根据 dataframe Python 中另一列的值计算一列的移动平均值（熊猫）

Question

I am trying to create a column of the 10-day moving average of points for nba players. My dataframe has game by game statistics for each player, and I would like to have the moving average column contain the 10 day moving average at that point. I have tried df.groupby('player')['points].rolling(10,1).mean, but this is just giving me the number of points scored on that day as the moving average. All of the players from each day are listed and then the dataframe moves onto the following day, so I could have a couple hundred rows with the same date but different players' stats. Any help would be greatly appreciated. Thanks.

Answer 1

As stated, you really should provide a sample dataset, and show what you are trying to achieve. However, I love working with sports data so don't mind puting in the minute or so to get a sample set.

So basically you need to do a rolling mean on a groupby. You'll notice obviously the first 10 rows of each player are blank, because it doesn't have 10 dates to take the mean of. You can change that by changing the min to 1. Also, when you do this, you want to make sure your data is sorted by date (which here it already is).

import pandas as pd

player_link_list = ['https://www.basketball-reference.com/players/l/lavinza01/gamelog/2021/',
                    'https://www.basketball-reference.com/players/v/vucevni01/gamelog/2021/',
                    'https://www.basketball-reference.com/players/j/jamesle01/gamelog/2021/',
                    'https://www.basketball-reference.com/players/d/davisan02/gamelog/2021/']

dfs = []
for link in player_link_list:
    w=1
    df = pd.read_html(link)[-1]
    df = df[df['Rk'].ne('Rk')]   
    df = df[df['PTS'].ne('Inactive')]   
    df['Player'] = link.split('/')[-4]
    df['PTS'] = df['PTS'].astype(int,errors = 'ignore')
    dfs.append(df)
    

df = pd.concat(dfs)

df['rolling_10_avg'] = df.groupby('Player')['PTS'].transform(lambda s: s.rolling(10, min_periods=10).mean())