I have some noise survey data telling me noise levels measured over the period of several days. I want to find the 5th highest noise level in each night-time period. I have made this into a Pandas Series and used groupby and nlargest methods to show me the 5 highest noise levels each night, but now I want to view only the 5th highest value for each period (i.e. 82, 86, 86, 87 etc.). What's the best way to achieve this?
night_time_lmax.groupby(by=night_time_lmax.index.date).nlargest(5)
Start date & time
2021-08-18 2021-08-18 23:00:00 82.0
2021-08-18 23:15:00 82.0
2021-08-18 23:30:00 82.0
2021-08-18 23:45:00 82.0
2021-08-19 2021-08-19 05:45:00 100.0
2021-08-19 01:15:00 91.0
2021-08-19 04:45:00 87.0
2021-08-19 06:15:00 87.0
2021-08-19 01:45:00 86.0
2021-08-20 2021-08-20 06:30:00 90.0
2021-08-20 06:00:00 88.0
2021-08-20 03:15:00 87.0
2021-08-20 05:30:00 87.0
2021-08-20 01:15:00 86.0
2021-08-21 2021-08-21 01:30:00 98.0
2021-08-21 03:00:00 93.0
2021-08-21 00:45:00 88.0
2021-08-21 06:00:00 88.0
2021-08-21 03:30:00 87.0
2021-08-22 2021-08-22 23:45:00 102.0
2021-08-22 00:30:00 96.0
2021-08-22 06:30:00 92.0
2021-08-22 05:00:00 91.0
2021-08-22 01:30:00 90.0
2021-08-23 2021-08-23 01:15:00 98.0
2021-08-23 02:15:00 88.0
2021-08-23 00:45:00 87.0
2021-08-23 03:00:00 86.0
2021-08-23 06:00:00 86.0
2021-08-24 2021-08-24 01:00:00 93.0
2021-08-24 00:30:00 89.0
2021-08-24 06:30:00 87.0
2021-08-24 02:45:00 86.0
2021-08-24 06:00:00 86.0```
I see two options here.
Either sort your data by your value and then take the nth element per group:
(night_time_lmax.sort_values(by='value_column', ascending=False)
.groupby(by=night_time_lmax.index.date).nth(5)
)
## below gives the same result for shorter syntax:
# (night_time_lmax.sort_values(by='value_column')
# .groupby(by=night_time_lmax.index.date).nth(-5)
# )
Or use a double groupby, once for the top 5 and once for the last:
(night_time_lmax.groupby(by=night_time_lmax.index.date).nlargest(5)
.groupby(by=night_time_lmax.index.date).last()
)
I want this output which is in dict to be converted into a pandas DataFrame with few columns of interest. Note my output is actually more but I have posted only part of the output but I hope you can understand what I actually want
My dataframe should have columns `['Date','Change in OI','Open interest']. preferably Date should be index.
strikes=[690,700,710]
data={}
for s in strikes:
data[s]=get_history(symbol="CIPLA",
start=date(2020,7,1),
end=date(2201,7,17),
option_type="CE",
strike_price=s,
expiry_date=date(2020,7,30))
OUTPUT
{690: Symbol Expiry Option Type Strike Price Open High Low \
Date
2020-07-01 CIPLA 2020-07-30 CE 690.0 11.50 11.50 7.70
2020-07-02 CIPLA 2020-07-30 CE 690.0 8.90 20.90 8.50
2020-07-03 CIPLA 2020-07-30 CE 690.0 17.75 17.75 12.00
2020-07-06 CIPLA 2020-07-30 CE 690.0 11.30 11.30 9.60
2020-07-07 CIPLA 2020-07-30 CE 690.0 10.70 12.25 10.60
2020-07-08 CIPLA 2020-07-30 CE 690.0 12.95 14.10 11.45
2020-07-09 CIPLA 2020-07-30 CE 690.0 14.00 14.00 11.60
2020-07-10 CIPLA 2020-07-30 CE 690.0 12.50 13.00 10.95
2020-07-13 CIPLA 2020-07-30 CE 690.0 11.10 11.65 9.65
2020-07-14 CIPLA 2020-07-30 CE 690.0 10.65 10.70 8.40
2020-07-15 CIPLA 2020-07-30 CE 690.0 7.55 10.00 7.55
2020-07-16 CIPLA 2020-07-30 CE 690.0 11.20 18.65 7.25
2020-07-17 CIPLA 2020-07-30 CE 690.0 18.85 25.75 14.80
Close Last Settle Price Number of Contracts Turnover \
Date
2020-07-01 8.85 8.85 8.85 66 5.995900e+07
2020-07-02 16.85 20.50 16.85 68 6.235500e+07
2020-07-03 13.00 13.25 13.00 117 1.069840e+08
2020-07-06 10.65 10.75 10.65 76 6.918800e+07
2020-07-07 11.00 11.00 11.00 64 5.836300e+07
2020-07-08 11.95 11.95 11.95 84 7.674300e+07
2020-07-09 12.00 12.00 12.00 25 2.284000e+07
2020-07-10 11.10 11.10 11.10 50 4.564100e+07
2020-07-13 10.05 10.05 10.05 36 3.278000e+07
2020-07-14 8.50 8.40 8.50 39 3.546700e+07
2020-07-15 8.45 8.40 8.45 31 2.816200e+07
2020-07-16 17.20 16.80 17.20 803 7.350000e+08
2020-07-17 20.05 19.30 20.05 1708 1.577693e+09
Premium Turnover Open Interest Change in OI Underlying
Date
2020-07-01 757000.0 119600 5200 NaN
2020-07-02 1359000.0 113100 -6500 646.20
2020-07-03 2035000.0 131300 18200 638.80
2020-07-06 1016000.0 123500 -7800 NaN
2020-07-07 955000.0 128700 5200 636.55
2020-07-08 1395000.0 130000 1300 NaN
2020-07-09 415000.0 130000 0 NaN
2020-07-10 791000.0 130000 0 NaN
2020-07-13 488000.0 123500 -6500 NaN
2020-07-14 484000.0 115700 -7800 NaN
2020-07-15 355000.0 124800 9100 NaN
2020-07-16 14709000.0 302900 178100 NaN
2020-07-17 45617000.0 243100 -59800 689.10 }
the same goes on for the values in strikes=[700,710] in output
Already tried using pd.DataFrame.from_dict(data) no use
You can try the following approaches
1.
import pandas as pd
df = pd.DatFrame(<<your dictionary>>) # you can pass the dictionary
Alternatively you can also use the following
import pandas as pd
cols = [<<list of column names>>] # to specify different column names
df = pd.DataFrame.from_dict(<<dictionary name>>,columns=cols)
use your dictionary in this way to convert it into a dataframe
import pandas as pd
df = pd.DataFrame({"your dictionary here"})
I have the following Pandas DataFrame object df, which denotes incidents that occurred between 2000-07-01 to 2018-03-31. Each row represents an incident that occurred on that particular date. FID_1 is the index column and can be used to uniquely identify each row of incident. The ICC_NAME column contains 33 unique values for where it occurred.
comb_date ICC_NAME
FID_1
267 2000-09-18 09:49:00 Alexandra
462 2000-10-19 01:00:00 Alexandra
696 2000-11-26 15:08:00 Alexandra
734 2000-11-27 19:20:00 Alexandra
760 2000-11-28 20:00:00 Alexandra
761 2000-11-28 20:30:00 Alexandra
945 2000-05-12 12:37:00 Alexandra
1242 2000-12-12 14:35:00 Alexandra
1440 2000-12-16 06:45:00 Alexandra
1523 2000-12-17 12:55:00 Alexandra
1701 2000-12-19 18:40:00 Alexandra
1899 2000-12-26 11:42:00 Alexandra
1963 2000-12-29 09:43:00 Alexandra
1975 2000-12-29 15:54:00 Alexandra
2004 2000-12-30 13:26:00 Alexandra
2044 2000-12-31 13:18:00 Alexandra
2100 2001-01-01 00:06:00 Alexandra
2202 2001-02-01 13:34:00 Alexandra
2826 2001-11-01 13:32:00 Alexandra
2991 2001-01-15 10:55:00 Alexandra
3175 2001-01-20 11:18:00 Alexandra
3176 2001-01-20 11:35:00 Alexandra
3212 2001-01-20 22:55:00 Alexandra
3371 2001-01-26 14:25:00 Alexandra
3386 2001-01-26 19:05:00 Alexandra
3395 2001-01-27 13:20:00 Alexandra
3432 2001-01-28 18:03:00 Alexandra
3701 2001-06-02 18:29:00 Alexandra
3881 2001-02-14 10:00:00 Alexandra
4131 2001-02-21 17:48:00 Alexandra
... ... ...
... ... ...
... ... Boort
... ... Boort
... ... ...
... ... ...
96968 2018-01-25 17:27:00 Woori Yallock
96983 2018-01-25 19:04:00 Woori Yallock
96995 2018-01-26 00:03:00 Woori Yallock
97002 2018-01-26 09:39:00 Woori Yallock
97105 2018-01-28 11:12:00 Woori Yallock
97143 2018-01-29 14:42:00 Woori Yallock
97144 2018-01-29 15:00:00 Woori Yallock
97160 2018-01-30 21:54:00 Woori Yallock
97249 2018-06-02 22:40:00 Woori Yallock
97314 2018-11-02 12:38:00 Woori Yallock
97361 2018-02-13 16:49:00 Woori Yallock
97362 2018-02-13 16:55:00 Woori Yallock
97368 2018-02-14 05:48:00 Woori Yallock
97446 2018-02-18 11:17:00 Woori Yallock
97475 2018-02-19 18:52:00 Woori Yallock
97485 2018-02-20 15:42:00 Woori Yallock
97496 2018-02-20 22:19:00 Woori Yallock
97514 2018-02-22 14:47:00 Woori Yallock
97563 2018-02-25 20:37:00 Woori Yallock
97641 2018-02-28 17:19:00 Woori Yallock
97642 2018-02-28 17:45:00 Woori Yallock
97769 2018-07-03 07:35:00 Woori Yallock
97786 2018-07-03 22:05:00 Woori Yallock
97902 2018-11-03 16:20:00 Woori Yallock
97938 2018-12-03 14:33:00 Woori Yallock
97939 2018-12-03 14:35:00 Woori Yallock
97946 2018-12-03 20:23:00 Woori Yallock
98046 2018-03-17 18:24:00 Woori Yallock
98090 2018-03-18 11:06:00 Woori Yallock
98207 2018-03-22 19:58:00 Woori Yallock
[98372 rows x 2 columns]
What I want to achieve is to get sum of incidents per YYYY-MM and for each ICC_NAME.
yyyy-mm Alexandra Boort ... Woori Yallock
2000-07 29 12 ... 8
2000-08 20 16 ... 13
... ...
... ...
2018-03 41 8 ... 28
I was thinking of using resample but not sure on which column the sum() should be applied.
Use crosstab with convert datetimes to month periods by Series.dt.to_period, last change index, columns names by DataFrame.rename_axis and convert PeriodIndex to column by DataFrame.reset_index:
df['comb_date'] = pd.to_datetime(df['comb_date'])
df1 = (pd.crosstab(df['comb_date'].dt.to_period('m'), df['ICC_NAME'])
.rename_axis(columns=None, index='yyy-mm')
.reset_index())
print (df1)
yyy-mm Alexandra Woori Yallock
0 2000-05 1 0
1 2000-09 1 0
2 2000-10 1 0
3 2000-11 4 0
4 2000-12 9 0
5 2001-01 9 0
6 2001-02 3 0
7 2001-06 1 0
8 2001-11 1 0
9 2018-01 0 8
10 2018-02 0 11
11 2018-03 0 3
12 2018-06 0 1
13 2018-07 0 2
14 2018-11 0 2
15 2018-12 0 3
I'm new in python and my English are not so good so i ll try to explain my problem with the example below.
In :ds # is my dataframe
Out :DateStarted DateCompleted DayStarted DayCompleted \
1460 2017-06-12 14:03:32 2017-06-12 14:04:07 2017-06-12 2017-06-12
14445 2017-06-13 13:39:16 2017-06-13 13:40:32 2017-06-13 2017-06-13
14109 2017-06-21 10:25:36 2017-06-21 10:32:17 2017-06-21 2017-06-21
16652 2017-06-27 15:44:28 2017-06-27 15:44:41 2017-06-27 2017-06-27
30062 2017-07-05 09:49:01 2017-07-05 10:04:00 2017-07-05 2017-07-05
22357 2017-08-31 09:06:00 2017-08-31 09:10:31 2017-08-31 2017-08-31
39117 2017-09-08 08:43:07 2017-09-08 08:44:51 2017-09-08 2017-09-08
41903 2017-09-15 12:54:40 2017-09-15 14:00:06 2017-09-15 2017-09-15
74633 2017-09-27 12:41:09 2017-09-27 13:16:04 2017-09-27 2017-09-27
69315 2017-10-23 08:25:28 2017-10-23 08:26:09 2017-10-23 2017-10-23
87508 2017-10-30 12:19:19 2017-10-30 12:19:45 2017-10-30 2017-10-30
86828 2017-11-03 12:20:09 2017-11-03 12:24:56 2017-11-03 2017-11-03
89877 2017-11-06 13:52:05 2017-11-06 13:52:50 2017-11-06 2017-11-06
94970 2017-11-07 08:09:53 2017-11-07 08:10:15 2017-11-07 2017-11-07
94866 2017-11-28 14:38:14 2017-11-30 07:51:04 2017-11-28 2017-11-30
DailyTotalActiveTime diff
1460 NaN 35.0
14445 NaN 76.0
14109 NaN 401.0
16652 NaN 13.0
30062 NaN 899.0
22357 NaN 271.0
39117 NaN 104.0
41903 NaN 3926.0
74633 NaN 2095.0
69315 NaN 41.0
87508 NaN 26.0
86828 NaN 287.0
89877 NaN 45.0
94970 NaN 22.0
94866 NaN 148370.0
In the DailyTotalActiveTime column, i want to calculate how much time,
the specific days, will have in total. The diff column is in seconds.
I tried this, but i had no results:
for i in ds['diff']:
if i <= 86400:
ds['DailyTotalActiveTime']==i
else:
ds['DailyTotalActiveTime']==86400
ds['DailyTotalActiveTime']+1 == i-86400
What can i do? Again, sorry for the explanation..
You should try with = instead of ==
To get you halfway there, you could do something like the following (I am sure there must an a more simple way but I can't see it right now):
df['datestarted'] = pd.to_datetime(df['datestarted'])
df['datecompleted'] = pd.to_datetime(df['datecompleted'])
df['daystarted'] = df['datestarted'].dt.date
df['daycompleted'] = df['datecompleted'].dt.date
df['Date'] = df['daystarted'] # This is the unqiue date per row.
for row in df.itertuples():
if (row.daycompleted - row.daystarted) > pd.Timedelta(days=0):
for i in range(1, (row.daycompleted - row.daystarted).days+1):
df2 = pd.DataFrame([row]).drop('Index', axis=1)
df2['Date'] = df2['Date'] + pd.Timedelta(days=i)
df = df.append(df2)
Similarly to this question, I'm interested in creating time series spirals. The solution doesn't necessarily have to be implemented in R or using ggplot, but it seems the majority of solutions have been implemented in R with ggplot, with a handful in Python and one in d3. My attempts so far have all used R. Unlike this question, I'm interested in displaying specific ranges of data without quantizing/binning the data. That is, I'd like to display a spiral timeline showing when particular events start and stop, where theta-min and theta-max of every event represent specific points in time.
Consider this travel data:
trip_start trip_stop dist
2017-04-01 17:42:00 2017-04-01 18:34:00 1.95
2017-04-01 18:42:00 2017-04-01 19:05:00 6.54
2017-04-02 01:09:00 2017-04-02 01:12:00 1.07
2017-04-02 01:22:00 2017-04-02 01:27:00 1.03
2017-04-02 08:17:00 2017-04-02 08:23:00 1.98
2017-04-02 11:23:00 2017-04-02 11:30:00 1.98
2017-04-02 15:44:00 2017-04-02 15:56:00 4.15
2017-04-02 16:29:00 2017-04-02 16:45:00 4.08
2017-04-03 10:24:00 2017-04-03 10:55:00 19.76
2017-04-03 14:01:00 2017-04-03 14:18:00 8.21
2017-04-03 14:25:00 2017-04-03 14:31:00 1.49
2017-04-03 14:45:00 2017-04-03 14:50:00 1.59
2017-04-03 15:44:00 2017-04-03 16:10:00 4.44
2017-04-03 16:14:00 2017-04-03 16:37:00 9.96
2017-04-03 16:40:00 2017-04-03 16:45:00 0.7
2017-04-03 17:15:00 2017-04-03 17:46:00 16.92
2017-04-03 17:56:00 2017-04-03 18:19:00 5.23
2017-04-03 18:42:00 2017-04-03 18:45:00 0.49
2017-04-03 19:02:00 2017-04-03 19:04:00 0.48
2017-04-04 07:24:00 2017-04-04 07:27:00 0.66
2017-04-04 07:30:00 2017-04-04 08:04:00 13.55
2017-04-04 08:32:00 2017-04-04 09:25:00 25.09
2017-04-04 13:32:00 2017-04-04 13:40:00 3.06
2017-04-04 13:52:00 2017-04-04 13:57:00 1.3
2017-04-04 14:55:00 2017-04-04 15:01:00 2.47
2017-04-04 18:40:00 2017-04-04 19:12:00 22.71
2017-04-04 22:16:00 2017-04-04 23:54:00 38.28
2017-04-04 23:59:00 2017-04-05 00:03:00 1.02
2017-04-05 11:04:00 2017-04-05 11:49:00 25.73
2017-04-05 12:05:00 2017-04-05 12:18:00 2.97
2017-04-05 15:19:00 2017-04-05 16:25:00 25.13
2017-04-05 16:38:00 2017-04-05 16:40:00 0.41
2017-04-05 18:58:00 2017-04-05 19:02:00 1.25
2017-04-05 19:13:00 2017-04-05 19:18:00 1.09
2017-04-05 19:25:00 2017-04-05 19:48:00 6.63
2017-04-06 10:01:00 2017-04-06 10:44:00 20.81
2017-04-06 13:22:00 2017-04-06 13:33:00 1.63
2017-04-06 20:58:00 2017-04-06 21:25:00 24.85
2017-04-06 21:32:00 2017-04-06 21:56:00 6.06
2017-04-07 10:55:00 2017-04-07 11:37:00 24.53
2017-04-07 17:14:00 2017-04-07 17:48:00 19.66
2017-04-07 17:57:00 2017-04-07 18:07:00 2.12
2017-04-08 20:57:00 2017-04-08 21:06:00 1.06
2017-04-08 21:23:00 2017-04-08 21:36:00 2.97
2017-04-09 08:14:00 2017-04-09 08:19:00 1.99
2017-04-09 11:40:00 2017-04-09 11:50:00 2.24
2017-04-09 11:50:00 2017-04-09 11:57:00 1.64
2017-04-09 16:29:00 2017-04-09 16:34:00 0.53
2017-04-09 16:43:00 2017-04-09 16:45:00 0.5
2017-04-09 17:46:00 2017-04-09 17:48:00 0.44
2017-04-09 17:53:00 2017-04-09 17:56:00 0.4
2017-04-09 21:33:00 2017-04-09 21:56:00 2.48
2017-04-09 21:57:00 2017-04-09 22:14:00 2.92
2017-04-09 22:22:00 2017-04-09 22:25:00 0.9
2017-04-10 10:37:00 2017-04-10 11:22:00 19.27
2017-04-10 16:12:00 2017-04-10 16:59:00 21.31
2017-04-11 11:14:00 2017-04-11 11:18:00 1.24
2017-04-11 11:21:00 2017-04-11 11:48:00 22.95
2017-04-11 18:24:00 2017-04-11 19:05:00 28.64
2017-04-11 19:21:00 2017-04-11 19:34:00 5.37
2017-04-12 11:00:00 2017-04-12 12:08:00 28.91
2017-04-12 14:03:00 2017-04-12 15:20:00 28.56
2017-04-12 20:24:00 2017-04-12 20:29:00 1.17
2017-04-12 20:32:00 2017-04-12 21:09:00 30.89
2017-04-13 01:37:00 2017-04-13 02:09:00 32.3
2017-04-13 08:08:00 2017-04-13 08:39:00 19.39
2017-04-13 10:53:00 2017-04-13 11:23:00 24.59
2017-04-13 18:56:00 2017-04-13 19:22:00 22.74
2017-04-14 01:06:00 2017-04-14 01:37:00 31.36
2017-04-14 01:48:00 2017-04-14 01:51:00 1.03
2017-04-14 12:08:00 2017-04-14 12:22:00 1.94
2017-04-14 12:29:00 2017-04-14 13:01:00 19.07
2017-04-14 16:17:00 2017-04-14 17:03:00 19.74
2017-04-14 17:05:00 2017-04-14 17:32:00 3.99
2017-04-14 21:57:00 2017-04-14 22:02:00 1.98
2017-04-15 01:46:00 2017-04-15 01:49:00 1.07
2017-04-15 01:56:00 2017-04-15 01:58:00 1.03
2017-04-15 07:13:00 2017-04-15 07:15:00 0.45
2017-04-15 07:19:00 2017-04-15 07:21:00 0.41
2017-04-15 15:54:00 2017-04-15 16:05:00 1.94
2017-04-15 22:23:00 2017-04-15 22:26:00 0.86
2017-04-15 22:46:00 2017-04-15 22:47:00 0.25
2017-04-15 22:51:00 2017-04-15 22:53:00 0.71
2017-04-16 11:35:00 2017-04-16 11:54:00 11.4
2017-04-16 11:58:00 2017-04-16 12:15:00 10.43
2017-04-17 10:44:00 2017-04-17 10:53:00 3.04
2017-04-17 10:55:00 2017-04-17 11:22:00 18.26
2017-04-17 18:09:00 2017-04-17 18:12:00 0.85
2017-04-17 18:21:00 2017-04-17 19:07:00 37.22
2017-04-18 02:07:00 2017-04-18 02:47:00 32.41
2017-04-18 10:55:00 2017-04-18 10:57:00 0.41
2017-04-18 11:02:00 2017-04-18 11:12:00 2.3
2017-04-18 11:15:00 2017-04-18 11:52:00 24.05
2017-04-18 16:59:00 2017-04-18 17:55:00 22.66
2017-04-19 00:46:00 2017-04-19 01:35:00 39.25
2017-04-19 10:57:00 2017-04-19 11:44:00 24.06
2017-04-19 13:23:00 2017-04-19 14:10:00 25.96
2017-04-19 16:21:00 2017-04-19 17:07:00 18.05
2017-04-19 23:32:00 2017-04-20 00:19:00 39.67
2017-04-20 10:47:00 2017-04-20 11:13:00 24.07
2017-04-20 16:21:00 2017-04-20 16:30:00 0.86
2017-04-20 16:36:00 2017-04-20 16:58:00 0.85
2017-04-20 17:41:00 2017-04-20 17:44:00 0.37
2017-04-20 17:49:00 2017-04-20 18:40:00 19.32
2017-04-20 22:22:00 2017-04-20 22:53:00 29.2
2017-04-20 23:07:00 2017-04-20 23:27:00 10.94
2017-04-21 08:29:00 2017-04-21 08:40:00 1.91
2017-04-21 11:30:00 2017-04-21 11:32:00 0.42
2017-04-21 11:38:00 2017-04-21 11:40:00 0.4
2017-04-21 11:42:00 2017-04-21 12:15:00 19.09
2017-04-21 16:50:00 2017-04-21 18:17:00 40.61
2017-04-21 18:55:00 2017-04-21 19:11:00 1.73
2017-04-21 22:20:00 2017-04-21 22:53:00 28.26
2017-04-21 23:01:00 2017-04-21 23:22:00 11.76
2017-04-22 08:56:00 2017-04-22 08:58:00 0.63
2017-04-22 09:04:00 2017-04-22 09:08:00 0.3
2017-04-22 09:12:00 2017-04-22 09:15:00 0.42
2017-04-22 16:48:00 2017-04-22 16:52:00 0.54
2017-04-22 17:06:00 2017-04-22 17:09:00 0.51
2017-04-22 17:10:00 2017-04-22 17:13:00 1.03
2017-04-22 17:22:00 2017-04-22 17:27:00 1.1
2017-04-23 08:13:00 2017-04-23 08:15:00 0.41
2017-04-23 08:19:00 2017-04-23 08:20:00 0.4
2017-04-23 08:21:00 2017-04-23 08:25:00 1.99
2017-04-23 11:41:00 2017-04-23 11:48:00 2.04
2017-04-23 12:35:00 2017-04-23 12:50:00 7.59
2017-04-23 14:08:00 2017-04-23 14:21:00 7.31
2017-04-23 14:33:00 2017-04-23 15:38:00 37.6
2017-04-24 00:26:00 2017-04-24 01:18:00 39.21
2017-04-24 10:24:00 2017-04-24 10:26:00 0.41
2017-04-24 10:31:00 2017-04-24 10:35:00 1.37
2017-04-24 10:38:00 2017-04-24 10:43:00 1.19
2017-04-24 10:49:00 2017-04-24 11:15:00 19.58
2017-04-24 17:13:00 2017-04-24 18:20:00 37.42
2017-04-24 19:02:00 2017-04-24 19:08:00 1.76
2017-04-24 19:49:00 2017-04-24 19:55:00 1.79
2017-04-24 20:41:00 2017-04-24 21:16:00 32.31
2017-04-25 10:53:00 2017-04-25 11:25:00 24.83
2017-04-25 15:15:00 2017-04-25 15:24:00 3.07
2017-04-25 15:30:00 2017-04-25 15:40:00 3.01
2017-04-25 17:34:00 2017-04-25 18:18:00 24.8
2017-04-26 09:59:00 2017-04-26 10:28:00 24.05
2017-04-26 12:56:00 2017-04-26 13:40:00 29.13
2017-04-26 14:37:00 2017-04-26 15:34:00 21
2017-04-27 08:57:00 2017-04-27 10:21:00 40.56
2017-04-27 16:12:00 2017-04-27 16:44:00 9.89
2017-04-27 17:09:00 2017-04-27 18:01:00 17.51
2017-04-28 05:18:00 2017-04-28 06:06:00 39.28
2017-04-28 12:57:00 2017-04-28 13:52:00 35.82
2017-04-28 16:48:00 2017-04-28 18:14:00 39.1
2017-05-01 11:41:00 2017-05-01 12:20:00 18.74
2017-05-01 18:53:00 2017-05-01 19:34:00 37.15
2017-05-01 23:08:00 2017-05-01 23:09:00 0.06
2017-05-01 23:18:00 2017-05-02 00:11:00 38.61
2017-05-02 11:05:00 2017-05-02 11:42:00 24.07
2017-05-02 17:34:00 2017-05-02 18:53:00 26.42
2017-05-03 12:13:00 2017-05-03 12:25:00 3.96
2017-05-03 12:25:00 2017-05-03 12:56:00 21.15
2017-05-03 13:26:00 2017-05-03 13:44:00 3.32
2017-05-03 13:57:00 2017-05-03 14:08:00 3.49
2017-05-03 18:39:00 2017-05-03 19:08:00 24.85
2017-05-03 19:09:00 2017-05-03 19:13:00 0.99
2017-05-03 19:29:00 2017-05-03 19:32:00 0.84
2017-05-04 10:38:00 2017-05-04 11:06:00 24.05
2017-05-04 13:34:00 2017-05-04 14:10:00 1.73
2017-05-04 17:14:00 2017-05-04 18:23:00 24.68
2017-05-05 20:38:00 2017-05-05 20:52:00 2.24
2017-05-06 11:45:00 2017-05-06 12:30:00 20.19
2017-05-06 14:36:00 2017-05-06 15:35:00 14.49
2017-05-06 15:48:00 2017-05-06 16:17:00 5.25
2017-05-06 17:11:00 2017-05-06 17:13:00 0.43
2017-05-06 17:19:00 2017-05-06 17:21:00 0.43
2017-05-07 08:16:00 2017-05-07 08:22:00 3.27
2017-05-07 12:09:00 2017-05-07 12:16:00 2.01
2017-05-07 17:28:00 2017-05-07 17:50:00 10.36
2017-05-07 17:54:00 2017-05-07 18:01:00 1.19
2017-05-07 18:02:00 2017-05-07 18:35:00 28.31
2017-05-07 21:48:00 2017-05-07 21:52:00 1.46
2017-05-07 22:01:00 2017-05-07 22:05:00 1.37
2017-05-08 00:59:00 2017-05-08 02:19:00 39.23
2017-05-08 11:30:00 2017-05-08 11:58:00 22.55
2017-05-08 18:08:00 2017-05-08 18:30:00 10.47
2017-05-08 18:33:00 2017-05-08 19:09:00 28.44
2017-05-08 22:25:00 2017-05-08 23:09:00 38.65
2017-05-08 23:14:00 2017-05-08 23:17:00 1.04
2017-05-09 11:35:00 2017-05-09 12:19:00 23.99
2017-05-09 17:57:00 2017-05-09 18:59:00 29.38
2017-05-09 20:03:00 2017-05-09 20:13:00 1.9
2017-05-10 10:18:00 2017-05-10 10:54:00 24.06
2017-05-10 15:43:00 2017-05-10 16:46:00 24.71
2017-05-11 12:28:00 2017-05-11 13:07:00 21.75
2017-05-11 18:00:00 2017-05-11 18:31:00 19.3
2017-05-12 08:26:00 2017-05-12 08:55:00 20.46
2017-05-12 13:00:00 2017-05-12 13:34:00 14.6
2017-05-13 08:44:00 2017-05-13 08:46:00 0.38
2017-05-13 08:57:00 2017-05-13 09:01:00 0.33
2017-05-13 14:22:00 2017-05-13 14:41:00 6.86
2017-05-13 15:17:00 2017-05-13 15:35:00 5.2
2017-05-13 18:10:00 2017-05-13 18:21:00 1.91
2017-05-14 11:22:00 2017-05-14 11:26:00 0.9
2017-05-14 11:36:00 2017-05-14 11:38:00 0.39
2017-05-14 14:56:00 2017-05-14 15:59:00 40.07
2017-05-14 16:34:00 2017-05-14 16:41:00 1.49
2017-05-14 16:56:00 2017-05-14 17:04:00 1.45
2017-05-14 19:05:00 2017-05-14 20:06:00 39.21
2017-05-15 11:24:00 2017-05-15 11:33:00 1.91
2017-05-15 11:41:00 2017-05-15 12:13:00 19.84
2017-05-15 17:41:00 2017-05-15 18:11:00 16
2017-05-15 18:15:00 2017-05-15 19:23:00 31.52
2017-05-15 23:41:00 2017-05-16 00:26:00 39.32
2017-05-16 09:49:00 2017-05-16 11:02:00 24.91
2017-05-16 16:08:00 2017-05-16 16:32:00 3.37
2017-05-16 17:11:00 2017-05-16 17:32:00 4.8
2017-05-16 17:42:00 2017-05-16 17:56:00 1.81
2017-05-16 18:13:00 2017-05-16 18:46:00 24.85
2017-05-16 21:07:00 2017-05-16 21:10:00 1.04
2017-05-16 21:26:00 2017-05-16 21:29:00 1.02
2017-07-28 16:10:00 2017-07-28 16:17:00 2.22
2017-07-28 16:17:00 2017-07-28 16:42:00 7.84
2017-08-10 12:00:00 2017-08-10 12:44:00 24.05
2017-08-10 14:56:00 2017-08-10 15:10:00 1.61
2017-08-10 18:51:00 2017-08-10 19:21:00 24.85
2017-08-10 19:46:00 2017-08-10 19:56:00 1.14
2017-08-10 20:08:00 2017-08-10 20:12:00 1.09
2017-08-11 12:44:00 2017-08-11 12:49:00 0.82
2017-08-11 12:59:00 2017-08-11 13:01:00 0.56
2017-08-11 13:18:00 2017-08-11 15:12:00 1.79
2017-08-11 15:14:00 2017-08-11 16:53:00 34.6
2017-08-11 19:27:00 2017-08-11 20:34:00 34.91
2017-08-12 13:52:00 2017-08-12 13:56:00 1.05
2017-08-12 13:59:00 2017-08-12 14:02:00 0.28
2017-08-12 14:10:00 2017-08-12 14:30:00 1.22
2017-08-12 17:15:00 2017-08-12 17:36:00 11.37
2017-08-12 20:49:00 2017-08-12 21:05:00 10.43
2017-08-13 12:16:00 2017-08-13 12:44:00 12.96
2017-08-13 16:03:00 2017-08-13 16:32:00 14.33
2017-08-13 18:19:00 2017-08-13 18:42:00 9.32
2017-08-13 18:52:00 2017-08-13 19:05:00 3.99
2017-08-13 21:42:00 2017-08-13 21:53:00 5.6
2017-08-14 08:50:00 2017-08-14 09:45:00 24.1
2017-08-14 13:22:00 2017-08-14 13:54:00 24.84
2017-08-14 14:02:00 2017-08-14 15:34:00 36.92
2017-08-14 15:58:00 2017-08-14 17:17:00 35.7
2017-08-14 17:35:00 2017-08-14 17:45:00 1.99
2017-08-14 18:07:00 2017-08-14 18:27:00 9.92
2017-08-15 10:15:00 2017-08-15 10:51:00 25
2017-08-15 19:23:00 2017-08-15 19:29:00 0.4
2017-08-15 19:51:00 2017-08-15 20:45:00 24.39
2017-08-15 20:56:00 2017-08-15 21:04:00 2.78
2017-08-15 21:09:00 2017-08-15 21:37:00 19.22
2017-08-16 00:03:00 2017-08-16 00:27:00 15.51
2017-08-16 00:36:00 2017-08-16 00:41:00 1.23
2017-08-16 00:46:00 2017-08-16 01:18:00 11.35
2017-08-16 09:38:00 2017-08-16 09:41:00 1.21
2017-08-16 09:41:00 2017-08-16 09:43:00 0.08
2017-08-16 09:47:00 2017-08-16 10:32:00 22.89
2017-08-16 16:51:00 2017-08-16 17:11:00 3.14
2017-08-16 17:12:00 2017-08-16 17:25:00 2.76
2017-08-16 17:41:00 2017-08-16 18:36:00 24.78
2017-08-17 09:34:00 2017-08-17 10:13:00 24.03
2017-08-17 12:32:00 2017-08-17 13:07:00 24.82
2017-08-17 13:35:00 2017-08-17 13:40:00 0.4
2017-08-17 13:47:00 2017-08-17 15:07:00 36.06
2017-08-17 15:18:00 2017-08-17 15:24:00 0.06
2017-08-17 16:03:00 2017-08-17 18:05:00 35.16
2017-08-18 09:47:00 2017-08-18 10:23:00 24.47
2017-08-18 16:04:00 2017-08-18 16:42:00 1.63
2017-08-18 17:56:00 2017-08-18 18:25:00 10.74
2017-08-18 18:27:00 2017-08-18 18:48:00 1.85
2017-08-19 00:07:00 2017-08-19 00:41:00 18.92
2017-08-19 00:52:00 2017-08-19 00:55:00 0.99
2017-08-19 11:52:00 2017-08-19 12:14:00 7.56
2017-08-19 15:57:00 2017-08-19 16:12:00 4.02
2017-08-19 16:37:00 2017-08-19 16:56:00 5.32
2017-08-19 23:32:00 2017-08-19 23:50:00 7.54
2017-08-19 23:51:00 2017-08-20 00:17:00 9.59
2017-08-20 09:03:00 2017-08-20 09:16:00 5.22
2017-08-20 19:17:00 2017-08-20 19:32:00 4.69
2017-08-21 09:24:00 2017-08-21 09:40:00 2.31
2017-08-21 10:59:00 2017-08-21 11:02:00 0.47
2017-08-21 13:40:00 2017-08-21 15:29:00 36.09
2017-08-21 15:54:00 2017-08-21 16:48:00 2.24
2017-08-21 16:57:00 2017-08-21 18:15:00 32.3
2017-08-22 08:38:00 2017-08-22 09:06:00 0.65
2017-08-22 09:18:00 2017-08-22 09:19:00 0.04
2017-08-22 09:22:00 2017-08-22 10:05:00 23.49
2017-08-22 14:30:00 2017-08-22 15:02:00 1.7
2017-08-22 16:37:00 2017-08-22 17:41:00 24.8
2017-08-23 17:16:00 2017-08-23 18:14:00 24.01
2017-08-23 18:27:00 2017-08-23 18:32:00 1.05
2017-08-23 19:24:00 2017-08-23 20:04:00 18.14
2017-08-23 22:01:00 2017-08-23 22:28:00 16.33
2017-08-23 22:46:00 2017-08-23 22:50:00 1.04
2017-08-24 09:41:00 2017-08-24 09:44:00 0.02
2017-08-24 09:59:00 2017-08-24 10:00:00 0.02
2017-08-24 13:57:00 2017-08-24 15:33:00 42.51
2017-08-24 16:43:00 2017-08-24 17:00:00 0.07
2017-08-24 17:06:00 2017-08-24 17:33:00 10.01
2017-08-24 18:12:00 2017-08-24 19:03:00 27.67
2017-08-25 09:36:00 2017-08-25 09:55:00 2.63
2017-08-25 10:01:00 2017-08-25 10:32:00 20.92
2017-08-25 20:40:00 2017-08-25 21:45:00 17.41
2017-08-25 21:49:00 2017-08-25 22:14:00 16.02
2017-08-26 00:10:00 2017-08-26 02:14:00 29.77
2017-08-26 16:31:00 2017-08-26 16:55:00 7.15
2017-08-26 17:54:00 2017-08-26 18:19:00 10
2017-08-26 20:07:00 2017-08-26 20:08:00 0.19
2017-08-26 20:08:00 2017-08-26 20:11:00 1.35
2017-08-27 12:39:00 2017-08-27 12:54:00 1
2017-08-27 12:55:00 2017-08-27 13:48:00 9.29
2017-08-27 14:00:00 2017-08-27 14:34:00 3.86
2017-08-27 15:56:00 2017-08-27 16:37:00 10.45
2017-08-27 16:44:00 2017-08-27 16:51:00 1.8
2017-08-27 16:55:00 2017-08-27 17:00:00 0.68
2017-08-27 17:04:00 2017-08-27 17:19:00 4.96
2017-08-27 17:28:00 2017-08-27 17:39:00 2.33
2017-08-27 17:47:00 2017-08-27 18:58:00 24.19
2017-08-27 22:17:00 2017-08-27 22:41:00 16.24
2017-08-28 00:33:00 2017-08-28 01:22:00 13.62
2017-08-28 12:48:00 2017-08-28 12:51:00 0.47
2017-08-28 14:01:00 2017-08-28 14:03:00 0.4
2017-08-28 14:12:00 2017-08-28 15:31:00 34.86
2017-08-28 15:56:00 2017-08-28 17:04:00 34.47
2017-08-28 22:15:00 2017-08-28 22:38:00 18.57
2017-08-29 01:42:00 2017-08-29 02:05:00 18.88
2017-08-29 11:40:00 2017-08-29 11:44:00 1.04
2017-08-29 11:48:00 2017-08-29 12:09:00 0.03
2017-08-29 12:18:00 2017-08-29 12:21:00 0.03
2017-08-29 12:26:00 2017-08-29 12:32:00 1.05
2017-08-29 12:35:00 2017-08-29 13:15:00 24.05
2017-08-29 19:40:00 2017-08-29 19:42:00 0.35
2017-08-29 19:50:00 2017-08-29 20:19:00 27.72
2017-08-29 20:25:00 2017-08-29 20:41:00 10.42
2017-08-30 10:00:00 2017-08-30 10:47:00 24.25
2017-08-30 14:31:00 2017-08-30 14:56:00 1.68
2017-08-30 17:19:00 2017-08-30 17:43:00 0.04
2017-08-30 17:43:00 2017-08-30 17:50:00 0.29
2017-08-30 17:56:00 2017-08-30 18:40:00 16.85
2017-08-30 22:57:00 2017-08-30 23:35:00 17.31
2017-08-31 11:30:00 2017-08-31 11:41:00 0.43
2017-08-31 14:04:00 2017-08-31 14:06:00 0.41
2017-08-31 14:24:00 2017-08-31 14:26:00 0.68
2017-08-31 14:31:00 2017-08-31 15:42:00 34.88
2017-08-31 16:01:00 2017-08-31 17:07:00 30.45
2017-08-31 20:54:00 2017-08-31 21:21:00 19.6
2017-09-01 10:30:00 2017-09-01 10:59:00 17.63
2017-09-01 14:07:00 2017-09-01 15:07:00 27.45
2017-09-01 17:17:00 2017-09-01 17:36:00 1.93
2017-09-01 18:16:00 2017-09-01 19:19:00 20.58
2017-09-01 19:25:00 2017-09-01 19:38:00 4.8
2017-09-01 21:30:00 2017-09-01 21:54:00 1.94
2017-09-02 15:46:00 2017-09-02 16:06:00 0.99
2017-09-02 16:13:00 2017-09-02 16:16:00 1.01
2017-09-02 16:56:00 2017-09-02 16:59:00 0.42
2017-09-02 17:04:00 2017-09-02 17:06:00 0.4
2017-09-02 22:52:00 2017-09-02 22:54:00 0.07
2017-09-02 22:55:00 2017-09-02 23:15:00 18.62
2017-09-03 01:46:00 2017-09-03 02:10:00 18.9
2017-09-03 14:49:00 2017-09-03 15:04:00 3.14
2017-09-03 15:50:00 2017-09-03 16:07:00 10.17
2017-09-03 16:21:00 2017-09-03 16:38:00 7.79
2017-09-03 16:47:00 2017-09-03 16:52:00 1.11
2017-09-03 18:32:00 2017-09-03 18:37:00 1.2
2017-09-03 18:37:00 2017-09-03 18:44:00 0.91
2017-09-04 15:50:00 2017-09-04 15:54:00 0.42
2017-09-04 15:59:00 2017-09-04 16:11:00 2.3
2017-09-04 16:21:00 2017-09-04 16:43:00 8.31
2017-09-04 17:05:00 2017-09-04 17:15:00 2.54
2017-09-04 17:26:00 2017-09-04 17:41:00 4.52
2017-09-04 17:49:00 2017-09-04 18:25:00 29.55
2017-09-04 19:36:00 2017-09-04 19:51:00 0.93
2017-09-04 19:54:00 2017-09-04 19:59:00 0.5
2017-09-04 21:21:00 2017-09-04 21:55:00 29.37
2017-09-05 11:08:00 2017-09-05 11:51:00 35.5
2017-09-05 12:36:00 2017-09-05 13:07:00 2.29
2017-09-05 13:19:00 2017-09-05 13:22:00 0.51
2017-09-05 13:26:00 2017-09-05 14:03:00 33.09
2017-09-05 14:13:00 2017-09-05 15:01:00 24.03
2017-09-05 17:33:00 2017-09-05 18:11:00 14.55
2017-09-05 19:01:00 2017-09-05 19:19:00 11.31
2017-09-06 09:21:00 2017-09-06 09:39:00 7.73
2017-09-06 10:14:00 2017-09-06 10:30:00 7.75
2017-09-06 10:37:00 2017-09-06 11:13:00 24.13
2017-09-06 16:48:00 2017-09-06 17:35:00 25.3
2017-09-06 17:49:00 2017-09-06 17:55:00 0.18
2017-09-06 17:58:00 2017-09-06 18:00:00 0.39
2017-09-06 18:38:00 2017-09-06 19:04:00 15.93
2017-09-06 23:45:00 2017-09-07 00:14:00 19.45
2017-09-07 00:26:00 2017-09-07 00:30:00 1.01
2017-09-07 10:42:00 2017-09-07 11:35:00 31.74
2017-09-07 14:04:00 2017-09-07 14:39:00 27.38
2017-09-07 14:43:00 2017-09-07 14:52:00 3.06
2017-09-07 14:54:00 2017-09-07 16:00:00 32.96
2017-09-07 16:32:00 2017-09-07 16:33:00 0.07
2017-09-07 16:38:00 2017-09-07 17:04:00 2.31
2017-09-07 17:23:00 2017-09-07 18:14:00 33.03
2017-09-08 10:02:00 2017-09-08 10:30:00 19.73
2017-09-08 18:09:00 2017-09-08 18:37:00 18.97
2017-09-08 19:04:00 2017-09-08 19:18:00 1.87
2017-09-09 02:25:00 2017-09-09 02:28:00 1.1
2017-09-09 02:33:00 2017-09-09 02:35:00 1.05
2017-09-10 17:09:00 2017-09-10 17:44:00 14.25
2017-09-10 22:50:00 2017-09-10 22:53:00 0.25
2017-09-10 22:56:00 2017-09-10 22:57:00 0.02
2017-09-10 23:00:00 2017-09-10 23:23:00 16.18
2017-09-11 00:01:00 2017-09-11 00:19:00 1.83
2017-09-11 09:59:00 2017-09-11 10:06:00 1.91
2017-09-11 10:12:00 2017-09-11 10:51:00 27.49
2017-09-11 13:39:00 2017-09-11 14:13:00 27.23
2017-09-11 14:31:00 2017-09-11 15:31:00 35.45
2017-09-11 16:03:00 2017-09-11 17:09:00 36.01
2017-09-11 17:39:00 2017-09-11 18:01:00 9.88
2017-09-11 23:01:00 2017-09-11 23:05:00 1.14
2017-09-11 23:16:00 2017-09-11 23:30:00 5.93
2017-09-11 23:30:00 2017-09-11 23:54:00 4.94
2017-09-12 02:56:00 2017-09-12 04:00:00 25.87
2017-09-12 10:06:00 2017-09-12 10:46:00 24.84
2017-09-12 16:33:00 2017-09-12 17:20:00 22.43
2017-09-12 19:38:00 2017-09-12 20:14:00 21.79
2017-09-13 06:24:00 2017-09-13 06:59:00 25.84
2017-09-13 07:02:00 2017-09-13 07:14:00 5.77
2017-09-13 11:14:00 2017-09-13 11:36:00 16.26
2017-09-13 16:01:00 2017-09-13 16:57:00 24.79
2017-09-13 17:07:00 2017-09-13 17:48:00 15.94
2017-09-13 23:13:00 2017-09-13 23:35:00 16.73
2017-09-14 12:00:00 2017-09-14 12:27:00 19.71
2017-09-14 12:28:00 2017-09-14 12:30:00 0.18
2017-09-14 14:36:00 2017-09-14 15:06:00 14.98
2017-09-14 15:11:00 2017-09-14 15:17:00 2.99
2017-09-14 15:26:00 2017-09-14 16:44:00 37.48
2017-09-14 17:03:00 2017-09-14 18:17:00 34.18
2017-09-14 18:32:00 2017-09-14 18:41:00 3.03
2017-09-15 10:25:00 2017-09-15 10:26:00 0.05
2017-09-15 10:45:00 2017-09-15 10:48:00 0.29
2017-09-15 10:59:00 2017-09-15 11:05:00 0.3
2017-09-15 11:09:00 2017-09-15 11:36:00 10.82
2017-09-15 13:00:00 2017-09-15 13:17:00 8.37
2017-09-15 13:36:00 2017-09-15 14:30:00 25.19
2017-09-15 14:37:00 2017-09-15 15:01:00 0.45
2017-09-15 15:04:00 2017-09-15 16:59:00 85.51
2017-09-15 17:06:00 2017-09-15 18:57:00 129.72
2017-09-15 19:03:00 2017-09-15 20:02:00 60.96
2017-09-16 10:18:00 2017-09-16 10:39:00 16.04
2017-09-16 11:52:00 2017-09-16 12:12:00 16.68
2017-09-16 12:28:00 2017-09-16 13:29:00 49
2017-09-16 18:36:00 2017-09-16 19:30:00 45.7
2017-09-16 19:39:00 2017-09-16 19:47:00 2.1
2017-09-17 13:32:00 2017-09-17 13:41:00 2.24
2017-09-17 14:19:00 2017-09-17 14:48:00 14.68
2017-09-17 18:25:00 2017-09-17 18:26:00 0.05
2017-09-17 18:36:00 2017-09-17 19:03:00 12.26
2017-09-18 07:52:00 2017-09-18 08:03:00 2.04
2017-09-18 08:21:00 2017-09-18 08:56:00 37.94
2017-09-18 09:01:00 2017-09-18 09:53:00 65.7
2017-09-18 10:04:00 2017-09-18 10:34:00 39.43
2017-09-18 10:46:00 2017-09-18 11:07:00 14.25
2017-09-18 11:19:00 2017-09-18 13:29:00 138.98
2017-09-18 14:24:00 2017-09-18 14:26:00 0.04
2017-09-18 14:28:00 2017-09-18 15:23:00 35.52
2017-09-18 15:53:00 2017-09-18 17:49:00 36.64
2017-09-19 09:24:00 2017-09-19 10:22:00 24.37
2017-09-19 15:55:00 2017-09-19 16:53:00 15.87
2017-09-19 16:53:00 2017-09-19 17:20:00 0.85
2017-09-19 17:33:00 2017-09-19 18:06:00 10.95
2017-09-19 18:10:00 2017-09-19 18:34:00 8.41
2017-09-19 21:06:00 2017-09-19 21:10:00 1.24
2017-09-19 21:17:00 2017-09-19 21:21:00 1.05
2017-09-20 11:12:00 2017-09-20 11:16:00 1.22
2017-09-20 11:18:00 2017-09-20 11:59:00 24.15
2017-09-20 17:20:00 2017-09-20 18:07:00 24.15
2017-09-20 18:50:00 2017-09-20 19:17:00 16.02
2017-09-20 22:05:00 2017-09-20 22:32:00 17.5
2017-09-21 13:38:00 2017-09-21 13:44:00 0.72
2017-09-21 13:50:00 2017-09-21 15:26:00 35.81
2017-09-21 15:59:00 2017-09-21 16:15:00 8.26
2017-09-21 16:19:00 2017-09-21 17:32:00 28.1
2017-09-21 18:49:00 2017-09-21 19:25:00 16.05
2017-09-21 22:30:00 2017-09-21 22:59:00 16.97
2017-09-22 10:19:00 2017-09-22 10:21:00 0.43
2017-09-22 10:25:00 2017-09-22 10:26:00 0.4
2017-09-22 10:30:00 2017-09-22 10:54:00 19.15
2017-09-22 11:58:00 2017-09-22 12:02:00 1.05
2017-09-22 18:32:00 2017-09-22 18:59:00 20.95
2017-09-23 08:34:00 2017-09-23 08:51:00 1.15
2017-09-23 09:19:00 2017-09-23 10:31:00 37.57
2017-09-23 11:09:00 2017-09-23 11:23:00 5.67
2017-09-23 11:51:00 2017-09-23 12:15:00 4.64
2017-09-23 12:47:00 2017-09-23 13:40:00 8.45
2017-09-23 13:56:00 2017-09-23 15:08:00 34.62
2017-09-23 15:37:00 2017-09-23 16:07:00 1.56
2017-09-24 14:59:00 2017-09-24 15:02:00 0.43
2017-09-24 15:14:00 2017-09-24 17:09:00 6.6
2017-09-24 17:37:00 2017-09-24 18:01:00 7.05
2017-09-24 18:05:00 2017-09-24 18:07:00 0.41
2017-09-24 19:35:00 2017-09-24 20:31:00 25.28
2017-09-25 00:24:00 2017-09-25 00:26:00 0.42
2017-09-25 00:30:00 2017-09-25 01:10:00 23.13
2017-09-25 12:12:00 2017-09-25 12:38:00 19.45
2017-09-25 14:22:00 2017-09-25 14:50:00 19.86
2017-09-25 14:52:00 2017-09-25 15:54:00 35.53
2017-09-25 16:37:00 2017-09-25 18:17:00 34.54
2017-09-25 20:36:00 2017-09-25 21:08:00 28.91
2017-09-26 01:46:00 2017-09-26 02:21:00 26.32
2017-09-26 09:36:00 2017-09-26 10:18:00 24.02
2017-09-26 14:05:00 2017-09-26 14:39:00 25.3
2017-09-26 15:49:00 2017-09-26 15:58:00 1.53
2017-09-26 16:15:00 2017-09-26 16:22:00 1.1
2017-09-27 09:15:00 2017-09-27 10:16:00 24.76
2017-09-27 16:26:00 2017-09-27 17:49:00 35.87
2017-09-27 17:58:00 2017-09-27 18:46:00 27.64
2017-09-27 18:51:00 2017-09-27 18:59:00 2.08
2017-09-27 19:10:00 2017-09-27 20:17:00 21.17
2017-09-27 20:25:00 2017-09-27 21:56:00 3.6
2017-09-27 22:04:00 2017-09-27 22:32:00 16.56
2017-09-28 06:46:00 2017-09-28 07:19:00 14.4
2017-09-28 09:05:00 2017-09-28 09:29:00 8.06
2017-09-28 10:41:00 2017-09-28 11:21:00 22.34
2017-09-28 14:26:00 2017-09-28 16:05:00 35.57
2017-09-28 16:09:00 2017-09-28 16:21:00 1.17
2017-09-28 20:37:00 2017-09-28 20:40:00 1.1
2017-09-28 20:56:00 2017-09-28 21:00:00 1.15
2017-09-29 09:32:00 2017-09-29 10:02:00 19.73
I'd like to plot these discrete events the same way the below plots do, but where 2pi is one week rather than 24 hours in order to illuminate the periodicity of these events, where color represents distance.
I've attempted modifying the solution linked at the beginning of this question, but it hasn't gotten me anywhere. My new approach is to modify this solution, but I'm having a difficult time getting anything but horizontal and vertical lines scattered about a spiral. Making them curve and display in the correct locations is tough.
I'm open to any approach that successfully displays the data in a spiral plot without quantizing/binning it into specific intervals but rather allows the intervals themselves to describe discrete events along a continuous spiralling timeline. Likewise, I'm not interested in converting this to a raw single-point time series format where I'd have a great deal of data representing the time between trips. I'd like to achieve this in a temporal format (one that describes a time window rather than an event at a particular time).
Still needs work, but it's a start, with python and matplotlib.
The idea is to plot a spiral timeline in polar coordinates with 1 week period, each event is an arc of this spiral with a color depending on dist data.
There are lots of overlapping intervals though that this visualization tends to hide... maybe semitransparent arcs could be better, with a carefully chosen colormap.
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
import matplotlib.patheffects as mpe
import pandas as pd
# styling
LINEWIDTH=4
EDGEWIDTH=1
CAPSTYLE="projecting"
COLORMAP="viridis_r"
ALPHA=1
FIRSTDAY=6 # 0=Mon, 6=Sun
# load dataset and parse timestamps
df = pd.read_csv('trips.csv')
df[['trip_start', 'trip_stop']] = df[['trip_start', 'trip_stop']].apply(pd.to_datetime)
# set origin at the first FIRSTDAY before the first trip, midnight
first_trip = df['trip_start'].min()
origin = (first_trip - pd.to_timedelta(first_trip.weekday() - FIRSTDAY, unit='d')).replace(hour=0, minute=0, second=0)
weekdays = pd.date_range(origin, origin + np.timedelta64(1, 'W')).strftime("%a").tolist()
# # convert trip timestamps to week fractions
df['start'] = (df['trip_start'] - origin) / np.timedelta64(1, 'W')
df['stop'] = (df['trip_stop'] - origin) / np.timedelta64(1, 'W')
# sort dataset so shortest trips are plotted last
# should prevent longer events to cover shorter ones, still suboptimal
df = df.sort_values('dist', ascending=False).reset_index()
fig = plt.figure(figsize=(8, 6))
ax = fig.gca(projection="polar")
for idx, event in df.iterrows():
# sample normalized distance from colormap
ndist = event['dist'] / df['dist'].max()
color = plt.cm.get_cmap(COLORMAP)(ndist)
tstart, tstop = event.loc[['start', 'stop']]
# timestamps are in week fractions, 2pi is one week
nsamples = int(1000. * (tstop - tstart))
t = np.linspace(tstart, tstop, nsamples)
theta = 2 * np.pi * t
arc, = ax.plot(theta, t, lw=LINEWIDTH, color=color, solid_capstyle=CAPSTYLE, alpha=ALPHA)
if EDGEWIDTH > 0:
arc.set_path_effects([mpe.Stroke(linewidth=LINEWIDTH+EDGEWIDTH, foreground='black'), mpe.Normal()])
# grid and labels
ax.set_rticks([])
ax.set_theta_zero_location("N")
ax.set_theta_direction(-1)
ax.set_xticks(np.linspace(0, 2*np.pi, 7, endpoint=False))
ax.set_xticklabels(weekdays)
ax.tick_params('x', pad=2)
ax.grid(True)
# setup a custom colorbar, everything's always a bit tricky with mpl colorbars
vmin = df['dist'].min()
vmax = df['dist'].max()
norm = mpl.colors.Normalize(vmin=vmin, vmax=vmax)
sm = plt.cm.ScalarMappable(cmap=COLORMAP, norm=norm)
sm.set_array([])
plt.colorbar(sm, ticks=np.linspace(vmin, vmax, 10), fraction=0.04, aspect=60, pad=0.1, label="distance", ax=ax)
plt.savefig("spiral.png", pad_inches=0, bbox_inches="tight")
Full timeline
To see it's a spiral that never overlaps and it works for longer events too you can plot the full timeline (here with LINEWIDTH=3.5 to limit moiré fringing).
fullt = np.linspace(df['start'].min(), df['stop'].max(), 10000)
theta = 2 * np.pi * fullt
ax.plot(theta, fullt, lw=LINEWIDTH,
path_effects=[mpe.Stroke(linewidth=LINEWIDTH+LINEBORDER, foreground='black'), mpe.Normal()])
Example with a random set...
Here's the plot for a random dataset of 200 mainly short trips with the occasional 1 to 2 weeks long ones.
N = 200
df = pd.DataFrame()
df["start"] = np.random.uniform(0, 20, size=N)
df["stop"] = df["start"] + np.random.choice([np.random.uniform(0, 0.1),
np.random.uniform(1., 2.)], p=[0.98, 0.02], size=N)
df["dist"] = np.random.random(size=N)
... and different styles
inferno_r color map, rounded or butted linecaps, semitransparent, bolder edges, etc (click for full size)
Here's a start. Let me know if this is what you had in mind.
I began with your data sample and put trip_start and trip_stop into POSIXct format before continuing with the code below.
library(tidyverse)
library(lubridate)
dat = dat %>%
mutate(start=(hour(trip_start)*60 + minute(trip_start) + second(trip_start))/(24*60) + wday(trip_start),
stop=(hour(trip_stop)*60 + minute(trip_stop) + second(trip_stop))/(24*60) + wday(trip_stop),
tod = case_when(hour(trip_start) < 6 ~ "night",
hour(trip_start) < 12 ~ "morning",
hour(trip_start) < 18 ~ "afternoon",
hour(trip_start) < 24 ~ "evening"))
ggplot(dat) +
geom_segment(aes(x=start, xend=stop,
y=trip_start,
yend=trip_stop,
colour=tod),
size=5, show.legend = FALSE) +
coord_polar() +
scale_y_datetime(breaks=seq(as.POSIXct("2017-09-01"), as.POSIXct("2017-12-31"), by="week")) +
scale_x_continuous(limits=c(1,8), breaks=1:7,
labels=weekdays(x=as.Date(seq(7)+2, origin="1970-01-01"),
abbreviate=TRUE))+
expand_limits(y=as.POSIXct("2017-08-25")) +
theme_bw() +
scale_colour_manual(values=c(night="black", morning="orange",
afternoon="orange", evening="blue")) +
labs(x="",y="")
This could be achieved relatively straightforwardly with d3. I'll use your data to create a rough template of one basic possible approach. Here's what the result of this approach might look like:
The key ingredient is d3's radial line component that lets us define a line by plotting angle and radius (here's a recent answer showing another spiral graph, that answer started me down the path on this answer).
All we need to do is scale angle and radius to be able to use this effectively (for which we need the first time and last time in the dataset):
var angle = d3.scaleTime()
.domain([start,end])
.range([0,Math.PI * 2 * numberWeeks])
var radius = d3.scaleTime()
.domain([start,end])
.range([minInnerRadius,maxOuterRadius])
And from there we can create a spiral quite easily, we sample some dates throughout the interval and then pass them to the radial line function:
var spiral = d3.radialLine()
.curve(d3.curveCardinal)
.angle(angle)
.radius(radius);
Here's a quick demonstration of just the spiral covering your time period. I'm assuming a base familiarity with d3 for this answer, so have not touched on a few parts of the code.
Once we have that, it's just a matter of adding sections from the data. The most simple way would be to plainly draw a stroke with some width and color it appropriately. This requires the same as above, but rather than sampling points from the start and end times of the dataset, we just need the start and end times of each datum:
// append segments on spiral:
var segments = g.selectAll()
.data(data)
.enter()
.append("path")
.attr("d", function(d) {
return /* sample points and feed to spiral function here */;
})
.style("stroke-width", /* appropriate width here */ )
.style("stroke",function(d) { return /* color logic here */ })
This might look something like this (with data mouseover).
This is just a proof of concept, if you were looking for more control and a nicer look, you could create a polygonal path for each data entry and use both fill & stroke. As is, you'll have to make do with layering strokes to get borders if desired and svg manipulations like line capping options.
Also, as it's d3, and longer timespans may be hard to show all at once, you could show less time but rotate the spiral so that it animates through your time span, dropping off events at the end and creating them in the origin. The actual chart might need to be canvas for this to happen smoothly depending on number of nodes, but to convert to canvas is relatively trivial in this case.
For the sake of filling out the visualization a little with a legend and day labels, this is what I have.