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 좌표 x and 2 other fieldsHigh correlation
좌표 x is highly overall correlated with 관리번호 and 1 other fieldsHigh correlation
좌표 y 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
좌표 x has unique valuesUnique
좌표 y has unique valuesUnique

Reproduction

Analysis started2023-12-11 10:08:24.405886
Analysis finished2023-12-11 10:08:26.224837
Duration1.82 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
2023-12-11T19:08:26.329532image/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
2023-12-11T19:08:26.517405image/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

2023-12-11T19:08:26.692762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T19:08:26.820617image/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
2023-12-11T19:08:27.140866image/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%
2023-12-11T19:08:27.713005image/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%

좌표 x
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
2023-12-11T19:08:27.863106image/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
2023-12-11T19:08:28.029219image/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%

좌표 y
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
2023-12-11T19:08:28.210938image/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
2023-12-11T19:08:28.366651image/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

2023-12-11T19:08:25.635715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T19:08:24.665459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T19:08:25.355982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T19:08:25.763742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T19:08:24.800077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T19:08:25.470382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T19:08:25.892529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T19:08:24.923618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T19:08:25.555972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T19:08:28.484574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관리번호권역구분설치위치좌표 x좌표 y
관리번호1.0001.0001.0000.9700.733
권역구분1.0001.0001.0000.9710.736
설치위치1.0001.0001.0001.0001.000
좌표 x0.9700.9711.0001.0000.536
좌표 y0.7330.7361.0000.5361.000
2023-12-11T19:08:28.602196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관리번호좌표 x좌표 y권역구분
관리번호1.0000.620-0.6991.000
좌표 x0.6201.000-0.2140.902
좌표 y-0.699-0.2141.0000.561
권역구분1.0000.9020.5611.000

Missing values

2023-12-11T19:08:26.064893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T19:08:26.177305image/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

관리번호권역구분설치위치좌표 x좌표 y
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
관리번호권역구분설치위치좌표 x좌표 y
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