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Python数据科学_32_时间数据处理_pandas库

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pandas

Timestamp对象

构造方法

利用字符串格式时间数据转化

pandas支持智能的将各种格式的时间数据转化为标准时间戳格式

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# 获取当前时间
tm1 = pd.Timestamp.now()
tm1
Timestamp('2022-11-22 18:04:34.850491')

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tm2 = pd.Timestamp('2022-11-21 16:12:12')
tm2
Timestamp('2022-11-21 16:12:12')

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tm3 = pd.Timestamp('2022/11/21 16:12:12')
tm3
Timestamp('2022-11-21 16:12:12')

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# 还可以使用pd.to_datetime()进行转换
tm4 = pd.to_datetime(time.ctime())
tm4
Timestamp('2022-11-22 18:04:59')

利用时间戳进行转换

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pd.Timestamp(1669112020.1238306, unit='s')
Timestamp('2022-11-22 10:13:40.123830557')

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d = pd.Timestamp(1669112020.1238306, unit='s', tz=tz1) # 添加时区(使用datetime.timezone对象)
d
Timestamp('2022-11-22 18:13:40.123830557+0800', tz='新时区')

通过直接指定相应时间属性值

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pd.Timestamp(year=2022, month=11, day=21, hour=20,
minute=28, second=30, microsecond=30)
Timestamp('2022-11-21 20:28:30.000030')

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d2 = pd.Timestamp(2022, 11, 30, 20, 26,30)
d2
Timestamp('2022-11-30 20:26:30')

属性

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# 查看时区信息
print(d.tz)
print(d.tzinfo)

# 查看标准时区时间
print(d.asm8)

# 查看当前时间是本年的第几周
print(d.weekofyear)
# 查看是周几
print(d.day_of_week)  # 0表示周一
# 查看今天的本年的第几天
print(d.day_of_year)
# 查看本月一共多少天
print(d.days_in_month)
print(d.daysinmonth)

print(d.year)  # 年
print(d.month)  # 月
print(d.day)  # 日
print(d.hour)  # 时
print(d.minute)  # 分
print(d.second)  # 秒
print(d.nanosecond)  # 毫秒
print(d.microsecond)  # 微妙
print(d.quarter)  # 刻钟(15min)数

print(d.is_leap_year)  # 判断是否是闰年
print(d2.is_month_end)  # 判断是否是本月最后一天
print(d.is_month_start)  # 判断是否是本月第一天
print(d.is_year_end)  # 判断是否是今年最后一天
print(d.is_year_start)  # 判断是否是今天第一天
新时区
新时区
2022-11-22T10:13:40.123830557
47
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326
30
30
2022
11
22
18
13
40
557
123830
4
False
True
False
False
False

方法

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# 查看标准字符串格式
print(d.ctime())
# 查看星期数完整英文
print(d.day_name())

# 查看时间戳
print(d.timestamp())
# 将时间戳转化为Timestamp对象(本地时区)
print(d.fromtimestamp(d.timestamp()))
# 将时间戳转化为Timestamp对象(国际标准时区)
print(d.utcfromtimestamp(d.timestamp()))

# 查看距离公元1年1月1日多少天
print(d.toordinal() )
# 查看公元1年1月1日XX天，是什么时间
print(d.fromordinal(d.toordinal()))

# 查看d本年经过的周数，和星期数
print(d.isocalendar())
# 转化为元组格式时间数据，类似于time.localtime()，time.gmtime()
print(d.timetuple())
# 查看时区名称
print(d.tzname())
# 查看字符串标准格式数据
print(d.isoformat())
Tue Nov 22 18:13:40 2022
Tuesday
1669112020.123831
2022-11-22 18:13:40.123831
2022-11-22 10:13:40.123831
738481
2022-11-22 00:00:00
(2022, 47, 2)
time.struct_time(tm_year=2022, tm_mon=11, tm_mday=22, tm_hour=18, tm_min=13, tm_sec=40, tm_wday=1, tm_yday=326, tm_isdst=-1)
新时区
2022-11-22T18:13:40.123830557+08:00

Timedelta时间段

构造方法

通过字符串格式进行定义

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# 完整写法
pd.Timedelta('2days 2hours 15minutes 30seconds')
Timedelta('2 days 02:15:30')

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# 简写
pd.Timedelta('2d2h15min30s')
Timedelta('2 days 02:15:30')

通过整数值与指定单位定义

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pd.Timedelta(150, unit='s')
Timedelta('0 days 00:02:30')

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pd.Timedelta(1.5, unit='d')
Timedelta('1 days 12:00:00')

直接指定相应属性定义

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pd.Timedelta(days=2, hours=15, minutes=30, seconds=30)
Timedelta('2 days 15:30:30')

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# 还可以使用to_timedelta()方法进行构造
print(pd.to_timedelta('2d'))
print(pd.to_timedelta('2d3m'))
print(pd.to_timedelta(200, unit='s'))
2 days 00:00:00
2 days 00:03:00
0 days 00:03:20

date_range时间区间

其中每个元素都为Timestamp对象

指定起点、终点和数据量

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pd.date_range(start='2022-11-1', end='2022-11-30', periods=30)
DatetimeIndex(['2022-11-01', '2022-11-02', '2022-11-03', '2022-11-04',
               '2022-11-05', '2022-11-06', '2022-11-07', '2022-11-08',
               '2022-11-09', '2022-11-10', '2022-11-11', '2022-11-12',
               '2022-11-13', '2022-11-14', '2022-11-15', '2022-11-16',
               '2022-11-17', '2022-11-18', '2022-11-19', '2022-11-20',
               '2022-11-21', '2022-11-22', '2022-11-23', '2022-11-24',
               '2022-11-25', '2022-11-26', '2022-11-27', '2022-11-28',
               '2022-11-29', '2022-11-30'],
              dtype='datetime64[ns]', freq=None)

指定起点、终点和步长

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pd.date_range(start='2022-11-1', end='2022-11-30', freq='3d')
DatetimeIndex(['2022-11-01', '2022-11-04', '2022-11-07', '2022-11-10',
               '2022-11-13', '2022-11-16', '2022-11-19', '2022-11-22',
               '2022-11-25', '2022-11-28'],
              dtype='datetime64[ns]', freq='3D')

指定起点、数据量和步长

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pd.date_range(start='2022-11-3', periods=10, freq='2d')
DatetimeIndex(['2022-11-03', '2022-11-05', '2022-11-07', '2022-11-09',
               '2022-11-11', '2022-11-13', '2022-11-15', '2022-11-17',
               '2022-11-19', '2022-11-21'],
              dtype='datetime64[ns]', freq='2D')

指定终点、数据量和步长

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pd.date_range(end='2022-11-3 9:00:00', periods=10, freq='3d')
DatetimeIndex(['2022-10-07 09:00:00', '2022-10-10 09:00:00',
               '2022-10-13 09:00:00', '2022-10-16 09:00:00',
               '2022-10-19 09:00:00', '2022-10-22 09:00:00',
               '2022-10-25 09:00:00', '2022-10-28 09:00:00',
               '2022-10-31 09:00:00', '2022-11-03 09:00:00'],
              dtype='datetime64[ns]', freq='3D')
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