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 10:50:13.510838
Analysis finished2023-12-10 10:50:21.072262
Duration7.56 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%
Mean57253.53
Minimum56892
Maximum57490
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:50:21.216390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum56892
5-th percentile56980.55
Q157185.75
median57292.5
Q357392.25
95-th percentile57422.35
Maximum57490
Range598
Interquartile range (IQR)206.5

Descriptive statistics

Standard deviation156.64787
Coefficient of variation (CV)0.0027360386
Kurtosis-0.36212545
Mean57253.53
Median Absolute Deviation (MAD)103
Skewness-0.66301692
Sum5725353
Variance24538.555
MonotonicityStrictly increasing
2023-12-10T19:50:21.576858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
56892 1
 
1.0%
57315 1
 
1.0%
57392 1
 
1.0%
57391 1
 
1.0%
57390 1
 
1.0%
57389 1
 
1.0%
57388 1
 
1.0%
57387 1
 
1.0%
57386 1
 
1.0%
57385 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
56892 1
1.0%
56893 1
1.0%
56894 1
1.0%
56895 1
1.0%
56896 1
1.0%
56985 1
1.0%
56991 1
1.0%
56992 1
1.0%
56993 1
1.0%
56994 1
1.0%
ValueCountFrequency (%)
57490 1
1.0%
57489 1
1.0%
57488 1
1.0%
57487 1
1.0%
57486 1
1.0%
57419 1
1.0%
57418 1
1.0%
57417 1
1.0%
57416 1
1.0%
57415 1
1.0%

gid
Text

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T19:50:22.134432image/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 row라마7742
2nd row라마7743
3rd row라마7744
4th row라마7745
5th row라마7746
ValueCountFrequency (%)
라마7742 1
 
1.0%
라마8163 1
 
1.0%
라마8241 1
 
1.0%
라마8240 1
 
1.0%
라마8239 1
 
1.0%
라마8238 1
 
1.0%
라마8237 1
 
1.0%
라마8236 1
 
1.0%
라마8235 1
 
1.0%
라마8167 1
 
1.0%
Other values (90) 90
90.0%
2023-12-10T19:50:22.983051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
라 100
16.7%
마 100
16.7%
8 87
14.5%
4 49
8.2%
2 42
7.0%
3 38
 
6.3%
5 36
 
6.0%
7 35
 
5.8%
1 35
 
5.8%
6 33
 
5.5%
Other values (2) 45
7.5%

Most occurring categories

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

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 87
21.8%
4 49
12.2%
2 42
10.5%
3 38
9.5%
5 36
9.0%
7 35
8.8%
1 35
8.8%
6 33
 
8.2%
0 26
 
6.5%
9 19
 
4.8%
Other Letter
ValueCountFrequency (%)
라 100
50.0%
마 100
50.0%

Most occurring scripts

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

Most frequent character per script

Common
ValueCountFrequency (%)
8 87
21.8%
4 49
12.2%
2 42
10.5%
3 38
9.5%
5 36
9.0%
7 35
8.8%
1 35
8.8%
6 33
 
8.2%
0 26
 
6.5%
9 19
 
4.8%
Hangul
ValueCountFrequency (%)
라 100
50.0%
마 100
50.0%

Most occurring blocks

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

Most frequent character per block

Hangul
ValueCountFrequency (%)
라 100
50.0%
마 100
50.0%
ASCII
ValueCountFrequency (%)
8 87
21.8%
4 49
12.2%
2 42
10.5%
3 38
9.5%
5 36
9.0%
7 35
8.8%
1 35
8.8%
6 33
 
8.2%
0 26
 
6.5%
9 19
 
4.8%

SD_CD
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
27
100 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row27
2nd row27
3rd row27
4th row27
5th row27

Common Values

ValueCountFrequency (%)
27 100
100.0%

Length

2023-12-10T19:50:23.224656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:50:23.398764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
27 100
100.0%

SD_NM
Categorical

CONSTANT 

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

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
대구 100
100.0%

Length

2023-12-10T19:50:23.567569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:50:23.718626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
대구 100
100.0%

SGG_CD
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
27710
100 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row27710
2nd row27710
3rd row27710
4th row27710
5th row27710

Common Values

ValueCountFrequency (%)
27710 100
100.0%

Length

2023-12-10T19:50:23.868045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:50:24.015884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
27710 100
100.0%

SGG_KOR_NM
Categorical

CONSTANT 

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

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
달성군 100
100.0%

Length

2023-12-10T19:50:24.178228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:50:24.344735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
달성군 100
100.0%

inclination
Real number (ℝ)

HIGH CORRELATION 

Distinct8
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.6901
Minimum5.42
Maximum20.65
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:50:24.521254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5.42
5-th percentile5.42
Q17.83
median7.83
Q38.54
95-th percentile18.5885
Maximum20.65
Range15.23
Interquartile range (IQR)0.71

Descriptive statistics

Standard deviation3.4149882
Coefficient of variation (CV)0.39297456
Kurtosis6.6732232
Mean8.6901
Median Absolute Deviation (MAD)0.71
Skewness2.6737385
Sum869.01
Variance11.662144
MonotonicityNot monotonic
2023-12-10T19:50:24.764729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
7.83 48
48.0%
8.54 18
 
18.0%
7.01 12
 
12.0%
5.42 10
 
10.0%
20.65 5
 
5.0%
11.48 4
 
4.0%
18.48 2
 
2.0%
15.0 1
 
1.0%
ValueCountFrequency (%)
5.42 10
 
10.0%
7.01 12
 
12.0%
7.83 48
48.0%
8.54 18
 
18.0%
11.48 4
 
4.0%
15.0 1
 
1.0%
18.48 2
 
2.0%
20.65 5
 
5.0%
ValueCountFrequency (%)
20.65 5
 
5.0%
18.48 2
 
2.0%
15.0 1
 
1.0%
11.48 4
 
4.0%
8.54 18
 
18.0%
7.83 48
48.0%
7.01 12
 
12.0%
5.42 10
 
10.0%

intercept
Real number (ℝ)

HIGH CORRELATION 

Distinct8
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-37.869
Minimum-93.76
Maximum-18.82
Zeros0
Zeros (%)0.0%
Negative100
Negative (%)100.0%
Memory size1.0 KiB
2023-12-10T19:50:24.979089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-93.76
5-th percentile-82.873
Q1-38.22
median-35.27
Q3-35.27
95-th percentile-18.82
Maximum-18.82
Range74.94
Interquartile range (IQR)2.95

Descriptive statistics

Standard deviation16.405186
Coefficient of variation (CV)-0.43320886
Kurtosis5.6629981
Mean-37.869
Median Absolute Deviation (MAD)2.95
Skewness-2.3254327
Sum-3786.9
Variance269.13014
MonotonicityNot monotonic
2023-12-10T19:50:25.179862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
-35.27 48
48.0%
-38.22 18
 
18.0%
-26.34 12
 
12.0%
-18.82 10
 
10.0%
-93.76 5
 
5.0%
-49.92 4
 
4.0%
-82.3 2
 
2.0%
-68.62 1
 
1.0%
ValueCountFrequency (%)
-93.76 5
 
5.0%
-82.3 2
 
2.0%
-68.62 1
 
1.0%
-49.92 4
 
4.0%
-38.22 18
 
18.0%
-35.27 48
48.0%
-26.34 12
 
12.0%
-18.82 10
 
10.0%
ValueCountFrequency (%)
-18.82 10
 
10.0%
-26.34 12
 
12.0%
-35.27 48
48.0%
-38.22 18
 
18.0%
-49.92 4
 
4.0%
-68.62 1
 
1.0%
-82.3 2
 
2.0%
-93.76 5
 
5.0%

Depth_10
Real number (ℝ)

HIGH CORRELATION 

Distinct8
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.0074
Minimum35.35
Maximum112.74
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:50:25.368979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.35
5-th percentile35.35
Q142.99
median42.99
Q347.18
95-th percentile103.012
Maximum112.74
Range77.39
Interquartile range (IQR)4.19

Descriptive statistics

Standard deviation18.002306
Coefficient of variation (CV)0.36733852
Kurtosis7.1906572
Mean49.0074
Median Absolute Deviation (MAD)0.75
Skewness2.8489573
Sum4900.74
Variance324.08302
MonotonicityNot monotonic
2023-12-10T19:50:25.569745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
42.99 48
48.0%
47.18 18
 
18.0%
43.74 12
 
12.0%
35.35 10
 
10.0%
112.74 5
 
5.0%
64.88 4
 
4.0%
102.5 2
 
2.0%
81.38 1
 
1.0%
ValueCountFrequency (%)
35.35 10
 
10.0%
42.99 48
48.0%
43.74 12
 
12.0%
47.18 18
 
18.0%
64.88 4
 
4.0%
81.38 1
 
1.0%
102.5 2
 
2.0%
112.74 5
 
5.0%
ValueCountFrequency (%)
112.74 5
 
5.0%
102.5 2
 
2.0%
81.38 1
 
1.0%
64.88 4
 
4.0%
47.18 18
 
18.0%
43.74 12
 
12.0%
42.99 48
48.0%
35.35 10
 
10.0%

Depth_20
Real number (ℝ)

HIGH CORRELATION 

Distinct8
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean135.8916
Minimum89.52
Maximum319.24
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:50:25.808269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum89.52
5-th percentile89.52
Q1121.26
median121.26
Q3132.59
95-th percentile288.897
Maximum319.24
Range229.72
Interquartile range (IQR)11.33

Descriptive statistics

Standard deviation52.081518
Coefficient of variation (CV)0.38325782
Kurtosis6.8995697
Mean135.8916
Median Absolute Deviation (MAD)7.43
Skewness2.7498246
Sum13589.16
Variance2712.4845
MonotonicityNot monotonic
2023-12-10T19:50:25.995433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
121.26 48
48.0%
132.59 18
 
18.0%
113.83 12
 
12.0%
89.52 10
 
10.0%
319.24 5
 
5.0%
179.68 4
 
4.0%
287.3 2
 
2.0%
231.38 1
 
1.0%
ValueCountFrequency (%)
89.52 10
 
10.0%
113.83 12
 
12.0%
121.26 48
48.0%
132.59 18
 
18.0%
179.68 4
 
4.0%
231.38 1
 
1.0%
287.3 2
 
2.0%
319.24 5
 
5.0%
ValueCountFrequency (%)
319.24 5
 
5.0%
287.3 2
 
2.0%
231.38 1
 
1.0%
179.68 4
 
4.0%
132.59 18
 
18.0%
121.26 48
48.0%
113.83 12
 
12.0%
89.52 10
 
10.0%

Depth_50
Real number (ℝ)

HIGH CORRELATION 

Distinct8
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean396.5264
Minimum252.02
Maximum938.74
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:50:26.178591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum252.02
5-th percentile252.02
Q1356.05
median356.05
Q3388.8
95-th percentile846.552
Maximum938.74
Range686.72
Interquartile range (IQR)32.75

Descriptive statistics

Standard deviation154.53068
Coefficient of variation (CV)0.38971094
Kurtosis6.7533803
Mean396.5264
Median Absolute Deviation (MAD)31.98
Skewness2.7009034
Sum39652.64
Variance23879.73
MonotonicityNot monotonic
2023-12-10T19:50:26.401009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
356.05 48
48.0%
388.8 18
 
18.0%
324.07 12
 
12.0%
252.02 10
 
10.0%
938.74 5
 
5.0%
524.08 4
 
4.0%
841.7 2
 
2.0%
681.38 1
 
1.0%
ValueCountFrequency (%)
252.02 10
 
10.0%
324.07 12
 
12.0%
356.05 48
48.0%
388.8 18
 
18.0%
524.08 4
 
4.0%
681.38 1
 
1.0%
841.7 2
 
2.0%
938.74 5
 
5.0%
ValueCountFrequency (%)
938.74 5
 
5.0%
841.7 2
 
2.0%
681.38 1
 
1.0%
524.08 4
 
4.0%
388.8 18
 
18.0%
356.05 48
48.0%
324.07 12
 
12.0%
252.02 10
 
10.0%

Interactions

2023-12-10T19:50:19.487163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:50:13.863769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:50:14.841141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:50:16.307581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:50:17.381507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:50:18.541857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:50:19.672293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:50:14.038290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:50:15.017764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:50:16.471864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:50:17.555595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:50:18.706589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:50:19.861210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:50:14.212474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:50:15.225047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:50:16.651328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:50:17.743707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:50:18.883304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:50:20.030105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:50:14.361738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:50:15.437029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:50:16.829053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:50:17.919710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:50:19.064553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:50:20.201502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:50:14.510084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:50:15.937707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:50:17.002069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:50:18.098445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:50:19.217234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:50:20.339828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:50:14.674380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:50:16.085691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:50:17.190676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:50:18.311061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:50:19.351803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T19:50:26.563280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
idgidinclinationinterceptDepth_10Depth_20Depth_50
id1.0001.0000.1170.3510.1560.0000.000
gid1.0001.0001.0001.0001.0001.0001.000
inclination0.1171.0001.0000.9970.9691.0001.000
intercept0.3511.0000.9971.0000.9611.0001.000
Depth_100.1561.0000.9690.9611.0000.9951.000
Depth_200.0001.0001.0001.0000.9951.0001.000
Depth_500.0001.0001.0001.0001.0001.0001.000
2023-12-10T19:50:26.794358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
idinclinationinterceptDepth_10Depth_20Depth_50
id1.000-0.0220.0220.151-0.022-0.022
inclination-0.0221.000-1.0000.7651.0001.000
intercept0.022-1.0001.000-0.765-1.000-1.000
Depth_100.1510.765-0.7651.0000.7650.765
Depth_20-0.0221.000-1.0000.7651.0001.000
Depth_50-0.0221.000-1.0000.7651.0001.000

Missing values

2023-12-10T19:50:20.557201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T19:50:20.872375image/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
056892라마774227대구27710달성군7.83-35.2742.99121.26356.05
156893라마774327대구27710달성군7.83-35.2742.99121.26356.05
256894라마774427대구27710달성군7.83-35.2742.99121.26356.05
356895라마774527대구27710달성군7.83-35.2742.99121.26356.05
456896라마774627대구27710달성군7.83-35.2742.99121.26356.05
556985라마783527대구27710달성군18.48-82.3102.5287.3841.7
656991라마784127대구27710달성군7.83-35.2742.99121.26356.05
756992라마784227대구27710달성군7.83-35.2742.99121.26356.05
856993라마784327대구27710달성군7.83-35.2742.99121.26356.05
956994라마784427대구27710달성군7.83-35.2742.99121.26356.05
idgidSD_CDSD_NMSGG_CDSGG_KOR_NMinclinationinterceptDepth_10Depth_20Depth_50
9057415라마826527대구27710달성군5.42-18.8235.3589.52252.02
9157416라마826627대구27710달성군5.42-18.8235.3589.52252.02
9257417라마826727대구27710달성군7.01-26.3443.74113.83324.07
9357418라마826827대구27710달성군7.01-26.3443.74113.83324.07
9457419라마826927대구27710달성군7.01-26.3443.74113.83324.07
9557486라마833627대구27710달성군7.83-35.2742.99121.26356.05
9657487라마833727대구27710달성군7.83-35.2742.99121.26356.05
9757488라마833827대구27710달성군7.83-35.2742.99121.26356.05
9857489라마833927대구27710달성군7.83-35.2742.99121.26356.05
9957490라마834027대구27710달성군11.48-49.9264.88179.68524.08