摘要：生成一个时间段生成一个时间区间段，间隔为小时生成一个，并制定索引为时间段改变时间间隔转换为日期格式数字生成日期格式字符生成日期
1、生成一个时间段
In [1]:import pandas as pd
In [2]:import numpy as np
1)生成一个时间区间段，间隔为小时
In [3]:rng = pd.date_range("1/1/2011", periods=72, freq="H")
2)生成一个Series，并制定索引为时间段
In [4]:ts = pd.Series(np.random.randn(len(rng)), index=rng)
In [5]:ts
Out[5]:
2011-01-01 00:00:00 -0.204085
2011-01-01 01:00:00 1.101711
2011-01-01 02:00:00 1.840500
2011-01-01 03:00:00 0.112426
2011-01-01 04:00:00 -0.310413
2011-01-01 05:00:00 1.180762
2011-01-01 06:00:00 0.087775
2011-01-01 07:00:00 1.087877
2011-01-01 08:00:00 -0.950237
2011-01-01 09:00:00 -0.468453
Freq: H, dtype: float64
3)改变时间间隔
In [6]:converted = ts.asfreq("45Min", method="pad")
In [7]:converted
Out[7]:
2011-01-01 00:00:00 -0.204085
2011-01-01 00:45:00 -0.204085
2011-01-01 01:30:00 1.101711
2011-01-01 02:15:00 1.840500
2011-01-01 03:00:00 0.112426
2011-01-01 03:45:00 0.112426
2011-01-01 04:30:00 -0.310413
2011-01-01 05:15:00 1.180762
2011-01-01 06:00:00 0.087775
2011-01-01 06:45:00 0.087775
2011-01-01 07:30:00 1.087877
2011-01-01 08:15:00 -0.950237
2011-01-01 09:00:00 -0.468453
Freq: 45T, dtype: float64
In [8]: pd.Timestamp(datetime(2012, 5, 1))
Out[8]: Timestamp("2012-05-01 00:00:00")
In [9]: pd.Timestamp("2012-05-01")
Out[9]: Timestamp("2012-05-01 00:00:00")
In [10]: pd.Period("2011-01")
Out[10]: Period("2011-01", "M")
In [11]: pd.Period("2012-05", freq="D")
Out[11]: Period("2012-05-01", "D")
In [22]: pd.to_datetime(pd.Series(["Jul 31, 2009", "2010-01-10", None]))
Out[22]:
0 2009-07-31
1 2010-01-10
2 NaT
dtype: datetime64[ns]
In [23]: pd.to_datetime(["2005/11/23", "2010.12.31"])
Out[23]: DatetimeIndex(["2005-11-23", "2010-12-31"], dtype="datetime64[ns]", freq=None)
In [35]: dates = [datetime(2012, 5, 1), datetime(2012, 5, 2), datetime(2012, 5, 3)]
Note the frequency information
In [36]: index = pd.DatetimeIndex(dates)
In [37]: index
Out[37]: DatetimeIndex(["2012-05-01", "2012-05-02", "2012-05-03"], dtype="datetime64[ns]", freq=None)
Automatically converted to DatetimeIndex
In [38]: index = pd.Index(dates)
In [39]: index
Out[39]: DatetimeIndex(["2012-05-01", "2012-05-02", "2012-05-03"], dtype="datetime64[ns]", freq=None)
date_range日历，bdate_range工作日
In [40]: index = pd.date_range("2000-1-1", periods=1000, freq="M")
In [41]: index
Out[41]:
DatetimeIndex(["2000-01-31", "2000-02-29", "2000-03-31", "2000-04-30",
"2000-05-31", "2000-06-30", "2000-07-31", "2000-08-31",
"2000-09-30", "2000-10-31",
...
"2082-07-31", "2082-08-31", "2082-09-30", "2082-10-31",
"2082-11-30", "2082-12-31", "2083-01-31", "2083-02-28",
"2083-03-31", "2083-04-30"],
dtype="datetime64[ns]", length=1000, freq="M")
In [42]: index = pd.bdate_range("2012-1-1", periods=250)
In [43]: index
Out[43]:
DatetimeIndex(["2012-01-02", "2012-01-03", "2012-01-04", "2012-01-05",
"2012-01-06", "2012-01-09", "2012-01-10", "2012-01-11",
"2012-01-12", "2012-01-13",
...
"2012-12-03", "2012-12-04", "2012-12-05", "2012-12-06",
"2012-12-07", "2012-12-10", "2012-12-11", "2012-12-12",
"2012-12-13", "2012-12-14"],
dtype="datetime64[ns]", length=250, freq="B")
In [44]: start = datetime(2011, 1, 1)
In [45]: end = datetime(2012, 1, 1)
In [46]: rng = pd.date_range(start, end)
In [47]: rng
Out[47]:
DatetimeIndex(["2011-01-01", "2011-01-02", "2011-01-03", "2011-01-04",
"2011-01-05", "2011-01-06", "2011-01-07", "2011-01-08",
"2011-01-09", "2011-01-10",
...
"2011-12-23", "2011-12-24", "2011-12-25", "2011-12-26",
"2011-12-27", "2011-12-28", "2011-12-29", "2011-12-30",
"2011-12-31", "2012-01-01"],
dtype="datetime64[ns]", length=366, freq="D")
In [48]: rng = pd.bdate_range(start, end)
In [49]: rng
Out[49]:
DatetimeIndex(["2011-01-03", "2011-01-04", "2011-01-05", "2011-01-06",
"2011-01-07", "2011-01-10", "2011-01-11", "2011-01-12",
"2011-01-13", "2011-01-14",
...
"2011-12-19", "2011-12-20", "2011-12-21", "2011-12-22",
"2011-12-23", "2011-12-26", "2011-12-27", "2011-12-28",
"2011-12-29", "2011-12-30"],
dtype="datetime64[ns]", length=260, freq="B")
3.2 每个月末，每隔一周
In [50]: pd.date_range(start, end, freq="BM")
Out[50]:
DatetimeIndex(["2011-01-31", "2011-02-28", "2011-03-31", "2011-04-29",
"2011-05-31", "2011-06-30", "2011-07-29", "2011-08-31",
"2011-09-30", "2011-10-31", "2011-11-30", "2011-12-30"],
dtype="datetime64[ns]", freq="BM")
In [51]: pd.date_range(start, end, freq="W")
Out[51]:
DatetimeIndex(["2011-01-02", "2011-01-09", "2011-01-16", "2011-01-23",
"2011-01-30", "2011-02-06", "2011-02-13", "2011-02-20",
"2011-02-27", "2011-03-06", "2011-03-13", "2011-03-20",
"2011-03-27", "2011-04-03", "2011-04-10", "2011-04-17",
"2011-04-24", "2011-05-01", "2011-05-08", "2011-05-15",
"2011-05-22", "2011-05-29", "2011-06-05", "2011-06-12",
"2011-06-19", "2011-06-26", "2011-07-03", "2011-07-10",
"2011-07-17", "2011-07-24", "2011-07-31", "2011-08-07",
"2011-08-14", "2011-08-21", "2011-08-28", "2011-09-04",
"2011-09-11", "2011-09-18", "2011-09-25", "2011-10-02",
"2011-10-09", "2011-10-16", "2011-10-23", "2011-10-30",
"2011-11-06", "2011-11-13", "2011-11-20", "2011-11-27",
"2011-12-04", "2011-12-11", "2011-12-18", "2011-12-25",
"2012-01-01"],
dtype="datetime64[ns]", freq="W-SUN")
3.3 从End往前数20个工作日，从start往后数20个工作日
In [52]: pd.bdate_range(end=end, periods=20)
Out[52]:
DatetimeIndex(["2011-12-05", "2011-12-06", "2011-12-07", "2011-12-08",
"2011-12-09", "2011-12-12", "2011-12-13", "2011-12-14",
"2011-12-15", "2011-12-16", "2011-12-19", "2011-12-20",
"2011-12-21", "2011-12-22", "2011-12-23", "2011-12-26",
"2011-12-27", "2011-12-28", "2011-12-29", "2011-12-30"],
dtype="datetime64[ns]", freq="B")
In [53]: pd.bdate_range(start=start, periods=20)
Out[53]:
DatetimeIndex(["2011-01-03", "2011-01-04", "2011-01-05", "2011-01-06",
"2011-01-07", "2011-01-10", "2011-01-11", "2011-01-12",
"2011-01-13", "2011-01-14", "2011-01-17", "2011-01-18",
"2011-01-19", "2011-01-20", "2011-01-21", "2011-01-24",
"2011-01-25", "2011-01-26", "2011-01-27", "2011-01-28"],
dtype="datetime64[ns]", freq="B")
4、根据部分索引选择，切片
In [56]: rng = pd.date_range(start, end, freq="BM")
In [57]: ts = pd.Series(np.random.randn(len(rng)), index=rng)
In [58]: ts.index
Out[58]:
DatetimeIndex(["2011-01-31", "2011-02-28", "2011-03-31", "2011-04-29",
"2011-05-31", "2011-06-30", "2011-07-29", "2011-08-31",
"2011-09-30", "2011-10-31", "2011-11-30", "2011-12-30"],
dtype="datetime64[ns]", freq="BM")
In [59]: ts[:5].index
Out[59]:
DatetimeIndex(["2011-01-31", "2011-02-28", "2011-03-31", "2011-04-29",
"2011-05-31"],
dtype="datetime64[ns]", freq="BM")
In [60]: ts[::2].index
Out[60]:
DatetimeIndex(["2011-01-31", "2011-03-31", "2011-05-31", "2011-07-29",
"2011-09-30", "2011-11-30"],
dtype="datetime64[ns]", freq="2BM")
In [61]: ts["1/31/2011"]
Out[61]: -1.2812473076599531
In [62]: ts[pd.datetime(2011, 12, 25):]
Out[62]:
2011-12-30 0.687738
Freq: BM, dtype: float64
In [63]: ts["10/31/2011":"12/31/2011"]
Out[63]:
2011-10-31 0.149748
2011-11-30 -0.732339
2011-12-30 0.687738
Freq: BM, dtype: float64
In [64]: ts["2011"]
Out[64]:
2011-01-31 -1.281247
2011-02-28 -0.727707
2011-03-31 -0.121306
2011-04-29 -0.097883
2011-05-31 0.695775
2011-06-30 0.341734
2011-07-29 0.959726
2011-08-31 -1.110336
2011-09-30 -0.619976
2011-10-31 0.149748
2011-11-30 -0.732339
2011-12-30 0.687738
Freq: BM, dtype: float64
In [65]: ts["2011-6"]
Out[65]:
2011-06-30 0.341734
Freq: BM, dtype: float64
DataFrame中指定了时间索引，可以根据时间索引提取子集
In [66]: dft = pd.DataFrame(np.random.randn(100000,1),columns=["A"],index=pd.date_range("20130101",periods=100000,freq="T"))
In [67]: dft
Out[67]:
A
2013-01-01 00:00:00 0.176444
2013-01-01 00:01:00 0.403310
2013-01-01 00:02:00 -0.154951
2013-01-01 00:03:00 0.301624
2013-01-01 00:04:00 -2.179861
2013-01-01 00:05:00 -1.369849
2013-01-01 00:06:00 -0.954208
... ...
2013-03-11 10:33:00 -0.293083
2013-03-11 10:34:00 -0.059881
2013-03-11 10:35:00 1.252450
2013-03-11 10:36:00 0.046611
2013-03-11 10:37:00 0.059478
2013-03-11 10:38:00 -0.286539
2013-03-11 10:39:00 0.841669
[100000 rows x 1 columns]
In [68]: dft["2013"]
Out[68]:
A
2013-01-01 00:00:00 0.176444
2013-01-01 00:01:00 0.403310
2013-01-01 00:02:00 -0.154951
2013-01-01 00:03:00 0.301624
2013-01-01 00:04:00 -2.179861
2013-01-01 00:05:00 -1.369849
2013-01-01 00:06:00 -0.954208
... ...
2013-03-11 10:33:00 -0.293083
2013-03-11 10:34:00 -0.059881
2013-03-11 10:35:00 1.252450
2013-03-11 10:36:00 0.046611
2013-03-11 10:37:00 0.059478
2013-03-11 10:38:00 -0.286539
2013-03-11 10:39:00 0.841669
[100000 rows x 1 columns]
In [69]: dft["2013-1":"2013-2"]
Out[69]:
A
2013-01-01 00:00:00 0.176444
2013-01-01 00:01:00 0.403310
2013-01-01 00:02:00 -0.154951
2013-01-01 00:03:00 0.301624
2013-01-01 00:04:00 -2.179861
2013-01-01 00:05:00 -1.369849
2013-01-01 00:06:00 -0.954208
... ...
2013-02-28 23:53:00 0.103114
2013-02-28 23:54:00 -1.303422
2013-02-28 23:55:00 0.451943
2013-02-28 23:56:00 0.220534
2013-02-28 23:57:00 -1.624220
2013-02-28 23:58:00 0.093915
2013-02-28 23:59:00 -1.087454
[84960 rows x 1 columns]
In [70]: dft["2013-1":"2013-2-28"]
Out[70]:
A
2013-01-01 00:00:00 0.176444
2013-01-01 00:01:00 0.403310
2013-01-01 00:02:00 -0.154951
2013-01-01 00:03:00 0.301624
2013-01-01 00:04:00 -2.179861
2013-01-01 00:05:00 -1.369849
2013-01-01 00:06:00 -0.954208
... ...
2013-02-28 23:53:00 0.103114
2013-02-28 23:54:00 -1.303422
2013-02-28 23:55:00 0.451943
2013-02-28 23:56:00 0.220534
2013-02-28 23:57:00 -1.624220
2013-02-28 23:58:00 0.093915
2013-02-28 23:59:00 -1.087454
[84960 rows x 1 columns]
In [71]: dft["2013-1":"2013-2-28 00:00:00"]
Out[71]:
A
2013-01-01 00:00:00 0.176444
2013-01-01 00:01:00 0.403310
2013-01-01 00:02:00 -0.154951
2013-01-01 00:03:00 0.301624
2013-01-01 00:04:00 -2.179861
2013-01-01 00:05:00 -1.369849
2013-01-01 00:06:00 -0.954208
... ...
2013-02-27 23:54:00 0.897051
2013-02-27 23:55:00 -0.309230
2013-02-27 23:56:00 1.944713
2013-02-27 23:57:00 0.369265
2013-02-27 23:58:00 0.053071
2013-02-27 23:59:00 -0.019734
2013-02-28 00:00:00 1.388189
[83521 rows x 1 columns]
In [72]: dft["2013-1-15":"2013-1-15 12:30:00"]
Out[72]:
A
2013-01-15 00:00:00 0.501288
2013-01-15 00:01:00 -0.605198
2013-01-15 00:02:00 0.215146
2013-01-15 00:03:00 0.924732
2013-01-15 00:04:00 -2.228519
2013-01-15 00:05:00 1.517331
2013-01-15 00:06:00 -1.188774
... ...
2013-01-15 12:24:00 1.358314
2013-01-15 12:25:00 -0.737727
2013-01-15 12:26:00 1.838323
2013-01-15 12:27:00 -0.774090
2013-01-15 12:28:00 0.622261
2013-01-15 12:29:00 -0.631649
2013-01-15 12:30:00 0.193284
[751 rows x 1 columns]
In [73]: dft.loc["2013-1-15 12:30:00"]
Out[73]:
A 0.193284
Name: 2013-01-15 12:30:00, dtype: float64
类别 解释
year 年
month 月
day 日
hour 时
minute 分钟
second 秒
microsecond 微秒
nanosecond 纳秒
date 返回日期
time 返回时间
dayofyear 年序日
weekofyear 年序周
week 周
dayofweek 周中的第几天，Monday=0, Sunday=6
weekday 周中的第几天，Monday=0, Sunday=6
weekday_name 周中的星期几，ex: Friday
quarter 季度
days_in_month 一个月中有多少天
is_month_start 是否月初第一天
is_month_end 是否月末最后一天
is_quarter_start 是否季度的最开始
is_quarter_end 是否季度的最后一个
is_year_start 是否年初第一天
is_year_end 是否年末第一天
类别 解释
BDay 工作日
CDay 自定义日期
Week 周
WeekOfMonth 月中的第几周
LastWeekOfMonth 月中的最后一周
MonthEnd 日历上月末
MonthBegin 日历上月初
BMonthEnd 工作月初
BMonthBegin 月开始营业
CBMonthEnd 自定义月末
CBMonthBegin 自定义月初
QuarterEnd 日历季末
QuarterBegin 日历季初
BQuarterEnd 工作季末
BQuarterBegin 工作季初
FY5253Quarter retail (aka 52-53 week) quarter
YearEnd 日历年末
YearBegin 日历年初
BYearEnd 工作年末
BYearBegin 工作年初
FY5253 retail (aka 52-53 week) year
BusinessHour 工作小时
CustomBusinessHour 自定义小时
Hour 小时
Minute 分钟
Second 秒
In [84]: d = pd.datetime(2008, 8, 18, 9, 0)
In [86]: from pandas.tseries.offsets import *
In [87]: d + DateOffset(months=4, days=5)
Out[87]: Timestamp("2008-12-23 09:00:00")
In [88]: d - 5 * BDay()
Out[88]: Timestamp("2008-08-11 09:00:00")
月末
In [89]: d + BMonthEnd()
Out[89]: Timestamp("2008-08-29 09:00:00")
In [90]: d
Out[90]: datetime.datetime(2008, 8, 18, 9, 0)
往前数月末
In [91]: offset = BMonthEnd()
In [92]: offset.rollforward(d)
Out[92]: Timestamp("2008-08-29 09:00:00")
往后数月末
In [93]: offset.rollback(d)
Out[93]: Timestamp("2008-07-31 09:00:00")
时间方面的
In [94]: day = Day()
In [95]: day.apply(pd.Timestamp("2014-01-01 09:00"))
Out[95]: Timestamp("2014-01-02 09:00:00")
In [96]: day = Day(normalize=True)
In [97]: day.apply(pd.Timestamp("2014-01-01 09:00"))
Out[97]: Timestamp("2014-01-02 00:00:00")
In [98]: hour = Hour()
In [99]: hour.apply(pd.Timestamp("2014-01-01 22:00"))
Out[99]: Timestamp("2014-01-01 23:00:00")
In [100]: hour = Hour(normalize=True)
In [101]: hour.apply(pd.Timestamp("2014-01-01 22:00"))
Out[101]: Timestamp("2014-01-01 00:00:00")
In [102]: hour.apply(pd.Timestamp("2014-01-01 23:00"))
Out[102]: Timestamp("2014-01-02 00:00:00")
周相关的
In [103]: d
Out[103]: datetime.datetime(2008, 8, 18, 9, 0)
In [104]: d + Week()
Out[104]: Timestamp("2008-08-25 09:00:00")
In [105]: d + Week(weekday=4)
Out[105]: Timestamp("2008-08-22 09:00:00")
In [106]: (d + Week(weekday=4)).weekday()
Out[106]: 4
In [107]: d - Week()
Out[107]: Timestamp("2008-08-11 09:00:00")
In [213]: ts = ts[:5]
In [214]: ts.shift(1)
Out[214]:
2011-01-31 NaN
2011-02-28 -1.281247
2011-03-31 -0.727707
2011-04-29 -0.121306
2011-05-31 -0.097883
Freq: BM, dtype: float64
In [215]: ts.shift(5, freq=datetools.bday)
Out[215]:
2011-02-07 -1.281247
2011-03-07 -0.727707
2011-04-07 -0.121306
2011-05-06 -0.097883
2011-06-07 0.695775
dtype: float64
In [216]: ts.shift(5, freq="BM")
Out[216]:
2011-06-30 -1.281247
2011-07-29 -0.727707
2011-08-31 -0.121306
2011-09-30 -0.097883
2011-10-31 0.695775
Freq: BM, dtype: float64
In [217]: ts.tshift(5, freq="D")
Out[217]:
2011-02-05 -1.281247
2011-03-05 -0.727707
2011-04-05 -0.121306
2011-05-04 -0.097883
2011-06-05 0.695775
dtype: float64
文章版权归作者所有，未经允许请勿转载,若此文章存在违规行为，您可以联系管理员删除。
转载请注明本文地址：https://www.ucloud.cn/yun/40859.html
摘要：下文统一称为数组是存储单一数据类型的多维数组同语言数组直接保存数值而则是能够对数组进行处理的函数。动态数据类型与的数组和的这些不可变数据类型的适用场景等可变数据类型适用于需要不断对原始数据进行修改的场景。 showImg(https://segmentfault.com/img/remote/1460000018925396);Numpy,是python中的一个矩阵计算包,功能类似ma...
摘要：它还使用执行所谓的链式索引，这通常会导致意外的结果。但这种方法的最大问题是计算的时间成本。这些都是一次产生一行的生成器方法，类似中使用的用法。在这种情况下，所花费的时间大约是方法的一半。根据每小时所属的应用一组标签。 作者：xiaoyu 微信公众号：Python数据科学 知乎：python数据分析师 showImg(https://segmentfault.com/img/bVboe...
摘要：和数据类型的用法在存储字符串时，可以使用或者类型相同点和都可以存储变长字符串且字符串长度上限为字节不同点速度快，不存在空间浪费，不处理尾部空格，上限为字节，但是有存储长度实际字节最大可用。 点赞再看，养成赞美的习惯，微信搜一搜【香菜聊游戏】关注我。 目录 1、DATETIME、TIME...
小编写这篇文章的主要目的，主要是给大家介绍关于pandas相关的一些问题解答，包括将datetime64[ns]转为字符串日期，那么，具体是怎么进行操作的呢？下面给大家做出一个解答。 将datetime64[ns]转为字符串日期 将datetime64[ns]转为字符串日期（%Y-%m-%d）最核心的用法是：pandas.series.dt.strftime('%Y-%m-%d&#...
摘要：小安分析的数据主要是用户使用代理访问日志记录信息，要分析的原始数据以的形式存储。下面小安带小伙伴们一起来管窥管窥这些数据。在此小安一定一定要告诉你，小安每次做数据分析时必定使用的方法方法。 随着网络安全信息数据大规模的增长,应用数据分析技术进行网络安全分析成为业界研究热点，小安在这次小讲堂中带大家用Python工具对风险数据作简单分析，主要是分析蜜罐日志数据，来看看一般大家都使用代理i...
阅读 1161·2021-11-15 18:14
阅读 3625·2021-11-15 11:37
阅读 752·2021-09-24 09:47
阅读 2425·2021-09-04 16:48
阅读 2181·2019-08-30 15:53
阅读 2377·2019-08-30 15:53
阅读 387·2019-08-30 11:20
阅读 1231·2019-08-29 16:08
