Overview

Dataset statistics

Number of variables5
Number of observations50
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.2 KiB
Average record size in memory45.6 B

Variable types

Numeric3
Categorical1
Text1

Dataset

Description서울시에서 운영하는 도로전광표지의 설치위치정보 입니다.
Author서울특별시
URLhttp://data.seoul.go.kr/dataList/OA-13671/S/1/datasetView.do

Alerts

관리번호 is highly overall correlated with 좌표x and 2 other fieldsHigh correlation
좌표x is highly overall correlated with 관리번호 and 1 other fieldsHigh correlation
좌표y is highly overall correlated with 관리번호 and 1 other fieldsHigh correlation
권역구분 is highly overall correlated with 관리번호 and 2 other fieldsHigh correlation
관리번호 has unique valuesUnique
설치위치 has unique valuesUnique
좌표x has unique valuesUnique
좌표y has unique valuesUnique

Reproduction

Analysis started2023-12-11 10:08:29.562500
Analysis finished2023-12-11 10:08:30.847521
Duration1.29 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

관리번호
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct50
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1830.66
Minimum1001
Maximum3018
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size582.0 B
2023-12-11T19:08:30.959488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1001
5-th percentile1003.45
Q11013.25
median1025.5
Q33004.75
95-th percentile3014.55
Maximum3018
Range2017
Interquartile range (IQR)1991.5

Descriptive statistics

Standard deviation916.74178
Coefficient of variation (CV)0.50077119
Kurtosis-1.7430532
Mean1830.66
Median Absolute Deviation (MAD)24
Skewness0.37705894
Sum91533
Variance840415.49
MonotonicityStrictly increasing
2023-12-11T19:08:31.123697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1001 1
 
2.0%
3006 1
 
2.0%
2003 1
 
2.0%
2004 1
 
2.0%
2005 1
 
2.0%
2006 1
 
2.0%
2007 1
 
2.0%
3001 1
 
2.0%
3002 1
 
2.0%
3003 1
 
2.0%
Other values (40) 40
80.0%
ValueCountFrequency (%)
1001 1
2.0%
1002 1
2.0%
1003 1
2.0%
1004 1
2.0%
1005 1
2.0%
1006 1
2.0%
1007 1
2.0%
1008 1
2.0%
1009 1
2.0%
1010 1
2.0%
ValueCountFrequency (%)
3018 1
2.0%
3016 1
2.0%
3015 1
2.0%
3014 1
2.0%
3013 1
2.0%
3012 1
2.0%
3011 1
2.0%
3010 1
2.0%
3009 1
2.0%
3008 1
2.0%

권역구분
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
도심권
26 
잠실권
17 
서남권
7 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row도심권
2nd row도심권
3rd row도심권
4th row도심권
5th row도심권

Common Values

ValueCountFrequency (%)
도심권 26
52.0%
잠실권 17
34.0%
서남권 7
 
14.0%

Length

2023-12-11T19:08:31.266324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T19:08:31.358689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
도심권 26
52.0%
잠실권 17
34.0%
서남권 7
 
14.0%

설치위치
Text

UNIQUE 

Distinct50
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
2023-12-11T19:08:31.646083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length17
Mean length14.26
Min length6

Characters and Unicode

Total characters713
Distinct characters179
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique50 ?
Unique (%)100.0%

Sample

1st row새안빌딩 앞(도심방향)
2nd row효자공영주차장 맞은편 (도심방향)
3rd row경복궁역 버스정류소(도심방향)
4th row독립문역 중앙정류소(도심방향)
5th row서대문우체국 중앙정류소(도심방향)
ValueCountFrequency (%)
앞 14
 
11.4%
중앙정류소(도심방향 10
 
8.1%
앞(도심방향 7
 
5.7%
송파대로 3
 
2.4%
중앙정류소 2
 
1.6%
성산대교 2
 
1.6%
에이스하이테크시티 1
 
0.8%
동재한의원 1
 
0.8%
자양로 1
 
0.8%
정류장 1
 
0.8%
Other values (81) 81
65.9%
2023-12-11T19:08:32.120513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
74
 
10.4%
( 31
 
4.3%
) 31
 
4.3%
방 28
 
3.9%
향 27
 
3.8%
도 25
 
3.5%
심 24
 
3.4%
앞 22
 
3.1%
류 16
 
2.2%
정 16
 
2.2%
Other values (169) 419
58.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 561
78.7%
Space Separator 74
 
10.4%
Open Punctuation 31
 
4.3%
Close Punctuation 31
 
4.3%
Decimal Number 10
 
1.4%
Uppercase Letter 5
 
0.7%
Lowercase Letter 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
방 28
 
5.0%
향 27
 
4.8%
도 25
 
4.5%
심 24
 
4.3%
앞 22
 
3.9%
류 16
 
2.9%
정 16
 
2.9%
소 14
 
2.5%
중 13
 
2.3%
교 13
 
2.3%
Other values (155) 363
64.7%
Decimal Number
ValueCountFrequency (%)
3 3
30.0%
1 2
20.0%
2 2
20.0%
5 1
 
10.0%
0 1
 
10.0%
4 1
 
10.0%
Uppercase Letter
ValueCountFrequency (%)
K 2
40.0%
B 1
20.0%
I 1
20.0%
T 1
20.0%
Space Separator
ValueCountFrequency (%)
74
100.0%
Open Punctuation
ValueCountFrequency (%)
( 31
100.0%
Close Punctuation
ValueCountFrequency (%)
) 31
100.0%
Lowercase Letter
ValueCountFrequency (%)
m 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 561
78.7%
Common 146
 
20.5%
Latin 6
 
0.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
방 28
 
5.0%
향 27
 
4.8%
도 25
 
4.5%
심 24
 
4.3%
앞 22
 
3.9%
류 16
 
2.9%
정 16
 
2.9%
소 14
 
2.5%
중 13
 
2.3%
교 13
 
2.3%
Other values (155) 363
64.7%
Common
ValueCountFrequency (%)
74
50.7%
( 31
21.2%
) 31
21.2%
3 3
 
2.1%
1 2
 
1.4%
2 2
 
1.4%
5 1
 
0.7%
0 1
 
0.7%
4 1
 
0.7%
Latin
ValueCountFrequency (%)
K 2
33.3%
B 1
16.7%
I 1
16.7%
m 1
16.7%
T 1
16.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 561
78.7%
ASCII 152
 
21.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
74
48.7%
( 31
20.4%
) 31
20.4%
3 3
 
2.0%
1 2
 
1.3%
2 2
 
1.3%
K 2
 
1.3%
B 1
 
0.7%
I 1
 
0.7%
5 1
 
0.7%
Other values (4) 4
 
2.6%
Hangul
ValueCountFrequency (%)
방 28
 
5.0%
향 27
 
4.8%
도 25
 
4.5%
심 24
 
4.3%
앞 22
 
3.9%
류 16
 
2.9%
정 16
 
2.9%
소 14
 
2.5%
중 13
 
2.3%
교 13
 
2.3%
Other values (155) 363
64.7%

좌표x
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct50
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean199953.32
Minimum185761
Maximum212188
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size582.0 B
2023-12-11T19:08:32.258609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum185761
5-th percentile187071.3
Q1195196.75
median198795.5
Q3207507.75
95-th percentile210848.55
Maximum212188
Range26427
Interquartile range (IQR)12311

Descriptive statistics

Standard deviation7727.2525
Coefficient of variation (CV)0.038645282
Kurtosis-1.0708203
Mean199953.32
Median Absolute Deviation (MAD)6814.5
Skewness-0.10830276
Sum9997666
Variance59710431
MonotonicityNot monotonic
2023-12-11T19:08:32.387667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
197699 1
 
2.0%
207524 1
 
2.0%
187112 1
 
2.0%
189784 1
 
2.0%
185919 1
 
2.0%
187038 1
 
2.0%
190498 1
 
2.0%
203878 1
 
2.0%
204978 1
 
2.0%
210288 1
 
2.0%
Other values (40) 40
80.0%
ValueCountFrequency (%)
185761 1
2.0%
185919 1
2.0%
187038 1
2.0%
187112 1
2.0%
188975 1
2.0%
189784 1
2.0%
190498 1
2.0%
191099 1
2.0%
191296 1
2.0%
191750 1
2.0%
ValueCountFrequency (%)
212188 1
2.0%
210958 1
2.0%
210921 1
2.0%
210760 1
2.0%
210458 1
2.0%
210288 1
2.0%
209788 1
2.0%
209554 1
2.0%
209264 1
2.0%
208526 1
2.0%

좌표y
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct50
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean448556.32
Minimum442740
Maximum455151
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size582.0 B
2023-12-11T19:08:32.541591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum442740
5-th percentile443694.65
Q1445522.75
median448310
Q3451351.75
95-th percentile453425.85
Maximum455151
Range12411
Interquartile range (IQR)5829

Descriptive statistics

Standard deviation3396.7605
Coefficient of variation (CV)0.0075726511
Kurtosis-1.2504875
Mean448556.32
Median Absolute Deviation (MAD)3004
Skewness0.074158179
Sum22427816
Variance11537982
MonotonicityNot monotonic
2023-12-11T19:08:32.965533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
452275 1
 
2.0%
443366 1
 
2.0%
446386 1
 
2.0%
445236 1
 
2.0%
443913 1
 
2.0%
444236 1
 
2.0%
445780 1
 
2.0%
444860 1
 
2.0%
443516 1
 
2.0%
443968 1
 
2.0%
Other values (40) 40
80.0%
ValueCountFrequency (%)
442740 1
2.0%
443366 1
2.0%
443516 1
2.0%
443913 1
2.0%
443968 1
2.0%
444236 1
2.0%
444860 1
2.0%
444973 1
2.0%
445022 1
2.0%
445233 1
2.0%
ValueCountFrequency (%)
455151 1
2.0%
454465 1
2.0%
453708 1
2.0%
453081 1
2.0%
452923 1
2.0%
452787 1
2.0%
452567 1
2.0%
452350 1
2.0%
452275 1
2.0%
452151 1
2.0%

Interactions

2023-12-11T19:08:30.356800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T19:08:29.829957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T19:08:30.101189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T19:08:30.463364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T19:08:29.929975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T19:08:30.200325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T19:08:30.562639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T19:08:30.014214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T19:08:30.273527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T19:08:33.047527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관리번호권역구분설치위치좌표x좌표y
관리번호1.0001.0001.0000.9700.733
권역구분1.0001.0001.0000.9710.736
설치위치1.0001.0001.0001.0001.000
좌표x0.9700.9711.0001.0000.536
좌표y0.7330.7361.0000.5361.000
2023-12-11T19:08:33.136240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관리번호좌표x좌표y권역구분
관리번호1.0000.620-0.6991.000
좌표x0.6201.000-0.2140.902
좌표y-0.699-0.2141.0000.561
권역구분1.0000.9020.5611.000

Missing values

2023-12-11T19:08:30.693734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T19:08:30.802508image/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

관리번호권역구분설치위치좌표x좌표y
01001도심권새안빌딩 앞(도심방향)197699452275
11002도심권효자공영주차장 맞은편 (도심방향)197380453708
21003도심권경복궁역 버스정류소(도심방향)197490452923
31004도심권독립문역 중앙정류소(도심방향)196266452787
41005도심권서대문우체국 중앙정류소(도심방향)194011451361
51006도심권성산대교 북단(도심방향)191099451324
61007도심권강변북로 합정역방향 진출로(도심방향)192212449913
71008도심권영등포 전화국 사거리(외곽방향)191750447346
81009도심권이대역 중앙정류소(도심방향)195126450802
91010도심권마포역 중앙정류소(도심방향)195409449069
관리번호권역구분설치위치좌표x좌표y
403008잠실권양재대로 건설인 넷 앞212188448141
413009잠실권올림픽공원 앞210921446034
423010잠실권신천역사거리 하류350m지점207886445824
433011잠실권잠실대교남단208526446504
443012잠실권강남 수병원 앞207792445022
453013잠실권송파농협 올림픽지점 앞209788446416
463014잠실권송파대로 석촌역 중앙정류소209554444973
473015잠실권송파대로 석촌호수 버스정류소209264445437
483016잠실권골프백화점 앞207066445233
493018잠실권오금로 방이 고분군210458445316