Question

我有类似下面的Data_df示例数据框的数据。我想知道是否有一种方法可以为一个时间戳字段（例如“ start_timestamp”字段）中的所有时间维度创建新列。我想根据“ start_timestamp”列为年，月，日，小时，分钟创建新列。我知道我可以手动为每个时间维度编写代码，但是我想知道是否有一种方法可以检查时间戳并自动创建它们。

Data_df：

   Unnamed: 0  call_history_id                            calllog_id  \
0       16358       1210746736  ca58d850-6fe6-4673-a049-ea4a2d8d7ecf   
1       16361       1210976828  c005329b-955d-4d88-98a5-1c47e6a1cb80   
2       16402       1217791595  050e9b83-54c2-4c87-abdd-32225c0d3189   
3       16471       1228495414  45705ed1-a8e2-4a15-8941-5b0a40b7d409   
4       27906       1245173592  04e56818-04a0-4704-ac86-31c31dac2370   

        call_id  connection_id  pbx_name    pbx_id  extension_number  \
0  1.509170e+12   1.509170e+12  sales8x8  sales8x8               595   
1  1.509170e+12   1.509170e+12  sales8x8  sales8x8               595   
2  1.509170e+12   1.509170e+12  sales8x8  sales8x8               595   
3  1.509170e+12   1.509170e+12  sales8x8  sales8x8               595   
4  1.509170e+12   1.509170e+12  sales8x8  sales8x8               595   

  extension_id   customer_id      address                 name  \
0          595  2.525100e+29  14086694428           Sun Basket   
1          595  2.525100e+29  13214371589          PEREZ,BRYAN   
2          595  2.525100e+29  14088566290          14088566290   
3          595  2.525100e+29   8059316676              Dialing   
4          595  2.525100e+29  12028071151  Implementation Team   

  start_timestamp direction  call_internal  call_missed  duration  \
0    1/8/18 19:49         I            0.0          0.0    4414.0   
1    1/8/18 20:09         I            0.0          0.0    8300.0   
2    1/9/18 20:31         I            0.0          0.0   14766.0   
3   1/11/18 17:16         I            0.0          0.0    1686.0   
4   1/15/18 22:55         I            0.0          0.0    3491.0   

  device_model  group_call group_name group_number           device_id  \
0  mediaserver         0.0          N            N  MasterSlaveService   
1  mediaserver         0.0          N            N  MasterSlaveService   
2  mediaserver         0.0          N            N  MasterSlaveService   
3  mediaserver         0.0          N            N  MasterSlaveService   
4  mediaserver         0.0          N            N  MasterSlaveService   

  history_event_state   created_time   updated_time group_type  
0                   A   1/8/18 19:49   1/8/18 19:49          N  
1                   A   1/8/18 20:09   1/8/18 20:09        NaN  
2                   A   1/9/18 20:31   1/9/18 20:31          N  
3                   A  1/11/18 17:16  1/11/18 17:16          N  
4                   A  1/15/18 22:55  1/15/18 22:55          N

更新：

def ts_periods(f_nm, d_list, d_df):
    t_df=d_df.copy()

    for i in d_list:
        if i=='year':
            t_df[f_nm+'_Year']=pd.DatetimeIndex(t_df[f_nm]).year
        elif i=='month':
            t_df[f_nm+'_month']=pd.DatetimeIndex(t_df[f_nm]).month
        elif i=='weekday':
            t_df[f_nm+'_weekday']=pd.DatetimeIndex(t_df[f_nm]).weekday_name
        elif i=='week' in d_list:
            t_df[f_nm+'_week']=pd.DatetimeIndex(t_df[f_nm]).week
        elif i=='hour':
            t_df[f_nm+'_hour']=pd.DatetimeIndex(t_df[f_nm]).hour
        elif i=='minute':
            t_df[f_nm+'_minute']=pd.DatetimeIndex(t_df[f_nm]).minute
    return t_df

Answer 1

使用数据和.dt访问器的简短示例。我们首先将数据转换为熊猫时间戳，然后访问所需的维度：

import pandas as pd

data = pd.DataFrame(
    {
        'time_stamp': ['1/8/18 19:49', '1/9/18 20:31', '1/11/18 17:16']
    }
)

data['time_stamp'] = pd.to_datetime(data['time_stamp'], dayfirst = True)

data['day_of_week'] = data['time_stamp'].dt.weekday

data['hour_of_day'] = data['time_stamp'].dt.hour

print(data)

礼物：

           time_stamp  day_of_week  hour_of_day
0 2018-08-01 19:49:00            2           19
1 2018-09-01 20:31:00            5           20
2 2018-11-01 17:16:00            3           17

文档：https://pandas.pydata.org/pandas-docs/stable/basics.html#basics-dt-accessors

为所有时间维度创建列

1 个答案: