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=CT09GSR022

Alerts

SAR_NM has constant value ""Constant
LYR_ATTRB_SE_CD has constant value ""Constant
PRDN_LO is highly overall correlated with PRDN_WTEMHigh correlation
PRDN_WTEM is highly overall correlated with PRDN_LOHigh correlation

Reproduction

Analysis started2024-01-14 07:02:13.964367
Analysis finished2024-01-14 07:02:14.694575
Duration0.73 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

2024-01-14T16:02:14.757684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-14T16:02:14.848862image/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

2024-01-14T16:02:14.944587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-14T16:02:15.045468image/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

2024-01-14T16:02:15.161382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-14T16:02:15.288149image/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
2024-01-14T16:02:15.432327image/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
2024-01-14T16:02:15.567735image/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%

PRDN_WTEM
Real number (ℝ)

HIGH CORRELATION 

Distinct98
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.376594
Minimum11.0334
Maximum11.9555
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-01-14T16:02:15.726445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11.0334
5-th percentile11.042495
Q111.080325
median11.1514
Q311.774825
95-th percentile11.92263
Maximum11.9555
Range0.9221
Interquartile range (IQR)0.6945

Descriptive statistics

Standard deviation0.35031359
Coefficient of variation (CV)0.030792484
Kurtosis-1.4508383
Mean11.376594
Median Absolute Deviation (MAD)0.10795
Skewness0.56526311
Sum1137.6594
Variance0.12271961
MonotonicityNot monotonic
2024-01-14T16:02:16.099149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11.0474 2
 
2.0%
11.1244 2
 
2.0%
11.1022 1
 
1.0%
11.7776 1
 
1.0%
11.6986 1
 
1.0%
11.5981 1
 
1.0%
11.4862 1
 
1.0%
11.3146 1
 
1.0%
11.176 1
 
1.0%
11.1511 1
 
1.0%
Other values (88) 88
88.0%
ValueCountFrequency (%)
11.0334 1
1.0%
11.0337 1
1.0%
11.0343 1
1.0%
11.0405 1
1.0%
11.0424 1
1.0%
11.0425 1
1.0%
11.0428 1
1.0%
11.0441 1
1.0%
11.0456 1
1.0%
11.0461 1
1.0%
ValueCountFrequency (%)
11.9555 1
1.0%
11.9435 1
1.0%
11.9379 1
1.0%
11.9366 1
1.0%
11.9327 1
1.0%
11.9221 1
1.0%
11.9215 1
1.0%
11.9189 1
1.0%
11.9164 1
1.0%
11.9161 1
1.0%

Interactions

2024-01-14T16:02:14.276516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T16:02:14.078360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T16:02:14.383955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T16:02:14.169031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-14T16:02:16.205279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
PRDN_LAPRDN_LOPRDN_WTEM
PRDN_LA1.0000.0000.000
PRDN_LO0.0001.0000.940
PRDN_WTEM0.0000.9401.000
2024-01-14T16:02:16.323821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
PRDN_LOPRDN_WTEMPRDN_LA
PRDN_LO1.0000.9920.000
PRDN_WTEM0.9921.0000.000
PRDN_LA0.0000.0001.000

Missing values

2024-01-14T16:02:14.536605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-14T16:02:14.656685image/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_LOPRDN_WTEM
0WSEATO32.9167124.511.0462
1WSEATO32.9167124.513911.0474
2WSEATO32.9167124.527811.0474
3WSEATO32.9167124.541711.0428
4WSEATO32.9167124.555611.0566
5WSEATO32.9167124.569511.0694
6WSEATO32.9167124.583411.0831
7WSEATO32.9167124.597311.0997
8WSEATO32.9167124.611111.1122
9WSEATO32.9167124.62511.1244
SAR_NMLYR_ATTRB_SE_CDPRDN_LAPRDN_LOPRDN_WTEM
90WSEATO32.9584124.597311.0989
91WSEATO32.9584124.611111.1116
92WSEATO32.9584124.62511.1244
93WSEATO32.9584124.638911.138
94WSEATO32.9584124.652811.1517
95WSEATO32.9584124.666711.1658
96WSEATO32.9584124.680611.2023
97WSEATO32.9584124.694511.3671
98WSEATO32.9584124.708411.5417
99WSEATO32.9584124.722311.6595