我有一个包含"day“、"month”、"type“和"value”的数据帧。我想按“日”、“月”和“类型”分组,然后求和并计算“值”。但是,我只想让总和考虑"value">15。我现在拥有的是: final = test.groupby(['DAY','MONTH','TYPE']).VALUE.aggregate(['sum','count']) 它返回的几乎就是我想要的,
我正在尝试执行一个sql查询,该查询根据due_date范围和balance_amount group by company_id的总和得出输出。 ? 我的尝试: select invoices.company_id,SUM(invoices_1month.balance_amount) as 1month,fr
假设Team1有Player1和Player1共打了4场比赛,1场在04/2020,2场在06/2020,1场在08/2020。SELECT SUM(ISNULL(P.[TotalMatchesPlayed], 0) * 5) AS [ParticipationPoints], CAST(MONTH(PA.[TeamId] = 45
GROUP BY CAST(MONTH(PA.[ActivityDate]) AS VARCHAR(2)), CAST(YEAR(PA.ActivityDate]) AS VARC
有3列,我正在尝试在pandas中做以下事情,并计划在group by output dataframe之后使用"sum“列来做更多的工作:
df_group_by=df.groupby('account').agg({'amount': [np.size, np.sum]},as_index=False).reset_index() # equal to "SELECTACCOUNT, SU