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In [5]:



    


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()

##++++++++++++Example
# data.groupby(['col5', 'col2']).size().groupby(level=1).max()

#Mean of values by vehicle Id and DataTime
mean = data.groupby(['vID', 'dateTime']).mean()

#Number of vehicles
num_v = data.groupby(['vID']).size()

#Number of registers by timestamp
ts_match = data.groupby(['Timestamp']).size()
ts_match_max = data.groupby(['Timestamp']).size().max()
ts_match_min = data.groupby(['Timestamp']).size().min()
ts_match_mean = data.groupby(['Timestamp']).size().mean()

#number of register by dataTime
dt_match = data.groupby(['dateTime']).size()
dt_match_max = data.groupby(['dateTime']).size().max()
dt_match_min = data.groupby(['dateTime']).size().min()
dt_match_mean = data.groupby(['dateTime']).size().mean()

#print (desc)
#print (mean [:10])

data.plot(kind='barh', stacked=True)

#print(num_v)

#print (ts_match_max, ts_match_min, ts_match_mean)
#print (dt_match_max, dt_match_min, dt_match_mean)



    


















    
Out[5]:








<matplotlib.axes._subplots.AxesSubplot at 0x48170fd0>

















In [6]:



    


df = pd.DataFrame(np.random.randn(1000, 4), index=ts.index, columns=list('ABCD'))
df = df.cumsum()
plt.figure(); df.plot();



    


















    






---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
<ipython-input-6-03d51c611a78> in <module>()
----> 1 df = pd.DataFrame(np.random.randn(1000, 4), index=ts.index, columns=list('ABCD'))
      2 df = df.cumsum()
      3 plt.figure(); df.plot();

NameError: name 'ts' is not defined
















In [7]:



    


import pandas as pd
import numpy as np
import csv
import matplotlib as plt

df = pd.DataFrame(np.random.randn(1000, 4), index=ts.index, columns=list('ABCD'))
df = df.cumsum()
plt.figure(); df.plot();



    


















    






---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
<ipython-input-7-c7c03188f057> in <module>()
      4 import matplotlib as plt
      5 
----> 6 df = pd.DataFrame(np.random.randn(1000, 4), index=ts.index, columns=list('ABCD'))
      7 df = df.cumsum()
      8 plt.figure(); df.plot();

NameError: name 'ts' is not defined
















In [9]:



    


import pandas as pd
import numpy as np
import csv
import matplotlib as plt

ts = pd.Series(np.random.randn(1000), index=pd.date_range('1/1/2000', periods=1000))

ts = ts.cumsum()

ts.plot()

df = pd.DataFrame(np.random.randn(1000, 4), index=ts.index, columns=list('ABCD'))
df = df.cumsum()



    











In [10]:



    


import pandas as pd
import numpy as np
import csv
import matplotlib as plt

ts = pd.Series(np.random.randn(1000), index=pd.date_range('1/1/2000', periods=1000))

ts = ts.cumsum()

ts.plot()



    


















    
Out[10]:








<matplotlib.axes._subplots.AxesSubplot at 0x48170fd0>

















In [11]:



    


%matplotlib inline

import pandas as pd
import numpy as np
import csv
import matplotlib as plt

ts = pd.Series(np.random.randn(1000), index=pd.date_range('1/1/2000', periods=1000))

ts = ts.cumsum()

ts.plot()



    


















    
Out[11]:








<matplotlib.axes._subplots.AxesSubplot at 0x2319bf28>










    
























In [19]:



    


%matplotlib inline

import pandas as pd
import numpy as np
import csv
import matplotlib as plt
from pandas.tools.plotting import andrews_curves


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\\dataset1DT.txt', sep='\t', header=None, names=c_names)

# Stast description of the whole dataset.
desc = data.describe()

##++++++++++++Example
# data.groupby(['col5', 'col2']).size().groupby(level=1).max()

#Mean of values by vehicle Id and DataTime
mean = data.groupby(['vID', 'dateTime']).mean()

#Number of vehicles
num_v = data.groupby(['vID']).size()

#Number of registers by timestamp
ts_match = data.groupby(['Timestamp']).size()
ts_match_max = data.groupby(['Timestamp']).size().max()
ts_match_min = data.groupby(['Timestamp']).size().min()
ts_match_mean = data.groupby(['Timestamp']).size().mean()

#number of register by dataTime
dt_match = data.groupby(['dateTime']).size()
dt_match_max = data.groupby(['dateTime']).size().max()
dt_match_min = data.groupby(['dateTime']).size().min()
dt_match_mean = data.groupby(['dateTime']).size().mean()

#print (desc)
#print (mean [:10])

num_v.plot(kind='barh', stacked=True)

#print(num_v)

#print (ts_match_max, ts_match_min, ts_match_mean)
#print (dt_match_max, dt_match_min, dt_match_mean)



    


















    
Out[19]:








<matplotlib.axes._subplots.AxesSubplot at 0xad826be0>










    
























In [3]:



    


%matplotlib inline

import pandas as pd
import numpy as np
import csv
import matplotlib as plt
from pandas.tools.plotting import andrews_curves


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()

##++++++++++++Example
# data.groupby(['col5', 'col2']).size().groupby(level=1).max()

#Mean of values by vehicle Id and DataTime
mean = data.groupby(['vID', 'dateTime']).mean()

#Number of vehicles
num_v = data.groupby(['vID']).size()


#Number of registers by timestamp
ts_match = data.groupby(['Timestamp']).size()
ts_match_max = data.groupby(['Timestamp']).size().max()
ts_match_min = data.groupby(['Timestamp']).size().min()
ts_match_mean = data.groupby(['Timestamp']).size().mean()

#number of register by dataTime
dt_match = data.groupby(['dateTime']).size()
dt_match_max = data.groupby(['dateTime']).size().max()
dt_match_min = data.groupby(['dateTime']).size().min()
dt_match_mean = data.groupby(['dateTime']).size().mean()

#print (desc)
#print (mean [:10])

num_v.plot(kind='barh', stacked=True)
print (data.groupby(['vID']).count())

#ts_match.plot(kind='barh', stacked=True)

#dt_match.plot(kind='barh', stacked=True)

#print(num_v)

#print (ts_match_max, ts_match_min, ts_match_mean)
#print (dt_match_max, dt_match_min, dt_match_mean)



    


















    






     frID  tFr  Timestamp  localX  localY  globalX  globalY  vLenght  vWidth  \
vID                                                                            
2     437  437        437     437     437      437      437      437     437   
4     351  351        351     351     351      351      351      351     351   
5     452  452        452     452     452      452      452      452     452   
6     357  357        357     357     357      357      357      357     357   

     vType  veloc  accel  line  pred  foll  spac  headway  dateTime  
vID                                                                  
2      437    437    437   437   437   437   437      437       437  
4      351    351    351   351   351   351   351      351       351  
5      452    452    452   452   452   452   452      452       452  
6      357    357    357   357   357   357   357      357       357  










    
























In [4]:



    


ts_match.plot(kind='barh', stacked=True)



    


















    
Out[4]:








<matplotlib.axes._subplots.AxesSubplot at 0x933cb00>










    
























In [5]:



    


dt_match.plot(kind='barh', stacked=True)



    


















    
Out[5]:








<matplotlib.axes._subplots.AxesSubplot at 0xc1891d0>










    
























In [7]:



    


%matplotlib inline

import pandas as pd
import numpy as np
import csv
import matplotlib as plt
from pandas.tools.plotting import andrews_curves


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\\dataset1DT.txt', sep='\t', header=None, names=c_names)

# Stast description of the whole dataset.
desc = data.describe()

##++++++++++++Example
# data.groupby(['col5', 'col2']).size().groupby(level=1).max()

#Mean of values by vehicle Id and DataTime
mean = data.groupby(['vID', 'dateTime']).mean()

#Number of vehicles
num_v = data.groupby(['vID']).size()


#Number of registers by timestamp
ts_match = data.groupby(['Timestamp', 'vID']).size()
ts_match_max = data.groupby(['Timestamp']).size().max()
ts_match_min = data.groupby(['Timestamp']).size().min()
ts_match_mean = data.groupby(['Timestamp']).size().mean()

#number of register by dataTime
dt_match = data.groupby(['dateTime', 'vID']).size()
dt_match_max = data.groupby(['dateTime']).size().max()
dt_match_min = data.groupby(['dateTime']).size().min()
dt_match_mean = data.groupby(['dateTime']).size().mean()



    











In [8]:



    


print (data.groupby(['vID']).count())



    


















    






      frID  tFr  Timestamp  localX  localY  globalX  globalY  vLenght  vWidth  \
vID                                                                             
2      437  437        437     437     437      437      437      437     437   
4      351  351        351     351     351      351      351      351     351   
5      452  452        452     452     452      452      452      452     452   
6      357  357        357     357     357      357      357      357     357   
8      448  448        448     448     448      448      448      448     448   
9      409  409        409     409     409      409      409      409     409   
10     436  436        436     436     436      436      436      436     436   
12     443  443        443     443     443      443      443      443     443   
13     432  432        432     432     432      432      432      432     432   
14     515  515        515     515     515      515      515      515     515   
18     291  291        291     291     291      291      291      291     291   
20     414  414        414     414     414      414      414      414     414   
21     439  439        439     439     439      439      439      439     439   
22     441  441        441     441     441      441      441      441     441   
23     438  438        438     438     438      438      438      438     438   
25     436  436        436     436     436      436      436      436     436   
26     438  438        438     438     438      438      438      438     438   
27     432  432        432     432     432      432      432      432     432   
31     465  465        465     465     465      465      465      465     465   
32     438  438        438     438     438      438      438      438     438   
33     383  383        383     383     383      383      383      383     383   
34     451  451        451     451     451      451      451      451     451   
35     280  280        280     280     280      280      280      280     280   
37     408  408        408     408     408      408      408      408     408   
39     450  450        450     450     450      450      450      450     450   
40     391  391        391     391     391      391      391      391     391   
42     389  389        389     389     389      389      389      389     389   
43     458  458        458     458     458      458      458      458     458   
44     379  379        379     379     379      379      379      379     379   
47     428  428        428     428     428      428      428      428     428   
...    ...  ...        ...     ...     ...      ...      ...      ...     ...   
3004   725  725        725     725     725      725      725      725     725   
3005   726  726        726     726     726      726      726      726     726   
3006   741  741        741     741     741      741      741      741     741   
3007   853  853        853     853     853      853      853      853     853   
3008   724  724        724     724     724      724      724      724     724   
3009   732  732        732     732     732      732      732      732     732   
3011   842  842        842     842     842      842      842      842     842   
3014   676  676        676     676     676      676      676      676     676   
3015   578  578        578     578     578      578      578      578     578   
3018   714  714        714     714     714      714      714      714     714   
3019   704  704        704     704     704      704      704      704     704   
3021   678  678        678     678     678      678      678      678     678   
3022   641  641        641     641     641      641      641      641     641   
3023   403  403        403     403     403      403      403      403     403   
3024   834  834        834     834     834      834      834      834     834   
3025   644  644        644     644     644      644      644      644     644   
3026   326  326        326     326     326      326      326      326     326   
3030   667  667        667     667     667      667      667      667     667   
3032   820  820        820     820     820      820      820      820     820   
3033   633  633        633     633     633      633      633      633     633   
3034   567  567        567     567     567      567      567      567     567   
3101   255  255        255     255     255      255      255      255     255   
3102   443  443        443     443     443      443      443      443     443   
3103   461  461        461     461     461      461      461      461     461   
3104   474  474        474     474     474      474      474      474     474   
3105   534  534        534     534     534      534      534      534     534   
3106   515  515        515     515     515      515      515      515     515   
3107   282  282        282     282     282      282      282      282     282   
3108   359  359        359     359     359      359      359      359     359   
3109   510  510        510     510     510      510      510      510     510   

      vType  veloc  accel  line  pred  foll  spac  headway  dateTime  
vID                                                                   
2       437    437    437   437   437   437   437      437       437  
4       351    351    351   351   351   351   351      351       351  
5       452    452    452   452   452   452   452      452       452  
6       357    357    357   357   357   357   357      357       357  
8       448    448    448   448   448   448   448      448       448  
9       409    409    409   409   409   409   409      409       409  
10      436    436    436   436   436   436   436      436       436  
12      443    443    443   443   443   443   443      443       443  
13      432    432    432   432   432   432   432      432       432  
14      515    515    515   515   515   515   515      515       515  
18      291    291    291   291   291   291   291      291       291  
20      414    414    414   414   414   414   414      414       414  
21      439    439    439   439   439   439   439      439       439  
22      441    441    441   441   441   441   441      441       441  
23      438    438    438   438   438   438   438      438       438  
25      436    436    436   436   436   436   436      436       436  
26      438    438    438   438   438   438   438      438       438  
27      432    432    432   432   432   432   432      432       432  
31      465    465    465   465   465   465   465      465       465  
32      438    438    438   438   438   438   438      438       438  
33      383    383    383   383   383   383   383      383       383  
34      451    451    451   451   451   451   451      451       451  
35      280    280    280   280   280   280   280      280       280  
37      408    408    408   408   408   408   408      408       408  
39      450    450    450   450   450   450   450      450       450  
40      391    391    391   391   391   391   391      391       391  
42      389    389    389   389   389   389   389      389       389  
43      458    458    458   458   458   458   458      458       458  
44      379    379    379   379   379   379   379      379       379  
47      428    428    428   428   428   428   428      428       428  
...     ...    ...    ...   ...   ...   ...   ...      ...       ...  
3004    725    725    725   725   725   725   725      725       725  
3005    726    726    726   726   726   726   726      726       726  
3006    741    741    741   741   741   741   741      741       741  
3007    853    853    853   853   853   853   853      853       853  
3008    724    724    724   724   724   724   724      724       724  
3009    732    732    732   732   732   732   732      732       732  
3011    842    842    842   842   842   842   842      842       842  
3014    676    676    676   676   676   676   676      676       676  
3015    578    578    578   578   578   578   578      578       578  
3018    714    714    714   714   714   714   714      714       714  
3019    704    704    704   704   704   704   704      704       704  
3021    678    678    678   678   678   678   678      678       678  
3022    641    641    641   641   641   641   641      641       641  
3023    403    403    403   403   403   403   403      403       403  
3024    834    834    834   834   834   834   834      834       834  
3025    644    644    644   644   644   644   644      644       644  
3026    326    326    326   326   326   326   326      326       326  
3030    667    667    667   667   667   667   667      667       667  
3032    820    820    820   820   820   820   820      820       820  
3033    633    633    633   633   633   633   633      633       633  
3034    567    567    567   567   567   567   567      567       567  
3101    255    255    255   255   255   255   255      255       255  
3102    443    443    443   443   443   443   443      443       443  
3103    461    461    461   461   461   461   461      461       461  
3104    474    474    474   474   474   474   474      474       474  
3105    534    534    534   534   534   534   534      534       534  
3106    515    515    515   515   515   515   515      515       515  
3107    282    282    282   282   282   282   282      282       282  
3108    359    359    359   359   359   359   359      359       359  
3109    510    510    510   510   510   510   510      510       510  

[2169 rows x 18 columns]

















In [9]:



    


num_v.plot(kind='barh', stacked=True)



    


















    
Out[9]:








<matplotlib.axes._subplots.AxesSubplot at 0xead6470>










    
























In [10]:



    


ts_match.plot(kind='barh', stacked=True)



    


















    
Out[10]:








<matplotlib.axes._subplots.AxesSubplot at 0xddcf550>










    
























In [11]:



    


dt_match.plot(kind='barh', stacked=True)



    


















    
Out[11]:








<matplotlib.axes._subplots.AxesSubplot at 0x18cfa6d8>










    
























In [12]:



    


print (ts_match_max, ts_match_min, ts_match_mean)
print (dt_match_max, dt_match_min, dt_match_mean)



    


















    






(194, 1, 123.89526707944171)
(1926, 3, 1237.524109014675)

















In [13]:



    


print (mean [:10])



    


















    






                          frID  tFr     Timestamp     localX      localY  \
vID dateTime                                                               
2   2005-06-15 14:49:40   16.5  437  1.118847e+12  16.395125   49.380625   
    2005-06-15 14:49:41   25.5  437  1.118847e+12  16.248700   85.049000   
    2005-06-15 14:49:42   35.5  437  1.118847e+12  16.549500  122.327800   
    2005-06-15 14:49:43   45.5  437  1.118847e+12  16.078200  158.535100   
    2005-06-15 14:49:44   55.5  437  1.118847e+12  14.532900  197.567300   
    2005-06-15 14:49:45   65.5  437  1.118847e+12  15.562200  237.208600   
    2005-06-15 14:49:46   75.5  437  1.118847e+12  16.657900  278.598900   
    2005-06-15 14:49:47   85.5  437  1.118847e+12  16.130900  322.998400   
    2005-06-15 14:49:48   95.5  437  1.118847e+12  16.430600  367.761600   
    2005-06-15 14:49:49  105.5  437  1.118847e+12  17.410300  413.588400   

                               globalX         globalY  vLenght  vWidth  \
vID dateTime                                                              
2   2005-06-15 14:49:40  6451147.05075  1873334.595875     14.5     4.9   
    2005-06-15 14:49:41  6451171.07880  1873308.121600     14.5     4.9   
    2005-06-15 14:49:42  6451196.26890  1873280.366000     14.5     4.9   
    2005-06-15 14:49:43  6451221.32770  1873254.149300     14.5     4.9   
    2005-06-15 14:49:44  6451249.44020  1873226.795300     14.5     4.9   
    2005-06-15 14:49:45  6451276.55360  1873197.369400     14.5     4.9   
    2005-06-15 14:49:46  6451305.62580  1873167.561400     14.5     4.9   
    2005-06-15 14:49:47  6451337.91880  1873137.082800     14.5     4.9   
    2005-06-15 14:49:48  6451370.43160  1873105.807000     14.5     4.9   
    2005-06-15 14:49:49  6451403.48850  1873073.998600     14.5     4.9   

                         vType    veloc    accel  line  pred  foll  spac  \
vID dateTime                                                               
2   2005-06-15 14:49:40      2  40.0025  0.03125     2     0   0.0     0   
    2005-06-15 14:49:41      2  39.0260 -1.85300     2     0  11.7     0   
    2005-06-15 14:49:42      2  35.4440 -2.79500     2     0  13.0     0   
    2005-06-15 14:49:43      2  38.1130  4.21500     2     0  13.0     0   
    2005-06-15 14:49:44      2  39.4610  0.59800     2     0  13.0     0   
    2005-06-15 14:49:45      2  39.8820  0.20500     2     0  13.0     0   
    2005-06-15 14:49:46      2  43.2380  3.94300     2     0  13.0     0   
    2005-06-15 14:49:47      2  44.8580 -0.37600     2     0  13.0     0   
    2005-06-15 14:49:48      2  45.4040  2.91600     2     0  13.0     0   
    2005-06-15 14:49:49      2  45.2830 -1.97200     2     0  13.0     0   

                         headway  
vID dateTime                      
2   2005-06-15 14:49:40        0  
    2005-06-15 14:49:41        0  
    2005-06-15 14:49:42        0  
    2005-06-15 14:49:43        0  
    2005-06-15 14:49:44        0  
    2005-06-15 14:49:45        0  
    2005-06-15 14:49:46        0  
    2005-06-15 14:49:47        0  
    2005-06-15 14:49:48        0  
    2005-06-15 14:49:49        0  

















In [14]:



    


print (mean [:50])



    


















    






                          frID  tFr     Timestamp     localX       localY  \
vID dateTime                                                                
2   2005-06-15 14:49:40   16.5  437  1.118847e+12  16.395125    49.380625   
    2005-06-15 14:49:41   25.5  437  1.118847e+12  16.248700    85.049000   
    2005-06-15 14:49:42   35.5  437  1.118847e+12  16.549500   122.327800   
    2005-06-15 14:49:43   45.5  437  1.118847e+12  16.078200   158.535100   
    2005-06-15 14:49:44   55.5  437  1.118847e+12  14.532900   197.567300   
    2005-06-15 14:49:45   65.5  437  1.118847e+12  15.562200   237.208600   
    2005-06-15 14:49:46   75.5  437  1.118847e+12  16.657900   278.598900   
    2005-06-15 14:49:47   85.5  437  1.118847e+12  16.130900   322.998400   
    2005-06-15 14:49:48   95.5  437  1.118847e+12  16.430600   367.761600   
    2005-06-15 14:49:49  105.5  437  1.118847e+12  17.410300   413.588400   
    2005-06-15 14:49:50  115.5  437  1.118847e+12  18.355500   458.081900   
    2005-06-15 14:49:51  125.5  437  1.118847e+12  17.591300   499.134600   
    2005-06-15 14:49:52  135.5  437  1.118847e+12  17.668100   535.880900   
    2005-06-15 14:49:53  145.5  437  1.118847e+12  18.431600   571.199700   
    2005-06-15 14:49:54  155.5  437  1.118847e+12  18.072300   607.279700   
    2005-06-15 14:49:55  165.5  437  1.118847e+12  17.601300   646.529900   
    2005-06-15 14:49:56  175.5  437  1.118847e+12  17.327600   687.665400   
    2005-06-15 14:49:57  185.5  437  1.118847e+12  17.056600   732.249800   
    2005-06-15 14:49:58  195.5  437  1.118847e+12  16.456900   777.691900   
    2005-06-15 14:49:59  205.5  437  1.118847e+12  12.999600   827.392800   
    2005-06-15 14:50:00  215.5  437  1.118847e+12  11.227900   876.678400   
    2005-06-15 14:50:01  225.5  437  1.118847e+12  11.653000   926.757400   
    2005-06-15 14:50:02  235.5  437  1.118847e+12  11.286600   978.255700   
    2005-06-15 14:50:03  245.5  437  1.118847e+12  10.747200  1034.549600   
    2005-06-15 14:50:04  255.5  437  1.118847e+12  10.852600  1092.173700   
    2005-06-15 14:50:05  265.5  437  1.118847e+12  10.917100  1147.714700   
    2005-06-15 14:50:06  275.5  437  1.118847e+12   9.421900  1198.508000   
    2005-06-15 14:50:07  285.5  437  1.118847e+12   7.389700  1246.675700   
    2005-06-15 14:50:08  295.5  437  1.118847e+12   6.532700  1292.783100   
    2005-06-15 14:50:09  305.5  437  1.118847e+12   7.086300  1334.235300   
    2005-06-15 14:50:10  315.5  437  1.118847e+12   7.533600  1374.400900   
    2005-06-15 14:50:11  325.5  437  1.118847e+12   7.642800  1415.181300   
    2005-06-15 14:50:12  335.5  437  1.118847e+12   7.623300  1459.687400   
    2005-06-15 14:50:13  345.5  437  1.118847e+12   7.526000  1505.457700   
    2005-06-15 14:50:14  355.5  437  1.118847e+12   7.349300  1554.578300   
    2005-06-15 14:50:15  365.5  437  1.118847e+12   8.097900  1605.381200   
    2005-06-15 14:50:16  375.5  437  1.118847e+12   7.969900  1659.391200   
    2005-06-15 14:50:17  385.5  437  1.118847e+12   7.225300  1715.205200   
    2005-06-15 14:50:18  395.5  437  1.118847e+12   7.262800  1774.544200   
    2005-06-15 14:50:19  405.5  437  1.118847e+12   7.155600  1836.173900   
    2005-06-15 14:50:20  415.5  437  1.118847e+12   8.351200  1901.437700   
    2005-06-15 14:50:21  425.5  437  1.118847e+12   8.829100  1967.929400   
    2005-06-15 14:50:22  435.5  437  1.118847e+12   9.098000  2035.809700   
    2005-06-15 14:50:23  445.0  437  1.118847e+12   8.816111  2102.117778   
4   2005-06-15 14:49:51  129.0  351  1.118847e+12  66.250667   508.664667   
    2005-06-15 14:49:52  135.5  351  1.118847e+12  65.464600   535.440900   
    2005-06-15 14:49:53  145.5  351  1.118847e+12  62.737400   578.772400   
    2005-06-15 14:49:54  155.5  351  1.118847e+12  61.667000   618.786200   
    2005-06-15 14:49:55  165.5  351  1.118847e+12  61.184700   658.677800   
    2005-06-15 14:49:56  175.5  351  1.118847e+12  58.948700   702.243200   

                                globalX         globalY  vLenght  vWidth  \
vID dateTime                                                               
2   2005-06-15 14:49:40  6451147.050750  1873334.595875     14.5     4.9   
    2005-06-15 14:49:41  6451171.078800  1873308.121600     14.5     4.9   
    2005-06-15 14:49:42  6451196.268900  1873280.366000     14.5     4.9   
    2005-06-15 14:49:43  6451221.327700  1873254.149300     14.5     4.9   
    2005-06-15 14:49:44  6451249.440200  1873226.795300     14.5     4.9   
    2005-06-15 14:49:45  6451276.553600  1873197.369400     14.5     4.9   
    2005-06-15 14:49:46  6451305.625800  1873167.561400     14.5     4.9   
    2005-06-15 14:49:47  6451337.918800  1873137.082800     14.5     4.9   
    2005-06-15 14:49:48  6451370.431600  1873105.807000     14.5     4.9   
    2005-06-15 14:49:49  6451403.488500  1873073.998600     14.5     4.9   
    2005-06-15 14:49:50  6451435.566900  1873043.150300     14.5     4.9   
    2005-06-15 14:49:51  6451466.487100  1873015.865700     14.5     4.9   
    2005-06-15 14:49:52  6451493.863000  1872991.230200     14.5     4.9   
    2005-06-15 14:49:53  6451519.682800  1872967.114800     14.5     4.9   
    2005-06-15 14:49:54  6451546.814900  1872943.332100     14.5     4.9   
    2005-06-15 14:49:55  6451576.569800  1872917.526900     14.5     4.9   
    2005-06-15 14:49:56  6451607.682900  1872890.569600     14.5     4.9   
    2005-06-15 14:49:57  6451641.366700  1872861.358500     14.5     4.9   
    2005-06-15 14:49:58  6451675.894900  1872831.836900     14.5     4.9   
    2005-06-15 14:49:59  6451715.504000  1872801.608200     14.5     4.9   
    2005-06-15 14:50:00  6451753.675600  1872770.389600     14.5     4.9   
    2005-06-15 14:50:01  6451791.118300  1872737.034000     14.5     4.9   
    2005-06-15 14:50:02  6451830.213800  1872703.530700     14.5     4.9   
    2005-06-15 14:50:03  6451873.033300  1872666.971500     14.5     4.9   
    2005-06-15 14:50:04  6451916.415900  1872629.039300     14.5     4.9   
    2005-06-15 14:50:05  6451958.253000  1872592.503000     14.5     4.9   
    2005-06-15 14:50:06  6451997.439200  1872560.216400     14.5     4.9   
    2005-06-15 14:50:07  6452035.007800  1872529.999700     14.5     4.9   
    2005-06-15 14:50:08  6452070.273700  1872500.239700     14.5     4.9   
    2005-06-15 14:50:09  6452101.140700  1872472.575300     14.5     4.9   
    2005-06-15 14:50:10  6452131.148500  1872445.871800     14.5     4.9   
    2005-06-15 14:50:11  6452161.823100  1872419.020700     14.5     4.9   
    2005-06-15 14:50:12  6452195.272300  1872389.700600     14.5     4.9   
    2005-06-15 14:50:13  6452229.703700  1872359.548500     14.5     4.9   
    2005-06-15 14:50:14  6452266.780300  1872327.273500     14.5     4.9   
    2005-06-15 14:50:15  6452304.676400  1872293.402800     14.5     4.9   
    2005-06-15 14:50:16  6452345.398100  1872258.038100     14.5     4.9   
    2005-06-15 14:50:17  6452387.552500  1872221.462900     14.5     4.9   
    2005-06-15 14:50:18  6452432.435400  1872182.497300     14.5     4.9   
    2005-06-15 14:50:19  6452479.042300  1872142.246200     14.5     4.9   
    2005-06-15 14:50:20  6452527.649900  1872098.558900     14.5     4.9   
    2005-06-15 14:50:21  6452578.143000  1872055.148600     14.5     4.9   
    2005-06-15 14:50:22  6452630.515200  1872011.771400     14.5     4.9   
    2005-06-15 14:50:23  6452682.455667  1871970.417222     14.5     4.9   
4   2005-06-15 14:49:51  6451441.192000  1872973.158000     16.0     4.9   
    2005-06-15 14:49:52  6451461.662500  1872955.905300     16.0     4.9   
    2005-06-15 14:49:53  6451495.791800  1872929.041600     16.0     4.9   
    2005-06-15 14:49:54  6451526.375300  1872903.130900     16.0     4.9   
    2005-06-15 14:49:55  6451556.954900  1872876.745300     16.0     4.9   
    2005-06-15 14:49:56  6451591.181200  1872849.671900     16.0     4.9   

                         vType      veloc     accel  line  pred  foll  spac  \
vID dateTime                                                                  
2   2005-06-15 14:49:40      2  40.002500  0.031250   2.0     0   0.0     0   
    2005-06-15 14:49:41      2  39.026000 -1.853000   2.0     0  11.7     0   
    2005-06-15 14:49:42      2  35.444000 -2.795000   2.0     0  13.0     0   
    2005-06-15 14:49:43      2  38.113000  4.215000   2.0     0  13.0     0   
    2005-06-15 14:49:44      2  39.461000  0.598000   2.0     0  13.0     0   
    2005-06-15 14:49:45      2  39.882000  0.205000   2.0     0  13.0     0   
    2005-06-15 14:49:46      2  43.238000  3.943000   2.0     0  13.0     0   
    2005-06-15 14:49:47      2  44.858000 -0.376000   2.0     0  13.0     0   
    2005-06-15 14:49:48      2  45.404000  2.916000   2.0     0  13.0     0   
    2005-06-15 14:49:49      2  45.283000 -1.972000   2.0     0  13.0     0   
    2005-06-15 14:49:50      2  42.870000 -4.140000   2.0     0  13.0     0   
    2005-06-15 14:49:51      2  39.434000 -4.553000   2.0     0  13.0     0   
    2005-06-15 14:49:52      2  35.005000  0.267000   2.0     0  13.0     0   
    2005-06-15 14:49:53      2  35.510000 -0.446000   2.0     0  13.0     0   
    2005-06-15 14:49:54      2  37.734000  4.918000   2.0     0  13.0     0   
    2005-06-15 14:49:55      2  39.891000  0.670000   2.0     0  13.0     0   
    2005-06-15 14:49:56      2  43.209000  4.440000   2.0     0  13.0     0   
    2005-06-15 14:49:57      2  45.000000  0.000000   2.0     0  13.0     0   
    2005-06-15 14:49:58      2  47.360000  6.275000   2.0     0  13.0     0   
    2005-06-15 14:49:59      2  49.574000 -4.008000   1.6     0  11.8     0   
    2005-06-15 14:50:00      2  50.124000  1.663000   1.0     0  10.0     0   
    2005-06-15 14:50:01      2  50.024000  1.104000   1.0     0  10.0     0   
    2005-06-15 14:50:02      2  53.912000  6.474000   1.0     0  10.0     0   
    2005-06-15 14:50:03      2  57.466000  0.426000   1.0     0  10.0     0   
    2005-06-15 14:50:04      2  57.238000 -3.704000   1.0     0  10.0     0   
    2005-06-15 14:50:05      2  53.398000 -5.018000   1.0     0  10.0     0   
    2005-06-15 14:50:06      2  49.199000 -1.014000   1.0     0  10.0     0   
    2005-06-15 14:50:07      2  47.242000 -0.035000   1.0     0  10.0     0   
    2005-06-15 14:50:08      2  43.434000 -4.889000   1.0     0  10.0     0   
    2005-06-15 14:50:09      2  40.679000 -1.014000   1.0     0  10.0     0   
    2005-06-15 14:50:10      2  39.998000 -0.026000   1.0     0  10.0     0   
    2005-06-15 14:50:11      2  42.791000  4.827000   1.0     0  10.0     0   
    2005-06-15 14:50:12      2  45.042000  0.586000   1.0     0  10.0     0   
    2005-06-15 14:50:13      2  47.215000  4.277000   1.0     0  10.0     0   
    2005-06-15 14:50:14      2  50.521000  0.865000   1.0     0  10.0     0   
    2005-06-15 14:50:15      2  51.835000  4.163000   1.0     0  10.0     0   
    2005-06-15 14:50:16      2  55.129000 -0.070000   1.0     0  10.0     0   
    2005-06-15 14:50:17      2  57.605000  5.065000   1.0     0  10.0     0   
    2005-06-15 14:50:18      2  60.097000  0.877000   1.0     0  10.0     0   
    2005-06-15 14:50:19      2  63.904000  4.823000   1.0     0  10.0     0   
    2005-06-15 14:50:20      2  65.980000  0.967000   1.0     0  10.0     0   
    2005-06-15 14:50:21      2  66.890000  0.866000   1.0     0  10.0     0   
    2005-06-15 14:50:22      2  69.209000  2.350000   1.0     0  10.0     0   
    2005-06-15 14:50:23      2  70.008889  0.112222   1.0     0  10.0     0   
4   2005-06-15 14:49:51      2  40.710000  0.000000   7.0     0   0.0     0   
    2005-06-15 14:49:52      2  42.905000  4.067000   7.0     0   0.0     0   
    2005-06-15 14:49:53      2  41.500000 -5.282000   7.0     0   6.0     0   
    2005-06-15 14:49:54      2  39.524000 -0.677000   6.4     0   2.4     0   
    2005-06-15 14:49:55      2  41.218000  5.117000   6.0     0   1.8     0   
    2005-06-15 14:49:56      2  44.814000 -1.848000   6.0     0   6.0     0   

                         headway  
vID dateTime                      
2   2005-06-15 14:49:40        0  
    2005-06-15 14:49:41        0  
    2005-06-15 14:49:42        0  
    2005-06-15 14:49:43        0  
    2005-06-15 14:49:44        0  
    2005-06-15 14:49:45        0  
    2005-06-15 14:49:46        0  
    2005-06-15 14:49:47        0  
    2005-06-15 14:49:48        0  
    2005-06-15 14:49:49        0  
    2005-06-15 14:49:50        0  
    2005-06-15 14:49:51        0  
    2005-06-15 14:49:52        0  
    2005-06-15 14:49:53        0  
    2005-06-15 14:49:54        0  
    2005-06-15 14:49:55        0  
    2005-06-15 14:49:56        0  
    2005-06-15 14:49:57        0  
    2005-06-15 14:49:58        0  
    2005-06-15 14:49:59        0  
    2005-06-15 14:50:00        0  
    2005-06-15 14:50:01        0  
    2005-06-15 14:50:02        0  
    2005-06-15 14:50:03        0  
    2005-06-15 14:50:04        0  
    2005-06-15 14:50:05        0  
    2005-06-15 14:50:06        0  
    2005-06-15 14:50:07        0  
    2005-06-15 14:50:08        0  
    2005-06-15 14:50:09        0  
    2005-06-15 14:50:10        0  
    2005-06-15 14:50:11        0  
    2005-06-15 14:50:12        0  
    2005-06-15 14:50:13        0  
    2005-06-15 14:50:14        0  
    2005-06-15 14:50:15        0  
    2005-06-15 14:50:16        0  
    2005-06-15 14:50:17        0  
    2005-06-15 14:50:18        0  
    2005-06-15 14:50:19        0  
    2005-06-15 14:50:20        0  
    2005-06-15 14:50:21        0  
    2005-06-15 14:50:22        0  
    2005-06-15 14:50:23        0  
4   2005-06-15 14:49:51        0  
    2005-06-15 14:49:52        0  
    2005-06-15 14:49:53        0  
    2005-06-15 14:49:54        0  
    2005-06-15 14:49:55        0  
    2005-06-15 14:49:56        0  

















In [15]:



    


#Number of registers by timestamp
ts_match = data.groupby(['Timestamp', 'vID']).size()
ts_match_max = ts_match.max()
ts_match_min = ts_match.min()
ts_match_mean = ts_match.mean()

#number of register by dataTime
dt_match = data.groupby(['dateTime', 'vID']).size()
dt_match_max = dt_match.max()
dt_match_min = dt_match.min()
dt_match_mean = dt_match.mean()



    











In [16]:



    


print (ts_match [:50])



    


















    






Timestamp      vID
1118846979700  5      1
1118846979800  5      1
1118846979900  5      1
1118846980000  5      1
1118846980100  5      1
1118846980200  2      1
               5      1
1118846980300  2      1
               5      1
1118846980400  2      1
               5      1
1118846980500  2      1
               5      1
1118846980600  2      1
               5      1
1118846980700  2      1
               5      1
1118846980800  2      1
               5      1
1118846980900  2      1
               5      1
1118846981000  2      1
               5      1
1118846981100  2      1
               5      1
               13     1
1118846981200  2      1
               5      1
               13     1
1118846981300  2      1
               5      1
               13     1
1118846981400  2      1
               5      1
               13     1
1118846981500  2      1
               5      1
               13     1
1118846981600  2      1
               5      1
               13     1
1118846981700  2      1
               5      1
               13     1
1118846981800  2      1
               5      1
               8      1
               13     1
1118846981900  2      1
               5      1
dtype: int64

















In [17]:



    


print (ts_match.count())



    


















    






1180598

















In [18]:



    


print (dt_match [:50])



    


















    






dateTime             vID
2005-06-15 14:49:39  5       3
2005-06-15 14:49:40  2       8
                     5      10
2005-06-15 14:49:41  2      10
                     5      10
                     8       2
                     13      9
2005-06-15 14:49:42  2      10
                     5      10
                     8      10
                     10      2
                     13     10
                     14      9
                     22      4
2005-06-15 14:49:43  2      10
                     5      10
                     8      10
                     9       6
                     10     10
                     13     10
                     14     10
                     18      5
                     22     10
2005-06-15 14:49:44  2      10
                     5      10
                     8      10
                     9      10
                     10     10
                     12     10
                     13     10
                     14     10
                     18     10
                     21      9
                     22     10
                     26      9
2005-06-15 14:49:45  2      10
                     5      10
                     8      10
                     9      10
                     10     10
                     12     10
                     13     10
                     14     10
                     18     10
                     20      1
                     21     10
                     22     10
                     23      6
                     26     10
                     31      8
dtype: int64

















In [19]:



    


print (dt_match.count())



    


















    






120032

















In [20]:



    


print (ts_match_max)



    


















    






1

















In [2]:



    


print(dt_match_max)



    


















    






---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
<ipython-input-2-1f083de4b68f> in <module>()
----> 1 print(dt_match_max)

NameError: name 'dt_match_max' is not defined
















In [1]:



    


dt_match



    


















    






---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
<ipython-input-1-c50c1b714d44> in <module>()
----> 1 dt_match

NameError: name 'dt_match' is not defined
















In [ ]:

Content source: lalonica/PhD

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