Overview

Dataset statistics

Number of variables11
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory9.5 KiB
Average record size in memory97.3 B

Variable types

Numeric6
Text1
Categorical4

Alerts

SD_CD has constant value ""Constant
SD_NM has constant value ""Constant
SGG_CD has constant value ""Constant
SGG_KOR_NM has constant value ""Constant
inclination is highly overall correlated with intercept and 3 other fieldsHigh correlation
intercept is highly overall correlated with inclination and 3 other fieldsHigh correlation
Depth_10 is highly overall correlated with inclination and 3 other fieldsHigh correlation
Depth_20 is highly overall correlated with inclination and 3 other fieldsHigh correlation
Depth_50 is highly overall correlated with inclination and 3 other fieldsHigh correlation
id has unique valuesUnique
gid has unique valuesUnique

Reproduction

Analysis started2023-12-10 10:49:28.389554
Analysis finished2023-12-10 10:49:35.609883
Duration7.22 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

id
Real number (ℝ)

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30400.66
Minimum29988
Maximum30583
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:49:35.736118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum29988
5-th percentile30173.55
Q130287.75
median30389.5
Q330489.25
95-th percentile30578.05
Maximum30583
Range595
Interquartile range (IQR)201.5

Descriptive statistics

Standard deviation142.85765
Coefficient of variation (CV)0.0046991627
Kurtosis-0.24919742
Mean30400.66
Median Absolute Deviation (MAD)101
Skewness-0.63675629
Sum3040066
Variance20408.307
MonotonicityStrictly increasing
2023-12-10T19:49:36.014529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29988 1
 
1.0%
30479 1
 
1.0%
30489 1
 
1.0%
30488 1
 
1.0%
30487 1
 
1.0%
30486 1
 
1.0%
30485 1
 
1.0%
30484 1
 
1.0%
30483 1
 
1.0%
30482 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
29988 1
1.0%
30086 1
1.0%
30087 1
1.0%
30088 1
1.0%
30089 1
1.0%
30178 1
1.0%
30179 1
1.0%
30181 1
1.0%
30186 1
1.0%
30187 1
1.0%
ValueCountFrequency (%)
30583 1
1.0%
30582 1
1.0%
30581 1
1.0%
30580 1
1.0%
30579 1
1.0%
30578 1
1.0%
30577 1
1.0%
30576 1
1.0%
30575 1
1.0%
30574 1
1.0%

gid
Text

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T19:49:36.544671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters600
Distinct characters12
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique100 ?
Unique (%)100.0%

Sample

1st row다바6656
2nd row다바6754
3rd row다바6755
4th row다바6756
5th row다바6757
ValueCountFrequency (%)
다바6656 1
 
1.0%
다바7145 1
 
1.0%
다바7156 1
 
1.0%
다바7155 1
 
1.0%
다바7154 1
 
1.0%
다바7153 1
 
1.0%
다바7152 1
 
1.0%
다바7151 1
 
1.0%
다바7150 1
 
1.0%
다바7149 1
 
1.0%
Other values (90) 90
90.0%
2023-12-10T19:49:37.331949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
다 100
16.7%
바 100
16.7%
7 88
14.7%
5 55
9.2%
6 44
7.3%
4 41
6.8%
3 33
 
5.5%
2 32
 
5.3%
1 30
 
5.0%
0 29
 
4.8%
Other values (2) 48
8.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 400
66.7%
Other Letter 200
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
7 88
22.0%
5 55
13.8%
6 44
11.0%
4 41
10.2%
3 33
 
8.2%
2 32
 
8.0%
1 30
 
7.5%
0 29
 
7.2%
9 27
 
6.8%
8 21
 
5.2%
Other Letter
ValueCountFrequency (%)
다 100
50.0%
바 100
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 400
66.7%
Hangul 200
33.3%

Most frequent character per script

Common
ValueCountFrequency (%)
7 88
22.0%
5 55
13.8%
6 44
11.0%
4 41
10.2%
3 33
 
8.2%
2 32
 
8.0%
1 30
 
7.5%
0 29
 
7.2%
9 27
 
6.8%
8 21
 
5.2%
Hangul
ValueCountFrequency (%)
다 100
50.0%
바 100
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 400
66.7%
Hangul 200
33.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
다 100
50.0%
바 100
50.0%
ASCII
ValueCountFrequency (%)
7 88
22.0%
5 55
13.8%
6 44
11.0%
4 41
10.2%
3 33
 
8.2%
2 32
 
8.0%
1 30
 
7.5%
0 29
 
7.2%
9 27
 
6.8%
8 21
 
5.2%

SD_CD
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
36
100 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row36
2nd row36
3rd row36
4th row36
5th row36

Common Values

ValueCountFrequency (%)
36 100
100.0%

Length

2023-12-10T19:49:37.592568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:49:38.120140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
36 100
100.0%

SD_NM
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
세종
100 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
세종 100
100.0%

Length

2023-12-10T19:49:38.292005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:49:38.461924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
세종 100
100.0%

SGG_CD
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
36110
100 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row36110
2nd row36110
3rd row36110
4th row36110
5th row36110

Common Values

ValueCountFrequency (%)
36110 100
100.0%

Length

2023-12-10T19:49:38.654869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:49:38.821951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
36110 100
100.0%

SGG_KOR_NM
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
세종특별자치시
100 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
세종특별자치시 100
100.0%

Length

2023-12-10T19:49:39.011332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:49:39.221895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
세종특별자치시 100
100.0%

inclination
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.5384
Minimum3.22
Maximum17.99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:49:39.372981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.22
5-th percentile3.22
Q13.22
median4.58
Q310.115
95-th percentile17.99
Maximum17.99
Range14.77
Interquartile range (IQR)6.895

Descriptive statistics

Standard deviation6.1013247
Coefficient of variation (CV)0.80936601
Kurtosis-0.6743093
Mean7.5384
Median Absolute Deviation (MAD)1.36
Skewness1.1317149
Sum753.84
Variance37.226163
MonotonicityNot monotonic
2023-12-10T19:49:39.599813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
4.58 42
42.0%
3.22 29
29.0%
17.99 25
25.0%
3.55 2
 
2.0%
7.49 1
 
1.0%
3.76 1
 
1.0%
ValueCountFrequency (%)
3.22 29
29.0%
3.55 2
 
2.0%
3.76 1
 
1.0%
4.58 42
42.0%
7.49 1
 
1.0%
17.99 25
25.0%
ValueCountFrequency (%)
17.99 25
25.0%
7.49 1
 
1.0%
4.58 42
42.0%
3.76 1
 
1.0%
3.55 2
 
2.0%
3.22 29
29.0%

intercept
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-31.5434
Minimum-75.15
Maximum-13.11
Zeros0
Zeros (%)0.0%
Negative100
Negative (%)100.0%
Memory size1.0 KiB
2023-12-10T19:49:39.822214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-75.15
5-th percentile-75.15
Q1-47.4075
median-19.18
Q3-13.11
95-th percentile-13.11
Maximum-13.11
Range62.04
Interquartile range (IQR)34.2975

Descriptive statistics

Standard deviation25.51989
Coefficient of variation (CV)-0.80904057
Kurtosis-0.6928742
Mean-31.5434
Median Absolute Deviation (MAD)6.07
Skewness-1.1183122
Sum-3154.34
Variance651.26479
MonotonicityNot monotonic
2023-12-10T19:49:40.151948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
-19.18 42
42.0%
-13.11 29
29.0%
-75.15 25
25.0%
-18.08 2
 
2.0%
-38.16 1
 
1.0%
-15.52 1
 
1.0%
ValueCountFrequency (%)
-75.15 25
25.0%
-38.16 1
 
1.0%
-19.18 42
42.0%
-18.08 2
 
2.0%
-15.52 1
 
1.0%
-13.11 29
29.0%
ValueCountFrequency (%)
-13.11 29
29.0%
-15.52 1
 
1.0%
-18.08 2
 
2.0%
-19.18 42
42.0%
-38.16 1
 
1.0%
-75.15 25
25.0%

Depth_10
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.8467
Minimum17.46
Maximum104.75
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:49:40.353378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum17.46
5-th percentile19.11
Q119.11
median26.62
Q353.7275
95-th percentile104.75
Maximum104.75
Range87.29
Interquartile range (IQR)34.6175

Descriptive statistics

Standard deviation35.513237
Coefficient of variation (CV)0.80994094
Kurtosis-0.66603394
Mean43.8467
Median Absolute Deviation (MAD)7.51
Skewness1.1383126
Sum4384.67
Variance1261.19
MonotonicityNot monotonic
2023-12-10T19:49:40.550564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
26.62 42
42.0%
19.11 29
29.0%
104.75 25
25.0%
17.46 2
 
2.0%
36.72 1
 
1.0%
22.05 1
 
1.0%
ValueCountFrequency (%)
17.46 2
 
2.0%
19.11 29
29.0%
22.05 1
 
1.0%
26.62 42
42.0%
36.72 1
 
1.0%
104.75 25
25.0%
ValueCountFrequency (%)
104.75 25
25.0%
36.72 1
 
1.0%
26.62 42
42.0%
22.05 1
 
1.0%
19.11 29
29.0%
17.46 2
 
2.0%

Depth_20
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean119.2396
Minimum51.34
Maximum284.65
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:49:40.723272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum51.34
5-th percentile51.34
Q151.34
median72.42
Q3154.87
95-th percentile284.65
Maximum284.65
Range233.31
Interquartile range (IQR)103.53

Descriptive statistics

Standard deviation96.51363
Coefficient of variation (CV)0.80940921
Kurtosis-0.67070306
Mean119.2396
Median Absolute Deviation (MAD)21.08
Skewness1.1345794
Sum11923.96
Variance9314.8808
MonotonicityNot monotonic
2023-12-10T19:49:40.926595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
72.42 42
42.0%
51.34 29
29.0%
284.65 25
25.0%
52.99 2
 
2.0%
111.61 1
 
1.0%
59.62 1
 
1.0%
ValueCountFrequency (%)
51.34 29
29.0%
52.99 2
 
2.0%
59.62 1
 
1.0%
72.42 42
42.0%
111.61 1
 
1.0%
284.65 25
25.0%
ValueCountFrequency (%)
284.65 25
25.0%
111.61 1
 
1.0%
72.42 42
42.0%
59.62 1
 
1.0%
52.99 2
 
2.0%
51.34 29
29.0%

Depth_50
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean345.4083
Minimum148.01
Maximum824.35
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:49:41.121849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum148.01
5-th percentile148.01
Q1148.01
median209.81
Q3458.275
95-th percentile824.35
Maximum824.35
Range676.34
Interquartile range (IQR)310.265

Descriptive statistics

Standard deviation279.53764
Coefficient of variation (CV)0.80929625
Kurtosis-0.67292884
Mean345.4083
Median Absolute Deviation (MAD)61.8
Skewness1.1328336
Sum34540.83
Variance78141.293
MonotonicityNot monotonic
2023-12-10T19:49:41.350308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
209.81 42
42.0%
148.01 29
29.0%
824.35 25
25.0%
159.6 2
 
2.0%
336.25 1
 
1.0%
172.32 1
 
1.0%
ValueCountFrequency (%)
148.01 29
29.0%
159.6 2
 
2.0%
172.32 1
 
1.0%
209.81 42
42.0%
336.25 1
 
1.0%
824.35 25
25.0%
ValueCountFrequency (%)
824.35 25
25.0%
336.25 1
 
1.0%
209.81 42
42.0%
172.32 1
 
1.0%
159.6 2
 
2.0%
148.01 29
29.0%

Interactions

2023-12-10T19:49:33.994525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:28.766718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:29.804727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:30.820049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:31.894696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:32.882945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:34.155324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:28.937894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:29.973245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:30.991704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:32.090556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:33.056331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:34.327186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:29.105780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:30.132092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:31.195863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:32.240230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:33.252197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:34.510520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:29.270962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:30.295233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:31.343108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:32.407130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:33.409086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:34.662549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:29.412994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:30.437815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:31.492998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:32.549874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:33.554833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:34.835373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:29.565695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:30.576742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:31.723931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:32.716962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:49:33.793565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T19:49:41.512397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
idgidinclinationinterceptDepth_10Depth_20Depth_50
id1.0001.0000.3690.3690.6770.3690.345
gid1.0001.0001.0001.0001.0001.0001.000
inclination0.3691.0001.0001.0001.0001.0001.000
intercept0.3691.0001.0001.0001.0001.0001.000
Depth_100.6771.0001.0001.0001.0001.0001.000
Depth_200.3691.0001.0001.0001.0001.0001.000
Depth_500.3451.0001.0001.0001.0001.0001.000
2023-12-10T19:49:41.708055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
idinclinationinterceptDepth_10Depth_20Depth_50
id1.0000.472-0.4700.4710.4720.472
inclination0.4721.000-1.0000.9881.0001.000
intercept-0.470-1.0001.000-0.987-1.000-1.000
Depth_100.4710.988-0.9871.0000.9880.988
Depth_200.4721.000-1.0000.9881.0001.000
Depth_500.4721.000-1.0000.9881.0001.000

Missing values

2023-12-10T19:49:35.092972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T19:49:35.404170image/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

idgidSD_CDSD_NMSGG_CDSGG_KOR_NMinclinationinterceptDepth_10Depth_20Depth_50
029988다바665636세종36110세종특별자치시3.22-13.1119.1151.34148.01
130086다바675436세종36110세종특별자치시3.22-13.1119.1151.34148.01
230087다바675536세종36110세종특별자치시3.22-13.1119.1151.34148.01
330088다바675636세종36110세종특별자치시3.22-13.1119.1151.34148.01
430089다바675736세종36110세종특별자치시3.22-13.1119.1151.34148.01
530178다바684636세종36110세종특별자치시4.58-19.1826.6272.42209.81
630179다바684736세종36110세종특별자치시4.58-19.1826.6272.42209.81
730181다바684936세종36110세종특별자치시4.58-19.1826.6272.42209.81
830186다바685436세종36110세종특별자치시3.22-13.1119.1151.34148.01
930187다바685536세종36110세종특별자치시3.22-13.1119.1151.34148.01
idgidSD_CDSD_NMSGG_CDSGG_KOR_NMinclinationinterceptDepth_10Depth_20Depth_50
9030574다바724236세종36110세종특별자치시3.76-15.5222.0559.62172.32
9130575다바724336세종36110세종특별자치시4.58-19.1826.6272.42209.81
9230576다바724436세종36110세종특별자치시4.58-19.1826.6272.42209.81
9330577다바724536세종36110세종특별자치시4.58-19.1826.6272.42209.81
9430578다바724636세종36110세종특별자치시4.58-19.1826.6272.42209.81
9530579다바724736세종36110세종특별자치시4.58-19.1826.6272.42209.81
9630580다바724836세종36110세종특별자치시4.58-19.1826.6272.42209.81
9730581다바724936세종36110세종특별자치시4.58-19.1826.6272.42209.81
9830582다바725036세종36110세종특별자치시4.58-19.1826.6272.42209.81
9930583다바725136세종36110세종특별자치시4.58-19.1826.6272.42209.81