Overview

Dataset statistics

Number of variables4
Number of observations362
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory11.8 KiB
Average record size in memory33.4 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 10 (2.8%) zerosZeros

Reproduction

Analysis started2024-03-13 12:33:49.471223
Analysis finished2024-03-13 12:33:50.170670
Duration0.7 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

GEOM
Text

UNIQUE 

Distinct362
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size3.0 KiB
2024-03-13T21:33:50.430530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length255
Median length222
Mean length189.17127
Min length178

Characters and Unicode

Total characters68480
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

Unique362 ?
Unique (%)100.0%

Sample

1st rowMULTIPOLYGON (((129.557641427719 35.9856663345936,129.557648606893 35.9856663012247,129.557648543478 35.9856572892933,129.557644565999 35.9856573077808,129.557641427719 35.9856663345936)))
2nd rowMULTIPOLYGON (((129.557638289438 35.9856753614063,129.557648670308 35.985675313156,129.557648606893 35.9856663012247,129.557641427719 35.9856663345936,129.557638289438 35.9856753614063)))
3rd rowMULTIPOLYGON (((129.557637595914 35.9856773562287,129.557637645313 35.9856843766262,129.557648733722 35.9856843250873,129.557648670308 35.985675313156,129.557638289438 35.9856753614063,129.557637595914 35.9856773562287)))
4th rowMULTIPOLYGON (((129.557639648869 35.9856933795398,129.557648797137 35.9856933370186,129.557648733722 35.9856843250873,129.557637645313 35.9856843766262,129.557637658795 35.9856862925666,129.557639648869 35.9856933795398)))
5th rowMULTIPOLYGON (((129.557642176269 35.9857023800185,129.557648860552 35.9857023489499,129.557648797137 35.9856933370186,129.557639648869 35.9856933795398,129.557642176269 35.9857023800185)))
ValueCountFrequency (%)
multipolygon 362
 
14.1%
35.9858821752219,129.557738772921 1
 
< 0.1%
35.9858732148384,129.557738772921 1
 
< 0.1%
35.985873163291,129.557738709495 1
 
< 0.1%
35.9858641513601,129.557727621062 1
 
< 0.1%
35.9858642029075 1
 
< 0.1%
129.557730795026 1
 
< 0.1%
35.9858822126041,129.557738836346 1
 
< 0.1%
35.985873163291,129.557727684486 1
 
< 0.1%
35.9858371671147,129.557727494213 1
 
< 0.1%
Other values (2189) 2189
85.5%
2024-03-13T21:33:51.041634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 10101
14.8%
7 6886
10.1%
9 6776
9.9%
3 5321
7.8%
8 5219
7.6%
2 5141
 
7.5%
1 4984
 
7.3%
6 4361
 
6.4%
. 3672
 
5.4%
4 3117
 
4.6%
Other values (15) 12902
18.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 54620
79.8%
Other Punctuation 5146
 
7.5%
Uppercase Letter 4344
 
6.3%
Space Separator 2198
 
3.2%
Close Punctuation 1086
 
1.6%
Open Punctuation 1086
 
1.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 10101
18.5%
7 6886
12.6%
9 6776
12.4%
3 5321
9.7%
8 5219
9.6%
2 5141
9.4%
1 4984
9.1%
6 4361
8.0%
4 3117
 
5.7%
0 2714
 
5.0%
Uppercase Letter
ValueCountFrequency (%)
O 724
16.7%
L 724
16.7%
U 362
8.3%
N 362
8.3%
G 362
8.3%
Y 362
8.3%
P 362
8.3%
I 362
8.3%
T 362
8.3%
M 362
8.3%
Other Punctuation
ValueCountFrequency (%)
. 3672
71.4%
, 1474
28.6%
Space Separator
ValueCountFrequency (%)
2198
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1086
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1086
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 64136
93.7%
Latin 4344
 
6.3%

Most frequent character per script

Common
ValueCountFrequency (%)
5 10101
15.7%
7 6886
10.7%
9 6776
10.6%
3 5321
8.3%
8 5219
8.1%
2 5141
8.0%
1 4984
7.8%
6 4361
6.8%
. 3672
 
5.7%
4 3117
 
4.9%
Other values (5) 8558
13.3%
Latin
ValueCountFrequency (%)
O 724
16.7%
L 724
16.7%
U 362
8.3%
N 362
8.3%
G 362
8.3%
Y 362
8.3%
P 362
8.3%
I 362
8.3%
T 362
8.3%
M 362
8.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 68480
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 10101
14.8%
7 6886
10.1%
9 6776
9.9%
3 5321
7.8%
8 5219
7.6%
2 5141
 
7.5%
1 4984
 
7.3%
6 4361
 
6.4%
. 3672
 
5.4%
4 3117
 
4.6%
Other values (15) 12902
18.8%

GAP_XTN
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct204
Distinct (%)56.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.13200276
Minimum0
Maximum0.508
Zeros10
Zeros (%)2.8%
Negative0
Negative (%)0.0%
Memory size3.3 KiB
2024-03-13T21:33:51.247331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.00505
Q10.042
median0.1125
Q30.21125
95-th percentile0.3
Maximum0.508
Range0.508
Interquartile range (IQR)0.16925

Descriptive statistics

Standard deviation0.10234355
Coefficient of variation (CV)0.7753137
Kurtosis-0.540983
Mean0.13200276
Median Absolute Deviation (MAD)0.0795
Skewness0.58665972
Sum47.785
Variance0.010474202
MonotonicityNot monotonic
2024-03-13T21:33:51.489965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.3 12
 
3.3%
0.0 10
 
2.8%
0.017 5
 
1.4%
0.009 5
 
1.4%
0.298 4
 
1.1%
0.028 4
 
1.1%
0.077 4
 
1.1%
0.142 4
 
1.1%
0.035 4
 
1.1%
0.063 4
 
1.1%
Other values (194) 306
84.5%
ValueCountFrequency (%)
0.0 10
2.8%
0.001 1
 
0.3%
0.002 1
 
0.3%
0.003 2
 
0.6%
0.004 2
 
0.6%
0.005 3
 
0.8%
0.006 1
 
0.3%
0.007 1
 
0.3%
0.008 1
 
0.3%
0.009 5
1.4%
ValueCountFrequency (%)
0.508 1
0.3%
0.433 1
0.3%
0.414 1
0.3%
0.345 1
0.3%
0.339 1
0.3%
0.33 1
0.3%
0.323 1
0.3%
0.321 1
0.3%
0.318 2
0.6%
0.31 1
0.3%

GAP_RISK_GRD_CD
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size3.0 KiB
C
177 
B
162 
D
 
12
A
 
10
E
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)0.3%

Sample

1st rowC
2nd rowC
3rd rowC
4th rowC
5th rowB

Common Values

ValueCountFrequency (%)
C 177
48.9%
B 162
44.8%
D 12
 
3.3%
A 10
 
2.8%
E 1
 
0.3%

Length

2024-03-13T21:33:51.726618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T21:33:51.901382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
c 177
48.9%
b 162
44.8%
d 12
 
3.3%
a 10
 
2.8%
e 1
 
0.3%

GAP_RISK_CN
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size3.0 KiB
주의
177 
보통
162 
위험
 
12
안전
 
10
매우위험
 
1

Length

Max length4
Median length2
Mean length2.0055249
Min length2

Unique

Unique1 ?
Unique (%)0.3%

Sample

1st row주의
2nd row주의
3rd row주의
4th row주의
5th row보통

Common Values

ValueCountFrequency (%)
주의 177
48.9%
보통 162
44.8%
위험 12
 
3.3%
안전 10
 
2.8%
매우위험 1
 
0.3%

Length

2024-03-13T21:33:52.164918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T21:33:52.379222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
주의 177
48.9%
보통 162
44.8%
위험 12
 
3.3%
안전 10
 
2.8%
매우위험 1
 
0.3%

Interactions

2024-03-13T21:33:49.701504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-13T21:33:52.498518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
GAP_XTNGAP_RISK_GRD_CDGAP_RISK_CN
GAP_XTN1.0000.9240.924
GAP_RISK_GRD_CD0.9241.0001.000
GAP_RISK_CN0.9241.0001.000
2024-03-13T21:33:52.643311image/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
2024-03-13T21:33:52.775347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
GAP_XTNGAP_RISK_GRD_CDGAP_RISK_CN
GAP_XTN1.0000.8380.838
GAP_RISK_GRD_CD0.8381.0001.000
GAP_RISK_CN0.8381.0001.000

Missing values

2024-03-13T21:33:49.916886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-13T21:33:50.103490image/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 (((129.557641427719 35.9856663345936,129.557648606893 35.9856663012247,129.557648543478 35.9856572892933,129.557644565999 35.9856573077808,129.557641427719 35.9856663345936)))0.3C주의
1MULTIPOLYGON (((129.557638289438 35.9856753614063,129.557648670308 35.985675313156,129.557648606893 35.9856663012247,129.557641427719 35.9856663345936,129.557638289438 35.9856753614063)))0.298C주의
2MULTIPOLYGON (((129.557637595914 35.9856773562287,129.557637645313 35.9856843766262,129.557648733722 35.9856843250873,129.557648670308 35.985675313156,129.557638289438 35.9856753614063,129.557637595914 35.9856773562287)))0.271C주의
3MULTIPOLYGON (((129.557639648869 35.9856933795398,129.557648797137 35.9856933370186,129.557648733722 35.9856843250873,129.557637645313 35.9856843766262,129.557637658795 35.9856862925666,129.557639648869 35.9856933795398)))0.227C주의
4MULTIPOLYGON (((129.557642176269 35.9857023800185,129.557648860552 35.9857023489499,129.557648797137 35.9856933370186,129.557639648869 35.9856933795398,129.557642176269 35.9857023800185)))0.063B보통
5MULTIPOLYGON (((129.55764470367 35.9857113804972,129.557648923967 35.9857113608812,129.557648860552 35.9857023489499,129.557642176269 35.9857023800185,129.55764470367 35.9857113804972)))0.266C주의
6MULTIPOLYGON (((129.557650778744 35.9856394377025,129.557648464698 35.9856460937288,129.557648480063 35.985648277362,129.557659568467 35.9856482258222,129.557659505051 35.9856392138909,129.557650945693 35.9856392536755,129.557650778744 35.9856394377025)))0.029B보통
7MULTIPOLYGON (((129.557648480063 35.985648277362,129.557648543478 35.9856572892933,129.557659631883 35.9856572377535,129.557659568467 35.9856482258222,129.557648480063 35.985648277362)))0.302D위험
8MULTIPOLYGON (((129.557648543478 35.9856572892933,129.557648606893 35.9856663012247,129.557659695299 35.9856662496848,129.557659631883 35.9856572377535,129.557648543478 35.9856572892933)))0.288C주의
9MULTIPOLYGON (((129.557648606893 35.9856663012247,129.557648670308 35.985675313156,129.557659758715 35.9856752616162,129.557659695299 35.9856662496848,129.557648606893 35.9856663012247)))0.262C주의
GEOMGAP_XTNGAP_RISK_GRD_CDGAP_RISK_CN
352MULTIPOLYGON (((129.557749480796 35.9858190401572,129.557749544223 35.9858280520881,129.557760632652 35.9858280005387,129.557760569224 35.9858189886078,129.557749480796 35.9858190401572)))0.032B보통
353MULTIPOLYGON (((129.557749544223 35.9858280520881,129.557749607649 35.985837064019,129.557760696079 35.9858370124696,129.557760632652 35.9858280005387,129.557749544223 35.9858280520881)))0.066B보통
354MULTIPOLYGON (((129.557749607649 35.985837064019,129.557749671076 35.9858460759499,129.557760759507 35.9858460244005,129.557760696079 35.9858370124696,129.557749607649 35.985837064019)))0.042B보통
355MULTIPOLYGON (((129.557749671076 35.9858460759499,129.557749734503 35.9858550878808,129.557760822935 35.9858550363314,129.557760759507 35.9858460244005,129.557749671076 35.9858460759499)))0.0A안전
356MULTIPOLYGON (((129.557749734503 35.9858550878808,129.557749797929 35.9858640998117,129.557760886363 35.9858640482623,129.557760822935 35.9858550363314,129.557749734503 35.9858550878808)))0.0A안전
357MULTIPOLYGON (((129.557749797929 35.9858640998117,129.557749861356 35.9858731117426,129.557760949791 35.9858730601931,129.557760886363 35.9858640482623,129.557749797929 35.9858640998117)))0.009B보통
358MULTIPOLYGON (((129.557749861356 35.9858731117426,129.557749924783 35.9858821236734,129.557761013219 35.985882072124,129.557760949791 35.9858730601931,129.557749861356 35.9858731117426)))0.024B보통
359MULTIPOLYGON (((129.557761046133 35.9858867485135,129.557761013219 35.985882072124,129.557749924783 35.9858821236734,129.557749945618 35.9858850840819,129.557761046133 35.9858867485135)))0.119C주의
360MULTIPOLYGON (((129.557758891797 35.9865129377529,129.557765453248 35.9865129072486,129.557765389818 35.9865038953187,129.557760646762 35.9865039173693,129.557758891797 35.9865129377529)))0.064B보통
361MULTIPOLYGON (((129.557757136831 35.9865219581365,129.557765516678 35.9865219191784,129.557765453248 35.9865129072486,129.557758891797 35.9865129377529,129.557757136831 35.9865219581365)))0.001B보통