Question

我有一个赛季篮球得分的数据框，我想找到他们在赛季中每场比赛每场比赛之间的天数。

示例框架：

  testDateFrame = pd.DataFrame({'HomeTeam': ['HOU', 'CHI', 'DAL', 'HOU'],
                          'AwayTeam' : ['CHI', 'DAL', 'CHI', 'DAL'],
                          'HomeGameNum': [1, 2, 2, 2],
                          'AwayGameNum' : [1, 1, 3, 3],
                          'Date' : [datetime.date(2014,3,11), datetime.date(2014,3,12),     datetime.date(2014,3,14), datetime.date(2014,3,15)]})

我想要的输出是：

  AwayGameNum AwayTeam Date  HomeGameNum HomeTeam AwayRest  HomeRest
        1      CHI  2014-03-11    1      HOU        nan       nan
        1      DAL  2014-03-12    2      CHI        nan        0
        3      CHI  2014-03-14    2      DAL         1         1
        3      DAL  2014-03-15    2      HOU         0         3

AwayRest，HomeRest列是AwayTeam游戏之间的天数，HomeTeam -1

Answer 1

我会稍微调整一下你的数据布局，以便它符合Hadley Wickhams对Tidy Data的定义。这使得计算更加简单。删除AwayTeam和HomeTeam的列，并使用Team创建一个列。然后创建一个布尔列（HomeTeam），以确定该团队是否为主队。

注意：我没有更改AwayGameNum和HomeGameNum，因此数字与您想要的输出不符。但该方法可行。

In [34]: df
Out[34]: 
   AwayGameNum Team       Date  HomeGameNum HomeTeam
0            1  CHI 2014-03-11            1    False
1            1  HOU 2014-03-11            1     True
2            1  DAL 2014-03-12            2    False
3            1  CHI 2014-03-12            2     True
4            3  CHI 2014-03-14            2    False
5            3  DAL 2014-03-14            2     True
6            3  DAL 2014-03-15            2    False
7            3  HOU 2014-03-15            2     True

[8 rows x 5 columns]

In [62]: rest = df.groupby(['Team'])['Date'].diff() - datetime.timedelta(1)

In [63]: df['HomeRest'] = rest[df.HomeTeam]

In [64]: df['AwayRest'] = rest[~df.HomeTeam]

In [65]: df
Out[65]: 
   AwayGameNum Team       Date  HomeGameNum HomeTeam  HomeRest  AwayRest
0            1  CHI 2014-03-11            1    False       NaT       NaT
1            1  HOU 2014-03-11            1     True       NaT       NaT
2            1  DAL 2014-03-12            2    False       NaT       NaT
3            1  CHI 2014-03-12            2     True    0 days       NaT
4            3  CHI 2014-03-14            2    False       NaT    1 days
5            3  DAL 2014-03-14            2     True    1 days       NaT
6            3  DAL 2014-03-15            2    False       NaT    0 days
7            3  HOU 2014-03-15            2     True    3 days       NaT

[8 rows x 7 columns]

python pandas timedelta具体行

1 个答案: