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Will
2022-08-04
目录

时间序列

import numpy as np
import pandas as pd
import datetime
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# 创建

# to_datetime

pd.to_datetime(arg, errors='raise', dayfirst=False, yearfirst=False, utc=None, box=True, format=None, exact=True, unit=None, infer_datetime_format=False, origin='unix', cache=True)

  • arg：integer, float, string, datetime, list, tuple, 1-d array, Series or DataFrame/dict-like
  • errors：{'ignore', 'raise', 'coerce'}, 默认为 'raise'
    • 如果为 ignore，遇到无法解析的字符串会返回原字符串
    • 如果为 raise，遇到无法解析的字符串会抛出异常
    • 如果为 coerce，遇到无法解析的字符串会转为 NaT
  • dayfirst：指定解析顺序（如果 arg 参数为字符串或类似于列表的对象）。如果为 True，10/11/12 会被解析为 2012/11/10
  • yearfirst：指定解析顺序（如果 arg 参数为字符串或类似于列表的对象）。如果为 True，10/11/12 会被解析为 2010/11/12
    • 如果 dayfirst 和 yearfirst 都为 True 的话，yearfirst 优先级高（默认）
  • format：指定解析格式。
    • pd.to_datetime('12-2010-10 00:00', format='%d-%Y-%m %H:%M') 会被解析为 2010-10-12 00:00:00

返回类型依赖于输入：

  • 输入标量，返回 Timestap
  • 输入数组，返回 DatetimeIndex
  • 输入一个 Series/DataFrame，返回 Series
# 输入标量，返回Timestap
pd.to_datetime('2019')
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Timestamp('2019-01-01 00:00:00')

# 输入数组，返回DatetimeIndex
pd.to_datetime(['20190101', '20190201', '20190301'])
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DatetimeIndex(['2019-01-01', '2019-02-01', '2019-03-01'], dtype='datetime64[ns]', freq=None)

# 输入一个Series，返回Series
s = pd.Series(['20190101', '20190201', '20190301'])
pd.to_datetime(s)
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0   2019-01-01
1   2019-02-01
2   2019-03-01
dtype: datetime64[ns]

也可以通过 DataFrame 来创建时间序列，但是需要通过列名称来指定时间单位：

year, month, day是必选列名

hour , minute, second, millisecond, microsecond, nanosecond是可选列名

# 输入一个DataFrame，返回一个Series
df = pd.DataFrame({'year': [2018, 2019],'month': [3, 4], 'day': [6, 8],'hour': [3, 1], 'minute': [10, 20]})
pd.to_datetime(df)

pd.to_datetime(df[['year', 'month', 'day']])
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0   2018-03-06 03:10:00
1   2019-04-08 01:20:00
dtype: datetime64[ns]

0   2018-03-06
1   2019-04-08
dtype: datetime64[ns]

# pandas中的时间解析是很灵活的
datestrs = ['2019-07-06 12:00:00', '1/09/2019', '20190101', 'Jul 31, 2019', np.datetime64('2018-01-01'), datetime.datetime.now()]
pd.to_datetime(datestrs)
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DatetimeIndex([       '2019-07-06 12:00:00',        '2019-01-09 00:00:00',
                      '2019-01-01 00:00:00',        '2019-07-31 00:00:00',
                      '2018-01-01 00:00:00', '2022-08-04 20:39:54.929756'],
              dtype='datetime64[ns]', freq=None)

# date_range

pd.date_range(start=None, end=None, periods=None, freq=None, tz=None, normalize=False, name=None, closed=None, **kwargs)

生成固定频率的DatetimeIndex

  • start：string 或 datetime-like，默认值是 None，表示日期的起点
  • end：string 或 datetime-like，默认值是 None，表示日期的终点
  • periods：integer 或 None，默认值是 None，表示你要从这个函数产生多少个日期索引值；如果是 None 的话，那么 start 和 end 必须不能为 None
  • freq：string 或 DateOffset，默认值是’D’，表示以自然日为单位，这个参数用来指定计时单位，比如’5H’表示每隔 5 个小时计算一次。在这里 (opens new window)可以看到所有可选值。
    • Y 表示年
    • M 表示月
    • D 表示日
    • W 表示周
    • H 表示时
    • T 表示分
    • S 表示秒
    • B 表示工作日
  • tz：string 或 None，表示时区，例如：’Asia/Hong_Kong’
  • normalize：bool，默认值为 False，如果为 True 的话，那么在产生时间索引值之前会先把 start 和 end 都转化为当日的午夜 0 点
  • name：str，默认值为 None，给返回的时间索引指定一个名字
  • closed：string 或者 None，默认值为 None，表示 start 和 end 这个区间端点是否包含在区间内，可以有三个值，’left’表示左闭右开区间，’right’表示左开右闭区间，None 表示两边都是闭区间

start, end, periods, freq这四个参数至少需要指定三个，其中freq默认为 D

pd.date_range('2019-01-01', periods=3, freq='T') # freq 默认为D，可以写成3D，表示间隔为3天
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DatetimeIndex(['2019-01-01 00:00:00', '2019-01-01 00:01:00',
               '2019-01-01 00:02:00'],
              dtype='datetime64[ns]', freq='T')

pd.date_range('20190101', periods=4, freq='10T')
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DatetimeIndex(['2019-01-01 00:00:00', '2019-01-01 00:10:00',
               '2019-01-01 00:20:00', '2019-01-01 00:30:00'],
              dtype='datetime64[ns]', freq='10T')

# bdate_range

pd.bdate_range(start=None, end=None, periods=None, freq='B', tz=None, normalize=True, name=None, weekmask=None, holidays=None, closed=None, **kwargs)

按照工作日计算

pd.bdate_range(start='2018-01-01', end='2019-01-01')
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DatetimeIndex(['2018-01-01', '2018-01-02', '2018-01-03', '2018-01-04',
               '2018-01-05', '2018-01-08', '2018-01-09', '2018-01-10',
               '2018-01-11', '2018-01-12',
               ...
               '2018-12-19', '2018-12-20', '2018-12-21', '2018-12-24',
               '2018-12-25', '2018-12-26', '2018-12-27', '2018-12-28',
               '2018-12-31', '2019-01-01'],
              dtype='datetime64[ns]', length=262, freq='B')

pd.date_range(start='2018-01-01', end='2019-01-01')
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DatetimeIndex(['2018-01-01', '2018-01-02', '2018-01-03', '2018-01-04',
               '2018-01-05', '2018-01-06', '2018-01-07', '2018-01-08',
               '2018-01-09', '2018-01-10',
               ...
               '2018-12-23', '2018-12-24', '2018-12-25', '2018-12-26',
               '2018-12-27', '2018-12-28', '2018-12-29', '2018-12-30',
               '2018-12-31', '2019-01-01'],
              dtype='datetime64[ns]', length=366, freq='D')

# 索引

d = pd.date_range('20180101', '20190601')
ds = pd.Series(np.random.randn(len(d)), index=d)
ds
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2018-01-01   -0.250983
2018-01-02    1.000452
2018-01-03   -0.619409
2018-01-04    0.838948
2018-01-05    1.530228
                ...
2019-05-28    0.148896
2019-05-29   -0.767799
2019-05-30   -1.536733
2019-05-31    0.190801
2019-06-01   -2.300904
Freq: D, Length: 517, dtype: float64

ds.index
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DatetimeIndex(['2018-01-01', '2018-01-02', '2018-01-03', '2018-01-04',
               '2018-01-05', '2018-01-06', '2018-01-07', '2018-01-08',
               '2018-01-09', '2018-01-10',
               ...
               '2019-05-23', '2019-05-24', '2019-05-25', '2019-05-26',
               '2019-05-27', '2019-05-28', '2019-05-29', '2019-05-30',
               '2019-05-31', '2019-06-01'],
              dtype='datetime64[ns]', length=517, freq='D')

ds['2019-05-01']
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1.7245340453263973

ds['2019-05-01':]
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2019-05-01    1.724534
2019-05-02    0.446775
2019-05-03    0.327490
2019-05-04   -0.016504
2019-05-05   -0.949755
2019-05-06   -0.412805
2019-05-07   -0.599558
2019-05-08    1.047344
2019-05-09    0.648256
2019-05-10    0.637291
2019-05-11   -0.519630
2019-05-12   -0.372287
2019-05-13    1.195050
2019-05-14   -0.527554
2019-05-15    0.951183
2019-05-16    0.153516
2019-05-17   -0.818237
2019-05-18    0.635959
2019-05-19    0.543335
2019-05-20    1.706608
2019-05-21   -1.126285
2019-05-22   -1.210101
2019-05-23   -0.096912
2019-05-24   -0.810127
2019-05-25    0.454461
2019-05-26    1.536894
2019-05-27   -0.029693
2019-05-28    0.148896
2019-05-29   -0.767799
2019-05-30   -1.536733
2019-05-31    0.190801
2019-06-01   -2.300904
Freq: D, dtype: float64

ds['2019-05']
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2019-05-01    1.724534
2019-05-02    0.446775
2019-05-03    0.327490
2019-05-04   -0.016504
2019-05-05   -0.949755
2019-05-06   -0.412805
2019-05-07   -0.599558
2019-05-08    1.047344
2019-05-09    0.648256
2019-05-10    0.637291
2019-05-11   -0.519630
2019-05-12   -0.372287
2019-05-13    1.195050
2019-05-14   -0.527554
2019-05-15    0.951183
2019-05-16    0.153516
2019-05-17   -0.818237
2019-05-18    0.635959
2019-05-19    0.543335
2019-05-20    1.706608
2019-05-21   -1.126285
2019-05-22   -1.210101
2019-05-23   -0.096912
2019-05-24   -0.810127
2019-05-25    0.454461
2019-05-26    1.536894
2019-05-27   -0.029693
2019-05-28    0.148896
2019-05-29   -0.767799
2019-05-30   -1.536733
2019-05-31    0.190801
Freq: D, dtype: float64

ds['2019-04':'2019-05']
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2019-04-01    1.745744
2019-04-02    0.809381
2019-04-03    0.418581
2019-04-04    0.580189
2019-04-05   -0.008010
                ...
2019-05-27   -0.029693
2019-05-28    0.148896
2019-05-29   -0.767799
2019-05-30   -1.536733
2019-05-31    0.190801
Freq: D, Length: 61, dtype: float64

ds['2019']
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2019-01-01   -1.278967
2019-01-02    0.023677
2019-01-03   -0.469362
2019-01-04    1.063142
2019-01-05    1.833249
                ...
2019-05-28    0.148896
2019-05-29   -0.767799
2019-05-30   -1.536733
2019-05-31    0.190801
2019-06-01   -2.300904
Freq: D, Length: 152, dtype: float64

# 时间/日期属性

属性 描述
year The year of the datetime
month The month of the datetime
day The days of the datetime
hour The hour of the datetime
minute The minutes of the datetime
second The seconds of the datetime
microsecond The microseconds of the datetime
nanosecond The nanoseconds of the datetime
date Returns datetime.date (does not contain timezone information)
time Returns datetime.time (does not contain timezone information)
timetz Returns datetime.time as local time with timezone information
dayofyear The ordinal day of year
weekofyear The week ordinal of the year
week The week ordinal of the year
dayofweek The number of the day of the week with Monday=0, Sunday=6
weekday The number of the day of the week with Monday=0, Sunday=6
weekday_name The name of the day in a week (ex: Friday)
quarter Quarter of the date: Jan-Mar = 1, Apr-Jun = 2, etc.
days_in_month The number of days in the month of the datetime
is_month_start Logical indicating if first day of month (defined by frequency)
is_month_end Logical indicating if last day of month (defined by frequency)
is_quarter_start Logical indicating if first day of quarter (defined by frequency)
is_quarter_end Logical indicating if last day of quarter (defined by frequency)
is_year_start Logical indicating if first day of year (defined by frequency)
is_year_end Logical indicating if last day of year (defined by frequency)
is_leap_year Logical indicating if the date belongs to a leap year
today = pd.to_datetime(datetime.datetime.now())
today
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Timestamp('2022-08-04 20:39:55.255040')

today.year
today.month
today.day
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2022

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上次更新: 2023/11/01, 03:11:44

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