Python按天生成时间范围序列的多种实现方法

在实际开发中，经常需要生成一段连续的时间序列，例如从 2022-01-01 00:00:00 开始的后续 10 天的 datetime 对象。手动计算涉及不同月份天数差异（如30天或31天）较为繁琐，因此寻找自动处理日期逻辑的方法显得尤为重要。

使用 timedelta

Python 标准库中的 datetime 模块提供了 timedelta 类，可用于实现基于固定时间增量的日期计算。

from datetime import datetime, timedelta, timezone
from pydantic.datetime_parse import parse_datetime
from loguru import logger


SECOND: int = 1
MINUTE: int = SECOND*60
HOUR: int = MINUTE*60
DAY: int = HOUR*24
WEEK: int = DAY*7
MONTH: int = DAY*30


def get_utc_now_timestamp(with_tzinfo: bool = True) -> datetime:
    """ https://blog.csdn.net/ball4022/article/details/101670024 """
    if not with_tzinfo:
        return datetime.utcnow()
    return datetime.utcnow().replace(tzinfo=timezone.utc)


def timedelta_seconds(start_time: datetime, end_time: datetime = None) -> int:
    """ 返回两个时间相差的秒数 """
    if not end_time:
        end_time = get_utc_now_timestamp()

    return int((end_time - start_time).total_seconds())


def custom_timestamp(base_timestamp: datetime, seconds: int, reduce=False):
    return base_timestamp + timedelta(seconds=seconds) \
        if not reduce \
        else base_timestamp - timedelta(seconds=seconds)


start_datetime = parse_datetime('2022-02-27 00:00:00')
data = [
    dt
    for dt in [
        custom_timestamp(start_datetime, DAY*i) for i in range(10)
    ]
]
logger.debug(data)

运行脚本后的控制台输出如下：

╰─➤ python -u "/Users/ponponon/Desktop/code/me/ideaboom/main.py" 2022-11-15 15:18:37.653 | DEBUG | __main__:<module>:67 - [datetime.datetime(2022, 2, 27, 0, 0), datetime.datetime(2022, 2, 28, 0, 0), datetime.datetime(2022, 3, 1, 0, 0), datetime.datetime(2022, 3, 2, 0, 0), datetime.datetime(2022, 3, 3, 0, 0), datetime.datetime(2022, 3, 4, 0, 0), datetime.datetime(2022, 3, 5, 0, 0), datetime.datetime(2022, 3, 6, 0, 0), datetime.datetime(2022, 3, 7, 0, 0), datetime.datetime(2022, 3, 8, 0, 0)]

使用 arrow 这个第三方库

import arrow
from loguru import logger
from pydantic.datetime_parse import parse_datetime

for crawl_date in arrow.Arrow.range('day', parse_datetime('2022-02-27 00:00:00'), parse_datetime('2022-03-10 00:00:00')):
    logger.debug(crawl_date.datetime)

通过 Arrow 库生成的日期序列输出结果如下：

╰─➤ python -u "/Users/ponponon/Desktop/code/me/ideaboom/datetime_arrow_range.py" 2022-11-15 15:28:52.130 | DEBUG | __main__:<module>:6 - 2022-02-27 00:00:00+00:00 2022-11-15 15:28:52.130 | DEBUG | __main__:<module>:6 - 2022-02-28 00:00:00+00:00 2022-11-15 15:28:52.130 | DEBUG | __main__:<module>:6 - 2022-03-01 00:00:00+00:00 2022-11-15 15:28:52.130 | DEBUG | __main__:<module>:6 - 2022-03-02 00:00:00+00:00 2022-11-15 15:28:52.130 | DEBUG | __main__:<module>:6 - 2022-03-03 00:00:00+00:00 2022-11-15 15:28:52.130 | DEBUG | __main__:<module>:6 - 2022-03-04 00:00:00+00:00 2022-11-15 15:28:52.130 | DEBUG | __main__:<module>:6 - 2022-03-05 00:00:00+00:00 2022-11-15 15:28:52.130 | DEBUG | __main__:<module>:6 - 2022-03-06 00:00:00+00:00 2022-11-15 15:28:52.130 | DEBUG | __main__:<module>:6 - 2022-03-07 00:00:00+00:00 2022-11-15 15:28:52.130 | DEBUG | __main__:<module>:6 - 2022-03-08 00:00:00+00:00 2022-11-15 15:28:52.130 | DEBUG | __main__:<module>:6 - 2022-03-09 00:00:00+00:00 2022-11-15 15:28:52.130 | DEBUG | __main__:<module>:6 - 2022-03-10 00:00:00+00:00

补充

除了上述方法，Python 生态中还有多种生成特定要求时间序列的工具。以下整理了一些常用技巧，供读者参考应用。

生成不同间隔的时间序列

import pandas as pd
import numpy as np
import datetime as dt

# 从2022-07-01开始，间隔3天，生成10条 时间数据
rng = pd.date_range('2022-07-01', periods = 10, freq = '3D')
print(rng)
print("#####################")

# 指定开始时间，结束时间  以及频率
data=pd.date_range('2022-01-01','2023-01-01',freq='M')
print(data)
print("#####################")

# 从2022-01-01开始，间隔1天，生成20条 时间数据
time=pd.Series(np.random.randn(20),
           index=pd.date_range(dt.datetime(2022,1,1),periods=20))
print(time)
print("#####################")

# 不规则的时间间隔
p1 = pd.period_range('2022-01-01 10:10', freq = '25H', periods = 10)
print(p1)
print("######################################")

# 指定索引
rng = pd.date_range('2022 Jul 1', periods = 10, freq = 'D')
print(pd.Series(range(len(rng)), index = rng))
print("######################################")

相关测试代码的执行记录如下：

DatetimeIndex(['2022-07-01', '2022-07-04', '2022-07-07', '2022-07-10', '2022-07-13', '2022-07-16', '2022-07-19', '2022-07-22', '2022-07-25', '2022-07-28'], dtype='datetime64[ns]', freq='3D') ##################### DatetimeIndex(['2022-01-31', '2022-02-28', '2022-03-31', '2022-04-30', '2022-05-31', '2022-06-30', '2022-07-31', '2022-08-31', '2022-09-30', '2022-10-31', '2022-11-30', '2022-12-31'], dtype='datetime64[ns]', freq='M') ##################### 2022-01-01 -0.957412 2022-01-02 -0.333720 2022-01-03 1.079960 2022-01-04 0.050675 2022-01-05 0.270313 2022-01-06 -0.222715 2022-01-07 -0.560258 2022-01-08 1.009430 2022-01-09 -0.678157 2022-01-10 0.213557 2022-01-11 -0.720791 2022-01-12 0.332096 2022-01-13 -0.986449 2022-01-14 -0.357303 2022-01-15 -0.559618 2022-01-16 0.480281 2022-01-17 -0.443998 2022-01-18 1.541631 2022-01-19 -0.094559 2022-01-20 1.875012 Freq: D, dtype: float64 ##################### PeriodIndex(['2022-01-01 10:00', '2022-01-02 11:00', '2022-01-03 12:00', '2022-01-04 13:00', '2022-01-05 14:00', '2022-01-06 15:00', '2022-01-07 16:00', '2022-01-08 17:00', '2022-01-09 18:00', '2022-01-10 19:00'], dtype='period[25H]', freq='25H') ###################################### 2022-07-01 0 2022-07-02 1 2022-07-03 2 2022-07-04 3 2022-07-05 4 2022-07-06 5 2022-07-07 6 2022-07-08 7 2022-07-09 8 2022-07-10 9 Freq: D, dtype: int64 ######################################

截断时间段

import pandas as pd
import numpy as np
import datetime as dt

# 从2022-01-01开始，间隔1天，生成20条 时间数据
time=pd.Series(np.random.randn(20),
           index=pd.date_range(dt.datetime(2022,1,1),periods=20))
print(time)
print("#####################")

# 只输出2022-01-10 之后的数据
print(time.truncate(before='2022-1-10'))
print("#####################")

# 只输出2022-01-10 之后的数据
print(time.truncate(after='2022-1-10'))
print("#####################")

# 输出区间段
print(time['2022-01-15':'2022-01-20'])
print("#####################")

截断操作的测试记录如下：

2022-01-01 -0.203552 2022-01-02 -1.035483 2022-01-03 0.252587 2022-01-04 -1.046993 2022-01-05 0.152435 2022-01-06 -0.534518 2022-01-07 0.770170 2022-01-08 -0.038129 2022-01-09 0.531485 2022-01-10 0.499937 2022-01-11 0.815295 2022-01-12 2.315740 2022-01-13 -0.443379 2022-01-14 -0.689247 2022-01-15 0.667250 2022-01-16 -2.067246 2022-01-17 -0.105151 2022-01-18 -0.420562 2022-01-19 1.012943 2022-01-20 0.509710 Freq: D, dtype: float64 ##################### 2022-01-10 0.499937 2022-01-11 0.815295 2022-01-12 2.315740 2022-01-13 -0.443379 2022-01-14 -0.689247 2022-01-15 0.667250 2022-01-16 -2.067246 2022-01-17 -0.105151 2022-01-18 -0.420562 2022-01-19 1.012943 2022-01-20 0.509710 Freq: D, dtype: float64 ##################### 2022-01-01 -0.203552 2022-01-02 -1.035483 2022-01-03 0.252587 2022-01-04 -1.046993 2022-01-05 0.152435 2022-01-06 -0.534518 2022-01-07 0.770170 2022-01-08 -0.038129 2022-01-09 0.531485 2022-01-10 0.499937 Freq: D, dtype: float64 ##################### 2022-01-15 0.667250 2022-01-16 -2.067246 2022-01-17 -0.105151 2022-01-18 -0.420562 2022-01-19 1.012943 2022-01-20 0.509710 Freq: D, dtype: float64 #####################

本文介绍了Python中按天生成时间范围序列的几种实现方式。如需了解更多Python时间序列处理相关内容，建议查阅本站过往文章或关注后续更新，感谢支持。