按时间间隔创建月平均值

本文介绍了按时间间隔创建月平均值的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！ 问题描述 我正在处理30年的月度温度观测，包括1960年1月到1989年12月。它看起来像这样： >头（df）日期温度 1 1960-01-01 22.92235 2 1960-02-01 23.07059 3 1960-03-01 23.10941 4 1960- 04-01 20.78353 5 1960-05-01 17.45176 6 1960-06-01 17.31765 首先，我需要做的是在整个时期内平均所有的januaries，februaries，marches等。 然后，我想在特定的时间段（3年，5年，10年等）做同样的事情。 例如， 1960年至1964年之间的所有jan，feb，mar等的平均值; 1965年至1969年之间的所有jan，feb，mar等的平均值; 等等。 最终结果将包括月，日期和温度，如下所示： 月份温度 1月1960-1989 17 Feb 1960-1989 12 Mar 1960-1989 7 1960年 - 1989年4月$ $ $ $ $ $ $ $ $ $ $ $ $ $ b Jun 1960-1989 12 Jul 1960-1989 17 Aug 1960-1989 22 Sep 1960-1989 21 Oct 1960-1989 21 Nov 1960- 1989 18 十二月1960-1989 17 一月1960-1964 17 二月1960-1964 12 三月1960-1964 7 四月1960-1964 9 May 1960-1964 9 Jun 1960-1964 11 Jul 1960-1964 14 Aug 1960-1964 18 Sep 1960-1964 13 Oct 1960-1964 12 Nov 1960-1964 17 Dec 1960-1964 11 有什么想法怎么做？ 如果您发现有用，以下是我的数据集的一个克隆： df< - structure（list（date = structure（c（-3653，-3622，-3593，-3562， -3532，-3501，-3471，-3440，-3409，-3379 ，-3348，-3318，-3287， -3256，-3228，-3197，-3167，-3136，-3106，-3075，-3044，-3014， -2983，-2953 ，-2922，-2891，-2863，-2832，-2802，-2771，-2741， -2710，-2679，-2649，-2618，-2588，-2557，-2526，-2498， -2467， -2437，-2406，-2376，-2345，-2314，-2284，-2253，-2223，-2192， -2161，-2132，-2101,2071， -2040，-2010，-1979，-1948，-1918， -1887，-1857，-1826，-1795，-1767，-1736，-1706，-1675，-1645， -1614，-1583，-1553，-1522，-1492，-1461，-1430，-1402，-1371， -1341，-1310，-1280，-1249，-1218，-1188， - 1157，-1127，-1096， -1065，-1037，-1006，-976，-945，-915，-884，-853，-823，-792， -762， 731，-700，-671，-640，-610，-579，-549，-518，-487，-457， -426，-396，-365，-334，-306，-275 ，-245,214，-184，-15 3，-122， -92，-61，-31,0,31,59,90,120,151,181,212,243,273,304， 334,365,396， 424,455,485,516,546,577,608,638,669,699,600,721,821,851,882,912,943,974,1004,1035,1065， 1096,1127,1155,1168,1216,1247,1277,1308,1339,1369,1400， 1430，1461，1492，1520,1551,1581,1612,1642,1673,1704,1734， 1765，1795，1826，1857,1885,1916,1946,1977,2007,2038,2069， 2099,2130,2160,2191,2222,25251,2282,2312,2343,2733， 2404， 2435，2465，2496，2526，2557，2588，2616，2647，2677，2708，2738， 2769,2800,2830,2861,2891,2922,2953,2981,3012， 3042,3073， 3103，3134,3125,3195,3226,3256,3287,3318,3346,3777,3407， 3438,3488,3499,3530,3560,3591,3621,3652， 3683,3712,3743， 3773,3804,3834,3865,39896,3926,3957,3987,4018,4049,4077， 4108,4138,4169,4199,4230,46161,4291， 4322,4352,4383,4414， 4442，4473，4503，4534，4564，4595，4626，4656，4687， 4717,4478， 4779,4807,4838,4868,48999,4929,4960,4991,5021,5052,5082， 5113,5144,5173,5204,5234,5265,5295,5326， 5357,5387,5418， 5448,5479,5510,5538,5569,5599,5630,5660,5691,5722,5752， 5783,5813,5844,5875,5903,5934,5964， 5995,6025,6056,6087， 6117,6148,6178,6209,6240,6268,62999,6299,66360,6390,6221， 6452,6482,6513,65343,65474,6605,6502,6502,6952,6952,6005,6005,6005,6056,6087,5165,6056,6087， 6634,6665,6695,66726,6756， 6787,6818,6848,6897,6909,6940,6971,6999,7030,7060,7091， 7121,7152,7183,7133,7244， 7274），temp = c（22.9223529411765， 23.0705882352941，23.1094117647059,20.7835294117647,17.4517647058824， 17.3176470588235，18.0494117647059，19.6188235294118，21.3023529411765， 23.1105882352941,22.2364705882353，22.7482352941176， 23.5870588235294， 24.0023529411765,23.0094117647059,22.0176470588235,19.4917647058824， 18.1011764705882,18.3164705882353,202.1623529411765,22.8717647058824， 2 3.2576470588235，23.68，22.3694117647059，22.9517647058824， 23.6976470588235，23.3294117647059，20.8564705882353，18.16， 15.8988235294118，15.7988235294118，18.4176470588235，20.8423529411765， 20.3247058823529，22.3070588235294，22.2035294117647，24.2235294117647， 23.6976470588235， 24.4082352941176，21.1752941176471，18.1023529411765， 16.1211764705882，18.3164705882353，19.7635294117647，23.1294117647059， 22.9964705882353，23.6552941176471，22.6964705882353，23.6011764705882， 23.6517647058824，23.7035294117647，22.4352941176471，18.5835294117647， 16.5976470588235，15.7741176470588， 19.2541176470588，20.8776470588235， 20.5729411764706，21.1729411764706，21.5870588235294，22.4576470588235， 23.6058823529412，21.84，21.6694117647059，19.2458823529412， 18.7517647058824，17.7811764705882，19.4764705882353，21.9270588235294， 21.5470588235294，22.88，23.2458823529412， 24.2776470588235， 25.2470588235294,23.4694117647 059，21.4435294117647，19.3941176470588， 18.5447058823529，17.6，18.3764705882353，19.8529411764706，22.0823529411765， 22.7294117647059，23.4011764705882，23.3611764705882，24.2505882352941， 23.2870588235294，21.9482352941176，20.5552941176471，18.0788235294118， 18.5929411764706， 20.8752941176471，21.9023529411765，23.6105882352941， 22.4070588235294，21.5635294117647，23.3129411764706，22.9741176470588， 23.3670588235294，19.6105882352941，16.9941176470588，17.7670588235294， 17.4858823529412，17.8517647058824，20.26，22.1576470588235， 23.8364705882353，23.4447058823529， 24.8129411764706，25.1764705882353， 24.2694117647059，21.5035294117647，20.0458823529412，18.4694117647059， 18.4541176470588，19.5388235294118，22.02，20.5364705882353， 22.9858823529412，21.9752941176471，23.7729411764706，24.0576470588235， 24.0941176470588，22.1552941176471，21.2329411764706， 19.5611764705882， 17.8788235294118，18.682352941176 5，20.1541176470588，21.6258823529412， 21.5211764705882，23.9811764705882，24.8352941176471，24.5882352941176， 24.1729411764706，21.1035294117647，19.0435294117647，17.08， 17.4529411764706，19.1458823529412，20.4447058823529，20.7129411764706， 21.5047058823529，22.6952941176471， 23.4364705882353，23.1 24.1847058823529， 19.8105882352941，19.9847058823529，20.5188235294118，17.7658823529412， 19.4435294117647，20.7588235294118，21.7835294117647，22.7788235294118， 23.2388235294118，24.9129411764706，25.6，23.5647058823529，24.0058823529412， 19.7823529411765， 19.3152941176471，18.7741176470588，19.0305882352941， 20.5576470588235，21.3611764705882，21.4247058823529，23.4811764705882， 23.6505882352941，25.1870588235294，23.3541176470588，21.4823529411765， 18.7364705882353，17.7235294117647，18.3976470588235，19.7235294117647， 21.0741176470588，21.6094117647059， 22.9635294117647，22.4011764705882， 23.4152941176471， 24.7741176470588，24.3270588235294，20.7976470588235， 18.8764705882353，17.7788235294118，16.4129411764706，21.4117647058824， 22.3317647058824，21.66，22.3694117647059，23.0917647058824， 24.4541176470588，23.2847058823529，23.3164705882353，21.2529411764706， 19.1258823529412，17.3882352941176， 17.3823529411765，19.0529411764706， 19.6576470588235，20.2976470588235，21.9023529411765，23.3094117647059， 24.0117647058824，25.5611764705882，24.9129411764706，21.3964705882353， 19.9870588235294，18.3929411764706，20.9917647058824，20.3058823529412， 21.4435294117647，23.1941176470588，22.8388235294118， 22.5176470588235， 24.6317647058824，24.6541176470588，24.2，20.84，18.4576470588235， 17.5011764705882，19.16，20.54，20.1517647058824，22.6776470588235， 22.7470588235294，22.7882352941176，22.0811764705882，24.2152941176471， 22.9235294117647，20.8411764705882， 19.6188235294118,17.16， 16.0529411764706,20.3223529411765， 19.9752941176471，22.5152941176471， 22.2705882352941，23.1541176470588，23.1047058823529，23.9517647058824， 24.8176470588235，22.18 20.5023529411765，17.3505882352941， 19.1917647058824，19.9894117647059，19.0235294117647，22.8235294117647， 22.7094117647059，23.8741176470588，24.0517647058824， 25.1764705882353， 23.9235294117647，21.2929411764706，20.6117647058824，17.1305882352941， 16.3470588235294，19.6470588235294，21.3341176470588，20.2176470588235， 23.7435294117647，22.6741176470588，22.9070588235294，24.7152941176471， 23.2905882352941，20.5776470588235，18.9635294117647，19.0658823529412， 18.8423529411765，20.0729411764706，21.3047058823529，22.1588235294118， 24.0388235294118，22.1917647058824，24.0517647058824，24.8729411764706， 23.0117647058824，23 21.3094117647059，19.4105882352941，20.3470588235294， 19.4482352941176，20.0670588235294，21.6364705882353，23.4211764705882， 23.16，25.4788235294118，26。 4741176470588，24.0482352941176， 21.4176470588235，21.7164705882353，19.0905882352941，19.6752941176471， 18.1611764705882，20.0482352941176，23.4917647058824，23.4894117647059， 22.5482352941176，23.1376470588235，24.9811764705882，24.1552941176471， 22.8423529411765，19.7435294117647，16.4， 17.3105882352941，20.5235294117647， 21.0494117647059，23.1352941176471，23.9435294117647，23.9058823529412， 24.9835294117647，24.6952941176471，24.0047058823529，23.3164705882353， 21.5823529411765，18.3447058823529，18.1964705882353，20.0035294117647， 20.7152941176471，22.5705882352941，24.6541176470588， 23.2329411764706， 25.0517647058824，24.3329411764706，23.5811764705882，22.9988235294118， 19.4976470588235，17.3188235294118，19.5635294117647，19.0211764705882， 19.7223529411765，22.6858823529412，23.9423529411765，23.6905882352941， 25.7129411764706，23.9505882352941，24.4376470588235，22.6070588235294， 19.8882352941176，1 7.2058823529412，16.4211764705882，20.02， 21.9458823529412，21.9341176470588，22.74，23.8，23.9611764705882， 24.4564705882353，24 23.2129411764706，19.4729411764706，17.7105882352941， 16.9682352941176，19.0341176470588，20.2917647058824，20.7776470588235， 22.9364705882353，22.7894117647059）），.Names = c（date，temp），row.names = c（NA，-360L），class =data.frame） 解决方案一个选项是使用 data.table 年份分组为 cut 或 findInterval 。对于第一种情况，即每个月收集意味着，我们将'date'转换为 Date 类，并提取 months ，将其用作分组变量，并获得temp的平均值。请注意，我们首先将data.frame转换为data.table（ setDT（df））。 library（data.table） setDT（df）[，list（Temp = mean（temp）），by =。（Months = months（as.Date ）abbr = TRUE））] ＃Months Temp ＃1：Jan 23.90506 ＃2：Feb 24.40012 ＃3：Mar 23.73714 ＃4：Apr 21.68584 ＃5：May 19.53863 ＃6：Jun 17.90322 ＃7：Jul 17.97675 ＃8：Aug 19.56051 ＃9：Sep 20.90125 ＃10：Oct 21.96886 ＃11：Nov 22.86102 ＃12：Dec 22.92537 对于周期性和每月分组，我们需要创建一个句点列。一种方式是 cut 或 findInterval 。例如，如果我们正在寻找一个5年的窗口，即1960-1964,1965-1969等，我们通过在 findInterval 中创建 vec 来创建期间列使用 seq ，将从 findInterval 导出的数字索引更改为从粘贴。使用'Month'和'Period'作为分组变量，其余与以前相同。 setDT（df）[， c（'Month'，'Period'）：= {tmp tmp1 tmp2 < - months（tmp，abbr = TRUE） i1 i2< - i1 + 4 lbl< -paste（i1，i2，sep =' - '） list（tmp2，lbl [findInterval（tmp1，i1）]）}] df [，list （Temp = mean（temp）），。（Month，Period）] ＃Month Period Temp ＃1：Jan 1960-1964 23.45718 ＃2：1960-1964 23.62400 ＃3：1960年6月19日23.51200 ＃4：1960年6月19日21.45365 ＃5：1960-1964 18.35788 ＃6：1960年6月19日16.80729 ＃7：Jul 1960-1964 17.25106 ＃8：1960-1964 19.42329 ＃9：Sep 1960-1964 21.80471 ＃10：Oct 1960-1964 22.05247 ＃11 ：1960年11月19日22.61035 ＃12：Dec 196 0-1964 22.32094 ＃13：Jan 1965-1969 23.64447 ＃14：1965-1969 24.25082 ＃15：1965-1933 23.24659 ＃16：1965年4月 - 1969 21.23506 ＃17：1965-1969 19.24706 ＃18：1965-1969 18.32235 ＃19：1965-1979 17.98282 ＃20：1965-1980 19.22376 ＃21：1965-1969 21.19247 ＃22：1965-1969 21.98682 ＃23：Nov 1965-1969 22.96776 ＃24：Dec 1965-1969 22.72612 ＃25：Jan 1970-1974 24.12165 ＃26：Feb 1970-1974 24.50659 ＃27：Mar 1970-1974 23.87412 ＃28：Apr 1970-1974 21.71153 ＃29：May 1970-1974 19.75600 ＃30：Jun 1970-1974 18.83976 ＃31：1970年7月19日18.05388 ＃32：1980年7月19日19.20518 ＃33 ：1970年7月19日20.59788 ＃34：1970年7月19日21.41859 ＃35：1970年11月19日22.03859 ＃36：1970年11月19日23.15953 ＃37：Jan 1975-1979 23.71882 ＃38：1975-1979 24.49788 ＃39：1975-1979 23.93600 ＃40：1975-1979 21.02565 ＃41：1975-1979 19.21318 ＃42：1975-1979 17.64424 ＃43：1975-1979 18.00000 ＃44：1975-1979 20.32659 ＃45 ：1975年9月19日20.71200 ＃46：1975-1979 22.06894 ＃47：1975-1979 22.42565 ＃48：1975-1979 22.97224 ＃49：Jan 1980-1984 23.91882 ＃50：1980-1984 25.03812 ＃51：1980-1984 23.81835 ＃52：1980-1984 21.69365 ＃53：1980年5月 - 1984 20.62071 ＃54：1980-1984 18.40965 ＃55：1980-1984 18.88071 ＃56：1980-1984 19.46376 ＃57：1980-1984 20.35553 ＃58：Oct 1980-1984 22.06565 ＃59：Nov 1980-1984 23.48047 ＃60：Dec 1980-1984 22.88965 ＃61：Jan 1985-1989 24.56941 ＃62：Feb 1985-1989 24.48329 ＃63：Mar 1985-1989 24.03576 ＃64：Apr 1985-1989 22.99553 ＃65：May 1985-1989 20.03694 ＃66：Jun 1985-1989 17.39600 ＃67：Jul 1985-1989 17.69200 ＃68：Aug 1985-1989 19.72047 ＃69：Sep 1985-198 9 20.74494 ＃70：Oct 1985-1989 22.22071 ＃71：Nov 1985-1989 23.64329 ＃72：Dec 1985-1989 23.48376 ＃Month Period Temp 以同样的方式，我们可以获得10年或其他窗口。 Sorry if this has already been posted but I looked really hard and could not find anything.I am working with monthly temperature observations for 30 years, comprising January 1960 to December 1989. It looks like this:> head(df) date temp1 1960-01-01 22.922352 1960-02-01 23.070593 1960-03-01 23.109414 1960-04-01 20.783535 1960-05-01 17.451766 1960-06-01 17.31765First, what I need to do is to average all januaries, februaries, marches and etc for the whole period. Then, I would like to do the same for specific periods of time (3 years, 5 years, 10 years etc).For example,The average of all jan, feb, mar etc between 1960 and 1964;The average of all jan, feb, mar etc between 1965 and 1969;and so on.The final result would consist of month, period and temperature, something like this:Month Period Temp Jan 1960-1989 17 Feb 1960-1989 12 Mar 1960-1989 7 Apr 1960-1989 9 May 1960-1989 15 Jun 1960-1989 12 Jul 1960-1989 17 Aug 1960-1989 22 Sep 1960-1989 21 Oct 1960-1989 21 Nov 1960-1989 18 Dec 1960-1989 17 Jan 1960-1964 17 Feb 1960-1964 12 Mar 1960-1964 7 Apr 1960-1964 9 May 1960-1964 9 Jun 1960-1964 11 Jul 1960-1964 14 Aug 1960-1964 18 Sep 1960-1964 13 Oct 1960-1964 12 Nov 1960-1964 17 Dec 1960-1964 11Any ideias on how to do this? In case you find useful, here is one clone of my dataset:df <- structure(list(date = structure(c(-3653, -3622, -3593, -3562, -3532, -3501, -3471, -3440, -3409, -3379, -3348, -3318, -3287, -3256, -3228, -3197, -3167, -3136, -3106, -3075, -3044, -3014, -2983, -2953, -2922, -2891, -2863, -2832, -2802, -2771, -2741, -2710, -2679, -2649, -2618, -2588, -2557, -2526, -2498, -2467, -2437, -2406, -2376, -2345, -2314, -2284, -2253, -2223, -2192, -2161, -2132, -2101, -2071, -2040, -2010, -1979, -1948, -1918, -1887, -1857, -1826, -1795, -1767, -1736, -1706, -1675, -1645, -1614, -1583, -1553, -1522, -1492, -1461, -1430, -1402, -1371, -1341, -1310, -1280, -1249, -1218, -1188, -1157, -1127, -1096, -1065, -1037, -1006, -976, -945, -915, -884, -853, -823, -792, -762, -731, -700, -671, -640, -610, -579, -549, -518, -487, -457, -426, -396, -365, -334, -306, -275, -245, -214, -184, -153, -122, -92, -61, -31, 0, 31, 59, 90, 120, 151, 181, 212, 243, 273, 304, 334, 365, 396, 424, 455, 485, 516, 546, 577, 608, 638, 669, 699, 730, 761, 790, 821, 851, 882, 912, 943, 974, 1004, 1035, 1065, 1096, 1127, 1155, 1186, 1216, 1247, 1277, 1308, 1339, 1369, 1400, 1430, 1461, 1492, 1520, 1551, 1581, 1612, 1642, 1673, 1704, 1734, 1765, 1795, 1826, 1857, 1885, 1916, 1946, 1977, 2007, 2038, 2069, 2099, 2130, 2160, 2191, 2222, 2251, 2282, 2312, 2343, 2373, 2404, 2435, 2465, 2496, 2526, 2557, 2588, 2616, 2647, 2677, 2708, 2738, 2769, 2800, 2830, 2861, 2891, 2922, 2953, 2981, 3012, 3042, 3073, 3103, 3134, 3165, 3195, 3226, 3256, 3287, 3318, 3346, 3377, 3407, 3438, 3468, 3499, 3530, 3560, 3591, 3621, 3652, 3683, 3712, 3743, 3773, 3804, 3834, 3865, 3896, 3926, 3957, 3987, 4018, 4049, 4077, 4108, 4138, 4169, 4199, 4230, 4261, 4291, 4322, 4352, 4383, 4414, 4442, 4473, 4503, 4534, 4564, 4595, 4626, 4656, 4687, 4717, 4748, 4779, 4807, 4838, 4868, 4899, 4929, 4960, 4991, 5021, 5052, 5082, 5113, 5144, 5173, 5204, 5234, 5265, 5295, 5326, 5357, 5387, 5418, 5448, 5479, 5510, 5538, 5569, 5599, 5630, 5660, 5691, 5722, 5752, 5783, 5813, 5844, 5875, 5903, 5934, 5964, 5995, 6025, 6056, 6087, 6117, 6148, 6178, 6209, 6240, 6268, 6299, 6329, 6360, 6390, 6421, 6452, 6482, 6513, 6543, 6574, 6605, 6634, 6665, 6695, 6726, 6756, 6787, 6818, 6848, 6879, 6909, 6940, 6971, 6999, 7030, 7060, 7091, 7121, 7152, 7183, 7213, 7244, 7274), class = "Date"), temp = c(22.9223529411765, 23.0705882352941, 23.1094117647059, 20.7835294117647, 17.4517647058824, 17.3176470588235, 18.0494117647059, 19.6188235294118, 21.3023529411765, 23.1105882352941, 22.2364705882353, 22.7482352941176, 23.5870588235294, 24.0023529411765, 23.0094117647059, 22.0176470588235, 19.4917647058824, 18.1011764705882, 18.3164705882353, 20.0623529411765, 22.8717647058824, 23.2576470588235, 23.68, 22.3694117647059, 22.9517647058824, 23.6976470588235, 23.3294117647059, 20.8564705882353, 18.16, 15.8988235294118, 15.7988235294118, 18.4176470588235, 20.8423529411765, 20.3247058823529, 22.3070588235294, 22.2035294117647, 24.2235294117647, 23.6976470588235, 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24.3329411764706, 23.5811764705882, 22.9988235294118, 19.4976470588235, 17.3188235294118, 19.5635294117647, 19.0211764705882, 19.7223529411765, 22.6858823529412, 23.9423529411765, 23.6905882352941, 25.7129411764706, 23.9505882352941, 24.4376470588235, 22.6070588235294, 19.8882352941176, 17.2058823529412, 16.4211764705882, 20.02, 21.9458823529412, 21.9341176470588, 22.74, 23.8, 23.9611764705882, 24.4564705882353, 24, 23.2129411764706, 19.4729411764706, 17.7105882352941, 16.9682352941176, 19.0341176470588, 20.2917647058824, 20.7776470588235, 22.9364705882353, 22.7894117647059)), .Names = c("date", "temp"), row.names = c(NA, -360L), class = "data.frame") 解决方案 One option would be to use data.table with year grouping by cut or findInterval. For the first case, ie. getting mean of each month aggregating over all the years, we convert the 'date' to Date class and extract the months, use it as grouping variable and get the mean of 'temp'. Note that we convert the 'data.frame' to 'data.table' first (setDT(df)).library(data.table)setDT(df)[, list(Temp=mean(temp)) , by = .(Months= months(as.Date(date), abbr=TRUE))]# Months Temp# 1: Jan 23.90506# 2: Feb 24.40012# 3: Mar 23.73714# 4: Apr 21.68584# 5: May 19.53863# 6: Jun 17.90322# 7: Jul 17.97675# 8: Aug 19.56051# 9: Sep 20.90125#10: Oct 21.96886#11: Nov 22.86102#12: Dec 22.92537For periodwise and monthly grouping, we need to create a period column. One way would be either cut or findInterval. For example, if we are looking for a 5 year window, ie. 1960-1964, 1965-1969, etc., we create the 'Period' column by creating the vec in findInterval using seq, change the numeric index derived from findInterval to 'lbl' created from paste. Use the 'Month' and 'Period' as grouping variable and the rest is same as before.setDT(df)[, c('Month', 'Period') := {tmp <- as.Date(date) tmp1 <- as.numeric(format(tmp, '%Y')) tmp2 <- months(tmp, abbr=TRUE) i1 <- seq(min(tmp1), max(tmp1)+4, by=5) i2 <- i1+4 lbl <-paste(i1, i2, sep='-') list(tmp2, lbl[findInterval(tmp1, i1)]) }]df[, list(Temp= mean(temp)), .(Month, Period)]# Month Period Temp# 1: Jan 1960-1964 23.45718# 2: Feb 1960-1964 23.62400# 3: Mar 1960-1964 23.51200# 4: Apr 1960-1964 21.45365# 5: May 1960-1964 18.35788# 6: Jun 1960-1964 16.80729# 7: Jul 1960-1964 17.25106# 8: Aug 1960-1964 19.42329# 9: Sep 1960-1964 21.80471#10: Oct 1960-1964 22.05247#11: Nov 1960-1964 22.61035#12: Dec 1960-1964 22.32094#13: Jan 1965-1969 23.64447#14: Feb 1965-1969 24.25082#15: Mar 1965-1969 23.24659#16: Apr 1965-1969 21.23506#17: May 1965-1969 19.24706#18: Jun 1965-1969 18.32235#19: Jul 1965-1969 17.98282#20: Aug 1965-1969 19.22376#21: Sep 1965-1969 21.19247#22: Oct 1965-1969 21.98682#23: Nov 1965-1969 22.96776#24: Dec 1965-1969 22.72612#25: Jan 1970-1974 24.12165#26: Feb 1970-1974 24.50659#27: Mar 1970-1974 23.87412#28: Apr 1970-1974 21.71153#29: May 1970-1974 19.75600#30: Jun 1970-1974 18.83976#31: Jul 1970-1974 18.05388#32: Aug 1970-1974 19.20518#33: Sep 1970-1974 20.59788#34: Oct 1970-1974 21.41859#35: Nov 1970-1974 22.03859#36: Dec 1970-1974 23.15953#37: Jan 1975-1979 23.71882#38: Feb 1975-1979 24.49788#39: Mar 1975-1979 23.93600#40: Apr 1975-1979 21.02565#41: May 1975-1979 19.21318#42: Jun 1975-1979 17.64424#43: Jul 1975-1979 18.00000#44: Aug 1975-1979 20.32659#45: Sep 1975-1979 20.71200#46: Oct 1975-1979 22.06894#47: Nov 1975-1979 22.42565#48: Dec 1975-1979 22.97224#49: Jan 1980-1984 23.91882#50: Feb 1980-1984 25.03812#51: Mar 1980-1984 23.81835#52: Apr 1980-1984 21.69365#53: May 1980-1984 20.62071#54: Jun 1980-1984 18.40965#55: Jul 1980-1984 18.88071#56: Aug 1980-1984 19.46376#57: Sep 1980-1984 20.35553#58: Oct 1980-1984 22.06565#59: Nov 1980-1984 23.48047#60: Dec 1980-1984 22.88965#61: Jan 1985-1989 24.56941#62: Feb 1985-1989 24.48329#63: Mar 1985-1989 24.03576#64: Apr 1985-1989 22.99553#65: May 1985-1989 20.03694#66: Jun 1985-1989 17.39600#67: Jul 1985-1989 17.69200#68: Aug 1985-1989 19.72047#69: Sep 1985-1989 20.74494#70: Oct 1985-1989 22.22071#71: Nov 1985-1989 23.64329#72: Dec 1985-1989 23.48376# Month Period TempIn the same way, we can get 10 year or other windows. 这篇关于按时间间隔创建月平均值的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持！