Pandas 计算列中元素在时间戳上出现的次数

对于Pandas

数据帧中的给定行，我需要计算当前列值的次数，例如“destination\u address\u IP”在过去（比如）2秒内使用“Time\u stamp”列出现的次数，并将这些值放入新的列“count”。
通过重复移动数据帧，您可以按如下方式进行操作。基本假设是，数据帧按时间戳列排序：

# define the threshold in milliseconds (2 seconds)
time_threshold= 2000000
df['ip_count']=1
df_shifted= df
# loop over the dataframe and shift it by one row
# until the time_threshold is violated for all rows
while True:
    # shift the copy of the dataframe
    df_shifted= df_shifted.shift(1)
    # check if time range is ok
    ser_time_diff= (df['Time_stamp'] - df_shifted['Time_stamp'])
    ser_in_time= ser_time_diff.dt.microseconds + ser_time_diff.dt.seconds * 1000000 < time_threshold
    if ser_in_time.any():
        # there are still rows left, where the shifted
        # frame's timestamp lies within the threshold
        # so we need to count the matches for those rows
        # if there are any
        ser_match= ser_in_time & (df['destination_address_IP'] == df_shifted['destination_address_IP'])
        df['ip_count']+= ser_match.astype('int')
    else:
        # none of the rows of the shifted df was within
        # the threshold of the original df
        # so further shifts will not change the result
        # anymore
        break

df

它是根据以下数据生成的：

import io
import pandas as pd

raw=\
"""Time_stamp                   destination_address_IP
2019-09-17T19:20:45.093209   157.111.73.31
2019-09-17T19:20:45.297932   127.0.0.1
2019-09-17T19:20:45.750725   157.111.73.31
2019-09-17T19:20:46.787009   192.168.21.15
2019-09-17T19:20:47.601051   52.18.181.18
2019-09-17T19:20:47.863428   52.18.181.17
2019-09-17T19:20:48.418591   52.18.181.18
2019-09-17T19:20:48.596764   52.18.181.17
2019-09-17T19:20:49.057553   192.168.21.15
2019-09-17T19:20:49.153256   192.168.21.15
2019-09-17T19:20:49.712312   127.0.0.1
2019-09-17T19:20:50.000119   52.18.181.17
2019-09-17T19:20:50.248562   52.18.181.18
2019-09-17T19:20:50.603783   52.18.181.18
2019-09-17T19:20:50.921631   52.18.181.17
2019-09-17T19:20:51.366193   52.18.181.18
2019-09-17T19:20:51.528611   52.18.181.18
2019-09-17T19:20:51.773429   131.53.97.59
2019-09-17T19:20:52.618215   192.168.21.15
2019-09-17T19:20:52.936181   52.18.181.18
"""

df= pd.read_csv(
        io.StringIO(raw), 
        sep='\s{2,}', dtype={
                'Time_stamp': 'datetime64', 
        'destination_address_IP': 'str'}, 
        engine='python')

你能提供样品数据和预期结果吗？欢迎访问。请检查并创建一个。所以这不是一个免费的代码编写服务，也不是一个教程网站。
import io
import pandas as pd

raw=\
"""Time_stamp                   destination_address_IP
2019-09-17T19:20:45.093209   157.111.73.31
2019-09-17T19:20:45.297932   127.0.0.1
2019-09-17T19:20:45.750725   157.111.73.31
2019-09-17T19:20:46.787009   192.168.21.15
2019-09-17T19:20:47.601051   52.18.181.18
2019-09-17T19:20:47.863428   52.18.181.17
2019-09-17T19:20:48.418591   52.18.181.18
2019-09-17T19:20:48.596764   52.18.181.17
2019-09-17T19:20:49.057553   192.168.21.15
2019-09-17T19:20:49.153256   192.168.21.15
2019-09-17T19:20:49.712312   127.0.0.1
2019-09-17T19:20:50.000119   52.18.181.17
2019-09-17T19:20:50.248562   52.18.181.18
2019-09-17T19:20:50.603783   52.18.181.18
2019-09-17T19:20:50.921631   52.18.181.17
2019-09-17T19:20:51.366193   52.18.181.18
2019-09-17T19:20:51.528611   52.18.181.18
2019-09-17T19:20:51.773429   131.53.97.59
2019-09-17T19:20:52.618215   192.168.21.15
2019-09-17T19:20:52.936181   52.18.181.18
"""

df= pd.read_csv(
        io.StringIO(raw), 
        sep='\s{2,}', dtype={
                'Time_stamp': 'datetime64', 
        'destination_address_IP': 'str'}, 
        engine='python')