Question

我正在使用脚本将停止时间从HH：MM：SS格式插入到微小的int值中。脚本如下。

# read in new csv file
reindexed = pd.read_csv('output/stop_times.csv')

for col in ('arrival_time', 'departure_time'):
    # extract hh:mm:ss values
    df = reindexed[col].str.extract(
        r'(?P<hour>\d+):(?P<minute>\d+):(?P<second>\d+)').astype('float')
    # convert to int value
    reindexed[col] = df['hour'] * 60 + df['minute']
    # interpolate
    reindexed[col] = reindexed[col].interpolate()
    reindexed[col] = np.round(reindexed[col], decimals=2)
    reindexed.to_csv('output/stop_times.csv', index=False)

# convert minutes back to HH:MM:SS

我现在想要的是将这些值转换回HH：MM：SS格式，但我很难搞清楚。我有一种预感，这个方法隐藏在timeseries documentation的某个地方，但我的结果还不够。

以下是从我正在使用的较大的stop_times.csv文件派生的示例CSV。 arrival_time 和 departure_time 列是重点：

stop_id,stop_code,stop_name,stop_desc,stop_lat,stop_lon,location_type,parent_station,trip_id,arrival_time,departure_time,stop_sequence,pickup_type,drop_off_type,stop_headsign
02303,02303,LCC Station Bay C,lcc_c,44.00981229999999,-123.0351463,,99994.0,1475360,707.0,707.0,1,0,0,82 EUGENE STATION
01092,01092,N/S of 30th E of University,,44.0242826,-123.07484540000002,,,1475360,709.67,709.67,2,0,0,82 EUGENE STATION
01089,01089,N/S of 30th W of Alder,,44.0242545,-123.08092409999999,,,1475360,712.33,712.33,3,0,0,82 EUGENE STATION
01409,01409,"Amazon Station, Bay A",amz_a,44.026660799999995,-123.08448870000001,,99993.0,1475360,715.0,715.0,4,0,0,82 EUGENE STATION
01222,01222,E/S of Amazon Prkwy N of 24th,,44.0339371,-123.0887632,,,1475360,715.75,715.75,5,0,0,82 EUGENE STATION
01548,01548,E/S of Amazon Pkwy S of 19th,,44.038014700000005,-123.0896553,,,1475360,716.5,716.5,6,0,0,82 EUGENE STATION

以下是从分钟的时间值中导出HH：MM：SS值的参考：

78.6 minutes can be converted to hours by dividing 78.6 minutes / 60 minutes/hour = 1.31 hours
1.31 hours can be broken down to 1 hour plus 0.31 hours - 1 hour
0.31 hours * 60 minutes/hour = 18.6 minutes - 18 minutes
0.6 minutes * 60 seconds/minute = 36 seconds - 36 seconds

非常感谢任何帮助。提前谢谢！

Answer 1

也许是

Per the previous question 最好的办法是保留原来的HH：MM：SS字符串：

所以而不是

for col in ('arrival_time', 'departure_time'):
    df = reindexed[col].str.extract(
        r'(?P<hour>\d+):(?P<minute>\d+):(?P<second>\d+)').astype('float')
    reindexed[col] = df['hour'] * 60 + df['minute']

使用

for col in ('arrival_time', 'departure_time'):
    newcol = '{}_minutes'.format(col)
    df = reindexed[col].str.extract(
        r'(?P<hour>\d+):(?P<minute>\d+):(?P<second>\d+)').astype('float')
    reindexed[newcol] = df['hour'] * 60 + df['minute']

然后您不必执行任何新计算来恢复HH：MM：SS字符串。 reindexed['arrival_time']仍然是原始的HH：MM：SS字符串，和 reindexed['arrival_time_minutes']将是以分钟为单位的持续时间。

以Jianxun Li's solution为基础，要切断微秒，你可以将分钟乘以60，然后拨打astype(int)：

import numpy as np
import pandas as pd

np.random.seed(0)
df = pd.DataFrame(np.random.rand(3) * 1000, columns=['minutes'])
df['HH:MM:SS'] = pd.to_timedelta((60*df['minutes']).astype('int'), unit='s')

产生

      minutes  HH:MM:SS
0  548.813504  09:08:48
1  715.189366  11:55:11
2  602.763376  10:02:45

请注意，df['HH:MM:SS']列包含pd.Timedelta s：

In [240]: df['HH:MM:SS'].iloc[0]
Out[240]: Timedelta('0 days 09:08:48')

但是，如果您尝试将此数据存储在csv

中

In [223]: df.to_csv('/tmp/out', date_format='%H:%M:%S')

你得到：

,minutes,HH:MM:SS
0,548.813503927,0 days 09:08:48.000000000
1,715.189366372,0 days 11:55:11.000000000
2,602.763376072,0 days 10:02:45.000000000

如果分钟值太大，您还会看到days作为timedelta字符串表示的一部分：

np.random.seed(0)
df = pd.DataFrame(np.random.rand(3) * 10000, columns=['minutes'])
df['HH:MM:SS'] = pd.to_timedelta((60*df['minutes']).astype('int'), unit='s')

产量

       minutes        HH:MM:SS
0  5488.135039 3 days 19:28:08
1  7151.893664 4 days 23:11:53
2  6027.633761 4 days 04:27:38

这可能不是你想要的。在这种情况下，而不是

df['HH:MM:SS'] = pd.to_timedelta((60*df['minutes']).astype('int'), unit='s')

每Phillip Cloud's solution你可以使用

import operator
fmt = operator.methodcaller('strftime', '%H:%M:%S')
df['HH:MM:SS'] = pd.to_datetime(df['minutes'], unit='m').map(fmt)

结果看起来相同，但现在df['HH:MM:SS']列包含字符串

In [244]: df['HH:MM:SS'].iloc[0]
Out[244]: '09:08:48'

请注意，这会切断（省略）整天和微秒。将DataFrame写入CSV

In [229]: df.to_csv('/tmp/out', date_format='%H:%M:%S')

现在收益

,minutes,HH:MM:SS
0,548.813503927,09:08:48
1,715.189366372,11:55:11
2,602.763376072,10:02:45

Answer 2

您可能需要考虑使用pd.to_timedelta。

import pandas as pd
import numpy as np

np.random.seed(0)
df = pd.DataFrame(np.random.rand(10) * 1000, columns=['time_in_minutes'])

Out[94]: 
   time_in_minutes
0         548.8135
1         715.1894
2         602.7634
3         544.8832
4         423.6548
5         645.8941
6         437.5872
7         891.7730
8         963.6628
9         383.4415

# As Jeff suggests, pd.to_timedelta is a very handy tool to do this
df['time_delta'] = pd.to_timedelta(df.time_in_minutes, unit='m')


Out[96]: 
   time_in_minutes      time_delta
0         548.8135 09:08:48.810235
1         715.1894 11:55:11.361982
2         602.7634 10:02:45.802564
3         544.8832 09:04:52.990979
4         423.6548 07:03:39.287960
5         645.8941 10:45:53.646784
6         437.5872 07:17:35.232675
7         891.7730 14:51:46.380046
8         963.6628 16:03:39.765630
9         383.4415 06:23:26.491129

将分钟格式的时间列转换为pandas中的HH：MM：SS格式的时间

2 个答案: