Overview

Dataset statistics

Number of variables10
Number of observations121
Missing cells242
Missing cells (%)20.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory9.9 KiB
Average record size in memory84.1 B

Variable types

Numeric1
Text2
Categorical5
Unsupported2

Dataset

Description순번,ID,도시계획코드,분류명,조서ID,고시ID,라벨명,고시일자,X좌표,Y좌표
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-15527/S/1/datasetView.do

Alerts

조서ID has constant value ""Constant
고시ID has constant value ""Constant
고시일자 has constant value ""Constant
도시계획코드 is highly overall correlated with 분류명High correlation
분류명 is highly overall correlated with 도시계획코드High correlation
도시계획코드 is highly imbalanced (75.2%)Imbalance
분류명 is highly imbalanced (75.2%)Imbalance
X좌표 has 121 (100.0%) missing valuesMissing
Y좌표 has 121 (100.0%) missing valuesMissing
순번 has unique valuesUnique
ID has unique valuesUnique
라벨명 has unique valuesUnique
X좌표 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Y좌표 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2024-05-11 09:51:53.318129
Analysis finished2024-05-11 09:51:55.107466
Duration1.79 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

순번
Real number (ℝ)

UNIQUE 

Distinct121
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10904.785
Minimum10675
Maximum10987
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2024-05-11T09:51:55.422813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10675
5-th percentile10681
Q110897
median10927
Q310957
95-th percentile10981
Maximum10987
Range312
Interquartile range (IQR)60

Descriptive statistics

Standard deviation86.198338
Coefficient of variation (CV)0.0079046342
Kurtosis2.5795798
Mean10904.785
Median Absolute Deviation (MAD)30
Skewness-1.9102555
Sum1319479
Variance7430.1534
MonotonicityNot monotonic
2024-05-11T09:51:55.907645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10682 1
 
0.8%
10987 1
 
0.8%
10985 1
 
0.8%
10984 1
 
0.8%
10983 1
 
0.8%
10982 1
 
0.8%
10952 1
 
0.8%
10951 1
 
0.8%
10950 1
 
0.8%
10949 1
 
0.8%
Other values (111) 111
91.7%
ValueCountFrequency (%)
10675 1
0.8%
10676 1
0.8%
10677 1
0.8%
10678 1
0.8%
10679 1
0.8%
10680 1
0.8%
10681 1
0.8%
10682 1
0.8%
10683 1
0.8%
10684 1
0.8%
ValueCountFrequency (%)
10987 1
0.8%
10986 1
0.8%
10985 1
0.8%
10984 1
0.8%
10983 1
0.8%
10982 1
0.8%
10981 1
0.8%
10980 1
0.8%
10979 1
0.8%
10978 1
0.8%

ID
Text

UNIQUE 

Distinct121
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2024-05-11T09:51:56.661472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters1089
Distinct characters16
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique121 ?
Unique (%)100.0%

Sample

1st row생활권경계_109
2nd row생활권경계_110
3rd row생활권경계_111
4th row생활권경계_112
5th row생활권경계_113
ValueCountFrequency (%)
생활권경계_109 1
 
0.8%
생활권경계_006 1
 
0.8%
생활권경계_046 1
 
0.8%
생활권경계_045 1
 
0.8%
생활권경계_040 1
 
0.8%
생활권경계_020 1
 
0.8%
생활권경계_075 1
 
0.8%
생활권경계_069 1
 
0.8%
생활권경계_068 1
 
0.8%
생활권경계_067 1
 
0.8%
Other values (111) 111
91.7%
2024-05-11T09:51:57.970616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 130
11.9%
생 121
11.1%
활 121
11.1%
권 121
11.1%
경 121
11.1%
계 121
11.1%
_ 121
11.1%
1 55
5.1%
2 24
 
2.2%
9 22
 
2.0%
Other values (6) 132
12.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 605
55.6%
Decimal Number 363
33.3%
Connector Punctuation 121
 
11.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 130
35.8%
1 55
15.2%
2 24
 
6.6%
9 22
 
6.1%
3 22
 
6.1%
4 22
 
6.1%
5 22
 
6.1%
7 22
 
6.1%
8 22
 
6.1%
6 22
 
6.1%
Other Letter
ValueCountFrequency (%)
생 121
20.0%
활 121
20.0%
권 121
20.0%
경 121
20.0%
계 121
20.0%
Connector Punctuation
ValueCountFrequency (%)
_ 121
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 605
55.6%
Common 484
44.4%

Most frequent character per script

Common
ValueCountFrequency (%)
0 130
26.9%
_ 121
25.0%
1 55
11.4%
2 24
 
5.0%
9 22
 
4.5%
3 22
 
4.5%
4 22
 
4.5%
5 22
 
4.5%
7 22
 
4.5%
8 22
 
4.5%
Hangul
ValueCountFrequency (%)
생 121
20.0%
활 121
20.0%
권 121
20.0%
경 121
20.0%
계 121
20.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 605
55.6%
ASCII 484
44.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 130
26.9%
_ 121
25.0%
1 55
11.4%
2 24
 
5.0%
9 22
 
4.5%
3 22
 
4.5%
4 22
 
4.5%
5 22
 
4.5%
7 22
 
4.5%
8 22
 
4.5%
Hangul
ValueCountFrequency (%)
생 121
20.0%
활 121
20.0%
권 121
20.0%
경 121
20.0%
계 121
20.0%

도시계획코드
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
ZON125
116 
ZON121
 
5

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowZON125
2nd rowZON125
3rd rowZON125
4th rowZON125
5th rowZON125

Common Values

ValueCountFrequency (%)
ZON125 116
95.9%
ZON121 5
 
4.1%

Length

2024-05-11T09:51:58.597242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T09:51:58.913608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
zon125 116
95.9%
zon121 5
 
4.1%

분류명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
지역생활권
116 
권역생활권
 
5

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
지역생활권 116
95.9%
권역생활권 5
 
4.1%

Length

2024-05-11T09:51:59.337104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T09:51:59.656179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
지역생활권 116
95.9%
권역생활권 5
 
4.1%

조서ID
Categorical

CONSTANT 

Distinct1
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
121 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
121
100.0%

Length

2024-05-11T09:52:00.037605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T09:52:00.372278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
No values found.

고시ID
Categorical

CONSTANT 

Distinct1
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
121 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
121
100.0%

Length

2024-05-11T09:52:00.859727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T09:52:01.260251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
No values found.

라벨명
Text

UNIQUE 

Distinct121
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2024-05-11T09:52:01.986766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length9
Mean length7.0330579
Min length3

Characters and Unicode

Total characters851
Distinct characters160
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique121 ?
Unique (%)100.0%

Sample

1st row강동구_길동둔촌
2nd row강동구_암사
3rd row송파구_석촌
4th row송파구_잠실1
5th row서초구_방배
ValueCountFrequency (%)
강동구_길동둔촌 1
 
0.8%
종로구_청운효자 1
 
0.8%
강북구_번동 1
 
0.8%
강북구_미아 1
 
0.8%
동대문구_장안 1
 
0.8%
노원구_상계 1
 
0.8%
영등포구_신길 1
 
0.8%
구로구_고척개봉 1
 
0.8%
강서구_공항방화 1
 
0.8%
양천구_신월1 1
 
0.8%
Other values (111) 111
91.7%
2024-05-11T09:52:03.169482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
구 124
 
14.6%
_ 116
 
13.6%
동 29
 
3.4%
강 24
 
2.8%
서 19
 
2.2%
권 17
 
2.0%
성 15
 
1.8%
활 13
 
1.5%
생 13
 
1.5%
포 13
 
1.5%
Other values (150) 468
55.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 728
85.5%
Connector Punctuation 116
 
13.6%
Decimal Number 6
 
0.7%
Other Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 124
 
17.0%
동 29
 
4.0%
강 24
 
3.3%
서 19
 
2.6%
권 17
 
2.3%
성 15
 
2.1%
활 13
 
1.8%
생 13
 
1.8%
포 13
 
1.8%
대 13
 
1.8%
Other values (146) 448
61.5%
Decimal Number
ValueCountFrequency (%)
1 3
50.0%
2 3
50.0%
Connector Punctuation
ValueCountFrequency (%)
_ 116
100.0%
Other Punctuation
ValueCountFrequency (%)
? 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 728
85.5%
Common 123
 
14.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 124
 
17.0%
동 29
 
4.0%
강 24
 
3.3%
서 19
 
2.6%
권 17
 
2.3%
성 15
 
2.1%
활 13
 
1.8%
생 13
 
1.8%
포 13
 
1.8%
대 13
 
1.8%
Other values (146) 448
61.5%
Common
ValueCountFrequency (%)
_ 116
94.3%
1 3
 
2.4%
2 3
 
2.4%
? 1
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 728
85.5%
ASCII 123
 
14.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
구 124
 
17.0%
동 29
 
4.0%
강 24
 
3.3%
서 19
 
2.6%
권 17
 
2.3%
성 15
 
2.1%
활 13
 
1.8%
생 13
 
1.8%
포 13
 
1.8%
대 13
 
1.8%
Other values (146) 448
61.5%
ASCII
ValueCountFrequency (%)
_ 116
94.3%
1 3
 
2.4%
2 3
 
2.4%
? 1
 
0.8%

고시일자
Categorical

CONSTANT 

Distinct1
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
121 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
121
100.0%

Length

2024-05-11T09:52:03.672920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T09:52:03.989365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
No values found.

X좌표
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing121
Missing (%)100.0%
Memory size1.2 KiB

Y좌표
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing121
Missing (%)100.0%
Memory size1.2 KiB

Interactions

2024-05-11T09:51:53.917828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-11T09:52:04.195683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번도시계획코드분류명
순번1.0000.0000.000
도시계획코드0.0001.0000.986
분류명0.0000.9861.000
2024-05-11T09:52:04.449506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
분류명도시계획코드
분류명1.0000.895
도시계획코드0.8951.000
2024-05-11T09:52:04.694149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번도시계획코드분류명
순번1.0000.0000.000
도시계획코드0.0001.0000.895
분류명0.0000.8951.000

Missing values

2024-05-11T09:51:54.374458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-11T09:51:54.930946image/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

순번ID도시계획코드분류명조서ID고시ID라벨명고시일자X좌표Y좌표
010682생활권경계_109ZON125지역생활권강동구_길동둔촌<NA><NA>
110683생활권경계_110ZON125지역생활권강동구_암사<NA><NA>
210684생활권경계_111ZON125지역생활권송파구_석촌<NA><NA>
310685생활권경계_112ZON125지역생활권송파구_잠실1<NA><NA>
410686생활권경계_113ZON125지역생활권서초구_방배<NA><NA>
510687생활권경계_114ZON125지역생활권송파구_가락<NA><NA>
610688생활권경계_115ZON125지역생활권강남구_역삼논현<NA><NA>
710881생활권경계_054ZON125지역생활권서대문구_홍제생활권<NA><NA>
810882생활권경계_055ZON125지역생활권서대문구_가좌생활권<NA><NA>
910883생활권경계_057ZON125지역생활권은평구_응암생활권<NA><NA>
순번ID도시계획코드분류명조서ID고시ID라벨명고시일자X좌표Y좌표
11110972생활권경계_036ZON125지역생활권성동구_마장용답<NA><NA>
11210973생활권경계_037ZON125지역생활권성동구_왕십리행당<NA><NA>
11310974생활권경계_038ZON125지역생활권성동구_금호옥수<NA><NA>
11410975생활권경계_001ZON121권역생활권도심권<NA><NA>
11510976생활권경계_019ZON125지역생활권노원구_마들<NA><NA>
11610977생활권경계_021ZON125지역생활권노원구_중계<NA><NA>
11710978생활권경계_022ZON125지역생활권노원구_공릉<NA><NA>
11810979생활권경계_024ZON125지역생활권노원구_하계<NA><NA>
11910980생활권경계_025ZON125지역생활권노원구_월계<NA><NA>
12010981생활권경계_026ZON125지역생활권광진구_구의<NA><NA>