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Edit and run



In [3]:

    
%cd /home/wallar/projects/ridesharing/c++/data-sim/
%cd v1000-c4-w300-p0-2-18-2013-1489260474









    



/home/wallar/projects/ridesharing/c++/data-sim
/home/wallar/projects/ridesharing/c++/data-sim/v1000-c4-w300-p0-2-18-2013-1489260474



In [4]:

    
import pandas as pd



In [5]:

    
df = pd.read_csv("metrics_its.csv")



In [6]:

    
df.describe()









    



/usr/local/lib/python2.7/dist-packages/numpy/lib/function_base.py:4116: RuntimeWarning: Invalid value encountered in percentile
  interpolation=interpolation)






    Out[6]:






  
    
      
      Unnamed: 0
      active_taxis
      capacity
      empty_moving_to_pickup
      empty_rebalancing
      empty_waiting
      is_long
      mean_delay
      mean_km_travelled
      mean_passengers
      ...
      time_pass_2
      time_pass_3
      time_pass_4
      time_pass_5
      time_pass_6
      time_pass_7
      time_pass_8
      time_pass_9
      total_km_travelled
      total_passengers
    
  
  
    
      count
      2879.000000
      2879.000000
      2879.0
      2879.000000
      2879.000000
      2879.0
      2879.0
      2875.000000
      2879.000000
      2879.000000
      ...
      2879.000000
      2879.000000
      2879.000000
      2879.0
      2879.0
      2879.0
      2879.0
      2879.0
      2879.000000
      2879.000000
    
    
      mean
      1439.000000
      516.654047
      4.0
      8.907954
      474.437999
      0.0
      1.0
      378.750128
      107.316655
      1.450488
      ...
      115.453630
      158.415769
      167.183050
      0.0
      0.0
      0.0
      0.0
      0.0
      107316.654862
      66261.895102
    
    
      std
      831.240038
      197.212540
      0.0
      12.796784
      194.915030
      0.0
      0.0
      50.263893
      73.871705
      0.658665
      ...
      39.050579
      76.416595
      97.219471
      0.0
      0.0
      0.0
      0.0
      0.0
      73871.704820
      50952.802306
    
    
      min
      0.000000
      0.000000
      4.0
      0.000000
      272.000000
      0.0
      1.0
      0.000000
      0.000000
      0.000000
      ...
      0.000000
      0.000000
      0.000000
      0.0
      0.0
      0.0
      0.0
      0.0
      0.000000
      0.000000
    
    
      25%
      719.500000
      415.000000
      4.0
      4.000000
      331.500000
      0.0
      1.0
      NaN
      34.237299
      0.998000
      ...
      100.000000
      108.500000
      68.000000
      0.0
      0.0
      0.0
      0.0
      0.0
      34237.298537
      13933.000000
    
    
      50%
      1439.000000
      610.000000
      4.0
      6.000000
      383.000000
      0.0
      1.0
      NaN
      96.345406
      1.772000
      ...
      123.000000
      189.000000
      215.000000
      0.0
      0.0
      0.0
      0.0
      0.0
      96345.405890
      57417.000000
    
    
      75%
      2158.500000
      663.000000
      4.0
      10.000000
      570.000000
      0.0
      1.0
      NaN
      174.157404
      1.982000
      ...
      142.000000
      219.500000
      246.000000
      0.0
      0.0
      0.0
      0.0
      0.0
      174157.403520
      113267.000000
    
    
      max
      2878.000000
      721.000000
      4.0
      198.000000
      902.000000
      0.0
      1.0
      585.600000
      240.990336
      2.166000
      ...
      209.000000
      271.000000
      314.000000
      0.0
      0.0
      0.0
      0.0
      0.0
      240990.336000
      155717.000000
    
  

8 rows × 42 columns



In [15]:

    
sp = df["n_pickups"].sum() / (df["n_pickups"].sum() + df["n_ignored"].sum())



In [17]:

    
df["serviced_percentage"]









    



0.396620055526



In [18]:

    
mtd = df["mean_delay"] - df["mean_waiting_time"]



In [19]:

    
print mtd.mean()









    



195.505325818



In [ ]:

	Unnamed: 0	active_taxis	capacity	empty_moving_to_pickup	empty_rebalancing	empty_waiting	is_long	mean_delay	mean_km_travelled	mean_passengers	...	time_pass_2	time_pass_3	time_pass_4	time_pass_5	time_pass_6	time_pass_7	time_pass_8	time_pass_9	total_km_travelled	total_passengers
count	2879.000000	2879.000000	2879.0	2879.000000	2879.000000	2879.0	2879.0	2875.000000	2879.000000	2879.000000	...	2879.000000	2879.000000	2879.000000	2879.0	2879.0	2879.0	2879.0	2879.0	2879.000000	2879.000000
mean	1439.000000	516.654047	4.0	8.907954	474.437999	0.0	1.0	378.750128	107.316655	1.450488	...	115.453630	158.415769	167.183050	0.0	0.0	0.0	0.0	0.0	107316.654862	66261.895102
std	831.240038	197.212540	0.0	12.796784	194.915030	0.0	0.0	50.263893	73.871705	0.658665	...	39.050579	76.416595	97.219471	0.0	0.0	0.0	0.0	0.0	73871.704820	50952.802306
min	0.000000	0.000000	4.0	0.000000	272.000000	0.0	1.0	0.000000	0.000000	0.000000	...	0.000000	0.000000	0.000000	0.0	0.0	0.0	0.0	0.0	0.000000	0.000000
25%	719.500000	415.000000	4.0	4.000000	331.500000	0.0	1.0	NaN	34.237299	0.998000	...	100.000000	108.500000	68.000000	0.0	0.0	0.0	0.0	0.0	34237.298537	13933.000000
50%	1439.000000	610.000000	4.0	6.000000	383.000000	0.0	1.0	NaN	96.345406	1.772000	...	123.000000	189.000000	215.000000	0.0	0.0	0.0	0.0	0.0	96345.405890	57417.000000
75%	2158.500000	663.000000	4.0	10.000000	570.000000	0.0	1.0	NaN	174.157404	1.982000	...	142.000000	219.500000	246.000000	0.0	0.0	0.0	0.0	0.0	174157.403520	113267.000000
max	2878.000000	721.000000	4.0	198.000000	902.000000	0.0	1.0	585.600000	240.990336	2.166000	...	209.000000	271.000000	314.000000	0.0	0.0	0.0	0.0	0.0	240990.336000	155717.000000