Overview

Dataset statistics

Number of variables18
Number of observations296
Missing cells33
Missing cells (%)0.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory44.4 KiB
Average record size in memory153.4 B

Variable types

Categorical9
Text1
Numeric8

Dataset

Description공공데이터 제공 표준데이터 속성정보(허용값, 표현형식/단위 등)는 [공공데이터 제공 표준] 전문을 참고하시기 바랍니다.(공공데이터포털>정보공유>자료실)
Author지방공기업
URLhttps://www.data.go.kr/data/15114145/standard.do

Alerts

측정연도 is highly overall correlated with 일산화탄소 and 11 other fieldsHigh correlation
시군구명 is highly overall correlated with 시도명 and 7 other fieldsHigh correlation
측정위치 is highly overall correlated with 일산화탄소 and 10 other fieldsHigh correlation
데이터기준일자 is highly overall correlated with 일산화탄소 and 11 other fieldsHigh correlation
제공기관명 is highly overall correlated with 일산화탄소 and 11 other fieldsHigh correlation
관리기관명 is highly overall correlated with 일산화탄소 and 11 other fieldsHigh correlation
시도명 is highly overall correlated with 일산화탄소 and 11 other fieldsHigh correlation
제공기관코드 is highly overall correlated with 일산화탄소 and 11 other fieldsHigh correlation
미세먼지 is highly overall correlated with 초미세먼지High correlation
초미세먼지 is highly overall correlated with 미세먼지High correlation
일산화탄소 is highly overall correlated with 시도명 and 6 other fieldsHigh correlation
이산화질소 is highly overall correlated with 라돈 and 8 other fieldsHigh correlation
라돈 is highly overall correlated with 이산화질소 and 8 other fieldsHigh correlation
휘발성유기화합물 is highly overall correlated with 이산화질소 and 8 other fieldsHigh correlation
지하철호선명 is highly overall correlated with 시도명 and 6 other fieldsHigh correlation
시도명 is highly imbalanced (51.6%)Imbalance
측정연도 is highly imbalanced (77.1%)Imbalance
측정위치 is highly imbalanced (67.8%)Imbalance
관리기관명 is highly imbalanced (61.7%)Imbalance
데이터기준일자 is highly imbalanced (61.7%)Imbalance
제공기관코드 is highly imbalanced (61.7%)Imbalance
제공기관명 is highly imbalanced (61.7%)Imbalance
이산화질소 has 11 (3.7%) missing valuesMissing
라돈 has 11 (3.7%) missing valuesMissing
휘발성유기화합물 has 11 (3.7%) missing valuesMissing
이산화질소 has 263 (88.9%) zerosZeros
라돈 has 263 (88.9%) zerosZeros
휘발성유기화합물 has 263 (88.9%) zerosZeros

Reproduction

Analysis started2023-12-12 20:12:10.075632
Analysis finished2023-12-12 20:12:18.240237
Duration8.16 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size2.4 KiB
서울특별시
251 
경기도
 
23
대전광역시
 
22

Length

Max length5
Median length5
Mean length4.8445946
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
서울특별시 251
84.8%
경기도 23
 
7.8%
대전광역시 22
 
7.4%

Length

2023-12-13T05:12:18.321767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:12:18.423495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서울특별시 251
84.8%
경기도 23
 
7.8%
대전광역시 22
 
7.4%

시군구명
Categorical

HIGH CORRELATION 

Distinct32
Distinct (%)10.8%
Missing0
Missing (%)0.0%
Memory size2.4 KiB
중구
27 
송파구
27 
강남구
21 
마포구
 
16
종로구
 
15
Other values (27)
190 

Length

Max length4
Median length3
Mean length2.9459459
Min length2

Unique

Unique2 ?
Unique (%)0.7%

Sample

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

Common Values

ValueCountFrequency (%)
중구 27
 
9.1%
송파구 27
 
9.1%
강남구 21
 
7.1%
마포구 16
 
5.4%
종로구 15
 
5.1%
은평구 13
 
4.4%
강동구 13
 
4.4%
서초구 12
 
4.1%
과천시 11
 
3.7%
영등포구 11
 
3.7%
Other values (22) 130
43.9%

Length

2023-12-13T05:12:18.533732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
중구 27
 
9.1%
송파구 27
 
9.1%
강남구 21
 
7.1%
마포구 16
 
5.4%
종로구 15
 
5.1%
은평구 13
 
4.4%
강동구 13
 
4.4%
서초구 12
 
4.1%
영등포구 11
 
3.7%
과천시 11
 
3.7%
Other values (22) 130
43.9%

측정연도
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size2.4 KiB
2022
285 
2023
 
11

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023
2nd row2023
3rd row2023
4th row2023
5th row2023

Common Values

ValueCountFrequency (%)
2022 285
96.3%
2023 11
 
3.7%

Length

2023-12-13T05:12:18.657856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:12:18.786146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022 285
96.3%
2023 11
 
3.7%

지하철호선명
Categorical

HIGH CORRELATION 

Distinct9
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size2.4 KiB
5호선
56 
7호선
39 
6호선
38 
2호선
37 
4호선
32 
Other values (4)
94 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4호선
2nd row4호선
3rd row4호선
4th row4호선
5th row4호선

Common Values

ValueCountFrequency (%)
5호선 56
18.9%
7호선 39
13.2%
6호선 38
12.8%
2호선 37
12.5%
4호선 32
10.8%
1호선 32
10.8%
3호선 32
10.8%
8호선 17
 
5.7%
9호선 13
 
4.4%

Length

2023-12-13T05:12:18.885262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:12:19.008744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
5호선 56
18.9%
7호선 39
13.2%
6호선 38
12.8%
2호선 37
12.5%
4호선 32
10.8%
1호선 32
10.8%
3호선 32
10.8%
8호선 17
 
5.7%
9호선 13
 
4.4%

지하철역사명
Text

Distinct287
Distinct (%)97.0%
Missing0
Missing (%)0.0%
Memory size2.4 KiB
2023-12-13T05:12:19.346047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length11
Mean length3.8344595
Min length2

Characters and Unicode

Total characters1135
Distinct characters223
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

Unique279 ?
Unique (%)94.3%

Sample

1st row정부청사역
2nd row정부청사역
3rd row경마공원역
4th row경마공원역
5th row과천역
ValueCountFrequency (%)
대공원역 3
 
1.0%
천호(5 2
 
0.7%
경마공원역 2
 
0.7%
월드컵경기장 2
 
0.7%
선바위역 2
 
0.7%
과천역 2
 
0.7%
정부청사역 2
 
0.7%
신흥 2
 
0.7%
망원 1
 
0.3%
공덕(6 1
 
0.3%
Other values (277) 277
93.6%
2023-12-13T05:12:19.755267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
( 82
 
7.2%
) 82
 
7.2%
대 34
 
3.0%
구 27
 
2.4%
신 22
 
1.9%
동 22
 
1.9%
원 20
 
1.8%
청 19
 
1.7%
역 18
 
1.6%
5 17
 
1.5%
Other values (213) 792
69.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 881
77.6%
Decimal Number 90
 
7.9%
Open Punctuation 82
 
7.2%
Close Punctuation 82
 
7.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
대 34
 
3.9%
구 27
 
3.1%
신 22
 
2.5%
동 22
 
2.5%
원 20
 
2.3%
청 19
 
2.2%
역 18
 
2.0%
산 17
 
1.9%
사 16
 
1.8%
문 16
 
1.8%
Other values (202) 670
76.0%
Decimal Number
ValueCountFrequency (%)
5 17
18.9%
2 17
18.9%
3 16
17.8%
6 12
13.3%
7 9
10.0%
4 9
10.0%
1 6
 
6.7%
8 3
 
3.3%
9 1
 
1.1%
Open Punctuation
ValueCountFrequency (%)
( 82
100.0%
Close Punctuation
ValueCountFrequency (%)
) 82
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 881
77.6%
Common 254
 
22.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
대 34
 
3.9%
구 27
 
3.1%
신 22
 
2.5%
동 22
 
2.5%
원 20
 
2.3%
청 19
 
2.2%
역 18
 
2.0%
산 17
 
1.9%
사 16
 
1.8%
문 16
 
1.8%
Other values (202) 670
76.0%
Common
ValueCountFrequency (%)
( 82
32.3%
) 82
32.3%
5 17
 
6.7%
2 17
 
6.7%
3 16
 
6.3%
6 12
 
4.7%
7 9
 
3.5%
4 9
 
3.5%
1 6
 
2.4%
8 3
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 881
77.6%
ASCII 254
 
22.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
( 82
32.3%
) 82
32.3%
5 17
 
6.7%
2 17
 
6.7%
3 16
 
6.3%
6 12
 
4.7%
7 9
 
3.5%
4 9
 
3.5%
1 6
 
2.4%
8 3
 
1.2%
Hangul
ValueCountFrequency (%)
대 34
 
3.9%
구 27
 
3.1%
신 22
 
2.5%
동 22
 
2.5%
원 20
 
2.3%
청 19
 
2.2%
역 18
 
2.0%
산 17
 
1.9%
사 16
 
1.8%
문 16
 
1.8%
Other values (202) 670
76.0%

측정위치
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size2.4 KiB
승강장 및 대합실
263 
<NA>
 
22
대합실
 
6
승강장
 
5

Length

Max length9
Median length9
Mean length8.4054054
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row대합실
2nd row승강장
3rd row승강장
4th row대합실
5th row승강장

Common Values

ValueCountFrequency (%)
승강장 및 대합실 263
88.9%
<NA> 22
 
7.4%
대합실 6
 
2.0%
승강장 5
 
1.7%

Length

2023-12-13T05:12:19.874707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:12:19.968630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
대합실 269
32.7%
승강장 268
32.6%
및 263
32.0%
na 22
 
2.7%

미세먼지
Real number (ℝ)

HIGH CORRELATION 

Distinct212
Distinct (%)71.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.624324
Minimum18.9
Maximum110
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2023-12-13T05:12:20.073255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum18.9
5-th percentile24.4
Q129.775
median36.6
Q346.25
95-th percentile64.925
Maximum110
Range91.1
Interquartile range (IQR)16.475

Descriptive statistics

Standard deviation13.990356
Coefficient of variation (CV)0.35307493
Kurtosis4.7210728
Mean39.624324
Median Absolute Deviation (MAD)7.7
Skewness1.7553702
Sum11728.8
Variance195.73005
MonotonicityNot monotonic
2023-12-13T05:12:20.195908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
46.4 4
 
1.4%
30.6 3
 
1.0%
44.3 3
 
1.0%
26.4 3
 
1.0%
24.4 3
 
1.0%
34.1 3
 
1.0%
30.9 3
 
1.0%
36.9 3
 
1.0%
45.6 3
 
1.0%
28.8 3
 
1.0%
Other values (202) 265
89.5%
ValueCountFrequency (%)
18.9 1
0.3%
19.4 1
0.3%
20.3 1
0.3%
21.4 2
0.7%
21.7 1
0.3%
23.0 1
0.3%
23.3 2
0.7%
23.6 1
0.3%
23.8 1
0.3%
24.2 2
0.7%
ValueCountFrequency (%)
110.0 1
0.3%
98.7 1
0.3%
96.9 1
0.3%
96.6 1
0.3%
96.0 1
0.3%
83.5 1
0.3%
74.2 1
0.3%
73.2 1
0.3%
71.0 1
0.3%
68.9 1
0.3%

초미세먼지
Real number (ℝ)

HIGH CORRELATION 

Distinct182
Distinct (%)61.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.221959
Minimum5.3
Maximum58
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2023-12-13T05:12:20.327301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5.3
5-th percentile10.9
Q116.2
median20.15
Q325.375
95-th percentile33.3
Maximum58
Range52.7
Interquartile range (IQR)9.175

Descriptive statistics

Standard deviation7.4340598
Coefficient of variation (CV)0.35030035
Kurtosis2.6291309
Mean21.221959
Median Absolute Deviation (MAD)4.45
Skewness0.98386171
Sum6281.7
Variance55.265245
MonotonicityNot monotonic
2023-12-13T05:12:20.447594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18.4 6
 
2.0%
16.8 5
 
1.7%
19.7 4
 
1.4%
21.3 4
 
1.4%
16.6 4
 
1.4%
19.4 4
 
1.4%
21.2 4
 
1.4%
24.2 4
 
1.4%
14.2 4
 
1.4%
16.2 4
 
1.4%
Other values (172) 253
85.5%
ValueCountFrequency (%)
5.3 1
0.3%
6.1 1
0.3%
6.4 1
0.3%
7.1 1
0.3%
7.2 1
0.3%
8.0 1
0.3%
8.3 2
0.7%
9.6 1
0.3%
10.0 1
0.3%
10.1 1
0.3%
ValueCountFrequency (%)
58.0 1
0.3%
54.0 1
0.3%
44.6 1
0.3%
40.9 1
0.3%
39.1 1
0.3%
38.5 1
0.3%
37.7 1
0.3%
37.6 1
0.3%
37.4 1
0.3%
36.2 1
0.3%

이산화탄소
Real number (ℝ)

Distinct167
Distinct (%)56.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean499.41216
Minimum331
Maximum885
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2023-12-13T05:12:20.565335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum331
5-th percentile378
Q1460
median486
Q3528
95-th percentile690.5
Maximum885
Range554
Interquartile range (IQR)68

Descriptive statistics

Standard deviation83.32464
Coefficient of variation (CV)0.16684544
Kurtosis3.9409297
Mean499.41216
Median Absolute Deviation (MAD)31.5
Skewness1.4476412
Sum147826
Variance6942.9956
MonotonicityNot monotonic
2023-12-13T05:12:20.712701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
463 6
 
2.0%
481 6
 
2.0%
505 6
 
2.0%
478 5
 
1.7%
482 5
 
1.7%
485 5
 
1.7%
491 4
 
1.4%
510 4
 
1.4%
479 4
 
1.4%
514 4
 
1.4%
Other values (157) 247
83.4%
ValueCountFrequency (%)
331 1
0.3%
341 1
0.3%
342 2
0.7%
350 1
0.3%
352 1
0.3%
355 1
0.3%
357 1
0.3%
360 1
0.3%
365 1
0.3%
368 2
0.7%
ValueCountFrequency (%)
885 1
0.3%
859 1
0.3%
826 1
0.3%
765 1
0.3%
756 1
0.3%
738 1
0.3%
734 1
0.3%
731 1
0.3%
724 1
0.3%
716 1
0.3%

폼알데하이드
Real number (ℝ)

Distinct118
Distinct (%)39.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.3226351
Minimum0.9
Maximum22.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2023-12-13T05:12:20.853477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.9
5-th percentile3.475
Q15.975
median7.85
Q310.025
95-th percentile15.525
Maximum22.7
Range21.8
Interquartile range (IQR)4.05

Descriptive statistics

Standard deviation3.5243263
Coefficient of variation (CV)0.42346279
Kurtosis1.5020357
Mean8.3226351
Median Absolute Deviation (MAD)2.1
Skewness0.98036575
Sum2463.5
Variance12.420876
MonotonicityNot monotonic
2023-12-13T05:12:20.973184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.6 9
 
3.0%
7.8 7
 
2.4%
6.1 7
 
2.4%
5.4 7
 
2.4%
10.0 7
 
2.4%
6.8 6
 
2.0%
7.9 6
 
2.0%
6.2 6
 
2.0%
9.0 5
 
1.7%
8.8 5
 
1.7%
Other values (108) 231
78.0%
ValueCountFrequency (%)
0.9 1
0.3%
1.0 1
0.3%
1.7 1
0.3%
2.1 1
0.3%
2.4 1
0.3%
2.5 1
0.3%
2.7 2
0.7%
2.9 1
0.3%
3.1 1
0.3%
3.2 2
0.7%
ValueCountFrequency (%)
22.7 1
0.3%
20.2 1
0.3%
19.6 1
0.3%
18.8 2
0.7%
17.9 1
0.3%
17.8 1
0.3%
16.9 1
0.3%
16.6 1
0.3%
16.5 1
0.3%
16.4 1
0.3%

일산화탄소
Real number (ℝ)

HIGH CORRELATION 

Distinct22
Distinct (%)7.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.72432432
Minimum0.3
Maximum2.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2023-12-13T05:12:21.072555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.3
5-th percentile0.3
Q10.5
median0.6
Q30.8
95-th percentile1.8
Maximum2.8
Range2.5
Interquartile range (IQR)0.3

Descriptive statistics

Standard deviation0.4350136
Coefficient of variation (CV)0.60057847
Kurtosis4.934879
Mean0.72432432
Median Absolute Deviation (MAD)0.2
Skewness2.1584981
Sum214.4
Variance0.18923683
MonotonicityNot monotonic
2023-12-13T05:12:21.170634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
0.5 63
21.3%
0.4 44
14.9%
0.6 40
13.5%
0.7 37
12.5%
0.8 29
9.8%
0.3 22
 
7.4%
0.9 13
 
4.4%
1.0 10
 
3.4%
1.2 6
 
2.0%
2.2 4
 
1.4%
Other values (12) 28
9.5%
ValueCountFrequency (%)
0.3 22
 
7.4%
0.4 44
14.9%
0.5 63
21.3%
0.6 40
13.5%
0.7 37
12.5%
0.8 29
9.8%
0.9 13
 
4.4%
1.0 10
 
3.4%
1.1 3
 
1.0%
1.2 6
 
2.0%
ValueCountFrequency (%)
2.8 1
 
0.3%
2.5 1
 
0.3%
2.3 1
 
0.3%
2.2 4
1.4%
2.1 3
1.0%
2.0 1
 
0.3%
1.9 2
0.7%
1.8 3
1.0%
1.7 3
1.0%
1.6 2
0.7%

이산화질소
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct18
Distinct (%)6.3%
Missing11
Missing (%)3.7%
Infinite0
Infinite (%)0.0%
Mean0.0016947368
Minimum0
Maximum0.042
Zeros263
Zeros (%)88.9%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2023-12-13T05:12:21.269805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.0162
Maximum0.042
Range0.042
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.0065435814
Coefficient of variation (CV)3.8611195
Kurtosis17.954387
Mean0.0016947368
Median Absolute Deviation (MAD)0
Skewness4.2143376
Sum0.483
Variance4.2818458 × 10-5
MonotonicityNot monotonic
2023-12-13T05:12:21.404470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
0.0 263
88.9%
0.022 3
 
1.0%
0.026 2
 
0.7%
0.013 2
 
0.7%
0.031 2
 
0.7%
0.005 1
 
0.3%
0.02 1
 
0.3%
0.021 1
 
0.3%
0.017 1
 
0.3%
0.024 1
 
0.3%
Other values (8) 8
 
2.7%
(Missing) 11
 
3.7%
ValueCountFrequency (%)
0.0 263
88.9%
0.005 1
 
0.3%
0.007 1
 
0.3%
0.009 1
 
0.3%
0.011 1
 
0.3%
0.012 1
 
0.3%
0.013 2
 
0.7%
0.017 1
 
0.3%
0.02 1
 
0.3%
0.021 1
 
0.3%
ValueCountFrequency (%)
0.042 1
 
0.3%
0.041 1
 
0.3%
0.038 1
 
0.3%
0.031 2
0.7%
0.03 1
 
0.3%
0.026 2
0.7%
0.024 1
 
0.3%
0.022 3
1.0%
0.021 1
 
0.3%
0.02 1
 
0.3%

라돈
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct21
Distinct (%)7.4%
Missing11
Missing (%)3.7%
Infinite0
Infinite (%)0.0%
Mean2.0880702
Minimum0
Maximum46.4
Zeros263
Zeros (%)88.9%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2023-12-13T05:12:21.512123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile21.8
Maximum46.4
Range46.4
Interquartile range (IQR)0

Descriptive statistics

Standard deviation7.6485752
Coefficient of variation (CV)3.6629876
Kurtosis13.81534
Mean2.0880702
Median Absolute Deviation (MAD)0
Skewness3.7840228
Sum595.1
Variance58.500702
MonotonicityNot monotonic
2023-12-13T05:12:21.625792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0.0 263
88.9%
14.2 2
 
0.7%
18.0 2
 
0.7%
29.6 1
 
0.3%
18.1 1
 
0.3%
16.3 1
 
0.3%
46.4 1
 
0.3%
27.6 1
 
0.3%
25.5 1
 
0.3%
38.9 1
 
0.3%
Other values (11) 11
 
3.7%
(Missing) 11
 
3.7%
ValueCountFrequency (%)
0.0 263
88.9%
14.2 2
 
0.7%
16.3 1
 
0.3%
18.0 2
 
0.7%
18.1 1
 
0.3%
21.0 1
 
0.3%
22.0 1
 
0.3%
23.0 1
 
0.3%
24.0 1
 
0.3%
25.5 1
 
0.3%
ValueCountFrequency (%)
46.4 1
0.3%
42.0 1
0.3%
38.9 1
0.3%
37.0 1
0.3%
34.0 1
0.3%
33.3 1
0.3%
33.0 1
0.3%
30.0 1
0.3%
29.6 1
0.3%
29.0 1
0.3%

휘발성유기화합물
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct20
Distinct (%)7.0%
Missing11
Missing (%)3.7%
Infinite0
Infinite (%)0.0%
Mean8.0905263
Minimum0
Maximum294.4
Zeros263
Zeros (%)88.9%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2023-12-13T05:12:21.733952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile30
Maximum294.4
Range294.4
Interquartile range (IQR)0

Descriptive statistics

Standard deviation37.571836
Coefficient of variation (CV)4.6439297
Kurtosis31.552937
Mean8.0905263
Median Absolute Deviation (MAD)0
Skewness5.4635104
Sum2305.8
Variance1411.6428
MonotonicityNot monotonic
2023-12-13T05:12:21.846989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0.0 263
88.9%
26.0 2
 
0.7%
31.0 2
 
0.7%
14.0 2
 
0.7%
12.0 1
 
0.3%
178.2 1
 
0.3%
186.4 1
 
0.3%
119.5 1
 
0.3%
294.4 1
 
0.3%
209.2 1
 
0.3%
Other values (10) 10
 
3.4%
(Missing) 11
 
3.7%
ValueCountFrequency (%)
0.0 263
88.9%
12.0 1
 
0.3%
14.0 2
 
0.7%
17.0 1
 
0.3%
22.0 1
 
0.3%
26.0 2
 
0.7%
31.0 2
 
0.7%
34.0 1
 
0.3%
47.0 1
 
0.3%
119.5 1
 
0.3%
ValueCountFrequency (%)
294.4 1
0.3%
292.3 1
0.3%
209.2 1
0.3%
186.4 1
0.3%
178.2 1
0.3%
172.9 1
0.3%
167.5 1
0.3%
156.3 1
0.3%
132.0 1
0.3%
123.1 1
0.3%

관리기관명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size2.4 KiB
서울교통공사
263 
대전교통공사
 
22
한국철도공사
 
11

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
서울교통공사 263
88.9%
대전교통공사 22
 
7.4%
한국철도공사 11
 
3.7%

Length

2023-12-13T05:12:21.993437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:12:22.121950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서울교통공사 263
88.9%
대전교통공사 22
 
7.4%
한국철도공사 11
 
3.7%

데이터기준일자
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size2.4 KiB
2023-07-11
263 
2023-07-01
 
22
2023-07-03
 
11

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-07-03
2nd row2023-07-03
3rd row2023-07-03
4th row2023-07-03
5th row2023-07-03

Common Values

ValueCountFrequency (%)
2023-07-11 263
88.9%
2023-07-01 22
 
7.4%
2023-07-03 11
 
3.7%

Length

2023-12-13T05:12:22.240271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:12:22.425210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023-07-11 263
88.9%
2023-07-01 22
 
7.4%
2023-07-03 11
 
3.7%

제공기관코드
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size2.4 KiB
B553766
263 
B554695
 
22
3970000
 
11

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3970000
2nd row3970000
3rd row3970000
4th row3970000
5th row3970000

Common Values

ValueCountFrequency (%)
B553766 263
88.9%
B554695 22
 
7.4%
3970000 11
 
3.7%

Length

2023-12-13T05:12:22.627849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:12:22.782132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
b553766 263
88.9%
b554695 22
 
7.4%
3970000 11
 
3.7%

제공기관명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size2.4 KiB
서울교통공사
263 
대전교통공사
 
22
경기도 과천시
 
11

Length

Max length7
Median length6
Mean length6.0371622
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row경기도 과천시
2nd row경기도 과천시
3rd row경기도 과천시
4th row경기도 과천시
5th row경기도 과천시

Common Values

ValueCountFrequency (%)
서울교통공사 263
88.9%
대전교통공사 22
 
7.4%
경기도 과천시 11
 
3.7%

Length

2023-12-13T05:12:22.936840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:12:23.067751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서울교통공사 263
85.7%
대전교통공사 22
 
7.2%
경기도 11
 
3.6%
과천시 11
 
3.6%

Interactions

2023-12-13T05:12:16.585508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:12:11.119514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:12:11.731791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:12:12.418522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:12:13.245209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:12:14.008584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:12:14.865720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:12:15.740850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:12:16.700601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:12:11.198410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:12:11.811526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:12:12.527498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:12:13.333010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:12:14.092196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:12:14.992688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:12:15.840940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:12:16.795016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:12:11.300335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:12:11.891709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:12:12.631353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:12:13.419029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:12:14.186417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:12:15.085802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:12:15.958986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:12:16.903399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:12:11.375700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:12:11.978016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:12:12.727312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:12:13.505375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:12:14.297869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:12:15.188780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:12:16.066166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:12:17.003195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:12:11.448142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:12:12.059534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:12:12.822503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:12:13.584370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:12:14.389813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:12:15.288468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:12:16.152212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:12:17.101291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:12:11.520298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:12:12.140789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:12:12.928837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:12:13.690091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:12:14.484643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:12:15.388880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:12:16.256508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:12:17.190850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:12:11.589241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:12:12.226180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:12:13.022824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:12:13.812138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:12:14.586750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:12:15.510841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:12:16.362832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:12:17.282344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:12:11.657899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:12:12.317874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:12:13.133817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:12:13.910304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:12:14.732419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:12:15.612455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:12:16.479582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T05:12:23.199767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시도명시군구명측정연도지하철호선명측정위치미세먼지초미세먼지이산화탄소폼알데하이드일산화탄소이산화질소라돈휘발성유기화합물관리기관명데이터기준일자제공기관코드제공기관명
시도명1.0000.9860.4320.9060.4310.1650.6890.6150.5100.6850.9330.7820.6600.9860.9860.9860.986
시군구명0.9861.0001.0000.8920.8500.2590.4590.5200.5630.5840.7490.7170.6260.9860.9860.9860.986
측정연도0.4321.0001.0000.5451.0000.1750.2720.1730.1820.875NaNNaNNaN1.0001.0001.0001.000
지하철호선명0.9060.8920.5451.0000.6490.2840.6140.5290.5040.6130.6130.4830.3870.9290.9290.9290.929
측정위치0.4310.8501.0000.6491.0000.1010.6930.0000.0000.808NaNNaNNaN1.0001.0001.0001.000
미세먼지0.1650.2590.1750.2840.1011.0000.7590.0000.0000.1240.0000.0000.0000.0830.0830.0830.083
초미세먼지0.6890.4590.2720.6140.6930.7591.0000.2240.2020.4120.6270.4340.4980.7230.7230.7230.723
이산화탄소0.6150.5200.1730.5290.0000.0000.2241.0000.4160.2650.3820.4130.3220.6270.6270.6270.627
폼알데하이드0.5100.5630.1820.5040.0000.0000.2020.4161.0000.1700.3880.3560.3810.5160.5160.5160.516
일산화탄소0.6850.5840.8750.6130.8080.1240.4120.2650.1701.0000.6840.7960.6550.7830.7830.7830.783
이산화질소0.9330.749NaN0.613NaN0.0000.6270.3820.3880.6841.0000.8970.7881.0001.0001.0001.000
라돈0.7820.717NaN0.483NaN0.0000.4340.4130.3560.7960.8971.0000.8011.0001.0001.0001.000
휘발성유기화합물0.6600.626NaN0.387NaN0.0000.4980.3220.3810.6550.7880.8011.0000.7480.7480.7480.748
관리기관명0.9860.9861.0000.9291.0000.0830.7230.6270.5160.7831.0001.0000.7481.0001.0001.0001.000
데이터기준일자0.9860.9861.0000.9291.0000.0830.7230.6270.5160.7831.0001.0000.7481.0001.0001.0001.000
제공기관코드0.9860.9861.0000.9291.0000.0830.7230.6270.5160.7831.0001.0000.7481.0001.0001.0001.000
제공기관명0.9860.9861.0000.9291.0000.0830.7230.6270.5160.7831.0001.0000.7481.0001.0001.0001.000
2023-12-13T05:12:23.418430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
측정연도시군구명측정위치데이터기준일자제공기관명지하철호선명관리기관명시도명제공기관코드
측정연도1.0000.9480.9980.9980.9980.5410.9980.6730.998
시군구명0.9481.0000.6330.9040.9040.5760.9040.9030.904
측정위치0.9980.6331.0000.9980.9980.3600.9980.6710.998
데이터기준일자0.9980.9040.9981.0001.0000.6821.0000.8521.000
제공기관명0.9980.9040.9981.0001.0000.6821.0000.8521.000
지하철호선명0.5410.5760.3600.6820.6821.0000.6820.6400.682
관리기관명0.9980.9040.9981.0001.0000.6821.0000.8521.000
시도명0.6730.9030.6710.8520.8520.6400.8521.0000.852
제공기관코드0.9980.9040.9981.0001.0000.6821.0000.8521.000
2023-12-13T05:12:23.584579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
미세먼지초미세먼지이산화탄소폼알데하이드일산화탄소이산화질소라돈휘발성유기화합물시도명시군구명측정연도지하철호선명측정위치관리기관명데이터기준일자제공기관코드제공기관명
미세먼지1.0000.838-0.0350.1250.160-0.138-0.137-0.1370.0980.0890.1320.1330.0580.0480.0480.0480.048
초미세먼지0.8381.0000.0430.2370.129-0.374-0.375-0.3740.3940.1800.2690.2370.3970.4250.4250.4250.425
이산화탄소-0.0350.0431.0000.376-0.206-0.421-0.422-0.4210.4640.2080.1320.2770.0000.4770.4770.4770.477
폼알데하이드0.1250.2370.3761.000-0.071-0.294-0.294-0.2960.3520.2260.1370.2560.0000.3570.3570.3570.357
일산화탄소0.1600.129-0.206-0.0711.0000.3920.3940.3940.5390.2570.7200.3350.7750.6690.6690.6690.669
이산화질소-0.138-0.374-0.421-0.2940.3921.0000.9980.9980.6890.3801.0000.2371.0000.9880.9880.9880.988
라돈-0.137-0.375-0.422-0.2940.3940.9981.0000.9980.6920.3591.0000.2611.0000.9890.9890.9890.989
휘발성유기화합물-0.137-0.374-0.421-0.2960.3940.9980.9981.0000.5600.3041.0000.2141.0000.8030.8030.8030.803
시도명0.0980.3940.4640.3520.5390.6890.6920.5601.0000.9030.6730.6400.6710.8520.8520.8520.852
시군구명0.0890.1800.2080.2260.2570.3800.3590.3040.9031.0000.9480.5760.6330.9040.9040.9040.904
측정연도0.1320.2690.1320.1370.7201.0001.0001.0000.6730.9481.0000.5410.9980.9980.9980.9980.998
지하철호선명0.1330.2370.2770.2560.3350.2370.2610.2140.6400.5760.5411.0000.3600.6820.6820.6820.682
측정위치0.0580.3970.0000.0000.7751.0001.0001.0000.6710.6330.9980.3601.0000.9980.9980.9980.998
관리기관명0.0480.4250.4770.3570.6690.9880.9890.8030.8520.9040.9980.6820.9981.0001.0001.0001.000
데이터기준일자0.0480.4250.4770.3570.6690.9880.9890.8030.8520.9040.9980.6820.9981.0001.0001.0001.000
제공기관코드0.0480.4250.4770.3570.6690.9880.9890.8030.8520.9040.9980.6820.9981.0001.0001.0001.000
제공기관명0.0480.4250.4770.3570.6690.9880.9890.8030.8520.9040.9980.6820.9981.0001.0001.0001.000

Missing values

2023-12-13T05:12:17.736419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T05:12:17.996470image/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.
2023-12-13T05:12:18.166050image/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

시도명시군구명측정연도지하철호선명지하철역사명측정위치미세먼지초미세먼지이산화탄소폼알데하이드일산화탄소이산화질소라돈휘발성유기화합물관리기관명데이터기준일자제공기관코드제공기관명
0경기도과천시20234호선정부청사역대합실31.523.94494.21.6<NA><NA><NA>한국철도공사2023-07-033970000경기도 과천시
1경기도과천시20234호선정부청사역승강장49.826.14625.21.5<NA><NA><NA>한국철도공사2023-07-033970000경기도 과천시
2경기도과천시20234호선경마공원역승강장28.010.54896.11.7<NA><NA><NA>한국철도공사2023-07-033970000경기도 과천시
3경기도과천시20234호선경마공원역대합실25.616.74826.81.8<NA><NA><NA>한국철도공사2023-07-033970000경기도 과천시
4경기도과천시20234호선과천역승강장29.110.255610.22.1<NA><NA><NA>한국철도공사2023-07-033970000경기도 과천시
5경기도과천시20234호선과천역대합실21.49.65655.62.3<NA><NA><NA>한국철도공사2023-07-033970000경기도 과천시
6경기도과천시20234호선대공원역대합실18.914.25744.31.6<NA><NA><NA>한국철도공사2023-07-033970000경기도 과천시
7경기도과천시20234호선대공원역대합실19.414.85465.41.7<NA><NA><NA>한국철도공사2023-07-033970000경기도 과천시
8경기도과천시20234호선대공원역승강장26.616.35955.92.2<NA><NA><NA>한국철도공사2023-07-033970000경기도 과천시
9경기도과천시20234호선선바위역대합실29.210.05193.91.8<NA><NA><NA>한국철도공사2023-07-033970000경기도 과천시
시도명시군구명측정연도지하철호선명지하철역사명측정위치미세먼지초미세먼지이산화탄소폼알데하이드일산화탄소이산화질소라돈휘발성유기화합물관리기관명데이터기준일자제공기관코드제공기관명
286서울특별시강남구20229호선봉은사승강장 및 대합실56.124.35057.42.80.00.00.0서울교통공사2023-07-11B553766서울교통공사
287서울특별시송파구20229호선종합운동장(9)승강장 및 대합실43.228.74585.72.10.00.00.0서울교통공사2023-07-11B553766서울교통공사
288서울특별시송파구20229호선삼전승강장 및 대합실56.630.84825.22.20.00.00.0서울교통공사2023-07-11B553766서울교통공사
289서울특별시송파구20229호선석촌고분승강장 및 대합실52.125.845010.21.30.00.00.0서울교통공사2023-07-11B553766서울교통공사
290서울특별시송파구20229호선석촌승강장 및 대합실51.733.04675.40.60.00.00.0서울교통공사2023-07-11B553766서울교통공사
291서울특별시송파구20229호선송파나루승강장 및 대합실44.325.94494.40.80.00.00.0서울교통공사2023-07-11B553766서울교통공사
292서울특별시송파구20229호선한성백제승강장 및 대합실40.135.54287.41.20.00.00.0서울교통공사2023-07-11B553766서울교통공사
293서울특별시송파구20229호선올림픽공원승강장 및 대합실49.127.84656.10.50.00.00.0서울교통공사2023-07-11B553766서울교통공사
294서울특별시강동구20229호선둔촌오륜승강장 및 대합실36.725.84195.70.50.00.00.0서울교통공사2023-07-11B553766서울교통공사
295서울특별시강동구20229호선중앙보훈병원승강장 및 대합실39.223.25105.80.70.00.00.0서울교통공사2023-07-11B553766서울교통공사