data.table 滚动平均时间戳 window
data.table rolling average timestamp window
我有以下 data.table（仅摘录）：
posix_dt sentiment score
1: 2019-11-02 08:45:06 0.0000 2
2: 2019-11-02 08:45:07 0.0000 5
3: 2019-11-02 08:45:08 0.0201 4
4: 2019-11-02 08:45:14 0.2732 7
5: 2019-11-02 08:45:25 0.0000 3
6: 2019-11-02 08:45:35 0.3182 16
7: 2019-11-02 08:45:48 0.0000 3
8: 2019-11-02 08:45:53 -0.3582 6
9: 2019-11-02 08:46:00 0.4003 6
10: 2019-11-02 08:46:00 0.0000 7
11: 2019-11-02 08:46:04 0.0000 4
12: 2019-11-02 08:46:07 0.0000 2
13: 2019-11-02 08:46:16 0.4939 0
14: 2019-11-02 08:46:19 0.0000 2
15: 2019-11-02 08:46:32 -0.5267 2
16: 2019-11-02 08:46:49 0.2960 0
17: 2019-11-02 08:47:05 0.9753 7
18: 2019-11-02 08:47:05 0.0000 9
19: 2019-11-02 08:47:07 0.0000 3
20: 2019-11-02 08:47:10 -0.2960 9
我想计算 score/sentiment 列在 2 分钟 window 内的移动平均值。正如您所看到的，每 2 分钟的数据速率没有模式（即我不能只有 window 行 n 行总是 2 分钟）。
在 Python Pandas 库 there is a function 中，它只需要一个时间间隔就可以为你做这件事。
我知道 zoo 包及其滚动平均函数，但据我所知它需要 fixed/predetermined window 大小？
作为参考，我的完整数据约为 12000 行，涵盖了大约 3 个小时。
这里有一些快速但效率很低但似乎有效的方法：
DT[, obs_back := vapply(seq_along(posix_dt), function(i) sum(as.integer(posix_dt[i] - posix_dt[seq_len(i-1)]) < 120) + 1L, integer(1))]
DT[, sentiment_2minmean := diag(as.matrix(DT[, frollmean(sentiment, obs_back)]))]
DT[, score_2minmean := diag(as.matrix(DT[, frollmean(score, obs_back)]))]
可复现示例（请下次自行提供）：
DT <- fread("
posix_dt, sentiment, score
2019-11-02 08:45:06,0.0000,2
2019-11-02 08:45:07,0.0000,5
2019-11-02 08:45:08,0.0201,4
2019-11-02 08:45:14,0.2732,7
2019-11-02 08:45:25,0.0000,3
2019-11-02 08:45:35,0.3182,16
2019-11-02 08:45:48,0.0000,3
2019-11-02 08:45:53,-0.3582,6
2019-11-02 08:46:00,0.4003,6
2019-11-02 08:46:00,0.0000,7
2019-11-02 08:46:04,0.0000,4
2019-11-02 08:46:07,0.0000,2
2019-11-02 08:46:16,0.4939,0
2019-11-02 08:46:19,0.0000,2
2019-11-02 08:46:32,-0.5267,2
2019-11-02 08:46:49,0.2960,0
2019-11-02 08:47:05,0.9753,7
2019-11-02 08:47:05,0.0000,9
2019-11-02 08:47:07,0.0000,3
2019-11-02 08:47:10,-0.2960,9")
DT[, posix_dt := as.POSIXct(posix_dt)]
data.table 中的另一个选项非等值连接：
DT[, posix_dt := as.POSIXct(posix_dt, format="%Y-%m-%d %T")]
DT[, c("start", "end") := .(posix_dt - 2*60, posix_dt)]
DT[, c("rm_sentiment", "rm_score") :=
.SD[.SD, on=.(posix_dt>=start, posix_dt<=end),
by=.EACHI, lapply(.SD, mean), .SDcols=c("sentiment", "score")][,
(1L:2L) := NULL]
]
输出：
posix_dt sentiment score start end rm_sentiment rm_score
1: 2019-11-02 08:45:06 0.0000 2 2019-11-02 08:43:06 2019-11-02 08:45:06 0.00000000 2.000000
2: 2019-11-02 08:45:07 0.0000 5 2019-11-02 08:43:07 2019-11-02 08:45:07 0.00000000 3.500000
3: 2019-11-02 08:45:08 0.0201 4 2019-11-02 08:43:08 2019-11-02 08:45:08 0.00670000 3.666667
4: 2019-11-02 08:45:14 0.2732 7 2019-11-02 08:43:14 2019-11-02 08:45:14 0.07332500 4.500000
5: 2019-11-02 08:45:25 0.0000 3 2019-11-02 08:43:25 2019-11-02 08:45:25 0.05866000 4.200000
6: 2019-11-02 08:45:35 0.3182 16 2019-11-02 08:43:35 2019-11-02 08:45:35 0.10191667 6.166667
7: 2019-11-02 08:45:48 0.0000 3 2019-11-02 08:43:48 2019-11-02 08:45:48 0.08735714 5.714286
8: 2019-11-02 08:45:53 -0.3582 6 2019-11-02 08:43:53 2019-11-02 08:45:53 0.03166250 5.750000
9: 2019-11-02 08:46:00 0.4003 6 2019-11-02 08:44:00 2019-11-02 08:46:00 0.06536000 5.900000
10: 2019-11-02 08:46:00 0.0000 7 2019-11-02 08:44:00 2019-11-02 08:46:00 0.06536000 5.900000
11: 2019-11-02 08:46:04 0.0000 4 2019-11-02 08:44:04 2019-11-02 08:46:04 0.05941818 5.727273
12: 2019-11-02 08:46:07 0.0000 2 2019-11-02 08:44:07 2019-11-02 08:46:07 0.05446667 5.416667
13: 2019-11-02 08:46:16 0.4939 0 2019-11-02 08:44:16 2019-11-02 08:46:16 0.08826923 5.000000
14: 2019-11-02 08:46:19 0.0000 2 2019-11-02 08:44:19 2019-11-02 08:46:19 0.08196429 4.785714
15: 2019-11-02 08:46:32 -0.5267 2 2019-11-02 08:44:32 2019-11-02 08:46:32 0.04138667 4.600000
16: 2019-11-02 08:46:49 0.2960 0 2019-11-02 08:44:49 2019-11-02 08:46:49 0.05730000 4.312500
17: 2019-11-02 08:47:05 0.9753 7 2019-11-02 08:45:05 2019-11-02 08:47:05 0.10511667 4.722222
18: 2019-11-02 08:47:05 0.0000 9 2019-11-02 08:45:05 2019-11-02 08:47:05 0.10511667 4.722222
19: 2019-11-02 08:47:07 0.0000 3 2019-11-02 08:45:07 2019-11-02 08:47:07 0.10511667 4.777778
20: 2019-11-02 08:47:10 -0.2960 9 2019-11-02 08:45:10 2019-11-02 08:47:10 0.09270588 5.058824
数据：
library(data.table)
DT <- fread("posix_dt,sentiment,score
2019-11-02 08:45:06, 0.0000 , 2
2019-11-02 08:45:07, 0.0000 , 5
2019-11-02 08:45:08, 0.0201 , 4
2019-11-02 08:45:14, 0.2732 , 7
2019-11-02 08:45:25, 0.0000 , 3
2019-11-02 08:45:35, 0.3182 , 16
2019-11-02 08:45:48, 0.0000 , 3
2019-11-02 08:45:53, -0.3582 , 6
2019-11-02 08:46:00, 0.4003 , 6
2019-11-02 08:46:00, 0.0000 , 7
2019-11-02 08:46:04, 0.0000 , 4
2019-11-02 08:46:07, 0.0000 , 2
2019-11-02 08:46:16, 0.4939 , 0
2019-11-02 08:46:19, 0.0000 , 2
2019-11-02 08:46:32, -0.5267 , 2
2019-11-02 08:46:49, 0.2960 , 0
2019-11-02 08:47:05, 0.9753 , 7
2019-11-02 08:47:05, 0.0000 , 9
2019-11-02 08:47:07, 0.0000 , 3
2019-11-02 08:47:10, -0.2960 ,9")
另一种使用滚动连接的方法应该更快：
#because there are duplicate of posix_dt,
#thats why there is a need to aggregate first to make posix_dt unique
twomins <- 2L * 60L
aggDT <- DT[, c(.(N=.N), lapply(.SD, sum)), .(posix_dt), .SDcols=cols]
#calculate cumulative sums for calculating means later
cols <- c("N", "sentiment", "score")
aggDT[, c("start", paste0("cs_", cols)) :=
c(.(posix_dt - twomins), lapply(.SD, cumsum)), .SDcols=cols]
#performing rolling join to find first timing that is >= time 2 minutes ago
#for current row
newcols <- c("rm_sentiment", "rm_score")
aggDT[, (newcols) := aggDT[aggDT, on=.(posix_dt=start), roll=-twomins,
.((i.cs_sentiment - x.cs_sentiment + x.sentiment) / (i.cs_N - x.cs_N + x.N),
(i.cs_score - x.cs_score + x.score) / (i.cs_N - x.cs_N + x.N))]
]
#lookup mean values into original DT using update join
DT[aggDT, on=.(posix_dt), paste0(newcols,"2") := mget(paste0("i.", newcols))]
DT
输出：
posix_dt sentiment score start end rm_sentiment rm_score rm_sentiment2 rm_score2
1: 2019-11-02 08:45:06 0.0000 2 2019-11-02 08:43:06 2019-11-02 08:45:06 0.00000000 2.000000 0.00000000 2.000000
2: 2019-11-02 08:45:07 0.0000 5 2019-11-02 08:43:07 2019-11-02 08:45:07 0.00000000 3.500000 0.00000000 3.500000
3: 2019-11-02 08:45:08 0.0201 4 2019-11-02 08:43:08 2019-11-02 08:45:08 0.00670000 3.666667 0.00670000 3.666667
4: 2019-11-02 08:45:14 0.2732 7 2019-11-02 08:43:14 2019-11-02 08:45:14 0.07332500 4.500000 0.07332500 4.500000
5: 2019-11-02 08:45:25 0.0000 3 2019-11-02 08:43:25 2019-11-02 08:45:25 0.05866000 4.200000 0.05866000 4.200000
6: 2019-11-02 08:45:35 0.3182 16 2019-11-02 08:43:35 2019-11-02 08:45:35 0.10191667 6.166667 0.10191667 6.166667
7: 2019-11-02 08:45:48 0.0000 3 2019-11-02 08:43:48 2019-11-02 08:45:48 0.08735714 5.714286 0.08735714 5.714286
8: 2019-11-02 08:45:53 -0.3582 6 2019-11-02 08:43:53 2019-11-02 08:45:53 0.03166250 5.750000 0.03166250 5.750000
9: 2019-11-02 08:46:00 0.4003 6 2019-11-02 08:44:00 2019-11-02 08:46:00 0.06536000 5.900000 0.06536000 5.900000
10: 2019-11-02 08:46:00 0.0000 7 2019-11-02 08:44:00 2019-11-02 08:46:00 0.06536000 5.900000 0.06536000 5.900000
11: 2019-11-02 08:46:04 0.0000 4 2019-11-02 08:44:04 2019-11-02 08:46:04 0.05941818 5.727273 0.05941818 5.727273
12: 2019-11-02 08:46:07 0.0000 2 2019-11-02 08:44:07 2019-11-02 08:46:07 0.05446667 5.416667 0.05446667 5.416667
13: 2019-11-02 08:46:16 0.4939 0 2019-11-02 08:44:16 2019-11-02 08:46:16 0.08826923 5.000000 0.08826923 5.000000
14: 2019-11-02 08:46:19 0.0000 2 2019-11-02 08:44:19 2019-11-02 08:46:19 0.08196429 4.785714 0.08196429 4.785714
15: 2019-11-02 08:46:32 -0.5267 2 2019-11-02 08:44:32 2019-11-02 08:46:32 0.04138667 4.600000 0.04138667 4.600000
16: 2019-11-02 08:46:49 0.2960 0 2019-11-02 08:44:49 2019-11-02 08:46:49 0.05730000 4.312500 0.05730000 4.312500
17: 2019-11-02 08:47:05 0.9753 7 2019-11-02 08:45:05 2019-11-02 08:47:05 0.10511667 4.722222 0.10511667 4.722222
18: 2019-11-02 08:47:05 0.0000 9 2019-11-02 08:45:05 2019-11-02 08:47:05 0.10511667 4.722222 0.10511667 4.722222
19: 2019-11-02 08:47:07 0.0000 3 2019-11-02 08:45:07 2019-11-02 08:47:07 0.10511667 4.777778 0.10511667 4.777778
20: 2019-11-02 08:47:10 -0.2960 9 2019-11-02 08:45:10 2019-11-02 08:47:10 0.09270588 5.058824 0.09270588 5.058824
我有以下 data.table（仅摘录）：
posix_dt sentiment score
1: 2019-11-02 08:45:06 0.0000 2
2: 2019-11-02 08:45:07 0.0000 5
3: 2019-11-02 08:45:08 0.0201 4
4: 2019-11-02 08:45:14 0.2732 7
5: 2019-11-02 08:45:25 0.0000 3
6: 2019-11-02 08:45:35 0.3182 16
7: 2019-11-02 08:45:48 0.0000 3
8: 2019-11-02 08:45:53 -0.3582 6
9: 2019-11-02 08:46:00 0.4003 6
10: 2019-11-02 08:46:00 0.0000 7
11: 2019-11-02 08:46:04 0.0000 4
12: 2019-11-02 08:46:07 0.0000 2
13: 2019-11-02 08:46:16 0.4939 0
14: 2019-11-02 08:46:19 0.0000 2
15: 2019-11-02 08:46:32 -0.5267 2
16: 2019-11-02 08:46:49 0.2960 0
17: 2019-11-02 08:47:05 0.9753 7
18: 2019-11-02 08:47:05 0.0000 9
19: 2019-11-02 08:47:07 0.0000 3
20: 2019-11-02 08:47:10 -0.2960 9
我想计算 score/sentiment 列在 2 分钟 window 内的移动平均值。正如您所看到的，每 2 分钟的数据速率没有模式（即我不能只有 window 行 n 行总是 2 分钟）。
在 Python Pandas 库 there is a function 中，它只需要一个时间间隔就可以为你做这件事。
我知道 zoo 包及其滚动平均函数，但据我所知它需要 fixed/predetermined window 大小？
作为参考，我的完整数据约为 12000 行，涵盖了大约 3 个小时。
这里有一些快速但效率很低但似乎有效的方法：
DT[, obs_back := vapply(seq_along(posix_dt), function(i) sum(as.integer(posix_dt[i] - posix_dt[seq_len(i-1)]) < 120) + 1L, integer(1))]
DT[, sentiment_2minmean := diag(as.matrix(DT[, frollmean(sentiment, obs_back)]))]
DT[, score_2minmean := diag(as.matrix(DT[, frollmean(score, obs_back)]))]
可复现示例（请下次自行提供）：
DT <- fread("
posix_dt, sentiment, score
2019-11-02 08:45:06,0.0000,2
2019-11-02 08:45:07,0.0000,5
2019-11-02 08:45:08,0.0201,4
2019-11-02 08:45:14,0.2732,7
2019-11-02 08:45:25,0.0000,3
2019-11-02 08:45:35,0.3182,16
2019-11-02 08:45:48,0.0000,3
2019-11-02 08:45:53,-0.3582,6
2019-11-02 08:46:00,0.4003,6
2019-11-02 08:46:00,0.0000,7
2019-11-02 08:46:04,0.0000,4
2019-11-02 08:46:07,0.0000,2
2019-11-02 08:46:16,0.4939,0
2019-11-02 08:46:19,0.0000,2
2019-11-02 08:46:32,-0.5267,2
2019-11-02 08:46:49,0.2960,0
2019-11-02 08:47:05,0.9753,7
2019-11-02 08:47:05,0.0000,9
2019-11-02 08:47:07,0.0000,3
2019-11-02 08:47:10,-0.2960,9")
DT[, posix_dt := as.POSIXct(posix_dt)]
data.table 中的另一个选项非等值连接：
DT[, posix_dt := as.POSIXct(posix_dt, format="%Y-%m-%d %T")]
DT[, c("start", "end") := .(posix_dt - 2*60, posix_dt)]
DT[, c("rm_sentiment", "rm_score") :=
.SD[.SD, on=.(posix_dt>=start, posix_dt<=end),
by=.EACHI, lapply(.SD, mean), .SDcols=c("sentiment", "score")][,
(1L:2L) := NULL]
]
输出：
posix_dt sentiment score start end rm_sentiment rm_score
1: 2019-11-02 08:45:06 0.0000 2 2019-11-02 08:43:06 2019-11-02 08:45:06 0.00000000 2.000000
2: 2019-11-02 08:45:07 0.0000 5 2019-11-02 08:43:07 2019-11-02 08:45:07 0.00000000 3.500000
3: 2019-11-02 08:45:08 0.0201 4 2019-11-02 08:43:08 2019-11-02 08:45:08 0.00670000 3.666667
4: 2019-11-02 08:45:14 0.2732 7 2019-11-02 08:43:14 2019-11-02 08:45:14 0.07332500 4.500000
5: 2019-11-02 08:45:25 0.0000 3 2019-11-02 08:43:25 2019-11-02 08:45:25 0.05866000 4.200000
6: 2019-11-02 08:45:35 0.3182 16 2019-11-02 08:43:35 2019-11-02 08:45:35 0.10191667 6.166667
7: 2019-11-02 08:45:48 0.0000 3 2019-11-02 08:43:48 2019-11-02 08:45:48 0.08735714 5.714286
8: 2019-11-02 08:45:53 -0.3582 6 2019-11-02 08:43:53 2019-11-02 08:45:53 0.03166250 5.750000
9: 2019-11-02 08:46:00 0.4003 6 2019-11-02 08:44:00 2019-11-02 08:46:00 0.06536000 5.900000
10: 2019-11-02 08:46:00 0.0000 7 2019-11-02 08:44:00 2019-11-02 08:46:00 0.06536000 5.900000
11: 2019-11-02 08:46:04 0.0000 4 2019-11-02 08:44:04 2019-11-02 08:46:04 0.05941818 5.727273
12: 2019-11-02 08:46:07 0.0000 2 2019-11-02 08:44:07 2019-11-02 08:46:07 0.05446667 5.416667
13: 2019-11-02 08:46:16 0.4939 0 2019-11-02 08:44:16 2019-11-02 08:46:16 0.08826923 5.000000
14: 2019-11-02 08:46:19 0.0000 2 2019-11-02 08:44:19 2019-11-02 08:46:19 0.08196429 4.785714
15: 2019-11-02 08:46:32 -0.5267 2 2019-11-02 08:44:32 2019-11-02 08:46:32 0.04138667 4.600000
16: 2019-11-02 08:46:49 0.2960 0 2019-11-02 08:44:49 2019-11-02 08:46:49 0.05730000 4.312500
17: 2019-11-02 08:47:05 0.9753 7 2019-11-02 08:45:05 2019-11-02 08:47:05 0.10511667 4.722222
18: 2019-11-02 08:47:05 0.0000 9 2019-11-02 08:45:05 2019-11-02 08:47:05 0.10511667 4.722222
19: 2019-11-02 08:47:07 0.0000 3 2019-11-02 08:45:07 2019-11-02 08:47:07 0.10511667 4.777778
20: 2019-11-02 08:47:10 -0.2960 9 2019-11-02 08:45:10 2019-11-02 08:47:10 0.09270588 5.058824
数据：
library(data.table)
DT <- fread("posix_dt,sentiment,score
2019-11-02 08:45:06, 0.0000 , 2
2019-11-02 08:45:07, 0.0000 , 5
2019-11-02 08:45:08, 0.0201 , 4
2019-11-02 08:45:14, 0.2732 , 7
2019-11-02 08:45:25, 0.0000 , 3
2019-11-02 08:45:35, 0.3182 , 16
2019-11-02 08:45:48, 0.0000 , 3
2019-11-02 08:45:53, -0.3582 , 6
2019-11-02 08:46:00, 0.4003 , 6
2019-11-02 08:46:00, 0.0000 , 7
2019-11-02 08:46:04, 0.0000 , 4
2019-11-02 08:46:07, 0.0000 , 2
2019-11-02 08:46:16, 0.4939 , 0
2019-11-02 08:46:19, 0.0000 , 2
2019-11-02 08:46:32, -0.5267 , 2
2019-11-02 08:46:49, 0.2960 , 0
2019-11-02 08:47:05, 0.9753 , 7
2019-11-02 08:47:05, 0.0000 , 9
2019-11-02 08:47:07, 0.0000 , 3
2019-11-02 08:47:10, -0.2960 ,9")
另一种使用滚动连接的方法应该更快：
#because there are duplicate of posix_dt,
#thats why there is a need to aggregate first to make posix_dt unique
twomins <- 2L * 60L
aggDT <- DT[, c(.(N=.N), lapply(.SD, sum)), .(posix_dt), .SDcols=cols]
#calculate cumulative sums for calculating means later
cols <- c("N", "sentiment", "score")
aggDT[, c("start", paste0("cs_", cols)) :=
c(.(posix_dt - twomins), lapply(.SD, cumsum)), .SDcols=cols]
#performing rolling join to find first timing that is >= time 2 minutes ago
#for current row
newcols <- c("rm_sentiment", "rm_score")
aggDT[, (newcols) := aggDT[aggDT, on=.(posix_dt=start), roll=-twomins,
.((i.cs_sentiment - x.cs_sentiment + x.sentiment) / (i.cs_N - x.cs_N + x.N),
(i.cs_score - x.cs_score + x.score) / (i.cs_N - x.cs_N + x.N))]
]
#lookup mean values into original DT using update join
DT[aggDT, on=.(posix_dt), paste0(newcols,"2") := mget(paste0("i.", newcols))]
DT
输出：
posix_dt sentiment score start end rm_sentiment rm_score rm_sentiment2 rm_score2
1: 2019-11-02 08:45:06 0.0000 2 2019-11-02 08:43:06 2019-11-02 08:45:06 0.00000000 2.000000 0.00000000 2.000000
2: 2019-11-02 08:45:07 0.0000 5 2019-11-02 08:43:07 2019-11-02 08:45:07 0.00000000 3.500000 0.00000000 3.500000
3: 2019-11-02 08:45:08 0.0201 4 2019-11-02 08:43:08 2019-11-02 08:45:08 0.00670000 3.666667 0.00670000 3.666667
4: 2019-11-02 08:45:14 0.2732 7 2019-11-02 08:43:14 2019-11-02 08:45:14 0.07332500 4.500000 0.07332500 4.500000
5: 2019-11-02 08:45:25 0.0000 3 2019-11-02 08:43:25 2019-11-02 08:45:25 0.05866000 4.200000 0.05866000 4.200000
6: 2019-11-02 08:45:35 0.3182 16 2019-11-02 08:43:35 2019-11-02 08:45:35 0.10191667 6.166667 0.10191667 6.166667
7: 2019-11-02 08:45:48 0.0000 3 2019-11-02 08:43:48 2019-11-02 08:45:48 0.08735714 5.714286 0.08735714 5.714286
8: 2019-11-02 08:45:53 -0.3582 6 2019-11-02 08:43:53 2019-11-02 08:45:53 0.03166250 5.750000 0.03166250 5.750000
9: 2019-11-02 08:46:00 0.4003 6 2019-11-02 08:44:00 2019-11-02 08:46:00 0.06536000 5.900000 0.06536000 5.900000
10: 2019-11-02 08:46:00 0.0000 7 2019-11-02 08:44:00 2019-11-02 08:46:00 0.06536000 5.900000 0.06536000 5.900000
11: 2019-11-02 08:46:04 0.0000 4 2019-11-02 08:44:04 2019-11-02 08:46:04 0.05941818 5.727273 0.05941818 5.727273
12: 2019-11-02 08:46:07 0.0000 2 2019-11-02 08:44:07 2019-11-02 08:46:07 0.05446667 5.416667 0.05446667 5.416667
13: 2019-11-02 08:46:16 0.4939 0 2019-11-02 08:44:16 2019-11-02 08:46:16 0.08826923 5.000000 0.08826923 5.000000
14: 2019-11-02 08:46:19 0.0000 2 2019-11-02 08:44:19 2019-11-02 08:46:19 0.08196429 4.785714 0.08196429 4.785714
15: 2019-11-02 08:46:32 -0.5267 2 2019-11-02 08:44:32 2019-11-02 08:46:32 0.04138667 4.600000 0.04138667 4.600000
16: 2019-11-02 08:46:49 0.2960 0 2019-11-02 08:44:49 2019-11-02 08:46:49 0.05730000 4.312500 0.05730000 4.312500
17: 2019-11-02 08:47:05 0.9753 7 2019-11-02 08:45:05 2019-11-02 08:47:05 0.10511667 4.722222 0.10511667 4.722222
18: 2019-11-02 08:47:05 0.0000 9 2019-11-02 08:45:05 2019-11-02 08:47:05 0.10511667 4.722222 0.10511667 4.722222
19: 2019-11-02 08:47:07 0.0000 3 2019-11-02 08:45:07 2019-11-02 08:47:07 0.10511667 4.777778 0.10511667 4.777778
20: 2019-11-02 08:47:10 -0.2960 9 2019-11-02 08:45:10 2019-11-02 08:47:10 0.09270588 5.058824 0.09270588 5.058824