Overview

Dataset statistics

Number of variables5
Number of observations400
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory17.3 KiB
Average record size in memory44.3 B

Variable types

Numeric4
Text1

Dataset

Description파일 다운로드
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-22238/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

Reproduction

Analysis started2024-03-13 17:57:02.976921
Analysis finished2024-03-13 17:57:04.444766
Duration1.47 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

표준ID
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct400
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1211522 × 108
Minimum1 × 108
Maximum1.2400045 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.6 KiB
2024-03-14T02:57:04.509408image/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)11999582

Descriptive statistics

Standard deviation7060231.3
Coefficient of variation (CV)0.062972995
Kurtosis-1.0628279
Mean1.1211522 × 108
Median Absolute Deviation (MAD)5999970
Skewness-0.08644689
Sum4.4846089 × 1010
Variance4.9846866 × 1013
MonotonicityNot monotonic
2024-03-14T02:57:04.629831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100000001 1
 
0.2%
116000003 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%
116000007 1
 
0.2%
116000006 1
 
0.2%
Other values (390) 390
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 

Distinct400
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13225.995
Minimum1001
Maximum25014
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.6 KiB
2024-03-14T02:57:04.741894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1001
5-th percentile1020.95
Q17011.75
median13030.5
Q319011.25
95-th percentile24012.05
Maximum25014
Range24013
Interquartile range (IQR)11999.5

Descriptive statistics

Standard deviation6967.2582
Coefficient of variation (CV)0.52678519
Kurtosis-1.0243465
Mean13225.995
Median Absolute Deviation (MAD)5984
Skewness-0.08889504
Sum5290398
Variance48542687
MonotonicityStrictly increasing
2024-03-14T02:57:04.846063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1001 1
 
0.2%
17003 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%
17007 1
 
0.2%
17006 1
 
0.2%
Other values (390) 390
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

Distinct228
Distinct (%)57.0%
Missing0
Missing (%)0.0%
Memory size3.3 KiB
2024-03-14T02:57:05.014218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length26
Median length15
Mean length8.05
Min length3

Characters and Unicode

Total characters3220
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

Unique62 ?
Unique (%)15.5%

Sample

1st row종로2가사거리
2nd row창경궁.서울대학교병원
3rd row명륜3가.성대입구
4th row종로2가.삼일교
5th row혜화동로터리.여운형활동터
ValueCountFrequency (%)
여의도환승센터 4
 
1.0%
서울역버스환승센터 4
 
1.0%
청량리역환승센터 3
 
0.8%
구로디지털단지역 3
 
0.8%
영천시장 2
 
0.5%
마포경찰서 2
 
0.5%
고속터미널 2
 
0.5%
홍제역.서대문세무서 2
 
0.5%
구반포역.세화고등학교 2
 
0.5%
무악재역 2
 
0.5%
Other values (218) 374
93.5%
2024-03-14T02:57:05.292119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 174
 
5.4%
역 171
 
5.3%
동 77
 
2.4%
서 77
 
2.4%
교 69
 
2.1%
리 62
 
1.9%
대 59
 
1.8%
구 55
 
1.7%
신 48
 
1.5%
시 47
 
1.5%
Other values (277) 2381
73.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2956
91.8%
Other Punctuation 177
 
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 171
 
5.8%
동 77
 
2.6%
서 77
 
2.6%
교 69
 
2.3%
리 62
 
2.1%
대 59
 
2.0%
구 55
 
1.9%
신 48
 
1.6%
시 47
 
1.6%
문 45
 
1.5%
Other values (253) 2246
76.0%
Uppercase Letter
ValueCountFrequency (%)
C 4
15.4%
T 4
15.4%
S 4
15.4%
V 2
7.7%
L 2
7.7%
G 2
7.7%
B 2
7.7%
D 2
7.7%
M 2
7.7%
K 2
7.7%
Decimal Number
ValueCountFrequency (%)
2 12
22.6%
1 10
18.9%
3 9
17.0%
0 8
15.1%
6 4
 
7.5%
5 4
 
7.5%
4 4
 
7.5%
9 2
 
3.8%
Other Punctuation
ValueCountFrequency (%)
. 174
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 2956
91.8%
Common 236
 
7.3%
Latin 28
 
0.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
역 171
 
5.8%
동 77
 
2.6%
서 77
 
2.6%
교 69
 
2.3%
리 62
 
2.1%
대 59
 
2.0%
구 55
 
1.9%
신 48
 
1.6%
시 47
 
1.6%
문 45
 
1.5%
Other values (253) 2246
76.0%
Common
ValueCountFrequency (%)
. 174
73.7%
2 12
 
5.1%
1 10
 
4.2%
3 9
 
3.8%
0 8
 
3.4%
6 4
 
1.7%
5 4
 
1.7%
4 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%
V 2
7.1%
L 2
7.1%
G 2
7.1%
e 2
7.1%
B 2
7.1%
D 2
7.1%
M 2
7.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2956
91.8%
ASCII 263
 
8.2%
None 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 174
66.2%
2 12
 
4.6%
1 10
 
3.8%
3 9
 
3.4%
0 8
 
3.0%
C 4
 
1.5%
6 4
 
1.5%
T 4
 
1.5%
5 4
 
1.5%
4 4
 
1.5%
Other values (13) 30
 
11.4%
Hangul
ValueCountFrequency (%)
역 171
 
5.8%
동 77
 
2.6%
서 77
 
2.6%
교 69
 
2.3%
리 62
 
2.1%
대 59
 
2.0%
구 55
 
1.9%
신 48
 
1.6%
시 47
 
1.6%
문 45
 
1.5%
Other values (253) 2246
76.0%
None
ValueCountFrequency (%)
· 1
100.0%

X좌표
Real number (ℝ)

UNIQUE 

Distinct400
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.984
Minimum126.80983
Maximum127.17136
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.6 KiB
2024-03-14T02:57:05.438375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.80983
5-th percentile126.85995
Q1126.92309
median126.98135
Q3127.04054
95-th percentile127.11734
Maximum127.17136
Range0.36153454
Interquartile range (IQR)0.11744951

Descriptive statistics

Standard deviation0.078698105
Coefficient of variation (CV)0.00061974818
Kurtosis-0.64741562
Mean126.984
Median Absolute Deviation (MAD)0.059213853
Skewness0.12100045
Sum50793.601
Variance0.0061933917
MonotonicityNot monotonic
2024-03-14T02:57:05.558532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.9877522923 1
 
0.2%
126.8839791469 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%
126.8679976691 1
 
0.2%
126.8745360708 1
 
0.2%
Other values (390) 390
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 

Distinct399
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.552834
Minimum37.440193
Maximum37.689203
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.6 KiB
2024-03-14T02:57:05.677457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.440193
5-th percentile37.470078
Q137.511671
median37.557357
Q337.581003
95-th percentile37.643469
Maximum37.689203
Range0.24900955
Interquartile range (IQR)0.069331248

Descriptive statistics

Standard deviation0.051430991
Coefficient of variation (CV)0.0013695635
Kurtosis-0.19169512
Mean37.552834
Median Absolute Deviation (MAD)0.033179484
Skewness0.17065018
Sum15021.134
Variance0.0026451469
MonotonicityNot monotonic
2024-03-14T02:57:05.803946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.5553932418 2
 
0.5%
37.5698055407 1
 
0.2%
37.5053435935 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%
37.4996343963 1
 
0.2%
Other values (389) 389
97.2%
ValueCountFrequency (%)
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%
37.4571774154 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

2024-03-14T02:57:04.011343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T02:57:03.169622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T02:57:03.433762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T02:57:03.704566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T02:57:04.110341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T02:57:03.235925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T02:57:03.504602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T02:57:03.774635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T02:57:04.182211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T02:57:03.300818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T02:57:03.565801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T02:57:03.841373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T02:57:04.252499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T02:57:03.369311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T02:57:03.637892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T02:57:03.919037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-14T02:57:05.885147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
표준IDARS-IDX좌표Y좌표
표준ID1.0001.0000.9450.902
ARS-ID1.0001.0000.9450.907
X좌표0.9450.9451.0000.765
Y좌표0.9020.9070.7651.000
2024-03-14T02:57:05.957512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
표준IDARS-IDX좌표Y좌표
표준ID1.0000.985-0.100-0.685
ARS-ID0.9851.000-0.106-0.691
X좌표-0.100-0.1061.0000.136
Y좌표-0.685-0.6910.1361.000

Missing values

2024-03-14T02:57:04.335714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T02:57:04.409992image/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
51010003051006서대문역사거리126.96689337.566137
61000003801007서울역사박물관.경희궁앞126.9703837.569135
71000003791008서울역사박물관.경희궁앞126.97075937.569512
81000003841009광화문126.97635337.570142
91000003851010광화문126.9778837.57024
표준IDARS-ID정류소명X좌표Y좌표
39012400036025005강동자이.프라자아파트127.1488437.536733
39112400036325006강동자이·프라자아파트127.14778937.53621
39212400035925007길동주민센터.둔촌2동주민센터127.14293337.534274
39312400036425008길동주민센터.둔촌2동주민센터127.14210537.533886
39412400035825009길동사거리.강동세무서127.136837.53452
39512400036525010길동사거리.강동세무서127.13585537.534733
39612400035725011강동역127.13244937.535902
39712400036625012강동역127.13151637.536108
39812400045325013천호역127.12705237.537653
39912400045425014천호역127.12553937.537948