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 10:50:28.629825
Analysis finished2023-12-10 10:50:35.672413
Duration7.04 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%
Mean8886.25
Minimum8478
Maximum9259
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:50:35.792917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8478
5-th percentile8482.95
Q18683.75
median8879.5
Q39073.25
95-th percentile9175.05
Maximum9259
Range781
Interquartile range (IQR)389.5

Descriptive statistics

Standard deviation226.68072
Coefficient of variation (CV)0.025509154
Kurtosis-1.0983159
Mean8886.25
Median Absolute Deviation (MAD)195
Skewness-0.1743456
Sum888625
Variance51384.149
MonotonicityStrictly increasing
2023-12-10T19:50:36.030687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8478 1
 
1.0%
8978 1
 
1.0%
9073 1
 
1.0%
9072 1
 
1.0%
9071 1
 
1.0%
9070 1
 
1.0%
9069 1
 
1.0%
9068 1
 
1.0%
9067 1
 
1.0%
9066 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
8478 1
1.0%
8479 1
1.0%
8480 1
1.0%
8481 1
1.0%
8482 1
1.0%
8483 1
1.0%
8486 1
1.0%
8577 1
1.0%
8578 1
1.0%
8579 1
1.0%
ValueCountFrequency (%)
9259 1
1.0%
9258 1
1.0%
9257 1
1.0%
9256 1
1.0%
9176 1
1.0%
9175 1
1.0%
9174 1
1.0%
9173 1
1.0%
9172 1
1.0%
9171 1
1.0%

gid
Text

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T19:50:36.583977image/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 row다라2280
2nd row다라2281
3rd row다라2282
4th row다라2283
5th row다라2284
ValueCountFrequency (%)
다라2280 1
 
1.0%
다라2787 1
 
1.0%
다라2886 1
 
1.0%
다라2885 1
 
1.0%
다라2884 1
 
1.0%
다라2883 1
 
1.0%
다라2882 1
 
1.0%
다라2881 1
 
1.0%
다라2880 1
 
1.0%
다라2879 1
 
1.0%
Other values (90) 90
90.0%
2023-12-10T19:50:37.278603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 114
19.0%
8 102
17.0%
다 100
16.7%
라 100
16.7%
9 43
 
7.2%
7 26
 
4.3%
3 23
 
3.8%
5 21
 
3.5%
4 20
 
3.3%
6 20
 
3.3%
Other values (2) 31
 
5.2%

Most occurring categories

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

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 114
28.5%
8 102
25.5%
9 43
 
10.8%
7 26
 
6.5%
3 23
 
5.8%
5 21
 
5.2%
4 20
 
5.0%
6 20
 
5.0%
0 18
 
4.5%
1 13
 
3.2%
Other Letter
ValueCountFrequency (%)
다 100
50.0%
라 100
50.0%

Most occurring scripts

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

Most frequent character per script

Common
ValueCountFrequency (%)
2 114
28.5%
8 102
25.5%
9 43
 
10.8%
7 26
 
6.5%
3 23
 
5.8%
5 21
 
5.2%
4 20
 
5.0%
6 20
 
5.0%
0 18
 
4.5%
1 13
 
3.2%
Hangul
ValueCountFrequency (%)
다 100
50.0%
라 100
50.0%

Most occurring blocks

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

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 114
28.5%
8 102
25.5%
9 43
 
10.8%
7 26
 
6.5%
3 23
 
5.8%
5 21
 
5.2%
4 20
 
5.0%
6 20
 
5.0%
0 18
 
4.5%
1 13
 
3.2%
Hangul
ValueCountFrequency (%)
다 100
50.0%
라 100
50.0%

SD_CD
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
29
100 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row29
2nd row29
3rd row29
4th row29
5th row29

Common Values

ValueCountFrequency (%)
29 100
100.0%

Length

2023-12-10T19:50:37.501925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:50:37.757113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
29 100
100.0%

SD_NM
Categorical

CONSTANT 

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

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
광주 100
100.0%

Length

2023-12-10T19:50:37.979040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:50:38.145810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
광주 100
100.0%

SGG_CD
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
29200
100 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row29200
2nd row29200
3rd row29200
4th row29200
5th row29200

Common Values

ValueCountFrequency (%)
29200 100
100.0%

Length

2023-12-10T19:50:38.320024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:50:38.480952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
29200 100
100.0%

SGG_KOR_NM
Categorical

CONSTANT 

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

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
광산구 100
100.0%

Length

2023-12-10T19:50:38.662243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:50:38.829656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
광산구 100
100.0%

inclination
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.1849
Minimum0.78
Maximum11.43
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:50:38.976713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.78
5-th percentile0.78
Q11.76
median1.76
Q33.15
95-th percentile11.43
Maximum11.43
Range10.65
Interquartile range (IQR)1.39

Descriptive statistics

Standard deviation3.4006323
Coefficient of variation (CV)1.067736
Kurtosis2.1498772
Mean3.1849
Median Absolute Deviation (MAD)0
Skewness1.9676882
Sum318.49
Variance11.5643
MonotonicityNot monotonic
2023-12-10T19:50:39.144468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1.76 55
55.0%
11.43 14
 
14.0%
3.15 12
 
12.0%
1.21 9
 
9.0%
0.78 8
 
8.0%
3.37 2
 
2.0%
ValueCountFrequency (%)
0.78 8
 
8.0%
1.21 9
 
9.0%
1.76 55
55.0%
3.15 12
 
12.0%
3.37 2
 
2.0%
11.43 14
 
14.0%
ValueCountFrequency (%)
11.43 14
 
14.0%
3.37 2
 
2.0%
3.15 12
 
12.0%
1.76 55
55.0%
1.21 9
 
9.0%
0.78 8
 
8.0%

intercept
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-12.2089
Minimum-38.84
Maximum-2.11
Zeros0
Zeros (%)0.0%
Negative100
Negative (%)100.0%
Memory size1.0 KiB
2023-12-10T19:50:39.312369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-38.84
5-th percentile-38.84
Q1-14.89
median-7.3
Q3-7.3
95-th percentile-2.11
Maximum-2.11
Range36.73
Interquartile range (IQR)7.59

Descriptive statistics

Standard deviation11.283434
Coefficient of variation (CV)-0.92419743
Kurtosis1.6592046
Mean-12.2089
Median Absolute Deviation (MAD)0
Skewness-1.7730973
Sum-1220.89
Variance127.31588
MonotonicityNot monotonic
2023-12-10T19:50:39.503821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
-7.3 55
55.0%
-38.84 14
 
14.0%
-14.89 12
 
12.0%
-5.37 9
 
9.0%
-2.11 8
 
8.0%
-15.87 2
 
2.0%
ValueCountFrequency (%)
-38.84 14
 
14.0%
-15.87 2
 
2.0%
-14.89 12
 
12.0%
-7.3 55
55.0%
-5.37 9
 
9.0%
-2.11 8
 
8.0%
ValueCountFrequency (%)
-2.11 8
 
8.0%
-5.37 9
 
9.0%
-7.3 55
55.0%
-14.89 12
 
12.0%
-15.87 2
 
2.0%
-38.84 14
 
14.0%

Depth_10
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.6545
Minimum5.69
Maximum75.46
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:50:39.663574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5.69
5-th percentile5.69
Q110.32
median10.32
Q316.61
95-th percentile75.46
Maximum75.46
Range69.77
Interquartile range (IQR)6.29

Descriptive statistics

Standard deviation22.822099
Coefficient of variation (CV)1.1611641
Kurtosis2.3168901
Mean19.6545
Median Absolute Deviation (MAD)0
Skewness2.0350742
Sum1965.45
Variance520.8482
MonotonicityNot monotonic
2023-12-10T19:50:40.163939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
10.32 55
55.0%
75.46 14
 
14.0%
16.61 12
 
12.0%
6.77 9
 
9.0%
5.69 8
 
8.0%
17.82 2
 
2.0%
ValueCountFrequency (%)
5.69 8
 
8.0%
6.77 9
 
9.0%
10.32 55
55.0%
16.61 12
 
12.0%
17.82 2
 
2.0%
75.46 14
 
14.0%
ValueCountFrequency (%)
75.46 14
 
14.0%
17.82 2
 
2.0%
16.61 12
 
12.0%
10.32 55
55.0%
6.77 9
 
9.0%
5.69 8
 
8.0%

Depth_20
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51.5169
Minimum13.49
Maximum189.76
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:50:40.352953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum13.49
5-th percentile13.49
Q127.94
median27.94
Q348.1
95-th percentile189.76
Maximum189.76
Range176.27
Interquartile range (IQR)20.16

Descriptive statistics

Standard deviation56.800766
Coefficient of variation (CV)1.1025657
Kurtosis2.224138
Mean51.5169
Median Absolute Deviation (MAD)0
Skewness1.9974818
Sum5151.69
Variance3226.3271
MonotonicityNot monotonic
2023-12-10T19:50:40.546357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
27.94 55
55.0%
189.76 14
 
14.0%
48.1 12
 
12.0%
18.91 9
 
9.0%
13.49 8
 
8.0%
51.52 2
 
2.0%
ValueCountFrequency (%)
13.49 8
 
8.0%
18.91 9
 
9.0%
27.94 55
55.0%
48.1 12
 
12.0%
51.52 2
 
2.0%
189.76 14
 
14.0%
ValueCountFrequency (%)
189.76 14
 
14.0%
51.52 2
 
2.0%
48.1 12
 
12.0%
27.94 55
55.0%
18.91 9
 
9.0%
13.49 8
 
8.0%

Depth_50
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean147.1105
Minimum36.89
Maximum532.66
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:50:40.748541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.89
5-th percentile36.89
Q180.81
median80.81
Q3142.59
95-th percentile532.66
Maximum532.66
Range495.77
Interquartile range (IQR)61.78

Descriptive statistics

Standard deviation158.78858
Coefficient of variation (CV)1.0793831
Kurtosis2.1778361
Mean147.1105
Median Absolute Deviation (MAD)0
Skewness1.97885
Sum14711.05
Variance25213.814
MonotonicityNot monotonic
2023-12-10T19:50:40.945302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
80.81 55
55.0%
532.66 14
 
14.0%
142.59 12
 
12.0%
55.32 9
 
9.0%
36.89 8
 
8.0%
152.59 2
 
2.0%
ValueCountFrequency (%)
36.89 8
 
8.0%
55.32 9
 
9.0%
80.81 55
55.0%
142.59 12
 
12.0%
152.59 2
 
2.0%
532.66 14
 
14.0%
ValueCountFrequency (%)
532.66 14
 
14.0%
152.59 2
 
2.0%
142.59 12
 
12.0%
80.81 55
55.0%
55.32 9
 
9.0%
36.89 8
 
8.0%

Interactions

2023-12-10T19:50:34.285148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:50:29.055439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:50:30.156496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:50:31.225530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:50:32.222854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:50:33.265852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:50:34.445344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:50:29.241202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:50:30.334863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:50:31.392676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:50:32.419710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:50:33.440894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:50:34.591713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:50:29.433297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:50:30.550710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:50:31.544632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:50:32.604746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:50:33.585462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:50:34.753563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:50:29.598672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:50:30.764124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:50:31.742521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:50:32.797509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:50:33.784747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:50:34.920893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:50:29.772613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:50:30.932625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:50:31.897595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:50:32.967650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:50:33.979693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:50:35.079353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:50:29.974797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:50:31.079313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:50:32.057017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:50:33.119388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:50:34.149927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T19:50:41.100332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
idgidinclinationinterceptDepth_10Depth_20Depth_50
id1.0001.0000.7430.6350.7430.5470.743
gid1.0001.0001.0001.0001.0001.0001.000
inclination0.7431.0001.0001.0001.0001.0001.000
intercept0.6351.0001.0001.0001.0000.9811.000
Depth_100.7431.0001.0001.0001.0001.0001.000
Depth_200.5471.0001.0000.9811.0001.0001.000
Depth_500.7431.0001.0001.0001.0001.0001.000
2023-12-10T19:50:41.318079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
idinclinationinterceptDepth_10Depth_20Depth_50
id1.0000.143-0.1430.1430.1430.143
inclination0.1431.000-1.0001.0001.0001.000
intercept-0.143-1.0001.000-1.000-1.000-1.000
Depth_100.1431.000-1.0001.0001.0001.000
Depth_200.1431.000-1.0001.0001.0001.000
Depth_500.1431.000-1.0001.0001.0001.000

Missing values

2023-12-10T19:50:35.287799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T19:50:35.574785image/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
08478다라228029광주29200광산구3.37-15.8717.8251.52152.59
18479다라228129광주29200광산구0.78-2.115.6913.4936.89
28480다라228229광주29200광산구0.78-2.115.6913.4936.89
38481다라228329광주29200광산구0.78-2.115.6913.4936.89
48482다라228429광주29200광산구0.78-2.115.6913.4936.89
58483다라228529광주29200광산구1.76-7.310.3227.9480.81
68486다라228829광주29200광산구1.76-7.310.3227.9480.81
78577다라238029광주29200광산구3.37-15.8717.8251.52152.59
88578다라238129광주29200광산구0.78-2.115.6913.4936.89
98579다라238229광주29200광산구0.78-2.115.6913.4936.89
idgidSD_CDSD_NMSGG_CDSGG_KOR_NMinclinationinterceptDepth_10Depth_20Depth_50
909171다라298929광주29200광산구1.21-5.376.7718.9155.32
919172다라299029광주29200광산구1.21-5.376.7718.9155.32
929173다라299129광주29200광산구1.21-5.376.7718.9155.32
939174다라299229광주29200광산구1.21-5.376.7718.9155.32
949175다라299329광주29200광산구1.21-5.376.7718.9155.32
959176다라299429광주29200광산구1.21-5.376.7718.9155.32
969256다라307929광주29200광산구11.43-38.8475.46189.76532.66
979257다라308029광주29200광산구11.43-38.8475.46189.76532.66
989258다라308129광주29200광산구11.43-38.8475.46189.76532.66
999259다라308229광주29200광산구11.43-38.8475.46189.76532.66