Overview

Dataset statistics

Number of variables4
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory429.7 KiB
Average record size in memory44.0 B

Variable types

Numeric4

Dataset

Description서울특별시 교통정보과에서 제공하는 교통소통 서비스링크 보간점 정보입니다.
Author서울특별시
URLhttp://data.seoul.go.kr/dataList/OA-15060/S/1/datasetView.do

Alerts

LINK_ID is highly overall correlated with GRS80TM_YHigh correlation
GRS80TM_Y is highly overall correlated with LINK_IDHigh correlation

Reproduction

Analysis started2023-12-11 06:46:02.922478
Analysis finished2023-12-11 06:46:04.940036
Duration2.02 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

LINK_ID
Real number (ℝ)

HIGH CORRELATION 

Distinct4367
Distinct (%)43.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4052841 × 109
Minimum1.0000001 × 109
Maximum3.2015107 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T15:46:05.026098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.0000001 × 109
5-th percentile1.0050029 × 109
Q11.0700056 × 109
median1.1700003 × 109
Q31.2400278 × 109
95-th percentile2.3500255 × 109
Maximum3.2015107 × 109
Range2.2015106 × 109
Interquartile range (IQR)1.7002218 × 108

Descriptive statistics

Standard deviation5.3641629 × 108
Coefficient of variation (CV)0.38171377
Kurtosis1.193888
Mean1.4052841 × 109
Median Absolute Deviation (MAD)99991500
Skewness1.5195606
Sum1.4052841 × 1013
Variance2.8774243 × 1017
MonotonicityNot monotonic
2023-12-11T15:46:05.190872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1000021200 49
 
0.5%
1000021100 47
 
0.5%
2300037801 35
 
0.4%
2370013901 33
 
0.3%
1005008600 31
 
0.3%
2370014002 29
 
0.3%
1000017000 28
 
0.3%
2240157200 23
 
0.2%
2390106400 22
 
0.2%
2300038101 22
 
0.2%
Other values (4357) 9681
96.8%
ValueCountFrequency (%)
1000000100 1
 
< 0.1%
1000000200 1
 
< 0.1%
1000000500 3
< 0.1%
1000000600 1
 
< 0.1%
1000000700 2
< 0.1%
1000000800 3
< 0.1%
1000000900 1
 
< 0.1%
1000001100 1
 
< 0.1%
1000001300 2
< 0.1%
1000001500 1
 
< 0.1%
ValueCountFrequency (%)
3201510700 1
< 0.1%
3201510600 2
< 0.1%
3201510300 1
< 0.1%
3201510200 2
< 0.1%
3201510000 1
< 0.1%
3201509900 1
< 0.1%
3201509800 1
< 0.1%
3201509600 1
< 0.1%
3201509100 1
< 0.1%
3201509000 2
< 0.1%

VER_SEQ
Real number (ℝ)

Distinct157
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.8672
Minimum1
Maximum207
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T15:46:05.387091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q310
95-th percentile36
Maximum207
Range206
Interquartile range (IQR)8

Descriptive statistics

Standard deviation17.194369
Coefficient of variation (CV)1.7425784
Kurtosis36.145216
Mean9.8672
Median Absolute Deviation (MAD)3
Skewness5.1805018
Sum98672
Variance295.64633
MonotonicityNot monotonic
2023-12-11T15:46:05.510411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1381
13.8%
3 1318
13.2%
2 1305
13.1%
4 1072
10.7%
5 625
 
6.2%
6 525
 
5.2%
7 433
 
4.3%
8 386
 
3.9%
9 298
 
3.0%
10 271
 
2.7%
Other values (147) 2386
23.9%
ValueCountFrequency (%)
1 1381
13.8%
2 1305
13.1%
3 1318
13.2%
4 1072
10.7%
5 625
6.2%
6 525
 
5.2%
7 433
 
4.3%
8 386
 
3.9%
9 298
 
3.0%
10 271
 
2.7%
ValueCountFrequency (%)
207 1
< 0.1%
204 1
< 0.1%
201 1
< 0.1%
200 1
< 0.1%
195 1
< 0.1%
190 1
< 0.1%
189 1
< 0.1%
187 2
< 0.1%
185 1
< 0.1%
184 1
< 0.1%

GRS80TM_X
Real number (ℝ)

Distinct9941
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean201375.63
Minimum154301.09
Maximum273019.78
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T15:46:05.651101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum154301.09
5-th percentile182393.98
Q1192608.05
median200651.17
Q3206763.12
95-th percentile229117.82
Maximum273019.78
Range118718.7
Interquartile range (IQR)14155.068

Descriptive statistics

Standard deviation14529.235
Coefficient of variation (CV)0.072149917
Kurtosis4.2611818
Mean201375.63
Median Absolute Deviation (MAD)7142.4321
Skewness1.3132399
Sum2.0137563 × 109
Variance2.1109866 × 108
MonotonicityNot monotonic
2023-12-11T15:46:05.783084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
194734.297483 2
 
< 0.1%
191451.43092 2
 
< 0.1%
208437.050741 2
 
< 0.1%
200181.809326 2
 
< 0.1%
193356.456954 2
 
< 0.1%
232103.029113 2
 
< 0.1%
214153.291317 2
 
< 0.1%
186640.310335 2
 
< 0.1%
190055.873824 2
 
< 0.1%
234595.611517 2
 
< 0.1%
Other values (9931) 9980
99.8%
ValueCountFrequency (%)
154301.089201 1
< 0.1%
154310.707641 1
< 0.1%
154456.699637 1
< 0.1%
154465.736167 1
< 0.1%
154466.010832 1
< 0.1%
154475.806881 1
< 0.1%
154510.80017 1
< 0.1%
154522.54932 1
< 0.1%
154562.217032 1
< 0.1%
154574.508548 1
< 0.1%
ValueCountFrequency (%)
273019.784702 1
< 0.1%
273010.224406 1
< 0.1%
272931.839967 1
< 0.1%
272736.731782 1
< 0.1%
272488.010887 1
< 0.1%
272452.849259 1
< 0.1%
272425.527098 1
< 0.1%
272391.288705 1
< 0.1%
272384.678375 1
< 0.1%
272367.394577 1
< 0.1%

GRS80TM_Y
Real number (ℝ)

HIGH CORRELATION 

Distinct9942
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean445172.62
Minimum378908.03
Maximum470565.94
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T15:46:05.909623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum378908.03
5-th percentile411443.85
Q1442789.64
median448727.14
Q3453758.38
95-th percentile460960.29
Maximum470565.94
Range91657.91
Interquartile range (IQR)10968.734

Descriptive statistics

Standard deviation14825.444
Coefficient of variation (CV)0.033302687
Kurtosis4.0285107
Mean445172.62
Median Absolute Deviation (MAD)5475.4542
Skewness-1.9231737
Sum4.4517262 × 109
Variance2.197938 × 108
MonotonicityNot monotonic
2023-12-11T15:46:06.033432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
449192.563606 2
 
< 0.1%
452916.490813 2
 
< 0.1%
449499.539519 2
 
< 0.1%
442863.593192 2
 
< 0.1%
451712.151591 2
 
< 0.1%
453556.237155 2
 
< 0.1%
459845.659804 2
 
< 0.1%
414918.935603 2
 
< 0.1%
448493.299271 2
 
< 0.1%
448043.775505 2
 
< 0.1%
Other values (9932) 9980
99.8%
ValueCountFrequency (%)
378908.034151 1
< 0.1%
380690.758921 1
< 0.1%
381703.797853 1
< 0.1%
382156.208796 1
< 0.1%
382456.642848 1
< 0.1%
382639.882468 1
< 0.1%
382681.471001 1
< 0.1%
382691.8851 1
< 0.1%
382692.02777 1
< 0.1%
382721.343128 1
< 0.1%
ValueCountFrequency (%)
470565.943652 1
< 0.1%
470362.182916 1
< 0.1%
470304.441161 1
< 0.1%
470213.832151 1
< 0.1%
470164.725015 1
< 0.1%
470125.506503 1
< 0.1%
469944.181902 1
< 0.1%
469889.754054 1
< 0.1%
469638.487581 1
< 0.1%
469272.917236 1
< 0.1%

Interactions

2023-12-11T15:46:04.547464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:46:03.754550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:46:04.208169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:46:04.624173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:46:03.853142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:46:04.289929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:46:04.710886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:46:03.968989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:46:04.375533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T15:46:06.111672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
LINK_IDVER_SEQGRS80TM_XGRS80TM_Y
LINK_ID1.0000.1810.7370.648
VER_SEQ0.1811.0000.4210.308
GRS80TM_X0.7370.4211.0000.666
GRS80TM_Y0.6480.3080.6661.000
2023-12-11T15:46:06.217497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
LINK_IDVER_SEQGRS80TM_XGRS80TM_Y
LINK_ID1.0000.0180.038-0.627
VER_SEQ0.0181.0000.120-0.006
GRS80TM_X0.0380.1201.0000.083
GRS80TM_Y-0.627-0.0060.0831.000

Missing values

2023-12-11T15:46:04.817231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T15:46:04.899825image/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

LINK_IDVER_SEQGRS80TM_XGRS80TM_Y
22802117000170014192598.414903438297.868066
138910000209001196627.501384455996.797967
17750112001390021195884.662225455219.921439
689010300069005201180.191799449318.499816
39705226001070126205409.796104432904.627398
1091610600201003206849.110678457387.307312
3077012300049002207993.260271447363.946531
112910000192002200113.17056454028.975268
554010200118004195692.871166448330.132309
36363214002910242205965.287089391840.398643
LINK_IDVER_SEQGRS80TM_XGRS80TM_Y
3743621800003031185325.907804455016.532076
26663120001010012192269.235659443128.104082
31331005008600108243223.016188462020.704746
1695611200018007194343.68065454251.032819
1414411000014009205342.354933459606.208357
1896711300218001192208.740887451759.678872
40818230003780196229677.327754425623.213507
2791512100153003202571.446419442720.779418
40934230003810141229793.656745425469.540502
648110300015001205956.026416449872.291007