Overview

Dataset statistics

Number of variables10
Number of observations82
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.9 KiB
Average record size in memory86.6 B

Variable types

Categorical5
Numeric4
Text1

Dataset

Description통영시 도시정보시스템의 선착장에 대하여 지형지물부호,관리번호,행정읍면동,도엽번호,관리기관,구분,대장초기화여부,구분ID,경도,위도 정보를 제공합니다.
Author경상남도 통영시
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=15062749

Alerts

지형지물부호 has constant value ""Constant
관리기관 has constant value ""Constant
대장초기화여부 has constant value ""Constant
행정읍면동 is highly overall correlated with 관리번호 and 2 other fieldsHigh correlation
구분 is highly overall correlated with 관리번호 and 3 other fieldsHigh correlation
관리번호 is highly overall correlated with 경도 and 3 other fieldsHigh correlation
구분ID is highly overall correlated with 위도 and 1 other fieldsHigh correlation
경도 is highly overall correlated with 관리번호 and 2 other fieldsHigh correlation
위도 is highly overall correlated with 관리번호 and 1 other fieldsHigh correlation
관리번호 has unique valuesUnique
구분ID has unique valuesUnique
경도 has unique valuesUnique
위도 has unique valuesUnique
구분ID has 1 (1.2%) zerosZeros

Reproduction

Analysis started2023-12-11 00:24:48.097557
Analysis finished2023-12-11 00:24:50.017935
Duration1.92 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

지형지물부호
Categorical

CONSTANT 

Distinct1
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size788.0 B
선착장
82 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
선착장 82
100.0%

Length

2023-12-11T09:24:50.069160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:24:50.157253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
선착장 82
100.0%

관리번호
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct82
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41.5
Minimum1
Maximum82
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size870.0 B
2023-12-11T09:24:50.244095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5.05
Q121.25
median41.5
Q361.75
95-th percentile77.95
Maximum82
Range81
Interquartile range (IQR)40.5

Descriptive statistics

Standard deviation23.815261
Coefficient of variation (CV)0.57386172
Kurtosis-1.2
Mean41.5
Median Absolute Deviation (MAD)20.5
Skewness0
Sum3403
Variance567.16667
MonotonicityNot monotonic
2023-12-11T09:24:50.354829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
34 1
 
1.2%
14 1
 
1.2%
12 1
 
1.2%
11 1
 
1.2%
10 1
 
1.2%
9 1
 
1.2%
8 1
 
1.2%
7 1
 
1.2%
6 1
 
1.2%
82 1
 
1.2%
Other values (72) 72
87.8%
ValueCountFrequency (%)
1 1
1.2%
2 1
1.2%
3 1
1.2%
4 1
1.2%
5 1
1.2%
6 1
1.2%
7 1
1.2%
8 1
1.2%
9 1
1.2%
10 1
1.2%
ValueCountFrequency (%)
82 1
1.2%
81 1
1.2%
80 1
1.2%
79 1
1.2%
78 1
1.2%
77 1
1.2%
76 1
1.2%
75 1
1.2%
74 1
1.2%
73 1
1.2%

행정읍면동
Categorical

HIGH CORRELATION 

Distinct9
Distinct (%)11.0%
Missing0
Missing (%)0.0%
Memory size788.0 B
사량면
39 
욕지면
23 
봉평동
6 
명정동
5 
산양읍
 
2
Other values (4)
7 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique1 ?
Unique (%)1.2%

Sample

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

Common Values

ValueCountFrequency (%)
사량면 39
47.6%
욕지면 23
28.0%
봉평동 6
 
7.3%
명정동 5
 
6.1%
산양읍 2
 
2.4%
정량동 2
 
2.4%
중앙동 2
 
2.4%
용남면 2
 
2.4%
광도면 1
 
1.2%

Length

2023-12-11T09:24:50.478439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:24:50.585766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
사량면 39
47.6%
욕지면 23
28.0%
봉평동 6
 
7.3%
명정동 5
 
6.1%
산양읍 2
 
2.4%
정량동 2
 
2.4%
중앙동 2
 
2.4%
용남면 2
 
2.4%
광도면 1
 
1.2%

도엽번호
Text

Distinct55
Distinct (%)67.1%
Missing0
Missing (%)0.0%
Memory size788.0 B
2023-12-11T09:24:50.777522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters820
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

Unique37 ?
Unique (%)45.1%

Sample

1st row348012085D
2nd row348011927C
3rd row348011489A
4th row348011487C
5th row348012023D
ValueCountFrequency (%)
348021947d 6
 
7.3%
348051070a 3
 
3.7%
348012074c 3
 
3.7%
348051001a 3
 
3.7%
348021925a 3
 
3.7%
348051047d 3
 
3.7%
348011487c 2
 
2.4%
348012053a 2
 
2.4%
348012098c 2
 
2.4%
348051070d 2
 
2.4%
Other values (45) 53
64.6%
2023-12-11T09:24:51.047796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 142
17.3%
4 116
14.1%
1 108
13.2%
8 107
13.0%
3 91
11.1%
2 61
7.4%
5 46
 
5.6%
9 34
 
4.1%
7 32
 
3.9%
A 29
 
3.5%
Other values (4) 54
 
6.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 738
90.0%
Uppercase Letter 82
 
10.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 142
19.2%
4 116
15.7%
1 108
14.6%
8 107
14.5%
3 91
12.3%
2 61
8.3%
5 46
 
6.2%
9 34
 
4.6%
7 32
 
4.3%
6 1
 
0.1%
Uppercase Letter
ValueCountFrequency (%)
A 29
35.4%
D 20
24.4%
C 18
22.0%
B 15
18.3%

Most occurring scripts

ValueCountFrequency (%)
Common 738
90.0%
Latin 82
 
10.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 142
19.2%
4 116
15.7%
1 108
14.6%
8 107
14.5%
3 91
12.3%
2 61
8.3%
5 46
 
6.2%
9 34
 
4.6%
7 32
 
4.3%
6 1
 
0.1%
Latin
ValueCountFrequency (%)
A 29
35.4%
D 20
24.4%
C 18
22.0%
B 15
18.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 820
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 142
17.3%
4 116
14.1%
1 108
13.2%
8 107
13.0%
3 91
11.1%
2 61
7.4%
5 46
 
5.6%
9 34
 
4.1%
7 32
 
3.9%
A 29
 
3.5%
Other values (4) 54
 
6.6%

관리기관
Categorical

CONSTANT 

Distinct1
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size788.0 B
통영시
82 

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 82
100.0%

Length

2023-12-11T09:24:51.168721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:24:51.240833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
통영시 82
100.0%

구분
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size788.0 B
1/5000
62 
1/1000
20 

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 62
75.6%
1/1000 20
 
24.4%

Length

2023-12-11T09:24:51.315131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:24:51.390790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1/5000 62
75.6%
1/1000 20
 
24.4%

대장초기화여부
Categorical

CONSTANT 

Distinct1
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size788.0 B
1
82 

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 82
100.0%

Length

2023-12-11T09:24:51.476416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:24:51.791949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 82
100.0%

구분ID
Real number (ℝ)

HIGH CORRELATION  UNIQUE  ZEROS 

Distinct82
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.5
Minimum0
Maximum81
Zeros1
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size870.0 B
2023-12-11T09:24:51.879272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4.05
Q120.25
median40.5
Q360.75
95-th percentile76.95
Maximum81
Range81
Interquartile range (IQR)40.5

Descriptive statistics

Standard deviation23.815261
Coefficient of variation (CV)0.58803114
Kurtosis-1.2
Mean40.5
Median Absolute Deviation (MAD)20.5
Skewness0
Sum3321
Variance567.16667
MonotonicityStrictly increasing
2023-12-11T09:24:51.989930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1
 
1.2%
62 1
 
1.2%
60 1
 
1.2%
59 1
 
1.2%
58 1
 
1.2%
57 1
 
1.2%
56 1
 
1.2%
55 1
 
1.2%
54 1
 
1.2%
53 1
 
1.2%
Other values (72) 72
87.8%
ValueCountFrequency (%)
0 1
1.2%
1 1
1.2%
2 1
1.2%
3 1
1.2%
4 1
1.2%
5 1
1.2%
6 1
1.2%
7 1
1.2%
8 1
1.2%
9 1
1.2%
ValueCountFrequency (%)
81 1
1.2%
80 1
1.2%
79 1
1.2%
78 1
1.2%
77 1
1.2%
76 1
1.2%
75 1
1.2%
74 1
1.2%
73 1
1.2%
72 1
1.2%

경도
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct82
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean128.26733
Minimum128.13755
Maximum128.45321
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size870.0 B
2023-12-11T09:24:52.100577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum128.13755
5-th percentile128.18127
Q1128.20588
median128.23274
Q3128.24924
95-th percentile128.43466
Maximum128.45321
Range0.3156633
Interquartile range (IQR)0.043356675

Descriptive statistics

Standard deviation0.094776268
Coefficient of variation (CV)0.00073889641
Kurtosis-0.58885929
Mean128.26733
Median Absolute Deviation (MAD)0.0268057
Skewness1.0572905
Sum10517.921
Variance0.0089825409
MonotonicityNot monotonic
2023-12-11T09:24:52.234346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
128.2236767 1
 
1.2%
128.2000385 1
 
1.2%
128.2330376 1
 
1.2%
128.2334121 1
 
1.2%
128.2334004 1
 
1.2%
128.1812599 1
 
1.2%
128.1815585 1
 
1.2%
128.183419 1
 
1.2%
128.1948187 1
 
1.2%
128.4532131 1
 
1.2%
Other values (72) 72
87.8%
ValueCountFrequency (%)
128.1375498 1
1.2%
128.1698923 1
1.2%
128.1805299 1
1.2%
128.1805584 1
1.2%
128.1812599 1
1.2%
128.1815585 1
1.2%
128.1819077 1
1.2%
128.182665 1
1.2%
128.183419 1
1.2%
128.190735 1
1.2%
ValueCountFrequency (%)
128.4532131 1
1.2%
128.45286 1
1.2%
128.4371645 1
1.2%
128.4359232 1
1.2%
128.4346812 1
1.2%
128.4342762 1
1.2%
128.4341675 1
1.2%
128.4340745 1
1.2%
128.4339984 1
1.2%
128.4338656 1
1.2%

위도
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct82
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.790799
Minimum34.619114
Maximum34.910171
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size870.0 B
2023-12-11T09:24:52.374715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum34.619114
5-th percentile34.652001
Q134.709494
median34.826252
Q334.840097
95-th percentile34.859047
Maximum34.910171
Range0.2910565
Interquartile range (IQR)0.13060327

Descriptive statistics

Standard deviation0.0755785
Coefficient of variation (CV)0.0021723703
Kurtosis-0.70392671
Mean34.790799
Median Absolute Deviation (MAD)0.018461115
Skewness-0.91054378
Sum2852.8455
Variance0.0057121097
MonotonicityNot monotonic
2023-12-11T09:24:52.484943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
34.80712976 1
 
1.2%
34.69754602 1
 
1.2%
34.67536656 1
 
1.2%
34.67544418 1
 
1.2%
34.67569515 1
 
1.2%
34.70965151 1
 
1.2%
34.70944097 1
 
1.2%
34.70971486 1
 
1.2%
34.70834309 1
 
1.2%
34.91017052 1
 
1.2%
Other values (72) 72
87.8%
ValueCountFrequency (%)
34.61911402 1
1.2%
34.63499095 1
1.2%
34.64160554 1
1.2%
34.65091584 1
1.2%
34.65127754 1
1.2%
34.66574269 1
1.2%
34.66581163 1
1.2%
34.66676925 1
1.2%
34.66888522 1
1.2%
34.66905845 1
1.2%
ValueCountFrequency (%)
34.91017052 1
1.2%
34.88972567 1
1.2%
34.8600371 1
1.2%
34.85920383 1
1.2%
34.85907348 1
1.2%
34.85855346 1
1.2%
34.85788602 1
1.2%
34.85756803 1
1.2%
34.85616469 1
1.2%
34.85599564 1
1.2%

Interactions

2023-12-11T09:24:49.487679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:48.423574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:48.792420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:49.125877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:49.567589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:48.528850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:48.870370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:49.221124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:49.647850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:48.610317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:48.959705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:49.315251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:49.723695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:48.706414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:49.041294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:49.412800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T09:24:52.567170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관리번호행정읍면동도엽번호구분구분ID경도위도
관리번호1.0000.7860.9960.9930.9740.7820.781
행정읍면동0.7861.0001.0001.0000.7600.8870.889
도엽번호0.9961.0001.0001.0000.9801.0001.000
구분0.9931.0001.0001.0000.9591.0000.501
구분ID0.9740.7600.9800.9591.0000.7860.729
경도0.7820.8871.0001.0000.7861.0000.860
위도0.7810.8891.0000.5010.7290.8601.000
2023-12-11T09:24:52.656730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정읍면동구분
행정읍면동1.0000.955
구분0.9551.000
2023-12-11T09:24:52.765078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관리번호구분ID경도위도행정읍면동구분
관리번호1.000-0.4810.6120.6590.5060.880
구분ID-0.4811.000-0.027-0.5400.4730.784
경도0.612-0.0271.000-0.0620.6030.968
위도0.659-0.540-0.0621.0000.4900.479
행정읍면동0.5060.4730.6030.4901.0000.955
구분0.8800.7840.9680.4790.9551.000

Missing values

2023-12-11T09:24:49.836946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T09:24:49.973088image/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

지형지물부호관리번호행정읍면동도엽번호관리기관구분대장초기화여부구분ID경도위도
0선착장34사량면348012085D통영시1/500010128.22367734.80713
1선착장35사량면348011927C통영시1/500011128.18055834.836721
2선착장36사량면348011489A통영시1/500012128.19177434.858553
3선착장40사량면348011487C통영시1/500013128.1805334.855996
4선착장41사량면348012023D통영시1/500014128.21462934.836222
5선착장42사량면348012011D통영시1/500015128.203134.841384
6선착장43사량면348011592B통영시1/500016128.20984434.853436
7선착장44사량면348011593A통영시1/500017128.21010734.854026
8선착장45사량면348011582A통영시1/500018128.20583134.857568
9선착장46사량면348011582A통영시1/500019128.20603734.857886
지형지물부호관리번호행정읍면동도엽번호관리기관구분대장초기화여부구분ID경도위도
72선착장24사량면348011838B통영시1/5000172128.1375534.833496
73선착장25사량면348011950A통영시1/5000173128.19595434.829842
74선착장26사량면348011940C통영시1/5000174128.19572734.83028
75선착장27사량면348011940C통영시1/5000175128.19529434.830376
76선착장28사량면348012073B통영시1/5000176128.21489134.81259
77선착장29사량면348012074C통영시1/5000177128.21544434.811947
78선착장30사량면348012074C통영시1/5000178128.2150634.812002
79선착장31사량면348012074C통영시1/5000179128.21575634.81235
80선착장32사량면348012053A통영시1/5000180128.21162934.823994
81선착장33사량면348012053A통영시1/5000181128.21072334.824199