資訊專欄INFORMATION COLUMN
摘要：生成一個(gè)時(shí)間段生成一個(gè)時(shí)間區(qū)間段，間隔為小時(shí)生成一個(gè)，并制定索引為時(shí)間段改變時(shí)間間隔轉(zhuǎn)換為日期格式數(shù)字生成日期格式字符生成日期
1、生成一個(gè)時(shí)間段
In [1]:import pandas as pd
In [2]:import numpy as np
1)生成一個(gè)時(shí)間區(qū)間段，間隔為小時(shí)
In [3]:rng = pd.date_range("1/1/2011", periods=72, freq="H")
2)生成一個(gè)Series，并制定索引為時(shí)間段
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)改變時(shí)間間隔
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日歷，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 每個(gè)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 從End往前數(shù)20個(gè)工作日，從start往后數(shù)20個(gè)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、根據(jù)部分索引選擇，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中指定了時(shí)間索引，可以根據(jù)時(shí)間索引提取子集
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
類別 解釋
year 年
month 月
day 日
hour 時(shí)
minute 分鐘
second 秒
microsecond 微秒
nanosecond 納秒
date 返回日期
time 返回時(shí)間
dayofyear 年序日
weekofyear 年序周
week 周
dayofweek 周中的第幾天，Monday=0, Sunday=6
weekday 周中的第幾天，Monday=0, Sunday=6
weekday_name 周中的星期幾，ex: Friday
quarter 季度
days_in_month 一個(gè)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 是否季度的最開(kāi)始
is_quarter_end 是否季度的最后一個(gè)
is_year_start 是否年初第一天
is_year_end 是否年末第一天
類別 解釋
BDay 工作日
CDay 自定義日期
Week 周
WeekOfMonth 月中的第幾周
LastWeekOfMonth 月中的最后一周
MonthEnd 日歷上月末
MonthBegin 日歷上月初
BMonthEnd 工作月初
BMonthBegin 月開(kāi)始營(yíng)業(yè)
CBMonthEnd 自定義月末
CBMonthBegin 自定義月初
QuarterEnd 日歷季末
QuarterBegin 日歷季初
BQuarterEnd 工作季末
BQuarterBegin 工作季初
FY5253Quarter retail (aka 52-53 week) quarter
YearEnd 日歷年末
YearBegin 日歷年初
BYearEnd 工作年末
BYearBegin 工作年初
FY5253 retail (aka 52-53 week) year
BusinessHour 工作小時(shí)
CustomBusinessHour 自定義小時(shí)
Hour 小時(shí)
Minute 分鐘
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)
往前數(shù)月末
In [91]: offset = BMonthEnd()
In [92]: offset.rollforward(d)
Out[92]: Timestamp("2008-08-29 09:00:00")
往后數(shù)月末
In [93]: offset.rollback(d)
Out[93]: Timestamp("2008-07-31 09:00:00")
時(shí)間方面的
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")
周相關(guān)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
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摘要：它還使用執(zhí)行所謂的鏈?zhǔn)剿饕?，這通常會(huì)導(dǎo)致意外的結(jié)果。但這種方法的最大問(wèn)題是計(jì)算的時(shí)間成本。這些都是一次產(chǎn)生一行的生成器方法，類似中使用的用法。在這種情況下，所花費(fèi)的時(shí)間大約是方法的一半。根據(jù)每小時(shí)所屬的應(yīng)用一組標(biāo)簽。 作者：xiaoyu 微信公眾號(hào)：Python數(shù)據(jù)科學(xué) 知乎：python數(shù)據(jù)分析師 showImg(https://segmentfault.com/img/bVboe...
摘要：和數(shù)據(jù)類型的用法在存儲(chǔ)字符串時(shí)，可以使用或者類型相同點(diǎn)和都可以存儲(chǔ)變長(zhǎng)字符串且字符串長(zhǎng)度上限為字節(jié)不同點(diǎn)速度快，不存在空間浪費(fèi)，不處理尾部空格，上限為字節(jié)，但是有存儲(chǔ)長(zhǎng)度實(shí)際字節(jié)最大可用。 點(diǎn)贊再看，養(yǎng)成贊美的習(xí)慣，微信搜一搜【香菜聊游戲】關(guān)注我。 目錄 1、DATETIME、TIME...
小編寫這篇文章的主要目的，主要是給大家介紹關(guān)于pandas相關(guān)的一些問(wèn)題解答，包括將datetime64[ns]轉(zhuǎn)為字符串日期，那么，具體是怎么進(jìn)行操作的呢？下面給大家做出一個(gè)解答。 將datetime64[ns]轉(zhuǎn)為字符串日期 將datetime64[ns]轉(zhuǎn)為字符串日期（%Y-%m-%d）最核心的用法是：pandas.series.dt.strftime('%Y-%m-%d&#...
摘要：小安分析的數(shù)據(jù)主要是用戶使用代理訪問(wèn)日志記錄信息，要分析的原始數(shù)據(jù)以的形式存儲(chǔ)。下面小安帶小伙伴們一起來(lái)管窺管窺這些數(shù)據(jù)。在此小安一定一定要告訴你，小安每次做數(shù)據(jù)分析時(shí)必定使用的方法方法。 隨著網(wǎng)絡(luò)安全信息數(shù)據(jù)大規(guī)模的增長(zhǎng),應(yīng)用數(shù)據(jù)分析技術(shù)進(jìn)行網(wǎng)絡(luò)安全分析成為業(yè)界研究熱點(diǎn)，小安在這次小講堂中帶大家用Python工具對(duì)風(fēng)險(xiǎn)數(shù)據(jù)作簡(jiǎn)單分析，主要是分析蜜罐日志數(shù)據(jù)，來(lái)看看一般大家都使用代理i...
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