Overview

Dataset statistics

Number of variables8
Number of observations27
Missing cells32
Missing cells (%)14.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.8 KiB
Average record size in memory69.9 B

Variable types

Text4
Numeric1
Categorical1
DateTime2

Dataset

Description경상남도 보건환경연구원에서 운영중인 대기환경측정망 설치 현황 자료입니다. 도시대기측정소 및 도로변대기측정소의 측정소명, 주소, 설치일자, 측정망 종류, 시군, 용도지역, 위경도 좌표 자료를 포함하고 있습니다.
Author경상남도
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=3078597

Alerts

장비교체연도 has 13 (48.1%) missing valuesMissing
비 고 has 19 (70.4%) missing valuesMissing
코드 has unique valuesUnique
측정소명 has unique valuesUnique
설치위치 has unique valuesUnique

Reproduction

Analysis started2023-12-11 00:05:07.188238
Analysis finished2023-12-11 00:05:07.953814
Duration0.77 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군
Text

Distinct14
Distinct (%)51.9%
Missing0
Missing (%)0.0%
Memory size348.0 B
2023-12-11T09:05:08.039582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters81
Distinct characters24
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

Unique10 ?
Unique (%)37.0%

Sample

1st row창원시
2nd row창원시
3rd row창원시
4th row창원시
5th row창원시
ValueCountFrequency (%)
창원시 9
33.3%
진주시 3
 
11.1%
김해시 3
 
11.1%
양산시 2
 
7.4%
사천시 1
 
3.7%
거제시 1
 
3.7%
통영시 1
 
3.7%
밀양시 1
 
3.7%
하동군 1
 
3.7%
함안군 1
 
3.7%
Other values (4) 4
14.8%
2023-12-11T09:05:08.656342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 21
25.9%
창 10
12.3%
원 9
11.1%
군 6
 
7.4%
해 4
 
4.9%
양 4
 
4.9%
진 3
 
3.7%
주 3
 
3.7%
김 3
 
3.7%
산 2
 
2.5%
Other values (14) 16
19.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 81
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 21
25.9%
창 10
12.3%
원 9
11.1%
군 6
 
7.4%
해 4
 
4.9%
양 4
 
4.9%
진 3
 
3.7%
주 3
 
3.7%
김 3
 
3.7%
산 2
 
2.5%
Other values (14) 16
19.8%

Most occurring scripts

ValueCountFrequency (%)
Hangul 81
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 21
25.9%
창 10
12.3%
원 9
11.1%
군 6
 
7.4%
해 4
 
4.9%
양 4
 
4.9%
진 3
 
3.7%
주 3
 
3.7%
김 3
 
3.7%
산 2
 
2.5%
Other values (14) 16
19.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 81
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 21
25.9%
창 10
12.3%
원 9
11.1%
군 6
 
7.4%
해 4
 
4.9%
양 4
 
4.9%
진 3
 
3.7%
주 3
 
3.7%
김 3
 
3.7%
산 2
 
2.5%
Other values (14) 16
19.8%

코드
Real number (ℝ)

UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean748.66667
Minimum701
Maximum801
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-11T09:05:08.797937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum701
5-th percentile702.3
Q1716.5
median743
Q3785.5
95-th percentile794.7
Maximum801
Range100
Interquartile range (IQR)69

Descriptive statistics

Standard deviation35.512728
Coefficient of variation (CV)0.047434632
Kurtosis-1.5720463
Mean748.66667
Median Absolute Deviation (MAD)38
Skewness0.077782918
Sum20214
Variance1261.1538
MonotonicityNot monotonic
2023-12-11T09:05:08.916708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
701 1
 
3.7%
702 1
 
3.7%
795 1
 
3.7%
794 1
 
3.7%
793 1
 
3.7%
792 1
 
3.7%
791 1
 
3.7%
761 1
 
3.7%
787 1
 
3.7%
784 1
 
3.7%
Other values (17) 17
63.0%
ValueCountFrequency (%)
701 1
3.7%
702 1
3.7%
703 1
3.7%
704 1
3.7%
705 1
3.7%
711 1
3.7%
712 1
3.7%
721 1
3.7%
722 1
3.7%
723 1
3.7%
ValueCountFrequency (%)
801 1
3.7%
795 1
3.7%
794 1
3.7%
793 1
3.7%
792 1
3.7%
791 1
3.7%
787 1
3.7%
784 1
3.7%
781 1
3.7%
771 1
3.7%

측정소명
Text

UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size348.0 B
2023-12-11T09:05:09.118263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters81
Distinct characters46
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

Unique27 ?
Unique (%)100.0%

Sample

1st row성주동
2nd row웅남동
3rd row명서동
4th row용지동
5th row사파동
ValueCountFrequency (%)
성주동 1
 
3.7%
삼방동 1
 
3.7%
함양읍 1
 
3.7%
남해읍 1
 
3.7%
고성읍 1
 
3.7%
가야읍 1
 
3.7%
하동읍 1
 
3.7%
내일동 1
 
3.7%
무전동 1
 
3.7%
웅상읍 1
 
3.7%
Other values (17) 17
63.0%
2023-12-11T09:05:09.462239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 19
23.5%
읍 8
 
9.9%
상 4
 
4.9%
성 2
 
2.5%
사 2
 
2.5%
주 2
 
2.5%
대 2
 
2.5%
봉 2
 
2.5%
웅 2
 
2.5%
남 2
 
2.5%
Other values (36) 36
44.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 81
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 19
23.5%
읍 8
 
9.9%
상 4
 
4.9%
성 2
 
2.5%
사 2
 
2.5%
주 2
 
2.5%
대 2
 
2.5%
봉 2
 
2.5%
웅 2
 
2.5%
남 2
 
2.5%
Other values (36) 36
44.4%

Most occurring scripts

ValueCountFrequency (%)
Hangul 81
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 19
23.5%
읍 8
 
9.9%
상 4
 
4.9%
성 2
 
2.5%
사 2
 
2.5%
주 2
 
2.5%
대 2
 
2.5%
봉 2
 
2.5%
웅 2
 
2.5%
남 2
 
2.5%
Other values (36) 36
44.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 81
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 19
23.5%
읍 8
 
9.9%
상 4
 
4.9%
성 2
 
2.5%
사 2
 
2.5%
주 2
 
2.5%
대 2
 
2.5%
봉 2
 
2.5%
웅 2
 
2.5%
남 2
 
2.5%
Other values (36) 36
44.4%

용도
Categorical

Distinct6
Distinct (%)22.2%
Missing0
Missing (%)0.0%
Memory size348.0 B
주거
14 
상업
4 
공업
3 
준주거
3 
녹지
2 

Length

Max length4
Median length2
Mean length2.1851852
Min length2

Unique

Unique1 ?
Unique (%)3.7%

Sample

1st row주거
2nd row공업
3rd row주거
4th row준주거
5th row주거

Common Values

ValueCountFrequency (%)
주거 14
51.9%
상업 4
 
14.8%
공업 3
 
11.1%
준주거 3
 
11.1%
녹지 2
 
7.4%
자연녹지 1
 
3.7%

Length

2023-12-11T09:05:09.617944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:05:09.754127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
주거 14
51.9%
상업 4
 
14.8%
공업 3
 
11.1%
준주거 3
 
11.1%
녹지 2
 
7.4%
자연녹지 1
 
3.7%

설치위치
Text

UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size348.0 B
2023-12-11T09:05:09.935247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length11
Mean length7.8148148
Min length4

Characters and Unicode

Total characters211
Distinct characters85
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique27 ?
Unique (%)100.0%

Sample

1st row성주동 민원센터
2nd row효성 굿스프링스
3rd row명서2동 민원센터
4th row용지동 주민센터
5th row사파동 주민센터
ValueCountFrequency (%)
주민센터 8
 
19.0%
민원센터 2
 
4.8%
중앙동 2
 
4.8%
성주동 1
 
2.4%
노인복지회관 1
 
2.4%
신어초등학교 1
 
2.4%
장유보건센터 1
 
2.4%
아주동주민센터 1
 
2.4%
웅상 1
 
2.4%
무전동주민센터 1
 
2.4%
Other values (23) 23
54.8%
2023-12-11T09:05:10.267997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 17
 
8.1%
15
 
7.1%
민 14
 
6.6%
센 14
 
6.6%
터 14
 
6.6%
주 14
 
6.6%
사 6
 
2.8%
지 5
 
2.4%
성 4
 
1.9%
원 4
 
1.9%
Other values (75) 104
49.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 189
89.6%
Space Separator 15
 
7.1%
Decimal Number 6
 
2.8%
Dash Punctuation 1
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 17
 
9.0%
민 14
 
7.4%
센 14
 
7.4%
터 14
 
7.4%
주 14
 
7.4%
사 6
 
3.2%
지 5
 
2.6%
성 4
 
2.1%
원 4
 
2.1%
읍 4
 
2.1%
Other values (68) 93
49.2%
Decimal Number
ValueCountFrequency (%)
1 2
33.3%
2 1
16.7%
5 1
16.7%
6 1
16.7%
4 1
16.7%
Space Separator
ValueCountFrequency (%)
15
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 189
89.6%
Common 22
 
10.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 17
 
9.0%
민 14
 
7.4%
센 14
 
7.4%
터 14
 
7.4%
주 14
 
7.4%
사 6
 
3.2%
지 5
 
2.6%
성 4
 
2.1%
원 4
 
2.1%
읍 4
 
2.1%
Other values (68) 93
49.2%
Common
ValueCountFrequency (%)
15
68.2%
1 2
 
9.1%
2 1
 
4.5%
5 1
 
4.5%
6 1
 
4.5%
- 1
 
4.5%
4 1
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 189
89.6%
ASCII 22
 
10.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 17
 
9.0%
민 14
 
7.4%
센 14
 
7.4%
터 14
 
7.4%
주 14
 
7.4%
사 6
 
3.2%
지 5
 
2.6%
성 4
 
2.1%
원 4
 
2.1%
읍 4
 
2.1%
Other values (68) 93
49.2%
ASCII
ValueCountFrequency (%)
15
68.2%
1 2
 
9.1%
2 1
 
4.5%
5 1
 
4.5%
6 1
 
4.5%
- 1
 
4.5%
4 1
 
4.5%

설치연도
Date

Distinct20
Distinct (%)74.1%
Missing0
Missing (%)0.0%
Memory size348.0 B
Minimum1993-09-01 00:00:00
Maximum2018-12-01 00:00:00
2023-12-11T09:05:10.417421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:05:10.532475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)

장비교체연도
Date

MISSING 

Distinct10
Distinct (%)71.4%
Missing13
Missing (%)48.1%
Memory size348.0 B
Minimum2006-09-01 00:00:00
Maximum2016-07-01 00:00:00
2023-12-11T09:05:10.627970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:05:10.731425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)

비 고
Text

MISSING 

Distinct7
Distinct (%)87.5%
Missing19
Missing (%)70.4%
Memory size348.0 B
2023-12-11T09:05:10.894392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length29
Median length18.5
Mean length12.375
Min length3

Characters and Unicode

Total characters99
Distinct characters27
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)75.0%

Sample

1st row2018.11 이전
2nd row대기중금속, (‘08.9,→ ‘12.11) 추이측정소
3rd row대기중금속, (‘08.9,→ ‘12.11)
4th row도로변
5th row추이측정소
ValueCountFrequency (%)
추이측정소 3
18.8%
대기중금속 3
18.8%
‘12.11 3
18.8%
이전 2
12.5%
‘08.9,→ 2
12.5%
2018.11 1
 
6.2%
도로변 1
 
6.2%
‘18.3.1 1
 
6.2%
2023-12-11T09:05:11.211883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 14
 
14.1%
. 8
 
8.1%
8
 
8.1%
‘ 6
 
6.1%
이 5
 
5.1%
2 4
 
4.0%
, 4
 
4.0%
8 4
 
4.0%
추 3
 
3.0%
중 3
 
3.0%
Other values (17) 40
40.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 37
37.4%
Decimal Number 28
28.3%
Other Punctuation 12
 
12.1%
Space Separator 8
 
8.1%
Initial Punctuation 6
 
6.1%
Close Punctuation 3
 
3.0%
Open Punctuation 3
 
3.0%
Math Symbol 2
 
2.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
이 5
13.5%
추 3
8.1%
중 3
8.1%
속 3
8.1%
금 3
8.1%
기 3
8.1%
대 3
8.1%
소 3
8.1%
정 3
8.1%
측 3
8.1%
Other values (4) 5
13.5%
Decimal Number
ValueCountFrequency (%)
1 14
50.0%
2 4
 
14.3%
8 4
 
14.3%
0 3
 
10.7%
9 2
 
7.1%
3 1
 
3.6%
Other Punctuation
ValueCountFrequency (%)
. 8
66.7%
, 4
33.3%
Space Separator
ValueCountFrequency (%)
8
100.0%
Initial Punctuation
ValueCountFrequency (%)
‘ 6
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Math Symbol
ValueCountFrequency (%)
→ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 62
62.6%
Hangul 37
37.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
이 5
13.5%
추 3
8.1%
중 3
8.1%
속 3
8.1%
금 3
8.1%
기 3
8.1%
대 3
8.1%
소 3
8.1%
정 3
8.1%
측 3
8.1%
Other values (4) 5
13.5%
Common
ValueCountFrequency (%)
1 14
22.6%
. 8
12.9%
8
12.9%
‘ 6
9.7%
2 4
 
6.5%
, 4
 
6.5%
8 4
 
6.5%
) 3
 
4.8%
( 3
 
4.8%
0 3
 
4.8%
Other values (3) 5
 
8.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 54
54.5%
Hangul 37
37.4%
Punctuation 6
 
6.1%
Arrows 2
 
2.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 14
25.9%
. 8
14.8%
8
14.8%
2 4
 
7.4%
, 4
 
7.4%
8 4
 
7.4%
) 3
 
5.6%
( 3
 
5.6%
0 3
 
5.6%
9 2
 
3.7%
Punctuation
ValueCountFrequency (%)
‘ 6
100.0%
Hangul
ValueCountFrequency (%)
이 5
13.5%
추 3
8.1%
중 3
8.1%
속 3
8.1%
금 3
8.1%
기 3
8.1%
대 3
8.1%
소 3
8.1%
정 3
8.1%
측 3
8.1%
Other values (4) 5
13.5%
Arrows
ValueCountFrequency (%)
→ 2
100.0%

Interactions

2023-12-11T09:05:07.539020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T09:05:11.316948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군코드측정소명용도설치위치설치연도장비교체연도비 고
시군1.0000.8441.0000.3631.0000.0000.6870.000
코드0.8441.0001.0000.0001.0000.8780.0000.956
측정소명1.0001.0001.0001.0001.0001.0001.0001.000
용도0.3630.0001.0001.0001.0000.9160.8831.000
설치위치1.0001.0001.0001.0001.0001.0001.0001.000
설치연도0.0000.8781.0000.9161.0001.0000.9421.000
장비교체연도0.6870.0001.0000.8831.0000.9421.0001.000
비 고0.0000.9561.0001.0001.0001.0001.0001.000
2023-12-11T09:05:11.438151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
코드용도
코드1.0000.000
용도0.0001.000

Missing values

2023-12-11T09:05:07.642890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T09:05:07.786137image/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.
2023-12-11T09:05:07.896955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

시군코드측정소명용도설치위치설치연도장비교체연도비 고
0창원시701성주동주거성주동 민원센터1997-022010-082018.11 이전
1창원시702웅남동공업효성 굿스프링스1999-062010-08<NA>
2창원시703명서동주거명서2동 민원센터1993-122010-11대기중금속, (‘08.9,→ ‘12.11) 추이측정소
3창원시704용지동준주거용지동 주민센터2007-072007-07<NA>
4창원시705사파동주거사파동 주민센터2009-03<NA><NA>
5창원시711회원동주거회원1동 주민센터1993-092016-07<NA>
6창원시712봉암동공업봉암동 주민센터1995-122006-09대기중금속, (‘08.9,→ ‘12.11)
7창원시731경화동주거병암동 주민센터1994-032011-06<NA>
8창원시801반송로자연녹지성산구 중앙동 516-42008-10<NA>도로변
9진주시721상봉동주거상봉동동 주민센터1995-072011-06추이측정소
시군코드측정소명용도설치위치설치연도장비교체연도비 고
17양산시751북부동주거중앙동 주민센터1999-042010-08<NA>
18양산시752웅상읍준주거웅상 노인복지회관2004-122015-08<NA>
19통영시784무전동상업무전동주민센터2017-08<NA><NA>
20밀양시787내일동상업내일동주민센터2017-08<NA><NA>
21하동군761하동읍녹지하동군청2007-082007-08대기중금속 (‘12.11)
22함안군791가야읍주거가야읍행정복지타운2018-12<NA><NA>
23고성군792고성읍상업고성읍보건지소2018-12<NA><NA>
24남해군793남해읍녹지남해유배문학관2018-12<NA><NA>
25함양군794함양읍주거함양군청민원봉사과2018-12<NA><NA>
26거창군795거창읍주거거창읍사무소2018-12<NA><NA>