在python中查找消息状态之间的时间差

Question

所以，我想计算时间差；文件看起来像这样

id	message_id	send_date	status
0	5f74b996a2b7e	2020-10-01 00:00:07	sent
1	5f74b996a2b7e	2020-10-01 00:00:09	delivered
2	5f74b99e85b3c	2020-10-02 02:00:14	sent
3	5f74b99e85b3c	2020-10-02 02:01:16	delivered
4	5f74b99e85b3c	2020-10-02 08:06:49	read
5	5f74b996a2b7e	2020-10-02 15:16:32	read
6	5f9d97ff1af9e	2020-10-14 13:45:43	sent
7	5f9d97ff1af9e	2020-10-14 13:45:45	delivered
8	5f9d97ff1af9e	2020-10-14 13:50:48	read
9	5f74b9a35b6c5	2020-10-16 19:01:19	sent
10	5f74b9a35b6c5	2020-10-16 19:01:25	deleted

里面是递增的id，message_id是每条消息唯一的，send_date是时间，status是消息状态（有5个状态：send、delivered、read、failed、deleted）。

我想计算消息发送然后传递的时间差，如果传递然后读取。

我知道这样的东西很方便，但我不确定如何将它唯一地分配给每个 message_id

from datetime import datetime
s1 = '2020-10-14 13:45:45'
s2 = '2020-10-14 13:50:48' # for example
FMT = '%Y-%m-%d %H:%M:%S'
tdelta = datetime.strptime(s2, FMT) - datetime.strptime(s1, FMT)
print(tdelta)

Ref: https://stackoverflow.com/questions/3096953/how-to-calculate-the-time-interval-between-two-time-strings

预期的输出是，

	message_id	delivered_diff	read_diff	deleted_diff
0	5f74b996a2b7e	00:00:02	1 day, 15:16:23
1	5f74b99e85b3c	00:01:02	6:05:33
2	5f9d97ff1af9e	00:00:02	0:05:03
3	5f74b9a35b6c5			0:00:06

Answer 1

您可以使用pandas 和datetime 来执行此操作。 对代码进行了注释，以便更好地理解和使用python 3.8实现。

import datetime
import pandas as pd

def time_delta(a, b):
    return datetime.datetime.strptime(b, '%Y-%m-%d %H:%M:%S') - datetime.datetime.strptime(a, '%Y-%m-%d %H:%M:%S') # calculate the timedelta

def calculate_diff(val, first_status, second_status):
    if not val['status'].str.contains(first_status).any() or not val['status'].str.contains(second_status).any(): # Check if the status exist
        return ''

    a = val.loc[val['status'] == first_status, 'send_date'].values[0] # Get the first send_date value for the first status value
    b = val.loc[val['status'] == second_status, 'send_date'].values[0] # Get the first send_date value for the second status value

    return time_delta(a, b) # calculate the delta

df = pd.read_csv('test.csv', sep=';') # Load csv file with ; as separator
grouped = df.groupby('message_id') # Group by message ids

final_df = pd.DataFrame(columns=['message_id', 'delivered_diff', 'read_diff', 'deleted_diff']) # create empty result dataframe
for message_id, values in grouped: # calculate the results for each group
    delivered_diff = calculate_diff(values, 'sent', 'delivered') # calculate delivered_diff as delta between sent status and delivered status
    read_diff = calculate_diff(values, 'delivered', 'read') # calculate read_diff as delta between delivered status and read status
    deleted_diff = calculate_diff(values, 'sent', 'deleted') # calculate deleted_diff as delta between sent status and deleted status

    res = {
        'message_id': message_id,
        'delivered_diff': delivered_diff,
        'read_diff': read_diff,
        'deleted_diff': deleted_diff
    }
    # append the results
    final_df = final_df.append(res, ignore_index=True)

# print final result
print(final_df)

结果：

      message_id   delivered_diff        read_diff     deleted_diff
0  5f74b996a2b7e  0 days 00:00:02  1 days 15:16:23
1  5f74b99e85b3c  0 days 00:01:02  0 days 06:05:33
2  5f74b9a35b6c5                                    0 days 00:00:06
3  5f9d97ff1af9e  0 days 00:00:02  0 days 00:05:03

Answer 2

import pandas as pd
from datetime import datetime, timedelta

final_dict = []
data = pd.read_csv('data.csv', names=['id','unique_id','time','status'])
data['time'] = pd.to_datetime(data['time'])
# data.info()
groupByUniqueId = data.groupby('unique_id') 
for name,group in groupByUniqueId:
  for row in group.iterrows():
        
    if row[1][3] == "sent":
      sent = row[1][2]
    if row[1][3] == "read":
      final_dict.append({row[1][1]: {"read": str(sent - row[1][2])}})
    elif row[1][3] == "delivered":
      final_dict.append({row[1][1]: {"delivered":str(sent - row[1][2])}})
    elif row[1][3] == "deleted":
      final_dict.append({row[1][1]: {"deleted":str(sent - row[1][2])}})

print(final_dict)

CSV 数据样本