Overview

Dataset statistics

Number of variables9
Number of observations232
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory17.8 KiB
Average record size in memory78.6 B

Variable types

Categorical3
Numeric6

Dataset

DescriptionSample
Author㈜지오시스템리서치
URLhttps://www.bigdata-coast.kr/gdsInfo/gdsInfoDetail.do?gdsCd=CT09GSR002

Alerts

SIDO_NM is highly overall correlated with MESR_DPNT_LA and 3 other fieldsHigh correlation
TRGET_AREA_NM is highly overall correlated with MESR_DPNT_LA and 4 other fieldsHigh correlation
SGG_NM is highly overall correlated with MESR_DPNT_LA and 3 other fieldsHigh correlation
MESR_DPNT_LA is highly overall correlated with MESR_DPNT_LO and 3 other fieldsHigh correlation
MESR_DPNT_LO is highly overall correlated with MESR_DPNT_LA and 3 other fieldsHigh correlation
MESR_BSLAR_VAL is highly overall correlated with TRGET_AREA_NMHigh correlation

Reproduction

Analysis started2024-03-13 12:47:51.439483
Analysis finished2024-03-13 12:47:58.168441
Duration6.73 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

SIDO_NM
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
인천광역시
140 
충청남도
54 
경기도
38 

Length

Max length5
Median length5
Mean length4.4396552
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
인천광역시 140
60.3%
충청남도 54
 
23.3%
경기도 38
 
16.4%

Length

2024-03-13T21:47:58.287412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T21:47:58.478113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
인천광역시 140
60.3%
충청남도 54
 
23.3%
경기도 38
 
16.4%

SGG_NM
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
옹진군
84 
태안군
54 
중구
46 
안산시
38 
강화군
10 

Length

Max length3
Median length3
Mean length2.8017241
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
옹진군 84
36.2%
태안군 54
23.3%
중구 46
19.8%
안산시 38
16.4%
강화군 10
 
4.3%

Length

2024-03-13T21:47:58.687173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T21:47:58.869388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
옹진군 84
36.2%
태안군 54
23.3%
중구 46
19.8%
안산시 38
16.4%
강화군 10
 
4.3%

TRGET_AREA_NM
Categorical

HIGH CORRELATION 

Distinct18
Distinct (%)7.8%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
신두리
30 
만리포
24 
장골
22 
실미
16 
서위
16 
Other values (13)
124 

Length

Max length5
Median length4
Mean length2.8189655
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
신두리 30
12.9%
만리포 24
 
10.3%
장골 22
 
9.5%
실미 16
 
6.9%
서위 16
 
6.9%
큰풀안 14
 
6.0%
구봉도남측 12
 
5.2%
장경리 12
 
5.2%
이일레 12
 
5.2%
서포리 10
 
4.3%
Other values (8) 64
27.6%

Length

2024-03-13T21:47:59.056163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
신두리 30
12.9%
만리포 24
 
10.3%
장골 22
 
9.5%
실미 16
 
6.9%
서위 16
 
6.9%
큰풀안 14
 
6.0%
구봉도남측 12
 
5.2%
장경리 12
 
5.2%
이일레 12
 
5.2%
동막 10
 
4.3%
Other values (8) 64
27.6%

MESR_BSLN_NO
Real number (ℝ)

Distinct15
Distinct (%)6.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.5172414
Minimum1
Maximum15
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2024-03-13T21:47:59.214496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q36
95-th percentile11
Maximum15
Range14
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.1731633
Coefficient of variation (CV)0.702456
Kurtosis0.94081187
Mean4.5172414
Median Absolute Deviation (MAD)2
Skewness1.1571367
Sum1048
Variance10.068966
MonotonicityNot monotonic
2024-03-13T21:47:59.355837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
1 36
15.5%
2 36
15.5%
3 36
15.5%
4 32
13.8%
5 24
10.3%
6 18
7.8%
7 12
 
5.2%
8 10
 
4.3%
9 6
 
2.6%
10 6
 
2.6%
Other values (5) 16
6.9%
ValueCountFrequency (%)
1 36
15.5%
2 36
15.5%
3 36
15.5%
4 32
13.8%
5 24
10.3%
6 18
7.8%
7 12
 
5.2%
8 10
 
4.3%
9 6
 
2.6%
10 6
 
2.6%
ValueCountFrequency (%)
15 2
 
0.9%
14 2
 
0.9%
13 2
 
0.9%
12 4
 
1.7%
11 6
 
2.6%
10 6
 
2.6%
9 6
 
2.6%
8 10
4.3%
7 12
5.2%
6 18
7.8%

MESR_AZ
Real number (ℝ)

Distinct110
Distinct (%)47.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean243.84741
Minimum4.2
Maximum359.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2024-03-13T21:47:59.558975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4.2
5-th percentile29.76
Q1209.05
median254.5
Q3310.575
95-th percentile338.185
Maximum359.9
Range355.7
Interquartile range (IQR)101.525

Descriptive statistics

Standard deviation82.785324
Coefficient of variation (CV)0.33949642
Kurtosis1.41071
Mean243.84741
Median Absolute Deviation (MAD)48.85
Skewness-1.2480001
Sum56572.6
Variance6853.4099
MonotonicityNot monotonic
2024-03-13T21:47:59.818245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
321.4 8
 
3.4%
209.9 4
 
1.7%
249.0 4
 
1.7%
314.2 4
 
1.7%
219.6 2
 
0.9%
359.9 2
 
0.9%
195.1 2
 
0.9%
189.7 2
 
0.9%
196.0 2
 
0.9%
235.6 2
 
0.9%
Other values (100) 200
86.2%
ValueCountFrequency (%)
4.2 2
0.9%
6.1 2
0.9%
18.6 2
0.9%
21.3 2
0.9%
25.8 2
0.9%
26.9 2
0.9%
32.1 2
0.9%
53.4 2
0.9%
60.4 2
0.9%
83.2 2
0.9%
ValueCountFrequency (%)
359.9 2
0.9%
356.4 2
0.9%
353.9 2
0.9%
346.3 2
0.9%
341.4 2
0.9%
338.9 2
0.9%
337.6 2
0.9%
336.6 2
0.9%
334.9 2
0.9%
334.2 2
0.9%

MESR_DPNT_LA
Real number (ℝ)

HIGH CORRELATION 

Distinct116
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.18959
Minimum36.782658
Maximum37.593472
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2024-03-13T21:48:00.080760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.782658
5-th percentile36.785692
Q137.162925
median37.249826
Q337.289033
95-th percentile37.457239
Maximum37.593472
Range0.81081389
Interquartile range (IQR)0.12610834

Descriptive statistics

Standard deviation0.2292315
Coefficient of variation (CV)0.006163862
Kurtosis-0.66713044
Mean37.18959
Median Absolute Deviation (MAD)0.08651528
Skewness-0.54994728
Sum8627.9849
Variance0.052547081
MonotonicityNot monotonic
2024-03-13T21:48:00.313239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.591525 2
 
0.9%
37.28971667 2
 
0.9%
37.28086389 2
 
0.9%
37.28056111 2
 
0.9%
37.27945278 2
 
0.9%
37.27813611 2
 
0.9%
37.28265278 2
 
0.9%
37.28040556 2
 
0.9%
37.27948056 2
 
0.9%
37.27841944 2
 
0.9%
Other values (106) 212
91.4%
ValueCountFrequency (%)
36.78265833 2
0.9%
36.78273056 2
0.9%
36.78310278 2
0.9%
36.78353611 2
0.9%
36.78483056 2
0.9%
36.78512778 2
0.9%
36.78615278 2
0.9%
36.787375 2
0.9%
36.78888333 2
0.9%
36.79044444 2
0.9%
ValueCountFrequency (%)
37.59347222 2
0.9%
37.59279167 2
0.9%
37.59238611 2
0.9%
37.59196667 2
0.9%
37.591525 2
0.9%
37.45781389 2
0.9%
37.45676944 2
0.9%
37.45525556 2
0.9%
37.45368611 2
0.9%
37.44917222 2
0.9%

MESR_DPNT_LO
Real number (ℝ)

HIGH CORRELATION 

Distinct116
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.33249
Minimum126.1121
Maximum126.5776
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2024-03-13T21:48:00.520243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.1121
5-th percentile126.13417
Q1126.19803
median126.3134
Q3126.44792
95-th percentile126.56812
Maximum126.5776
Range0.4655
Interquartile range (IQR)0.249893

Descriptive statistics

Standard deviation0.14162277
Coefficient of variation (CV)0.0011210321
Kurtosis-1.0648757
Mean126.33249
Median Absolute Deviation (MAD)0.11645135
Skewness0.2061447
Sum29309.137
Variance0.020057008
MonotonicityNot monotonic
2024-03-13T21:48:00.729182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.4604861 2
 
0.9%
126.5776028 2
 
0.9%
126.5449222 2
 
0.9%
126.5468028 2
 
0.9%
126.5491167 2
 
0.9%
126.5495361 2
 
0.9%
126.5684444 2
 
0.9%
126.5678583 2
 
0.9%
126.5668944 2
 
0.9%
126.5652944 2
 
0.9%
Other values (106) 212
91.4%
ValueCountFrequency (%)
126.1121028 2
0.9%
126.1146806 2
0.9%
126.1152444 2
0.9%
126.1154278 2
0.9%
126.1162083 2
0.9%
126.1331361 2
0.9%
126.1350222 2
0.9%
126.1365472 2
0.9%
126.1381111 2
0.9%
126.1414167 2
0.9%
ValueCountFrequency (%)
126.5776028 2
0.9%
126.5763944 2
0.9%
126.5741472 2
0.9%
126.5724028 2
0.9%
126.570225 2
0.9%
126.5684444 2
0.9%
126.5678583 2
0.9%
126.5668944 2
0.9%
126.5652944 2
0.9%
126.563325 2
0.9%

MESR_WTCH_YMD
Real number (ℝ)

Distinct15
Distinct (%)6.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20210662
Minimum20210401
Maximum20210917
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2024-03-13T21:48:00.953992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20210401
5-th percentile20210401
Q120210413
median20210667
Q320210909
95-th percentile20210916
Maximum20210917
Range516
Interquartile range (IQR)496

Descriptive statistics

Standard deviation248.84844
Coefficient of variation (CV)1.2312731 × 10-5
Kurtosis-2.0143177
Mean20210662
Median Absolute Deviation (MAD)248
Skewness-0.0015695914
Sum4.6888736 × 109
Variance61925.546
MonotonicityNot monotonic
2024-03-13T21:48:01.602681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
20210908 29
12.5%
20210412 28
12.1%
20210401 25
10.8%
20210909 21
9.1%
20210906 17
 
7.3%
20210427 15
 
6.5%
20210916 15
 
6.5%
20210414 14
 
6.0%
20210428 12
 
5.2%
20210917 12
 
5.2%
Other values (5) 44
19.0%
ValueCountFrequency (%)
20210401 25
10.8%
20210412 28
12.1%
20210413 11
 
4.7%
20210414 14
6.0%
20210415 5
 
2.2%
20210416 6
 
2.6%
20210427 15
6.5%
20210428 12
5.2%
20210906 17
7.3%
20210907 11
 
4.7%
ValueCountFrequency (%)
20210917 12
5.2%
20210916 15
6.5%
20210914 11
 
4.7%
20210909 21
9.1%
20210908 29
12.5%
20210907 11
 
4.7%
20210906 17
7.3%
20210428 12
5.2%
20210427 15
6.5%
20210416 6
 
2.6%

MESR_BSLAR_VAL
Real number (ℝ)

HIGH CORRELATION 

Distinct219
Distinct (%)94.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean115.50862
Minimum0
Maximum848.5
Zeros1
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2024-03-13T21:48:01.824940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile13.855
Q151.15
median90.75
Q3149.3
95-th percentile259.735
Maximum848.5
Range848.5
Interquartile range (IQR)98.15

Descriptive statistics

Standard deviation106.88085
Coefficient of variation (CV)0.92530623
Kurtosis17.675367
Mean115.50862
Median Absolute Deviation (MAD)43.95
Skewness3.2430356
Sum26798
Variance11423.515
MonotonicityNot monotonic
2024-03-13T21:48:02.072132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
73.2 2
 
0.9%
100.1 2
 
0.9%
114.0 2
 
0.9%
104.4 2
 
0.9%
33.2 2
 
0.9%
77.0 2
 
0.9%
98.8 2
 
0.9%
106.6 2
 
0.9%
78.8 2
 
0.9%
85.1 2
 
0.9%
Other values (209) 212
91.4%
ValueCountFrequency (%)
0.0 1
0.4%
0.1 1
0.4%
5.3 1
0.4%
6.2 1
0.4%
8.0 1
0.4%
8.4 1
0.4%
8.5 1
0.4%
9.5 1
0.4%
9.8 1
0.4%
10.7 1
0.4%
ValueCountFrequency (%)
848.5 1
0.4%
833.4 1
0.4%
491.9 1
0.4%
466.6 1
0.4%
335.4 1
0.4%
311.6 1
0.4%
300.0 1
0.4%
296.7 1
0.4%
268.1 1
0.4%
265.0 1
0.4%

Interactions

2024-03-13T21:47:57.052010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:47:52.080798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:47:53.365008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:47:54.303375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:47:55.187257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:47:56.120091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:47:57.182969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:47:52.212307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:47:53.520960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:47:54.437493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:47:55.364541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:47:56.270727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:47:57.310227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:47:52.741268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:47:53.685126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:47:54.583130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:47:55.522565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:47:56.434802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:47:57.470844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:47:52.918706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:47:53.817513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:47:54.734172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:47:55.674309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:47:56.620339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:47:57.632264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:47:53.059867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:47:53.982378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:47:54.862418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:47:55.846408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:47:56.774587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:47:57.744408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:47:53.227412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:47:54.146204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:47:55.023207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:47:55.986769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:47:56.922027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-13T21:48:02.246097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
SIDO_NMSGG_NMTRGET_AREA_NMMESR_BSLN_NOMESR_AZMESR_DPNT_LAMESR_DPNT_LOMESR_WTCH_YMDMESR_BSLAR_VAL
SIDO_NM1.0001.0001.0000.4730.7010.9110.9980.1610.564
SGG_NM1.0001.0001.0000.4400.5640.9590.9440.2050.351
TRGET_AREA_NM1.0001.0001.0000.0000.8781.0001.0000.2120.846
MESR_BSLN_NO0.4730.4400.0001.0000.5650.3100.2730.0000.120
MESR_AZ0.7010.5640.8780.5651.0000.6220.8040.0000.654
MESR_DPNT_LA0.9110.9591.0000.3100.6221.0000.9420.2660.516
MESR_DPNT_LO0.9980.9441.0000.2730.8040.9421.0000.2300.598
MESR_WTCH_YMD0.1610.2050.2120.0000.0000.2660.2301.0000.000
MESR_BSLAR_VAL0.5640.3510.8460.1200.6540.5160.5980.0001.000
2024-03-13T21:48:02.459253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
SIDO_NMTRGET_AREA_NMSGG_NM
SIDO_NM1.0000.9670.996
TRGET_AREA_NM0.9671.0000.971
SGG_NM0.9960.9711.000
2024-03-13T21:48:02.621298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
MESR_BSLN_NOMESR_AZMESR_DPNT_LAMESR_DPNT_LOMESR_WTCH_YMDMESR_BSLAR_VALSIDO_NMSGG_NMTRGET_AREA_NM
MESR_BSLN_NO1.0000.143-0.279-0.2060.150-0.0550.3240.2020.022
MESR_AZ0.1431.000-0.1270.1200.026-0.1220.4030.3660.496
MESR_DPNT_LA-0.279-0.1271.0000.792-0.3980.1880.9100.9500.975
MESR_DPNT_LO-0.2060.1200.7921.000-0.424-0.0700.9390.8790.980
MESR_WTCH_YMD0.1500.026-0.398-0.4241.000-0.0090.0000.0000.000
MESR_BSLAR_VAL-0.055-0.1220.188-0.070-0.0091.0000.2770.2460.501
SIDO_NM0.3240.4030.9100.9390.0000.2771.0000.9960.967
SGG_NM0.2020.3660.9500.8790.0000.2460.9961.0000.971
TRGET_AREA_NM0.0220.4960.9750.9800.0000.5010.9670.9711.000

Missing values

2024-03-13T21:47:57.909378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-13T21:47:58.091742image/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

SIDO_NMSGG_NMTRGET_AREA_NMMESR_BSLN_NOMESR_AZMESR_DPNT_LAMESR_DPNT_LOMESR_WTCH_YMDMESR_BSLAR_VAL
0인천광역시강화군동막1219.637.591525126.4604862021041256.2
1인천광역시강화군동막1219.637.591525126.4604862021090647.1
2인천광역시강화군동막2216.037.591967126.4598532021041275.8
3인천광역시강화군동막2216.037.591967126.4598532021090670.4
4인천광역시강화군동막3208.937.592386126.4587142021041227.2
5인천광역시강화군동막3208.937.592386126.4587142021090623.7
6인천광역시강화군동막4209.937.592792126.4577062021041214.5
7인천광역시강화군동막4209.937.592792126.4577062021090613.8
8인천광역시강화군동막5198.937.593472126.4560782021041251.4
9인천광역시강화군동막5198.937.593472126.4560782021090641.2
SIDO_NMSGG_NMTRGET_AREA_NMMESR_BSLN_NOMESR_AZMESR_DPNT_LAMESR_DPNT_LOMESR_WTCH_YMDMESR_BSLAR_VAL
222충청남도태안군만리포8302.236.787375126.1440862021042852.2
223충청남도태안군만리포8302.236.787375126.1440862021091757.6
224충청남도태안군만리포9295.436.788883126.1451222021042849.1
225충청남도태안군만리포9295.436.788883126.1451222021091745.9
226충청남도태안군만리포10293.336.790444126.1460222021042850.4
227충청남도태안군만리포10293.336.790444126.1460222021091753.3
228충청남도태안군만리포11285.436.792083126.1467222021042836.1
229충청남도태안군만리포11285.436.792083126.1467222021091744.0
230충청남도태안군만리포12274.336.793189126.1469362021042848.3
231충청남도태안군만리포12274.336.793189126.1469362021091752.9