Question

我有一个包含三列的数据框df，即Date, Time, Name（可以有更多的额外列）。 df按Time的升序排序。在任何给定的Date上，可能存在多个Time值，这些值可以相隔5分钟或者>相隔15分钟。在任何一天，5分钟内的任何事情都应视为相同。我想在5分钟内添加列TimeRank，在任何特定日期将Time类似地聚集在一起，并为它们提供相同的TimeRank。例如，

        Date      Name                 Time   TimeRank
0 2017-01-01     Henry  2017-01-01 09:21:01          1  
1 2017-01-01      John  2017-01-01 09:23:43          1
2 2017-01-01  Svetlana  2017-01-01 10:15:01          2
3 2017-01-01      Sara  2017-01-01 11:01:01          3 
4 2017-01-01   Whitney  2017-01-01 11:03:03          3
5 2017-01-02      Lara  2017-01-02 11:03:03          1
6 2017-01-02    Eugene  2017-01-02 16:46:00          2
7 2017-01-02   Richard  2017-01-02 16:46:00          2
8 2017-01-03      Andy  2017-01-03 11:01:01          1
9 2017-01-03      Paul  2017-01-03 11:03:03          1

下面我创建了一个示例df。不幸的是，我受限于使用较早版本的pandas 0.16。

import pandas as pd
from random import randint
from datetime import time
dates = pd.date_range('2017-01-01', '2017-01-04')
dates2 = [dates[i] for i in [randint(0, len(dates) -1) for i in range (0, 100)]]
timelist = [time(9,20,45), time(9,21,0), time(9,23,43), time(9,50,0), time(10,15,1), time(11,1,1), time(11,3,3), time(16,45,0), time(16,46,0)]
timelist2 = [timelist[i] for i in [randint(0, len(timelist) -1) for i in range (0, 100)]]
names = ['henry', 'tom', 'andy', 'lara', 'whitney', 'eleanor', 'paloma', 'john', 'james', 'svetlana', 'paul']
names2 = [names[i] for i in [randint(0, len(names)-1) for i in range (0, 100)]]
df = pd.DataFrame({'Date':dates2, 'Time':timelist2, 'Name':names2})
df['Time'] = df.apply(lambda r:pd.datetime.combine(r['Date'],r['Time']), axis=1)
df.sort('Time', inplace=True)

Answer 1

df.loc[:, 'minutes'] = df.apply(lambda x:x['Time'].minute + 60*x['Time'].hour, axis=1)
df.loc[:, 'delTime'] = df.groupby('Date')['minutes'].diff()
df.loc[(df['delTime'] <=5) & (df['delTime'] >=-5), 'delTime'] = 0
df.loc[np.isnan(df['delTime']), 'delTime'] = 1.
df.loc[(df['delTime']) == 0, 'delTime'] = np.nan
df.loc[~np.isnan(df['delTime']), 'delTime'] = df['minutes']
df = df.ffill()
df.loc[:, 'TimeRank'] = df.groupby('Date')['delTime'].rank(method='dense')
df.drop(['minutes', 'delTime'], inplace=True, axis=1)

时间排名DataFrame在噪声的约束下

1 个答案: