In [32]:
import pandas as pd
import numpy as np
import csv
import matplotlib as plt
c_names = ['vID', 'frID', 'tFr','Timestamp', 'localX', 'localY', 'globalX','globalY', 'vLenght', 'vWidth', 'vType', 'veloc','accel', 'line', 'pred', 'foll', 'spac', 'headway', 'dateTime']
data = pd.read_table('D:\\zzzLola\\PhD\\DataSet\\US101\\test\\portion1Set2DT.txt', sep='\t', header=None, names=c_names)
# Stast description of the whole dataset.
desc = data.describe()
In [33]:
desc
Out[33]:
vID
frID
tFr
Timestamp
localX
localY
globalX
globalY
vLenght
vWidth
vType
veloc
accel
line
pred
foll
spac
headway
count
1597.000000
1597.000000
1597.000000
1.597000e+03
1597.000000
1597.000000
1597.000000
1597.000000
1597.000000
1597.000000
1597
1597.000000
1597.000000
1597.000000
1597.000000
1597.000000
1597.000000
1597.000000
mean
4.182843
267.204133
404.460238
1.118847e+12
40.166214
1114.140043
6451915.031800
1872594.324443
15.425485
5.749092
2
46.853181
0.427790
3.865373
0.706324
9.244208
11.302549
0.241935
std
1.496573
123.996664
45.413270
1.239967e+04
18.506289
559.918912
419.122022
372.802068
1.209852
1.351837
0
7.709628
4.989681
1.647519
1.525733
3.987194
24.613803
0.523286
min
2.000000
8.000000
351.000000
1.118847e+12
6.263000
35.381000
6451122.815000
1871894.687000
14.000000
4.900000
2
32.640000
-11.200000
1.000000
0.000000
0.000000
0.000000
0.000000
25%
2.000000
172.000000
357.000000
1.118847e+12
17.813000
664.639000
6451572.038000
1872291.603000
14.500000
4.900000
2
40.710000
-1.460000
2.000000
0.000000
6.000000
0.000000
0.000000
50%
5.000000
272.000000
437.000000
1.118847e+12
42.818000
1102.629000
6451903.487000
1872598.226000
16.000000
4.900000
2
45.000000
0.000000
4.000000
0.000000
8.000000
0.000000
0.000000
75%
5.000000
371.000000
452.000000
1.118847e+12
54.753000
1572.348000
6452258.323000
1872883.465000
17.000000
7.900000
2
51.360000
2.660000
5.000000
0.000000
13.000000
0.000000
0.000000
max
6.000000
497.000000
452.000000
1.118847e+12
71.498000
2161.090000
6452704.608000
1873344.962000
17.000000
7.900000
2
70.030000
11.200000
7.000000
4.000000
18.000000
78.580000
1.500000
In [34]:
data[:50]
Out[34]:
vID
frID
tFr
Timestamp
localX
localY
globalX
globalY
vLenght
vWidth
vType
veloc
accel
line
pred
foll
spac
headway
dateTime
0
2
13
437
1118846980200
16.467
35.381
6451137.641
1873344.962
14.5
4.9
2
40.00
0.00
2
0
0
0
0
2005-06-15 14:49:40
1
2
14
437
1118846980300
16.447
39.381
6451140.329
1873342.000
14.5
4.9
2
40.00
0.00
2
0
0
0
0
2005-06-15 14:49:40
2
2
15
437
1118846980400
16.426
43.381
6451143.018
1873339.038
14.5
4.9
2
40.00
0.00
2
0
0
0
0
2005-06-15 14:49:40
3
2
16
437
1118846980500
16.405
47.380
6451145.706
1873336.077
14.5
4.9
2
40.00
0.00
2
0
0
0
0
2005-06-15 14:49:40
4
2
17
437
1118846980600
16.385
51.381
6451148.395
1873333.115
14.5
4.9
2
40.00
0.00
2
0
0
0
0
2005-06-15 14:49:40
5
2
18
437
1118846980700
16.364
55.381
6451151.084
1873330.153
14.5
4.9
2
40.00
0.00
2
0
0
0
0
2005-06-15 14:49:40
6
2
19
437
1118846980800
16.344
59.381
6451153.772
1873327.192
14.5
4.9
2
40.00
0.00
2
0
0
0
0
2005-06-15 14:49:40
7
2
20
437
1118846980900
16.323
63.379
6451156.461
1873324.230
14.5
4.9
2
40.02
0.25
2
0
0
0
0
2005-06-15 14:49:40
8
2
21
437
1118846981000
16.303
67.383
6451159.149
1873321.268
14.5
4.9
2
40.03
0.13
2
0
0
0
0
2005-06-15 14:49:41
9
2
22
437
1118846981100
16.282
71.398
6451161.838
1873318.307
14.5
4.9
2
39.93
-1.63
2
0
13
0
0
2005-06-15 14:49:41
10
2
23
437
1118846981200
16.262
75.401
6451164.546
1873315.323
14.5
4.9
2
39.61
-4.54
2
0
13
0
0
2005-06-15 14:49:41
11
2
24
437
1118846981300
16.254
79.349
6451167.199
1873312.382
14.5
4.9
2
39.14
-5.73
2
0
13
0
0
2005-06-15 14:49:41
12
2
25
437
1118846981400
16.221
83.233
6451169.802
1873309.533
14.5
4.9
2
38.61
-5.15
2
0
13
0
0
2005-06-15 14:49:41
13
2
26
437
1118846981500
16.201
87.043
6451172.358
1873306.719
14.5
4.9
2
38.28
-1.61
2
0
13
0
0
2005-06-15 14:49:41
14
2
27
437
1118846981600
16.169
90.829
6451174.961
1873303.870
14.5
4.9
2
38.42
3.73
2
0
13
0
0
2005-06-15 14:49:41
15
2
28
437
1118846981700
16.204
94.683
6451177.613
1873300.929
14.5
4.9
2
38.78
4.86
2
0
13
0
0
2005-06-15 14:49:41
16
2
29
437
1118846981800
16.252
98.611
6451180.342
1873297.924
14.5
4.9
2
38.92
0.00
2
0
13
0
0
2005-06-15 14:49:41
17
2
30
437
1118846981900
16.339
102.560
6451182.980
1873294.961
14.5
4.9
2
38.54
-8.59
2
0
13
0
0
2005-06-15 14:49:41
18
2
31
437
1118846982000
16.400
106.385
6451185.537
1873292.122
14.5
4.9
2
37.51
-11.20
2
0
13
0
0
2005-06-15 14:49:42
19
2
32
437
1118846982100
16.430
110.079
6451188.021
1873289.408
14.5
4.9
2
36.34
-10.86
2
0
13
0
0
2005-06-15 14:49:42
20
2
33
437
1118846982200
16.435
113.628
6451190.424
1873286.817
14.5
4.9
2
35.50
-6.20
2
0
13
0
0
2005-06-15 14:49:42
21
2
34
437
1118846982300
16.478
117.118
6451192.757
1873284.247
14.5
4.9
2
35.08
-1.89
2
0
13
0
0
2005-06-15 14:49:42
22
2
35
437
1118846982400
16.520
120.600
6451195.109
1873281.656
14.5
4.9
2
34.96
0.18
2
0
13
0
0
2005-06-15 14:49:42
23
2
36
437
1118846982500
16.562
124.096
6451197.462
1873279.065
14.5
4.9
2
34.98
0.25
2
0
13
0
0
2005-06-15 14:49:42
24
2
37
437
1118846982600
16.605
127.597
6451199.814
1873276.473
14.5
4.9
2
35.00
0.04
2
0
13
0
0
2005-06-15 14:49:42
25
2
38
437
1118846982700
16.647
131.099
6451202.167
1873273.882
14.5
4.9
2
34.99
-0.20
2
0
13
0
0
2005-06-15 14:49:42
26
2
39
437
1118846982800
16.691
134.595
6451204.519
1873271.290
14.5
4.9
2
34.98
-0.02
2
0
13
0
0
2005-06-15 14:49:42
27
2
40
437
1118846982900
16.727
138.081
6451206.879
1873268.700
14.5
4.9
2
35.10
1.95
2
0
13
0
0
2005-06-15 14:49:42
28
2
41
437
1118846983000
16.796
141.578
6451209.191
1873266.113
14.5
4.9
2
35.49
5.55
2
0
13
0
0
2005-06-15 14:49:43
29
2
42
437
1118846983100
16.795
145.131
6451211.610
1873263.514
14.5
4.9
2
36.20
8.99
2
0
13
0
0
2005-06-15 14:49:43
30
2
43
437
1118846983200
16.724
148.784
6451214.156
1873260.882
14.5
4.9
2
37.15
10.44
2
0
13
0
0
2005-06-15 14:49:43
31
2
44
437
1118846983300
16.588
152.559
6451216.824
1873258.213
14.5
4.9
2
38.12
9.30
2
0
13
0
0
2005-06-15 14:49:43
32
2
45
437
1118846983400
16.376
156.449
6451219.616
1873255.522
14.5
4.9
2
38.76
4.36
2
0
13
0
0
2005-06-15 14:49:43
33
2
46
437
1118846983500
16.064
160.379
6451222.548
1873252.829
14.5
4.9
2
38.95
-0.73
2
0
13
0
0
2005-06-15 14:49:43
34
2
47
437
1118846983600
15.763
164.277
6451225.462
1873250.139
14.5
4.9
2
38.95
-1.15
2
0
13
0
0
2005-06-15 14:49:43
35
2
48
437
1118846983700
15.471
168.150
6451228.376
1873247.450
14.5
4.9
2
38.99
1.90
2
0
13
0
0
2005-06-15 14:49:43
36
2
49
437
1118846983800
15.226
172.044
6451231.290
1873244.760
14.5
4.9
2
39.18
3.47
2
0
13
0
0
2005-06-15 14:49:43
37
2
50
437
1118846983900
14.979
176.000
6451234.204
1873242.071
14.5
4.9
2
39.34
0.02
2
0
13
0
0
2005-06-15 14:49:43
38
2
51
437
1118846984000
14.720
179.959
6451237.144
1873239.374
14.5
4.9
2
39.20
-3.52
2
0
13
0
0
2005-06-15 14:49:44
39
2
52
437
1118846984100
14.508
183.862
6451239.988
1873236.708
14.5
4.9
2
38.89
-3.28
2
0
13
0
0
2005-06-15 14:49:44
40
2
53
437
1118846984200
14.331
187.716
6451242.770
1873234.057
14.5
4.9
2
38.73
-0.33
2
0
13
0
0
2005-06-15 14:49:44
41
2
54
437
1118846984300
14.240
191.561
6451245.501
1873231.336
14.5
4.9
2
38.88
3.49
2
0
13
0
0
2005-06-15 14:49:44
42
2
55
437
1118846984400
14.309
195.455
6451248.125
1873228.494
14.5
4.9
2
39.28
5.00
2
0
13
0
0
2005-06-15 14:49:44
43
2
56
437
1118846984500
14.428
199.414
6451250.788
1873225.539
14.5
4.9
2
39.68
3.76
2
0
13
0
0
2005-06-15 14:49:44
44
2
57
437
1118846984600
14.540
203.417
6451253.489
1873222.554
14.5
4.9
2
39.94
1.29
2
0
13
0
0
2005-06-15 14:49:44
45
2
58
437
1118846984700
14.646
207.430
6451256.177
1873219.592
14.5
4.9
2
40.02
-0.22
2
0
13
0
0
2005-06-15 14:49:44
46
2
59
437
1118846984800
14.751
211.431
6451258.866
1873216.630
14.5
4.9
2
40.00
-0.21
2
0
13
0
0
2005-06-15 14:49:44
47
2
60
437
1118846984900
14.856
215.428
6451261.554
1873213.669
14.5
4.9
2
39.99
0.00
2
0
13
0
0
2005-06-15 14:49:44
48
2
61
437
1118846985000
14.962
219.427
6451264.243
1873210.707
14.5
4.9
2
39.99
0.00
2
0
13
0
0
2005-06-15 14:49:45
49
2
62
437
1118846985100
15.067
223.462
6451266.932
1873207.745
14.5
4.9
2
39.65
-5.35
2
0
13
0
0
2005-06-15 14:49:45
In [35]:
ts1 = data[19:25]
In [36]:
ts2 = data[19:26]
In [37]:
ts1
Out[37]:
vID
frID
tFr
Timestamp
localX
localY
globalX
globalY
vLenght
vWidth
vType
veloc
accel
line
pred
foll
spac
headway
dateTime
19
2
32
437
1118846982100
16.430
110.079
6451188.021
1873289.408
14.5
4.9
2
36.34
-10.86
2
0
13
0
0
2005-06-15 14:49:42
20
2
33
437
1118846982200
16.435
113.628
6451190.424
1873286.817
14.5
4.9
2
35.50
-6.20
2
0
13
0
0
2005-06-15 14:49:42
21
2
34
437
1118846982300
16.478
117.118
6451192.757
1873284.247
14.5
4.9
2
35.08
-1.89
2
0
13
0
0
2005-06-15 14:49:42
22
2
35
437
1118846982400
16.520
120.600
6451195.109
1873281.656
14.5
4.9
2
34.96
0.18
2
0
13
0
0
2005-06-15 14:49:42
23
2
36
437
1118846982500
16.562
124.096
6451197.462
1873279.065
14.5
4.9
2
34.98
0.25
2
0
13
0
0
2005-06-15 14:49:42
24
2
37
437
1118846982600
16.605
127.597
6451199.814
1873276.473
14.5
4.9
2
35.00
0.04
2
0
13
0
0
2005-06-15 14:49:42
In [7]:
data(12:19)
File "<ipython-input-7-e30932e2ca69>", line 1
data(12:19)
^
SyntaxError: invalid syntax
In [8]:
data[(12:19)]
File "<ipython-input-8-a09065cf9c5e>", line 1
data[(12:19)]
^
SyntaxError: invalid syntax
In [9]:
data.iloc[[16]]
Out[9]:
vID
frID
tFr
Timestamp
localX
localY
globalX
globalY
vLenght
vWidth
vType
veloc
accel
line
pred
foll
spac
headway
dateTime
16
2
29
437
1118846981800
16.252
98.611
6451180.342
1873297.924
14.5
4.9
2
38.92
0
2
0
13
0
0
2005-06-15 14:49:41
In [12]:
data.at[[16, 19]]
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-12-8544e4cc8bb5> in <module>()
----> 1 data.at[[16, 19]]
C:\Anaconda2\lib\site-packages\pandas\core\indexing.pyc in __getitem__(self, key)
1550 key = tuple([key])
1551 else:
-> 1552 raise ValueError('Invalid call for scalar access (getting)!')
1553
1554 key = self._convert_key(key)
ValueError: Invalid call for scalar access (getting)!
In [40]:
dt1 = data.iat[16, 18]
In [41]:
dt2 = data.iat[25, 18]
In [42]:
dt1
Out[42]:
'2005-06-15 14:49:41'
In [43]:
dt2
Out[43]:
'2005-06-15 14:49:42'
In [15]:
from datetime import datetime
date_object1 = datetime.strptime(dt1, "%Y-%m-%d %H:%M:%S")
In [16]:
date_object2 = datetime.strptime(dt2, "%Y-%m-%d %H:%M:%S")
In [30]:
date_object1
Out[30]:
datetime.datetime(2005, 6, 15, 14, 49, 41)
In [17]:
date_object2
Out[17]:
datetime.datetime(2005, 6, 15, 14, 49, 42)
In [18]:
delta = date_object2 - date_object1
delta.total_seconds()
Out[18]:
1.0
In [21]:
v = 0
t = 0
for line in data:
vID = line[0]
if v == vID:
dt = datetime.strptime(line[v,18], "%Y-%m-%d %H:%M:%S")
if dt > t:
d_max = dt
t = dt
print d_max
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
<ipython-input-21-b9b134213971> in <module>()
8 d_max = dt
9 t = dt
---> 10 print d_max
11
12
NameError: name 'd_max' is not defined
In [30]:
v = 0
t = 0
time_list = []
for line in data:
vID = data.iat[int(line),0]
if data.iat[int(line+1),0]:
print data.iat[int(line+1),0]
print vID
if v == vID:
time = data[3]
while (number != -1):
num_list.append(time)
number = int(time)
t_max = max(time)
t_min = min(time)
print (v, t_max, t_min)
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-30-b986b1a517c0> in <module>()
3 time_list = []
4 for line in data:
----> 5 vID = data.iat[line,0]
6 print vID
7 if v == vID:
C:\Anaconda2\lib\site-packages\pandas\core\indexing.pyc in __getitem__(self, key)
1552 raise ValueError('Invalid call for scalar access (getting)!')
1553
-> 1554 key = self._convert_key(key)
1555 return self.obj.get_value(*key, takeable=self._takeable)
1556
C:\Anaconda2\lib\site-packages\pandas\core\indexing.pyc in _convert_key(self, key, is_setter)
1613 for a, i in zip(self.obj.axes, key):
1614 if not is_integer(i):
-> 1615 raise ValueError("iAt based indexing can only have integer "
1616 "indexers")
1617 return key
ValueError: iAt based indexing can only have integer indexers
In [61]:
for i, row in data.iterrows():
vID = data.irow(i)['vID']
if data.irow(i+1)['vID']:
vID1 = data.irow(i+1)['vID']
print vID, vID1
2 2
2 2
2 2
2 2
2 2
2 2
2 2
2 2
2 2
2 2
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C:\Anaconda2\lib\site-packages\ipykernel\__main__.py:2: FutureWarning: irow(i) is deprecated. Please use .iloc[i]
from ipykernel import kernelapp as app
C:\Anaconda2\lib\site-packages\ipykernel\__main__.py:3: FutureWarning: irow(i) is deprecated. Please use .iloc[i]
app.launch_new_instance()
C:\Anaconda2\lib\site-packages\ipykernel\__main__.py:4: FutureWarning: irow(i) is deprecated. Please use .iloc[i]
---------------------------------------------------------------------------
IndexError Traceback (most recent call last)
<ipython-input-61-8232852309f9> in <module>()
1 for i, row in data.iterrows():
2 vID = data.irow(i)['vID']
----> 3 if data.irow(i+1)['vID']:
4 vID1 = data.irow(i+1)['vID']
5 print vID, vID1
C:\Anaconda2\lib\site-packages\pandas\core\frame.pyc in irow(self, i, copy)
1854 warnings.warn("irow(i) is deprecated. Please use .iloc[i]",
1855 FutureWarning, stacklevel=2)
-> 1856 return self._ixs(i, axis=0)
1857
1858 def icol(self, i):
C:\Anaconda2\lib\site-packages\pandas\core\frame.pyc in _ixs(self, i, axis)
1882 return self[i]
1883 else:
-> 1884 label = self.index[i]
1885 if isinstance(label, Index):
1886 # a location index by definition
C:\Anaconda2\lib\site-packages\pandas\core\index.pyc in __getitem__(self, key)
1147
1148 if np.isscalar(key):
-> 1149 return getitem(key)
1150
1151 if isinstance(key, slice):
IndexError: index 1597 is out of bounds for axis 0 with size 1597
In [ ]:
for
Content source: lalonica/PhD
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