Overview

Dataset statistics

Number of variables4
Number of observations510
Missing cells1
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory16.6 KiB
Average record size in memory33.3 B

Variable types

Text3
Numeric1

Dataset

Description한국환경공단에서 운영하는 실내공기질 관리 종합정보망(www.inair.or.kr)에 등록된 시스템 관련 정보를 제공합니다.
Author한국환경공단
URLhttps://www.data.go.kr/data/15093406/fileData.do

Reproduction

Analysis started2023-12-11 23:12:25.025486
Analysis finished2023-12-11 23:12:25.556153
Duration0.53 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Text

Distinct57
Distinct (%)11.2%
Missing0
Missing (%)0.0%
Memory size4.1 KiB
2023-12-12T08:12:25.681907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length4
Mean length4.3470588
Min length2

Characters and Unicode

Total characters2217
Distinct characters104
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.2%

Sample

1st row시설군
2nd row시설군
3rd row시설군
4th row시설군
5th row시설군
ValueCountFrequency (%)
측정지점 138
24.3%
선별코드 33
 
5.8%
항목 27
 
4.8%
공동주택 25
 
4.4%
시간 24
 
4.2%
시설군-자가측정 21
 
3.7%
지점 20
 
3.5%
6시간 19
 
3.4%
구분 15
 
2.6%
자가측정 12
 
2.1%
Other values (46) 233
41.1%
2023-12-12T08:12:26.001966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
정 195
 
8.8%
측 188
 
8.5%
지 167
 
7.5%
점 167
 
7.5%
시 85
 
3.8%
자 65
 
2.9%
목 58
 
2.6%
항 58
 
2.6%
57
 
2.6%
코 44
 
2.0%
Other values (94) 1133
51.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2091
94.3%
Space Separator 57
 
2.6%
Uppercase Letter 29
 
1.3%
Dash Punctuation 21
 
0.9%
Decimal Number 19
 
0.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
정 195
 
9.3%
측 188
 
9.0%
지 167
 
8.0%
점 167
 
8.0%
시 85
 
4.1%
자 65
 
3.1%
목 58
 
2.8%
항 58
 
2.8%
코 44
 
2.1%
드 44
 
2.1%
Other values (83) 1020
48.8%
Uppercase Letter
ValueCountFrequency (%)
S 12
41.4%
M 6
20.7%
D 4
 
13.8%
L 3
 
10.3%
R 1
 
3.4%
A 1
 
3.4%
O 1
 
3.4%
N 1
 
3.4%
Space Separator
ValueCountFrequency (%)
57
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 21
100.0%
Decimal Number
ValueCountFrequency (%)
6 19
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2091
94.3%
Common 97
 
4.4%
Latin 29
 
1.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
정 195
 
9.3%
측 188
 
9.0%
지 167
 
8.0%
점 167
 
8.0%
시 85
 
4.1%
자 65
 
3.1%
목 58
 
2.8%
항 58
 
2.8%
코 44
 
2.1%
드 44
 
2.1%
Other values (83) 1020
48.8%
Latin
ValueCountFrequency (%)
S 12
41.4%
M 6
20.7%
D 4
 
13.8%
L 3
 
10.3%
R 1
 
3.4%
A 1
 
3.4%
O 1
 
3.4%
N 1
 
3.4%
Common
ValueCountFrequency (%)
57
58.8%
- 21
 
21.6%
6 19
 
19.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2091
94.3%
ASCII 126
 
5.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
정 195
 
9.3%
측 188
 
9.0%
지 167
 
8.0%
점 167
 
8.0%
시 85
 
4.1%
자 65
 
3.1%
목 58
 
2.8%
항 58
 
2.8%
코 44
 
2.1%
드 44
 
2.1%
Other values (83) 1020
48.8%
ASCII
ValueCountFrequency (%)
57
45.2%
- 21
 
16.7%
6 19
 
15.1%
S 12
 
9.5%
M 6
 
4.8%
D 4
 
3.2%
L 3
 
2.4%
R 1
 
0.8%
A 1
 
0.8%
O 1
 
0.8%

코드
Text

Distinct262
Distinct (%)51.4%
Missing0
Missing (%)0.0%
Memory size4.1 KiB
2023-12-12T08:12:26.393701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length9
Mean length2.0215686
Min length1

Characters and Unicode

Total characters1031
Distinct characters43
Distinct categories3 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique206 ?
Unique (%)40.4%

Sample

1st rowA
2nd rowB
3rd rowC
4th rowD
5th rowG
ValueCountFrequency (%)
1 34
 
6.7%
2 26
 
5.1%
4 20
 
3.9%
3 19
 
3.7%
5 14
 
2.7%
6 13
 
2.5%
8 12
 
2.4%
0 10
 
2.0%
7 10
 
2.0%
9 9
 
1.8%
Other values (252) 343
67.3%
2023-12-12T08:12:27.032403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 201
19.5%
1 103
 
10.0%
2 77
 
7.5%
3 52
 
5.0%
4 52
 
5.0%
5 42
 
4.1%
A 40
 
3.9%
6 35
 
3.4%
8 29
 
2.8%
7 28
 
2.7%
Other values (33) 372
36.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 635
61.6%
Uppercase Letter 366
35.5%
Other Letter 30
 
2.9%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 40
 
10.9%
B 26
 
7.1%
D 24
 
6.6%
S 21
 
5.7%
E 21
 
5.7%
T 20
 
5.5%
C 20
 
5.5%
R 18
 
4.9%
M 18
 
4.9%
F 16
 
4.4%
Other values (13) 142
38.8%
Decimal Number
ValueCountFrequency (%)
0 201
31.7%
1 103
16.2%
2 77
 
12.1%
3 52
 
8.2%
4 52
 
8.2%
5 42
 
6.6%
6 35
 
5.5%
8 29
 
4.6%
7 28
 
4.4%
9 16
 
2.5%
Other Letter
ValueCountFrequency (%)
향 8
26.7%
동 5
16.7%
서 3
 
10.0%
북 3
 
10.0%
남 3
 
10.0%
가 2
 
6.7%
혼 2
 
6.7%
잡 2
 
6.7%
미 1
 
3.3%
비 1
 
3.3%

Most occurring scripts

ValueCountFrequency (%)
Common 635
61.6%
Latin 366
35.5%
Hangul 30
 
2.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 40
 
10.9%
B 26
 
7.1%
D 24
 
6.6%
S 21
 
5.7%
E 21
 
5.7%
T 20
 
5.5%
C 20
 
5.5%
R 18
 
4.9%
M 18
 
4.9%
F 16
 
4.4%
Other values (13) 142
38.8%
Common
ValueCountFrequency (%)
0 201
31.7%
1 103
16.2%
2 77
 
12.1%
3 52
 
8.2%
4 52
 
8.2%
5 42
 
6.6%
6 35
 
5.5%
8 29
 
4.6%
7 28
 
4.4%
9 16
 
2.5%
Hangul
ValueCountFrequency (%)
향 8
26.7%
동 5
16.7%
서 3
 
10.0%
북 3
 
10.0%
남 3
 
10.0%
가 2
 
6.7%
혼 2
 
6.7%
잡 2
 
6.7%
미 1
 
3.3%
비 1
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1001
97.1%
Hangul 30
 
2.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 201
20.1%
1 103
 
10.3%
2 77
 
7.7%
3 52
 
5.2%
4 52
 
5.2%
5 42
 
4.2%
A 40
 
4.0%
6 35
 
3.5%
8 29
 
2.9%
7 28
 
2.8%
Other values (23) 342
34.2%
Hangul
ValueCountFrequency (%)
향 8
26.7%
동 5
16.7%
서 3
 
10.0%
북 3
 
10.0%
남 3
 
10.0%
가 2
 
6.7%
혼 2
 
6.7%
잡 2
 
6.7%
미 1
 
3.3%
비 1
 
3.3%

코드명
Text

Distinct333
Distinct (%)65.3%
Missing0
Missing (%)0.0%
Memory size4.1 KiB
2023-12-12T08:12:27.433975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length33
Median length25
Mean length4.1
Min length1

Characters and Unicode

Total characters2091
Distinct characters278
Distinct categories11 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique260 ?
Unique (%)51.0%

Sample

1st row지하역사
2nd row지하도상가
3rd row공항터미널
4th row여객터미널
5th row도서관
ValueCountFrequency (%)
기타 27
 
4.4%
외기 23
 
3.7%
환풍구 21
 
3.4%
19
 
3.1%
대합실 10
 
1.6%
부대장비 9
 
1.5%
연결통로 8
 
1.3%
이상 8
 
1.3%
정상 6
 
1.0%
휴게실 6
 
1.0%
Other values (324) 481
77.8%
2023-12-12T08:12:27.945351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
108
 
5.2%
기 86
 
4.1%
실 48
 
2.3%
1 48
 
2.3%
시 45
 
2.2%
대 39
 
1.9%
상 38
 
1.8%
2 36
 
1.7%
정 34
 
1.6%
구 31
 
1.5%
Other values (268) 1578
75.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1606
76.8%
Decimal Number 178
 
8.5%
Space Separator 108
 
5.2%
Uppercase Letter 104
 
5.0%
Lowercase Letter 22
 
1.1%
Math Symbol 19
 
0.9%
Close Punctuation 17
 
0.8%
Open Punctuation 17
 
0.8%
Other Punctuation 13
 
0.6%
Connector Punctuation 6
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
기 86
 
5.4%
실 48
 
3.0%
시 45
 
2.8%
대 39
 
2.4%
상 38
 
2.4%
정 34
 
2.1%
구 31
 
1.9%
외 28
 
1.7%
환 28
 
1.7%
타 28
 
1.7%
Other values (220) 1201
74.8%
Uppercase Letter
ValueCountFrequency (%)
O 20
19.2%
P 17
16.3%
M 15
14.4%
C 15
14.4%
N 9
8.7%
H 8
 
7.7%
L 4
 
3.8%
D 4
 
3.8%
T 3
 
2.9%
R 3
 
2.9%
Other values (5) 6
 
5.8%
Lowercase Letter
ValueCountFrequency (%)
b 6
27.3%
p 2
 
9.1%
u 2
 
9.1%
m 2
 
9.1%
n 1
 
4.5%
l 1
 
4.5%
a 1
 
4.5%
h 1
 
4.5%
r 1
 
4.5%
e 1
 
4.5%
Other values (4) 4
18.2%
Decimal Number
ValueCountFrequency (%)
1 48
27.0%
2 36
20.2%
0 28
15.7%
5 18
 
10.1%
3 10
 
5.6%
8 9
 
5.1%
4 8
 
4.5%
6 8
 
4.5%
7 7
 
3.9%
9 6
 
3.4%
Other Punctuation
ValueCountFrequency (%)
/ 6
46.2%
. 5
38.5%
, 2
 
15.4%
Space Separator
ValueCountFrequency (%)
108
100.0%
Math Symbol
ValueCountFrequency (%)
~ 19
100.0%
Close Punctuation
ValueCountFrequency (%)
) 17
100.0%
Open Punctuation
ValueCountFrequency (%)
( 17
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 6
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1606
76.8%
Common 359
 
17.2%
Latin 126
 
6.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
기 86
 
5.4%
실 48
 
3.0%
시 45
 
2.8%
대 39
 
2.4%
상 38
 
2.4%
정 34
 
2.1%
구 31
 
1.9%
외 28
 
1.7%
환 28
 
1.7%
타 28
 
1.7%
Other values (220) 1201
74.8%
Latin
ValueCountFrequency (%)
O 20
15.9%
P 17
13.5%
M 15
11.9%
C 15
11.9%
N 9
 
7.1%
H 8
 
6.3%
b 6
 
4.8%
L 4
 
3.2%
D 4
 
3.2%
T 3
 
2.4%
Other values (19) 25
19.8%
Common
ValueCountFrequency (%)
108
30.1%
1 48
13.4%
2 36
 
10.0%
0 28
 
7.8%
~ 19
 
5.3%
5 18
 
5.0%
) 17
 
4.7%
( 17
 
4.7%
3 10
 
2.8%
8 9
 
2.5%
Other values (9) 49
13.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1606
76.8%
ASCII 485
 
23.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
108
22.3%
1 48
 
9.9%
2 36
 
7.4%
0 28
 
5.8%
O 20
 
4.1%
~ 19
 
3.9%
5 18
 
3.7%
P 17
 
3.5%
) 17
 
3.5%
( 17
 
3.5%
Other values (38) 157
32.4%
Hangul
ValueCountFrequency (%)
기 86
 
5.4%
실 48
 
3.0%
시 45
 
2.8%
대 39
 
2.4%
상 38
 
2.4%
정 34
 
2.1%
구 31
 
1.9%
외 28
 
1.7%
환 28
 
1.7%
타 28
 
1.7%
Other values (220) 1201
74.8%

순서
Real number (ℝ)

Distinct99
Distinct (%)19.4%
Missing1
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean16.227898
Minimum1
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 KiB
2023-12-12T08:12:28.410391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median7
Q318
95-th percentile73.6
Maximum99
Range98
Interquartile range (IQR)15

Descriptive statistics

Standard deviation22.037886
Coefficient of variation (CV)1.3580247
Kurtosis3.8301416
Mean16.227898
Median Absolute Deviation (MAD)5
Skewness2.1242004
Sum8260
Variance485.66843
MonotonicityNot monotonic
2023-12-12T08:12:28.541283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 57
 
11.2%
1 56
 
11.0%
3 39
 
7.6%
4 34
 
6.7%
5 28
 
5.5%
6 22
 
4.3%
7 22
 
4.3%
10 20
 
3.9%
11 18
 
3.5%
8 18
 
3.5%
Other values (89) 195
38.2%
ValueCountFrequency (%)
1 56
11.0%
2 57
11.2%
3 39
7.6%
4 34
6.7%
5 28
5.5%
6 22
 
4.3%
7 22
 
4.3%
8 18
 
3.5%
9 15
 
2.9%
10 20
 
3.9%
ValueCountFrequency (%)
99 1
0.2%
98 1
0.2%
97 1
0.2%
96 1
0.2%
95 1
0.2%
94 1
0.2%
93 1
0.2%
92 1
0.2%
91 1
0.2%
90 1
0.2%

Interactions

2023-12-12T08:12:25.312037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T08:12:28.627913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분순서
구분1.0000.414
순서0.4141.000

Missing values

2023-12-12T08:12:25.421673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T08:12:25.523345image/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

구분코드코드명순서
0시설군A지하역사1
1시설군B지하도상가2
2시설군C공항터미널3
3시설군D여객터미널4
4시설군G도서관7
5시설군H항만시설8
6시설군I박물관9
7시설군K철도역사11
8지점A외기21
9지점B대합실22
구분코드코드명순서
500위반여부1권고기준위반1
501위반여부2측정결과 미공고2
502위반여부3관계공무원 출입방해 검사3
503측정데이터 구분11시간2
504측정데이터 구분55분1
505통계구분11시간1
506통계구분2020시간3
507통계구분24일평균4
508통계구분30월평균5
509통계구분88시간2