Overview

Dataset statistics

Number of variables4
Number of observations421
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory13.7 KiB
Average record size in memory33.3 B

Variable types

Text1
Numeric1
Categorical2

Alerts

GAP_RISK_CN is highly overall correlated with GAP_XTN and 1 other fieldsHigh correlation
GAP_RISK_GRD_CD 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
GAP_XTN has 8 (1.9%) zerosZeros

Reproduction

Analysis started2024-03-13 12:50:39.282310
Analysis finished2024-03-13 12:50:39.851638
Duration0.57 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

GEOM
Text

UNIQUE 

Distinct421
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size3.4 KiB
2024-03-13T21:50:40.066143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length222
Median length221
Mean length189.08551
Min length182

Characters and Unicode

Total characters79605
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

Unique421 ?
Unique (%)100.0%

Sample

1st rowMULTIPOLYGON (((129.23314444587 35.2154647854891,129.233150944381 35.2154647729749,129.233150918608 35.215455759536,129.233145764053 35.2154557694622,129.23314444587 35.2154647854891)))
2nd rowMULTIPOLYGON (((129.233143127687 35.215473801516,129.233150970153 35.2154737864137,129.233150944381 35.2154647729749,129.23314444587 35.2154647854891,129.233143127687 35.215473801516)))
3rd rowMULTIPOLYGON (((129.233141809504 35.2154828175428,129.233150995926 35.2154827998525,129.233150970153 35.2154737864137,129.233143127687 35.215473801516,129.233141809504 35.2154828175428)))
4th rowMULTIPOLYGON (((129.233140491321 35.2154918335697,129.233151021698 35.2154918132914,129.233150995926 35.2154827998525,129.233141809504 35.2154828175428,129.233140491321 35.2154918335697)))
5th rowMULTIPOLYGON (((129.233140047277 35.2154948707128,129.233140064367 35.2155008478803,129.233151047471 35.2155008267302,129.233151021698 35.2154918132914,129.233140491321 35.2154918335697,129.233140047277 35.2154948707128)))
ValueCountFrequency (%)
multipolygon 421
 
14.2%
35.2155276343295,129.233260955863 1
 
< 0.1%
35.2155276343295,129.233271913184 1
 
< 0.1%
35.2155186208908,129.233260930078 1
 
< 0.1%
35.2155186420519 1
 
< 0.1%
129.233260955863 1
 
< 0.1%
35.2155276554906,129.233260981648 1
 
< 0.1%
35.2155366689293,129.233271964756 1
 
< 0.1%
35.2155366477682,129.23327193897 1
 
< 0.1%
35.2155276554906 1
 
< 0.1%
Other values (2544) 2544
85.5%
2024-03-13T21:50:40.688891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 11248
14.1%
3 10355
13.0%
5 8730
11.0%
1 8195
10.3%
9 5744
 
7.2%
4 4455
 
5.6%
. 4264
 
5.4%
6 3993
 
5.0%
7 3971
 
5.0%
8 3748
 
4.7%
Other values (15) 14902
18.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 63499
79.8%
Other Punctuation 5975
 
7.5%
Uppercase Letter 5052
 
6.3%
Space Separator 2553
 
3.2%
Close Punctuation 1263
 
1.6%
Open Punctuation 1263
 
1.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 11248
17.7%
3 10355
16.3%
5 8730
13.7%
1 8195
12.9%
9 5744
9.0%
4 4455
 
7.0%
6 3993
 
6.3%
7 3971
 
6.3%
8 3748
 
5.9%
0 3060
 
4.8%
Uppercase Letter
ValueCountFrequency (%)
O 842
16.7%
L 842
16.7%
U 421
8.3%
N 421
8.3%
G 421
8.3%
Y 421
8.3%
P 421
8.3%
I 421
8.3%
T 421
8.3%
M 421
8.3%
Other Punctuation
ValueCountFrequency (%)
. 4264
71.4%
, 1711
28.6%
Space Separator
ValueCountFrequency (%)
2553
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1263
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1263
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 74553
93.7%
Latin 5052
 
6.3%

Most frequent character per script

Common
ValueCountFrequency (%)
2 11248
15.1%
3 10355
13.9%
5 8730
11.7%
1 8195
11.0%
9 5744
7.7%
4 4455
 
6.0%
. 4264
 
5.7%
6 3993
 
5.4%
7 3971
 
5.3%
8 3748
 
5.0%
Other values (5) 9850
13.2%
Latin
ValueCountFrequency (%)
O 842
16.7%
L 842
16.7%
U 421
8.3%
N 421
8.3%
G 421
8.3%
Y 421
8.3%
P 421
8.3%
I 421
8.3%
T 421
8.3%
M 421
8.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 79605
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 11248
14.1%
3 10355
13.0%
5 8730
11.0%
1 8195
10.3%
9 5744
 
7.2%
4 4455
 
5.6%
. 4264
 
5.4%
6 3993
 
5.0%
7 3971
 
5.0%
8 3748
 
4.7%
Other values (15) 14902
18.7%

GAP_XTN
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct338
Distinct (%)80.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.40782185
Minimum0
Maximum1
Zeros8
Zeros (%)1.9%
Negative0
Negative (%)0.0%
Memory size3.8 KiB
2024-03-13T21:50:40.921365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.01
Q10.139
median0.354
Q30.668
95-th percentile0.959
Maximum1
Range1
Interquartile range (IQR)0.529

Descriptive statistics

Standard deviation0.30521301
Coefficient of variation (CV)0.74839787
Kurtosis-1.0600215
Mean0.40782185
Median Absolute Deviation (MAD)0.252
Skewness0.38808833
Sum171.693
Variance0.09315498
MonotonicityNot monotonic
2024-03-13T21:50:41.148566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.0 10
 
2.4%
0.0 8
 
1.9%
0.141 4
 
1.0%
0.164 3
 
0.7%
0.056 3
 
0.7%
0.668 3
 
0.7%
0.284 3
 
0.7%
0.982 3
 
0.7%
0.475 3
 
0.7%
0.013 3
 
0.7%
Other values (328) 378
89.8%
ValueCountFrequency (%)
0.0 8
1.9%
0.001 2
 
0.5%
0.002 2
 
0.5%
0.003 1
 
0.2%
0.004 2
 
0.5%
0.005 2
 
0.5%
0.007 1
 
0.2%
0.009 2
 
0.5%
0.01 2
 
0.5%
0.011 2
 
0.5%
ValueCountFrequency (%)
1.0 10
2.4%
0.998 1
 
0.2%
0.996 1
 
0.2%
0.995 1
 
0.2%
0.993 1
 
0.2%
0.982 3
 
0.7%
0.979 1
 
0.2%
0.974 1
 
0.2%
0.968 1
 
0.2%
0.96 1
 
0.2%

GAP_RISK_GRD_CD
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size3.4 KiB
E
152 
C
102 
D
83 
B
76 
A
 
8

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
E 152
36.1%
C 102
24.2%
D 83
19.7%
B 76
18.1%
A 8
 
1.9%

Length

2024-03-13T21:50:41.348207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T21:50:41.530945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
e 152
36.1%
c 102
24.2%
d 83
19.7%
b 76
18.1%
a 8
 
1.9%

GAP_RISK_CN
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size3.4 KiB
매우위험
152 
주의
102 
위험
83 
보통
76 
안전
 
8

Length

Max length4
Median length2
Mean length2.7220903
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
매우위험 152
36.1%
주의 102
24.2%
위험 83
19.7%
보통 76
18.1%
안전 8
 
1.9%

Length

2024-03-13T21:50:41.729360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T21:50:41.943047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
매우위험 152
36.1%
주의 102
24.2%
위험 83
19.7%
보통 76
18.1%
안전 8
 
1.9%

Interactions

2024-03-13T21:50:39.478847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-13T21:50:42.082216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
GAP_XTNGAP_RISK_GRD_CDGAP_RISK_CN
GAP_XTN1.0000.9910.991
GAP_RISK_GRD_CD0.9911.0001.000
GAP_RISK_CN0.9911.0001.000
2024-03-13T21:50:42.207207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
GAP_RISK_CNGAP_RISK_GRD_CD
GAP_RISK_CN1.0001.000
GAP_RISK_GRD_CD1.0001.000
2024-03-13T21:50:42.347319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
GAP_XTNGAP_RISK_GRD_CDGAP_RISK_CN
GAP_XTN1.0000.8580.858
GAP_RISK_GRD_CD0.8581.0001.000
GAP_RISK_CN0.8581.0001.000

Missing values

2024-03-13T21:50:39.663656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-13T21:50:39.802198image/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 (((129.23314444587 35.2154647854891,129.233150944381 35.2154647729749,129.233150918608 35.215455759536,129.233145764053 35.2154557694622,129.23314444587 35.2154647854891)))0.84E매우위험
1MULTIPOLYGON (((129.233143127687 35.215473801516,129.233150970153 35.2154737864137,129.233150944381 35.2154647729749,129.23314444587 35.2154647854891,129.233143127687 35.215473801516)))0.608E매우위험
2MULTIPOLYGON (((129.233141809504 35.2154828175428,129.233150995926 35.2154827998525,129.233150970153 35.2154737864137,129.233143127687 35.215473801516,129.233141809504 35.2154828175428)))0.805E매우위험
3MULTIPOLYGON (((129.233140491321 35.2154918335697,129.233151021698 35.2154918132914,129.233150995926 35.2154827998525,129.233141809504 35.2154828175428,129.233140491321 35.2154918335697)))0.246C주의
4MULTIPOLYGON (((129.233140047277 35.2154948707128,129.233140064367 35.2155008478803,129.233151047471 35.2155008267302,129.233151021698 35.2154918132914,129.233140491321 35.2154918335697,129.233140047277 35.2154948707128)))0.0A안전
5MULTIPOLYGON (((129.233140064367 35.2155008478803,129.233140090138 35.2155098613191,129.233151073243 35.215509840169,129.233151047471 35.2155008267302,129.233140064367 35.2155008478803)))0.0A안전
6MULTIPOLYGON (((129.233140090138 35.2155098613191,129.23314011591 35.2155188747578,129.233151099016 35.2155188536077,129.233151073243 35.215509840169,129.233140090138 35.2155098613191)))0.038B보통
7MULTIPOLYGON (((129.233144712181 35.2155278793954,129.233151124788 35.2155278670465,129.233151099016 35.2155188536077,129.23314011591 35.2155188747578,129.233140128058 35.2155231237456,129.233144712181 35.2155278793954)))0.324D위험
8MULTIPOLYGON (((129.233153450063 35.2154287141955,129.233161824385 35.2154286980683,129.233161798611 35.2154196846294,129.233158692423 35.2154196906113,129.233153450063 35.2154287141955)))1.0E매우위험
9MULTIPOLYGON (((129.233150854053 35.2154331826614,129.233150867063 35.2154377326582,129.233161850159 35.2154377115072,129.233161824385 35.2154286980683,129.233153450063 35.2154287141955,129.233150854053 35.2154331826614)))0.522E매우위험
GEOMGAP_XTNGAP_RISK_GRD_CDGAP_RISK_CN
411MULTIPOLYGON (((129.233300813749 35.2156898214802,129.233305352506 35.2156898127341,129.233305326717 35.2156807992956,129.233294343589 35.2156808204598,129.233294350157 35.2156831160707,129.233300813749 35.2156898214802)))0.878E매우위험
412MULTIPOLYGON (((129.233304274329 35.2153129899655,129.233304295136 35.2153202617475,129.233315278215 35.2153202405826,129.233315252424 35.2153112271436,129.233306708084 35.215311243609,129.233304274329 35.2153129899655)))1.0E매우위험
413MULTIPOLYGON (((129.233304295136 35.2153202617475,129.233304320925 35.2153292751865,129.233315304005 35.2153292540216,129.233315278215 35.2153202405826,129.233304295136 35.2153202617475)))1.0E매우위험
414MULTIPOLYGON (((129.233304320925 35.2153292751865,129.233304346714 35.2153382886254,129.233315329796 35.2153382674605,129.233315304005 35.2153292540216,129.233304320925 35.2153292751865)))0.69E매우위험
415MULTIPOLYGON (((129.233304346714 35.2153382886254,129.233304372503 35.2153473020644,129.233315355587 35.2153472808994,129.233315329796 35.2153382674605,129.233304346714 35.2153382886254)))0.784E매우위험
416MULTIPOLYGON (((129.233304372503 35.2153473020644,129.233304398293 35.2153563155033,129.233315381377 35.2153562943383,129.233315355587 35.2153472808994,129.233304372503 35.2153473020644)))0.732E매우위험
417MULTIPOLYGON (((129.233304398293 35.2153563155033,129.233304424082 35.2153653289422,129.233315407168 35.2153653077773,129.233315381377 35.2153562943383,129.233304398293 35.2153563155033)))0.288C주의
418MULTIPOLYGON (((129.233304424082 35.2153653289422,129.233304449871 35.2153743423811,129.233315432958 35.2153743212162,129.233315407168 35.2153653077773,129.233304424082 35.2153653289422)))0.031B보통
419MULTIPOLYGON (((129.233304449871 35.2153743423811,129.233304475661 35.21538335582,129.233315458749 35.215383334655,129.233315432958 35.2153743212162,129.233304449871 35.2153743423811)))0.53E매우위험
420MULTIPOLYGON (((129.233304475661 35.21538335582,129.23330450145 35.2153923692589,129.233315484539 35.2153923480939,129.233315458749 35.215383334655,129.233304475661 35.21538335582)))0.056B보통