在 pandas 中将通话数据拆分为 15 分钟的间隔

Splitting call data to 15 minute intervals in pandas

我是 python 和 pandas 的新手,尽管我研究了很多关于时间间隔的问题,但我找不到解决问题的方法,希望有人能提供帮助

这是我的 DF 的示例

df = pd.DataFrame(
    data=[['Mel Gibson', 'German', '2021-9-23 14:22:38', 301 ],
          ['Jim Carrey', 'German', '2021-9-23 14:27:39', 1041 ],
          ['Mel Gibson', 'German','2021-9-24 13:33:22',12]],
    columns=['specialist', 'Language', 'Interval Start', 'status_duration']
)
df['Interval Start'] = pd.to_datetime(df['Interval Start'])

我想做的是将状态持续时间转换为 15 分钟的间隔,并将它们按专家和每天分组。

我想要的输出应该如下所示:

df = pd.DataFrame(
    data=[['Mel Gibson', 'German', '2021-9-23 14:15:00', 301 ],
          ['Jim Carrey', 'German', '2021-9-23 14:15:00', 141 ],
          ['Jim Carrey', 'German', '2021-9-23 14:30:00', 900 ],
          ['Mel Gibson', 'German','2021-9-24 13:30:00',12]],
    columns=['specialist', 'Language', 'Interval Start', 'status_duration']
)

所以基本上我需要将状态持续时间的秒数分成 15 分钟的间隔,直到没有剩余的持续时间。

编辑:

我原来的数据是这样的:

    df = pd.DataFrame(
            data=[['Mel Gibson', 'German', '2021-9-23 14:22:38', 301 ],
                  ['Mel Gibson', 'German', '2021-9-23 14:27:40', 4678 ],
                  ['Mel Gibson', 'German','2021-9-24 13:33:22',12]],
            columns=['specialist', 'Language', 'Interval Start', 'status_duration']
        )
        df['Interval Start'] = pd.to_datetime(df['Interval Start'])

Henry 的代码只给出第一行的输出,第二行被跳过。

另外假设如果呼叫在 10:35:00 开始,此间隔 (10:30-10:45) 不能超过 600 秒,因为距离开始时间仅剩 10 分钟。

可以使用dt.floor()函数进行四舍五入:

df['Interval Start'] = df['Interval Start'].dt.floor("15min")

结果(基于您编辑的数据):

   specialist Language      Interval Start  status_duration
0  Mel Gibson   German 2021-09-23 14:15:00              301
1  Mel Gibson   German 2021-09-23 14:15:00             4678
2  Mel Gibson   German 2021-09-24 13:30:00               12

然后我添加了一列,其中包含您期望的间隔数:

df['len'] = 1 + df['status_duration']//900

结果:

0  Mel Gibson   German 2021-09-23 14:15:00              301    1
1  Mel Gibson   German 2021-09-23 14:15:00             4678    6
2  Mel Gibson   German 2021-09-24 13:30:00               12    1

然后您可以使用 numpy.repeat() 复制相应的行和列表理解,使用 timedelta() 构建相应的间隔。

import numpy as np
from datetime import timedelta

new_df = pd.DataFrame({'specialist': np.repeat(df['specialist'], df['len']),
                'Language': np.repeat(df['Language'], df['len']),
                'Interval Start': [el for sublist in [[x['Interval Start'] + timedelta(minutes=15*y) for y in range(0, x['len'])] for i, x in df.iterrows()] for el in sublist],
                'status_duration': [el for sublist in [([900]*(x['len']-1)+[x['status_duration']%900]) for i, x in df.iterrows()] for el in sublist]
})

结果:

   specialist Language      Interval Start  status_duration
0  Mel Gibson   German 2021-09-23 14:15:00              301
1  Mel Gibson   German 2021-09-23 14:15:00              900
1  Mel Gibson   German 2021-09-23 14:30:00              900
1  Mel Gibson   German 2021-09-23 14:45:00              900
1  Mel Gibson   German 2021-09-23 15:00:00              900
1  Mel Gibson   German 2021-09-23 15:15:00              900
1  Mel Gibson   German 2021-09-23 15:30:00              178
2  Mel Gibson   German 2021-09-24 13:30:00               12

最后,您可能想要重置索引:

new_df = new_df.reset_index(drop=True)

结果:

   specialist Language      Interval Start  status_duration
0  Mel Gibson   German 2021-09-23 14:15:00              301
1  Mel Gibson   German 2021-09-23 14:15:00              900
2  Mel Gibson   German 2021-09-23 14:30:00              900
3  Mel Gibson   German 2021-09-23 14:45:00              900
4  Mel Gibson   German 2021-09-23 15:00:00              900
5  Mel Gibson   German 2021-09-23 15:15:00              900
6  Mel Gibson   German 2021-09-23 15:30:00              178
7  Mel Gibson   German 2021-09-24 13:30:00               12

一种方法是利用status_duration的商和余数,explode结果,最后以秒为单位累加时间:

ref = (df.groupby(["specialist", "Language", pd.Grouper(key="Interval Start", freq="D")], as_index=False)
         .agg(status_duration=("status_duration", lambda d: [*([900]*(d.iat[0]//900)), d.iat[0]%900]),
              Interval=("Interval Start", "first"))
         .explode("status_duration"))

ref["Interval"] = ref["Interval"].dt.floor("15min")+pd.to_timedelta(ref.groupby(ref.index).cumcount()*900, unit="sec")

print (ref)

   specialist Language status_duration            Interval
0  Jim Carrey   German             900 2021-09-23 14:15:00
0  Jim Carrey   German             141 2021-09-23 14:30:00
1  Mel Gibson   German             301 2021-09-23 14:15:00
2  Mel Gibson   German              12 2021-09-24 13:30:00