Overview

Dataset statistics

Number of variables5
Number of observations403
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory17.4 KiB
Average record size in memory44.3 B

Variable types

Numeric4
Text1

Dataset

Description파일 다운로드
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-21288/F/1/datasetView.do

Alerts

표준ID is highly overall correlated with ARS_ID and 1 other fieldsHigh correlation
ARS_ID is highly overall correlated with 표준ID and 1 other fieldsHigh correlation
Y좌표 is highly overall correlated with 표준ID and 1 other fieldsHigh correlation
표준ID has unique valuesUnique
ARS_ID has unique valuesUnique
X좌표 has unique valuesUnique
Y좌표 has unique valuesUnique

Reproduction

Analysis started2024-05-11 06:10:06.511792
Analysis finished2024-05-11 06:10:09.684655
Duration3.17 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

표준ID
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct403
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1214662 × 108
Minimum1 × 108
Maximum1.2400045 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.7 KiB
2024-05-11T15:10:09.785023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1 × 108
5-th percentile1.0000039 × 108
Q11.0600043 × 108
median1.120004 × 108
Q31.1800001 × 108
95-th percentile1.2300001 × 108
Maximum1.2400045 × 108
Range24000453
Interquartile range (IQR)11999581

Descriptive statistics

Standard deviation7043318.7
Coefficient of variation (CV)0.062804554
Kurtosis-1.0560534
Mean1.1214662 × 108
Median Absolute Deviation (MAD)5999970
Skewness-0.098186989
Sum4.5195089 × 1010
Variance4.9608339 × 1013
MonotonicityStrictly increasing
2024-05-11T15:10:09.989112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100000001 1
 
0.2%
116000005 1
 
0.2%
116000372 1
 
0.2%
116000014 1
 
0.2%
116000013 1
 
0.2%
116000012 1
 
0.2%
116000011 1
 
0.2%
116000010 1
 
0.2%
116000009 1
 
0.2%
116000008 1
 
0.2%
Other values (393) 393
97.5%
ValueCountFrequency (%)
100000001 1
0.2%
100000002 1
0.2%
100000003 1
0.2%
100000004 1
0.2%
100000005 1
0.2%
100000362 1
0.2%
100000363 1
0.2%
100000365 1
0.2%
100000366 1
0.2%
100000367 1
0.2%
ValueCountFrequency (%)
124000454 1
0.2%
124000453 1
0.2%
124000370 1
0.2%
124000369 1
0.2%
124000366 1
0.2%
124000365 1
0.2%
124000364 1
0.2%
124000363 1
0.2%
124000362 1
0.2%
124000361 1
0.2%

ARS_ID
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct403
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13258.568
Minimum1001
Maximum25014
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.7 KiB
2024-05-11T15:10:10.157435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1001
5-th percentile1021.1
Q17012.5
median13032
Q319010.5
95-th percentile24011.9
Maximum25014
Range24013
Interquartile range (IQR)11998

Descriptive statistics

Standard deviation6951.7908
Coefficient of variation (CV)0.5243244
Kurtosis-1.0183557
Mean13258.568
Median Absolute Deviation (MAD)5981
Skewness-0.1008642
Sum5343203
Variance48327395
MonotonicityNot monotonic
2024-05-11T15:10:10.363610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1001 1
 
0.2%
17005 1
 
0.2%
17015 1
 
0.2%
17014 1
 
0.2%
17013 1
 
0.2%
17012 1
 
0.2%
17011 1
 
0.2%
17010 1
 
0.2%
17009 1
 
0.2%
17008 1
 
0.2%
Other values (393) 393
97.5%
ValueCountFrequency (%)
1001 1
0.2%
1002 1
0.2%
1003 1
0.2%
1004 1
0.2%
1005 1
0.2%
1006 1
0.2%
1007 1
0.2%
1008 1
0.2%
1009 1
0.2%
1010 1
0.2%
ValueCountFrequency (%)
25014 1
0.2%
25013 1
0.2%
25012 1
0.2%
25011 1
0.2%
25010 1
0.2%
25009 1
0.2%
25008 1
0.2%
25007 1
0.2%
25006 1
0.2%
25005 1
0.2%

정류소명
Text

Distinct230
Distinct (%)57.1%
Missing0
Missing (%)0.0%
Memory size3.3 KiB
2024-05-11T15:10:10.596250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length26
Median length16
Mean length8.0769231
Min length3

Characters and Unicode

Total characters3255
Distinct characters287
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique63 ?
Unique (%)15.6%

Sample

1st row종로2가사거리
2nd row창경궁.서울대학교병원
3rd row명륜3가.성대입구
4th row종로2가.삼일교
5th row혜화동로터리.여운형활동터
ValueCountFrequency (%)
서울역버스환승센터 4
 
1.0%
여의도환승센터 4
 
1.0%
청량리역환승센터 3
 
0.7%
구로디지털단지역 3
 
0.7%
강서구청사거리.서울디지털대학교 2
 
0.5%
신도림역 2
 
0.5%
북가좌동삼거리 2
 
0.5%
신촌오거리.현대백화점 2
 
0.5%
웨딩타운 2
 
0.5%
아현역 2
 
0.5%
Other values (220) 377
93.5%
2024-05-11T15:10:11.040891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 176
 
5.4%
역 174
 
5.3%
서 79
 
2.4%
동 77
 
2.4%
교 69
 
2.1%
리 62
 
1.9%
대 59
 
1.8%
구 57
 
1.8%
신 48
 
1.5%
시 47
 
1.4%
Other values (277) 2407
73.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2989
91.8%
Other Punctuation 179
 
5.5%
Decimal Number 53
 
1.6%
Uppercase Letter 26
 
0.8%
Close Punctuation 3
 
0.1%
Open Punctuation 3
 
0.1%
Lowercase Letter 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
역 174
 
5.8%
서 79
 
2.6%
동 77
 
2.6%
교 69
 
2.3%
리 62
 
2.1%
대 59
 
2.0%
구 57
 
1.9%
신 48
 
1.6%
시 47
 
1.6%
문 45
 
1.5%
Other values (253) 2272
76.0%
Uppercase Letter
ValueCountFrequency (%)
C 4
15.4%
T 4
15.4%
S 4
15.4%
B 2
7.7%
K 2
7.7%
G 2
7.7%
V 2
7.7%
L 2
7.7%
M 2
7.7%
D 2
7.7%
Decimal Number
ValueCountFrequency (%)
2 12
22.6%
1 10
18.9%
3 9
17.0%
0 8
15.1%
4 4
 
7.5%
5 4
 
7.5%
6 4
 
7.5%
9 2
 
3.8%
Other Punctuation
ValueCountFrequency (%)
. 176
98.3%
, 2
 
1.1%
· 1
 
0.6%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Lowercase Letter
ValueCountFrequency (%)
e 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2989
91.8%
Common 238
 
7.3%
Latin 28
 
0.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
역 174
 
5.8%
서 79
 
2.6%
동 77
 
2.6%
교 69
 
2.3%
리 62
 
2.1%
대 59
 
2.0%
구 57
 
1.9%
신 48
 
1.6%
시 47
 
1.6%
문 45
 
1.5%
Other values (253) 2272
76.0%
Common
ValueCountFrequency (%)
. 176
73.9%
2 12
 
5.0%
1 10
 
4.2%
3 9
 
3.8%
0 8
 
3.4%
4 4
 
1.7%
5 4
 
1.7%
6 4
 
1.7%
) 3
 
1.3%
( 3
 
1.3%
Other values (3) 5
 
2.1%
Latin
ValueCountFrequency (%)
C 4
14.3%
T 4
14.3%
S 4
14.3%
B 2
7.1%
K 2
7.1%
G 2
7.1%
V 2
7.1%
L 2
7.1%
e 2
7.1%
M 2
7.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2989
91.8%
ASCII 265
 
8.1%
None 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 176
66.4%
2 12
 
4.5%
1 10
 
3.8%
3 9
 
3.4%
0 8
 
3.0%
C 4
 
1.5%
4 4
 
1.5%
5 4
 
1.5%
6 4
 
1.5%
T 4
 
1.5%
Other values (13) 30
 
11.3%
Hangul
ValueCountFrequency (%)
역 174
 
5.8%
서 79
 
2.6%
동 77
 
2.6%
교 69
 
2.3%
리 62
 
2.1%
대 59
 
2.0%
구 57
 
1.9%
신 48
 
1.6%
시 47
 
1.6%
문 45
 
1.5%
Other values (253) 2272
76.0%
None
ValueCountFrequency (%)
· 1
100.0%

X좌표
Real number (ℝ)

UNIQUE 

Distinct403
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.9834
Minimum126.80983
Maximum127.17136
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.7 KiB
2024-05-11T15:10:11.210389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.80983
5-th percentile126.86023
Q1126.92187
median126.97788
Q3127.04025
95-th percentile127.11709
Maximum127.17136
Range0.36153454
Interquartile range (IQR)0.11838386

Descriptive statistics

Standard deviation0.078718582
Coefficient of variation (CV)0.0006199124
Kurtosis-0.65504619
Mean126.9834
Median Absolute Deviation (MAD)0.05869478
Skewness0.13486419
Sum51174.309
Variance0.0061966152
MonotonicityNot monotonic
2024-05-11T15:10:11.426080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.9877522923 1
 
0.2%
126.8746997523 1
 
0.2%
126.902058345 1
 
0.2%
126.9020198843 1
 
0.2%
126.9015481288 1
 
0.2%
126.8551315518 1
 
0.2%
126.8554228318 1
 
0.2%
126.8620566339 1
 
0.2%
126.8601789687 1
 
0.2%
126.8670186731 1
 
0.2%
Other values (393) 393
97.5%
ValueCountFrequency (%)
126.8098252947 1
0.2%
126.8109375565 1
0.2%
126.8155482638 1
0.2%
126.8171527623 1
0.2%
126.8221992473 1
0.2%
126.8228597284 1
0.2%
126.8262227042 1
0.2%
126.827513 1
0.2%
126.8324754968 1
0.2%
126.8339921625 1
0.2%
ValueCountFrequency (%)
127.1713598368 1
0.2%
127.1706308587 1
0.2%
127.1615969135 1
0.2%
127.1610838354 1
0.2%
127.1488398094 1
0.2%
127.1477890164 1
0.2%
127.1429328548 1
0.2%
127.142104768 1
0.2%
127.1367996291 1
0.2%
127.1358552825 1
0.2%

Y좌표
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct403
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.552203
Minimum37.434859
Maximum37.689203
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.7 KiB
2024-05-11T15:10:11.685889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.434859
5-th percentile37.469094
Q137.509392
median37.557294
Q337.580701
95-th percentile37.643361
Maximum37.689203
Range0.25434426
Interquartile range (IQR)0.071308832

Descriptive statistics

Standard deviation0.051795366
Coefficient of variation (CV)0.0013792897
Kurtosis-0.20101803
Mean37.552203
Median Absolute Deviation (MAD)0.034130617
Skewness0.16161065
Sum15133.538
Variance0.00268276
MonotonicityNot monotonic
2024-05-11T15:10:11.931195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.5698055407 1
 
0.2%
37.4998806283 1
 
0.2%
37.4846452474 1
 
0.2%
37.4846560288 1
 
0.2%
37.4830994287 1
 
0.2%
37.4971710251 1
 
0.2%
37.4973830961 1
 
0.2%
37.4964773551 1
 
0.2%
37.496906783 1
 
0.2%
37.4993764843 1
 
0.2%
Other values (393) 393
97.5%
ValueCountFrequency (%)
37.4348585994 1
0.2%
37.4401933055 1
0.2%
37.4409634671 1
0.2%
37.4478432162 1
0.2%
37.449067 1
0.2%
37.4522224011 1
0.2%
37.4530892573 1
0.2%
37.4557109244 1
0.2%
37.4557491207 1
0.2%
37.4568017217 1
0.2%
ValueCountFrequency (%)
37.689202857 1
0.2%
37.688568 1
0.2%
37.6839229454 1
0.2%
37.682672 1
0.2%
37.6777606492 1
0.2%
37.677262 1
0.2%
37.673289362 1
0.2%
37.67275 1
0.2%
37.6699992027 1
0.2%
37.669197 1
0.2%

Interactions

2024-05-11T15:10:08.846061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:10:06.887407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:10:07.465341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:10:08.331582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:10:08.967020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:10:07.030825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:10:07.598154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:10:08.452532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:10:09.115421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:10:07.174162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:10:08.048031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:10:08.578097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:10:09.301449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:10:07.333108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:10:08.186608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:10:08.718389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-11T15:10:12.085068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
표준IDARS_IDX좌표Y좌표
표준ID1.0001.0000.9430.900
ARS_ID1.0001.0000.9430.902
X좌표0.9430.9431.0000.746
Y좌표0.9000.9020.7461.000
2024-05-11T15:10:12.222995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
표준IDARS_IDX좌표Y좌표
표준ID1.0000.986-0.104-0.688
ARS_ID0.9861.000-0.110-0.694
X좌표-0.104-0.1101.0000.148
Y좌표-0.688-0.6940.1481.000

Missing values

2024-05-11T15:10:09.493217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-11T15:10:09.640852image/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

표준IDARS_ID정류소명X좌표Y좌표
01000000011001종로2가사거리126.98775237.569806
11000000021002창경궁.서울대학교병원126.99652137.579433
21000000031003명륜3가.성대입구126.99825137.58258
31000000041004종로2가.삼일교126.98761337.568579
41000000051005혜화동로터리.여운형활동터127.00174437.586243
510000036213037영천시장126.96313537.569528
610000036313036영천시장126.96253337.569939
71000003651037동대문역.흥인지문127.01245237.572105
81000003661041동묘앞127.0186137.573832
91000003671044동묘앞127.01955337.574246
표준IDARS_ID정류소명X좌표Y좌표
39312400036125001상일초교127.1713637.546125
39412400036225002상일초교127.17063137.545794
39512400036325006강동자이·프라자아파트127.14778937.53621
39612400036425008길동주민센터.둔촌2동주민센터127.14210537.533886
39712400036525010길동사거리.강동세무서127.13585537.534733
39812400036625012강동역127.13151637.536108
39912400036925003초이동127.16159737.542139
40012400037025004초이동127.16108437.541846
40112400045325013천호역127.12705237.537653
40212400045425014천호역127.12553937.537948