GMS 200¶
The following script reads trip files from TripScanner App (repo: ... ) and plots the following:
- Speed versus Time
- Trajectory on a Leaflet Map
In [3]:
from matplotlib import pyplot as plt
import seaborn
import re
import pandas as pd
import datetime
from dateutil import parser
import folium
/projects/sage/sage-6.10/local/lib/python2.7/site-packages/matplotlib-1.5.0-py2.7-linux-x86_64.egg/matplotlib/__init__.py:872: UserWarning: axes.color_cycle is deprecated and replaced with axes.prop_cycle; please use the latter. warnings.warn(self.msg_depr % (key, alt_key))
Reading the text file¶
In [1]:
#we open the text file with the read permission
with open('trip_2.txt', 'r') as myfile:
data=myfile.read()
#Clean up the data and split it
#data = data[2:-2].split('\n')
In [37]:
data
Out[37]:
'(\n "<+37.86698125,-122.26613173> +/- 30.00m (speed 0.88 mps / course -1.00) @ 3/10/16, 11:08:48 AM Pacific Standard Time",\n "<+37.86698125,-122.26613173> +/- 30.00m (speed 0.88 mps / course -1.00) @ 3/10/16, 11:08:48 AM Pacific Standard Time",\n "<+37.86698125,-122.26613173> +/- 10.00m (speed 0.00 mps / course -1.00) @ 3/10/16, 11:08:49 AM Pacific Standard Time",\n "<+37.86698125,-122.26613173> +/- 10.00m (speed 0.00 mps / course -1.00) @ 3/10/16, 11:08:50 AM Pacific Standard Time",\n "<+37.86698125,-122.26613173> +/- 10.00m (speed 0.00 mps / course -1.00) @ 3/10/16, 11:08:51 AM Pacific Standard Time"\n)'
Parsing the data¶
In [4]:
# for parsing we will use the following regEx
pattern = "\<(.+)\,(.+)\>\s\+\/\-\s(.+)m\s\(speed\s(.+)\smps\s\/\scourse\s(.+)\)\s\@\s(.+)\sPacific Standard Time"
paresed_data = re.findall(pattern, data)
In [5]:
#show the data
paresed_data
Out[5]:
[('+37.33417438',
'-122.07050306',
'5.00',
'33.60',
'257.34',
'3/11/16, 11:58:15 AM'),
('+37.33410968',
'-122.07087404',
'5.00',
'33.62',
'256.99',
'3/11/16, 11:58:16 AM'),
('+37.33403923',
'-122.07123245',
'5.00',
'33.61',
'256.64',
'3/11/16, 11:58:17 AM'),
('+37.33396475',
'-122.07160897',
'5.00',
'33.51',
'256.64',
'3/11/16, 11:58:18 AM'),
('+37.33389384',
'-122.07197743',
'5.00',
'33.67',
'257.34',
'3/11/16, 11:58:19 AM'),
('+37.33382775',
'-122.07235512',
'5.00',
'33.85',
'258.05',
'3/11/16, 11:58:20 AM'),
('+37.33375801',
'-122.07272963',
'5.00',
'33.36',
'257.70',
'3/11/16, 11:58:21 AM'),
('+37.33369775',
'-122.07309474',
'5.00',
'33.08',
'258.05',
'3/11/16, 11:58:22 AM'),
('+37.33363907',
'-122.07346522',
'5.00',
'32.79',
'259.80',
'3/11/16, 11:58:23 AM'),
('+37.33359159',
'-122.07381886',
'5.00',
'32.16',
'261.21',
'3/11/16, 11:58:24 AM'),
('+37.33355651',
'-122.07417592',
'5.00',
'32.04',
'264.02',
'3/11/16, 11:58:25 AM'),
('+37.33353728',
'-122.07453283',
'5.00',
'31.86',
'266.84',
'3/11/16, 11:58:26 AM'),
('+37.33353065',
'-122.07488445',
'5.00',
'31.54',
'269.65',
'3/11/16, 11:58:27 AM'),
('+37.33353363',
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'5.00',
'31.34',
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'3/11/16, 11:58:28 AM'),
('+37.33355228',
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'5.00',
'31.58',
'274.57',
'3/11/16, 11:58:29 AM'),
('+37.33358308',
'-122.07595104',
'5.00',
'31.84',
'276.68',
'3/11/16, 11:58:30 AM'),
('+37.33362663',
'-122.07630560',
'5.00',
'31.90',
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'3/11/16, 11:58:31 AM'),
('+37.33368006',
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'3/11/16, 11:58:32 AM'),
('+37.33374934',
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'5.00',
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'3/11/16, 11:58:33 AM'),
('+37.33382825',
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'3/11/16, 11:58:34 AM'),
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'5.00',
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'3/11/16, 11:58:35 AM'),
('+37.33402871',
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'3/11/16, 11:58:36 AM'),
('+37.33415037',
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'3/11/16, 11:58:37 AM'),
('+37.33428050',
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'3/11/16, 11:58:38 AM'),
('+37.33442102',
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'5.00',
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'3/11/16, 11:58:39 AM'),
('+37.33457244',
'-122.07936390',
'5.00',
'33.02',
'301.99',
'3/11/16, 11:58:40 AM'),
('+37.33473735',
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'5.00',
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'3/11/16, 11:58:41 AM'),
('+37.33492184',
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'5.00',
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'3/11/16, 11:58:42 AM'),
('+37.33511119',
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'5.00',
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'3/11/16, 11:58:43 AM'),
('+37.33531428',
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'5.00',
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'3/11/16, 11:58:44 AM'),
('+37.33552177',
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'3/11/16, 11:58:45 AM'),
('+37.33573891',
'-122.08110667',
'5.00',
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'3/11/16, 11:58:46 AM'),
('+37.33595575',
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'3/11/16, 11:58:48 AM'),
('+37.33638536',
'-122.08193111',
'5.00',
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'3/11/16, 11:58:49 AM'),
('+37.33659906',
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'5.00',
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'3/11/16, 11:58:50 AM'),
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('+37.33845490',
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'3/11/16, 11:59:02 AM'),
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'3/11/16, 11:59:03 AM'),
('+37.33913576',
'-122.08640269',
'5.00',
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'3/11/16, 11:59:04 AM'),
('+37.33930008',
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'3/11/16, 11:59:05 AM'),
('+37.33947623',
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'5.00',
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'3/11/16, 11:59:06 AM'),
('+37.33965020',
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'3/11/16, 11:59:07 AM'),
('+37.33981846',
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'5.00',
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'3/11/16, 11:59:08 AM'),
('+37.33998434',
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Put the data in a DataFrame¶
In [6]:
df_data = pd.DataFrame(paresed_data, columns=["lat","long","accuracy","speed","course","timestamp"])
In [7]:
df_data #Notice that the timestamp is a string (just text!) we need to convert to dataTime object so that python can help us in ploting
Out[7]:
| lat | long | accuracy | speed | course | timestamp | |
|---|---|---|---|---|---|---|
| 0 | +37.33417438 | -122.07050306 | 5.00 | 33.60 | 257.34 | 3/11/16, 11:58:15 AM |
| 1 | +37.33410968 | -122.07087404 | 5.00 | 33.62 | 256.99 | 3/11/16, 11:58:16 AM |
| 2 | +37.33403923 | -122.07123245 | 5.00 | 33.61 | 256.64 | 3/11/16, 11:58:17 AM |
| 3 | +37.33396475 | -122.07160897 | 5.00 | 33.51 | 256.64 | 3/11/16, 11:58:18 AM |
| 4 | +37.33389384 | -122.07197743 | 5.00 | 33.67 | 257.34 | 3/11/16, 11:58:19 AM |
| 5 | +37.33382775 | -122.07235512 | 5.00 | 33.85 | 258.05 | 3/11/16, 11:58:20 AM |
| 6 | +37.33375801 | -122.07272963 | 5.00 | 33.36 | 257.70 | 3/11/16, 11:58:21 AM |
| 7 | +37.33369775 | -122.07309474 | 5.00 | 33.08 | 258.05 | 3/11/16, 11:58:22 AM |
| 8 | +37.33363907 | -122.07346522 | 5.00 | 32.79 | 259.80 | 3/11/16, 11:58:23 AM |
| 9 | +37.33359159 | -122.07381886 | 5.00 | 32.16 | 261.21 | 3/11/16, 11:58:24 AM |
| 10 | +37.33355651 | -122.07417592 | 5.00 | 32.04 | 264.02 | 3/11/16, 11:58:25 AM |
| 11 | +37.33353728 | -122.07453283 | 5.00 | 31.86 | 266.84 | 3/11/16, 11:58:26 AM |
| 12 | +37.33353065 | -122.07488445 | 5.00 | 31.54 | 269.65 | 3/11/16, 11:58:27 AM |
| 13 | +37.33353363 | -122.07523112 | 5.00 | 31.34 | 272.11 | 3/11/16, 11:58:28 AM |
| 14 | +37.33355228 | -122.07558936 | 5.00 | 31.58 | 274.57 | 3/11/16, 11:58:29 AM |
| 15 | +37.33358308 | -122.07595104 | 5.00 | 31.84 | 276.68 | 3/11/16, 11:58:30 AM |
| 16 | +37.33362663 | -122.07630560 | 5.00 | 31.90 | 279.14 | 3/11/16, 11:58:31 AM |
| 17 | +37.33368006 | -122.07665613 | 5.00 | 31.92 | 281.60 | 3/11/16, 11:58:32 AM |
| 18 | +37.33374934 | -122.07700482 | 5.00 | 32.16 | 284.06 | 3/11/16, 11:58:33 AM |
| 19 | +37.33382825 | -122.07735141 | 5.00 | 32.58 | 287.23 | 3/11/16, 11:58:34 AM |
| 20 | +37.33392322 | -122.07769942 | 5.00 | 32.48 | 289.34 | 3/11/16, 11:58:35 AM |
| 21 | +37.33402871 | -122.07804543 | 5.00 | 32.47 | 292.15 | 3/11/16, 11:58:36 AM |
| 22 | +37.33415037 | -122.07838406 | 5.00 | 32.74 | 294.61 | 3/11/16, 11:58:37 AM |
| 23 | +37.33428050 | -122.07872034 | 5.00 | 32.85 | 296.72 | 3/11/16, 11:58:38 AM |
| 24 | +37.33442102 | -122.07904631 | 5.00 | 32.75 | 299.18 | 3/11/16, 11:58:39 AM |
| 25 | +37.33457244 | -122.07936390 | 5.00 | 33.02 | 301.99 | 3/11/16, 11:58:40 AM |
| 26 | +37.33473735 | -122.07967470 | 5.00 | 33.21 | 304.80 | 3/11/16, 11:58:41 AM |
| 27 | +37.33492184 | -122.07997503 | 5.00 | 33.41 | 307.97 | 3/11/16, 11:58:42 AM |
| 28 | +37.33511119 | -122.08026077 | 5.00 | 33.53 | 310.78 | 3/11/16, 11:58:43 AM |
| 29 | +37.33531428 | -122.08054952 | 5.00 | 33.75 | 312.54 | 3/11/16, 11:58:44 AM |
| ... | ... | ... | ... | ... | ... | ... |
| 641 | +37.44400414 | -122.25598745 | 5.00 | 32.57 | 284.41 | 3/11/16, 12:08:56 PM |
| 642 | +37.44406453 | -122.25634385 | 5.00 | 32.57 | 282.30 | 3/11/16, 12:08:57 PM |
| 643 | +37.44411847 | -122.25671014 | 5.00 | 32.43 | 279.49 | 3/11/16, 12:08:58 PM |
| 644 | +37.44416092 | -122.25706779 | 5.00 | 32.31 | 278.09 | 3/11/16, 12:08:59 PM |
| 645 | +37.44419763 | -122.25743668 | 5.00 | 32.30 | 276.68 | 3/11/16, 12:09:00 PM |
| 646 | +37.44422018 | -122.25779040 | 5.00 | 32.25 | 274.22 | 3/11/16, 12:09:01 PM |
| 647 | +37.44423451 | -122.25815644 | 5.00 | 32.29 | 272.46 | 3/11/16, 12:09:02 PM |
| 648 | +37.44423900 | -122.25851887 | 5.00 | 32.57 | 270.35 | 3/11/16, 12:09:03 PM |
| 649 | +37.44423414 | -122.25888331 | 5.00 | 32.66 | 268.24 | 3/11/16, 12:09:04 PM |
| 650 | +37.44422039 | -122.25925044 | 5.00 | 32.48 | 266.13 | 3/11/16, 12:09:05 PM |
| 651 | +37.44420115 | -122.25962444 | 5.00 | 32.48 | 265.08 | 3/11/16, 12:09:06 PM |
| 652 | +37.44417706 | -122.25999425 | 5.00 | 32.60 | 264.73 | 3/11/16, 12:09:07 PM |
| 653 | +37.44414135 | -122.26035660 | 5.00 | 32.45 | 264.02 | 3/11/16, 12:09:08 PM |
| 654 | +37.44411151 | -122.26071711 | 5.00 | 32.51 | 264.02 | 3/11/16, 12:09:09 PM |
| 655 | +37.44407878 | -122.26109077 | 5.00 | 32.65 | 264.38 | 3/11/16, 12:09:10 PM |
| 656 | +37.44404961 | -122.26145748 | 5.00 | 32.57 | 264.38 | 3/11/16, 12:09:11 PM |
| 657 | +37.44401797 | -122.26182293 | 5.00 | 32.35 | 264.02 | 3/11/16, 12:09:12 PM |
| 658 | +37.44398880 | -122.26217958 | 5.00 | 32.49 | 264.38 | 3/11/16, 12:09:13 PM |
| 659 | +37.44395519 | -122.26255308 | 5.00 | 32.35 | 264.38 | 3/11/16, 12:09:14 PM |
| 660 | +37.44392480 | -122.26291149 | 5.00 | 32.18 | 264.38 | 3/11/16, 12:09:15 PM |
| 661 | +37.44389345 | -122.26327367 | 5.00 | 32.04 | 264.38 | 3/11/16, 12:09:16 PM |
| 662 | +37.44386206 | -122.26362152 | 5.00 | 31.85 | 264.38 | 3/11/16, 12:09:17 PM |
| 663 | +37.44383398 | -122.26398513 | 5.00 | 31.86 | 265.43 | 3/11/16, 12:09:18 PM |
| 664 | +37.44382032 | -122.26434982 | 5.00 | 32.01 | 266.84 | 3/11/16, 12:09:19 PM |
| 665 | +37.44380955 | -122.26471217 | 5.00 | 32.07 | 268.59 | 3/11/16, 12:09:20 PM |
| 666 | +37.44380964 | -122.26507343 | 5.00 | 32.24 | 270.70 | 3/11/16, 12:09:21 PM |
| 667 | +37.44381697 | -122.26544651 | 5.00 | 32.14 | 272.46 | 3/11/16, 12:09:22 PM |
| 668 | +37.44383755 | -122.26580291 | 5.00 | 32.20 | 274.92 | 3/11/16, 12:09:23 PM |
| 669 | +37.44387418 | -122.26616560 | 5.00 | 32.18 | 276.68 | 3/11/16, 12:09:24 PM |
| 670 | +37.44391160 | -122.26651906 | 5.00 | 32.20 | 278.44 | 3/11/16, 12:09:25 PM |
671 rows × 6 columns
In [8]:
df_data['datestamp']= pd.Series()
pd.to_datetime(df_data['datestamp'])
def parse_date(df):
df['datestamp'] = parser.parse(df['timestamp'])
return df
df_data = df_data.apply(parse_date, axis = 1)
df_data.set_index(df_data['datestamp'])
Out[8]:
| lat | long | accuracy | speed | course | timestamp | datestamp | |
|---|---|---|---|---|---|---|---|
| datestamp | |||||||
| 2016-03-11 11:58:15 | +37.33417438 | -122.07050306 | 5.00 | 33.60 | 257.34 | 3/11/16, 11:58:15 AM | 2016-03-11 11:58:15 |
| 2016-03-11 11:58:16 | +37.33410968 | -122.07087404 | 5.00 | 33.62 | 256.99 | 3/11/16, 11:58:16 AM | 2016-03-11 11:58:16 |
| 2016-03-11 11:58:17 | +37.33403923 | -122.07123245 | 5.00 | 33.61 | 256.64 | 3/11/16, 11:58:17 AM | 2016-03-11 11:58:17 |
| 2016-03-11 11:58:18 | +37.33396475 | -122.07160897 | 5.00 | 33.51 | 256.64 | 3/11/16, 11:58:18 AM | 2016-03-11 11:58:18 |
| 2016-03-11 11:58:19 | +37.33389384 | -122.07197743 | 5.00 | 33.67 | 257.34 | 3/11/16, 11:58:19 AM | 2016-03-11 11:58:19 |
| 2016-03-11 11:58:20 | +37.33382775 | -122.07235512 | 5.00 | 33.85 | 258.05 | 3/11/16, 11:58:20 AM | 2016-03-11 11:58:20 |
| 2016-03-11 11:58:21 | +37.33375801 | -122.07272963 | 5.00 | 33.36 | 257.70 | 3/11/16, 11:58:21 AM | 2016-03-11 11:58:21 |
| 2016-03-11 11:58:22 | +37.33369775 | -122.07309474 | 5.00 | 33.08 | 258.05 | 3/11/16, 11:58:22 AM | 2016-03-11 11:58:22 |
| 2016-03-11 11:58:23 | +37.33363907 | -122.07346522 | 5.00 | 32.79 | 259.80 | 3/11/16, 11:58:23 AM | 2016-03-11 11:58:23 |
| 2016-03-11 11:58:24 | +37.33359159 | -122.07381886 | 5.00 | 32.16 | 261.21 | 3/11/16, 11:58:24 AM | 2016-03-11 11:58:24 |
| 2016-03-11 11:58:25 | +37.33355651 | -122.07417592 | 5.00 | 32.04 | 264.02 | 3/11/16, 11:58:25 AM | 2016-03-11 11:58:25 |
| 2016-03-11 11:58:26 | +37.33353728 | -122.07453283 | 5.00 | 31.86 | 266.84 | 3/11/16, 11:58:26 AM | 2016-03-11 11:58:26 |
| 2016-03-11 11:58:27 | +37.33353065 | -122.07488445 | 5.00 | 31.54 | 269.65 | 3/11/16, 11:58:27 AM | 2016-03-11 11:58:27 |
| 2016-03-11 11:58:28 | +37.33353363 | -122.07523112 | 5.00 | 31.34 | 272.11 | 3/11/16, 11:58:28 AM | 2016-03-11 11:58:28 |
| 2016-03-11 11:58:29 | +37.33355228 | -122.07558936 | 5.00 | 31.58 | 274.57 | 3/11/16, 11:58:29 AM | 2016-03-11 11:58:29 |
| 2016-03-11 11:58:30 | +37.33358308 | -122.07595104 | 5.00 | 31.84 | 276.68 | 3/11/16, 11:58:30 AM | 2016-03-11 11:58:30 |
| 2016-03-11 11:58:31 | +37.33362663 | -122.07630560 | 5.00 | 31.90 | 279.14 | 3/11/16, 11:58:31 AM | 2016-03-11 11:58:31 |
| 2016-03-11 11:58:32 | +37.33368006 | -122.07665613 | 5.00 | 31.92 | 281.60 | 3/11/16, 11:58:32 AM | 2016-03-11 11:58:32 |
| 2016-03-11 11:58:33 | +37.33374934 | -122.07700482 | 5.00 | 32.16 | 284.06 | 3/11/16, 11:58:33 AM | 2016-03-11 11:58:33 |
| 2016-03-11 11:58:34 | +37.33382825 | -122.07735141 | 5.00 | 32.58 | 287.23 | 3/11/16, 11:58:34 AM | 2016-03-11 11:58:34 |
| 2016-03-11 11:58:35 | +37.33392322 | -122.07769942 | 5.00 | 32.48 | 289.34 | 3/11/16, 11:58:35 AM | 2016-03-11 11:58:35 |
| 2016-03-11 11:58:36 | +37.33402871 | -122.07804543 | 5.00 | 32.47 | 292.15 | 3/11/16, 11:58:36 AM | 2016-03-11 11:58:36 |
| 2016-03-11 11:58:37 | +37.33415037 | -122.07838406 | 5.00 | 32.74 | 294.61 | 3/11/16, 11:58:37 AM | 2016-03-11 11:58:37 |
| 2016-03-11 11:58:38 | +37.33428050 | -122.07872034 | 5.00 | 32.85 | 296.72 | 3/11/16, 11:58:38 AM | 2016-03-11 11:58:38 |
| 2016-03-11 11:58:39 | +37.33442102 | -122.07904631 | 5.00 | 32.75 | 299.18 | 3/11/16, 11:58:39 AM | 2016-03-11 11:58:39 |
| 2016-03-11 11:58:40 | +37.33457244 | -122.07936390 | 5.00 | 33.02 | 301.99 | 3/11/16, 11:58:40 AM | 2016-03-11 11:58:40 |
| 2016-03-11 11:58:41 | +37.33473735 | -122.07967470 | 5.00 | 33.21 | 304.80 | 3/11/16, 11:58:41 AM | 2016-03-11 11:58:41 |
| 2016-03-11 11:58:42 | +37.33492184 | -122.07997503 | 5.00 | 33.41 | 307.97 | 3/11/16, 11:58:42 AM | 2016-03-11 11:58:42 |
| 2016-03-11 11:58:43 | +37.33511119 | -122.08026077 | 5.00 | 33.53 | 310.78 | 3/11/16, 11:58:43 AM | 2016-03-11 11:58:43 |
| 2016-03-11 11:58:44 | +37.33531428 | -122.08054952 | 5.00 | 33.75 | 312.54 | 3/11/16, 11:58:44 AM | 2016-03-11 11:58:44 |
| ... | ... | ... | ... | ... | ... | ... | ... |
| 2016-03-11 12:08:56 | +37.44400414 | -122.25598745 | 5.00 | 32.57 | 284.41 | 3/11/16, 12:08:56 PM | 2016-03-11 12:08:56 |
| 2016-03-11 12:08:57 | +37.44406453 | -122.25634385 | 5.00 | 32.57 | 282.30 | 3/11/16, 12:08:57 PM | 2016-03-11 12:08:57 |
| 2016-03-11 12:08:58 | +37.44411847 | -122.25671014 | 5.00 | 32.43 | 279.49 | 3/11/16, 12:08:58 PM | 2016-03-11 12:08:58 |
| 2016-03-11 12:08:59 | +37.44416092 | -122.25706779 | 5.00 | 32.31 | 278.09 | 3/11/16, 12:08:59 PM | 2016-03-11 12:08:59 |
| 2016-03-11 12:09:00 | +37.44419763 | -122.25743668 | 5.00 | 32.30 | 276.68 | 3/11/16, 12:09:00 PM | 2016-03-11 12:09:00 |
| 2016-03-11 12:09:01 | +37.44422018 | -122.25779040 | 5.00 | 32.25 | 274.22 | 3/11/16, 12:09:01 PM | 2016-03-11 12:09:01 |
| 2016-03-11 12:09:02 | +37.44423451 | -122.25815644 | 5.00 | 32.29 | 272.46 | 3/11/16, 12:09:02 PM | 2016-03-11 12:09:02 |
| 2016-03-11 12:09:03 | +37.44423900 | -122.25851887 | 5.00 | 32.57 | 270.35 | 3/11/16, 12:09:03 PM | 2016-03-11 12:09:03 |
| 2016-03-11 12:09:04 | +37.44423414 | -122.25888331 | 5.00 | 32.66 | 268.24 | 3/11/16, 12:09:04 PM | 2016-03-11 12:09:04 |
| 2016-03-11 12:09:05 | +37.44422039 | -122.25925044 | 5.00 | 32.48 | 266.13 | 3/11/16, 12:09:05 PM | 2016-03-11 12:09:05 |
| 2016-03-11 12:09:06 | +37.44420115 | -122.25962444 | 5.00 | 32.48 | 265.08 | 3/11/16, 12:09:06 PM | 2016-03-11 12:09:06 |
| 2016-03-11 12:09:07 | +37.44417706 | -122.25999425 | 5.00 | 32.60 | 264.73 | 3/11/16, 12:09:07 PM | 2016-03-11 12:09:07 |
| 2016-03-11 12:09:08 | +37.44414135 | -122.26035660 | 5.00 | 32.45 | 264.02 | 3/11/16, 12:09:08 PM | 2016-03-11 12:09:08 |
| 2016-03-11 12:09:09 | +37.44411151 | -122.26071711 | 5.00 | 32.51 | 264.02 | 3/11/16, 12:09:09 PM | 2016-03-11 12:09:09 |
| 2016-03-11 12:09:10 | +37.44407878 | -122.26109077 | 5.00 | 32.65 | 264.38 | 3/11/16, 12:09:10 PM | 2016-03-11 12:09:10 |
| 2016-03-11 12:09:11 | +37.44404961 | -122.26145748 | 5.00 | 32.57 | 264.38 | 3/11/16, 12:09:11 PM | 2016-03-11 12:09:11 |
| 2016-03-11 12:09:12 | +37.44401797 | -122.26182293 | 5.00 | 32.35 | 264.02 | 3/11/16, 12:09:12 PM | 2016-03-11 12:09:12 |
| 2016-03-11 12:09:13 | +37.44398880 | -122.26217958 | 5.00 | 32.49 | 264.38 | 3/11/16, 12:09:13 PM | 2016-03-11 12:09:13 |
| 2016-03-11 12:09:14 | +37.44395519 | -122.26255308 | 5.00 | 32.35 | 264.38 | 3/11/16, 12:09:14 PM | 2016-03-11 12:09:14 |
| 2016-03-11 12:09:15 | +37.44392480 | -122.26291149 | 5.00 | 32.18 | 264.38 | 3/11/16, 12:09:15 PM | 2016-03-11 12:09:15 |
| 2016-03-11 12:09:16 | +37.44389345 | -122.26327367 | 5.00 | 32.04 | 264.38 | 3/11/16, 12:09:16 PM | 2016-03-11 12:09:16 |
| 2016-03-11 12:09:17 | +37.44386206 | -122.26362152 | 5.00 | 31.85 | 264.38 | 3/11/16, 12:09:17 PM | 2016-03-11 12:09:17 |
| 2016-03-11 12:09:18 | +37.44383398 | -122.26398513 | 5.00 | 31.86 | 265.43 | 3/11/16, 12:09:18 PM | 2016-03-11 12:09:18 |
| 2016-03-11 12:09:19 | +37.44382032 | -122.26434982 | 5.00 | 32.01 | 266.84 | 3/11/16, 12:09:19 PM | 2016-03-11 12:09:19 |
| 2016-03-11 12:09:20 | +37.44380955 | -122.26471217 | 5.00 | 32.07 | 268.59 | 3/11/16, 12:09:20 PM | 2016-03-11 12:09:20 |
| 2016-03-11 12:09:21 | +37.44380964 | -122.26507343 | 5.00 | 32.24 | 270.70 | 3/11/16, 12:09:21 PM | 2016-03-11 12:09:21 |
| 2016-03-11 12:09:22 | +37.44381697 | -122.26544651 | 5.00 | 32.14 | 272.46 | 3/11/16, 12:09:22 PM | 2016-03-11 12:09:22 |
| 2016-03-11 12:09:23 | +37.44383755 | -122.26580291 | 5.00 | 32.20 | 274.92 | 3/11/16, 12:09:23 PM | 2016-03-11 12:09:23 |
| 2016-03-11 12:09:24 | +37.44387418 | -122.26616560 | 5.00 | 32.18 | 276.68 | 3/11/16, 12:09:24 PM | 2016-03-11 12:09:24 |
| 2016-03-11 12:09:25 | +37.44391160 | -122.26651906 | 5.00 | 32.20 | 278.44 | 3/11/16, 12:09:25 PM | 2016-03-11 12:09:25 |
671 rows × 7 columns
Plotting Trajectory on Map¶
In [9]:
df_data.columns
Out[9]:
Index([u'lat', u'long', u'accuracy', u'speed', u'course', u'timestamp',
u'datestamp'],
dtype='object')
In [10]:
data_list = df_data.ix[:,[0,1,5]].values.tolist()
In [11]:
point_0 = data_list[0]
stamen = folium.Map(location=[point_0[0], point_0[1]], tiles='Stamen Toner',
zoom_start=13)
for point_i in data_list:
stamen.simple_marker(location=[point_i[0], point_i[1]], popup=point_i[2])
stamen
Out[11]:
Speed Versus Time¶
In [29]:
epoch = datetime.datetime(1970, 1, 1)
times = df_data.ix[:,'datestamp'].map(lambda x: (x - epoch).total_seconds())
In [30]:
plt.figure(figsize=(10,10))
plt.plot(list(times),list(df_data.ix[:,'speed']))
plt.title('Speed vs Time', fontsize = 20)
plt.xlabel('Epoch Time', fontsize = 15)
plt.ylabel('Speed', fontsize = 15)
Out[30]:
<matplotlib.text.Text at 0x7f735afb5750>
In [ ]: