Overview

Dataset statistics

Number of variables8
Number of observations221
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory15.0 KiB
Average record size in memory69.6 B

Variable types

Categorical3
Numeric5

Dataset

DescriptionSample
Author㈜지오시스템리서치
URLhttps://www.bigdata-coast.kr/gdsInfo/gdsInfoDetail.do?gdsCd=CT09GSR003

Alerts

SIDO_NM is highly overall correlated with SFCLYR_SDMNT_STA_LA and 3 other fieldsHigh correlation
TRGET_AREA_NM is highly overall correlated with SFCLYR_SDMNT_STA_LA and 3 other fieldsHigh correlation
SGG_NM is highly overall correlated with SFCLYR_SDMNT_STA_LA and 3 other fieldsHigh correlation
SFCLYR_SDMNT_STA_LA is highly overall correlated with SFCLYR_SDMNT_STA_LO and 3 other fieldsHigh correlation
SFCLYR_SDMNT_STA_LO is highly overall correlated with SFCLYR_SDMNT_STA_LA and 3 other fieldsHigh correlation

Reproduction

Analysis started2024-03-13 12:49:06.143982
Analysis finished2024-03-13 12:49:11.326563
Duration5.18 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

SIDO_NM
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
인천광역시
139 
충청남도
54 
경기도
28 

Length

Max length5
Median length5
Mean length4.5022624
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
인천광역시 139
62.9%
충청남도 54
 
24.4%
경기도 28
 
12.7%

Length

2024-03-13T21:49:11.440968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T21:49:11.629806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
인천광역시 139
62.9%
충청남도 54
 
24.4%
경기도 28
 
12.7%

SGG_NM
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
옹진군
83 
태안군
54 
중구
46 
안산시
28 
강화군
10 

Length

Max length3
Median length3
Mean length2.7918552
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
옹진군 83
37.6%
태안군 54
24.4%
중구 46
20.8%
안산시 28
 
12.7%
강화군 10
 
4.5%

Length

2024-03-13T21:49:11.800585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T21:49:11.990369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
옹진군 83
37.6%
태안군 54
24.4%
중구 46
20.8%
안산시 28
 
12.7%
강화군 10
 
4.5%

TRGET_AREA_NM
Categorical

HIGH CORRELATION 

Distinct18
Distinct (%)8.1%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
신두리
30 
만리포
24 
장골
22 
실미
16 
큰풀안
14 
Other values (13)
115 

Length

Max length5
Median length4
Mean length2.8144796
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
신두리 30
13.6%
만리포 24
 
10.9%
장골 22
 
10.0%
실미 16
 
7.2%
큰풀안 14
 
6.3%
이일레 12
 
5.4%
장경리 11
 
5.0%
동막 10
 
4.5%
서포리 10
 
4.5%
방아머리 10
 
4.5%
Other values (8) 62
28.1%

Length

2024-03-13T21:49:12.175389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
신두리 30
13.6%
만리포 24
 
10.9%
장골 22
 
10.0%
실미 16
 
7.2%
큰풀안 14
 
6.3%
이일레 12
 
5.4%
장경리 11
 
5.0%
서포리 10
 
4.5%
방아머리 10
 
4.5%
동막 10
 
4.5%
Other values (8) 62
28.1%

MESR_BSLN_NO
Real number (ℝ)

Distinct15
Distinct (%)6.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.4977376
Minimum1
Maximum15
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2024-03-13T21:49:12.298764image/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 deviation3.2173174
Coefficient of variation (CV)0.71531906
Kurtosis0.929657
Mean4.4977376
Median Absolute Deviation (MAD)2
Skewness1.1834633
Sum994
Variance10.351131
MonotonicityNot monotonic
2024-03-13T21:49:12.427379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
1 35
15.8%
2 35
15.8%
3 35
15.8%
4 31
14.0%
5 23
10.4%
6 14
 
6.3%
7 11
 
5.0%
8 9
 
4.1%
9 6
 
2.7%
10 6
 
2.7%
Other values (5) 16
7.2%
ValueCountFrequency (%)
1 35
15.8%
2 35
15.8%
3 35
15.8%
4 31
14.0%
5 23
10.4%
6 14
 
6.3%
7 11
 
5.0%
8 9
 
4.1%
9 6
 
2.7%
10 6
 
2.7%
ValueCountFrequency (%)
15 2
 
0.9%
14 2
 
0.9%
13 2
 
0.9%
12 4
 
1.8%
11 6
2.7%
10 6
2.7%
9 6
2.7%
8 9
4.1%
7 11
5.0%
6 14
6.3%

SFCLYR_SDMNT_STA_LA
Real number (ℝ)

HIGH CORRELATION 

Distinct114
Distinct (%)51.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.186315
Minimum36.784515
Maximum37.593307
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2024-03-13T21:49:12.662330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.784515
5-th percentile36.786547
Q137.161148
median37.248991
Q337.385182
95-th percentile37.457493
Maximum37.593307
Range0.80879208
Interquartile range (IQR)0.22403348

Descriptive statistics

Standard deviation0.23392954
Coefficient of variation (CV)0.0062907429
Kurtosis-0.79562083
Mean37.186315
Median Absolute Deviation (MAD)0.13444048
Skewness-0.48724151
Sum8218.1755
Variance0.054723032
MonotonicityNot monotonic
2024-03-13T21:49:12.942033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.38685464 4
 
1.8%
37.59124675 2
 
0.9%
37.16509653 2
 
0.9%
37.28110789 2
 
0.9%
37.28028811 2
 
0.9%
37.27953386 2
 
0.9%
37.27802958 2
 
0.9%
37.27792956 2
 
0.9%
37.29036308 2
 
0.9%
37.28942239 2
 
0.9%
Other values (104) 199
90.0%
ValueCountFrequency (%)
36.78451489 2
0.9%
36.78452208 2
0.9%
36.78493697 2
0.9%
36.78503381 2
0.9%
36.78550731 2
0.9%
36.78654697 2
0.9%
36.78749242 2
0.9%
36.78845086 2
0.9%
36.78973958 2
0.9%
36.79147703 2
0.9%
ValueCountFrequency (%)
37.59330697 2
0.9%
37.59262222 2
0.9%
37.59222667 2
0.9%
37.5917 2
0.9%
37.59124675 2
0.9%
37.45749269 2
0.9%
37.45656342 2
0.9%
37.45524694 2
0.9%
37.45365425 2
0.9%
37.44877306 2
0.9%

SFCLYR_SDMNT_STA_LO
Real number (ℝ)

HIGH CORRELATION 

Distinct115
Distinct (%)52.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.32093
Minimum126.11154
Maximum126.57701
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2024-03-13T21:49:13.190900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.11154
5-th percentile126.13405
Q1126.19316
median126.31169
Q3126.40773
95-th percentile126.56792
Maximum126.57701
Range0.4654698
Interquartile range (IQR)0.2145627

Descriptive statistics

Standard deviation0.13672509
Coefficient of variation (CV)0.0010823629
Kurtosis-0.93306715
Mean126.32093
Median Absolute Deviation (MAD)0.1161775
Skewness0.26691703
Sum27916.926
Variance0.01869375
MonotonicityNot monotonic
2024-03-13T21:49:13.414641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.4601314 2
 
0.9%
126.3016301 2
 
0.9%
126.5428481 2
 
0.9%
126.5470139 2
 
0.9%
126.5488183 2
 
0.9%
126.5491658 2
 
0.9%
126.5630536 2
 
0.9%
126.5770131 2
 
0.9%
126.5757492 2
 
0.9%
126.57369 2
 
0.9%
Other values (105) 201
91.0%
ValueCountFrequency (%)
126.1115433 2
0.9%
126.1134869 2
0.9%
126.1147678 2
0.9%
126.1153481 2
0.9%
126.1153553 2
0.9%
126.1340455 2
0.9%
126.1351899 2
0.9%
126.1363192 2
0.9%
126.1375458 2
0.9%
126.13879 2
0.9%
ValueCountFrequency (%)
126.5770131 2
0.9%
126.5757492 2
0.9%
126.57369 2
0.9%
126.5719675 2
0.9%
126.5697361 2
0.9%
126.56834 1
0.5%
126.5679175 1
0.5%
126.5666119 1
0.5%
126.56501 1
0.5%
126.5630536 2
0.9%

MESR_WTCH_YMD
Real number (ℝ)

Distinct15
Distinct (%)6.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20210671
Minimum20210401
Maximum20210917
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2024-03-13T21:49:13.626262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20210401
5-th percentile20210401
Q120210414
median20210906
Q320210909
95-th percentile20210917
Maximum20210917
Range516
Interquartile range (IQR)495

Descriptive statistics

Standard deviation248.23496
Coefficient of variation (CV)1.2282371 × 10-5
Kurtosis-2.0112355
Mean20210671
Median Absolute Deviation (MAD)11
Skewness-0.065120446
Sum4.4665582 × 109
Variance61620.595
MonotonicityNot monotonic
2024-03-13T21:49:13.838569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
20210908 29
13.1%
20210412 28
12.7%
20210909 21
9.5%
20210401 16
 
7.2%
20210906 15
 
6.8%
20210427 15
 
6.8%
20210916 15
 
6.8%
20210414 14
 
6.3%
20210428 12
 
5.4%
20210917 12
 
5.4%
Other values (5) 44
19.9%
ValueCountFrequency (%)
20210401 16
7.2%
20210412 28
12.7%
20210413 11
 
5.0%
20210414 14
6.3%
20210415 5
 
2.3%
20210416 6
 
2.7%
20210427 15
6.8%
20210428 12
5.4%
20210906 15
6.8%
20210907 11
 
5.0%
ValueCountFrequency (%)
20210917 12
5.4%
20210916 15
6.8%
20210914 11
 
5.0%
20210909 21
9.5%
20210908 29
13.1%
20210907 11
 
5.0%
20210906 15
6.8%
20210428 12
5.4%
20210427 15
6.8%
20210416 6
 
2.7%

SFCLYR_SDMNT_AVG_PTCSZ_VAL
Real number (ℝ)

Distinct86
Distinct (%)38.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5899095
Minimum0.17
Maximum1.92
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2024-03-13T21:49:14.069435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.17
5-th percentile0.25
Q10.35
median0.48
Q30.71
95-th percentile1.45
Maximum1.92
Range1.75
Interquartile range (IQR)0.36

Descriptive statistics

Standard deviation0.35056576
Coefficient of variation (CV)0.59427042
Kurtosis2.4957506
Mean0.5899095
Median Absolute Deviation (MAD)0.14
Skewness1.6533465
Sum130.37
Variance0.12289636
MonotonicityNot monotonic
2024-03-13T21:49:14.290610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.35 12
 
5.4%
0.36 11
 
5.0%
0.34 7
 
3.2%
0.37 6
 
2.7%
0.49 6
 
2.7%
0.29 6
 
2.7%
0.59 6
 
2.7%
0.51 5
 
2.3%
0.71 5
 
2.3%
0.4 5
 
2.3%
Other values (76) 152
68.8%
ValueCountFrequency (%)
0.17 3
1.4%
0.22 1
 
0.5%
0.23 3
1.4%
0.24 4
1.8%
0.25 1
 
0.5%
0.26 4
1.8%
0.27 3
1.4%
0.28 2
 
0.9%
0.29 6
2.7%
0.3 2
 
0.9%
ValueCountFrequency (%)
1.92 1
0.5%
1.81 1
0.5%
1.76 1
0.5%
1.69 1
0.5%
1.62 2
0.9%
1.58 1
0.5%
1.51 1
0.5%
1.49 1
0.5%
1.48 1
0.5%
1.46 1
0.5%

Interactions

2024-03-13T21:49:10.181939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:49:06.583179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:49:07.648114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:49:08.569618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:49:09.407522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:49:10.366526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:49:06.706870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:49:07.837652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:49:08.710266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:49:09.567348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:49:10.566704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:49:07.245301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:49:08.100338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:49:08.883968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:49:09.745662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:49:10.705839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:49:07.367339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:49:08.253757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:49:09.077478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:49:09.891435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:49:10.852368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:49:07.515665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:49:08.406440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:49:09.258319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:49:10.030905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-13T21:49:14.450526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
SIDO_NMSGG_NMTRGET_AREA_NMMESR_BSLN_NOSFCLYR_SDMNT_STA_LASFCLYR_SDMNT_STA_LOMESR_WTCH_YMDSFCLYR_SDMNT_AVG_PTCSZ_VAL
SIDO_NM1.0001.0001.0000.4750.8910.9980.1530.356
SGG_NM1.0001.0001.0000.4370.9490.9460.1920.489
TRGET_AREA_NM1.0001.0001.0000.0000.9961.0000.1780.635
MESR_BSLN_NO0.4750.4370.0001.0000.3100.2720.0000.270
SFCLYR_SDMNT_STA_LA0.8910.9490.9960.3101.0000.9360.2450.471
SFCLYR_SDMNT_STA_LO0.9980.9461.0000.2720.9361.0000.2130.506
MESR_WTCH_YMD0.1530.1920.1780.0000.2450.2131.0000.437
SFCLYR_SDMNT_AVG_PTCSZ_VAL0.3560.4890.6350.2700.4710.5060.4371.000
2024-03-13T21:49:14.631326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
SIDO_NMTRGET_AREA_NMSGG_NM
SIDO_NM1.0000.9650.995
TRGET_AREA_NM0.9651.0000.969
SGG_NM0.9950.9691.000
2024-03-13T21:49:14.770120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
MESR_BSLN_NOSFCLYR_SDMNT_STA_LASFCLYR_SDMNT_STA_LOMESR_WTCH_YMDSFCLYR_SDMNT_AVG_PTCSZ_VALSIDO_NMSGG_NMTRGET_AREA_NM
MESR_BSLN_NO1.000-0.296-0.2440.180-0.1360.3230.1980.000
SFCLYR_SDMNT_STA_LA-0.2961.0000.809-0.3970.4870.8800.9330.965
SFCLYR_SDMNT_STA_LO-0.2440.8091.000-0.3790.3510.9390.8820.979
MESR_WTCH_YMD0.180-0.397-0.3791.000-0.0300.0000.0000.000
SFCLYR_SDMNT_AVG_PTCSZ_VAL-0.1360.4870.351-0.0301.0000.2170.2190.289
SIDO_NM0.3230.8800.9390.0000.2171.0000.9950.965
SGG_NM0.1980.9330.8820.0000.2190.9951.0000.969
TRGET_AREA_NM0.0000.9650.9790.0000.2890.9650.9691.000

Missing values

2024-03-13T21:49:11.040791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-13T21:49:11.243408image/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_NOSFCLYR_SDMNT_STA_LASFCLYR_SDMNT_STA_LOMESR_WTCH_YMDSFCLYR_SDMNT_AVG_PTCSZ_VAL
0인천광역시강화군동막137.591247126.460131202104120.61
1인천광역시강화군동막137.591247126.460131202109060.68
2인천광역시강화군동막237.5917126.459432202104121.06
3인천광역시강화군동막237.5917126.459432202109061.16
4인천광역시강화군동막337.592227126.45848202104121.48
5인천광역시강화군동막337.592227126.45848202109060.95
6인천광역시강화군동막437.592622126.457594202104121.26
7인천광역시강화군동막437.592622126.457594202109060.7
8인천광역시강화군동막537.593307126.455917202104121.02
9인천광역시강화군동막537.593307126.455917202109060.8
SIDO_NMSGG_NMTRGET_AREA_NMMESR_BSLN_NOSFCLYR_SDMNT_STA_LASFCLYR_SDMNT_STA_LOMESR_WTCH_YMDSFCLYR_SDMNT_AVG_PTCSZ_VAL
211충청남도태안군만리포836.788451126.141934202104280.38
212충청남도태안군만리포836.788451126.141934202109170.81
213충청남도태안군만리포936.78974126.142956202104280.37
214충청남도태안군만리포936.78974126.142956202109171.09
215충청남도태안군만리포1036.791477126.144085202104280.37
216충청남도태안군만리포1036.791477126.144085202109171.14
217충청남도태안군만리포1136.792596126.144601202104280.36
218충청남도태안군만리포1136.792596126.144601202109171.62
219충청남도태안군만리포1236.793112126.144709202104280.45
220충청남도태안군만리포1236.793112126.144709202109170.84