Overview

Dataset statistics

Number of variables9
Number of observations372
Missing cells42
Missing cells (%)1.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory28.5 KiB
Average record size in memory78.4 B

Variable types

Categorical3
DateTime1
Numeric5

Dataset

Description발전소의 발전설비별 황산화물, 질소산화물, 먼지 등 대기오염물질 배출현황을 제공하는 서비스이며, 항목은 "발전기명", "호기명", "배출년월" , "이산화황 허용기준(Sox_ppm)", "이산화황 평균농도(Sox_ppm)", "질소산화물 허용기준(Nox_ppm)", "질소산화물 평균농도(Nox_ppm)", "먼지 허용기준(표준세제곱미터당 밀리그램)", "먼지 평균농도(표준세제곱미터당 밀리그램)"로 구성되어 있음.
Author한국중부발전(주)
URLhttps://www.data.go.kr/data/15082977/fileData.do

Alerts

이산화황 허용기준(Sox_ppm) is highly overall correlated with 이산화황 평균농도(Sox_ppm) and 5 other fieldsHigh correlation
이산화황 평균농도(Sox_ppm) is highly overall correlated with 이산화황 허용기준(Sox_ppm) and 2 other fieldsHigh correlation
질소산화물 허용기준(Nox_ppm) is highly overall correlated with 이산화황 허용기준(Sox_ppm) and 6 other fieldsHigh correlation
질소산화물 평균농도(Nox_ppm) is highly overall correlated with 질소산화물 허용기준(Nox_ppm)High correlation
먼지 평균농도(표준세제곱미터당 밀리그램) is highly overall correlated with 이산화황 허용기준(Sox_ppm) and 3 other fieldsHigh correlation
발전기명 is highly overall correlated with 이산화황 허용기준(Sox_ppm) and 3 other fieldsHigh correlation
호기명 is highly overall correlated with 이산화황 허용기준(Sox_ppm) and 3 other fieldsHigh correlation
먼지 허용기준(표준세제곱미터당 밀리그램) is highly overall correlated with 이산화황 허용기준(Sox_ppm) and 4 other fieldsHigh correlation
이산화황 평균농도(Sox_ppm) has 9 (2.4%) missing valuesMissing
질소산화물 평균농도(Nox_ppm) has 24 (6.5%) missing valuesMissing
먼지 평균농도(표준세제곱미터당 밀리그램) has 9 (2.4%) missing valuesMissing
이산화황 평균농도(Sox_ppm) has 217 (58.3%) zerosZeros
먼지 평균농도(표준세제곱미터당 밀리그램) has 193 (51.9%) zerosZeros

Reproduction

Analysis started2024-05-04 08:00:57.529143
Analysis finished2024-05-04 08:01:09.092615
Duration11.56 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

발전기명
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size3.0 KiB
보령
144 
제주
72 
인천
72 
신보령
24 
세종
24 
Other values (2)
36 

Length

Max length3
Median length2
Mean length2.0967742
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
보령 144
38.7%
제주 72
19.4%
인천 72
19.4%
신보령 24
 
6.5%
세종 24
 
6.5%
서울 24
 
6.5%
신서천 12
 
3.2%

Length

2024-05-04T08:01:09.295441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T08:01:09.666521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
보령 144
38.7%
제주 72
19.4%
인천 72
19.4%
신보령 24
 
6.5%
세종 24
 
6.5%
서울 24
 
6.5%
신서천 12
 
3.2%

호기명
Categorical

HIGH CORRELATION 

Distinct20
Distinct (%)5.4%
Missing0
Missing (%)0.0%
Memory size3.0 KiB
GT1
48 
GT2
48 
GT5
24 
GT3
24 
GT4
24 
Other values (15)
204 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3호기
2nd row3호기
3rd row3호기
4th row3호기
5th row3호기

Common Values

ValueCountFrequency (%)
GT1 48
 
12.9%
GT2 48
 
12.9%
GT5 24
 
6.5%
GT3 24
 
6.5%
GT4 24
 
6.5%
GT6 24
 
6.5%
1호기 24
 
6.5%
3호기 12
 
3.2%
5호기 12
 
3.2%
6호기 12
 
3.2%
Other values (10) 120
32.3%

Length

2024-05-04T08:01:10.120548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
gt1 48
 
12.9%
gt2 48
 
12.9%
gt5 24
 
6.5%
gt3 24
 
6.5%
gt4 24
 
6.5%
gt6 24
 
6.5%
1호기 24
 
6.5%
내연1 12
 
3.2%
2호기 12
 
3.2%
복합1 12
 
3.2%
Other values (10) 120
32.3%

배출년월
Date

Distinct12
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size3.0 KiB
Minimum2023-01-01 00:00:00
Maximum2023-12-01 00:00:00
2024-05-04T08:01:10.436736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:01:10.890568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)

이산화황 허용기준(Sox_ppm)
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.935484
Minimum15
Maximum140
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.4 KiB
2024-05-04T08:01:11.228200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile15
Q120
median20
Q350
95-th percentile140
Maximum140
Range125
Interquartile range (IQR)30

Descriptive statistics

Standard deviation31.168028
Coefficient of variation (CV)0.84385054
Kurtosis4.9438584
Mean36.935484
Median Absolute Deviation (MAD)0
Skewness2.3023504
Sum13740
Variance971.44596
MonotonicityNot monotonic
2024-05-04T08:01:11.632727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
20 192
51.6%
60 48
 
12.9%
50 48
 
12.9%
25 36
 
9.7%
140 24
 
6.5%
15 24
 
6.5%
ValueCountFrequency (%)
15 24
 
6.5%
20 192
51.6%
25 36
 
9.7%
50 48
 
12.9%
60 48
 
12.9%
140 24
 
6.5%
ValueCountFrequency (%)
140 24
 
6.5%
60 48
 
12.9%
50 48
 
12.9%
25 36
 
9.7%
20 192
51.6%
15 24
 
6.5%

이산화황 평균농도(Sox_ppm)
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct140
Distinct (%)38.6%
Missing9
Missing (%)2.4%
Infinite0
Infinite (%)0.0%
Mean2.3173829
Minimum0
Maximum21.55
Zeros217
Zeros (%)58.3%
Negative0
Negative (%)0.0%
Memory size3.4 KiB
2024-05-04T08:01:12.256018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33.15
95-th percentile12.05
Maximum21.55
Range21.55
Interquartile range (IQR)3.15

Descriptive statistics

Standard deviation4.2708515
Coefficient of variation (CV)1.8429632
Kurtosis5.2844868
Mean2.3173829
Median Absolute Deviation (MAD)0
Skewness2.2944366
Sum841.21
Variance18.240172
MonotonicityNot monotonic
2024-05-04T08:01:12.809207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 217
58.3%
3.71 2
 
0.5%
1.69 2
 
0.5%
0.02 2
 
0.5%
4.5 2
 
0.5%
0.01 2
 
0.5%
10.15 2
 
0.5%
3.97 2
 
0.5%
1.96 1
 
0.3%
0.11 1
 
0.3%
Other values (130) 130
34.9%
(Missing) 9
 
2.4%
ValueCountFrequency (%)
0.0 217
58.3%
0.01 2
 
0.5%
0.02 2
 
0.5%
0.07 1
 
0.3%
0.09 1
 
0.3%
0.1 1
 
0.3%
0.11 1
 
0.3%
0.14 1
 
0.3%
0.22 1
 
0.3%
0.38 1
 
0.3%
ValueCountFrequency (%)
21.55 1
0.3%
21.27 1
0.3%
20.76 1
0.3%
20.32 1
0.3%
20.12 1
0.3%
18.31 1
0.3%
16.64 1
0.3%
15.08 1
0.3%
14.87 1
0.3%
14.76 1
0.3%

질소산화물 허용기준(Nox_ppm)
Real number (ℝ)

HIGH CORRELATION 

Distinct8
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51.451613
Minimum15
Maximum200
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.4 KiB
2024-05-04T08:01:13.107830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile20
Q125
median40
Q370
95-th percentile200
Maximum200
Range185
Interquartile range (IQR)45

Descriptive statistics

Standard deviation44.111966
Coefficient of variation (CV)0.85734856
Kurtosis5.5571188
Mean51.451613
Median Absolute Deviation (MAD)15
Skewness2.4118426
Sum19140
Variance1945.8656
MonotonicityNot monotonic
2024-05-04T08:01:13.548430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
25 96
25.8%
40 72
19.4%
70 48
12.9%
50 48
12.9%
20 48
12.9%
90 24
 
6.5%
200 24
 
6.5%
15 12
 
3.2%
ValueCountFrequency (%)
15 12
 
3.2%
20 48
12.9%
25 96
25.8%
40 72
19.4%
50 48
12.9%
70 48
12.9%
90 24
 
6.5%
200 24
 
6.5%
ValueCountFrequency (%)
200 24
 
6.5%
90 24
 
6.5%
70 48
12.9%
50 48
12.9%
40 72
19.4%
25 96
25.8%
20 48
12.9%
15 12
 
3.2%

질소산화물 평균농도(Nox_ppm)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct318
Distinct (%)91.4%
Missing24
Missing (%)6.5%
Infinite0
Infinite (%)0.0%
Mean20.237759
Minimum1.08
Maximum354.95
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.4 KiB
2024-05-04T08:01:14.119324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.08
5-th percentile1.9
Q14.3475
median7.12
Q317.0325
95-th percentile75.608
Maximum354.95
Range353.87
Interquartile range (IQR)12.685

Descriptive statistics

Standard deviation43.576528
Coefficient of variation (CV)2.153229
Kurtosis26.758644
Mean20.237759
Median Absolute Deviation (MAD)4.565
Skewness4.8752266
Sum7042.74
Variance1898.9138
MonotonicityNot monotonic
2024-05-04T08:01:14.720052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.9 5
 
1.3%
2.62 3
 
0.8%
1.72 2
 
0.5%
11.35 2
 
0.5%
4.66 2
 
0.5%
5.48 2
 
0.5%
4.92 2
 
0.5%
4.32 2
 
0.5%
6.17 2
 
0.5%
4.72 2
 
0.5%
Other values (308) 324
87.1%
(Missing) 24
 
6.5%
ValueCountFrequency (%)
1.08 1
0.3%
1.21 1
0.3%
1.47 1
0.3%
1.51 1
0.3%
1.59 1
0.3%
1.61 1
0.3%
1.72 2
0.5%
1.74 1
0.3%
1.75 1
0.3%
1.76 1
0.3%
ValueCountFrequency (%)
354.95 1
0.3%
327.97 1
0.3%
285.39 1
0.3%
251.3 1
0.3%
250.85 1
0.3%
209.5 1
0.3%
187.21 1
0.3%
176.22 1
0.3%
168.88 1
0.3%
161.0 1
0.3%

먼지 허용기준(표준세제곱미터당 밀리그램)
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size3.0 KiB
10
264 
12
48 
20
48 
5
 
12

Length

Max length2
Median length2
Mean length1.9677419
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row12
2nd row12
3rd row12
4th row12
5th row12

Common Values

ValueCountFrequency (%)
10 264
71.0%
12 48
 
12.9%
20 48
 
12.9%
5 12
 
3.2%

Length

2024-05-04T08:01:15.442747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T08:01:15.848556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
10 264
71.0%
12 48
 
12.9%
20 48
 
12.9%
5 12
 
3.2%

먼지 평균농도(표준세제곱미터당 밀리그램)
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct142
Distinct (%)39.1%
Missing9
Missing (%)2.4%
Infinite0
Infinite (%)0.0%
Mean1.1709642
Minimum0
Maximum8.78
Zeros193
Zeros (%)51.9%
Negative0
Negative (%)0.0%
Memory size3.4 KiB
2024-05-04T08:01:16.215809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32.22
95-th percentile5.019
Maximum8.78
Range8.78
Interquartile range (IQR)2.22

Descriptive statistics

Standard deviation1.7291544
Coefficient of variation (CV)1.4766928
Kurtosis2.4656924
Mean1.1709642
Median Absolute Deviation (MAD)0
Skewness1.6268629
Sum425.06
Variance2.989975
MonotonicityNot monotonic
2024-05-04T08:01:16.636736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 193
51.9%
2.0 3
 
0.8%
0.34 3
 
0.8%
0.37 3
 
0.8%
2.21 3
 
0.8%
2.49 3
 
0.8%
5.03 3
 
0.8%
2.18 3
 
0.8%
3.94 2
 
0.5%
3.2 2
 
0.5%
Other values (132) 145
39.0%
(Missing) 9
 
2.4%
ValueCountFrequency (%)
0.0 193
51.9%
0.01 2
 
0.5%
0.02 1
 
0.3%
0.03 1
 
0.3%
0.09 1
 
0.3%
0.13 1
 
0.3%
0.22 2
 
0.5%
0.26 1
 
0.3%
0.27 1
 
0.3%
0.28 2
 
0.5%
ValueCountFrequency (%)
8.78 1
0.3%
8.1 1
0.3%
7.59 1
0.3%
7.38 1
0.3%
7.03 1
0.3%
6.33 1
0.3%
6.11 1
0.3%
6.02 1
0.3%
5.95 1
0.3%
5.85 1
0.3%

Interactions

2024-05-04T08:01:06.013559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:00:58.450215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:00:59.725231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:01:01.602705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:01:03.872137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:01:06.336001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:00:58.716821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:00:59.974636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:01:01.999650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:01:04.303861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:01:06.986134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:00:58.919406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:01:00.326530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:01:02.448509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:01:04.830579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:01:07.262181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:00:59.192373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:01:00.750552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:01:02.914698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:01:05.319361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:01:07.571732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:00:59.473369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:01:01.206425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:01:03.404419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:01:05.738143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-04T08:01:16.926362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
발전기명호기명배출년월이산화황 허용기준(Sox_ppm)이산화황 평균농도(Sox_ppm)질소산화물 허용기준(Nox_ppm)질소산화물 평균농도(Nox_ppm)먼지 허용기준(표준세제곱미터당 밀리그램)먼지 평균농도(표준세제곱미터당 밀리그램)
발전기명1.0000.8990.0000.7110.4580.7650.2760.8500.602
호기명0.8991.0000.0001.0000.8460.9950.6690.9950.886
배출년월0.0000.0001.0000.0000.0000.0000.0000.0000.000
이산화황 허용기준(Sox_ppm)0.7111.0000.0001.0000.7540.8790.0000.9490.781
이산화황 평균농도(Sox_ppm)0.4580.8460.0000.7541.0000.7030.0000.5930.700
질소산화물 허용기준(Nox_ppm)0.7650.9950.0000.8790.7031.0000.6620.8450.888
질소산화물 평균농도(Nox_ppm)0.2760.6690.0000.0000.0000.6621.0000.5180.710
먼지 허용기준(표준세제곱미터당 밀리그램)0.8500.9950.0000.9490.5930.8450.5181.0000.745
먼지 평균농도(표준세제곱미터당 밀리그램)0.6020.8860.0000.7810.7000.8880.7100.7451.000
2024-05-04T08:01:17.212566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
발전기명호기명먼지 허용기준(표준세제곱미터당 밀리그램)
발전기명1.0000.6600.771
호기명0.6601.0000.884
먼지 허용기준(표준세제곱미터당 밀리그램)0.7710.8841.000
2024-05-04T08:01:17.461546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
이산화황 허용기준(Sox_ppm)이산화황 평균농도(Sox_ppm)질소산화물 허용기준(Nox_ppm)질소산화물 평균농도(Nox_ppm)먼지 평균농도(표준세제곱미터당 밀리그램)발전기명호기명먼지 허용기준(표준세제곱미터당 밀리그램)
이산화황 허용기준(Sox_ppm)1.0000.8810.7920.4580.7870.5780.9520.699
이산화황 평균농도(Sox_ppm)0.8811.0000.6690.3390.8550.2530.4390.396
질소산화물 허용기준(Nox_ppm)0.7920.6691.0000.6380.7050.6290.8830.820
질소산화물 평균농도(Nox_ppm)0.4580.3390.6381.0000.3990.1480.3340.355
먼지 평균농도(표준세제곱미터당 밀리그램)0.7870.8550.7050.3991.0000.3580.4990.548
발전기명0.5780.2530.6290.1480.3581.0000.6600.771
호기명0.9520.4390.8830.3340.4990.6601.0000.884
먼지 허용기준(표준세제곱미터당 밀리그램)0.6990.3960.8200.3550.5480.7710.8841.000

Missing values

2024-05-04T08:01:07.932694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-04T08:01:08.430204image/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.
2024-05-04T08:01:08.920010image/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

발전기명호기명배출년월이산화황 허용기준(Sox_ppm)이산화황 평균농도(Sox_ppm)질소산화물 허용기준(Nox_ppm)질소산화물 평균농도(Nox_ppm)먼지 허용기준(표준세제곱미터당 밀리그램)먼지 평균농도(표준세제곱미터당 밀리그램)
0보령3호기2023-01601.61701.21123.3
1보령3호기2023-02601.69701.74123.03
2보령3호기2023-0360<NA>70<NA>12<NA>
3보령3호기2023-04600.68704.34121.72
4보령3호기2023-05602.03708.73121.85
5보령3호기2023-06603.97702.0121.8
6보령3호기2023-07604.41702.36122.16
7보령3호기2023-08604.05701.08122.83
8보령3호기2023-09602.57702.19122.86
9보령3호기2023-10604.267013.4122.8
발전기명호기명배출년월이산화황 허용기준(Sox_ppm)이산화황 평균농도(Sox_ppm)질소산화물 허용기준(Nox_ppm)질소산화물 평균농도(Nox_ppm)먼지 허용기준(표준세제곱미터당 밀리그램)먼지 평균농도(표준세제곱미터당 밀리그램)
362서울GT22023-03200.0204.32100.0
363서울GT22023-04200.0203.79100.0
364서울GT22023-05200.0203.89100.0
365서울GT22023-06200.0204.25100.0
366서울GT22023-07200.0204.83100.0
367서울GT22023-08200.0205.1100.0
368서울GT22023-09200.0205.43100.0
369서울GT22023-10200.0205.98100.0
370서울GT22023-11200.0206.26100.0
371서울GT22023-12200.0204.89100.0