分组依据并做汇总函数，例如求和和对值列表的计数

Question

按显示分组，计算相应的VideoID，对观看次数求和，对收入求和。我该如何实现？

我的桌子看起来像

    show.    views.  revenue  VideoID
    batman.   100.     10.    v1
    batman.   200.     20.    v2
    joker.    100.     10.    v3
    joker.    300.     15.    v4
    superman. 500.     30.    v5

我的预期输出是

Show          total_views    total_revenue.    video_count
batman.           300.            30.           2
joker             400.            25            2
superman          500             30            1

我该如何实现？

这是我到目前为止尝试过的，但是输出错误

def grouping_series(df_series):
    t = defaultdict(list)
    gp = df_series.groupby('show')
    for i, k in gp:
        t['total_views'].append(k['views'].sum())
        t['total_revenue'].append(k['revenue'].sum())
        t['video_count'].append(k['VideoID'].count())
    return pd.DataFrame(t)

df = grouping_series(df_series)    

Answer 1

我们通常会做agg

s=df.groupby('show').agg(total_views=('views', 'sum'), 
                         total_revenue=('revenue', 'sum'),
                         video_count=('VideoID', 'count')).reset_index()
        show  total_views  total_revenue  video_count
0    batman.        300.0           30.0            2
1     joker.        400.0           25.0            2
2  superman.        500.0           30.0            1

Answer 2

这是我的建议：

iimport pandas as pd

frame = {
    "show": ["batman", "batman", "joker", "joker", "superman"],
    "views": [100, 200, 100, 300, 500],
    "revenue": [10, 20, 10, 15, 30],
    "VideoID": ["v1", "v2", "v3", "v4", "v5"],
}

df = pd.DataFrame(frame)

aggretations = {"views": "sum", "revenue": "sum", "VideoID": "nunique"}

df.groupby(["show"]).agg(aggretations)