Overview

Dataset statistics

Number of variables5
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.3 KiB
Average record size in memory44.3 B

Variable types

Categorical3
Numeric2

Dataset

DescriptionSample
Author㈜지오시스템리서치
URLhttps://www.bigdata-coast.kr/gdsInfo/gdsInfoDetail.do?gdsCd=CT09GSR023

Alerts

SAR_NM has constant value ""Constant
LYR_ATTRB_SE_CD has constant value ""Constant
PRDN_LO is highly overall correlated with OC_STS_WTEMHigh correlation
OC_STS_WTEM is highly overall correlated with PRDN_LOHigh correlation
OC_STS_WTEM has unique valuesUnique

Reproduction

Analysis started2024-03-13 12:42:03.562088
Analysis finished2024-03-13 12:42:04.546813
Duration0.98 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

SAR_NM
Categorical

CONSTANT 

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

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
WSEA 100
100.0%

Length

2024-03-13T21:42:04.646423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T21:42:04.777565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
wsea 100
100.0%

LYR_ATTRB_SE_CD
Categorical

CONSTANT 

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

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
TO 100
100.0%

Length

2024-03-13T21:42:04.915262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T21:42:05.052617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
to 100
100.0%

PRDN_LA
Categorical

Distinct4
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
32.9306
28 
32.9445
28 
32.9167
27 
32.9584
17 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row32.9167
2nd row32.9167
3rd row32.9167
4th row32.9167
5th row32.9167

Common Values

ValueCountFrequency (%)
32.9306 28
28.0%
32.9445 28
28.0%
32.9167 27
27.0%
32.9584 17
17.0%

Length

2024-03-13T21:42:05.188159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T21:42:05.392762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
32.9306 28
28.0%
32.9445 28
28.0%
32.9167 27
27.0%
32.9584 17
17.0%

PRDN_LO
Real number (ℝ)

HIGH CORRELATION 

Distinct28
Distinct (%)28.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean124.6727
Minimum124.5
Maximum124.8751
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-03-13T21:42:05.590282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum124.5
5-th percentile124.5139
Q1124.5834
median124.6667
Q3124.764
95-th percentile124.84799
Maximum124.8751
Range0.3751
Interquartile range (IQR)0.1806

Descriptive statistics

Standard deviation0.10911255
Coefficient of variation (CV)0.000875192
Kurtosis-1.0977398
Mean124.6727
Median Absolute Deviation (MAD)0.0903
Skewness0.16396478
Sum12467.27
Variance0.011905548
MonotonicityNot monotonic
2024-03-13T21:42:05.785028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
124.5 4
 
4.0%
124.625 4
 
4.0%
124.7223 4
 
4.0%
124.7084 4
 
4.0%
124.5139 4
 
4.0%
124.6806 4
 
4.0%
124.6667 4
 
4.0%
124.6528 4
 
4.0%
124.6389 4
 
4.0%
124.6945 4
 
4.0%
Other values (18) 60
60.0%
ValueCountFrequency (%)
124.5 4
4.0%
124.5139 4
4.0%
124.5278 4
4.0%
124.5417 4
4.0%
124.5556 4
4.0%
124.5695 4
4.0%
124.5834 4
4.0%
124.5973 4
4.0%
124.6111 4
4.0%
124.625 4
4.0%
ValueCountFrequency (%)
124.8751 2
2.0%
124.8612 3
3.0%
124.8473 3
3.0%
124.8334 3
3.0%
124.8196 3
3.0%
124.8057 3
3.0%
124.7918 3
3.0%
124.7779 3
3.0%
124.764 3
3.0%
124.7501 3
3.0%

OC_STS_WTEM
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.16302
Minimum11.8157
Maximum12.6978
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-03-13T21:42:06.028063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11.8157
5-th percentile11.851925
Q111.90545
median12.1238
Q312.38105
95-th percentile12.626645
Maximum12.6978
Range0.8821
Interquartile range (IQR)0.4756

Descriptive statistics

Standard deviation0.26864236
Coefficient of variation (CV)0.022086814
Kurtosis-1.185286
Mean12.16302
Median Absolute Deviation (MAD)0.2308
Skewness0.41716847
Sum1216.302
Variance0.072168716
MonotonicityNot monotonic
2024-03-13T21:42:06.225550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11.8157 1
 
1.0%
11.9827 1
 
1.0%
12.4104 1
 
1.0%
12.3751 1
 
1.0%
12.339 1
 
1.0%
12.3004 1
 
1.0%
12.2617 1
 
1.0%
12.2216 1
 
1.0%
12.1777 1
 
1.0%
12.1288 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
11.8157 1
1.0%
11.8322 1
1.0%
11.8352 1
1.0%
11.8441 1
1.0%
11.8467 1
1.0%
11.8522 1
1.0%
11.8526 1
1.0%
11.8556 1
1.0%
11.86 1
1.0%
11.8623 1
1.0%
ValueCountFrequency (%)
12.6978 1
1.0%
12.6874 1
1.0%
12.6631 1
1.0%
12.6528 1
1.0%
12.6294 1
1.0%
12.6265 1
1.0%
12.6163 1
1.0%
12.5924 1
1.0%
12.5891 1
1.0%
12.5792 1
1.0%

Interactions

2024-03-13T21:42:03.998095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:03.720793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:04.132385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:03.867119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-13T21:42:06.350662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
PRDN_LAPRDN_LOOC_STS_WTEM
PRDN_LA1.0000.0000.000
PRDN_LO0.0001.0000.973
OC_STS_WTEM0.0000.9731.000
2024-03-13T21:42:06.507479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
PRDN_LOOC_STS_WTEMPRDN_LA
PRDN_LO1.0000.9970.000
OC_STS_WTEM0.9971.0000.000
PRDN_LA0.0000.0001.000

Missing values

2024-03-13T21:42:04.331072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-13T21:42:04.494085image/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

SAR_NMLYR_ATTRB_SE_CDPRDN_LAPRDN_LOOC_STS_WTEM
0WSEATO32.9167124.511.8157
1WSEATO32.9167124.513911.8322
2WSEATO32.9167124.527811.8441
3WSEATO32.9167124.541711.8526
4WSEATO32.9167124.555611.8654
5WSEATO32.9167124.569511.8816
6WSEATO32.9167124.583411.9003
7WSEATO32.9167124.597311.9178
8WSEATO32.9167124.611111.9363
9WSEATO32.9167124.62511.9617
SAR_NMLYR_ATTRB_SE_CDPRDN_LAPRDN_LOOC_STS_WTEM
90WSEATO32.9584124.597311.9392
91WSEATO32.9584124.611111.9608
92WSEATO32.9584124.62511.9892
93WSEATO32.9584124.638912.0303
94WSEATO32.9584124.652812.0807
95WSEATO32.9584124.666712.1325
96WSEATO32.9584124.680612.1819
97WSEATO32.9584124.694512.2252
98WSEATO32.9584124.708412.2664
99WSEATO32.9584124.722312.3058