Overview

Dataset statistics

Number of variables11
Number of observations516
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory47.5 KiB
Average record size in memory94.3 B

Variable types

Categorical6
Numeric4
Text1

Dataset

Description통영시 도시정보시스템의 방파제에 대하여 지형지물부호,관리번호,행정읍면동,도엽번호,관리기관,어항관리번호,구분,대장초기화여부,구분id,경도,위도 정보를 제공합니다.
URLhttps://www.data.go.kr/data/15062764/fileData.do

Alerts

지형지물부호 has constant value ""Constant
관리기관 has constant value ""Constant
어항관리번호 has constant value ""Constant
대장초기화여부 has constant value ""Constant
관리번호 is highly overall correlated with 경도 and 1 other fieldsHigh correlation
구분id is highly overall correlated with 행정읍면동High correlation
경도 is highly overall correlated with 관리번호 and 1 other fieldsHigh correlation
위도 is highly overall correlated with 행정읍면동High correlation
행정읍면동 is highly overall correlated with 관리번호 and 3 other fieldsHigh correlation
구분 is highly imbalanced (93.5%)Imbalance
관리번호 has unique valuesUnique
구분id has unique valuesUnique
경도 has unique valuesUnique
위도 has unique valuesUnique

Reproduction

Analysis started2023-12-12 22:18:54.230768
Analysis finished2023-12-12 22:18:56.428337
Duration2.2 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

지형지물부호
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
방파제
516 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
방파제 516
100.0%

Length

2023-12-13T07:18:56.485717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:18:56.568245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
방파제 516
100.0%

관리번호
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct516
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean265.35271
Minimum1
Maximum568
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.7 KiB
2023-12-13T07:18:56.663104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile26.75
Q1129.75
median258.5
Q3387.25
95-th percentile542.25
Maximum568
Range567
Interquartile range (IQR)257.5

Descriptive statistics

Standard deviation160.05299
Coefficient of variation (CV)0.60317072
Kurtosis-1.011539
Mean265.35271
Median Absolute Deviation (MAD)129
Skewness0.18926595
Sum136922
Variance25616.959
MonotonicityNot monotonic
2023-12-13T07:18:57.032798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
228 1
 
0.2%
550 1
 
0.2%
520 1
 
0.2%
521 1
 
0.2%
522 1
 
0.2%
523 1
 
0.2%
524 1
 
0.2%
525 1
 
0.2%
526 1
 
0.2%
527 1
 
0.2%
Other values (506) 506
98.1%
ValueCountFrequency (%)
1 1
0.2%
2 1
0.2%
3 1
0.2%
4 1
0.2%
5 1
0.2%
6 1
0.2%
7 1
0.2%
8 1
0.2%
9 1
0.2%
10 1
0.2%
ValueCountFrequency (%)
568 1
0.2%
567 1
0.2%
566 1
0.2%
565 1
0.2%
564 1
0.2%
563 1
0.2%
562 1
0.2%
561 1
0.2%
560 1
0.2%
559 1
0.2%

행정읍면동
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
한산면
142 
산양읍
87 
사량면
87 
욕지면
67 
도산면
59 
Other values (2)
74 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row도산면
2nd row도산면
3rd row도산면
4th row한산면
5th row한산면

Common Values

ValueCountFrequency (%)
한산면 142
27.5%
산양읍 87
16.9%
사량면 87
16.9%
욕지면 67
13.0%
도산면 59
11.4%
용남면 50
 
9.7%
광도면 24
 
4.7%

Length

2023-12-13T07:18:57.143549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:18:57.241459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
한산면 142
27.5%
산양읍 87
16.9%
사량면 87
16.9%
욕지면 67
13.0%
도산면 59
11.4%
용남면 50
 
9.7%
광도면 24
 
4.7%

도엽번호
Text

Distinct336
Distinct (%)65.1%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
2023-12-13T07:18:57.469987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters5160
Distinct characters14
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique212 ?
Unique (%)41.1%

Sample

1st row348020758A
2nd row348020785C
3rd row348020755C
4th row348060533C
5th row348060533C
ValueCountFrequency (%)
348011593a 5
 
1.0%
348012015b 4
 
0.8%
348032291b 4
 
0.8%
348061146c 4
 
0.8%
348061146b 4
 
0.8%
348012053a 4
 
0.8%
348021327a 4
 
0.8%
348011487c 4
 
0.8%
348022505d 4
 
0.8%
348022589a 4
 
0.8%
Other values (326) 475
92.1%
2023-12-13T07:18:57.851884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 843
16.3%
3 705
13.7%
4 674
13.1%
8 659
12.8%
2 621
12.0%
1 467
9.1%
5 229
 
4.4%
6 186
 
3.6%
7 171
 
3.3%
A 140
 
2.7%
Other values (4) 465
9.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4644
90.0%
Uppercase Letter 516
 
10.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 843
18.2%
3 705
15.2%
4 674
14.5%
8 659
14.2%
2 621
13.4%
1 467
10.1%
5 229
 
4.9%
6 186
 
4.0%
7 171
 
3.7%
9 89
 
1.9%
Uppercase Letter
ValueCountFrequency (%)
A 140
27.1%
D 129
25.0%
C 128
24.8%
B 119
23.1%

Most occurring scripts

ValueCountFrequency (%)
Common 4644
90.0%
Latin 516
 
10.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 843
18.2%
3 705
15.2%
4 674
14.5%
8 659
14.2%
2 621
13.4%
1 467
10.1%
5 229
 
4.9%
6 186
 
4.0%
7 171
 
3.7%
9 89
 
1.9%
Latin
ValueCountFrequency (%)
A 140
27.1%
D 129
25.0%
C 128
24.8%
B 119
23.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5160
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 843
16.3%
3 705
13.7%
4 674
13.1%
8 659
12.8%
2 621
12.0%
1 467
9.1%
5 229
 
4.4%
6 186
 
3.6%
7 171
 
3.3%
A 140
 
2.7%
Other values (4) 465
9.0%

관리기관
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
통영시
516 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
통영시 516
100.0%

Length

2023-12-13T07:18:57.974787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:18:58.073164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
통영시 516
100.0%

어항관리번호
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
0
516 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 516
100.0%

Length

2023-12-13T07:18:58.158496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:18:58.232457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 516
100.0%

구분
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
1/5000
512 
1/1000
 
4

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1/5000
2nd row1/5000
3rd row1/5000
4th row1/5000
5th row1/5000

Common Values

ValueCountFrequency (%)
1/5000 512
99.2%
1/1000 4
 
0.8%

Length

2023-12-13T07:18:58.320055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:18:58.418171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1/5000 512
99.2%
1/1000 4
 
0.8%

대장초기화여부
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
1
516 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 516
100.0%

Length

2023-12-13T07:18:58.506989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:18:58.598393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 516
100.0%

구분id
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct516
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean257.5
Minimum0
Maximum515
Zeros1
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size4.7 KiB
2023-12-13T07:18:58.694481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile25.75
Q1128.75
median257.5
Q3386.25
95-th percentile489.25
Maximum515
Range515
Interquartile range (IQR)257.5

Descriptive statistics

Standard deviation149.10064
Coefficient of variation (CV)0.5790316
Kurtosis-1.2
Mean257.5
Median Absolute Deviation (MAD)129
Skewness0
Sum132870
Variance22231
MonotonicityStrictly increasing
2023-12-13T07:18:58.817155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1
 
0.2%
324 1
 
0.2%
354 1
 
0.2%
353 1
 
0.2%
352 1
 
0.2%
351 1
 
0.2%
350 1
 
0.2%
349 1
 
0.2%
348 1
 
0.2%
347 1
 
0.2%
Other values (506) 506
98.1%
ValueCountFrequency (%)
0 1
0.2%
1 1
0.2%
2 1
0.2%
3 1
0.2%
4 1
0.2%
5 1
0.2%
6 1
0.2%
7 1
0.2%
8 1
0.2%
9 1
0.2%
ValueCountFrequency (%)
515 1
0.2%
514 1
0.2%
513 1
0.2%
512 1
0.2%
511 1
0.2%
510 1
0.2%
509 1
0.2%
508 1
0.2%
507 1
0.2%
506 1
0.2%

경도
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct516
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean128.37361
Minimum128.13712
Maximum128.57469
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.7 KiB
2023-12-13T07:18:58.956183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum128.13712
5-th percentile128.20014
Q1128.2746
median128.381
Q3128.47456
95-th percentile128.52849
Maximum128.57469
Range0.4375654
Interquartile range (IQR)0.19995447

Descriptive statistics

Standard deviation0.11115645
Coefficient of variation (CV)0.00086588241
Kurtosis-1.2613499
Mean128.37361
Median Absolute Deviation (MAD)0.09870775
Skewness-0.21207614
Sum66240.781
Variance0.012355756
MonotonicityNot monotonic
2023-12-13T07:18:59.094403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
128.3368671 1
 
0.2%
128.2162618 1
 
0.2%
128.5087061 1
 
0.2%
128.5067689 1
 
0.2%
128.5031258 1
 
0.2%
128.502497 1
 
0.2%
128.4919014 1
 
0.2%
128.4910374 1
 
0.2%
128.4825962 1
 
0.2%
128.4773812 1
 
0.2%
Other values (506) 506
98.1%
ValueCountFrequency (%)
128.1371211 1
0.2%
128.1375499 1
0.2%
128.1377693 1
0.2%
128.169884 1
0.2%
128.1805145 1
0.2%
128.1805218 1
0.2%
128.1805378 1
0.2%
128.1807675 1
0.2%
128.1809646 1
0.2%
128.1814632 1
0.2%
ValueCountFrequency (%)
128.5746865 1
0.2%
128.5734859 1
0.2%
128.5702953 1
0.2%
128.5672323 1
0.2%
128.5572116 1
0.2%
128.5571866 1
0.2%
128.5546915 1
0.2%
128.5546834 1
0.2%
128.5537998 1
0.2%
128.5537074 1
0.2%

위도
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct516
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.807519
Minimum34.619134
Maximum34.995523
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.7 KiB
2023-12-13T07:18:59.214755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum34.619134
5-th percentile34.634792
Q134.757998
median34.812238
Q334.865744
95-th percentile34.935122
Maximum34.995523
Range0.37638834
Interquartile range (IQR)0.10774626

Descriptive statistics

Standard deviation0.086305061
Coefficient of variation (CV)0.0024794948
Kurtosis-0.26774992
Mean34.807519
Median Absolute Deviation (MAD)0.05424416
Skewness-0.29820442
Sum17960.68
Variance0.0074485635
MonotonicityNot monotonic
2023-12-13T07:18:59.376621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
34.92271313 1
 
0.2%
34.8429472 1
 
0.2%
34.76742455 1
 
0.2%
34.7666229 1
 
0.2%
34.7658342 1
 
0.2%
34.76580216 1
 
0.2%
34.75828307 1
 
0.2%
34.75759589 1
 
0.2%
34.76001564 1
 
0.2%
34.76044059 1
 
0.2%
Other values (506) 506
98.1%
ValueCountFrequency (%)
34.61913432 1
0.2%
34.62475386 1
0.2%
34.62542463 1
0.2%
34.62542833 1
0.2%
34.62555859 1
0.2%
34.6255666 1
0.2%
34.62558885 1
0.2%
34.62618669 1
0.2%
34.62618789 1
0.2%
34.62680465 1
0.2%
ValueCountFrequency (%)
34.99552266 1
0.2%
34.99529428 1
0.2%
34.99424482 1
0.2%
34.99411783 1
0.2%
34.99298169 1
0.2%
34.99288882 1
0.2%
34.9928233 1
0.2%
34.98871877 1
0.2%
34.98628704 1
0.2%
34.9852014 1
0.2%

Interactions

2023-12-13T07:18:55.798097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:18:54.578404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:18:54.979126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:18:55.406396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:18:55.905504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:18:54.668614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:18:55.085749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:18:55.499147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:18:56.001997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:18:54.776905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:18:55.193079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:18:55.603217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:18:56.091729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:18:54.866039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:18:55.292712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:18:55.693900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T07:18:59.494403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관리번호행정읍면동구분구분id경도위도
관리번호1.0000.8510.2810.9400.8420.876
행정읍면동0.8511.0000.2320.7880.8570.845
구분0.2810.2321.0000.2000.1460.148
구분id0.9400.7880.2001.0000.7940.760
경도0.8420.8570.1460.7941.0000.825
위도0.8760.8450.1480.7600.8251.000
2023-12-13T07:18:59.600163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정읍면동구분
행정읍면동1.0000.248
구분0.2481.000
2023-12-13T07:18:59.687916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관리번호구분id경도위도행정읍면동구분
관리번호1.000-0.065-0.603-0.3200.6540.213
구분id-0.0651.0000.393-0.2200.5570.152
경도-0.6030.3931.000-0.0890.6640.111
위도-0.320-0.220-0.0891.0000.6440.112
행정읍면동0.6540.5570.6640.6441.0000.248
구분0.2130.1520.1110.1120.2481.000

Missing values

2023-12-13T07:18:56.231807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T07:18:56.375618image/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

지형지물부호관리번호행정읍면동도엽번호관리기관어항관리번호구분대장초기화여부구분id경도위도
0방파제228도산면348020758A통영시01/500010128.33686734.922713
1방파제229도산면348020785C통영시01/500011128.3211434.907368
2방파제230도산면348020755C통영시01/500012128.32087334.922489
3방파제178한산면348060533C통영시01/500013128.46045734.731925
4방파제179한산면348060533C통영시01/500014128.46046334.730682
5방파제180한산면348060532D통영시01/500015128.45992734.730641
6방파제181산양읍348060456B통영시01/500016128.42766834.723551
7방파제314사량면348012078D통영시01/500017128.23855134.810226
8방파제315사량면348012039C통영시01/500018128.2417934.830845
9방파제316사량면348012038B통영시01/500019128.23968834.832842
지형지물부호관리번호행정읍면동도엽번호관리기관어항관리번호구분대장초기화여부구분id경도위도
506방파제146한산면348070122A통영시01/50001506128.50501934.739667
507방파제1용남면348020578A통영시01/50001507128.48619934.962894
508방파제2용남면348020578A통영시01/50001508128.486634.963054
509방파제3용남면348020578C통영시01/50001509128.48627834.962069
510방파제4광도면348020420B통영시01/50001510128.44922834.992889
511방파제5광도면348020426C통영시01/50001511128.42654234.985201
512방파제6광도면348020418B통영시01/50001512128.43850834.994245
513방파제7광도면348020418A통영시01/50001513128.43610834.994118
514방파제8광도면348020427C통영시01/50001514128.43074234.984972
515방파제9광도면348020459B통영시01/50001515128.44345534.973958