Overview

Dataset statistics

Number of variables4
Number of observations323
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory10.5 KiB
Average record size in memory33.4 B

Variable types

Text1
Numeric1
Categorical2

Alerts

GAP_RISK_GRD_CD is highly overall correlated with GAP_XTN and 1 other fieldsHigh correlation
GAP_RISK_CN is highly overall correlated with GAP_XTN and 1 other fieldsHigh correlation
GAP_XTN is highly overall correlated with GAP_RISK_GRD_CD and 1 other fieldsHigh correlation
GEOM has unique valuesUnique

Reproduction

Analysis started2024-01-14 06:58:18.286581
Analysis finished2024-01-14 06:58:18.741816
Duration0.46 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

GEOM
Text

UNIQUE 

Distinct323
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size2.7 KiB
2024-01-14T15:58:18.888055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length222
Median length188
Mean length188.42724
Min length182

Characters and Unicode

Total characters60862
Distinct characters25
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique323 ?
Unique (%)100.0%

Sample

1st rowMULTIPOLYGON (((129.124752167967 35.1529447465965,129.12476128837 35.1529447372058,129.124761274611 35.1529357236202,129.124754773955 35.1529357303137,129.124752167967 35.1529447465965)))
2nd rowMULTIPOLYGON (((129.124750323353 35.1529511286558,129.124750327372 35.1529537620913,129.124761302129 35.1529537507913,129.12476128837 35.1529447372058,129.124752167967 35.1529447465965,129.124750323353 35.1529511286558)))
3rd rowMULTIPOLYGON (((129.124750327372 35.1529537620913,129.12475034113 35.1529627756767,129.124761315888 35.1529627643767,129.124761302129 35.1529537507913,129.124750327372 35.1529537620913)))
4th rowMULTIPOLYGON (((129.124756244149 35.1529717831985,129.124761329648 35.1529717779622,129.124761315888 35.1529627643767,129.12475034113 35.1529627756767,129.124750352809 35.1529704273368,129.124756244149 35.1529717831985)))
5th rowMULTIPOLYGON (((129.124765197899 35.1528996651816,129.124772194324 35.1528996579771,129.124772180563 35.1528906443915,129.124767803883 35.1528906488984,129.124765197899 35.1528996651816)))
ValueCountFrequency (%)
multipolygon 323
 
14.2%
35.1528454407744,129.124892833929 1
 
< 0.1%
35.1528544656729,129.124903822447 1
 
< 0.1%
35.1528454520873,129.124892847703 1
 
< 0.1%
129.124892833929 1
 
< 0.1%
35.1528364385017 1
 
< 0.1%
35.1528364271888,129.124892820156 1
 
< 0.1%
35.1528454407744,129.124903794897 1
 
< 0.1%
35.1528454520873,129.124903808672 1
 
< 0.1%
35.1528364385017,129.124892833929 1
 
< 0.1%
Other values (1942) 1942
85.4%
2024-01-14T15:58:19.316622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 7633
12.5%
2 7584
12.5%
5 6220
10.2%
9 5761
9.5%
4 4529
 
7.4%
8 4296
 
7.1%
3 4121
 
6.8%
. 3256
 
5.3%
7 3124
 
5.1%
6 2860
 
4.7%
Other values (15) 11478
18.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 48536
79.7%
Other Punctuation 4561
 
7.5%
Uppercase Letter 3876
 
6.4%
Space Separator 1951
 
3.2%
Close Punctuation 969
 
1.6%
Open Punctuation 969
 
1.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 7633
15.7%
2 7584
15.6%
5 6220
12.8%
9 5761
11.9%
4 4529
9.3%
8 4296
8.9%
3 4121
8.5%
7 3124
6.4%
6 2860
 
5.9%
0 2408
 
5.0%
Uppercase Letter
ValueCountFrequency (%)
O 646
16.7%
L 646
16.7%
U 323
8.3%
N 323
8.3%
G 323
8.3%
Y 323
8.3%
P 323
8.3%
I 323
8.3%
T 323
8.3%
M 323
8.3%
Other Punctuation
ValueCountFrequency (%)
. 3256
71.4%
, 1305
28.6%
Space Separator
ValueCountFrequency (%)
1951
100.0%
Close Punctuation
ValueCountFrequency (%)
) 969
100.0%
Open Punctuation
ValueCountFrequency (%)
( 969
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 56986
93.6%
Latin 3876
 
6.4%

Most frequent character per script

Common
ValueCountFrequency (%)
1 7633
13.4%
2 7584
13.3%
5 6220
10.9%
9 5761
10.1%
4 4529
7.9%
8 4296
7.5%
3 4121
7.2%
. 3256
5.7%
7 3124
5.5%
6 2860
 
5.0%
Other values (5) 7602
13.3%
Latin
ValueCountFrequency (%)
O 646
16.7%
L 646
16.7%
U 323
8.3%
N 323
8.3%
G 323
8.3%
Y 323
8.3%
P 323
8.3%
I 323
8.3%
T 323
8.3%
M 323
8.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 60862
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 7633
12.5%
2 7584
12.5%
5 6220
10.2%
9 5761
9.5%
4 4529
 
7.4%
8 4296
 
7.1%
3 4121
 
6.8%
. 3256
 
5.3%
7 3124
 
5.1%
6 2860
 
4.7%
Other values (15) 11478
18.9%

GAP_XTN
Real number (ℝ)

HIGH CORRELATION 

Distinct267
Distinct (%)82.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.37501238
Minimum0.016
Maximum0.991
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2024-01-14T15:58:19.508870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.016
5-th percentile0.0541
Q10.2
median0.346
Q30.4995
95-th percentile0.8217
Maximum0.991
Range0.975
Interquartile range (IQR)0.2995

Descriptive statistics

Standard deviation0.22824858
Coefficient of variation (CV)0.60864279
Kurtosis-0.13319981
Mean0.37501238
Median Absolute Deviation (MAD)0.149
Skewness0.65173656
Sum121.129
Variance0.052097416
MonotonicityNot monotonic
2024-01-14T15:58:19.685390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.343 3
 
0.9%
0.417 3
 
0.9%
0.476 3
 
0.9%
0.293 3
 
0.9%
0.174 3
 
0.9%
0.204 3
 
0.9%
0.202 3
 
0.9%
0.442 2
 
0.6%
0.396 2
 
0.6%
0.481 2
 
0.6%
Other values (257) 296
91.6%
ValueCountFrequency (%)
0.016 1
0.3%
0.019 1
0.3%
0.022 1
0.3%
0.025 1
0.3%
0.027 1
0.3%
0.028 1
0.3%
0.032 2
0.6%
0.033 2
0.6%
0.045 1
0.3%
0.046 2
0.6%
ValueCountFrequency (%)
0.991 1
0.3%
0.978 1
0.3%
0.975 1
0.3%
0.959 1
0.3%
0.95 1
0.3%
0.944 1
0.3%
0.94 1
0.3%
0.932 1
0.3%
0.899 1
0.3%
0.888 1
0.3%

GAP_RISK_GRD_CD
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size2.7 KiB
C
108 
D
105 
E
80 
B
30 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowE
2nd rowE
3rd rowC
4th rowC
5th rowD

Common Values

ValueCountFrequency (%)
C 108
33.4%
D 105
32.5%
E 80
24.8%
B 30
 
9.3%

Length

2024-01-14T15:58:19.847002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-14T15:58:19.944929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
c 108
33.4%
d 105
32.5%
e 80
24.8%
b 30
 
9.3%

GAP_RISK_CN
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size2.7 KiB
주의
108 
위험
105 
매우위험
80 
보통
30 

Length

Max length4
Median length2
Mean length2.495356
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row매우위험
2nd row매우위험
3rd row주의
4th row주의
5th row위험

Common Values

ValueCountFrequency (%)
주의 108
33.4%
위험 105
32.5%
매우위험 80
24.8%
보통 30
 
9.3%

Length

2024-01-14T15:58:20.065924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-14T15:58:20.220286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
주의 108
33.4%
위험 105
32.5%
매우위험 80
24.8%
보통 30
 
9.3%

Interactions

2024-01-14T15:58:18.435805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-14T15:58:20.306987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
GAP_XTNGAP_RISK_GRD_CDGAP_RISK_CN
GAP_XTN1.0000.9880.988
GAP_RISK_GRD_CD0.9881.0001.000
GAP_RISK_CN0.9881.0001.000
2024-01-14T15:58:20.408132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
GAP_RISK_GRD_CDGAP_RISK_CN
GAP_RISK_GRD_CD1.0001.000
GAP_RISK_CN1.0001.000
2024-01-14T15:58:20.501473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
GAP_XTNGAP_RISK_GRD_CDGAP_RISK_CN
GAP_XTN1.0000.9360.936
GAP_RISK_GRD_CD0.9361.0001.000
GAP_RISK_CN0.9361.0001.000

Missing values

2024-01-14T15:58:18.600326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-14T15:58:18.707021image/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

GEOMGAP_XTNGAP_RISK_GRD_CDGAP_RISK_CN
0MULTIPOLYGON (((129.124752167967 35.1529447465965,129.12476128837 35.1529447372058,129.124761274611 35.1529357236202,129.124754773955 35.1529357303137,129.124752167967 35.1529447465965)))0.523E매우위험
1MULTIPOLYGON (((129.124750323353 35.1529511286558,129.124750327372 35.1529537620913,129.124761302129 35.1529537507913,129.12476128837 35.1529447372058,129.124752167967 35.1529447465965,129.124750323353 35.1529511286558)))0.633E매우위험
2MULTIPOLYGON (((129.124750327372 35.1529537620913,129.12475034113 35.1529627756767,129.124761315888 35.1529627643767,129.124761302129 35.1529537507913,129.124750327372 35.1529537620913)))0.233C주의
3MULTIPOLYGON (((129.124756244149 35.1529717831985,129.124761329648 35.1529717779622,129.124761315888 35.1529627643767,129.12475034113 35.1529627756767,129.124750352809 35.1529704273368,129.124756244149 35.1529717831985)))0.285C주의
4MULTIPOLYGON (((129.124765197899 35.1528996651816,129.124772194324 35.1528996579771,129.124772180563 35.1528906443915,129.124767803883 35.1528906488984,129.124765197899 35.1528996651816)))0.468D위험
5MULTIPOLYGON (((129.124762591913 35.1529086814647,129.124772208084 35.1529086715626,129.124772194324 35.1528996579771,129.124765197899 35.1528996651816,129.124762591913 35.1529086814647)))0.351D위험
6MULTIPOLYGON (((129.124761240468 35.1529133572431,129.124761247092 35.1529176964492,129.124772221844 35.1529176851482,129.124772208084 35.1529086715626,129.124762591913 35.1529086814647,129.124761240468 35.1529133572431)))0.453D위험
7MULTIPOLYGON (((129.124761247092 35.1529176964492,129.124761260851 35.1529267100347,129.124772235605 35.1529266987337,129.124772221844 35.1529176851482,129.124761247092 35.1529176964492)))0.817E매우위험
8MULTIPOLYGON (((129.124761260851 35.1529267100347,129.124761274611 35.1529357236202,129.124772249365 35.1529357123192,129.124772235605 35.1529266987337,129.124761260851 35.1529267100347)))0.697E매우위험
9MULTIPOLYGON (((129.124761274611 35.1529357236202,129.12476128837 35.1529447372058,129.124772263126 35.1529447259048,129.124772249365 35.1529357123192,129.124761274611 35.1529357236202)))0.582E매우위험
GEOMGAP_XTNGAP_RISK_GRD_CDGAP_RISK_CN
313MULTIPOLYGON (((129.124947721422 35.1528544090985,129.124947735201 35.1528634226841,129.124958709946 35.1528634113662,129.124958696165 35.1528543977806,129.124947721422 35.1528544090985)))0.755E매우위험
314MULTIPOLYGON (((129.124947735201 35.1528634226841,129.124947748981 35.1528724362696,129.124958723727 35.1528724249518,129.124958709946 35.1528634113662,129.124947735201 35.1528634226841)))0.651E매우위험
315MULTIPOLYGON (((129.124947748981 35.1528724362696,129.124947762761 35.1528814498552,129.124958737508 35.1528814385373,129.124958723727 35.1528724249518,129.124947748981 35.1528724362696)))0.147C주의
316MULTIPOLYGON (((129.124947762761 35.1528814498552,129.124947776541 35.1528904634407,129.124958751289 35.1528904521228,129.124958737508 35.1528814385373,129.124947762761 35.1528814498552)))0.378D위험
317MULTIPOLYGON (((129.124947776541 35.1528904634407,129.124947790321 35.1528994770262,129.12495876507 35.1528994657083,129.124958751289 35.1528904521228,129.124947776541 35.1528904634407)))0.41D위험
318MULTIPOLYGON (((129.124947790321 35.1528994770262,129.1249478041 35.1529084906117,129.124958778851 35.1529084792938,129.12495876507 35.1528994657083,129.124947790321 35.1528994770262)))0.514E매우위험
319MULTIPOLYGON (((129.1249478041 35.1529084906117,129.12494781788 35.1529175041972,129.124958792632 35.1529174928793,129.124958778851 35.1529084792938,129.1249478041 35.1529084906117)))0.197C주의
320MULTIPOLYGON (((129.12494781788 35.1529175041972,129.12494783166 35.1529265177826,129.124958806413 35.1529265064648,129.124958792632 35.1529174928793,129.12494781788 35.1529175041972)))0.167C주의
321MULTIPOLYGON (((129.12494783166 35.1529265177826,129.12494784544 35.1529355313681,129.124958820194 35.1529355200502,129.124958806413 35.1529265064648,129.12494783166 35.1529265177826)))0.135C주의
322MULTIPOLYGON (((129.12494784544 35.1529355313681,129.12494785922 35.1529445449536,129.124958833975 35.1529445336357,129.124958820194 35.1529355200502,129.12494784544 35.1529355313681)))0.119C주의