Overview

Dataset statistics

Number of variables5
Number of observations25
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.2 KiB
Average record size in memory48.3 B

Variable types

Numeric3
Text2

Dataset

Description측정소 코드,측정소 이름,측정소 주소,표시 순서,공인코드
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-15516/S/1/datasetView.do

Alerts

측정소 코드 is highly overall correlated with 표시 순서 and 1 other fieldsHigh correlation
표시 순서 is highly overall correlated with 측정소 코드 and 1 other fieldsHigh correlation
공인코드 is highly overall correlated with 측정소 코드 and 1 other fieldsHigh correlation
측정소 코드 has unique valuesUnique
측정소 이름 has unique valuesUnique
측정소 주소 has unique valuesUnique
표시 순서 has unique valuesUnique
공인코드 has unique valuesUnique

Reproduction

Analysis started2024-05-11 05:40:03.144357
Analysis finished2024-05-11 05:40:05.117054
Duration1.97 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

측정소 코드
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct25
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean113
Minimum101
Maximum125
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size357.0 B
2024-05-11T14:40:05.251777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum101
5-th percentile102.2
Q1107
median113
Q3119
95-th percentile123.8
Maximum125
Range24
Interquartile range (IQR)12

Descriptive statistics

Standard deviation7.3598007
Coefficient of variation (CV)0.06513098
Kurtosis-1.2
Mean113
Median Absolute Deviation (MAD)6
Skewness0
Sum2825
Variance54.166667
MonotonicityStrictly increasing
2024-05-11T14:40:05.523852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
101 1
 
4.0%
102 1
 
4.0%
125 1
 
4.0%
124 1
 
4.0%
123 1
 
4.0%
122 1
 
4.0%
121 1
 
4.0%
120 1
 
4.0%
119 1
 
4.0%
118 1
 
4.0%
Other values (15) 15
60.0%
ValueCountFrequency (%)
101 1
4.0%
102 1
4.0%
103 1
4.0%
104 1
4.0%
105 1
4.0%
106 1
4.0%
107 1
4.0%
108 1
4.0%
109 1
4.0%
110 1
4.0%
ValueCountFrequency (%)
125 1
4.0%
124 1
4.0%
123 1
4.0%
122 1
4.0%
121 1
4.0%
120 1
4.0%
119 1
4.0%
118 1
4.0%
117 1
4.0%
116 1
4.0%

측정소 이름
Text

UNIQUE 

Distinct25
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
2024-05-11T14:40:05.873258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.08
Min length2

Characters and Unicode

Total characters77
Distinct characters36
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique25 ?
Unique (%)100.0%

Sample

1st row종로구
2nd row중구
3rd row용산구
4th row은평구
5th row서대문구
ValueCountFrequency (%)
종로구 1
 
4.0%
노원구 1
 
4.0%
송파구 1
 
4.0%
강남구 1
 
4.0%
서초구 1
 
4.0%
관악구 1
 
4.0%
동작구 1
 
4.0%
영등포구 1
 
4.0%
금천구 1
 
4.0%
구로구 1
 
4.0%
Other values (15) 15
60.0%
2024-05-11T14:40:06.496328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
구 26
33.8%
강 4
 
5.2%
동 4
 
5.2%
서 3
 
3.9%
북 2
 
2.6%
중 2
 
2.6%
천 2
 
2.6%
로 2
 
2.6%
대 2
 
2.6%
문 2
 
2.6%
Other values (26) 28
36.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 77
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 26
33.8%
강 4
 
5.2%
동 4
 
5.2%
서 3
 
3.9%
북 2
 
2.6%
중 2
 
2.6%
천 2
 
2.6%
로 2
 
2.6%
대 2
 
2.6%
문 2
 
2.6%
Other values (26) 28
36.4%

Most occurring scripts

ValueCountFrequency (%)
Hangul 77
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 26
33.8%
강 4
 
5.2%
동 4
 
5.2%
서 3
 
3.9%
북 2
 
2.6%
중 2
 
2.6%
천 2
 
2.6%
로 2
 
2.6%
대 2
 
2.6%
문 2
 
2.6%
Other values (26) 28
36.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 77
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
구 26
33.8%
강 4
 
5.2%
동 4
 
5.2%
서 3
 
3.9%
북 2
 
2.6%
중 2
 
2.6%
천 2
 
2.6%
로 2
 
2.6%
대 2
 
2.6%
문 2
 
2.6%
Other values (26) 28
36.4%

측정소 주소
Text

UNIQUE 

Distinct25
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
2024-05-11T14:40:06.934340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length45
Median length32
Mean length28.16
Min length23

Characters and Unicode

Total characters704
Distinct characters128
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique25 ?
Unique (%)100.0%

Sample

1st row서울 종로구 종로35가길 19 종로5,6가 동 주민센터
2nd row서울 중구 덕수궁길 15 시청서소문별관 3동
3rd row서울 용산구 한남대로 136 서울특별시중부기술교육원
4th row서울 은평구 진흥로 215 (한국환경산업기술원 온실동2층 )
5th row서울 서대문구 세검정로4길 32(홍제3동 주민센터)
ValueCountFrequency (%)
서울 23
 
14.6%
주민센터 11
 
7.0%
서울특별시 2
 
1.3%
삼전동 2
 
1.3%
45 2
 
1.3%
노원구 2
 
1.3%
화곡3동 1
 
0.6%
영등포구 1
 
0.6%
118 1
 
0.6%
시흥5동 1
 
0.6%
Other values (112) 112
70.9%
2024-05-11T14:40:07.666603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
134
 
19.0%
서 32
 
4.5%
구 32
 
4.5%
로 27
 
3.8%
동 27
 
3.8%
울 26
 
3.7%
1 24
 
3.4%
길 19
 
2.7%
터 17
 
2.4%
2 17
 
2.4%
Other values (118) 349
49.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 454
64.5%
Space Separator 134
 
19.0%
Decimal Number 105
 
14.9%
Open Punctuation 5
 
0.7%
Close Punctuation 5
 
0.7%
Other Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
서 32
 
7.0%
구 32
 
7.0%
로 27
 
5.9%
동 27
 
5.9%
울 26
 
5.7%
길 19
 
4.2%
터 17
 
3.7%
센 16
 
3.5%
민 13
 
2.9%
주 13
 
2.9%
Other values (104) 232
51.1%
Decimal Number
ValueCountFrequency (%)
1 24
22.9%
2 17
16.2%
3 16
15.2%
5 12
11.4%
4 11
10.5%
6 10
9.5%
7 5
 
4.8%
9 5
 
4.8%
0 3
 
2.9%
8 2
 
1.9%
Space Separator
ValueCountFrequency (%)
134
100.0%
Open Punctuation
ValueCountFrequency (%)
( 5
100.0%
Close Punctuation
ValueCountFrequency (%)
) 5
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 454
64.5%
Common 250
35.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
서 32
 
7.0%
구 32
 
7.0%
로 27
 
5.9%
동 27
 
5.9%
울 26
 
5.7%
길 19
 
4.2%
터 17
 
3.7%
센 16
 
3.5%
민 13
 
2.9%
주 13
 
2.9%
Other values (104) 232
51.1%
Common
ValueCountFrequency (%)
134
53.6%
1 24
 
9.6%
2 17
 
6.8%
3 16
 
6.4%
5 12
 
4.8%
4 11
 
4.4%
6 10
 
4.0%
( 5
 
2.0%
7 5
 
2.0%
) 5
 
2.0%
Other values (4) 11
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 454
64.5%
ASCII 250
35.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
134
53.6%
1 24
 
9.6%
2 17
 
6.8%
3 16
 
6.4%
5 12
 
4.8%
4 11
 
4.4%
6 10
 
4.0%
( 5
 
2.0%
7 5
 
2.0%
) 5
 
2.0%
Other values (4) 11
 
4.4%
Hangul
ValueCountFrequency (%)
서 32
 
7.0%
구 32
 
7.0%
로 27
 
5.9%
동 27
 
5.9%
울 26
 
5.7%
길 19
 
4.2%
터 17
 
3.7%
센 16
 
3.5%
민 13
 
2.9%
주 13
 
2.9%
Other values (104) 232
51.1%

표시 순서
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct25
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13
Minimum1
Maximum25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size357.0 B
2024-05-11T14:40:07.925500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.2
Q17
median13
Q319
95-th percentile23.8
Maximum25
Range24
Interquartile range (IQR)12

Descriptive statistics

Standard deviation7.3598007
Coefficient of variation (CV)0.56613852
Kurtosis-1.2
Mean13
Median Absolute Deviation (MAD)6
Skewness0
Sum325
Variance54.166667
MonotonicityStrictly increasing
2024-05-11T14:40:08.150948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
1 1
 
4.0%
2 1
 
4.0%
25 1
 
4.0%
24 1
 
4.0%
23 1
 
4.0%
22 1
 
4.0%
21 1
 
4.0%
20 1
 
4.0%
19 1
 
4.0%
18 1
 
4.0%
Other values (15) 15
60.0%
ValueCountFrequency (%)
1 1
4.0%
2 1
4.0%
3 1
4.0%
4 1
4.0%
5 1
4.0%
6 1
4.0%
7 1
4.0%
8 1
4.0%
9 1
4.0%
10 1
4.0%
ValueCountFrequency (%)
25 1
4.0%
24 1
4.0%
23 1
4.0%
22 1
4.0%
21 1
4.0%
20 1
4.0%
19 1
4.0%
18 1
4.0%
17 1
4.0%
16 1
4.0%

공인코드
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct25
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean111211.04
Minimum111121
Maximum111311
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size357.0 B
2024-05-11T14:40:08.385125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum111121
5-th percentile111124.6
Q1111152
median111212
Q3111262
95-th percentile111299
Maximum111311
Range190
Interquartile range (IQR)110

Descriptive statistics

Standard deviation61.169491
Coefficient of variation (CV)0.00055003075
Kurtosis-1.4051597
Mean111211.04
Median Absolute Deviation (MAD)60
Skewness0.035398473
Sum2780276
Variance3741.7067
MonotonicityNot monotonic
2024-05-11T14:40:08.646875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
111123 1
 
4.0%
111121 1
 
4.0%
111274 1
 
4.0%
111273 1
 
4.0%
111261 1
 
4.0%
111262 1
 
4.0%
111251 1
 
4.0%
111241 1
 
4.0%
111231 1
 
4.0%
111281 1
 
4.0%
Other values (15) 15
60.0%
ValueCountFrequency (%)
111121 1
4.0%
111123 1
4.0%
111131 1
4.0%
111141 1
4.0%
111142 1
4.0%
111151 1
4.0%
111152 1
4.0%
111161 1
4.0%
111171 1
4.0%
111181 1
4.0%
ValueCountFrequency (%)
111311 1
4.0%
111301 1
4.0%
111291 1
4.0%
111281 1
4.0%
111274 1
4.0%
111273 1
4.0%
111262 1
4.0%
111261 1
4.0%
111251 1
4.0%
111241 1
4.0%

Interactions

2024-05-11T14:40:04.407103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:40:03.454205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:40:04.001768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:40:04.562658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:40:03.654525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:40:04.121133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:40:04.677424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:40:03.822996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:40:04.257364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-11T14:40:08.802494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
측정소 코드측정소 이름측정소 주소표시 순서공인코드
측정소 코드1.0001.0001.0000.9990.956
측정소 이름1.0001.0001.0001.0001.000
측정소 주소1.0001.0001.0001.0001.000
표시 순서0.9991.0001.0001.0000.930
공인코드0.9561.0001.0000.9301.000
2024-05-11T14:40:09.273716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
측정소 코드표시 순서공인코드
측정소 코드1.0001.0000.752
표시 순서1.0001.0000.752
공인코드0.7520.7521.000

Missing values

2024-05-11T14:40:04.867872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-11T14:40:05.048456image/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

측정소 코드측정소 이름측정소 주소표시 순서공인코드
0101종로구서울 종로구 종로35가길 19 종로5,6가 동 주민센터1111123
1102중구서울 중구 덕수궁길 15 시청서소문별관 3동2111121
2103용산구서울 용산구 한남대로 136 서울특별시중부기술교육원3111131
3104은평구서울 은평구 진흥로 215 (한국환경산업기술원 온실동2층 )4111181
4105서대문구서울 서대문구 세검정로4길 32(홍제3동 주민센터)5111191
5106마포구서울 마포구 포은로 6길 10 망원1동주민센터 옥상6111201
6107성동구서울 성동구 뚝섬로3길 18 성수1가1동주민센터7111142
7108광진구서울특별시 광진구 광나루로 571 구의 아리수정수센터8111141
8109동대문구서울 동대문구 천호대로13길 43 용두초등학교9111152
9110중랑구서울 중랑구 용마산로 369 건강가정지원센터10111151
측정소 코드측정소 이름측정소 주소표시 순서공인코드
15116강서구서울 강서구 강서로 45 다길 71 화곡3동 푸른들청소년도서관16111212
16117구로구서울 구로구 가마산로 27길 45 구로고등학교17111221
17118금천구서울 금천구 금하로21길 20 시흥5동 주민센터18111281
18119영등포구서울특별시 영등포구 당산로 123 영등포구청 (당산동3가)19111231
19120동작구서울 동작구 사당로16아길 6 사당4동 주민센터20111241
20121관악구서울 관악구 신림동길 14 신림동 주민센터21111251
21122서초구서울 서초구 신반포로15길 16 반포 2동 주민센터22111262
22123강남구서울 강남구 학동로 426 강남구청 별관 1동23111261
23124송파구서울 송파구 백제고분로 236 삼전동 주민센터 (삼전동)24111273
24125강동구서울 강동구 구천면로 42길 59 천호1동 주민센터25111274