如何在Pandas/Python中按星期几计算平均骑行次数

我有一个包含500万行、覆盖12个月数据的DataFrame（命名为copydf），其中包含day_of_week（取值为周一至周日）列和唯一标识列ride_id。需要按星期几计算平均骑行次数。

目前已经通过以下代码算出每周各天的总骑行次数：

copydf.groupby(['day_of_week']).agg(number_of_rides=('day_of_week', 'count'))

但尝试以下两种方法计算平均次数均失败：
第一种方法：

copydf.groupby(['day_of_week']).agg(number_of_rides=('ride_id', 'count')).mean()

第二种方法：

avg_days = copydf.groupby(['day_of_week']).agg(number_of_rides=('ride_id', 'count'))
avg_days.groupby(['day_of_week']).agg('number_of_rides', 'mean')

期望输出 #

我需要的结果是两种形式之一：

• 包含day_of_week、number_of_rides、avg_num_of_ride三列的DataFrame
• 仅包含day_of_week（或weekday_num）和avg_num_of_rides两列的DataFrame

DataFrame示例 #

ride_id rideable_type   started_at  ended_at    start_station_name  start_station_id    end_station_name    end_station_id  start_lat   start_lng   end_lat end_lng member_or_casual    ride_length year    month   day_of_week hour    weekday_num
0   9DC7B962304CBFD8    electric_bike   2021-09-28 16:07:10 2021-09-28 16:09:54 Streeter Dr & Grand Ave 13022   Streeter Dr & Grand Ave 13022   41.89   -87.68  41.89   -87.67  casual  2   2021    September   Tuesday 16  1
1   F930E2C6872D6B32    electric_bike   2021-09-28 14:24:51 2021-09-28 14:40:05 Streeter Dr & Grand Ave 13022   Streeter Dr & Grand Ave 13022   41.94   -87.64  41.98   -87.67  casual  15  2021    September   Tuesday 14  1
2   6EF72137900BB910    electric_bike   2021-09-28 00:20:16 2021-09-28 00:23:57 Streeter Dr & Grand Ave 13022   Streeter Dr & Grand Ave 13022   41.81   -87.72  41.80   -87.72  casual  3   2021    September   Tuesday 0   1

期望输出示例 #

number_of_rides average_number_of_rides
day_of_week 
Saturday    964079  50.4
Sunday  841919       70.9
Wednesday   840272   90.2
Thursday    836973    77.2
Friday  818205        34.4
Tuesday 814496       34.4
Monday  767002        200.3

再次说明：已算出每周各天的总骑行次数，仅需添加平均次数列，或生成仅包含星期几与平均次数的DataFrame。

内容的提问来源于stack exchange，提问作者Kdott