128

我有两列，fromdate并且todate，在数据框中。

import pandas as pd

data = {'todate': [pd.Timestamp('2014-01-24 13:03:12.050000'), pd.Timestamp('2014-01-27 11:57:18.240000'), pd.Timestamp('2014-01-23 10:07:47.660000')],
        'fromdate': [pd.Timestamp('2014-01-26 23:41:21.870000'), pd.Timestamp('2014-01-27 15:38:22.540000'), pd.Timestamp('2014-01-23 18:50:41.420000')]}

df = pd.DataFrame(data)

我添加了一个新列，diff来查找两个日期之间的差异，使用

df['diff'] = df['fromdate'] - df['todate']

我得到了该diff列，但它包含days, 超过 24 小时。

                   todate                fromdate                   diff
0 2014-01-24 13:03:12.050 2014-01-26 23:41:21.870 2 days 10:38:09.820000
1 2014-01-27 11:57:18.240 2014-01-27 15:38:22.540 0 days 03:41:04.300000
2 2014-01-23 10:07:47.660 2014-01-23 18:50:41.420 0 days 08:42:53.760000

如何将我的结果转换为小时和分钟（即天转换为小时）？

4

3 回答 3

168

Pandas 时间戳差异返回一个 datetime.timedelta 对象。这可以通过使用 *as_type* 方法轻松转换为小时，就像这样

import pandas
df = pandas.DataFrame(columns=['to','fr','ans'])
df.to = [pandas.Timestamp('2014-01-24 13:03:12.050000'), pandas.Timestamp('2014-01-27 11:57:18.240000'), pandas.Timestamp('2014-01-23 10:07:47.660000')]
df.fr = [pandas.Timestamp('2014-01-26 23:41:21.870000'), pandas.Timestamp('2014-01-27 15:38:22.540000'), pandas.Timestamp('2014-01-23 18:50:41.420000')]
(df.fr-df.to).astype('timedelta64[h]')

屈服，

0    58
1     3
2     8
dtype: float64
于 2014-04-07T23:01:08.143 回答
71

这让我发疯，因为.astype()上面的解决方案对我不起作用。但我找到了另一种方法。没有计时或其他任何东西，但可能对其他人有用：

t1 = pd.to_datetime('1/1/2015 01:00')
t2 = pd.to_datetime('1/1/2015 03:30')

print pd.Timedelta(t2 - t1).seconds / 3600.0

...如果你想要几个小时。或者：

print pd.Timedelta(t2 - t1).seconds / 60.0

...如果你想要几分钟。

更新：这里曾经有一个有用的评论提到使用.total_seconds()跨越多天的时间段。既然它消失了，我已经更新了答案。

于 2016-12-27T06:52:54.437 回答
34
  • 如何将我的结果转换为小时和分钟
    • 接受的答案只返回days + hours。不包括分钟。
  • 要提供一个小时和分钟为hh:mmor的列，x hours y minutes需要额外的计算和字符串格式。
  • 这个答案显示了如何使用timedelta数学将总小时数或总分钟数作为浮点数，并且比使用.astype('timedelta64[h]').
  • Pandas 时间增量用户指南
  • Pandas 时间序列/日期功能用户指南
  • pythontimedelta对象：查看支持的操作。
  • 以下示例数据已经是datetime64[ns] dtype. 需要使用 转换所有相关列pandas.to_datetime()。
import pandas as pd

# test data from OP, with values already in a datetime format
data = {'to_date': [pd.Timestamp('2014-01-24 13:03:12.050000'), pd.Timestamp('2014-01-27 11:57:18.240000'), pd.Timestamp('2014-01-23 10:07:47.660000')],
        'from_date': [pd.Timestamp('2014-01-26 23:41:21.870000'), pd.Timestamp('2014-01-27 15:38:22.540000'), pd.Timestamp('2014-01-23 18:50:41.420000')]}

# test dataframe; the columns must be in a datetime format; use pandas.to_datetime if needed
df = pd.DataFrame(data)

# add a timedelta column if wanted. It's added here for information only
# df['time_delta_with_sub'] = df.from_date.sub(df.to_date)  # also works
df['time_delta'] = (df.from_date - df.to_date)

# create a column with timedelta as total hours, as a float type
df['tot_hour_diff'] = (df.from_date - df.to_date) / pd.Timedelta(hours=1)

# create a colume with timedelta as total minutes, as a float type
df['tot_mins_diff'] = (df.from_date - df.to_date) / pd.Timedelta(minutes=1)

# display(df)
                  to_date               from_date             time_delta  tot_hour_diff  tot_mins_diff
0 2014-01-24 13:03:12.050 2014-01-26 23:41:21.870 2 days 10:38:09.820000      58.636061    3518.163667
1 2014-01-27 11:57:18.240 2014-01-27 15:38:22.540 0 days 03:41:04.300000       3.684528     221.071667
2 2014-01-23 10:07:47.660 2014-01-23 18:50:41.420 0 days 08:42:53.760000       8.714933     522.896000

其他方法

  • 其他资源中播客中的一个注释项是.total_seconds()在核心开发人员休假时添加和合并的，并且不会被批准。
    • 这也是为什么没有其他.total_xx方法的原因。
# convert the entire timedelta to seconds
# this is the same as td / timedelta(seconds=1)
(df.from_date - df.to_date).dt.total_seconds()
[out]:
0    211089.82
1     13264.30
2     31373.76
dtype: float64

# get the number of days
(df.from_date - df.to_date).dt.days
[out]:
0    2
1    0
2    0
dtype: int64

# get the seconds for hours + minutes + seconds, but not days
# note the difference from total_seconds
(df.from_date - df.to_date).dt.seconds
[out]:
0    38289
1    13264
2    31373
dtype: int64

其他资源

%%timeit测试

import pandas as pd

# dataframe with 2M rows
data = {'to_date': [pd.Timestamp('2014-01-24 13:03:12.050000'), pd.Timestamp('2014-01-27 11:57:18.240000')], 'from_date': [pd.Timestamp('2014-01-26 23:41:21.870000'), pd.Timestamp('2014-01-27 15:38:22.540000')]}
df = pd.DataFrame(data)
df = pd.concat([df] * 1000000).reset_index(drop=True)

%%timeit
(df.from_date - df.to_date) / pd.Timedelta(hours=1)
[out]:
43.1 ms ± 1.05 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)

%%timeit
(df.from_date - df.to_date).astype('timedelta64[h]')
[out]:
59.8 ms ± 1.29 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)
于 2020-08-21T00:40:40.757 回答