对每 5 分钟的值进行分组和求和 / 用字符串值重新采样 5 分钟的数据

Question

我想对每 5 分钟时间戳的每个性别的值求和。

主表：-

Time   Gender  value

10:01  Male      5
10:02  Female    1
10:03  Male      5
10:04  Male      5
10:05  Female    1
10:06  Female    1
10:07  Male      5
10:08  Male      5
10:09  Male      5
10:10  Male      5

要求的结果：-

Time   Gender  value
10:00  Male     15
10:00  Female   2
10:05  Male     20
10:05  Female   1

Answer 1

您可以转换为TimeDelta ， groupby floor agg ：

t = pd.to_timedelta(df['Time']+':00')
(df
 .groupby([t.dt.floor('5min'), 'Gender']) 
 .agg({'value': 'sum'})
 .reset_index()
)

output：

             Time  Gender  value
0 0 days 10:00:00  Female      1
1 0 days 10:00:00    Male     15
2 0 days 10:05:00  Female      2
3 0 days 10:05:00    Male     15
4 0 days 10:10:00    Male      5

匹配提供的 output

为了匹配您提供的 output，它需要更多的东西。

• 在“00:00:00”上减去一分钟到“00:05:00”下限
• 转换回字符串

t = pd.to_timedelta(df['Time']+':00').sub(pd.to_timedelta('1min'))

(df
 .groupby([t.dt.floor('5min'), 'Gender']) 
 .agg({'value': 'sum'})
 .reset_index()
 .assign(Time=lambda d: (pd.to_datetime(0)+d['Time']).dt.strftime('%H:%M'))
)

output：

    Time  Gender  value
0  10:00  Female      2
1  10:00    Male     15
2  10:05  Female      1
3  10:05    Male     20

变体

t = pd.to_timedelta(df['Time']+':00').sub(pd.to_timedelta('1min'))
(df.assign(Time=t.dt.floor('5min').astype(str).str[-8:-3])
   .groupby(['Time', 'Gender'])
   ['value'].sum().reset_index()
)