Overview

Dataset statistics

Number of variables10
Number of observations76
Missing cells152
Missing cells (%)20.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.3 KiB
Average record size in memory84.7 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-15531/S/1/datasetView.do

Alerts

분류명 has constant value ""Constant
조서ID has constant value ""Constant
고시ID has constant value ""Constant
고시일자 has constant value ""Constant
X좌표 has 76 (100.0%) missing valuesMissing
Y좌표 has 76 (100.0%) missing valuesMissing
순번 has unique valuesUnique
ID 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 01:04:38.418138
Analysis finished2024-05-11 01:04:40.397370
Duration1.98 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

순번
Real number (ℝ)

UNIQUE 

Distinct76
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11205.237
Minimum10953
Maximum11268
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size816.0 B
2024-05-11T01:04:40.809116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10953
5-th percentile10956.75
Q111211.75
median11230.5
Q311249.25
95-th percentile11264.25
Maximum11268
Range315
Interquartile range (IQR)37.5

Descriptive statistics

Standard deviation87.896131
Coefficient of variation (CV)0.0078442011
Kurtosis4.3396762
Mean11205.237
Median Absolute Deviation (MAD)19
Skewness-2.4025776
Sum851598
Variance7725.7298
MonotonicityNot monotonic
2024-05-11T01:04:41.504920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10953 1
 
1.3%
11209 1
 
1.3%
11216 1
 
1.3%
11215 1
 
1.3%
11214 1
 
1.3%
11213 1
 
1.3%
11212 1
 
1.3%
11211 1
 
1.3%
11210 1
 
1.3%
11208 1
 
1.3%
Other values (66) 66
86.8%
ValueCountFrequency (%)
10953 1
1.3%
10954 1
1.3%
10955 1
1.3%
10956 1
1.3%
10957 1
1.3%
10958 1
1.3%
10959 1
1.3%
10960 1
1.3%
11201 1
1.3%
11202 1
1.3%
ValueCountFrequency (%)
11268 1
1.3%
11267 1
1.3%
11266 1
1.3%
11265 1
1.3%
11264 1
1.3%
11263 1
1.3%
11262 1
1.3%
11261 1
1.3%
11260 1
1.3%
11259 1
1.3%

ID
Text

UNIQUE 

Distinct76
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size740.0 B
2024-05-11T01:04:42.300453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters456
Distinct characters14
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

Unique76 ?
Unique (%)100.0%

Sample

1st row중심지_51
2nd row중심지_52
3rd row중심지_53
4th row중심지_58
5th row중심지_59
ValueCountFrequency (%)
중심지_51 1
 
1.3%
중심지_17 1
 
1.3%
중심지_37 1
 
1.3%
중심지_35 1
 
1.3%
중심지_34 1
 
1.3%
중심지_16 1
 
1.3%
중심지_02 1
 
1.3%
중심지_61 1
 
1.3%
중심지_39 1
 
1.3%
중심지_52 1
 
1.3%
Other values (66) 66
86.8%
2024-05-11T01:04:43.617033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
중 76
16.7%
심 76
16.7%
지 76
16.7%
_ 76
16.7%
5 18
 
3.9%
6 18
 
3.9%
2 18
 
3.9%
4 18
 
3.9%
3 17
 
3.7%
7 17
 
3.7%
Other values (4) 46
10.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 228
50.0%
Decimal Number 152
33.3%
Connector Punctuation 76
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 18
11.8%
6 18
11.8%
2 18
11.8%
4 18
11.8%
3 17
11.2%
7 17
11.2%
1 16
10.5%
0 15
9.9%
8 8
5.3%
9 7
 
4.6%
Other Letter
ValueCountFrequency (%)
중 76
33.3%
심 76
33.3%
지 76
33.3%
Connector Punctuation
ValueCountFrequency (%)
_ 76
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 228
50.0%
Common 228
50.0%

Most frequent character per script

Common
ValueCountFrequency (%)
_ 76
33.3%
5 18
 
7.9%
6 18
 
7.9%
2 18
 
7.9%
4 18
 
7.9%
3 17
 
7.5%
7 17
 
7.5%
1 16
 
7.0%
0 15
 
6.6%
8 8
 
3.5%
Hangul
ValueCountFrequency (%)
중 76
33.3%
심 76
33.3%
지 76
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 228
50.0%
ASCII 228
50.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
중 76
33.3%
심 76
33.3%
지 76
33.3%
ASCII
ValueCountFrequency (%)
_ 76
33.3%
5 18
 
7.9%
6 18
 
7.9%
2 18
 
7.9%
4 18
 
7.9%
3 17
 
7.5%
7 17
 
7.5%
1 16
 
7.0%
0 15
 
6.6%
8 8
 
3.5%

도시계획코드
Categorical

Distinct4
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Memory size740.0 B
ZON540
53 
ZON530
13 
ZON520
7 
ZON510
 
3

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowZON540
2nd rowZON540
3rd rowZON510
4th rowZON540
5th rowZON540

Common Values

ValueCountFrequency (%)
ZON540 53
69.7%
ZON530 13
 
17.1%
ZON520 7
 
9.2%
ZON510 3
 
3.9%

Length

2024-05-11T01:04:44.116785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T01:04:44.567563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
zon540 53
69.7%
zon530 13
 
17.1%
zon520 7
 
9.2%
zon510 3
 
3.9%

분류명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size740.0 B
중심지
76 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
중심지 76
100.0%

Length

2024-05-11T01:04:45.204156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T01:04:45.612867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
중심지 76
100.0%

조서ID
Categorical

CONSTANT 

Distinct1
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size740.0 B
76 

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 (%)
76
100.0%

Length

2024-05-11T01:04:46.148576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T01:04:46.670749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
No values found.

고시ID
Categorical

CONSTANT 

Distinct1
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size740.0 B
76 

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 (%)
76
100.0%

Length

2024-05-11T01:04:46.986011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T01:04:47.406865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
No values found.

라벨명
Text

Distinct75
Distinct (%)98.7%
Missing0
Missing (%)0.0%
Memory size740.0 B
2024-05-11T01:04:48.086983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length11
Mean length11.473684
Min length9

Characters and Unicode

Total characters872
Distinct characters113
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

Unique74 ?
Unique (%)97.4%

Sample

1st row중곡지구중심(동북권)
2nd row신대방지구중심(서남권)
3rd row영등포여의도도심(서남권)
4th row신풍지구중심(서남권)
5th row오류지구중심(서남권)
ValueCountFrequency (%)
연신내불광지역중심(서북권 2
 
2.6%
신사지구중심(서북권 1
 
1.3%
수락지구중심(동북권 1
 
1.3%
한양도성도심(도심권 1
 
1.3%
청량리왕십리광역중심(동북권 1
 
1.3%
종암지구중심(동북권 1
 
1.3%
가락지구중심(동남권 1
 
1.3%
구로지구중심(서남권 1
 
1.3%
마포공덕지역중심(서북권 1
 
1.3%
석관지구중심(동북권 1
 
1.3%
Other values (65) 65
85.5%
2024-05-11T01:04:49.611200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
심 80
 
9.2%
권 76
 
8.7%
( 76
 
8.7%
) 76
 
8.7%
중 74
 
8.5%
지 66
 
7.6%
구 55
 
6.3%
남 42
 
4.8%
동 41
 
4.7%
서 39
 
4.5%
Other values (103) 247
28.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 720
82.6%
Open Punctuation 76
 
8.7%
Close Punctuation 76
 
8.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
심 80
11.1%
권 76
 
10.6%
중 74
 
10.3%
지 66
 
9.2%
구 55
 
7.6%
남 42
 
5.8%
동 41
 
5.7%
서 39
 
5.4%
북 33
 
4.6%
역 20
 
2.8%
Other values (101) 194
26.9%
Open Punctuation
ValueCountFrequency (%)
( 76
100.0%
Close Punctuation
ValueCountFrequency (%)
) 76
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 720
82.6%
Common 152
 
17.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
심 80
11.1%
권 76
 
10.6%
중 74
 
10.3%
지 66
 
9.2%
구 55
 
7.6%
남 42
 
5.8%
동 41
 
5.7%
서 39
 
5.4%
북 33
 
4.6%
역 20
 
2.8%
Other values (101) 194
26.9%
Common
ValueCountFrequency (%)
( 76
50.0%
) 76
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 720
82.6%
ASCII 152
 
17.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
심 80
11.1%
권 76
 
10.6%
중 74
 
10.3%
지 66
 
9.2%
구 55
 
7.6%
남 42
 
5.8%
동 41
 
5.7%
서 39
 
5.4%
북 33
 
4.6%
역 20
 
2.8%
Other values (101) 194
26.9%
ASCII
ValueCountFrequency (%)
( 76
50.0%
) 76
50.0%

고시일자
Categorical

CONSTANT 

Distinct1
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size740.0 B
76 

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 (%)
76
100.0%

Length

2024-05-11T01:04:50.420248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T01:04:50.903959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
No values found.

X좌표
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing76
Missing (%)100.0%
Memory size816.0 B

Y좌표
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing76
Missing (%)100.0%
Memory size816.0 B

Interactions

2024-05-11T01:04:39.127383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-11T01:04:51.142897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번ID도시계획코드라벨명
순번1.0001.0000.3520.946
ID1.0001.0001.0001.000
도시계획코드0.3521.0001.0001.000
라벨명0.9461.0001.0001.000
2024-05-11T01:04:51.586539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번도시계획코드
순번1.0000.134
도시계획코드0.1341.000

Missing values

2024-05-11T01:04:39.569998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-11T01:04:40.144274image/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
010953중심지_51ZON540중심지중곡지구중심(동북권)<NA><NA>
110954중심지_52ZON540중심지신대방지구중심(서남권)<NA><NA>
210955중심지_53ZON510중심지영등포여의도도심(서남권)<NA><NA>
310956중심지_58ZON540중심지신풍지구중심(서남권)<NA><NA>
410957중심지_59ZON540중심지오류지구중심(서남권)<NA><NA>
510958중심지_62ZON540중심지화곡지구중심(서남권)<NA><NA>
610959중심지_63ZON530중심지목동지역중심(서남권)<NA><NA>
711236중심지_33ZON540중심지군자지구중심(동북권)<NA><NA>
811237중심지_65ZON530중심지봉천지역중심(서남권)<NA><NA>
911238중심지_66ZON540중심지까치산지구중심(서남권)<NA><NA>
순번ID도시계획코드분류명조서ID고시ID라벨명고시일자X좌표Y좌표
6611226중심지_09ZON510중심지강남도심(동남권)<NA><NA>
6711227중심지_11ZON530중심지수서문정지역중심(동남권)<NA><NA>
6811228중심지_14ZON520중심지잠실광역중심(동남권)<NA><NA>
6911229중심지_18ZON540중심지월계지구중심(동북권)<NA><NA>
7011230중심지_19ZON520중심지창동상계광역중심(동북권)<NA><NA>
7111231중심지_20ZON540중심지쌍문지구중심(동북권)<NA><NA>
7211232중심지_21ZON540중심지수유지구중심(동북권)<NA><NA>
7311233중심지_25ZON540중심지방학지구중심(동북권)<NA><NA>
7411234중심지_31ZON540중심지구의지구중심(동북권)<NA><NA>
7511235중심지_54ZON520중심지가산대림광역중심(서남권)<NA><NA>