric4 h_team1 h_team2 h_team3 h_team4 a_team1 a_team2 a_team3 a_team4
1 10 10 10 10 10 10 1 0 0 0 0 1 0 0
如何获得此结果?
编辑:我造成了太多混乱,发布了我的代码,因此您可以更好地了解我要解决的问题。
import numpy as np
import pandas as pd
import requests
import json
from sklearn import preprocessing
from sklearn.preprocessing import OneHotEncoder
results = []
for game_id in range(2017020001, 2017020010, 1):
url = 'https://statsapi.web.nhl.com/api/v1/game/{}/boxscore'.format(game_id)
r = requests.get(url)
game_data = r.json()
for homeaway in ['home','away']:
game_dict = game_data.get('teams').get(homeaway).get('teamStats').get('teamSkaterStats')
game_dict['team'] = game_data.get('teams').get(homeaway).get('team').get('name')
game_dict['homeaway'] = homeaway
game_dict['game_id'] = game_id
results.append(game_dict)
df = pd.DataFrame(results)
df['Won/Lost'] = df.groupby('game_id')['goals'].apply(lambda g: (g == g.max()).map({True: 1, False: 0}))
df["faceOffWinPercentage"] = df["faceOffWinPercentage"].astype('float')
df["powerPlayPercentage"] = df["powerPlayPercentage"].astype('float')
df["team"] = df["team"].astype('category')
df = pd.get_dummies(df, columns=['homeaway'])
df = pd.get_dummies(df, columns=['team'])
答案 0 :(得分:1)
我只是假设,您正在使用面包和黄油: numpy,pandas&co?
如果是这样,我还假设您的表当前存储在名为'df'的pandas.DataFrame-instance中:
将您的df分为两个df,然后将它们加入:
df_team1 = df[df['Won/Lost']==1]
df_team2 = df[df['Won/Lost']==0]
final_df = df_team1.join(df_team2, lsuffix='_team1', rsuffix='_team2', on='gameID')
您当然可以编辑它,以更好地满足您的目的。例如,根据“居家/离开”列等创建df。
BR 本 :]
答案 1 :(得分:1)
这是基于以下假设:每个gameID
恰好有两行,并且您要根据该ID进行分组。 (它也假设我理解这个问题。)
改进的解决方案
给出一个数据帧df
,例如
gameID Won/Lost Home Away metric2 metric3 metric4 team1 team2 team3 team4
0 2017020001 1 1 0 10 10 10 1 0 0 0
1 2017020001 0 0 1 10 10 10 0 1 0 0
2 2017020002 1 1 0 10 10 10 1 0 0 0
3 2017020002 0 0 1 10 10 10 0 1 0 0
您可以像这样使用pd.merge
(以及一些数据处理):
>>> is_home = df['Home'] == 1
>>> home = df[is_home].drop(['Home', 'Away'], axis=1).add_prefix('h_').rename(columns={'h_gameID':'gameID'})
>>> away = df[~is_home].drop(['Won/Lost', 'Home', 'Away'], axis=1).add_prefix('a_').rename(columns={'a_gameID':'gameID'})
>>> pd.merge(home, away, on='gameID')
gameID h_Won/Lost h_metric2 h_metric3 h_metric4 h_team1 h_team2 h_team3 h_team4 a_metric2 a_metric3 a_metric4 a_team1 a_team2 a_team3 a_team4
0 2017020001 1 10 10 10 1 0 0 0 10 10 10 0 1 0 0
1 2017020002 1 10 10 10 1 0 0 0 10 10 10 0 1 0 0
(我保留了Won/Lost
的前缀,因为它表明这是主队的统计数据。此外,如果有人知道如何更优雅地添加前缀而不必重命名gameID
请发表评论。)
原始尝试
您可以在分组后应用以下功能
def munge(group):
is_home = group.Home == 1
wonlost = group.loc[is_home, 'Won/Lost'].reset_index(drop=True)
group = group.loc[:, 'metric2':]
home = group[is_home].add_prefix('h_').reset_index(drop=True)
away = group[~is_home].add_prefix('a_').reset_index(drop=True)
return pd.concat([wonlost, home, away], axis=1)
...像这样
>>> df.groupby('gameID').apply(munge).reset_index(level=1, drop=True)
Won/Lost h_metric2 h_metric3 h_metric4 h_team1 h_team2 h_team3 h_team4 a_metric2 a_metric3 a_metric4 a_team1 a_team2 a_team3 a_team4
gameID
2017020001 1 10 10 10 1 0 0 0 10 10 10 0 1 0 0
2017020002 1 10 10 10 1 0 0 0 10 10 10 0 1 0 0