Overview

Dataset statistics

Number of variables9
Number of observations56
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.4 KiB
Average record size in memory80.4 B

Variable types

DateTime1
Categorical2
Numeric6

Alerts

카테고리명 is highly overall correlated with 도메인명High correlation
도메인명 is highly overall correlated with 카테고리명High correlation
이산화질소 값 is highly overall correlated with pm2.5 값 and 1 other fieldsHigh correlation
pm2.5 값 is highly overall correlated with 이산화질소 값 and 1 other fieldsHigh correlation
pm10 값 is highly overall correlated with 이산화질소 값 and 1 other fieldsHigh correlation
버즈량 합계 has unique valuesUnique

Reproduction

Analysis started2023-12-10 12:36:22.618012
Analysis finished2023-12-10 12:36:29.077539
Duration6.46 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

날짜
Date

Distinct28
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size580.0 B
Minimum2019-02-01 00:00:00
Maximum2019-02-28 00:00:00
2023-12-10T21:36:29.173625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:36:29.375879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)

도메인명
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Memory size580.0 B
생활환경
28 
자연환경
28 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row생활환경
2nd row자연환경
3rd row생활환경
4th row자연환경
5th row생활환경

Common Values

ValueCountFrequency (%)
생활환경 28
50.0%
자연환경 28
50.0%

Length

2023-12-10T21:36:29.575226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:36:29.742806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
생활환경 28
50.0%
자연환경 28
50.0%

카테고리명
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Memory size580.0 B
대기
28 
기후변화
28 

Length

Max length4
Median length3
Mean length3
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row대기
2nd row기후변화
3rd row대기
4th row기후변화
5th row대기

Common Values

ValueCountFrequency (%)
대기 28
50.0%
기후변화 28
50.0%

Length

2023-12-10T21:36:29.977053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:36:30.160827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
대기 28
50.0%
기후변화 28
50.0%

버즈량 합계
Real number (ℝ)

UNIQUE 

Distinct56
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean75261.732
Minimum33515
Maximum162745
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size636.0 B
2023-12-10T21:36:30.374547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum33515
5-th percentile41953.75
Q154167.75
median66589
Q381923.5
95-th percentile141619.25
Maximum162745
Range129230
Interquartile range (IQR)27755.75

Descriptive statistics

Standard deviation31841.325
Coefficient of variation (CV)0.42307457
Kurtosis0.85511382
Mean75261.732
Median Absolute Deviation (MAD)14613.5
Skewness1.2376511
Sum4214657
Variance1.01387 × 109
MonotonicityNot monotonic
2023-12-10T21:36:30.602031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
65491 1
 
1.8%
79386 1
 
1.8%
44775 1
 
1.8%
45002 1
 
1.8%
42989 1
 
1.8%
67309 1
 
1.8%
70049 1
 
1.8%
59916 1
 
1.8%
107686 1
 
1.8%
82489 1
 
1.8%
Other values (46) 46
82.1%
ValueCountFrequency (%)
33515 1
1.8%
36909 1
1.8%
38848 1
1.8%
42989 1
1.8%
44657 1
1.8%
44775 1
1.8%
45002 1
1.8%
45032 1
1.8%
47802 1
1.8%
50036 1
1.8%
ValueCountFrequency (%)
162745 1
1.8%
157042 1
1.8%
153560 1
1.8%
137639 1
1.8%
132793 1
1.8%
132158 1
1.8%
124436 1
1.8%
115672 1
1.8%
107686 1
1.8%
106405 1
1.8%

아황산가스 값
Real number (ℝ)

Distinct28
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0040004695
Minimum0.0039892221
Maximum0.0040399259
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size636.0 B
2023-12-10T21:36:30.812217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.0039892221
5-th percentile0.0039949141
Q10.0039959855
median0.003997758
Q30.0040005149
95-th percentile0.0040241857
Maximum0.0040399259
Range5.0703763 × 10-5
Interquartile range (IQR)4.5293781 × 10-6

Descriptive statistics

Standard deviation9.7649844 × 10-6
Coefficient of variation (CV)0.0024409596
Kurtosis9.1775598
Mean0.0040004695
Median Absolute Deviation (MAD)2.2562316 × 10-6
Skewness2.9476606
Sum0.22402629
Variance9.535492 × 10-11
MonotonicityNot monotonic
2023-12-10T21:36:31.030127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
0.0039957153568748 2
 
3.6%
0.0040016448936768 2
 
3.6%
0.0039949141219305 2
 
3.6%
0.003996041154103 2
 
3.6%
0.0040001136157051 2
 
3.6%
0.004000165823728 2
 
3.6%
0.0039999205508466 2
 
3.6%
0.0039963568353157 2
 
3.6%
0.0039960035689043 2
 
3.6%
0.0039892221094383 2
 
3.6%
Other values (18) 36
64.3%
ValueCountFrequency (%)
0.0039892221094383 2
3.6%
0.0039949141219305 2
3.6%
0.0039949968024676 2
3.6%
0.0039952913478509 2
3.6%
0.0039954080876908 2
3.6%
0.0039957153568748 2
3.6%
0.0039959453630476 2
3.6%
0.0039959989056833 2
3.6%
0.0039960035689043 2
3.6%
0.003996041154103 2
3.6%
ValueCountFrequency (%)
0.0040399258725405 2
3.6%
0.0040241857180682 2
3.6%
0.0040093827745517 2
3.6%
0.0040027459175729 2
3.6%
0.0040026015745169 2
3.6%
0.0040016448936768 2
3.6%
0.0040015621212521 2
3.6%
0.004000165823728 2
3.6%
0.0040001136157051 2
3.6%
0.0039999205508466 2
3.6%

이산화질소 값
Real number (ℝ)

HIGH CORRELATION 

Distinct28
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.013979239
Minimum0.013953473
Maximum0.014215122
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size636.0 B
2023-12-10T21:36:31.271123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.013953473
5-th percentile0.013958531
Q10.013965314
median0.013968198
Q30.013972998
95-th percentile0.014032581
Maximum0.014215122
Range0.00026164899
Interquartile range (IQR)7.6841324 × 10-6

Descriptive statistics

Standard deviation4.7687361 × 10-5
Coefficient of variation (CV)0.0034112987
Kurtosis21.1809
Mean0.013979239
Median Absolute Deviation (MAD)3.5151482 × 10-6
Skewness4.5940554
Sum0.78283739
Variance2.2740844 × 10-9
MonotonicityNot monotonic
2023-12-10T21:36:31.496715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
0.013977362802156 2
 
3.6%
0.0139700104891748 2
 
3.6%
0.0139534728358048 2
 
3.6%
0.0139689331245778 2
 
3.6%
0.0139633475731658 2
 
3.6%
0.013964886825301 2
 
3.6%
0.0142151218220326 2
 
3.6%
0.0139675427146406 2
 
3.6%
0.0139606305067057 2
 
3.6%
0.0139585309587477 2
 
3.6%
Other values (18) 36
64.3%
ValueCountFrequency (%)
0.0139534728358048 2
3.6%
0.0139585309587477 2
3.6%
0.0139606305067057 2
3.6%
0.0139629717993966 2
3.6%
0.0139633475731658 2
3.6%
0.0139644800970922 2
3.6%
0.013964886825301 2
3.6%
0.0139654560007986 2
3.6%
0.0139662813761022 2
3.6%
0.0139673558953913 2
3.6%
ValueCountFrequency (%)
0.0142151218220326 2
3.6%
0.0140325808770661 2
3.6%
0.0139792359114159 2
3.6%
0.013977362802156 2
3.6%
0.0139762287300074 2
3.6%
0.0139741847295445 2
3.6%
0.0139732462946109 2
3.6%
0.0139729150208775 2
3.6%
0.0139715103935216 2
3.6%
0.0139707535234053 2
3.6%

일산화탄소 값
Real number (ℝ)

Distinct28
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.19545943
Minimum0.14852587
Maximum0.24761644
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size636.0 B
2023-12-10T21:36:31.896094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.14852587
5-th percentile0.15122698
Q10.17583264
median0.19399686
Q30.21119348
95-th percentile0.24370398
Maximum0.24761644
Range0.099090571
Interquartile range (IQR)0.035360839

Descriptive statistics

Standard deviation0.025167722
Coefficient of variation (CV)0.12876187
Kurtosis-0.39541418
Mean0.19545943
Median Absolute Deviation (MAD)0.018248847
Skewness0.18051095
Sum10.945728
Variance0.00063341425
MonotonicityNot monotonic
2023-12-10T21:36:32.196595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
0.217402792159125 2
 
3.6%
0.1822375321827134 2
 
3.6%
0.2136823050234177 2
 
3.6%
0.2291165287407807 2
 
3.6%
0.1845369023814246 2
 
3.6%
0.1873787413979121 2
 
3.6%
0.2053734110169729 2
 
3.6%
0.2069609772254191 2
 
3.6%
0.2026952674821258 2
 
3.6%
0.2437039771154288 2
 
3.6%
Other values (18) 36
64.3%
ValueCountFrequency (%)
0.1485258708331097 2
3.6%
0.1512269787191988 2
3.6%
0.1606303692325529 2
3.6%
0.170138551742168 2
3.6%
0.1743959177547871 2
3.6%
0.1749931653973781 2
3.6%
0.1755787628715392 2
3.6%
0.1759172624827399 2
3.6%
0.1817402099085902 2
3.6%
0.1822375321827134 2
3.6%
ValueCountFrequency (%)
0.247616442103851 2
3.6%
0.2437039771154288 2
3.6%
0.2303984350870217 2
3.6%
0.2291165287407807 2
3.6%
0.217402792159125 2
3.6%
0.2138561781779639 2
3.6%
0.2136823050234177 2
3.6%
0.2103638665194745 2
3.6%
0.2069609772254191 2
3.6%
0.2053734110169729 2
3.6%

pm2.5 값
Real number (ℝ)

HIGH CORRELATION 

Distinct28
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.975919
Minimum21.24584
Maximum93.331346
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size636.0 B
2023-12-10T21:36:32.375524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum21.24584
5-th percentile24.25515
Q136.273351
median50.82075
Q367.454267
95-th percentile78.91076
Maximum93.331346
Range72.085506
Interquartile range (IQR)31.180917

Descriptive statistics

Standard deviation18.833796
Coefficient of variation (CV)0.36946456
Kurtosis-0.69482881
Mean50.975919
Median Absolute Deviation (MAD)17.072176
Skewness0.22089519
Sum2854.6515
Variance354.71185
MonotonicityNot monotonic
2023-12-10T21:36:32.557997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
24.25515049808976 2
 
3.6%
56.965910174501765 2
 
3.6%
78.91076008016724 2
 
3.6%
57.36920152091255 2
 
3.6%
54.75 2
 
3.6%
67.01560815852748 2
 
3.6%
51.8635593220339 2
 
3.6%
70.15059712832901 2
 
3.6%
76.50055764154801 2
 
3.6%
93.3313463135554 2
 
3.6%
Other values (18) 36
64.3%
ValueCountFrequency (%)
21.245840122451 2
3.6%
24.25515049808976 2
3.6%
25.050463740597387 2
3.6%
26.43429017769967 2
3.6%
28.55900387465673 2
3.6%
30.007544544919472 2
3.6%
31.44960227060537 2
3.6%
37.88126667122632 2
3.6%
38.74642703091301 2
3.6%
42.273604613528704 2
3.6%
ValueCountFrequency (%)
93.3313463135554 2
3.6%
78.91076008016724 2
3.6%
76.50055764154801 2
3.6%
73.48384639143416 2
3.6%
70.15059712832901 2
3.6%
69.29161214465874 2
3.6%
68.77024383260962 2
3.6%
67.01560815852748 2
3.6%
57.36920152091255 2
3.6%
56.965910174501765 2
3.6%

pm10 값
Real number (ℝ)

HIGH CORRELATION 

Distinct28
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean58.834957
Minimum25.812917
Maximum108.69859
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size636.0 B
2023-12-10T21:36:32.834730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum25.812917
5-th percentile30.695173
Q142.137192
median57.008668
Q376.563351
95-th percentile91.212206
Maximum108.69859
Range82.885672
Interquartile range (IQR)34.426158

Descriptive statistics

Standard deviation20.994768
Coefficient of variation (CV)0.35684174
Kurtosis-0.47223415
Mean58.834957
Median Absolute Deviation (MAD)18.898243
Skewness0.3868191
Sum3294.7576
Variance440.78029
MonotonicityNot monotonic
2023-12-10T21:36:33.035554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
30.7650230299568 2
 
3.6%
65.25533994469343 2
 
3.6%
91.21220610736374 2
 
3.6%
62.24573920823082 2
 
3.6%
61.32819032903109 2
 
3.6%
75.9519525744134 2
 
3.6%
58.54494173728813 2
 
3.6%
82.43601083208323 2
 
3.6%
89.44180081043905 2
 
3.6%
108.6985881701578 2
 
3.6%
Other values (18) 36
64.3%
ValueCountFrequency (%)
25.81291660494741 2
3.6%
30.69517271598627 2
3.6%
30.7650230299568 2
3.6%
33.41266975134484 2
3.6%
33.4397969146651 2
3.6%
37.09287821637973 2
3.6%
38.15546578201081 2
3.6%
43.46443471718761 2
3.6%
47.0608195542775 2
3.6%
49.6841733604025 2
3.6%
ValueCountFrequency (%)
108.6985881701578 2
3.6%
91.21220610736374 2
3.6%
89.44180081043905 2
3.6%
82.50466385256873 2
3.6%
82.43601083208323 2
3.6%
78.52944123681824 2
3.6%
78.39754603553887 2
3.6%
75.9519525744134 2
3.6%
65.25533994469343 2
3.6%
63.46188840184812 2
3.6%

Interactions

2023-12-10T21:36:27.718248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:36:23.101319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:36:23.989728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:36:24.821349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:36:25.650658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:36:26.516017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:36:27.840752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:36:23.312506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:36:24.136798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:36:24.979280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:36:25.796846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:36:26.652120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:36:27.974635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:36:23.442288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:36:24.262483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:36:25.122920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:36:25.968951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:36:26.787949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:36:28.222292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:36:23.576376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:36:24.406340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:36:25.256833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:36:26.112321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:36:26.918242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:36:28.437110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:36:23.702663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:36:24.580317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:36:25.376451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:36:26.258305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:36:27.052661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:36:28.588345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:36:23.859258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:36:24.710933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:36:25.520377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:36:26.414013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:36:27.584795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T21:36:33.171146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
날짜도메인명카테고리명버즈량 합계아황산가스 값이산화질소 값일산화탄소 값pm2.5 값pm10 값
날짜1.0000.0000.0000.4751.0001.0001.0001.0001.000
도메인명0.0001.0000.9980.0000.0000.0000.0000.0000.000
카테고리명0.0000.9981.0000.0000.0000.0000.0000.0000.000
버즈량 합계0.4750.0000.0001.0000.0000.3840.0590.3110.341
아황산가스 값1.0000.0000.0000.0001.0000.0000.7470.8460.771
이산화질소 값1.0000.0000.0000.3840.0001.0000.4110.5690.493
일산화탄소 값1.0000.0000.0000.0590.7470.4111.0000.8530.652
pm2.5 값1.0000.0000.0000.3110.8460.5690.8531.0000.957
pm10 값1.0000.0000.0000.3410.7710.4930.6520.9571.000
2023-12-10T21:36:33.356260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
카테고리명도메인명
카테고리명1.0000.964
도메인명0.9641.000
2023-12-10T21:36:33.531171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
버즈량 합계아황산가스 값이산화질소 값일산화탄소 값pm2.5 값pm10 값도메인명카테고리명
버즈량 합계1.0000.076-0.111-0.0000.0910.0730.0520.052
아황산가스 값0.0761.0000.078-0.0550.1060.1170.0000.000
이산화질소 값-0.1110.0781.000-0.257-0.713-0.7270.0000.000
일산화탄소 값-0.000-0.055-0.2571.0000.4470.4380.0000.000
pm2.5 값0.0910.106-0.7130.4471.0000.9950.0000.000
pm10 값0.0730.117-0.7270.4380.9951.0000.0000.000
도메인명0.0520.0000.0000.0000.0000.0001.0000.964
카테고리명0.0520.0000.0000.0000.0000.0000.9641.000

Missing values

2023-12-10T21:36:28.773891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T21:36:28.995791image/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

날짜도메인명카테고리명버즈량 합계아황산가스 값이산화질소 값일산화탄소 값pm2.5 값pm10 값
02019-02-01생활환경대기654910.0039960.0139770.21740324.2551530.765023
12019-02-01자연환경기후변화806700.0039960.0139770.21740324.2551530.765023
22019-02-02생활환경대기561080.0039980.0139680.20404146.28737354.228128
32019-02-02자연환경기후변화801910.0039980.0139680.20404146.28737354.228128
42019-02-03생활환경대기446570.004040.013970.23039873.48384682.504664
52019-02-03자연환경기후변화767120.004040.013970.23039873.48384682.504664
62019-02-04생활환경대기564590.0039950.0139730.14852630.00754538.155466
72019-02-04자연환경기후변화501380.0039950.0139730.14852630.00754538.155466
82019-02-05생활환경대기388480.0039960.0140330.20187238.74642747.06082
92019-02-05자연환경기후변화369090.0039960.0140330.20187238.74642747.06082
날짜도메인명카테고리명버즈량 합계아황산가스 값이산화질소 값일산화탄소 값pm2.5 값pm10 값
462019-02-24생활환경대기1047920.0040.0142150.20537351.86355958.544942
472019-02-24자연환경기후변화513040.0040.0142150.20537351.86355958.544942
482019-02-25생활환경대기1627450.0040.0139650.18737967.01560875.951953
492019-02-25자연환경기후변화658690.0040.0139650.18737967.01560875.951953
502019-02-26생활환경대기1535600.0040.0139630.18453754.7561.32819
512019-02-26자연환경기후변화548510.0040.0139630.18453754.7561.32819
522019-02-27생활환경대기1376390.0039960.0139690.22911757.36920262.245739
532019-02-27자연환경기후변화557720.0039960.0139690.22911757.36920262.245739
542019-02-28생활환경대기1327930.0039950.0139530.21368278.9107691.212206
552019-02-28자연환경기후변화478020.0039950.0139530.21368278.9107691.212206