Overview

Dataset statistics

Number of variables11
Number of observations500
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory48.5 KiB
Average record size in memory99.3 B

Variable types

Numeric11

Dataset

Description샘플 데이터
Author녹색교통운동본부
URLhttps://bigdata.seoul.go.kr/data/selectSampleData.do?sample_data_seq=16

Alerts

데이터_저장시간(SAVE_TIME) has unique valuesUnique
현재속도(SPEED) has 33 (6.6%) zerosZeros
방위(DIRECTION) has 35 (7.0%) zerosZeros

Reproduction

Analysis started2023-12-10 15:03:27.207037
Analysis finished2023-12-10 15:03:58.074374
Duration30.87 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

경로식별번호(ID)
Real number (ℝ)

Distinct199
Distinct (%)39.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean750758.64
Minimum750222
Maximum751295
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-11T00:03:58.248226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum750222
5-th percentile750256
Q1750534
median750709
Q3751085
95-th percentile751292
Maximum751295
Range1073
Interquartile range (IQR)551

Descriptive statistics

Standard deviation345.39262
Coefficient of variation (CV)0.00046005814
Kurtosis-1.3114207
Mean750758.64
Median Absolute Deviation (MAD)319
Skewness0.015277791
Sum3.7537932 × 108
Variance119296.06
MonotonicityNot monotonic
2023-12-11T00:03:58.523447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
750258 15
 
3.0%
750556 15
 
3.0%
750691 14
 
2.8%
751292 14
 
2.8%
750563 12
 
2.4%
750428 11
 
2.2%
751076 10
 
2.0%
750222 10
 
2.0%
751294 9
 
1.8%
750248 8
 
1.6%
Other values (189) 382
76.4%
ValueCountFrequency (%)
750222 10
2.0%
750248 8
1.6%
750250 2
 
0.4%
750254 3
 
0.6%
750256 3
 
0.6%
750257 4
 
0.8%
750258 15
3.0%
750259 7
1.4%
750261 6
 
1.2%
750262 1
 
0.2%
ValueCountFrequency (%)
751295 1
 
0.2%
751294 9
1.8%
751293 6
1.2%
751292 14
2.8%
751291 7
1.4%
751290 3
 
0.6%
751289 1
 
0.2%
751286 1
 
0.2%
751281 2
 
0.4%
751278 1
 
0.2%

경로별순서(SEQ)
Real number (ℝ)

Distinct457
Distinct (%)91.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2015.824
Minimum2
Maximum15002
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-11T00:03:58.801688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile59.95
Q1357
median858.5
Q32741.75
95-th percentile7174
Maximum15002
Range15000
Interquartile range (IQR)2384.75

Descriptive statistics

Standard deviation2588.5859
Coefficient of variation (CV)1.2841329
Kurtosis4.5856655
Mean2015.824
Median Absolute Deviation (MAD)658
Skewness2.07323
Sum1007912
Variance6700776.8
MonotonicityNot monotonic
2023-12-11T00:03:59.068234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
322 3
 
0.6%
328 3
 
0.6%
691 3
 
0.6%
67 2
 
0.4%
295 2
 
0.4%
754 2
 
0.4%
280 2
 
0.4%
431 2
 
0.4%
7 2
 
0.4%
237 2
 
0.4%
Other values (447) 477
95.4%
ValueCountFrequency (%)
2 2
0.4%
7 2
0.4%
8 1
0.2%
9 1
0.2%
10 1
0.2%
14 1
0.2%
22 1
0.2%
27 2
0.4%
29 1
0.2%
30 2
0.4%
ValueCountFrequency (%)
15002 1
0.2%
13132 1
0.2%
12821 1
0.2%
12711 1
0.2%
12237 1
0.2%
11659 1
0.2%
11412 1
0.2%
11377 1
0.2%
11221 1
0.2%
10710 1
0.2%

위도(LATITUDE)
Real number (ℝ)

Distinct499
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.54515
Minimum37.286941
Maximum37.679338
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-11T00:03:59.312382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.286941
5-th percentile37.461876
Q137.509086
median37.55351
Q337.577759
95-th percentile37.643327
Maximum37.679338
Range0.39239696
Interquartile range (IQR)0.068673678

Descriptive statistics

Standard deviation0.057785378
Coefficient of variation (CV)0.0015390903
Kurtosis1.7362376
Mean37.54515
Median Absolute Deviation (MAD)0.037042134
Skewness-0.59881993
Sum18772.575
Variance0.0033391499
MonotonicityNot monotonic
2023-12-11T00:03:59.558611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.566778 2
 
0.4%
37.48352386 1
 
0.2%
37.5674126824443 1
 
0.2%
37.5258239961555 1
 
0.2%
37.4968990416767 1
 
0.2%
37.51881316 1
 
0.2%
37.51382375 1
 
0.2%
37.60961629 1
 
0.2%
37.5560294355018 1
 
0.2%
37.57025419 1
 
0.2%
Other values (489) 489
97.8%
ValueCountFrequency (%)
37.2869410524488 1
0.2%
37.2990175858517 1
0.2%
37.3027331657539 1
0.2%
37.3146161970154 1
0.2%
37.3321869530743 1
0.2%
37.3889136473088 1
0.2%
37.3944072284869 1
0.2%
37.4475779699184 1
0.2%
37.45235844 1
0.2%
37.45342998 1
0.2%
ValueCountFrequency (%)
37.67933801 1
0.2%
37.67523685 1
0.2%
37.66982059 1
0.2%
37.66726956 1
0.2%
37.66317436 1
0.2%
37.6627752508993 1
0.2%
37.66003776 1
0.2%
37.65432623 1
0.2%
37.65312136 1
0.2%
37.648492835193 1
0.2%

경도(LONGITUDE)
Real number (ℝ)

Distinct499
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.93659
Minimum126.49367
Maximum127.24196
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-11T00:03:59.804847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.49367
5-th percentile126.79367
Q1126.87593
median126.91276
Q3127.04103
95-th percentile127.07743
Maximum127.24196
Range0.74828452
Interquartile range (IQR)0.165097

Descriptive statistics

Standard deviation0.10566013
Coefficient of variation (CV)0.00083238509
Kurtosis0.87535627
Mean126.93659
Median Absolute Deviation (MAD)0.062155542
Skewness-0.32560899
Sum63468.296
Variance0.011164062
MonotonicityNot monotonic
2023-12-11T00:04:00.029756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.072978 2
 
0.4%
126.92832445 1
 
0.2%
126.87695858 1
 
0.2%
126.878345987628 1
 
0.2%
126.8659529884 1
 
0.2%
126.87572201 1
 
0.2%
126.87663595651 1
 
0.2%
127.07740089 1
 
0.2%
126.8779648 1
 
0.2%
126.73102086 1
 
0.2%
Other values (489) 489
97.8%
ValueCountFrequency (%)
126.49367230896 1
0.2%
126.55002782 1
0.2%
126.55543874 1
0.2%
126.58619491 1
0.2%
126.5956212 1
0.2%
126.65564872 1
0.2%
126.65769043 1
0.2%
126.6631601 1
0.2%
126.66638176 1
0.2%
126.673037963184 1
0.2%
ValueCountFrequency (%)
127.24195683 1
0.2%
127.23206503 1
0.2%
127.21607143 1
0.2%
127.17285579 1
0.2%
127.12828073 1
0.2%
127.12086834 1
0.2%
127.120515588494 1
0.2%
127.117969225317 1
0.2%
127.117861320456 1
0.2%
127.117769123804 1
0.2%

현재고도(ELEVATION)
Real number (ℝ)

Distinct350
Distinct (%)70.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42.678121
Minimum-1.240845
Maximum203.60001
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)0.2%
Memory size4.5 KiB
2023-12-11T00:04:00.248759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-1.240845
5-th percentile19.935572
Q128
median39.137533
Q350.055822
95-th percentile83.035176
Maximum203.60001
Range204.84085
Interquartile range (IQR)22.055822

Descriptive statistics

Standard deviation22.173959
Coefficient of variation (CV)0.51956267
Kurtosis9.0723276
Mean42.678121
Median Absolute Deviation (MAD)11.137533
Skewness2.2106464
Sum21339.061
Variance491.68444
MonotonicityNot monotonic
2023-12-11T00:04:00.452452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
26.0 17
 
3.4%
44.0 8
 
1.6%
29.0 8
 
1.6%
45.0 8
 
1.6%
27.0 8
 
1.6%
25.0 8
 
1.6%
42.0 8
 
1.6%
38.0 7
 
1.4%
36.0 6
 
1.2%
28.0 6
 
1.2%
Other values (340) 416
83.2%
ValueCountFrequency (%)
-1.240845 1
0.2%
4.60866465726372 1
0.2%
5.322612 1
0.2%
5.381073 1
0.2%
6.333575 1
0.2%
7.30989029613126 1
0.2%
8.118003 1
0.2%
10.101631 1
0.2%
10.847552 1
0.2%
12.806007 1
0.2%
ValueCountFrequency (%)
203.600006103516 1
0.2%
164.993933735754 1
0.2%
147.0 1
0.2%
130.0 1
0.2%
123.0 1
0.2%
122.0 1
0.2%
119.994263 1
0.2%
112.5 1
0.2%
111.484928933826 1
0.2%
110.669017647671 1
0.2%

현재속도(SPEED)
Real number (ℝ)

ZEROS 

Distinct302
Distinct (%)60.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.6259125
Minimum0
Maximum24.25
Zeros33
Zeros (%)6.6%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-11T00:04:00.671273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median5
Q36.1329954
95-th percentile7.8697608
Maximum24.25
Range24.25
Interquartile range (IQR)3.1329954

Descriptive statistics

Standard deviation2.5479634
Coefficient of variation (CV)0.55080233
Kurtosis7.3633754
Mean4.6259125
Median Absolute Deviation (MAD)1.5
Skewness1.0162966
Sum2312.9563
Variance6.4921174
MonotonicityNot monotonic
2023-12-11T00:04:00.889262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 33
 
6.6%
5.75 17
 
3.4%
6.0 16
 
3.2%
5.25 14
 
2.8%
6.5 11
 
2.2%
7.0 10
 
2.0%
5.5 9
 
1.8%
3.0 9
 
1.8%
4.5 8
 
1.6%
4.25 7
 
1.4%
Other values (292) 366
73.2%
ValueCountFrequency (%)
0.0 33
6.6%
0.5 2
 
0.4%
0.75 2
 
0.4%
0.77 1
 
0.2%
0.84 1
 
0.2%
0.91148126 1
 
0.2%
0.96 1
 
0.2%
1.0 4
 
0.8%
1.07 1
 
0.2%
1.09 1
 
0.2%
ValueCountFrequency (%)
24.25 1
0.2%
16.02 1
0.2%
14.77 1
0.2%
13.491876 1
0.2%
11.25 1
0.2%
10.5 1
0.2%
10.030229 1
0.2%
9.5 1
0.2%
9.298756 1
0.2%
8.994359 1
0.2%

방위(DIRECTION)
Real number (ℝ)

ZEROS 

Distinct452
Distinct (%)90.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean164.01071
Minimum0
Maximum357.1
Zeros35
Zeros (%)7.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-11T00:04:01.074123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q160.013418
median160.15
Q3268.025
95-th percentile342.06684
Maximum357.1
Range357.1
Interquartile range (IQR)208.01158

Descriptive statistics

Standard deviation112.97036
Coefficient of variation (CV)0.6887987
Kurtosis-1.27764
Mean164.01071
Median Absolute Deviation (MAD)105.65
Skewness0.13177591
Sum82005.355
Variance12762.303
MonotonicityNot monotonic
2023-12-11T00:04:01.276274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 35
 
7.0%
54.0 3
 
0.6%
72.6 2
 
0.4%
312.5 2
 
0.4%
116.4 2
 
0.4%
160.2 2
 
0.4%
346.9 2
 
0.4%
297.4 2
 
0.4%
155.5 2
 
0.4%
48.0 2
 
0.4%
Other values (442) 446
89.2%
ValueCountFrequency (%)
0.0 35
7.0%
0.4 1
 
0.2%
1.0434681 1
 
0.2%
1.1118833 1
 
0.2%
1.8 1
 
0.2%
1.9 1
 
0.2%
2.0457654 1
 
0.2%
2.5 1
 
0.2%
3.6 1
 
0.2%
4.0 1
 
0.2%
ValueCountFrequency (%)
357.1 1
0.2%
353.6 1
0.2%
353.08307 1
0.2%
351.8 1
0.2%
351.2 1
0.2%
351.1 1
0.2%
350.84024 1
0.2%
350.7 1
0.2%
350.47086 1
0.2%
350.0 1
0.2%
Distinct498
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16650.422
Minimum5.021
Maximum137643.08
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-11T00:04:01.625202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5.021
5-th percentile470.68041
Q12880.7257
median7821.4282
Q321092.868
95-th percentile52602.192
Maximum137643.08
Range137638.06
Interquartile range (IQR)18212.142

Descriptive statistics

Standard deviation22589.942
Coefficient of variation (CV)1.3567189
Kurtosis9.4013464
Mean16650.422
Median Absolute Deviation (MAD)6247.411
Skewness2.7535062
Sum8325211
Variance5.103055 × 108
MonotonicityNot monotonic
2023-12-11T00:04:02.019034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21395.191406 3
 
0.6%
3168.71088814736 1
 
0.2%
9313.70371219516 1
 
0.2%
2186.54375743866 1
 
0.2%
11261.799529314 1
 
0.2%
7992.87607502938 1
 
0.2%
7933.58428139985 1
 
0.2%
8811.19720256329 1
 
0.2%
17971.9615399838 1
 
0.2%
15529.4737993432 1
 
0.2%
Other values (488) 488
97.6%
ValueCountFrequency (%)
5.021 1
0.2%
14.4647394700878 1
0.2%
44.3784828186035 1
0.2%
68.6535987854004 1
0.2%
80.1015396118164 1
0.2%
94.6363053321838 1
0.2%
105.064060688019 1
0.2%
127.516680240631 1
0.2%
168.853991985321 1
0.2%
169.403361320496 1
0.2%
ValueCountFrequency (%)
137643.081054268 1
0.2%
137096.11103302 1
0.2%
133042.132714389 1
0.2%
129094.039227588 1
0.2%
119034.886044366 1
0.2%
117909.060513837 1
0.2%
117281.102730615 1
0.2%
116634.570179591 1
0.2%
112118.256913816 1
0.2%
108792.242717415 1
0.2%

총_칼로리(TOT_CAL)
Real number (ℝ)

Distinct498
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean904.00538
Minimum0
Maximum8345.483
Zeros1
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-11T00:04:02.315770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile33.164128
Q1200.07904
median443.85359
Q31232.5362
95-th percentile3135.7835
Maximum8345.483
Range8345.483
Interquartile range (IQR)1032.4571

Descriptive statistics

Standard deviation1097.1375
Coefficient of variation (CV)1.2136405
Kurtosis9.3304715
Mean904.00538
Median Absolute Deviation (MAD)336.39297
Skewness2.5129128
Sum452002.69
Variance1203710.7
MonotonicityNot monotonic
2023-12-11T00:04:02.584275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1348.192017 3
 
0.6%
23.347439 1
 
0.2%
2757.63774010402 1
 
0.2%
3435.48284075409 1
 
0.2%
1303.854248 1
 
0.2%
840.289780343998 1
 
0.2%
1027.95114265618 1
 
0.2%
128.78054557105 1
 
0.2%
1170.59336613704 1
 
0.2%
51.956978370252 1
 
0.2%
Other values (488) 488
97.6%
ValueCountFrequency (%)
0.0 1
0.2%
0.618061 1
0.2%
1.71101753568617 1
0.2%
1.8327943234816 1
0.2%
2.72728107877194 1
0.2%
3.03648268035084 1
0.2%
4.13104886166203 1
0.2%
8.45946962994354 1
0.2%
9.76473616425609 1
0.2%
10.0465508082669 1
0.2%
ValueCountFrequency (%)
8345.48301491442 1
0.2%
7544.44464559555 1
0.2%
6970.25934527141 1
0.2%
5152.00578795092 1
0.2%
5055.97156294738 1
0.2%
4862.52186525764 1
0.2%
4779.16836625829 1
0.2%
4598.69551696879 1
0.2%
4562.36395461739 1
0.2%
3758.3843522521 1
0.2%
Distinct484
Distinct (%)96.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4403.868
Minimum43
Maximum40509
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-11T00:04:03.229758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum43
5-th percentile322.75
Q1973.25
median2191.5
Q35212.25
95-th percentile14967.25
Maximum40509
Range40466
Interquartile range (IQR)4239

Descriptive statistics

Standard deviation6043.9582
Coefficient of variation (CV)1.3724204
Kurtosis11.784379
Mean4403.868
Median Absolute Deviation (MAD)1552.5
Skewness3.0898855
Sum2201934
Variance36529431
MonotonicityNot monotonic
2023-12-11T00:04:03.492452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
732 2
 
0.4%
2472 2
 
0.4%
473 2
 
0.4%
231 2
 
0.4%
4304 2
 
0.4%
371 2
 
0.4%
1106 2
 
0.4%
2701 2
 
0.4%
1078 2
 
0.4%
947 2
 
0.4%
Other values (474) 480
96.0%
ValueCountFrequency (%)
43 1
0.2%
46 1
0.2%
51 1
0.2%
55 1
0.2%
65 1
0.2%
76 1
0.2%
98 1
0.2%
108 1
0.2%
113 1
0.2%
121 1
0.2%
ValueCountFrequency (%)
40509 1
0.2%
40436 1
0.2%
35819 1
0.2%
35248 1
0.2%
34875 1
0.2%
34128 1
0.2%
31270 1
0.2%
29529 1
0.2%
28390 1
0.2%
27596 1
0.2%

데이터_저장시간(SAVE_TIME)
Real number (ℝ)

UNIQUE 

Distinct500
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0160215 × 1013
Minimum2.0160201 × 1013
Maximum2.0160229 × 1013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-11T00:04:03.768059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.0160201 × 1013
5-th percentile2.0160202 × 1013
Q12.0160207 × 1013
median2.0160215 × 1013
Q32.0160222 × 1013
95-th percentile2.0160227 × 1013
Maximum2.0160229 × 1013
Range28170105
Interquartile range (IQR)14935064

Descriptive statistics

Standard deviation8146923.6
Coefficient of variation (CV)4.0410896 × 10-7
Kurtosis-1.2422315
Mean2.0160215 × 1013
Median Absolute Deviation (MAD)6967857
Skewness-0.024360521
Sum1.0080107 × 1016
Variance6.6372364 × 1013
MonotonicityNot monotonic
2023-12-11T00:04:04.026818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20160226213525 1
 
0.2%
20160207162948 1
 
0.2%
20160220151841 1
 
0.2%
20160222123856 1
 
0.2%
20160220160212 1
 
0.2%
20160202115405 1
 
0.2%
20160211195020 1
 
0.2%
20160216181320 1
 
0.2%
20160220151503 1
 
0.2%
20160222130843 1
 
0.2%
Other values (490) 490
98.0%
ValueCountFrequency (%)
20160201045222 1
0.2%
20160201052816 1
0.2%
20160201093806 1
0.2%
20160201095702 1
0.2%
20160201100052 1
0.2%
20160201180555 1
0.2%
20160201181924 1
0.2%
20160201182155 1
0.2%
20160201182448 1
0.2%
20160201183704 1
0.2%
ValueCountFrequency (%)
20160229215327 1
0.2%
20160229215017 1
0.2%
20160229203139 1
0.2%
20160229200345 1
0.2%
20160229183730 1
0.2%
20160229183358 1
0.2%
20160229182357 1
0.2%
20160229144020 1
0.2%
20160229143942 1
0.2%
20160229143123 1
0.2%

Interactions

2023-12-11T00:03:54.431041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:03:28.230801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:03:31.293130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:03:33.831223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:03:36.445579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:03:39.124301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:03:41.738901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:03:44.555055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:03:46.979617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:03:49.366304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:03:51.867618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:03:54.633443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:03:28.914701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:03:31.549194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:03:34.015862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:03:36.746947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:03:39.336664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:03:42.468645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:03:44.769866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:03:47.166040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:03:49.559904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:03:52.231540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:03:55.316704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:03:29.115402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:03:31.789324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:03:34.218575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:03:36.992797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:03:39.626376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:03:42.669211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:03:44.977312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:03:47.390104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:03:49.747038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:03:52.454052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:03:55.566060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:03:29.325140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:03:32.054719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:03:34.473227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:03:37.264932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:03:39.872574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:03:42.914612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:03:45.232309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:03:47.704105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:03:49.930321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:03:52.681996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:03:55.820358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:03:29.567094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:03:32.274995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:03:34.693461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:03:37.530452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:03:40.097276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:03:43.122006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:03:45.450586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:03:47.930283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:03:50.121233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:03:52.915602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:03:56.110416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:03:29.798426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:03:32.515710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:03:34.886210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:03:37.728201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:03:40.275740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:03:43.320785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:03:45.644924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:03:48.107297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:03:50.330444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:03:53.111168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:03:56.357396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:03:30.046970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:03:32.730052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:03:35.099407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:03:37.931736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:03:40.486200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:03:43.511171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:03:45.821859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:03:48.317529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:03:50.578031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:03:53.298633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:03:56.600525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:03:30.298287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:03:32.967011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:03:35.308221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:03:38.134971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:03:40.713889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:03:43.737661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:03:46.041912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:03:48.527011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:03:50.823468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:03:53.515209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:03:56.863017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:03:30.545108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:03:33.222674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:03:35.623946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:03:38.340751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:03:40.963665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:03:43.960728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:03:46.343202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:03:48.741574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:03:51.039097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:03:53.735472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:03:57.104347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:03:30.766080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:03:33.408650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:03:35.929920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:03:38.653328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:03:41.158044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:03:44.142962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:03:46.540271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:03:48.949804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:03:51.219307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:03:53.955469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:03:57.325852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:03:31.032968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:03:33.646226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:03:36.171478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:03:38.866866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:03:41.509262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:03:44.349028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:03:46.753120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:03:49.152744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:03:51.474004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:03:54.239928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T00:04:04.189986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
경로식별번호(ID)경로별순서(SEQ)위도(LATITUDE)경도(LONGITUDE)현재고도(ELEVATION)현재속도(SPEED)방위(DIRECTION)총_이동거리(TOT_DIST)총_칼로리(TOT_CAL)총_사용시간(TOT_TIME)데이터_저장시간(SAVE_TIME)
경로식별번호(ID)1.0000.0880.0000.1130.0600.1170.0750.0000.1060.0000.000
경로별순서(SEQ)0.0881.0000.0000.2400.0000.3500.1760.2970.0000.0000.000
위도(LATITUDE)0.0000.0001.0000.0000.1840.0000.1380.0000.2160.1040.145
경도(LONGITUDE)0.1130.2400.0001.0000.0000.0000.0310.0000.0000.0000.000
현재고도(ELEVATION)0.0600.0000.1840.0001.0000.0000.0490.0000.2700.0000.000
현재속도(SPEED)0.1170.3500.0000.0000.0001.0000.0000.2480.0000.0000.064
방위(DIRECTION)0.0750.1760.1380.0310.0490.0001.0000.0000.0000.1240.000
총_이동거리(TOT_DIST)0.0000.2970.0000.0000.0000.2480.0001.0000.0000.5000.000
총_칼로리(TOT_CAL)0.1060.0000.2160.0000.2700.0000.0000.0001.0000.0000.010
총_사용시간(TOT_TIME)0.0000.0000.1040.0000.0000.0000.1240.5000.0001.0000.154
데이터_저장시간(SAVE_TIME)0.0000.0000.1450.0000.0000.0640.0000.0000.0100.1541.000
2023-12-11T00:04:04.426967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
경로식별번호(ID)경로별순서(SEQ)위도(LATITUDE)경도(LONGITUDE)현재고도(ELEVATION)현재속도(SPEED)방위(DIRECTION)총_이동거리(TOT_DIST)총_칼로리(TOT_CAL)총_사용시간(TOT_TIME)데이터_저장시간(SAVE_TIME)
경로식별번호(ID)1.0000.0230.066-0.049-0.063-0.0530.0240.010-0.008-0.0170.038
경로별순서(SEQ)0.0231.0000.088-0.027-0.0120.023-0.054-0.0230.0460.046-0.034
위도(LATITUDE)0.0660.0881.0000.021-0.1030.079-0.005-0.0370.0600.0120.110
경도(LONGITUDE)-0.049-0.0270.0211.000-0.089-0.0090.086-0.003-0.086-0.0320.054
현재고도(ELEVATION)-0.063-0.012-0.103-0.0891.000-0.041-0.1000.001-0.0520.0010.003
현재속도(SPEED)-0.0530.0230.079-0.009-0.0411.0000.029-0.063-0.0290.0080.027
방위(DIRECTION)0.024-0.054-0.0050.086-0.1000.0291.0000.059-0.015-0.004-0.010
총_이동거리(TOT_DIST)0.010-0.023-0.037-0.0030.001-0.0630.0591.0000.093-0.007-0.017
총_칼로리(TOT_CAL)-0.0080.0460.060-0.086-0.052-0.029-0.0150.0931.000-0.0860.032
총_사용시간(TOT_TIME)-0.0170.0460.012-0.0320.0010.008-0.004-0.007-0.0861.000-0.033
데이터_저장시간(SAVE_TIME)0.038-0.0340.1100.0540.0030.027-0.010-0.0170.032-0.0331.000

Missing values

2023-12-11T00:03:57.627143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T00:03:57.949466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

경로식별번호(ID)경로별순서(SEQ)위도(LATITUDE)경도(LONGITUDE)현재고도(ELEVATION)현재속도(SPEED)방위(DIRECTION)총_이동거리(TOT_DIST)총_칼로리(TOT_CAL)총_사용시간(TOT_TIME)데이터_저장시간(SAVE_TIME)
07502682937.483524126.92832452.3593261.75175.017303.86638323.3474392149120160226213525
175107632037.519571126.87882348.8292091.318137209.021453.1685392394.487061247020160215205256
27506911069837.523494127.0597216.3139327.0230.2392.470199365.507566980020160210172439
3750549198137.574162126.90982620.7807014.75315.9556.554316439.758283930920160205083939
475079277037.461035127.02076232.9000021.378691288.474889852.00395610.046551147720160218174730
5750556610637.485392126.85163256.05.75192.0928332866.0804911122.915969195820160225154423
67508252737.473916127.02274268.00.061.734439.859375864.849595101320160229200345
77512957437.580845127.02242729.02.186649208.5389438392.428548777.868159144020160225075318
875054269137.597313126.9792132.01.052.44027.319493136.16340265220160224201345
9750549617937.543687126.82215141.54.0132.26952.89059146.2920411823120160210084434
경로식별번호(ID)경로별순서(SEQ)위도(LATITUDE)경도(LONGITUDE)현재고도(ELEVATION)현재속도(SPEED)방위(DIRECTION)총_이동거리(TOT_DIST)총_칼로리(TOT_CAL)총_사용시간(TOT_TIME)데이터_저장시간(SAVE_TIME)
49075029380337.573195126.9070125.7283696.0266.938147.36502493.09048317920160207074358
491751076133437.51076127.06555351.05.75296.163063660.294487139.3301631422020160225211329
49275054346237.653121126.91307422.05.75196.3490.143441771.778117119320160224123401
493751095128537.459864127.21607151.05.83013734.8578575288.361219202.093922267820160218180945
49475076113137.598733126.8424347.3810156.5350.04610.241956485.1322591288420160220130252
49575069117237.547039126.880732-1.2408457.5336.149633745.19803323.633498781120160214142531
496750792580137.46194126.79746568.05.94225289.39411.763124645.6739786720160226215400
49775029288737.644687127.07314757.02.047078210.1640.83864853.461535186120160202091051
498750563683837.644858126.91369871.03.032.68724.476087508.701339625220160226221118
4997502481271137.562337127.1110152.5535813.1296390.0313.8529973521.37880816420160217101758