如果 Date < X，则使用 Groupby 计算平均值

Question

I am trying to use a data frame that includes historical game statistics like the below df1, and build a second data frame that shows what the various column averages were going into each game (as I show in df2). How can I use grouby or something else to find the various averages for each team but only for games that have a date prior to the date in that specific row. Example of historical games column:

Df1    =     Date         Team      Opponent     Points     Points Against   1st Downs      Win?    
             4/16/20      Eagles    Ravens       10         20               10             0
             2/10/20      Eagles    Falcons      30         40               8              0
             12/15/19     Eagles    Cardinals    40         10               7              1
             11/15/19     Eagles    Giants       20         15               5              1
             10/12/19     Jets      Giants       10         18               2              1

Below is the dataframe that i'm trying to create. As you can see, it is showing the averages for each column but only for the games that happened prior to each game. Note: this is a simplified example of a much larger data set that i'm working with. In case the context helps, I'm trying to create this dataframe so I can analyze the correlation between the averages and whether the team won.

Df2    =     Date         Team      Opponent     Avg Pts    Avg Pts Against  Avg 1st Downs      Win %   
             4/16/20      Eagles    Ravens       25.0       21.3             7.5                75%
             2/10/20      Eagles    Falcons      30.0       12.0             6.0                100%
             12/15/19     Eagles    Cardinals    20.0       15.0             5.0                100%
             11/15/19     Eagles    Giants       NaN        NaN              NaN                NaN               
             10/12/19     Jets      Giants       NaN        NaN              NaN                NaN

Let me know if anything above isn't clear, appreciate the help.

Answer 1

The easiest way is to turn your dataframe into a Time Series. Run this for a file:

data=pd.read_csv(r'C:\Users\...csv',index_col='Date',parse_dates=True)

This is an example with a CSV file. You can run this after:

data[:'#The Date you want to have all the dates before it']

If you want build a Series that has time indexed:

index=pd.DatetimeIndex(['2014-07-04',...,'2015-08-04'])
data=pd.Series([0, 1, 2, 3], index=index)

Answer 2

Define your own function

def aggs_under_date(df, date):
    first_team = df.Team.iloc[0]
    first_opponent= df.Opponent.iloc[0]

    if df.date.iloc[0] <= date:
        avg_points = df.Points.mean()
        avg_againts = df['Points Against'].mean()
        avg_downs = df['1st Downs'].mean()
        win_perc = f'{win_perc.sum()/win_perc.count()*100} %'

        return [first_team, first_opponent, avg_points, avg_againts, avg_downs, win_perc]
    else:
        return [first_team, first_opponent, np.nan, np.nan, np.nan, np.nan]

And do the groupby applying the function you just defined

 date_max = pd.to_datetime('11/15/19')
 Df1.groupby(['Date']).agg(aggs_under_date, date_max)